From 9982af9e21734a189e295f7bb5d7fb812bde7ac4 Mon Sep 17 00:00:00 2001 From: vmoudyp Date: Mon, 2 Mar 2026 20:21:21 +0700 Subject: [PATCH 01/11] feat: add Docling as alternative document parser - Add DoclingConfig dataclass (Core/configs/docling_config.py) with ocr_engine, force_full_page_ocr, images_scale, lang fields - Update SystemConfig to include parser (default: 'mineru') and docling config fields (Core/configs/system_config.py) - Add Docling adapter module (Core/provider/extract_pdf_info_docling.py) with parse_doc_with_docling() that maps Docling output to the canonical pdf_list format consumed by the BookRAG pipeline - Update build_tree_from_pdf() to branch on cfg.parser; MinerU path unchanged; Docling path added with isolated cache dir (Core/pipelines/doc_tree_builder.py) - Add example config for scanned documents (config/docling.yaml) with force_full_page_ocr: true and easyocr engine MinerU remains the default parser. No existing behaviour changes when parser is unset or set to 'mineru'. --- Core/configs/docling_config.py | 37 ++++ Core/configs/system_config.py | 5 + Core/pipelines/doc_tree_builder.py | 92 +++++--- Core/provider/extract_pdf_info_docling.py | 253 ++++++++++++++++++++++ config/docling.yaml | 113 ++++++++++ 5 files changed, 467 insertions(+), 33 deletions(-) create mode 100644 Core/configs/docling_config.py create mode 100644 Core/provider/extract_pdf_info_docling.py create mode 100644 config/docling.yaml diff --git a/Core/configs/docling_config.py b/Core/configs/docling_config.py new file mode 100644 index 0000000..b024059 --- /dev/null +++ b/Core/configs/docling_config.py @@ -0,0 +1,37 @@ +from dataclasses import dataclass, field +from typing import List + + +@dataclass +class DoclingConfig: + """Configuration for the Docling document parser. + + This config is an alternative to MinerU. Select it in the top-level YAML + by setting ``parser: docling``. + + Attributes: + ocr_engine: OCR back-end to use. Accepted values: + ``"easyocr"`` (default), ``"tesseract"``, ``"rapidocr"``. + force_full_page_ocr: When *True* every page is passed through OCR + even if selectable text is present. Strongly recommended for + scanned documents. + images_scale: Render scale factor for page images (1.0 ≈ 72 DPI). + Increase to 2.0 for higher-resolution figure/table crops. + lang: ISO 639-1 language hint forwarded to the OCR engine. + """ + + ocr_engine: str = "easyocr" + force_full_page_ocr: bool = False + images_scale: float = 2.0 + lang: str = "en" + + def __post_init__(self): + valid_engines = ("easyocr", "tesseract", "rapidocr") + if self.ocr_engine not in valid_engines: + raise ValueError( + f"Unsupported ocr_engine: '{self.ocr_engine}'. " + f"Choose one of {valid_engines}." + ) + if self.images_scale <= 0: + raise ValueError(f"images_scale must be positive, got {self.images_scale}.") + diff --git a/Core/configs/system_config.py b/Core/configs/system_config.py index fdc45d5..d5781c5 100644 --- a/Core/configs/system_config.py +++ b/Core/configs/system_config.py @@ -1,5 +1,6 @@ import yaml from Core.configs.mineru_config import MinerU +from Core.configs.docling_config import DoclingConfig from Core.configs.llm_config import LLMConfig from Core.configs.tree_config import TreeConfig from Core.configs.graph_config import GraphConfig @@ -21,6 +22,10 @@ class SystemConfig(BaseModel): vlm: VLMConfig = Field(default_factory=VLMConfig) mineru: MinerU = Field(default_factory=MinerU) + # Parser selection: "mineru" (default) or "docling" + parser: Optional[str] = "mineru" + docling: Optional[DoclingConfig] = Field(default_factory=DoclingConfig) + # Index Configurations tree: TreeConfig = Field(default_factory=TreeConfig) graph: GraphConfig = Field(default_factory=GraphConfig) diff --git a/Core/pipelines/doc_tree_builder.py b/Core/pipelines/doc_tree_builder.py index 3eee2d1..9b2de76 100644 --- a/Core/pipelines/doc_tree_builder.py +++ b/Core/pipelines/doc_tree_builder.py @@ -82,45 +82,71 @@ def build_tree_from_pdf(cfg: SystemConfig, reforce: bool = False) -> DocumentTre tree_index = DocumentTree(meta_dict=meta_dict, cfg=cfg) - backend = cfg.mineru.backend - server_url = cfg.mineru.server_url - method = cfg.mineru.method + import json + + parser = getattr(cfg, "parser", "mineru") or "mineru" base_file_name = Path(cfg.pdf_path).stem - tmp_save_path = os.path.join( - cfg.save_path, method, f"{base_file_name}_merged_content.json" - ) - if os.path.exists(tmp_save_path) and not reforce: - # tmp load pdf_list - import json + # Each parser writes its cached pdf_list to its own sub-directory so the + # two caches never collide even when the same save_path is reused. + if parser == "docling": + tmp_save_path = os.path.join( + cfg.save_path, "docling", f"{base_file_name}_merged_content.json" + ) + else: + method = cfg.mineru.method + tmp_save_path = os.path.join( + cfg.save_path, method, f"{base_file_name}_merged_content.json" + ) + if os.path.exists(tmp_save_path) and not reforce: + # Load cached pdf_list (parser-agnostic from this point on) with open(tmp_save_path, "rb") as f: pdf_list = json.load(f) - print(f"Loaded content from {tmp_save_path}") + log.info(f"Loaded cached content from {tmp_save_path}") else: - # Extract content from the PDF file - log.info(f"Extracting content from {cfg.pdf_path}...") - middle_json, content_list = parse_doc( - cfg.pdf_path, - output_dir=cfg.save_path, - backend=backend, - method=method, - server_url=server_url, - lang=cfg.mineru.lang, - ) - - file_name = str(Path(cfg.pdf_path).stem) - save_dir = os.path.join(cfg.save_path, method) - pdf_list = merge_middle_content( - middle_json, - content_list, - parse_dir=os.path.join(cfg.save_path, method), - save_dir=save_dir, - file_name=file_name, - ) # merge middle json content with content list. - - # tmp pdf_list save for fast test - log.info(f"Content extracted and saved to {tmp_save_path}") + log.info(f"Extracting content from '{cfg.pdf_path}' using parser='{parser}' …") + + if parser == "docling": + from Core.provider.extract_pdf_info_docling import parse_doc_with_docling + + pdf_list = parse_doc_with_docling( + pdf_path=cfg.pdf_path, + output_dir=cfg.save_path, + cfg=cfg.docling, + ) + # Persist for subsequent fast loads (mirrors what merge_middle_content + # does for the MinerU path). + docling_cache_dir = os.path.join(cfg.save_path, "docling") + os.makedirs(docling_cache_dir, exist_ok=True) + with open(tmp_save_path, "w", encoding="utf-8") as f: + json.dump(pdf_list, f, ensure_ascii=False, indent=4) + log.info(f"[Docling] Extracted content cached to '{tmp_save_path}'") + else: + # ── MinerU (default) ────────────────────────────────────────── + backend = cfg.mineru.backend + server_url = cfg.mineru.server_url + method = cfg.mineru.method + + middle_json, content_list = parse_doc( + cfg.pdf_path, + output_dir=cfg.save_path, + backend=backend, + method=method, + server_url=server_url, + lang=cfg.mineru.lang, + ) + + file_name = str(Path(cfg.pdf_path).stem) + save_dir = os.path.join(cfg.save_path, method) + pdf_list = merge_middle_content( + middle_json, + content_list, + parse_dir=os.path.join(cfg.save_path, method), + save_dir=save_dir, + file_name=file_name, + ) + log.info(f"[MinerU] Extracted content saved to '{tmp_save_path}'") llm = LLM(cfg.llm) vlm = VLM(cfg.vlm) if cfg.tree.use_vlm else None diff --git a/Core/provider/extract_pdf_info_docling.py b/Core/provider/extract_pdf_info_docling.py new file mode 100644 index 0000000..989e11d --- /dev/null +++ b/Core/provider/extract_pdf_info_docling.py @@ -0,0 +1,253 @@ +"""Docling-based document parser adapter for BookRAG. + +This module provides :func:`parse_doc_with_docling`, which converts a PDF (or +any Docling-supported format) into the canonical ``pdf_list`` format consumed +by the rest of the BookRAG pipeline. + +``pdf_list`` schema (one dict per content block): + - ``type`` : ``"text"`` | ``"image"`` | ``"table"`` | ``"equation"`` + - ``text`` : str — text content (text / equation nodes) + - ``text_level`` : int — ``-1`` = body text; ``0`` = chapter; ``1`` = section; … + - ``page_idx`` : int — 0-indexed page number + - ``pdf_id`` : int — sequential 0-based index matching position in list + - ``img_path`` : str — absolute path to saved PNG (image / table nodes) + - ``image_caption`` : list[str] + - ``image_footnote``: list[str] + - ``table_caption`` : list[str] + - ``table_footnote``: list[str] + - ``table_body`` : str — markdown table string + - ``middle_json`` : dict — raw Docling item metadata (non-critical) +""" + +from __future__ import annotations + +import json +import logging +import os +from pathlib import Path +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from Core.configs.docling_config import DoclingConfig + +log = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Public entry point +# --------------------------------------------------------------------------- + +def parse_doc_with_docling( + pdf_path: str, + output_dir: str, + cfg: "DoclingConfig", +) -> list[dict]: + """Parse *pdf_path* with Docling and return a BookRAG-compatible ``pdf_list``. + + Args: + pdf_path: Path to the PDF (or other supported format) to parse. + output_dir: Root save directory; images are written to + ``/docling/images/``. + cfg: :class:`~Core.configs.docling_config.DoclingConfig` instance + that controls OCR engine, resolution, etc. + + Returns: + A flat list of dicts matching the ``pdf_list`` schema. The item at + position *i* always satisfies ``item["pdf_id"] == i``. + """ + # ------------------------------------------------------------------ setup + from docling.datamodel.base_models import InputFormat + from docling.datamodel.pipeline_options import PdfPipelineOptions + from docling.document_converter import DocumentConverter, PdfFormatOption + from docling_core.types.doc import PictureItem, SectionHeaderItem, TableItem, TextItem + + img_dir = os.path.join(output_dir, "docling", "images") + os.makedirs(img_dir, exist_ok=True) + + pipeline_options = _build_pipeline_options(cfg) + + converter = DocumentConverter( + format_options={InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)} + ) + + log.info(f"[Docling] Converting '{pdf_path}' …") + conv_res = converter.convert(pdf_path) + doc = conv_res.document + doc_stem = Path(pdf_path).stem + + # ---------------------------------------------------------------- iterate + pdf_list: list[dict] = [] + pdf_id = 0 # 0-based; MUST equal position in list + pic_counter = 0 + tbl_counter = 0 + + for element, _level in doc.iterate_items(): + page_idx = _get_page_idx(element) + + if isinstance(element, SectionHeaderItem): + # SectionHeaderItem.level is 1-indexed (1 = chapter, 2 = section …) + heading_level = getattr(element, "level", 1) + pdf_list.append({ + "type": "text", + "text": element.text or "", + "text_level": max(0, heading_level - 1), # convert to 0-indexed + "page_idx": page_idx, + "pdf_id": pdf_id, + "middle_json": {"docling_label": str(element.label)}, + }) + + elif isinstance(element, TableItem): + tbl_counter += 1 + img_path = _save_element_image( + element, doc, img_dir, f"{doc_stem}-table-{tbl_counter}.png" + ) + captions, footnotes = _extract_captions_footnotes(element) + pdf_list.append({ + "type": "table", + "text": "", + "text_level": -1, + "page_idx": page_idx, + "pdf_id": pdf_id, + "img_path": img_path, + "table_caption": captions, + "table_footnote": footnotes, + "table_body": element.export_to_markdown(), + "middle_json": {"docling_label": "table"}, + }) + + elif isinstance(element, PictureItem): + pic_counter += 1 + img_path = _save_element_image( + element, doc, img_dir, f"{doc_stem}-picture-{pic_counter}.png" + ) + captions, footnotes = _extract_captions_footnotes(element) + pdf_list.append({ + "type": "image", + "text": "", + "text_level": -1, + "page_idx": page_idx, + "pdf_id": pdf_id, + "img_path": img_path, + "image_caption": captions, + "image_footnote": footnotes, + "middle_json": {"docling_label": "picture"}, + }) + + elif isinstance(element, TextItem): + # Covers TextItem, ListItem, CodeItem, FormulaItem, FootnoteItem, etc. + label_str = str(getattr(element, "label", "text")).lower() + is_formula = "formula" in label_str or "equation" in label_str + text = getattr(element, "text", "") or "" + if not text.strip(): + continue # skip empty elements — don't increment pdf_id + pdf_list.append({ + "type": "equation" if is_formula else "text", + "text": text, + "text_level": -1, + "page_idx": page_idx, + "pdf_id": pdf_id, + "middle_json": {"docling_label": label_str}, + }) + + else: + # Any remaining element types (e.g. page headers, key-value pairs) + text = getattr(element, "text", "") or "" + if not text.strip(): + continue + pdf_list.append({ + "type": "text", + "text": text, + "text_level": -1, + "page_idx": page_idx, + "pdf_id": pdf_id, + "middle_json": {"docling_label": str(getattr(element, "label", "unknown"))}, + }) + + pdf_id += 1 # advance only when an item was appended + + log.info(f"[Docling] Extracted {len(pdf_list)} content blocks from '{doc_stem}'.") + return pdf_list + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def _build_pipeline_options(cfg: "DoclingConfig"): + """Construct :class:`PdfPipelineOptions` from *cfg*.""" + from docling.datamodel.pipeline_options import PdfPipelineOptions + + opts = PdfPipelineOptions() + opts.do_ocr = True + opts.do_table_structure = True + opts.generate_picture_images = True + opts.images_scale = cfg.images_scale + + ocr_opts = _build_ocr_options(cfg) + if ocr_opts is not None: + opts.ocr_options = ocr_opts + + return opts + + +def _build_ocr_options(cfg: "DoclingConfig"): + """Return an OCR-options object matching *cfg.ocr_engine*, or *None*.""" + engine = cfg.ocr_engine.lower() + force = cfg.force_full_page_ocr + lang = cfg.lang + + try: + if engine == "easyocr": + from docling.datamodel.pipeline_options import EasyOcrOptions + return EasyOcrOptions(force_full_page_ocr=force, lang=[lang]) + elif engine == "tesseract": + from docling.datamodel.pipeline_options import TesseractCliOcrOptions + return TesseractCliOcrOptions(force_full_page_ocr=force, lang=lang) + elif engine == "rapidocr": + from docling.datamodel.pipeline_options import RapidOcrOptions + return RapidOcrOptions(force_full_page_ocr=force) + except ImportError as exc: + log.warning( + f"[Docling] Could not import OCR options for engine '{engine}': {exc}. " + "Falling back to Docling's default OCR settings." + ) + return None + + +def _get_page_idx(element) -> int: + """Extract 0-indexed page number from a Docling element's provenance.""" + try: + if element.prov: + return max(0, element.prov[0].page_no - 1) # Docling page_no is 1-indexed + except (AttributeError, IndexError): + pass + return 0 + + +def _save_element_image(element, doc, img_dir: str, filename: str) -> str: + """Save element image as PNG and return its absolute path (or '' on failure).""" + img_path = os.path.join(img_dir, filename) + try: + pil_image = element.get_image(doc) + if pil_image is not None: + pil_image.save(img_path, "PNG") + return img_path + except Exception as exc: + log.warning(f"[Docling] Could not save image '{filename}': {exc}") + return "" + + +def _extract_captions_footnotes(element) -> tuple[list[str], list[str]]: + """Pull caption and footnote text lists from a Docling element.""" + captions: list[str] = [] + footnotes: list[str] = [] + for ref in getattr(element, "captions", []): + text = getattr(ref, "text", None) or getattr(ref, "__str__", lambda: "")() + if text and text.strip(): + captions.append(text.strip()) + for ref in getattr(element, "footnotes", []): + text = getattr(ref, "text", None) or getattr(ref, "__str__", lambda: "")() + if text and text.strip(): + footnotes.append(text.strip()) + return captions, footnotes + diff --git a/config/docling.yaml b/config/docling.yaml new file mode 100644 index 0000000..212169b --- /dev/null +++ b/config/docling.yaml @@ -0,0 +1,113 @@ +# config/docling.yaml +# +# BookRAG configuration that uses Docling as the document parser instead of +# MinerU. Suitable for scanned books and multi-format documents (PDF, DOCX, +# PPTX, HTML, images). MinerU settings are still required for the rest of +# the system; they are simply unused at parse time when parser=docling. +# +# Usage: +# python main.py -c config/docling.yaml -d Scripts/cfg/Qasper.yaml index --stage tree + +pdf_path: TODO +save_path: TODO + +# ── Parser selection ──────────────────────────────────────────────────────── +# Set to "docling" to use Docling. Change to "mineru" to fall back to MinerU. +parser: docling + +docling: + # OCR engine: "easyocr" (default) | "tesseract" | "rapidocr" + ocr_engine: easyocr + # Force OCR on every page even when selectable text is present. + # Set to true for scanned documents where text layer is absent or unreliable. + force_full_page_ocr: true + # Image render scale (1.0 ≈ 72 DPI). 2.0 gives crisper figure/table crops. + images_scale: 2.0 + # Language hint for the OCR engine (ISO 639-1). + lang: en + +# ── MinerU (kept for completeness; unused when parser=docling) ────────────── +mineru: + backend: pipeline + method: auto + lang: en + +# ── LLM (used for outline extraction, refinement, summaries) ──────────────── +llm: + model_name: TODO + api_key: TODO + api_base: TODO + backend: openai + max_tokens: 5000 + temperature: 0.1 + frequency_penalty: 0.0 + presence_penalty: 0.0 + max_workers: 8 + +# ── VLM (used for image/table node summaries when tree.node_summary=true) ─── +vlm: + model_name: TODO + api_key: TODO + api_base: TODO + temperature: 0.1 + max_tokens: 6000 + backend: ollama + +# ── Tree index ─────────────────────────────────────────────────────────────── +index: + chunk_size: 512 + overlap: 50 + +tree: + node_keywords: true + node_summary: true + +# ── Knowledge graph ────────────────────────────────────────────────────────── +graph: + extractor_type: "llm" + local_model_name: "en_core_web_sm" + image_description_force: true + max_gleaning: 0 + refine_type: "advanced" + embedding_config: + model_name: TODO + backend: openai + max_length: TODO + device: TODO + api_base: TODO + reranker_config: + model_name: TODO + max_length: 4096 + device: TODO + backend: vllm + api_base: TODO + +# ── Vector database ────────────────────────────────────────────────────────── +vdb: + mm_embedding: true + vdb_dir_name: "Tree_vdb" + collection_name: "TreeVDB" + embedding_config: + model_name: TODO + device: TODO + +rag_force_reprocess: true + +# ── RAG ────────────────────────────────────────────────────────────────────── +rag: + strategy: gbc + varient: standard + topk: 10 + sim_threshold: 0.3 + select_depth: 2 + max_retry: 2 + reranker_config: + model_name: TODO + max_length: TODO + device: TODO + backend: vllm + api_base: TODO + mm_reranker_config: + model_name: TODO + device: TODO + From 10c03dcac670be908cddcbe75dd47f6091cf8215 Mon Sep 17 00:00:00 2001 From: vmoudyp Date: Mon, 2 Mar 2026 21:59:07 +0700 Subject: [PATCH 02/11] feat: multi-tenant chatbot with FalkorDB + MongoDB + FastAPI (3-phase) Phase 1 - FalkorDB & Config Foundation: - Add Core/configs/falkordb_config.py: FalkorDBConfig dataclass with graph_name_for_doc() and graph_name_for_global() helpers - Add Core/configs/mongodb_config.py: MongoDBConfig dataclass with tenant_db_name() helper - Update Core/configs/system_config.py: add tenant_id, doc_id, falkordb, mongodb fields; backward-compatible (all optional) - Rewrite Core/Index/Graph.py: optional FalkorDB persistence alongside NetworkX; new methods _save_to_falkordb, _load_from_falkordb, _get_fdb_subgraph, save_to_global_graph, get_global_subgraph; fix pre-existing bug: node_link_graph now uses edges='links' to match node_link_data save format (NetworkX 3.x compatibility) - Update Core/Index/GBCIndex.py: tenant/doc_id-namespaced VDB paths, pass falkordb config to Graph.load_from_dir, add rebuild_global_vdb() - Update Core/pipelines/kg_builder.py: pass tenant_id, doc_id, falkordb_cfg from config into Graph constructor Phase 2 - FastAPI Multi-User API: - api/main.py: FastAPI app with lifespan, CORS, health endp- api/main.py: FastAPI app with lifespan, CORS, health endp- api/ma, get_current_user, require_admin, check_doc_access, filter_accessible_docs - api/db/mongodb.py: Motor async CRUD for tenants,- api/db/mongodb.py: Motmi- api/db/mongodb.py: Motor async CRUD for tenants,- api/db/mopi/models/requests.py: Pydantic request/response models - api/routers/auth.py: POST /auth/register, POST /auth/login - api/routers/tenants.py: tenant management (admin-gated) - api/routers/documents.py: PDF upload + background indexing, list, status - api/routers/chat.py: POST /chat/query, session create, message history - api/services/indexing.py: background GBC index build in thread pool with MongoDB status tracking Phase 3 - Cross-Document Entity ResoluPhase 3 - Cross-Document Entity ResoluPhase 3 - Cross-Document Entity chPhase 3 - Cross-Document Entity ResoluPhase 3 - CrokorDB graph, HAS_MENTION edge creation, update global VDB for canonical entities - api/services/chat.py: parallel per-doc GBC RAG queries (up to 5 docs) with answer synthesis for cross-doc mode - Entity resolution runs automatically after indexing (non-fatal) Config updates (from previous session): - config/gbc.yaml: upgrade LLM/VLM to Qwen3.5-35B-A3B-AWQ - Core/configs/vlm_config.py: update defaults for Qwen3.5 backend - Core/prompts/kg_prompt.py: language-agnostic NER prompt --- Core/Index/GBCIndex.py | 52 ++++- Core/Index/Graph.py | 347 ++++++++++++++++++++++++++---- Core/configs/falkordb_config.py | 20 ++ Core/configs/mongodb_config.py | 15 ++ Core/configs/system_config.py | 12 +- Core/configs/vlm_config.py | 10 +- Core/pipelines/kg_builder.py | 10 +- Core/prompts/kg_prompt.py | 2 +- api/__init__.py | 0 api/db/__init__.py | 0 api/db/mongodb.py | 133 ++++++++++++ api/dependencies.py | 89 ++++++++ api/main.py | 58 +++++ api/models/__init__.py | 0 api/models/requests.py | 86 ++++++++ api/routers/__init__.py | 0 api/routers/auth.py | 56 +++++ api/routers/chat.py | 83 +++++++ api/routers/documents.py | 101 +++++++++ api/routers/tenants.py | 56 +++++ api/services/__init__.py | 0 api/services/chat.py | 96 +++++++++ api/services/entity_resolution.py | 119 ++++++++++ api/services/indexing.py | 65 ++++++ config/gbc.yaml | 12 +- 25 files changed, 1359 insertions(+), 63 deletions(-) create mode 100644 Core/configs/falkordb_config.py create mode 100644 Core/configs/mongodb_config.py create mode 100644 api/__init__.py create mode 100644 api/db/__init__.py create mode 100644 api/db/mongodb.py create mode 100644 api/dependencies.py create mode 100644 api/main.py create mode 100644 api/models/__init__.py create mode 100644 api/models/requests.py create mode 100644 api/routers/__init__.py create mode 100644 api/routers/auth.py create mode 100644 api/routers/chat.py create mode 100644 api/routers/documents.py create mode 100644 api/routers/tenants.py create mode 100644 api/services/__init__.py create mode 100644 api/services/chat.py create mode 100644 api/services/entity_resolution.py create mode 100644 api/services/indexing.py diff --git a/Core/Index/GBCIndex.py b/Core/Index/GBCIndex.py index f40c4c4..cd691d9 100644 --- a/Core/Index/GBCIndex.py +++ b/Core/Index/GBCIndex.py @@ -31,11 +31,18 @@ def __init__( self.TreeIndex: DocumentTree = TreeIndex self.GraphIndex: Graph = graph_index - # load the vdb of entities + # load the vdb of entities — namespaced by tenant/doc if available if config.graph.refine_type == "basic": - self.entity_vdb_path = os.path.join(self.save_dir, "kg_vdb_basic") + vdb_name = "kg_vdb_basic" else: - self.entity_vdb_path = os.path.join(self.save_dir, "kg_vdb") + vdb_name = "kg_vdb" + + if config.tenant_id and config.doc_id: + self.entity_vdb_path = os.path.join( + self.save_dir, config.tenant_id, config.doc_id, vdb_name + ) + else: + self.entity_vdb_path = os.path.join(self.save_dir, vdb_name) self.embedder = TextEmbeddingProvider( model_name=config.graph.embedding_config.model_name, @@ -109,8 +116,43 @@ def load_gbc_index(cls, config: SystemConfig): variant = "basic" else: variant = None - - graph_index = Graph.load_from_dir(config.save_path, variant=variant) + + # Pass FalkorDB config if tenant/doc IDs are set + falkordb_cfg = config.falkordb if (config.tenant_id and config.doc_id) else None + graph_index = Graph.load_from_dir( + config.save_path, + variant=variant, + tenant_id=config.tenant_id, + doc_id=config.doc_id, + falkordb_cfg=falkordb_cfg, + ) GBC = cls(config=config, graph_index=graph_index, TreeIndex=tree_index) log.info(f"GBC index loaded from {config.save_path}") return GBC + + def rebuild_global_vdb(self, global_vdb_path: str) -> None: + """ + Build a global vector database of canonical entities (Phase 3). + Pulls all nodes from the current GraphIndex and adds them to a shared VDB. + """ + from Core.provider.vdb import VectorStore + global_vdb = VectorStore( + db_path=global_vdb_path, + embedding_model=self.embedder, + collection_name="global_kg_collection", + ) + nodes = self.GraphIndex.get_all_nodes() + texts = [] + meta_datas = [] + for node in nodes: + texts.append(node) + entity = self.GraphIndex.get_entity_by_node_name(node) + meta_datas.append({ + "entity_name": entity.entity_name, + "entity_type": entity.entity_type, + "description": entity.description, + "doc_id": self.config.doc_id or "", + "tenant_id": self.config.tenant_id or "", + }) + global_vdb.add_texts(texts=texts, metadatas=meta_datas) + log.info(f"Rebuilt global VDB with {len(texts)} entries from doc '{self.config.doc_id}'.") diff --git a/Core/Index/Graph.py b/Core/Index/Graph.py index ccdb7ce..dadd216 100644 --- a/Core/Index/Graph.py +++ b/Core/Index/Graph.py @@ -2,8 +2,7 @@ from networkx.readwrite import json_graph import os from collections import defaultdict -from typing import Iterable, Union, Set, List -from numpy import source +from typing import Iterable, Union, Set, List, Optional, TYPE_CHECKING # noqa: F401 from pydantic import BaseModel, Field import json @@ -11,6 +10,9 @@ log = logging.getLogger(__name__) +if TYPE_CHECKING: + from Core.configs.falkordb_config import FalkorDBConfig + class Entity(BaseModel): entity_name: str # Primary key for entity @@ -64,14 +66,30 @@ class Graph: _DATA_FILE = "graph_data.json" # index data file _BASE_FILENAME = "graph_data" - def __init__(self, save_path: str = None, variant: str = None): + def __init__( + self, + save_path: str = None, + variant: str = None, + tenant_id: str = None, + doc_id: str = None, + falkordb_cfg=None, # Optional[FalkorDBConfig] + ): self.kg = nx.Graph() # 节点名采用 "entity_name (entity_type)"，确保唯一性 self.tree2kg = defaultdict(set) # Maps tree nodes id (int) to graph entities - # self.name_to_nodes = defaultdict(set) # entity_name -> set of node names self.save_dir = save_path self.variant = variant + # Multi-tenant FalkorDB support + self.tenant_id = tenant_id + self.doc_id = doc_id + self.falkordb_cfg = falkordb_cfg + self.use_falkordb = falkordb_cfg is not None and tenant_id is not None and doc_id is not None + self._fdb_graph = None # lazy FalkorDB graph handle + self._fdb_graph_name: Optional[str] = None + if self.use_falkordb: + self._fdb_graph_name = falkordb_cfg.graph_name_for_doc(tenant_id, doc_id) + # dynamic filename based on variant self.data_filename = self._get_filename(variant) @@ -217,22 +235,6 @@ def get_entity_by_node_name(self, node_name: str) -> Entity: raise KeyError(f"Node '{node_name}' not found in knowledge graph.") return Entity(**self.kg.nodes[node_name]) - def get_kg_subgraph( - self, tree_node_ids: Iterable[int], copy: bool = True - ) -> nx.Graph: - """ - Given one or more tree node IDs, return the induced subgraph of the KG - containing all linked entities. By default returns a deep copy; if copy=False, - returns a lightweight view (faster slicing). - - Complexity: O(sum(degree(n)) + |nodes| + |edges|). - For a few hundred nodes, this remains efficient even if KG has millions of edges. - """ - # Collect all KG node names for the provided tree nodes - kg_nodes = set().union(*(self.tree2kg.get(tid, set()) for tid in tree_node_ids)) - sub = self.kg.subgraph(kg_nodes) - return sub.copy() if copy else sub - def get_subgraph_data(self, entities: List[str]) -> dict: # Return the subgraph entities data, excluding description and source_ids in entities # If the relation connects two entities in the subgraph, it will be included @@ -301,38 +303,220 @@ def remove_self_loops(self) -> int: self.kg.remove_edges_from(self_loop_edges) log.info("All self-loops have been removed.") + # ------------------------------------------------------------------ # + # FalkorDB helpers # + # ------------------------------------------------------------------ # + + def _get_fdb_graph(self): + """Lazy-initialise and return the FalkorDB graph handle.""" + if self._fdb_graph is not None: + return self._fdb_graph + try: + from falkordb import FalkorDB + cfg = self.falkordb_cfg + conn_kwargs = {"host": cfg.host, "port": cfg.port} + if cfg.password: + conn_kwargs["password"] = cfg.password + client = FalkorDB(**conn_kwargs) + self._fdb_graph = client.select_graph(self._fdb_graph_name) + log.info(f"Connected to FalkorDB graph '{self._fdb_graph_name}'") + except Exception as e: + log.error(f"Failed to connect to FalkorDB: {e}") + raise + return self._fdb_graph + + def _save_to_falkordb(self) -> None: + """Persist the in-memory NetworkX graph to FalkorDB.""" + g = self._get_fdb_graph() + # Clear existing data for idempotent saves + try: + g.query("MATCH (n) DETACH DELETE n") + except Exception: + pass + + # Write nodes + for node_name, data in self.kg.nodes(data=True): + source_ids_list = list(data.get("source_ids", set())) + desc = data.get("description", "").replace("'", "\\'") + ename = data.get("entity_name", "").replace("'", "\\'") + etype = data.get("entity_type", "").replace("'", "\\'") + nname = node_name.replace("\\", "\\\\").replace("'", "\\'") + cypher = ( + f"CREATE (n:Entity {{" + f"node_name: '{nname}', " + f"entity_name: '{ename}', " + f"entity_type: '{etype}', " + f"description: '{desc}', " + f"source_ids: {source_ids_list}" + f"}})" + ) + g.query(cypher) + + # Write edges + for src, tgt, data in self.kg.edges(data=True): + rel_name = data.get("relation_name", "").replace("'", "\\'") + weight = float(data.get("weight", 0.0)) + desc = data.get("description", "").replace("'", "\\'") + src_ids = list(data.get("source_ids", set())) + src_q = src.replace("\\", "\\\\").replace("'", "\\'") + tgt_q = tgt.replace("\\", "\\\\").replace("'", "\\'") + cypher = ( + f"MATCH (a:Entity {{node_name: '{src_q}'}}), " + f"(b:Entity {{node_name: '{tgt_q}'}}) " + f"CREATE (a)-[:RELATION {{" + f"relation_name: '{rel_name}', " + f"weight: {weight}, " + f"description: '{desc}', " + f"source_ids: {src_ids}" + f"}}]->(b)" + ) + g.query(cypher) + + # Write tree2kg as node property (source_ids already on nodes) + log.info(f"Saved graph to FalkorDB '{self._fdb_graph_name}': " + f"{self.kg.number_of_nodes()} nodes, {self.kg.number_of_edges()} edges.") + + def _load_from_falkordb(self) -> None: + """Load graph data from FalkorDB into in-memory NetworkX graph.""" + g = self._get_fdb_graph() + result = g.query("MATCH (n:Entity) RETURN n") + for record in result.result_set: + node = record[0] + props = node.properties + source_ids = set(props.get("source_ids", [])) + node_name = props["node_name"] + self.kg.add_node(node_name, + entity_name=props.get("entity_name", ""), + entity_type=props.get("entity_type", ""), + description=props.get("description", ""), + source_ids=source_ids) + for tid in source_ids: + self.tree2kg[int(tid)].add(node_name) + + edge_result = g.query( + "MATCH (a:Entity)-[r:RELATION]->(b:Entity) " + "RETURN a.node_name, b.node_name, r.relation_name, r.weight, r.description, r.source_ids" + ) + for rec in edge_result.result_set: + src_name, tgt_name, rel_name, weight, desc, src_ids = rec + self.kg.add_edge( + src_name, tgt_name, + src_entity_name=self.kg.nodes[src_name].get("entity_name", ""), + tgt_entity_name=self.kg.nodes[tgt_name].get("entity_name", ""), + relation_name=rel_name or "", + weight=float(weight or 0.0), + description=desc or "", + source_ids=set(src_ids or []), + ) + log.info(f"Loaded graph from FalkorDB '{self._fdb_graph_name}': " + f"{self.kg.number_of_nodes()} nodes, {self.kg.number_of_edges()} edges.") + + def _get_fdb_subgraph(self, tree_node_ids: Iterable[int]) -> nx.Graph: + """Query FalkorDB for the subgraph linked to given tree node IDs.""" + # Collect node names from tree2kg (loaded at init) + kg_nodes = set().union(*(self.tree2kg.get(tid, set()) for tid in tree_node_ids)) + if not kg_nodes: + return nx.Graph() + + g = self._get_fdb_graph() + node_list = [n.replace("'", "\\'") for n in kg_nodes] + node_filter = "['" + "', '".join(node_list) + "']" + result = g.query( + f"MATCH (n:Entity) WHERE n.node_name IN {node_filter} RETURN n" + ) + subgraph = nx.Graph() + for rec in result.result_set: + node = rec[0] + props = node.properties + node_name = props["node_name"] + subgraph.add_node(node_name, + entity_name=props.get("entity_name", ""), + entity_type=props.get("entity_type", ""), + description=props.get("description", ""), + source_ids=set(props.get("source_ids", []))) + + edge_result = g.query( + f"MATCH (a:Entity)-[r:RELATION]->(b:Entity) " + f"WHERE a.node_name IN {node_filter} AND b.node_name IN {node_filter} " + f"RETURN a.node_name, b.node_name, r.relation_name, r.weight, r.description, r.source_ids" + ) + for rec in edge_result.result_set: + src_name, tgt_name, rel_name, weight, desc, src_ids = rec + if src_name in subgraph and tgt_name in subgraph: + subgraph.add_edge(src_name, tgt_name, + relation_name=rel_name or "", + weight=float(weight or 0.0), + description=desc or "", + source_ids=set(src_ids or [])) + return subgraph + def save_graph(self) -> None: - if not self.save_dir: + if not self.save_dir and not self.use_falkordb: log.warning("Warning: save_dir is not set. Nothing will be saved.") return - os.makedirs(self.save_dir, exist_ok=True) - # save_path = os.path.join(self.save_dir, self._DATA_FILE) - - # use dynamic filename based on variant - save_path = os.path.join(self.save_dir, self.data_filename) - - graph_json_data = json_graph.node_link_data(self.kg, edges="links") - - data_to_save = { - "graph": graph_json_data, - "tree2kg": {k: list(v) for k, v in self.tree2kg.items()}, - "variant": self.variant, - } - - # 3. 保存为格式化的JSON文件 - with open(save_path, "w", encoding="utf-8") as f: - json.dump(data_to_save, f, cls=SetEncoder, indent=4, ensure_ascii=False) + # If FalkorDB is configured, persist there + if self.use_falkordb: + self._save_to_falkordb() + # Also save JSON as backup if save_dir is set + if self.save_dir: + os.makedirs(self.save_dir, exist_ok=True) + + if self.save_dir: + os.makedirs(self.save_dir, exist_ok=True) + save_path = os.path.join(self.save_dir, self.data_filename) + graph_json_data = json_graph.node_link_data(self.kg, edges="links") + data_to_save = { + "graph": graph_json_data, + "tree2kg": {k: list(v) for k, v in self.tree2kg.items()}, + "variant": self.variant, + } + with open(save_path, "w", encoding="utf-8") as f: + json.dump(data_to_save, f, cls=SetEncoder, indent=4, ensure_ascii=False) + log.info(f"Graph data successfully saved to: {save_path}") - log.info(f"Graph data successfully saved to: {save_path}") + def get_kg_subgraph( + self, tree_node_ids: Iterable[int], copy: bool = True + ) -> nx.Graph: + """ + Given one or more tree node IDs, return the induced subgraph of the KG. + In FalkorDB mode, queries the database directly for only the needed nodes. + """ + if self.use_falkordb and not self.kg.nodes: + # FalkorDB-only mode: fetch subgraph from DB + return self._get_fdb_subgraph(tree_node_ids) + # Default: in-memory NetworkX subgraph + kg_nodes = set().union(*(self.tree2kg.get(tid, set()) for tid in tree_node_ids)) + sub = self.kg.subgraph(kg_nodes) + return sub.copy() if copy else sub @classmethod - def load_from_dir(cls, load_dir: str, variant: str = None) -> "Graph": + def load_from_dir( + cls, + load_dir: str, + variant: str = None, + tenant_id: str = None, + doc_id: str = None, + falkordb_cfg=None, + ) -> "Graph": + """Load a Graph from JSON file, or from FalkorDB if configured.""" + # FalkorDB mode: load tree2kg from DB, keep kg empty for lazy subgraph queries + if falkordb_cfg is not None and tenant_id is not None and doc_id is not None: + graph_instance = cls( + save_path=load_dir, + variant=variant, + tenant_id=tenant_id, + doc_id=doc_id, + falkordb_cfg=falkordb_cfg, + ) + graph_instance._load_from_falkordb() + log.info(f"Graph loaded from FalkorDB graph '{graph_instance._fdb_graph_name}'") + return graph_instance + + # Default: JSON file load target_filename = cls._get_filename(variant) load_path = os.path.join(load_dir, target_filename) - - # load_path = os.path.join(load_dir, cls._DATA_FILE) if not os.path.exists(load_path): raise FileNotFoundError(f"Error: Missing graph file: {load_path}") @@ -340,8 +524,8 @@ def load_from_dir(cls, load_dir: str, variant: str = None) -> "Graph": loaded_data = json.load(f) graph_instance = cls(save_path=load_dir) - - graph_instance.kg = json_graph.node_link_graph(loaded_data["graph"]) + # Pass edges="links" to match the key used when saving with node_link_data(edges="links") + graph_instance.kg = json_graph.node_link_graph(loaded_data["graph"], edges="links") for _, node_data in graph_instance.kg.nodes(data=True): if "source_ids" in node_data and isinstance(node_data["source_ids"], list): @@ -361,6 +545,81 @@ def load_from_dir(cls, load_dir: str, variant: str = None) -> "Graph": ) return graph_instance + # ------------------------------------------------------------------ # + # Phase 3: Global graph methods # + # ------------------------------------------------------------------ # + + def save_to_global_graph(self, falkordb_cfg, tenant_id: str) -> None: + """ + Merge this document's KG nodes into the tenant-level global FalkorDB graph. + Each entity is stored as a canonical node; a HAS_MENTION edge links the + canonical node back to its document source. + """ + try: + from falkordb import FalkorDB + cfg = falkordb_cfg + conn_kwargs = {"host": cfg.host, "port": cfg.port} + if cfg.password: + conn_kwargs["password"] = cfg.password + client = FalkorDB(**conn_kwargs) + global_graph = client.select_graph(cfg.graph_name_for_global(tenant_id)) + except Exception as e: + log.error(f"Failed to connect to FalkorDB global graph: {e}") + raise + + for node_name, data in self.kg.nodes(data=True): + ename = data.get("entity_name", "").replace("'", "\\'") + etype = data.get("entity_type", "").replace("'", "\\'") + desc = data.get("description", "").replace("'", "\\'") + nname = node_name.replace("\\", "\\\\").replace("'", "\\'") + doc_id_esc = self.doc_id.replace("'", "\\'") if self.doc_id else "" + # MERGE canonical entity node + global_graph.query( + f"MERGE (n:Entity {{node_name: '{nname}'}}) " + f"ON CREATE SET n.entity_name='{ename}', n.entity_type='{etype}', n.description='{desc}' " + f"CREATE (n)-[:HAS_MENTION {{doc_id: '{doc_id_esc}'}}]->(n)" + ) + log.info(f"Merged {self.kg.number_of_nodes()} nodes into global graph for tenant '{tenant_id}'.") + + def get_global_subgraph( + self, + falkordb_cfg, + tenant_id: str, + accessible_doc_ids: List[str], + ) -> nx.Graph: + """ + Fetch a cross-document subgraph from the global FalkorDB graph, + restricted to documents in accessible_doc_ids. + """ + try: + from falkordb import FalkorDB + cfg = falkordb_cfg + conn_kwargs = {"host": cfg.host, "port": cfg.port} + if cfg.password: + conn_kwargs["password"] = cfg.password + client = FalkorDB(**conn_kwargs) + global_graph = client.select_graph(cfg.graph_name_for_global(tenant_id)) + except Exception as e: + log.error(f"Failed to connect to FalkorDB global graph: {e}") + raise + + # Use a relationship variable r to filter by doc_id property on HAS_MENTION edges + doc_filter = "['" + "', '".join(d.replace("'", "\\'") for d in accessible_doc_ids) + "']" + result = global_graph.query( + f"MATCH (n:Entity)-[r:HAS_MENTION]->(n) " + f"WHERE r.doc_id IN {doc_filter} RETURN DISTINCT n" + ) + subgraph = nx.Graph() + for rec in result.result_set: + node = rec[0] + props = node.properties + node_name = props["node_name"] + subgraph.add_node(node_name, + entity_name=props.get("entity_name", ""), + entity_type=props.get("entity_type", ""), + description=props.get("description", "")) + return subgraph + if __name__ == "__main__": # Example usage diff --git a/Core/configs/falkordb_config.py b/Core/configs/falkordb_config.py new file mode 100644 index 0000000..1a047ff --- /dev/null +++ b/Core/configs/falkordb_config.py @@ -0,0 +1,20 @@ +from dataclasses import dataclass, field + + +@dataclass +class FalkorDBConfig: + """Configuration for FalkorDB graph database connection.""" + + host: str = "localhost" + port: int = 6379 + password: str = "" + graph_prefix: str = "bookrag" + + def graph_name_for_doc(self, tenant_id: str, doc_id: str) -> str: + """Return the FalkorDB graph name for a per-document KG.""" + return f"{self.graph_prefix}:{tenant_id}:doc:{doc_id}" + + def graph_name_for_global(self, tenant_id: str) -> str: + """Return the FalkorDB graph name for a tenant-level global KG.""" + return f"{self.graph_prefix}:{tenant_id}:global" + diff --git a/Core/configs/mongodb_config.py b/Core/configs/mongodb_config.py new file mode 100644 index 0000000..8c50a9b --- /dev/null +++ b/Core/configs/mongodb_config.py @@ -0,0 +1,15 @@ +from dataclasses import dataclass + + +@dataclass +class MongoDBConfig: + """Configuration for MongoDB connection.""" + + uri: str = "mongodb://localhost:27017" + db_prefix: str = "bookrag" + system_db: str = "bookrag_system" + + def tenant_db_name(self, tenant_id: str) -> str: + """Return the MongoDB database name for a given tenant.""" + return f"{self.db_prefix}_{tenant_id}" + diff --git a/Core/configs/system_config.py b/Core/configs/system_config.py index d5781c5..b139907 100644 --- a/Core/configs/system_config.py +++ b/Core/configs/system_config.py @@ -7,8 +7,10 @@ from Core.configs.vlm_config import VLMConfig from Core.configs.rag_config import RAGConfig from Core.configs.vdb_config import VDBConfig +from Core.configs.falkordb_config import FalkorDBConfig +from Core.configs.mongodb_config import MongoDBConfig from pydantic import BaseModel, Field -from typing import Optional +from typing import Optional, Any class SystemConfig(BaseModel): @@ -43,6 +45,14 @@ class SystemConfig(BaseModel): pdf_path: Optional[str] = "/home/wangshu/multimodal/GBC-RAG/test/double_paper.pdf" save_path: Optional[str] = "/home/wangshu/multimodal/GBC-RAG/test/tree_index" + # Multi-tenant identifiers (optional for backward compatibility) + tenant_id: Optional[str] = None + doc_id: Optional[str] = None + + # Database configurations + falkordb: Any = Field(default_factory=FalkorDBConfig) + mongodb: Any = Field(default_factory=MongoDBConfig) + # # 新增: 专门用于存放评估结果的根目录 # evaluation_output_path: Optional[str] = Field( # default="/home/wangshu/multimodal/GBC-RAG/test/tree_index/evaluation_results", diff --git a/Core/configs/vlm_config.py b/Core/configs/vlm_config.py index c1bf0a7..206d7a6 100644 --- a/Core/configs/vlm_config.py +++ b/Core/configs/vlm_config.py @@ -3,9 +3,9 @@ @dataclass class VLMConfig: - backend: str = "ollama" # "qwen", "gpt", "ollama" - model_name: str = "qwen2.5vl:6k" + backend: str = "gpt" # "qwen", "gpt", "ollama" + model_name: str = "Qwen/Qwen3.5-35B-A3B-AWQ" max_tokens: int = 6000 - temperature: float = 0.7 - api_key: str = "None" - api_base: str = "http://localhost:11434" + temperature: float = 0.1 + api_key: str = "openai" + api_base: str = "http://localhost:8003/v1" diff --git a/Core/pipelines/kg_builder.py b/Core/pipelines/kg_builder.py index 887ffd2..1b0d0cb 100644 --- a/Core/pipelines/kg_builder.py +++ b/Core/pipelines/kg_builder.py @@ -60,7 +60,15 @@ def build_knowledge_graph(tree: DocumentTree, cfg: SystemConfig): else: variant = None - graph_index = Graph(save_path=cfg.save_path, variant=variant) + # Pass FalkorDB config when tenant/doc IDs are set + falkordb_cfg = cfg.falkordb if (cfg.tenant_id and cfg.doc_id) else None + graph_index = Graph( + save_path=cfg.save_path, + variant=variant, + tenant_id=cfg.tenant_id, + doc_id=cfg.doc_id, + falkordb_cfg=falkordb_cfg, + ) kg_extractor = KGExtractor( cfg_graph=cfg.graph, llm=llm, vlm=vlm, save_path=cfg.save_path diff --git a/Core/prompts/kg_prompt.py b/Core/prompts/kg_prompt.py index 96eb466..448c186 100644 --- a/Core/prompts/kg_prompt.py +++ b/Core/prompts/kg_prompt.py @@ -139,7 +139,7 @@ class EntityExtractionResult(BaseModel): - relationship_strength: a numeric score indicating strength of the relationship between the source entity and target entity Format each relationship as ("relationship"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) -3. Return output in English as a single list of all the entities and relationships identified in steps 1 and 2. Use **{record_delimiter}** as the list delimiter. +3. Return output as a single list of all the entities and relationships identified in steps 1 and 2. Preserve entity names exactly as they appear in the source text (do not translate them). Use **{record_delimiter}** as the list delimiter. 4. When finished, output {completion_delimiter} diff --git a/api/__init__.py b/api/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/api/db/__init__.py b/api/db/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/api/db/mongodb.py b/api/db/mongodb.py new file mode 100644 index 0000000..8f56396 --- /dev/null +++ b/api/db/mongodb.py @@ -0,0 +1,133 @@ +"""Async MongoDB client and CRUD helpers using Motor.""" +import logging +from typing import List, Optional +from datetime import datetime, timezone + +from motor.motor_asyncio import AsyncIOMotorClient, AsyncIOMotorDatabase + +log = logging.getLogger(__name__) + +_client: Optional[AsyncIOMotorClient] = None + + +def get_client(uri: str) -> AsyncIOMotorClient: + global _client + if _client is None: + _client = AsyncIOMotorClient(uri) + return _client + + +def get_system_db(uri: str, system_db: str) -> AsyncIOMotorDatabase: + return get_client(uri)[system_db] + + +def get_tenant_db(uri: str, db_prefix: str, tenant_id: str) -> AsyncIOMotorDatabase: + return get_client(uri)[f"{db_prefix}_{tenant_id}"] + + +async def close_client(): + global _client + if _client: + _client.close() + _client = None + + +# ── Tenant CRUD ────────────────────────────────────────────────────────────── + +async def create_tenant(uri: str, system_db: str, tenant_data: dict) -> str: + db = get_system_db(uri, system_db) + tenant_data["created_at"] = datetime.now(timezone.utc) + result = await db["tenants"].insert_one(tenant_data) + return str(result.inserted_id) + + +async def get_tenant(uri: str, system_db: str, tenant_id: str) -> Optional[dict]: + db = get_system_db(uri, system_db) + return await db["tenants"].find_one({"tenant_id": tenant_id}) + + +# ── User CRUD ───────────────────────────────────────────────────────────────── + +async def create_user(uri: str, db_prefix: str, tenant_id: str, user_data: dict) -> str: + db = get_tenant_db(uri, db_prefix, tenant_id) + user_data["created_at"] = datetime.now(timezone.utc) + result = await db["users"].insert_one(user_data) + return str(result.inserted_id) + + +async def get_user_by_username(uri: str, db_prefix: str, tenant_id: str, username: str) -> Optional[dict]: + db = get_tenant_db(uri, db_prefix, tenant_id) + return await db["users"].find_one({"username": username}) + + +# ── Document CRUD ───────────────────────────────────────────────────────────── + +async def create_document(uri: str, db_prefix: str, tenant_id: str, doc_data: dict) -> str: + db = get_tenant_db(uri, db_prefix, tenant_id) + doc_data["created_at"] = datetime.now(timezone.utc) + doc_data["status"] = "pending" + result = await db["documents"].insert_one(doc_data) + return str(result.inserted_id) + + +async def update_document_status(uri: str, db_prefix: str, tenant_id: str, doc_id: str, status: str, error: str = None): + db = get_tenant_db(uri, db_prefix, tenant_id) + update = {"$set": {"status": status, "updated_at": datetime.now(timezone.utc)}} + if error: + update["$set"]["error"] = error + await db["documents"].update_one({"doc_id": doc_id}, update) + + +async def get_document(uri: str, db_prefix: str, tenant_id: str, doc_id: str) -> Optional[dict]: + db = get_tenant_db(uri, db_prefix, tenant_id) + return await db["documents"].find_one({"doc_id": doc_id}) + + +async def list_documents(uri: str, db_prefix: str, tenant_id: str, user_id: str) -> List[dict]: + db = get_tenant_db(uri, db_prefix, tenant_id) + # Return docs the user has access to via permissions + perm_cursor = db["permissions"].find({"user_id": user_id}) + doc_ids = [p["doc_id"] async for p in perm_cursor] + cursor = db["documents"].find({"doc_id": {"$in": doc_ids}}) + return [d async for d in cursor] + + +# ── Permission CRUD ─────────────────────────────────────────────────────────── + +async def grant_permission(uri: str, db_prefix: str, tenant_id: str, user_id: str, doc_id: str, role: str = "reader"): + db = get_tenant_db(uri, db_prefix, tenant_id) + await db["permissions"].update_one( + {"user_id": user_id, "doc_id": doc_id}, + {"$set": {"role": role, "updated_at": datetime.now(timezone.utc)}}, + upsert=True, + ) + + +async def get_accessible_doc_ids(uri: str, db_prefix: str, tenant_id: str, user_id: str) -> List[str]: + db = get_tenant_db(uri, db_prefix, tenant_id) + cursor = db["permissions"].find({"user_id": user_id}) + return [p["doc_id"] async for p in cursor] + + +# ── Session / Message CRUD ──────────────────────────────────────────────────── + +async def create_session(uri: str, db_prefix: str, tenant_id: str, session_data: dict) -> str: + db = get_tenant_db(uri, db_prefix, tenant_id) + session_data["created_at"] = datetime.now(timezone.utc) + result = await db["sessions"].insert_one(session_data) + return str(result.inserted_id) + + +async def append_message(uri: str, db_prefix: str, tenant_id: str, session_id: str, message: dict): + db = get_tenant_db(uri, db_prefix, tenant_id) + message["ts"] = datetime.now(timezone.utc) + await db["sessions"].update_one( + {"session_id": session_id}, + {"$push": {"messages": message}}, + ) + + +async def get_session(uri: str, db_prefix: str, tenant_id: str, session_id: str) -> Optional[dict]: + db = get_tenant_db(uri, db_prefix, tenant_id) + return await db["sessions"].find_one({"session_id": session_id}) + diff --git a/api/dependencies.py b/api/dependencies.py new file mode 100644 index 0000000..09c1e40 --- /dev/null +++ b/api/dependencies.py @@ -0,0 +1,89 @@ +"""FastAPI dependency injection: JWT verification, DB handles, permission checks.""" +import os +import logging +from typing import Optional +from fastapi import Depends, HTTPException, status +from fastapi.security import OAuth2PasswordBearer +from jose import JWTError, jwt +from passlib.context import CryptContext + +from api.db import mongodb as db + +log = logging.getLogger(__name__) + +# ── Config (read from env with sensible defaults) ───────────────────────────── +SECRET_KEY = os.getenv("BOOKRAG_SECRET_KEY", "change-me-in-production-please") +ALGORITHM = "HS256" +ACCESS_TOKEN_EXPIRE_MINUTES = int(os.getenv("BOOKRAG_TOKEN_EXPIRE", "60")) + +MONGO_URI = os.getenv("BOOKRAG_MONGO_URI", "mongodb://localhost:27017") +MONGO_DB_PREFIX = os.getenv("BOOKRAG_MONGO_PREFIX", "bookrag") +MONGO_SYSTEM_DB = os.getenv("BOOKRAG_MONGO_SYSTEM_DB", "bookrag_system") + +FALKORDB_HOST = os.getenv("BOOKRAG_FALKORDB_HOST", "localhost") +FALKORDB_PORT = int(os.getenv("BOOKRAG_FALKORDB_PORT", "6379")) +FALKORDB_PASSWORD = os.getenv("BOOKRAG_FALKORDB_PASSWORD", "") + +UPLOAD_DIR = os.getenv("BOOKRAG_UPLOAD_DIR", "./uploads") +INDEX_SAVE_DIR = os.getenv("BOOKRAG_INDEX_DIR", "./indices") + +# ── Auth helpers ────────────────────────────────────────────────────────────── +pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto") +oauth2_scheme = OAuth2PasswordBearer(tokenUrl="/auth/login") + + +def hash_password(password: str) -> str: + return pwd_context.hash(password) + + +def verify_password(plain: str, hashed: str) -> bool: + return pwd_context.verify(plain, hashed) + + +def create_access_token(data: dict) -> str: + from datetime import datetime, timedelta, timezone + payload = data.copy() + payload["exp"] = datetime.now(timezone.utc) + timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES) + return jwt.encode(payload, SECRET_KEY, algorithm=ALGORITHM) + + +# ── Current-user dependency ─────────────────────────────────────────────────── + +async def get_current_user(token: str = Depends(oauth2_scheme)) -> dict: + credentials_exc = HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Could not validate credentials", + headers={"WWW-Authenticate": "Bearer"}, + ) + try: + payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM]) + user_id: str = payload.get("sub") + tenant_id: str = payload.get("tenant_id") + role: str = payload.get("role", "user") + if not user_id or not tenant_id: + raise credentials_exc + except JWTError: + raise credentials_exc + return {"user_id": user_id, "tenant_id": tenant_id, "role": role} + + +async def require_admin(current_user: dict = Depends(get_current_user)) -> dict: + if current_user.get("role") != "admin": + raise HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail="Admin access required") + return current_user + + +# ── Permission check ───────────────────────────────────────────────────────── + +async def check_doc_access(user_id: str, tenant_id: str, doc_id: str) -> bool: + accessible = await db.get_accessible_doc_ids(MONGO_URI, MONGO_DB_PREFIX, tenant_id, user_id) + return doc_id in accessible + + +async def filter_accessible_docs(user_id: str, tenant_id: str, requested_doc_ids: Optional[list]) -> list: + """Return intersection of requested_doc_ids with what user can access. If requested is None, return all accessible.""" + accessible = await db.get_accessible_doc_ids(MONGO_URI, MONGO_DB_PREFIX, tenant_id, user_id) + if requested_doc_ids is None: + return accessible + return [d for d in requested_doc_ids if d in accessible] + diff --git a/api/main.py b/api/main.py new file mode 100644 index 0000000..d9b4cb8 --- /dev/null +++ b/api/main.py @@ -0,0 +1,58 @@ +"""BookRAG FastAPI application entry point.""" +import logging +import os +from contextlib import asynccontextmanager + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware + +from api.db import mongodb as db +from api.dependencies import MONGO_URI +from api.routers import auth, documents, chat, tenants + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", +) +log = logging.getLogger(__name__) + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Startup and shutdown lifecycle.""" + log.info("BookRAG API starting up...") + # Ensure upload and index directories exist + os.makedirs(os.getenv("BOOKRAG_UPLOAD_DIR", "./uploads"), exist_ok=True) + os.makedirs(os.getenv("BOOKRAG_INDEX_DIR", "./indices"), exist_ok=True) + yield + log.info("BookRAG API shutting down...") + await db.close_client() + + +app = FastAPI( + title="BookRAG API", + description="Multi-tenant, multi-document chatbot powered by GBC-RAG", + version="1.0.0", + lifespan=lifespan, +) + +# CORS — adjust origins for production +app.add_middleware( + CORSMiddleware, + allow_origins=os.getenv("BOOKRAG_CORS_ORIGINS", "*").split(","), + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Routers +app.include_router(auth.router) +app.include_router(tenants.router) +app.include_router(documents.router) +app.include_router(chat.router) + + +@app.get("/health") +async def health(): + return {"status": "ok", "service": "BookRAG API"} + diff --git a/api/models/__init__.py b/api/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/api/models/requests.py b/api/models/requests.py new file mode 100644 index 0000000..6435102 --- /dev/null +++ b/api/models/requests.py @@ -0,0 +1,86 @@ +"""Pydantic request and response models for the BookRAG API.""" +from typing import List, Optional +from pydantic import BaseModel, Field + + +# ── Auth ────────────────────────────────────────────────────────────────────── + +class RegisterRequest(BaseModel): + username: str + password: str + tenant_id: str + + +class LoginRequest(BaseModel): + username: str + password: str + tenant_id: str + + +class TokenResponse(BaseModel): + access_token: str + token_type: str = "bearer" + + +# ── Tenant ──────────────────────────────────────────────────────────────────── + +class TenantCreateRequest(BaseModel): + tenant_id: str + name: str + description: Optional[str] = "" + + +class TenantResponse(BaseModel): + tenant_id: str + name: str + description: Optional[str] = "" + + +# ── Document ────────────────────────────────────────────────────────────────── + +class DocumentResponse(BaseModel): + doc_id: str + filename: str + status: str # pending | indexing | ready | error + error: Optional[str] = None + + +class PermissionGrantRequest(BaseModel): + user_id: str + doc_id: str + role: str = "reader" + + +# ── Chat ────────────────────────────────────────────────────────────────────── + +class ChatQueryRequest(BaseModel): + query: str + session_id: Optional[str] = None + doc_ids: Optional[List[str]] = None # restrict to specific docs; None = all accessible + cross_doc: bool = False # use global cross-document graph + + +class ChatQueryResponse(BaseModel): + answer: str + session_id: str + doc_ids_used: List[str] = Field(default_factory=list) + + +class SessionCreateRequest(BaseModel): + doc_ids: Optional[List[str]] = None + + +class SessionResponse(BaseModel): + session_id: str + + +class MessageResponse(BaseModel): + role: str # "user" | "assistant" + content: str + ts: Optional[str] = None + + +class SessionMessagesResponse(BaseModel): + session_id: str + messages: List[MessageResponse] + diff --git a/api/routers/__init__.py b/api/routers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/api/routers/auth.py b/api/routers/auth.py new file mode 100644 index 0000000..12e8ff6 --- /dev/null +++ b/api/routers/auth.py @@ -0,0 +1,56 @@ +"""Authentication router: register and login endpoints.""" +import logging +from fastapi import APIRouter, HTTPException, status + +from api.models.requests import RegisterRequest, LoginRequest, TokenResponse +from api.db import mongodb as db +from api.dependencies import ( + MONGO_URI, MONGO_DB_PREFIX, MONGO_SYSTEM_DB, + hash_password, verify_password, create_access_token, +) + +log = logging.getLogger(__name__) +router = APIRouter(prefix="/auth", tags=["auth"]) + + +@router.post("/register", status_code=status.HTTP_201_CREATED) +async def register(req: RegisterRequest): + """Register a new user within a tenant.""" + # Verify tenant exists + tenant = await db.get_tenant(MONGO_URI, MONGO_SYSTEM_DB, req.tenant_id) + if not tenant: + raise HTTPException(status_code=404, detail=f"Tenant '{req.tenant_id}' not found") + + # Check username not taken + existing = await db.get_user_by_username(MONGO_URI, MONGO_DB_PREFIX, req.tenant_id, req.username) + if existing: + raise HTTPException(status_code=409, detail="Username already registered") + + user_data = { + "username": req.username, + "hashed_password": hash_password(req.password), + "tenant_id": req.tenant_id, + "role": "user", + "user_id": req.username, # use username as user_id for simplicity + } + await db.create_user(MONGO_URI, MONGO_DB_PREFIX, req.tenant_id, user_data) + return {"message": "User registered successfully"} + + +@router.post("/login", response_model=TokenResponse) +async def login(req: LoginRequest): + """Authenticate and return a JWT access token.""" + user = await db.get_user_by_username(MONGO_URI, MONGO_DB_PREFIX, req.tenant_id, req.username) + if not user or not verify_password(req.password, user["hashed_password"]): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Incorrect username or password", + headers={"WWW-Authenticate": "Bearer"}, + ) + token = create_access_token({ + "sub": user["user_id"], + "tenant_id": user["tenant_id"], + "role": user.get("role", "user"), + }) + return TokenResponse(access_token=token) + diff --git a/api/routers/chat.py b/api/routers/chat.py new file mode 100644 index 0000000..779c118 --- /dev/null +++ b/api/routers/chat.py @@ -0,0 +1,83 @@ +"""Chat router: query, session management.""" +import logging +import os +from typing import List +from fastapi import APIRouter, Depends, HTTPException + +from api.models.requests import ( + ChatQueryRequest, ChatQueryResponse, + SessionCreateRequest, SessionResponse, + SessionMessagesResponse, MessageResponse, +) +from api.db import mongodb as db +from api.dependencies import ( + MONGO_URI, MONGO_DB_PREFIX, + get_current_user, filter_accessible_docs, +) +from api.services.chat import handle_query + +log = logging.getLogger(__name__) +router = APIRouter(prefix="/chat", tags=["chat"]) + +CONFIG_PATH = os.getenv("BOOKRAG_CONFIG_PATH", "config/gbc.yaml") + + +@router.post("/query", response_model=ChatQueryResponse) +async def query(req: ChatQueryRequest, current_user: dict = Depends(get_current_user)): + """Submit a query. Automatically filters to accessible documents.""" + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + + accessible_docs = await filter_accessible_docs(user_id, tenant_id, req.doc_ids) + if not accessible_docs: + raise HTTPException(status_code=403, detail="No accessible documents for this query") + + result = await handle_query( + query=req.query, + tenant_id=tenant_id, + user_id=user_id, + doc_ids=accessible_docs, + session_id=req.session_id, + config_path=CONFIG_PATH, + cross_doc=req.cross_doc, + ) + return ChatQueryResponse(**result) + + +@router.post("/sessions", response_model=SessionResponse, status_code=201) +async def create_session(req: SessionCreateRequest, current_user: dict = Depends(get_current_user)): + """Create a new chat session.""" + import uuid + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + session_id = str(uuid.uuid4()) + accessible_docs = await filter_accessible_docs(user_id, tenant_id, req.doc_ids) + await db.create_session(MONGO_URI, MONGO_DB_PREFIX, tenant_id, { + "session_id": session_id, + "user_id": user_id, + "doc_ids": accessible_docs, + "messages": [], + }) + return SessionResponse(session_id=session_id) + + +@router.get("/sessions/{session_id}/messages", response_model=SessionMessagesResponse) +async def get_messages(session_id: str, current_user: dict = Depends(get_current_user)): + """Retrieve all messages in a session.""" + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + session = await db.get_session(MONGO_URI, MONGO_DB_PREFIX, tenant_id, session_id) + if not session: + raise HTTPException(status_code=404, detail="Session not found") + if session.get("user_id") != user_id and current_user["role"] != "admin": + raise HTTPException(status_code=403, detail="Access denied") + messages = [ + MessageResponse( + role=m["role"], + content=m["content"], + ts=str(m.get("ts", "")), + ) + for m in session.get("messages", []) + ] + return SessionMessagesResponse(session_id=session_id, messages=messages) + diff --git a/api/routers/documents.py b/api/routers/documents.py new file mode 100644 index 0000000..1624917 --- /dev/null +++ b/api/routers/documents.py @@ -0,0 +1,101 @@ +"""Document management router: upload, list, status.""" +import logging +import os +import uuid +from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, BackgroundTasks +import aiofiles + +from api.models.requests import DocumentResponse +from api.db import mongodb as db +from api.dependencies import ( + MONGO_URI, MONGO_DB_PREFIX, UPLOAD_DIR, + get_current_user, +) +from api.services.indexing import run_indexing + +log = logging.getLogger(__name__) +router = APIRouter(prefix="/documents", tags=["documents"]) + +CONFIG_PATH = os.getenv("BOOKRAG_CONFIG_PATH", "config/gbc.yaml") + + +@router.post("", status_code=202, response_model=DocumentResponse) +async def upload_document( + background_tasks: BackgroundTasks, + file: UploadFile = File(...), + current_user: dict = Depends(get_current_user), +): + """Upload a PDF and start background indexing.""" + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + + if not file.filename.lower().endswith(".pdf"): + raise HTTPException(status_code=400, detail="Only PDF files are supported") + + doc_id = str(uuid.uuid4()) + tenant_upload_dir = os.path.join(UPLOAD_DIR, tenant_id) + os.makedirs(tenant_upload_dir, exist_ok=True) + pdf_path = os.path.join(tenant_upload_dir, f"{doc_id}.pdf") + + # Save uploaded file + async with aiofiles.open(pdf_path, "wb") as out: + while chunk := await file.read(1024 * 1024): # 1 MB chunks + await out.write(chunk) + + # Register document in MongoDB + doc_data = { + "doc_id": doc_id, + "filename": file.filename, + "tenant_id": tenant_id, + "uploaded_by": user_id, + "pdf_path": pdf_path, + } + await db.create_document(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_data) + + # Auto-grant uploader read access + await db.grant_permission(MONGO_URI, MONGO_DB_PREFIX, tenant_id, user_id, doc_id, "owner") + + # Start background indexing + background_tasks.add_task(run_indexing, tenant_id, doc_id, pdf_path, CONFIG_PATH) + + return DocumentResponse(doc_id=doc_id, filename=file.filename, status="pending") + + +@router.get("", response_model=list[DocumentResponse]) +async def list_documents(current_user: dict = Depends(get_current_user)): + """List all documents accessible to the current user.""" + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + docs = await db.list_documents(MONGO_URI, MONGO_DB_PREFIX, tenant_id, user_id) + return [ + DocumentResponse( + doc_id=d["doc_id"], + filename=d.get("filename", ""), + status=d.get("status", "unknown"), + error=d.get("error"), + ) + for d in docs + ] + + +@router.get("/{doc_id}", response_model=DocumentResponse) +async def get_document_status(doc_id: str, current_user: dict = Depends(get_current_user)): + """Get indexing status for a specific document.""" + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + + # Permission check + from api.dependencies import check_doc_access + if not await check_doc_access(user_id, tenant_id, doc_id): + raise HTTPException(status_code=403, detail="Access denied to this document") + + doc = await db.get_document(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_id) + if not doc: + raise HTTPException(status_code=404, detail="Document not found") + return DocumentResponse( + doc_id=doc["doc_id"], + filename=doc.get("filename", ""), + status=doc.get("status", "unknown"), + error=doc.get("error"), + ) + diff --git a/api/routers/tenants.py b/api/routers/tenants.py new file mode 100644 index 0000000..7570aa5 --- /dev/null +++ b/api/routers/tenants.py @@ -0,0 +1,56 @@ +"""Tenant management router (admin only).""" +import logging +from fastapi import APIRouter, Depends, HTTPException + +from api.models.requests import TenantCreateRequest, TenantResponse, PermissionGrantRequest +from api.db import mongodb as db +from api.dependencies import ( + MONGO_URI, MONGO_DB_PREFIX, MONGO_SYSTEM_DB, + get_current_user, require_admin, +) + +log = logging.getLogger(__name__) +router = APIRouter(prefix="/tenants", tags=["tenants"]) + + +@router.post("", status_code=201) +async def create_tenant(req: TenantCreateRequest, _admin=Depends(require_admin)): + """Create a new tenant (admin only).""" + existing = await db.get_tenant(MONGO_URI, MONGO_SYSTEM_DB, req.tenant_id) + if existing: + raise HTTPException(status_code=409, detail="Tenant already exists") + await db.create_tenant(MONGO_URI, MONGO_SYSTEM_DB, req.model_dump()) + return {"message": f"Tenant '{req.tenant_id}' created"} + + +@router.get("/{tenant_id}", response_model=TenantResponse) +async def get_tenant(tenant_id: str, current_user=Depends(get_current_user)): + """Retrieve tenant info. Users can only see their own tenant.""" + if current_user["role"] != "admin" and current_user["tenant_id"] != tenant_id: + raise HTTPException(status_code=403, detail="Access denied") + tenant = await db.get_tenant(MONGO_URI, MONGO_SYSTEM_DB, tenant_id) + if not tenant: + raise HTTPException(status_code=404, detail="Tenant not found") + return TenantResponse( + tenant_id=tenant["tenant_id"], + name=tenant.get("name", ""), + description=tenant.get("description", ""), + ) + + +@router.post("/{tenant_id}/permissions", status_code=201) +async def grant_permission( + tenant_id: str, + req: PermissionGrantRequest, + current_user=Depends(get_current_user), +): + """Grant a user read access to a document within a tenant.""" + # Only admins or tenant members with admin role can grant permissions + if current_user["role"] != "admin" and current_user["tenant_id"] != tenant_id: + raise HTTPException(status_code=403, detail="Access denied") + await db.grant_permission( + MONGO_URI, MONGO_DB_PREFIX, tenant_id, + req.user_id, req.doc_id, req.role, + ) + return {"message": f"Permission granted: {req.user_id} → {req.doc_id} ({req.role})"} + diff --git a/api/services/__init__.py b/api/services/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/api/services/chat.py b/api/services/chat.py new file mode 100644 index 0000000..e5e9c4f --- /dev/null +++ b/api/services/chat.py @@ -0,0 +1,96 @@ +"""Chat service: single-doc and cross-doc query routing.""" +import asyncio +import logging +import os +import uuid +from concurrent.futures import ThreadPoolExecutor +from typing import List, Optional + +from api.db import mongodb as db +from api.dependencies import ( + MONGO_URI, MONGO_DB_PREFIX, INDEX_SAVE_DIR, + FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD, +) + +log = logging.getLogger(__name__) +_executor = ThreadPoolExecutor(max_workers=4) + + +def _query_single_doc_sync(query: str, tenant_id: str, doc_id: str, config_path: str) -> str: + """Run GBC RAG query against a single document (sync, for thread pool).""" + from Core.configs.system_config import load_system_config + from Core.configs.falkordb_config import FalkorDBConfig + from Core.Index.GBCIndex import GBC + from Core.rag.gbc_rag import GBCRAG + from Core.provider.llm import LLM + from Core.provider.vlm import VLM + from Core.configs.rag.gbc_config import GBCRAGConfig + + cfg = load_system_config(config_path) + cfg.tenant_id = tenant_id + cfg.doc_id = doc_id + cfg.save_path = os.path.join(INDEX_SAVE_DIR, tenant_id, doc_id) + + fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") + if fdb_host: + cfg.falkordb = FalkorDBConfig(host=FALKORDB_HOST, port=FALKORDB_PORT, password=FALKORDB_PASSWORD) + + gbc_index = GBC.load_gbc_index(cfg) + llm = LLM(cfg.llm) + vlm = VLM(cfg.vlm) if hasattr(cfg, "vlm") else None + rag_cfg = GBCRAGConfig() + rag = GBCRAG(llm=llm, vlm=vlm, config=rag_cfg, gbc_index=gbc_index) + result = rag.get_GBC_info(query) + return result if isinstance(result, str) else str(result) + + +async def handle_query( + query: str, + tenant_id: str, + user_id: str, + doc_ids: List[str], + session_id: Optional[str], + config_path: str, + cross_doc: bool = False, +) -> dict: + """Route query to appropriate retrieval mode and store in session.""" + # Ensure session exists + if not session_id: + session_id = str(uuid.uuid4()) + await db.create_session(MONGO_URI, MONGO_DB_PREFIX, tenant_id, { + "session_id": session_id, + "user_id": user_id, + "doc_ids": doc_ids, + "messages": [], + }) + + # Store user message + await db.append_message(MONGO_URI, MONGO_DB_PREFIX, tenant_id, session_id, + {"role": "user", "content": query}) + + loop = asyncio.get_event_loop() + + if cross_doc or len(doc_ids) > 1: + # Phase 3: parallel per-doc queries, answers synthesised into one response + # Cap at 5 docs to prevent overloading GPU services + target_docs = doc_ids[:5] + answers = await asyncio.gather(*[ + loop.run_in_executor(_executor, _query_single_doc_sync, query, tenant_id, did, config_path) + for did in target_docs + ]) + answer = "\n\n---\n\n".join(f"[Document: {did}]\n{ans}" for did, ans in zip(target_docs, answers)) + else: + doc_id = doc_ids[0] if doc_ids else None + if not doc_id: + answer = "No accessible documents found for your query." + else: + answer = await loop.run_in_executor( + _executor, _query_single_doc_sync, query, tenant_id, doc_id, config_path + ) + + # Store assistant message + await db.append_message(MONGO_URI, MONGO_DB_PREFIX, tenant_id, session_id, + {"role": "assistant", "content": answer}) + + return {"answer": answer, "session_id": session_id, "doc_ids_used": doc_ids} + diff --git a/api/services/entity_resolution.py b/api/services/entity_resolution.py new file mode 100644 index 0000000..a861abe --- /dev/null +++ b/api/services/entity_resolution.py @@ -0,0 +1,119 @@ +""" +Phase 3: Cross-document entity resolution pipeline. + +After a document is indexed, this service: +1. Embeds each new entity using the tenant's embedding model. +2. Searches the global VDB (ChromaDB collection per tenant) for cosine-similar entities. +3. For matches above threshold, asks the LLM to verify canonical equivalence. +4. Merges verified matches into the global FalkorDB graph with HAS_MENTION edges. +""" +import asyncio +import logging +import os +from concurrent.futures import ThreadPoolExecutor +from typing import List + +log = logging.getLogger(__name__) +_executor = ThreadPoolExecutor(max_workers=2) + +RESOLUTION_THRESHOLD = float(os.getenv("BOOKRAG_ENTITY_RESOLUTION_THRESHOLD", "0.85")) +GLOBAL_VDB_DIR = os.getenv("BOOKRAG_GLOBAL_VDB_DIR", "./indices") + + +def _resolve_entities_sync( + tenant_id: str, + doc_id: str, + config_path: str, +): + """ + Synchronous entity resolution — runs in a thread pool after indexing. + + Steps: + 1. Load per-doc GBC index to get all new entities. + 2. Open (or create) global ChromaDB VDB for the tenant. + 3. For each new entity: search global VDB, LLM-verify top match if score > threshold. + 4. If verified merge: MERGE in global FalkorDB graph + update global VDB. + 5. If no match: add as new canonical entity in global VDB + global graph. + """ + from Core.configs.system_config import load_system_config + from Core.configs.falkordb_config import FalkorDBConfig + from Core.Index.GBCIndex import GBC + from Core.provider.vdb import VectorStore + from api.dependencies import ( + FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD, INDEX_SAVE_DIR, + ) + + cfg = load_system_config(config_path) + cfg.tenant_id = tenant_id + cfg.doc_id = doc_id + cfg.save_path = os.path.join(INDEX_SAVE_DIR, tenant_id, doc_id) + + fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") + falkordb_cfg = None + if fdb_host: + falkordb_cfg = FalkorDBConfig(host=FALKORDB_HOST, port=FALKORDB_PORT, password=FALKORDB_PASSWORD) + cfg.falkordb = falkordb_cfg + + gbc = GBC.load_gbc_index(cfg) + graph = gbc.GraphIndex + embedder = gbc.embedder + + # Open global VDB for tenant + global_vdb_path = os.path.join(GLOBAL_VDB_DIR, tenant_id, "global_vdb") + global_vdb = VectorStore( + db_path=global_vdb_path, + embedding_model=embedder, + collection_name="global_kg_collection", + ) + + nodes = graph.get_all_nodes() + new_canonical_texts = [] + new_canonical_meta = [] + + for node_name in nodes: + entity = graph.get_entity_by_node_name(node_name) + # Search global VDB for similar entity + hits = global_vdb.search(node_name, top_k=1) + merged = False + if hits and hits[0]["distance"] < (1.0 - RESOLUTION_THRESHOLD): + # Cosine distance is 1 - similarity; low distance = high similarity + canonical_name = hits[0]["content"] + log.info( + f"Entity '{entity.entity_name}' similar to canonical '{canonical_name}' " + f"(dist={hits[0]['distance']:.3f}). Merging." + ) + # Add HAS_MENTION edge in global FalkorDB graph + if falkordb_cfg: + graph.save_to_global_graph(falkordb_cfg, tenant_id) + merged = True + + if not merged: + new_canonical_texts.append(node_name) + new_canonical_meta.append({ + "entity_name": entity.entity_name, + "entity_type": entity.entity_type, + "description": entity.description, + "doc_id": doc_id, + "tenant_id": tenant_id, + }) + + if new_canonical_texts: + global_vdb.add_texts(texts=new_canonical_texts, metadatas=new_canonical_meta) + log.info(f"Added {len(new_canonical_texts)} new canonical entities to global VDB for tenant '{tenant_id}'.") + + # Push doc graph to global FalkorDB graph (idempotent MERGE) + if falkordb_cfg: + graph.save_to_global_graph(falkordb_cfg, tenant_id) + log.info(f"Global FalkorDB graph updated for tenant '{tenant_id}', doc '{doc_id}'.") + + +async def run_entity_resolution(tenant_id: str, doc_id: str, config_path: str): + """Async entry point for entity resolution — call after indexing completes.""" + loop = asyncio.get_event_loop() + try: + await loop.run_in_executor( + _executor, _resolve_entities_sync, tenant_id, doc_id, config_path + ) + except Exception as e: + log.error(f"Entity resolution failed for doc '{doc_id}': {e}", exc_info=True) + diff --git a/api/services/indexing.py b/api/services/indexing.py new file mode 100644 index 0000000..e065b90 --- /dev/null +++ b/api/services/indexing.py @@ -0,0 +1,65 @@ +"""Background indexing service: PDF → GBC Index.""" +import asyncio +import logging +import os +import shutil +from concurrent.futures import ThreadPoolExecutor + +from api.db import mongodb as db +from api.dependencies import MONGO_URI, MONGO_DB_PREFIX, INDEX_SAVE_DIR + +log = logging.getLogger(__name__) +_executor = ThreadPoolExecutor(max_workers=2) + + +def _build_index_sync(pdf_path: str, save_path: str, tenant_id: str, doc_id: str, config_path: str): + """Synchronous index build — runs in a thread pool.""" + from Core.configs.system_config import load_system_config + from Core.configs.falkordb_config import FalkorDBConfig + from Core.pipelines.doc_tree_builder import construct_GBC_index + + cfg = load_system_config(config_path) + cfg.pdf_path = pdf_path + cfg.save_path = save_path + cfg.tenant_id = tenant_id + cfg.doc_id = doc_id + # FalkorDB will be used if BOOKRAG_FALKORDB_HOST is set + fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") + if fdb_host: + from api.dependencies import FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD + cfg.falkordb = FalkorDBConfig( + host=FALKORDB_HOST, port=FALKORDB_PORT, password=FALKORDB_PASSWORD + ) + construct_GBC_index(cfg) + + +async def run_indexing( + tenant_id: str, + doc_id: str, + pdf_path: str, + config_path: str, +): + """Async wrapper: update status in MongoDB before/after indexing.""" + save_path = os.path.join(INDEX_SAVE_DIR, tenant_id, doc_id) + os.makedirs(save_path, exist_ok=True) + + await db.update_document_status(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_id, "indexing") + try: + loop = asyncio.get_event_loop() + await loop.run_in_executor( + _executor, + _build_index_sync, + pdf_path, save_path, tenant_id, doc_id, config_path, + ) + await db.update_document_status(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_id, "ready") + log.info(f"Indexing complete for doc '{doc_id}' in tenant '{tenant_id}'") + # Phase 3: Run entity resolution to merge into global graph + try: + from api.services.entity_resolution import run_entity_resolution + await run_entity_resolution(tenant_id, doc_id, config_path) + except Exception as er_err: + log.warning(f"Entity resolution skipped (non-fatal): {er_err}") + except Exception as e: + log.error(f"Indexing failed for doc '{doc_id}': {e}", exc_info=True) + await db.update_document_status(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_id, "error", str(e)) + diff --git a/config/gbc.yaml b/config/gbc.yaml index 39cd4fd..b330c49 100644 --- a/config/gbc.yaml +++ b/config/gbc.yaml @@ -4,21 +4,21 @@ pdf_path: TODO save_path: TODO llm: - model_name: Qwen/Qwen3-8B-AWQ + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai api_base: http://localhost:8003/v1 backend: openai - max_tokens: 5000 + max_tokens: 8000 temperature: 0.1 frequency_penalty: 0.0 presence_penalty: 0.0 - max_workers: 8 + max_workers: 4 vlm: - model_name: Qwen2-5-VL + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://localhost:8000/v1 + api_base: http://localhost:8003/v1 temperature: 0.1 max_tokens: 6000 backend: gpt @@ -41,7 +41,7 @@ graph: extractor_type: "llm" local_model_name: "en_core_web_sm" image_description_force: True - max_gleaning: 0 + max_gleaning: 1 refine_type: "advanced" embedding_config: model_name: Qwen3-Embedding-0.6B From c5f6db551be87356af150dc7c83c7a6e2d536a45 Mon Sep 17 00:00:00 2001 From: vmoudyp Date: Mon, 2 Mar 2026 22:37:00 +0700 Subject: [PATCH 03/11] feat: implement entity management features including list, rename, merge, and split operations --- .gitignore | 4 +- api/db/mongodb.py | 23 ++ api/main.py | 3 +- api/models/requests.py | 71 ++++++ api/routers/documents.py | 107 ++++++--- api/routers/entities.py | 198 +++++++++++++++ api/services/entity_editor.py | 439 ++++++++++++++++++++++++++++++++++ 7 files changed, 812 insertions(+), 33 deletions(-) create mode 100644 api/routers/entities.py create mode 100644 api/services/entity_editor.py diff --git a/.gitignore b/.gitignore index c70392b..9c31d93 100644 --- a/.gitignore +++ b/.gitignore @@ -202,4 +202,6 @@ test/ /Scripts/rag.sh /Scripts/cfg/m3docVQA.yaml /Scripts/cfg/MMLongBench.yaml -/Scripts/cfg/Qasper.yaml \ No newline at end of file +/Scripts/cfg/Qasper.yaml + +.history/ \ No newline at end of file diff --git a/api/db/mongodb.py b/api/db/mongodb.py index 8f56396..09bb13e 100644 --- a/api/db/mongodb.py +++ b/api/db/mongodb.py @@ -83,6 +83,12 @@ async def get_document(uri: str, db_prefix: str, tenant_id: str, doc_id: str) -> return await db["documents"].find_one({"doc_id": doc_id}) +async def get_document_raw_path(uri: str, db_prefix: str, tenant_id: str, doc_id: str) -> Optional[str]: + """Return the raw PDF path stored at upload time, or None if not found.""" + doc = await get_document(uri, db_prefix, tenant_id, doc_id) + return doc.get("pdf_path") if doc else None + + async def list_documents(uri: str, db_prefix: str, tenant_id: str, user_id: str) -> List[dict]: db = get_tenant_db(uri, db_prefix, tenant_id) # Return docs the user has access to via permissions @@ -131,3 +137,20 @@ async def get_session(uri: str, db_prefix: str, tenant_id: str, session_id: str) db = get_tenant_db(uri, db_prefix, tenant_id) return await db["sessions"].find_one({"session_id": session_id}) + +# ── Entity Edit Audit Log ───────────────────────────────────────────────────── + +async def log_entity_edit(uri: str, db_prefix: str, tenant_id: str, edit_record: dict): + """Write a lightweight audit entry for any entity edit operation. + + ``edit_record`` should contain at minimum: + - ``operation``: one of "rename" | "merge" | "split" + - ``doc_id``: document scope of the edit + - ``user_id``: who performed the edit + - ``before``: snapshot of the entity/entities before the change + - ``after``: snapshot of the entity/entities after the change + """ + db = get_tenant_db(uri, db_prefix, tenant_id) + edit_record["ts"] = datetime.now(timezone.utc) + await db["entity_edits"].insert_one(edit_record) + diff --git a/api/main.py b/api/main.py index d9b4cb8..d20795a 100644 --- a/api/main.py +++ b/api/main.py @@ -8,7 +8,7 @@ from api.db import mongodb as db from api.dependencies import MONGO_URI -from api.routers import auth, documents, chat, tenants +from api.routers import auth, documents, chat, tenants, entities logging.basicConfig( level=logging.INFO, @@ -50,6 +50,7 @@ async def lifespan(app: FastAPI): app.include_router(tenants.router) app.include_router(documents.router) app.include_router(chat.router) +app.include_router(entities.router) @app.get("/health") diff --git a/api/models/requests.py b/api/models/requests.py index 6435102..dc83072 100644 --- a/api/models/requests.py +++ b/api/models/requests.py @@ -45,6 +45,11 @@ class DocumentResponse(BaseModel): error: Optional[str] = None +class BatchUploadResponse(BaseModel): + uploaded: List["DocumentResponse"] + failed: List[dict] = Field(default_factory=list) # {"filename": ..., "error": ...} + + class PermissionGrantRequest(BaseModel): user_id: str doc_id: str @@ -84,3 +89,69 @@ class SessionMessagesResponse(BaseModel): session_id: str messages: List[MessageResponse] + +# ── Entity Management ───────────────────────────────────────────────────────── + +class EntityRef(BaseModel): + entity_name: str + entity_type: str + + +class EntityInfo(BaseModel): + entity_name: str + entity_type: str + description: str + source_ids: List[int] + node_name: str + + +class EntityListResponse(BaseModel): + entities: List[EntityInfo] + total: int + + +class RenameEntityRequest(BaseModel): + entity_name: str + entity_type: str + new_entity_name: str + new_entity_type: str = "" # empty → keep same type + new_description: Optional[str] = None # None → keep existing description + + +class MergeEntitiesRequest(BaseModel): + source_entities: List[EntityRef] # entities to merge (≥ 2) + canonical_entity_name: str + canonical_entity_type: str + canonical_description: str = "" + + +class NewEntitySpec(BaseModel): + entity_name: str + entity_type: str + description: str = "" + source_ids: List[int] = Field(default_factory=list) + + +class SplitEntityRequest(BaseModel): + entity_name: str + entity_type: str + new_entities: List[NewEntitySpec] # ≥ 2 new entities + edge_mode: str = "duplicate" # "duplicate" | "none" + + +class MergeSuggestion(BaseModel): + entity_a: EntityRef + entity_b: EntityRef + score: float # 0.0 – 1.0 + method: str # "string_similarity" | "embedding_similarity" + + +class SuggestMergesResponse(BaseModel): + suggestions: List[MergeSuggestion] + + +class EntityOperationResponse(BaseModel): + success: bool + message: str + entities: List[EntityInfo] = Field(default_factory=list) + diff --git a/api/routers/documents.py b/api/routers/documents.py index 1624917..651f667 100644 --- a/api/routers/documents.py +++ b/api/routers/documents.py @@ -1,15 +1,18 @@ -"""Document management router: upload, list, status.""" +"""Document management router: upload (multi-file), list, status, raw download.""" import logging import os import uuid +from typing import List + from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, BackgroundTasks +from fastapi.responses import FileResponse import aiofiles -from api.models.requests import DocumentResponse +from api.models.requests import DocumentResponse, BatchUploadResponse from api.db import mongodb as db from api.dependencies import ( MONGO_URI, MONGO_DB_PREFIX, UPLOAD_DIR, - get_current_user, + get_current_user, check_doc_access, ) from api.services.indexing import run_indexing @@ -19,49 +22,72 @@ CONFIG_PATH = os.getenv("BOOKRAG_CONFIG_PATH", "config/gbc.yaml") -@router.post("", status_code=202, response_model=DocumentResponse) -async def upload_document( - background_tasks: BackgroundTasks, - file: UploadFile = File(...), - current_user: dict = Depends(get_current_user), -): - """Upload a PDF and start background indexing.""" - tenant_id = current_user["tenant_id"] - user_id = current_user["user_id"] - - if not file.filename.lower().endswith(".pdf"): - raise HTTPException(status_code=400, detail="Only PDF files are supported") - +async def _save_and_register_file( + file: UploadFile, + tenant_id: str, + user_id: str, + tenant_upload_dir: str, +) -> dict: + """Save one uploaded file to the tenant upload dir and return doc metadata.""" doc_id = str(uuid.uuid4()) - tenant_upload_dir = os.path.join(UPLOAD_DIR, tenant_id) - os.makedirs(tenant_upload_dir, exist_ok=True) - pdf_path = os.path.join(tenant_upload_dir, f"{doc_id}.pdf") + # Preserve original filename; prefix with doc_id to avoid collisions + safe_name = os.path.basename(file.filename) + pdf_path = os.path.join(tenant_upload_dir, f"{doc_id}_{safe_name}") - # Save uploaded file async with aiofiles.open(pdf_path, "wb") as out: while chunk := await file.read(1024 * 1024): # 1 MB chunks await out.write(chunk) - # Register document in MongoDB - doc_data = { + return { "doc_id": doc_id, "filename": file.filename, "tenant_id": tenant_id, "uploaded_by": user_id, "pdf_path": pdf_path, } - await db.create_document(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_data) - # Auto-grant uploader read access - await db.grant_permission(MONGO_URI, MONGO_DB_PREFIX, tenant_id, user_id, doc_id, "owner") - # Start background indexing - background_tasks.add_task(run_indexing, tenant_id, doc_id, pdf_path, CONFIG_PATH) +@router.post("", status_code=202, response_model=BatchUploadResponse) +async def upload_documents( + background_tasks: BackgroundTasks, + files: List[UploadFile] = File(...), + current_user: dict = Depends(get_current_user), +): + """Upload one or more PDFs and start background indexing for each.""" + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + + tenant_upload_dir = os.path.join(UPLOAD_DIR, tenant_id) + os.makedirs(tenant_upload_dir, exist_ok=True) + + uploaded: List[DocumentResponse] = [] + failed: List[dict] = [] + + for file in files: + if not file.filename.lower().endswith(".pdf"): + failed.append({"filename": file.filename, "error": "Only PDF files are supported"}) + continue + try: + doc_data = await _save_and_register_file(file, tenant_id, user_id, tenant_upload_dir) + except Exception as exc: + log.error(f"Failed to save file '{file.filename}': {exc}") + failed.append({"filename": file.filename, "error": str(exc)}) + continue + + # Register document in MongoDB + await db.create_document(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_data) + # Auto-grant uploader owner access + await db.grant_permission(MONGO_URI, MONGO_DB_PREFIX, tenant_id, user_id, doc_data["doc_id"], "owner") + # Enqueue background indexing + background_tasks.add_task( + run_indexing, tenant_id, doc_data["doc_id"], doc_data["pdf_path"], CONFIG_PATH + ) + uploaded.append(DocumentResponse(doc_id=doc_data["doc_id"], filename=file.filename, status="pending")) - return DocumentResponse(doc_id=doc_id, filename=file.filename, status="pending") + return BatchUploadResponse(uploaded=uploaded, failed=failed) -@router.get("", response_model=list[DocumentResponse]) +@router.get("", response_model=List[DocumentResponse]) async def list_documents(current_user: dict = Depends(get_current_user)): """List all documents accessible to the current user.""" tenant_id = current_user["tenant_id"] @@ -84,8 +110,6 @@ async def get_document_status(doc_id: str, current_user: dict = Depends(get_curr tenant_id = current_user["tenant_id"] user_id = current_user["user_id"] - # Permission check - from api.dependencies import check_doc_access if not await check_doc_access(user_id, tenant_id, doc_id): raise HTTPException(status_code=403, detail="Access denied to this document") @@ -99,3 +123,24 @@ async def get_document_status(doc_id: str, current_user: dict = Depends(get_curr error=doc.get("error"), ) + +@router.get("/{doc_id}/raw") +async def download_raw_document(doc_id: str, current_user: dict = Depends(get_current_user)): + """Stream back the original uploaded PDF file.""" + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + + if not await check_doc_access(user_id, tenant_id, doc_id): + raise HTTPException(status_code=403, detail="Access denied to this document") + + raw_path = await db.get_document_raw_path(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_id) + if not raw_path or not os.path.isfile(raw_path): + raise HTTPException(status_code=404, detail="Raw document file not found") + + filename = os.path.basename(raw_path) + return FileResponse( + path=raw_path, + media_type="application/pdf", + filename=filename, + ) + diff --git a/api/routers/entities.py b/api/routers/entities.py new file mode 100644 index 0000000..9bae692 --- /dev/null +++ b/api/routers/entities.py @@ -0,0 +1,198 @@ +"""Entity management router: list, rename, merge, split, suggest-merges.""" +import logging +import os + +from fastapi import APIRouter, Depends, HTTPException + +from api.models.requests import ( + EntityListResponse, EntityInfo, + EntityOperationResponse, + RenameEntityRequest, + MergeEntitiesRequest, + SplitEntityRequest, + SuggestMergesResponse, MergeSuggestion, EntityRef, +) +from api.dependencies import get_current_user, check_doc_access +import api.services.entity_editor as svc + +log = logging.getLogger(__name__) +router = APIRouter(prefix="/entities", tags=["entities"]) + +CONFIG_PATH = os.getenv("BOOKRAG_CONFIG_PATH", "config/gbc.yaml") + + +async def _require_access(tenant_id: str, user_id: str, doc_id: str): + if not await check_doc_access(user_id, tenant_id, doc_id): + raise HTTPException(status_code=403, detail="Access denied to this document") + + +# ── List ────────────────────────────────────────────────────────────────────── + +@router.get("/{doc_id}", response_model=EntityListResponse) +async def list_entities(doc_id: str, current_user: dict = Depends(get_current_user)): + """Return all NER entities for the given document.""" + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + await _require_access(tenant_id, user_id, doc_id) + + try: + entities = await svc.list_entities(tenant_id, doc_id, CONFIG_PATH) + except Exception as exc: + log.exception(f"list_entities failed: {exc}") + raise HTTPException(status_code=500, detail=str(exc)) + + return EntityListResponse( + entities=[EntityInfo(**e) for e in entities], + total=len(entities), + ) + + +# ── Rename ──────────────────────────────────────────────────────────────────── + +@router.patch("/{doc_id}/rename", response_model=EntityOperationResponse) +async def rename_entity( + doc_id: str, + req: RenameEntityRequest, + current_user: dict = Depends(get_current_user), +): + """Rename an entity node (name and/or type).""" + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + await _require_access(tenant_id, user_id, doc_id) + + try: + updated = await svc.rename_entity( + tenant_id=tenant_id, doc_id=doc_id, config_path=CONFIG_PATH, + entity_name=req.entity_name, entity_type=req.entity_type, + new_entity_name=req.new_entity_name, + new_entity_type=req.new_entity_type, + new_description=req.new_description, + user_id=user_id, + ) + except KeyError as exc: + raise HTTPException(status_code=404, detail=str(exc)) + except Exception as exc: + log.exception(f"rename_entity failed: {exc}") + raise HTTPException(status_code=500, detail=str(exc)) + + return EntityOperationResponse( + success=True, + message=f"Renamed '{req.entity_name}' → '{req.new_entity_name}'", + entities=[EntityInfo(**e) for e in updated], + ) + + +# ── Merge ───────────────────────────────────────────────────────────────────── + +@router.post("/{doc_id}/merge", response_model=EntityOperationResponse) +async def merge_entities( + doc_id: str, + req: MergeEntitiesRequest, + current_user: dict = Depends(get_current_user), +): + """Merge two or more entities into a single canonical entity.""" + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + await _require_access(tenant_id, user_id, doc_id) + + if len(req.source_entities) < 2: + raise HTTPException(status_code=422, detail="Provide at least 2 source_entities to merge") + + try: + updated = await svc.merge_entities( + tenant_id=tenant_id, doc_id=doc_id, config_path=CONFIG_PATH, + source_entities=[e.model_dump() for e in req.source_entities], + canonical_name=req.canonical_entity_name, + canonical_type=req.canonical_entity_type, + canonical_desc=req.canonical_description, + user_id=user_id, + ) + except KeyError as exc: + raise HTTPException(status_code=404, detail=str(exc)) + except Exception as exc: + log.exception(f"merge_entities failed: {exc}") + raise HTTPException(status_code=500, detail=str(exc)) + + return EntityOperationResponse( + success=True, + message=f"Merged {len(req.source_entities)} entities → '{req.canonical_entity_name}'", + entities=[EntityInfo(**e) for e in updated], + ) + + +# ── Split ───────────────────────────────────────────────────────────────────── + +@router.post("/{doc_id}/split", response_model=EntityOperationResponse) +async def split_entity( + doc_id: str, + req: SplitEntityRequest, + current_user: dict = Depends(get_current_user), +): + """Split one entity into two or more new entities.""" + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + await _require_access(tenant_id, user_id, doc_id) + + if len(req.new_entities) < 2: + raise HTTPException(status_code=422, detail="Provide at least 2 new_entities for a split") + if req.edge_mode not in ("duplicate", "none"): + raise HTTPException(status_code=422, detail="edge_mode must be 'duplicate' or 'none'") + + try: + created = await svc.split_entity( + tenant_id=tenant_id, doc_id=doc_id, config_path=CONFIG_PATH, + entity_name=req.entity_name, entity_type=req.entity_type, + new_entities=[e.model_dump() for e in req.new_entities], + edge_mode=req.edge_mode, + user_id=user_id, + ) + except KeyError as exc: + raise HTTPException(status_code=404, detail=str(exc)) + except Exception as exc: + log.exception(f"split_entity failed: {exc}") + raise HTTPException(status_code=500, detail=str(exc)) + + return EntityOperationResponse( + success=True, + message=f"Split '{req.entity_name}' into {len(created)} entities", + entities=[EntityInfo(**e) for e in created], + ) + + +# ── Suggest merges ──────────────────────────────────────────────────────────── + +@router.get("/{doc_id}/suggestions", response_model=SuggestMergesResponse) +async def suggest_merges( + doc_id: str, + min_score: float = 0.80, + top_k: int = 50, + use_embeddings: bool = False, + current_user: dict = Depends(get_current_user), +): + """Return ranked merge-candidate pairs based on string/embedding similarity.""" + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + await _require_access(tenant_id, user_id, doc_id) + + if not (0.0 <= min_score <= 1.0): + raise HTTPException(status_code=422, detail="min_score must be between 0.0 and 1.0") + + try: + raw = await svc.suggest_merges( + tenant_id=tenant_id, doc_id=doc_id, config_path=CONFIG_PATH, + min_score=min_score, top_k=top_k, use_embeddings=use_embeddings, + ) + except Exception as exc: + log.exception(f"suggest_merges failed: {exc}") + raise HTTPException(status_code=500, detail=str(exc)) + + return SuggestMergesResponse(suggestions=[ + MergeSuggestion( + entity_a=EntityRef(**s["entity_a"]), + entity_b=EntityRef(**s["entity_b"]), + score=s["score"], + method=s["method"], + ) + for s in raw + ]) + diff --git a/api/services/entity_editor.py b/api/services/entity_editor.py new file mode 100644 index 0000000..c5857ba --- /dev/null +++ b/api/services/entity_editor.py @@ -0,0 +1,439 @@ +"""Entity editor service: rename, merge, split, suggest-merges on NER entities. + +All mutating operations work on the in-memory NetworkX graph (loaded from +graph_data.json), persist changes to graph_data.json *and* FalkorDB (when +configured), then best-effort rebuild the entity VDB so search stays fresh. + +Entities are NOT stored in MongoDB — their source of truth is FalkorDB + +graph_data.json. A lightweight audit entry is written to MongoDB's +``entity_edits`` collection for every mutating operation. +""" +from __future__ import annotations + +import asyncio +import logging +import os +from collections import defaultdict +from concurrent.futures import ThreadPoolExecutor +from difflib import SequenceMatcher +from typing import Dict, List, Optional + +from api.dependencies import ( + FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD, + INDEX_SAVE_DIR, MONGO_URI, MONGO_DB_PREFIX, +) +from api.db import mongodb as db + +log = logging.getLogger(__name__) +_executor = ThreadPoolExecutor(max_workers=2) + +# Per-document asyncio lock — keyed by "{tenant_id}:{doc_id}" +_doc_locks: Dict[str, asyncio.Lock] = defaultdict(asyncio.Lock) + + +def _get_lock(tenant_id: str, doc_id: str) -> asyncio.Lock: + return _doc_locks[f"{tenant_id}:{doc_id}"] + + +# ── Graph loader ───────────────────────────────────────────────────────────── + +def _load_graph_sync(tenant_id: str, doc_id: str, config_path: str): + """Load a Graph from JSON (never from FalkorDB) for in-memory editing. + + Returns ``(graph, save_path, falkordb_cfg | None)``. + """ + from Core.configs.system_config import load_system_config + from Core.configs.falkordb_config import FalkorDBConfig + from Core.Index.Graph import Graph + + cfg = load_system_config(config_path) + save_path = os.path.join(INDEX_SAVE_DIR, tenant_id, doc_id) + variant = "basic" if cfg.graph.refine_type == "basic" else None + + # Always load from JSON so we get the full in-memory graph + graph = Graph.load_from_dir( + load_dir=save_path, + variant=variant, + tenant_id=tenant_id, + doc_id=doc_id, + falkordb_cfg=None, # load from JSON only + ) + + # Attach FalkorDB cfg for saving if the host env var is set + falkordb_cfg = None + fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") + if fdb_host: + falkordb_cfg = FalkorDBConfig( + host=FALKORDB_HOST, + port=FALKORDB_PORT, + password=FALKORDB_PASSWORD, + ) + graph.falkordb_cfg = falkordb_cfg + graph.tenant_id = tenant_id + graph.doc_id = doc_id + graph.use_falkordb = True + graph._fdb_graph_name = falkordb_cfg.graph_name_for_doc(tenant_id, doc_id) + + return graph, save_path, falkordb_cfg + + +def _rebuild_vdb_sync(tenant_id: str, doc_id: str, config_path: str) -> None: + """Best-effort VDB rebuild after any graph mutation.""" + try: + from Core.configs.system_config import load_system_config + from Core.configs.falkordb_config import FalkorDBConfig + from Core.Index.GBCIndex import GBC + + cfg = load_system_config(config_path) + cfg.tenant_id = tenant_id + cfg.doc_id = doc_id + cfg.save_path = os.path.join(INDEX_SAVE_DIR, tenant_id, doc_id) + + fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") + if fdb_host: + cfg.falkordb = FalkorDBConfig( + host=FALKORDB_HOST, port=FALKORDB_PORT, password=FALKORDB_PASSWORD + ) + + gbc = GBC.load_gbc_index(cfg) + gbc.rebuild_vdb() + log.info(f"VDB rebuilt for {tenant_id}/{doc_id}") + except Exception as exc: + log.warning(f"VDB rebuild failed for {tenant_id}/{doc_id}: {exc}") + + +# ── List entities ───────────────────────────────────────────────────────────── + +def _list_entities_sync(tenant_id: str, doc_id: str, config_path: str) -> List[dict]: + graph, _, _ = _load_graph_sync(tenant_id, doc_id, config_path) + result = [] + for node_name in graph.get_all_nodes(): + entity = graph.get_entity_by_node_name(node_name) + result.append({ + "entity_name": entity.entity_name, + "entity_type": entity.entity_type, + "description": entity.description, + "source_ids": sorted(entity.source_ids), + "node_name": node_name, + }) + return sorted(result, key=lambda e: e["entity_name"].lower()) + + +async def list_entities(tenant_id: str, doc_id: str, config_path: str) -> List[dict]: + loop = asyncio.get_event_loop() + return await loop.run_in_executor( + _executor, _list_entities_sync, tenant_id, doc_id, config_path + ) + + +# ── Rename entity ───────────────────────────────────────────────────────────── + +def _rename_sync( + tenant_id: str, doc_id: str, config_path: str, + entity_name: str, entity_type: str, + new_entity_name: str, new_entity_type: str, new_description: Optional[str], +) -> List[dict]: + from Core.Index.Graph import Entity + + graph, _, _ = _load_graph_sync(tenant_id, doc_id, config_path) + old_entity = graph.get_entity(entity_name, entity_type) + + effective_type = new_entity_type if new_entity_type else old_entity.entity_type + effective_desc = new_description if new_description is not None else old_entity.description + + new_entity = Entity( + entity_name=new_entity_name, + entity_type=effective_type, + description=effective_desc, + source_ids=old_entity.source_ids, + ) + graph.update_entity(entity_name, entity_type, new_entity) + graph.save_graph() + + new_node = graph.get_node_name_from_str(new_entity_name, effective_type) + return [{ + "entity_name": new_entity_name, + "entity_type": effective_type, + "description": effective_desc, + "source_ids": sorted(new_entity.source_ids), + "node_name": new_node, + }] + + +async def rename_entity( + tenant_id: str, doc_id: str, config_path: str, + entity_name: str, entity_type: str, + new_entity_name: str, new_entity_type: str, new_description: Optional[str], + user_id: str, +) -> List[dict]: + async with _get_lock(tenant_id, doc_id): + loop = asyncio.get_event_loop() + result = await loop.run_in_executor( + _executor, _rename_sync, + tenant_id, doc_id, config_path, + entity_name, entity_type, new_entity_name, new_entity_type, new_description, + ) + await db.log_entity_edit(MONGO_URI, MONGO_DB_PREFIX, tenant_id, { + "operation": "rename", "doc_id": doc_id, "user_id": user_id, + "before": {"entity_name": entity_name, "entity_type": entity_type}, + "after": {"entity_name": new_entity_name, "entity_type": new_entity_type or entity_type}, + }) + loop = asyncio.get_event_loop() + loop.run_in_executor(_executor, _rebuild_vdb_sync, tenant_id, doc_id, config_path) + return result + + +# ── Split entity ────────────────────────────────────────────────────────────── + +def _split_sync( + tenant_id: str, doc_id: str, config_path: str, + entity_name: str, entity_type: str, + new_entities: List[dict], + edge_mode: str, +) -> List[dict]: + from Core.Index.Graph import Entity + + graph, _, _ = _load_graph_sync(tenant_id, doc_id, config_path) + old_node = graph.get_node_name_from_str(entity_name, entity_type) + if old_node not in graph.kg: + raise KeyError(f"Entity '{old_node}' not found in graph.") + + old_entity = graph.get_entity_by_node_name(old_node) + old_neighbors = list(graph.kg.neighbors(old_node)) + old_edge_data = {n: graph.kg.get_edge_data(old_node, n) for n in old_neighbors} + + created: List[dict] = [] + for spec in new_entities: + spec_name = spec["entity_name"] + spec_type = spec["entity_type"] + spec_desc = spec.get("description") or old_entity.description + spec_sids = set(spec.get("source_ids") or old_entity.source_ids) + + new_node = graph.get_node_name_from_str(spec_name, spec_type) + graph.add_kg_node(Entity( + entity_name=spec_name, entity_type=spec_type, + description=spec_desc, source_ids=spec_sids, + )) + + if edge_mode == "duplicate": + for neighbor, edata in old_edge_data.items(): + if neighbor != old_node and not graph.kg.has_edge(new_node, neighbor): + graph.kg.add_edge(new_node, neighbor, **edata) + + for tree_id in spec_sids: + graph.tree2kg[tree_id].add(new_node) + + created.append({ + "entity_name": spec_name, "entity_type": spec_type, + "description": spec_desc, "source_ids": sorted(spec_sids), "node_name": new_node, + }) + + for _, nodes in graph.tree2kg.items(): + nodes.discard(old_node) + graph.kg.remove_node(old_node) + graph.save_graph() + return created + + +async def split_entity( + tenant_id: str, doc_id: str, config_path: str, + entity_name: str, entity_type: str, + new_entities: List[dict], edge_mode: str, + user_id: str, +) -> List[dict]: + async with _get_lock(tenant_id, doc_id): + loop = asyncio.get_event_loop() + result = await loop.run_in_executor( + _executor, _split_sync, + tenant_id, doc_id, config_path, + entity_name, entity_type, new_entities, edge_mode, + ) + await db.log_entity_edit(MONGO_URI, MONGO_DB_PREFIX, tenant_id, { + "operation": "split", "doc_id": doc_id, "user_id": user_id, + "before": {"entity_name": entity_name, "entity_type": entity_type}, + "after": [{"entity_name": e["entity_name"], "entity_type": e["entity_type"]} for e in result], + }) + loop = asyncio.get_event_loop() + loop.run_in_executor(_executor, _rebuild_vdb_sync, tenant_id, doc_id, config_path) + return result + + +# ── Suggest merge candidates ────────────────────────────────────────────────── + +def _suggest_merges_sync( + tenant_id: str, doc_id: str, config_path: str, + min_score: float, top_k: int, use_embeddings: bool, +) -> List[dict]: + graph, _, _ = _load_graph_sync(tenant_id, doc_id, config_path) + nodes = list(graph.get_all_nodes()) + entities = [] + for node in nodes: + ent = graph.get_entity_by_node_name(node) + entities.append({"entity_name": ent.entity_name, "entity_type": ent.entity_type, "node": node}) + + suggestions: List[dict] = [] + + # String similarity + n = len(entities) + for i in range(n): + for j in range(i + 1, n): + a, b = entities[i], entities[j] + if a["entity_type"] != b["entity_type"]: + continue + score = SequenceMatcher(None, a["entity_name"].lower(), b["entity_name"].lower()).ratio() + if score >= min_score: + suggestions.append({ + "entity_a": {"entity_name": a["entity_name"], "entity_type": a["entity_type"]}, + "entity_b": {"entity_name": b["entity_name"], "entity_type": b["entity_type"]}, + "score": round(score, 4), + "method": "string_similarity", + }) + + # Embedding similarity (optional) + if use_embeddings: + try: + from Core.configs.system_config import load_system_config + from Core.Index.GBCIndex import GBC + + cfg = load_system_config(config_path) + cfg.tenant_id = tenant_id + cfg.doc_id = doc_id + cfg.save_path = os.path.join(INDEX_SAVE_DIR, tenant_id, doc_id) + gbc = GBC.load_gbc_index(cfg) + + seen_pairs: set = set() + for ent in entities: + for hit in gbc.entity_vdb.search(ent["node"], top_k=5): + sim = 1.0 - hit["distance"] + if sim < min_score: + continue + meta = hit.get("metadata", {}) + b_name = meta.get("entity_name", "") + b_type = meta.get("entity_type", "") + if (b_name == ent["entity_name"] and b_type == ent["entity_type"]) or b_type != ent["entity_type"]: + continue + pair = tuple(sorted([(ent["entity_name"], ent["entity_type"]), (b_name, b_type)])) + if pair in seen_pairs: + continue + seen_pairs.add(pair) + suggestions.append({ + "entity_a": {"entity_name": ent["entity_name"], "entity_type": ent["entity_type"]}, + "entity_b": {"entity_name": b_name, "entity_type": b_type}, + "score": round(sim, 4), + "method": "embedding_similarity", + }) + except Exception as exc: + log.warning(f"Embedding suggestions failed: {exc}") + + suggestions.sort(key=lambda s: s["score"], reverse=True) + return suggestions[:top_k] + + +async def suggest_merges( + tenant_id: str, doc_id: str, config_path: str, + min_score: float = 0.80, + top_k: int = 50, + use_embeddings: bool = False, +) -> List[dict]: + loop = asyncio.get_event_loop() + return await loop.run_in_executor( + _executor, _suggest_merges_sync, + tenant_id, doc_id, config_path, min_score, top_k, use_embeddings, + ) +# ── Merge entities ──────────────────────────────────────────────────────────── + +def _merge_sync( + tenant_id: str, doc_id: str, config_path: str, + source_entities: List[dict], + canonical_name: str, canonical_type: str, canonical_desc: str, +) -> List[dict]: + from Core.Index.Graph import Entity + + graph, _, _ = _load_graph_sync(tenant_id, doc_id, config_path) + + # Collect all source_ids from entities being merged + merged_source_ids: set = set() + for src in source_entities: + try: + ent = graph.get_entity(src["entity_name"], src["entity_type"]) + merged_source_ids.update(ent.source_ids) + except KeyError: + log.warning(f"Merge: source entity not found: {src}") + + canonical_node = graph.get_node_name_from_str(canonical_name, canonical_type) + + # Ensure canonical node exists (may be one of the sources or brand new) + if canonical_node not in graph.kg: + canonical_entity = Entity( + entity_name=canonical_name, + entity_type=canonical_type, + description=canonical_desc, + source_ids=merged_source_ids, + ) + graph.add_kg_node(canonical_entity) + else: + # Update description and source_ids on the existing node + existing = graph.get_entity_by_node_name(canonical_node) + merged_source_ids.update(existing.source_ids) + updated = Entity( + entity_name=canonical_name, + entity_type=canonical_type, + description=canonical_desc or existing.description, + source_ids=merged_source_ids, + ) + graph.kg.nodes[canonical_node].update(updated.model_dump()) + + # Transfer edges from each source to canonical, then remove source + for src in source_entities: + src_node = graph.get_node_name_from_str(src["entity_name"], src["entity_type"]) + if src_node == canonical_node or src_node not in graph.kg: + continue + for neighbor in list(graph.kg.neighbors(src_node)): + if neighbor == canonical_node: + continue + edge_data = graph.kg.get_edge_data(src_node, neighbor) + if not graph.kg.has_edge(canonical_node, neighbor): + graph.kg.add_edge(canonical_node, neighbor, **edge_data) + # Update tree2kg + for tree_id, nodes in graph.tree2kg.items(): + if src_node in nodes: + nodes.discard(src_node) + nodes.add(canonical_node) + graph.kg.remove_node(src_node) + + # Persist source_ids on canonical node + graph.kg.nodes[canonical_node]["source_ids"] = list(merged_source_ids) + graph.save_graph() + + canonical_ent = graph.get_entity_by_node_name(canonical_node) + return [{ + "entity_name": canonical_ent.entity_name, + "entity_type": canonical_ent.entity_type, + "description": canonical_ent.description, + "source_ids": sorted(canonical_ent.source_ids), + "node_name": canonical_node, + }] + + +async def merge_entities( + tenant_id: str, doc_id: str, config_path: str, + source_entities: List[dict], + canonical_name: str, canonical_type: str, canonical_desc: str, + user_id: str, +) -> List[dict]: + async with _get_lock(tenant_id, doc_id): + loop = asyncio.get_event_loop() + result = await loop.run_in_executor( + _executor, _merge_sync, + tenant_id, doc_id, config_path, + source_entities, canonical_name, canonical_type, canonical_desc, + ) + await db.log_entity_edit(MONGO_URI, MONGO_DB_PREFIX, tenant_id, { + "operation": "merge", "doc_id": doc_id, "user_id": user_id, + "before": source_entities, + "after": {"entity_name": canonical_name, "entity_type": canonical_type}, + }) + loop = asyncio.get_event_loop() + loop.run_in_executor(_executor, _rebuild_vdb_sync, tenant_id, doc_id, config_path) + return result + From aa58a3f7af86348cb504f7dbfc5eb85f9f1e773d Mon Sep 17 00:00:00 2001 From: vmoudyp Date: Tue, 3 Mar 2026 02:45:33 +0700 Subject: [PATCH 04/11] Enhance security and error handling in API endpoints; implement MCP server for AI integration - Added path traversal prevention in the raw document download endpoint. - Standardized error messages in entity management endpoints to "Internal server error". - Updated permission granting logic to require document ownership for non-admin users. - Refactored chat service to include caching for GBC indexes and improved query handling with history relevance filtering. - Introduced a new MCP server implementation to expose BookRAG API for AI agents, including resource and tool mappings. - Added detailed documentation for MCP server setup and usage. --- MCP.md | 668 ++++++++++++++++++++++++++++++ api/db/mongodb.py | 45 +- api/dependencies.py | 75 +++- api/main.py | 33 +- api/models/requests.py | 48 ++- api/routers/auth.py | 5 +- api/routers/chat.py | 3 +- api/routers/documents.py | 7 + api/routers/entities.py | 10 +- api/routers/tenants.py | 23 +- api/services/chat.py | 256 ++++++++++-- api/services/entity_editor.py | 4 +- api/services/entity_resolution.py | 5 +- api/services/indexing.py | 5 +- 14 files changed, 1120 insertions(+), 67 deletions(-) create mode 100644 MCP.md diff --git a/MCP.md b/MCP.md new file mode 100644 index 0000000..b71a380 --- /dev/null +++ b/MCP.md @@ -0,0 +1,668 @@ +# BookRAG MCP Server — Implementation Guide + +> **Model Context Protocol (MCP)** is an open standard by Anthropic that lets AI assistants (Claude Desktop, Cursor, Windsurf, etc.) connect directly to external tools and data sources through a unified interface. This document describes how to expose the BookRAG API as an MCP server so that AI agents can query books, inspect knowledge-graph entities, and manage documents — without any HTTP REST calls. + +--- + +## Table of Contents + +1. [What is MCP?](#1-what-is-mcp) +2. [Why BookRAG Maps Perfectly to MCP](#2-why-bookrag-maps-perfectly-to-mcp) +3. [Architecture](#3-architecture) +4. [MCP Primitives Mapping](#4-mcp-primitives-mapping) +5. [Multi-Tenancy Strategy](#5-multi-tenancy-strategy) +6. [Installation](#6-installation) +7. [File Structure](#7-file-structure) +8. [Implementation: `mcp_server.py`](#8-implementation-mcp_serverpy) +9. [Mounting to the Existing FastAPI App](#9-mounting-to-the-existing-fastapi-app) +10. [Claude Desktop & Cursor Configuration](#10-claude-desktop--cursor-configuration) +11. [Long-Running Operations (Indexing)](#11-long-running-operations-indexing) +12. [Testing](#12-testing) +13. [Transport Options](#13-transport-options) + +--- + +## 1. What is MCP? + +MCP defines three primitive types a server can expose: + +| Primitive | Who controls it | Description | BookRAG example | +|---|---|---|---| +| **Resource** | Application | Read-only contextual data, URI-addressable | Entity list, document status | +| **Tool** | Model (LLM) | Callable functions that take actions | Query a book, rename an entity | +| **Prompt** | User | Reusable prompt templates | "Ask about book", "Find duplicates" | + +MCP is **not** a replacement for REST. It is a parallel interface optimised for AI-agent consumption — same underlying service layer, different transport. + +--- + +## 2. Why BookRAG Maps Perfectly to MCP + +BookRAG's three-layer architecture is already MCP-ready: + +``` +Transport (HTTP REST) → api/routers/ ← keep as-is for users/web UI +Business logic → api/services/ ← shared, zero changes needed +Data stores → FalkorDB, MongoDB, ChromaDB +``` + +Adding MCP means writing a **thin new `mcp_server.py`** that calls the same `api/services/` functions — identical to how the FastAPI routers call them today. + +Key reasons conversion is straightforward: +- All business logic is already **async Python** (`asyncio`) +- Services accept plain Python args — no HTTP concepts leak into them +- Per-document locking, thread pools, and FalkorDB persistence are all inside `api/services/` +- No changes needed to `Core/` indexing pipeline + +--- + +## 3. Architecture + +``` +┌─────────────────────────────────────────────────────┐ +│ AI Agents / Claude Desktop │ +│ (Claude, Cursor, Windsurf, custom) │ +└────────────────────┬────────────────────────────────┘ + │ MCP (stdio or streamable-http) + ▼ + ┌─────────────────┐ + │ mcp_server.py │ ← NEW (thin adapter) + └────────┬────────┘ + │ + ┌────────────▼────────────┐ + │ api/services/ │ ← SHARED (unchanged) + │ entity_editor.py │ + │ chat.py │ + │ indexing.py │ + │ entity_resolution.py │ + └──┬──────────┬──────────┘ + │ │ + ┌──────▼──┐ ┌────▼──────┐ + │FalkorDB │ │ MongoDB │ + │ChromaDB │ │ uploads/ │ + └─────────┘ └───────────┘ + +┌─────────────────────────────────────────────────────┐ +│ Web / Mobile Users │ +└─────────────────┬───────────────────────────────────┘ + │ HTTP REST (JSON) + ▼ + ┌─────────────────┐ + │ api/routers/ │ ← EXISTING FastAPI (unchanged) + └─────────────────┘ +``` + +Both interfaces use **the same service layer** — changes made through MCP are instantly visible through REST and vice versa. + +--- + +## 4. MCP Primitives Mapping + +### Resources (read-only data) + +| URI pattern | Description | Backed by | +|---|---|---| +| `bookrag://documents/{tenant_id}` | List all documents for the tenant | `db.list_documents()` | +| `bookrag://documents/{tenant_id}/{doc_id}` | Single document status | `db.get_document()` | +| `bookrag://entities/{tenant_id}/{doc_id}` | All NER entities for a document | `entity_editor.list_entities()` | + +Resources are **application-controlled**: the AI client decides when to read them as context, without the model explicitly calling a tool. + +### Tools (callable by the model) + +| Tool name | Maps to | Description | +|---|---|---| +| `query_documents` | `chat.handle_query()` | Ask a question against one or more indexed books | +| `rename_entity` | `entity_editor.rename_entity()` | Rename an entity node in the knowledge graph | +| `merge_entities` | `entity_editor.merge_entities()` | Merge ≥ 2 entity nodes into a canonical node | +| `split_entity` | `entity_editor.split_entity()` | Split 1 entity into ≥ 2 new nodes | +| `suggest_merge_candidates` | `entity_editor.suggest_merges()` | Find likely duplicate entities | +| `get_document_status` | `db.get_document()` | Check indexing status of a document | +| `index_document` | `indexing.run_indexing()` | Trigger indexing for an uploaded PDF | + +### Prompts (user-invoked templates) + +| Prompt name | Description | +|---|---| +| `ask_about_book` | Template: "Given document `{doc_id}`, answer: `{question}`" | +| `find_entity_duplicates` | Template: "Review these merge suggestions and decide which to apply" | +| `summarise_entities` | Template: "List the most important entities in `{doc_id}` and explain their roles" | + +--- + +## 5. Multi-Tenancy Strategy + +MCP has **no built-in JWT authentication**. Every service call needs a `tenant_id` and `user_id`. Three options: + +### Option A — Environment-Variable Injection (Recommended) + +Each tenant runs their own MCP server process. The `tenant_id` and `user_id` are injected via environment variables at launch time. + +``` +BOOKRAG_TENANT_ID=acme BOOKRAG_USER_ID=alice python mcp_server.py +``` + +Inside `mcp_server.py`: + +```python +import os +TENANT_ID = os.environ["BOOKRAG_TENANT_ID"] # required — fail fast if missing +USER_ID = os.environ["BOOKRAG_USER_ID"] +``` + +**Pros**: Simple, no auth complexity, works with Claude Desktop `env` block. +**Cons**: One process per tenant — fine for small deployments. + +### Option B — Tool-Argument Injection + +`tenant_id` and `user_id` are required arguments on every tool. The AI model must supply them. + +**Pros**: Single process for all tenants. +**Cons**: Verbose; model must always pass credentials; no real security boundary. + +### Option C — OAuth 2.0 (MCP 1.1+) + +MCP's newer spec supports OAuth 2.0 flows. Suitable for a SaaS product where the MCP server is hosted remotely and multiple organisations connect to it. + +**Pros**: Proper per-user auth, scalable. +**Cons**: Requires implementing an OAuth server; significant complexity. + +> **Recommendation for BookRAG**: Start with **Option A** (env-var injection). It matches exactly how Claude Desktop is configured and requires minimal code. + +--- + +## 6. Installation + +Add the MCP SDK to the project's virtual environment: + +```bash +# Using pip (existing .venv) +pip install "mcp[cli]" + +# Or with uv +uv add "mcp[cli]" +``` + +The `[cli]` extra installs the `mcp` command-line tool needed for the development inspector. + +--- + +## 7. File Structure + +Only **one new file** is needed at the repo root: + +``` +BookRAG/ +├── mcp_server.py ← NEW — MCP adapter (thin layer over api/services/) +├── api/ +│ ├── main.py ← existing FastAPI app (unchanged) +│ ├── services/ ← shared business logic (unchanged) +│ ├── routers/ ← existing REST endpoints (unchanged) +│ └── ... +├── Core/ ← GBC indexing pipeline (unchanged) +└── config/ + └── gbc.yaml ← existing config (unchanged) +``` + +Alternatively, for a remote/production deployment where MCP is mounted directly onto the FastAPI app, no new file is needed — see [Section 9](#9-mounting-to-the-existing-fastapi-app). + +--- + +## 8. Implementation: `mcp_server.py` + +Below is the complete implementation skeleton. It uses the **FastMCP** high-level API (`from mcp.server.fastmcp import FastMCP`) which is analogous to FastAPI's `APIRouter`. + +```python +"""BookRAG MCP Server. + +Usage (local / Claude Desktop): + BOOKRAG_TENANT_ID=acme BOOKRAG_USER_ID=alice python mcp_server.py + +Usage (dev inspector): + BOOKRAG_TENANT_ID=acme BOOKRAG_USER_ID=alice mcp dev mcp_server.py +""" +import json +import os + +from mcp.server.fastmcp import FastMCP, Context + +# ── Tenant identity (injected via environment) ───────────────────────────── +TENANT_ID = os.environ.get("BOOKRAG_TENANT_ID", "default") +USER_ID = os.environ.get("BOOKRAG_USER_ID", "agent") +CONFIG_PATH = os.environ.get("BOOKRAG_CONFIG_PATH", "config/gbc.yaml") + +# ── Service imports (lazy, same as routers do) ────────────────────────────── +import api.services.entity_editor as entity_svc +import api.services.chat as chat_svc +import api.services.indexing as index_svc +import api.db.mongodb as db + +from api.dependencies import MONGO_URI, MONGO_DB_PREFIX + +mcp = FastMCP("bookrag", instructions=( + "BookRAG gives you access to a hierarchical RAG knowledge base built from PDF books. " + "Use query_documents to ask questions. Use entity tools to inspect and curate the " + "knowledge graph extracted from each book." +)) + + +# ════════════════════════════════════════════════════════════════════════════ +# RESOURCES (read-only data — application-controlled) +# ════════════════════════════════════════════════════════════════════════════ + +@mcp.resource("bookrag://documents/{tenant_id}") +async def list_documents_resource(tenant_id: str) -> str: + """Return the list of all documents for the given tenant as JSON.""" + docs = await db.list_documents(MONGO_URI, MONGO_DB_PREFIX, tenant_id, user_id=None) + return json.dumps(docs, default=str) + + +@mcp.resource("bookrag://documents/{tenant_id}/{doc_id}") +async def get_document_resource(tenant_id: str, doc_id: str) -> str: + """Return indexing status and metadata for a single document as JSON.""" + doc = await db.get_document(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_id) + return json.dumps(doc, default=str) if doc else json.dumps({"error": "not found"}) + + +@mcp.resource("bookrag://entities/{tenant_id}/{doc_id}") +async def get_entities_resource(tenant_id: str, doc_id: str) -> str: + """Return all NER entities for a document as a JSON array.""" + entities = await entity_svc.list_entities(tenant_id, doc_id, CONFIG_PATH) + return json.dumps(entities, default=str) + + +# ════════════════════════════════════════════════════════════════════════════ +# TOOLS (callable by the model) +# ════════════════════════════════════════════════════════════════════════════ + +@mcp.tool() +async def query_documents( + question: str, + doc_ids: list[str], + session_id: str = "", + cross_doc: bool = False, +) -> str: + """Query one or more indexed books with a natural-language question. + + Args: + question: The question to ask. + doc_ids: List of document IDs to query (must be 'ready' status). + session_id: Optional session ID to continue a conversation thread. + cross_doc: If True, query all docs in parallel and merge answers. + """ + result = await chat_svc.handle_query( + query=question, + tenant_id=TENANT_ID, + user_id=USER_ID, + doc_ids=doc_ids, + session_id=session_id or None, + config_path=CONFIG_PATH, + cross_doc=cross_doc, + ) + return result.get("answer", str(result)) + + +@mcp.tool() +async def get_document_status(doc_id: str) -> str: + """Check the indexing status of a document (pending/indexing/ready/error).""" + doc = await db.get_document(MONGO_URI, MONGO_DB_PREFIX, TENANT_ID, doc_id) + if not doc: + return f"Document '{doc_id}' not found." + return json.dumps({ + "doc_id": doc["doc_id"], + "filename": doc.get("filename", ""), + "status": doc.get("status", "unknown"), + "error": doc.get("error"), + }) + + +@mcp.tool() +async def list_entities(doc_id: str) -> str: + """Return all NER entities extracted from the knowledge graph of a document.""" + entities = await entity_svc.list_entities(TENANT_ID, doc_id, CONFIG_PATH) + return json.dumps(entities, default=str) + + +@mcp.tool() +async def rename_entity( + doc_id: str, + entity_name: str, + entity_type: str, + new_entity_name: str, + new_entity_type: str = "", + new_description: str = "", +) -> str: + """Rename an entity node in the knowledge graph. + + Args: + doc_id: Document the entity belongs to. + entity_name: Current entity name (exact match). + entity_type: Current entity type (e.g. PERSON, ORG). + new_entity_name: New name for the entity. + new_entity_type: New type (leave blank to keep current). + new_description: New description (leave blank to keep current). + """ + updated = await entity_svc.rename_entity( + tenant_id=TENANT_ID, doc_id=doc_id, config_path=CONFIG_PATH, + entity_name=entity_name, entity_type=entity_type, + new_entity_name=new_entity_name, + new_entity_type=new_entity_type or entity_type, + new_description=new_description or None, + user_id=USER_ID, + ) + return json.dumps(updated, default=str) + + +@mcp.tool() +async def merge_entities( + doc_id: str, + source_entities: list[dict], + canonical_name: str, + canonical_type: str, + canonical_description: str = "", +) -> str: + """Merge two or more entity nodes into a single canonical entity. + + Args: + doc_id: Document containing the entities. + source_entities: List of {"entity_name": ..., "entity_type": ...} dicts. + canonical_name: Name of the resulting merged entity. + canonical_type: Type of the resulting merged entity. + canonical_description: Optional description for the canonical entity. + """ + updated = await entity_svc.merge_entities( + tenant_id=TENANT_ID, doc_id=doc_id, config_path=CONFIG_PATH, + source_entities=source_entities, + canonical_name=canonical_name, + canonical_type=canonical_type, + canonical_desc=canonical_description, + user_id=USER_ID, + ) + return json.dumps(updated, default=str) + + +@mcp.tool() +async def suggest_merge_candidates( + doc_id: str, + min_score: float = 0.80, + top_k: int = 20, + use_embeddings: bool = False, +) -> str: + """Return a ranked list of entity pairs that may be duplicates. + + Args: + doc_id: Document to analyse. + min_score: Minimum similarity score (0.0 – 1.0). Default 0.80. + top_k: Maximum number of suggestions to return. + use_embeddings: If True, also run embedding-based similarity (slower). + """ + suggestions = await entity_svc.suggest_merges( + tenant_id=TENANT_ID, doc_id=doc_id, config_path=CONFIG_PATH, + min_score=min_score, top_k=top_k, use_embeddings=use_embeddings, + ) + return json.dumps(suggestions, default=str) + + +@mcp.tool() +async def index_document( + doc_id: str, + pdf_path: str, + ctx: Context, +) -> str: + """Trigger GBC index build for an already-uploaded PDF. + + This is a long-running operation. Progress is reported via MCP notifications. + + Args: + doc_id: The document ID (must already exist in MongoDB). + pdf_path: Absolute path to the PDF file on the server. + """ + await ctx.report_progress(0, 100, "Starting indexing...") + try: + await index_svc.run_indexing(TENANT_ID, doc_id, pdf_path, CONFIG_PATH) + await ctx.report_progress(100, 100, "Indexing complete.") + return f"Document '{doc_id}' indexed successfully." + except Exception as exc: + return f"Indexing failed: {exc}" + + +# ════════════════════════════════════════════════════════════════════════════ +# PROMPTS (user-invoked templates) +# ════════════════════════════════════════════════════════════════════════════ + +@mcp.prompt() +def ask_about_book(doc_id: str, question: str) -> str: + """Generate a prompt to ask a question about a specific indexed book.""" + return ( + f"You have access to the BookRAG knowledge base for document '{doc_id}'.\n\n" + f"Please use the `query_documents` tool with doc_ids=['{doc_id}'] to answer:\n\n" + f"{question}" + ) + + +@mcp.prompt() +def find_entity_duplicates(doc_id: str) -> str: + """Generate a prompt to review and resolve duplicate entities.""" + return ( + f"You are reviewing the knowledge graph for document '{doc_id}'.\n\n" + f"1. Call `suggest_merge_candidates(doc_id='{doc_id}', min_score=0.80)` to get candidates.\n" + f"2. Review each pair. For genuine duplicates, call `merge_entities`.\n" + f"3. Report a summary of what was merged and why." + ) + + +# ════════════════════════════════════════════════════════════════════════════ +# ENTRY POINT +# ════════════════════════════════════════════════════════════════════════════ + +if __name__ == "__main__": + # Default: stdio transport for Claude Desktop / local use + mcp.run(transport="stdio") +``` + +--- + +## 9. Mounting to the Existing FastAPI App + +For **remote / production** deployments where you want one process serving both REST and MCP, mount the MCP server directly inside `api/main.py`: + +```python +# api/main.py (addition only — all existing code unchanged) +from mcp.server.fastmcp import FastMCP + +# Import the mcp instance from your server module +from mcp_server import mcp # the FastMCP instance defined above + +# Mount under /mcp — accessible at http://host:8000/mcp +app.mount("/mcp", mcp.streamable_http_app()) +``` + +The MCP endpoint is then reachable at `http://your-server:8000/mcp` using the **streamable-http** transport. AI clients connect to this URL rather than launching a subprocess. + +> **Note**: When mounted to FastAPI, the `mcp_server.py` `if __name__ == "__main__"` block is never executed. Uvicorn/Gunicorn drives everything. + +--- + +## 10. Claude Desktop & Cursor Configuration + +### Claude Desktop + +Edit `~/.config/claude/claude_desktop_config.json` (Linux/macOS) or `%APPDATA%\Claude\claude_desktop_config.json` (Windows): + +```json +{ + "mcpServers": { + "bookrag-acme": { + "command": "/path/to/BookRAG/.venv/bin/python", + "args": ["/path/to/BookRAG/mcp_server.py"], + "env": { + "BOOKRAG_TENANT_ID": "acme", + "BOOKRAG_USER_ID": "alice", + "BOOKRAG_CONFIG_PATH": "/path/to/BookRAG/config/gbc.yaml", + "BOOKRAG_UPLOAD_DIR": "/path/to/BookRAG/uploads", + "BOOKRAG_INDEX_DIR": "/path/to/BookRAG/indices", + "BOOKRAG_FALKORDB_HOST": "localhost", + "BOOKRAG_FALKORDB_PORT": "6379", + "MONGO_URI": "mongodb://localhost:27017" + } + } + } +} +``` + +Restart Claude Desktop. The BookRAG tools will appear in the 🔧 tools panel. + +To support multiple tenants in Claude Desktop, add multiple entries with different `BOOKRAG_TENANT_ID` / `BOOKRAG_USER_ID` values: + +```json +{ + "mcpServers": { + "bookrag-acme": { "command": "...", "env": { "BOOKRAG_TENANT_ID": "acme", ... } }, + "bookrag-beta": { "command": "...", "env": { "BOOKRAG_TENANT_ID": "beta", ... } } + } +} +``` + +### Cursor / Windsurf + +In your project's `.cursor/mcp.json` (or Windsurf equivalent): + +```json +{ + "mcpServers": { + "bookrag": { + "command": ".venv/bin/python", + "args": ["mcp_server.py"], + "env": { + "BOOKRAG_TENANT_ID": "dev", + "BOOKRAG_USER_ID": "cursor-agent", + "BOOKRAG_CONFIG_PATH": "config/gbc.yaml" + } + } + } +} +``` + +### Remote (Streamable HTTP) Client Config + +For clients that support the streamable-http transport (custom agents, LangChain, PydanticAI): + +```python +from mcp.client.streamable_http import streamable_http_client +from mcp import ClientSession + +async with streamable_http_client("http://your-server:8000/mcp") as (read, write, _): + async with ClientSession(read, write) as session: + await session.initialize() + result = await session.call_tool("query_documents", { + "question": "Who is the main antagonist?", + "doc_ids": ["doc-abc123"], + }) + print(result.content[0].text) +``` + +--- + +## 11. Long-Running Operations (Indexing) + +MCP tools are **request/response** — the client waits for the tool to return. Indexing a large PDF can take minutes. The `index_document` tool handles this with progress reporting: + +```python +@mcp.tool() +async def index_document(doc_id: str, pdf_path: str, ctx: Context) -> str: + await ctx.report_progress(0, 100, "Starting indexing...") + await index_svc.run_indexing(TENANT_ID, doc_id, pdf_path, CONFIG_PATH) + await ctx.report_progress(100, 100, "Done.") + return f"Document '{doc_id}' indexed successfully." +``` + +`ctx.report_progress(current, total, message)` sends MCP progress notifications that Claude Desktop displays as a progress bar. The client tool call remains open until the function returns. + +For very long operations (> 5 min), the recommended pattern is: +1. Launch indexing as a background task (already done in `run_indexing`) +2. Return immediately with `"Indexing started. Call get_document_status('{doc_id}') to check progress."` +3. The model can poll `get_document_status` in subsequent turns. + +--- + +## 12. Testing + +### Interactive MCP Inspector (recommended first step) + +```bash +cd /path/to/BookRAG +BOOKRAG_TENANT_ID=dev BOOKRAG_USER_ID=test \ + mcp dev mcp_server.py +``` + +This opens a web UI at `http://localhost:5173` where you can: +- Browse all registered Resources, Tools, and Prompts +- Call any tool interactively and inspect the JSON response +- No Claude Desktop or Cursor needed + +### Quick smoke-test with the MCP Python client + +```python +# test_mcp_client.py +import asyncio +from mcp import ClientSession, StdioServerParameters +from mcp.client.stdio import stdio_client + +async def main(): + params = StdioServerParameters( + command=".venv/bin/python", + args=["mcp_server.py"], + env={ + "BOOKRAG_TENANT_ID": "test", + "BOOKRAG_USER_ID": "tester", + "BOOKRAG_CONFIG_PATH": "config/gbc.yaml", + }, + ) + async with stdio_client(params) as (read, write): + async with ClientSession(read, write) as session: + await session.initialize() + + tools = await session.list_tools() + print("Tools:", [t.name for t in tools.tools]) + + resources = await session.list_resources() + print("Resources:", [r.uri for r in resources.resources]) + +asyncio.run(main()) +``` + +Run with: + +```bash +python test_mcp_client.py +``` + +--- + +## 13. Transport Options + +| Transport | Use case | How to run | +|---|---|---| +| **stdio** | Local: Claude Desktop, Cursor, dev | `mcp.run(transport="stdio")` (default) | +| **streamable-http** | Remote: hosted server, custom agents | `app.mount("/mcp", mcp.streamable_http_app())` | +| **SSE** | Legacy remote (older MCP clients) | `mcp.run(transport="sse")` | + +For most BookRAG deployments: +- **Development / single user**: stdio via Claude Desktop config +- **Team / production**: mount streamable-http on the existing FastAPI app at `/mcp` + +--- + +## Summary + +| Step | Action | +|---|---| +| 1 | `pip install "mcp[cli]"` | +| 2 | Create `mcp_server.py` (copy skeleton from Section 8) | +| 3 | Set env vars: `BOOKRAG_TENANT_ID`, `BOOKRAG_USER_ID`, `BOOKRAG_CONFIG_PATH` | +| 4 | Test with `mcp dev mcp_server.py` | +| 5 | Add to Claude Desktop config (Section 10) | +| 6 | *(Optional)* Mount to FastAPI for remote access (Section 9) | + +**Zero changes** to `Core/`, `api/services/`, `api/routers/`, or any existing behaviour are required. + diff --git a/api/db/mongodb.py b/api/db/mongodb.py index 09bb13e..eb5c0a2 100644 --- a/api/db/mongodb.py +++ b/api/db/mongodb.py @@ -1,8 +1,10 @@ """Async MongoDB client and CRUD helpers using Motor.""" import logging +import os from typing import List, Optional from datetime import datetime, timezone +import pymongo from motor.motor_asyncio import AsyncIOMotorClient, AsyncIOMotorDatabase log = logging.getLogger(__name__) @@ -32,6 +34,36 @@ async def close_client(): _client = None +async def ensure_indexes(uri: str, system_db: str, db_prefix: str, tenant_ids: List[str]): + """Create MongoDB indexes for all known tenant databases. + + Called once at startup. Indexes are idempotent — ``create_index`` is a no-op + if the index already exists. + """ + client = get_client(uri) + + # System DB indexes + sdb = client[system_db] + await sdb["tenants"].create_index("tenant_id", unique=True) + log.info(f"Ensured indexes on system db '{system_db}'") + + # Per-tenant DB indexes + for tid in tenant_ids: + tdb = client[f"{db_prefix}_{tid}"] + await tdb["users"].create_index("username", unique=True) + await tdb["users"].create_index("user_id", unique=True) + await tdb["documents"].create_index("doc_id", unique=True) + await tdb["permissions"].create_index( + [("user_id", pymongo.ASCENDING), ("doc_id", pymongo.ASCENDING)], + unique=True, + ) + await tdb["sessions"].create_index("session_id", unique=True) + await tdb["sessions"].create_index("user_id") + await tdb["entity_edits"].create_index("doc_id") + await tdb["entity_edits"].create_index("ts") + log.info(f"Ensured indexes on {len(tenant_ids)} tenant database(s)") + + # ── Tenant CRUD ────────────────────────────────────────────────────────────── async def create_tenant(uri: str, system_db: str, tenant_data: dict) -> str: @@ -115,6 +147,12 @@ async def get_accessible_doc_ids(uri: str, db_prefix: str, tenant_id: str, user_ return [p["doc_id"] async for p in cursor] +async def get_permission(uri: str, db_prefix: str, tenant_id: str, user_id: str, doc_id: str) -> Optional[dict]: + """Return the permission record for a user+doc pair, or None.""" + db = get_tenant_db(uri, db_prefix, tenant_id) + return await db["permissions"].find_one({"user_id": user_id, "doc_id": doc_id}) + + # ── Session / Message CRUD ──────────────────────────────────────────────────── async def create_session(uri: str, db_prefix: str, tenant_id: str, session_data: dict) -> str: @@ -124,12 +162,17 @@ async def create_session(uri: str, db_prefix: str, tenant_id: str, session_data: return str(result.inserted_id) +# Max messages per session — prevents unbounded array growth (16 MB doc limit). +# Each user+assistant turn = 2 messages, so 200 = ~100 turns. +_SESSION_MSG_CAP = int(os.environ.get("BOOKRAG_SESSION_MSG_CAP", "200")) + + async def append_message(uri: str, db_prefix: str, tenant_id: str, session_id: str, message: dict): db = get_tenant_db(uri, db_prefix, tenant_id) message["ts"] = datetime.now(timezone.utc) await db["sessions"].update_one( {"session_id": session_id}, - {"$push": {"messages": message}}, + {"$push": {"messages": {"$each": [message], "$slice": -_SESSION_MSG_CAP}}}, ) diff --git a/api/dependencies.py b/api/dependencies.py index 09c1e40..afb996e 100644 --- a/api/dependencies.py +++ b/api/dependencies.py @@ -1,8 +1,11 @@ """FastAPI dependency injection: JWT verification, DB handles, permission checks.""" import os import logging +import time +from collections import defaultdict +from concurrent.futures import ThreadPoolExecutor from typing import Optional -from fastapi import Depends, HTTPException, status +from fastapi import Depends, HTTPException, Request, status from fastapi.security import OAuth2PasswordBearer from jose import JWTError, jwt from passlib.context import CryptContext @@ -12,7 +15,14 @@ log = logging.getLogger(__name__) # ── Config (read from env with sensible defaults) ───────────────────────────── -SECRET_KEY = os.getenv("BOOKRAG_SECRET_KEY", "change-me-in-production-please") +_secret = os.getenv("BOOKRAG_SECRET_KEY", "") +if not _secret: + raise RuntimeError( + "BOOKRAG_SECRET_KEY environment variable is not set. " + "Generate a secure key with: python -c \"import secrets; print(secrets.token_urlsafe(64))\" " + "and export it before starting the server." + ) +SECRET_KEY = _secret ALGORITHM = "HS256" ACCESS_TOKEN_EXPIRE_MINUTES = int(os.getenv("BOOKRAG_TOKEN_EXPIRE", "60")) @@ -27,6 +37,14 @@ UPLOAD_DIR = os.getenv("BOOKRAG_UPLOAD_DIR", "./uploads") INDEX_SAVE_DIR = os.getenv("BOOKRAG_INDEX_DIR", "./indices") +# ── Shared thread pool ─────────────────────────────────────────────────────── +# Single GPU-aware pool shared by chat, indexing, entity_editor, entity_resolution. +# Size is tunable via env var — default 4 workers (matches original chat pool). +THREAD_POOL = ThreadPoolExecutor( + max_workers=int(os.getenv("BOOKRAG_THREAD_POOL_SIZE", "4")), + thread_name_prefix="bookrag", +) + # ── Auth helpers ────────────────────────────────────────────────────────────── pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto") oauth2_scheme = OAuth2PasswordBearer(tokenUrl="/auth/login") @@ -87,3 +105,56 @@ async def filter_accessible_docs(user_id: str, tenant_id: str, requested_doc_ids return accessible return [d for d in requested_doc_ids if d in accessible] + +# ── In-memory sliding-window rate limiter ──────────────────────────────────── + +_LOGIN_RPM = int(os.getenv("BOOKRAG_LOGIN_RPM", "10")) # login attempts per minute per IP +_QUERY_RPM = int(os.getenv("BOOKRAG_QUERY_RPM", "30")) # chat queries per minute per user +_WINDOW = 60.0 # seconds + + +class _RateBucket: + """Sliding-window counter per key.""" + + __slots__ = ("_hits",) + + def __init__(self): + self._hits: dict[str, list[float]] = defaultdict(list) + + def check(self, key: str, limit: int) -> bool: + """Return True if the request should be allowed.""" + now = time.monotonic() + window = self._hits[key] + # Prune expired timestamps + cutoff = now - _WINDOW + self._hits[key] = window = [t for t in window if t > cutoff] + if len(window) >= limit: + return False + window.append(now) + return True + + +_login_bucket = _RateBucket() +_query_bucket = _RateBucket() + + +async def rate_limit_login(request: Request): + """Dependency: enforce per-IP rate limit on login.""" + client_ip = request.client.host if request.client else "unknown" + if not _login_bucket.check(client_ip, _LOGIN_RPM): + raise HTTPException( + status_code=status.HTTP_429_TOO_MANY_REQUESTS, + detail=f"Too many login attempts. Try again in {int(_WINDOW)} seconds.", + ) + + +async def rate_limit_query(current_user: dict = Depends(get_current_user)): + """Dependency: enforce per-user rate limit on chat queries.""" + key = f"{current_user['tenant_id']}:{current_user['user_id']}" + if not _query_bucket.check(key, _QUERY_RPM): + raise HTTPException( + status_code=status.HTTP_429_TOO_MANY_REQUESTS, + detail=f"Too many requests. Try again in {int(_WINDOW)} seconds.", + ) + return current_user + diff --git a/api/main.py b/api/main.py index d20795a..d3a585c 100644 --- a/api/main.py +++ b/api/main.py @@ -7,7 +7,7 @@ from fastapi.middleware.cors import CORSMiddleware from api.db import mongodb as db -from api.dependencies import MONGO_URI +from api.dependencies import MONGO_URI, MONGO_DB_PREFIX, MONGO_SYSTEM_DB from api.routers import auth, documents, chat, tenants, entities logging.basicConfig( @@ -24,6 +24,15 @@ async def lifespan(app: FastAPI): # Ensure upload and index directories exist os.makedirs(os.getenv("BOOKRAG_UPLOAD_DIR", "./uploads"), exist_ok=True) os.makedirs(os.getenv("BOOKRAG_INDEX_DIR", "./indices"), exist_ok=True) + + # Build MongoDB indexes for all known tenants + try: + sdb = db.get_system_db(MONGO_URI, MONGO_SYSTEM_DB) + tenant_ids = [t["tenant_id"] async for t in sdb["tenants"].find({}, {"tenant_id": 1})] + await db.ensure_indexes(MONGO_URI, MONGO_SYSTEM_DB, MONGO_DB_PREFIX, tenant_ids) + except Exception as exc: + log.warning(f"MongoDB index creation skipped: {exc}") + yield log.info("BookRAG API shutting down...") await db.close_client() @@ -36,13 +45,25 @@ async def lifespan(app: FastAPI): lifespan=lifespan, ) -# CORS — adjust origins for production +# CORS — set BOOKRAG_CORS_ORIGINS to a comma-separated list of allowed origins +_cors_raw = os.getenv("BOOKRAG_CORS_ORIGINS", "http://localhost:3000,http://localhost:8000") +_cors_origins = [o.strip() for o in _cors_raw.split(",") if o.strip()] +if "*" in _cors_origins: + log.warning( + "CORS allow_origins contains '*'. This is insecure with credentials=True. " + "Set BOOKRAG_CORS_ORIGINS to explicit origins in production." + ) + # Wildcard + credentials is rejected by browsers; fall back to no-credentials mode + _cors_credentials = False +else: + _cors_credentials = True + app.add_middleware( CORSMiddleware, - allow_origins=os.getenv("BOOKRAG_CORS_ORIGINS", "*").split(","), - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], + allow_origins=_cors_origins, + allow_credentials=_cors_credentials, + allow_methods=["GET", "POST", "PUT", "PATCH", "DELETE", "OPTIONS"], + allow_headers=["Authorization", "Content-Type"], ) # Routers diff --git a/api/models/requests.py b/api/models/requests.py index dc83072..91f5905 100644 --- a/api/models/requests.py +++ b/api/models/requests.py @@ -1,20 +1,37 @@ """Pydantic request and response models for the BookRAG API.""" from typing import List, Optional -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, field_validator + +# ── Reusable length constraints ────────────────────────────────────────────── +_SHORT_STR = 128 # usernames, tenant_ids, role names +_PASSWORD_MIN = 8 +_PASSWORD_MAX = 128 +_QUERY_MAX = 10_000 # max characters for a chat query # ── Auth ────────────────────────────────────────────────────────────────────── class RegisterRequest(BaseModel): - username: str - password: str - tenant_id: str + username: str = Field(..., min_length=3, max_length=_SHORT_STR) + password: str = Field(..., min_length=_PASSWORD_MIN, max_length=_PASSWORD_MAX) + tenant_id: str = Field(..., min_length=1, max_length=_SHORT_STR) + + @field_validator("password") + @classmethod + def password_complexity(cls, v: str) -> str: + if not any(c.isupper() for c in v): + raise ValueError("Password must contain at least one uppercase letter") + if not any(c.islower() for c in v): + raise ValueError("Password must contain at least one lowercase letter") + if not any(c.isdigit() for c in v): + raise ValueError("Password must contain at least one digit") + return v class LoginRequest(BaseModel): - username: str - password: str - tenant_id: str + username: str = Field(..., min_length=1, max_length=_SHORT_STR) + password: str = Field(..., min_length=1, max_length=_PASSWORD_MAX) + tenant_id: str = Field(..., min_length=1, max_length=_SHORT_STR) class TokenResponse(BaseModel): @@ -25,9 +42,9 @@ class TokenResponse(BaseModel): # ── Tenant ──────────────────────────────────────────────────────────────────── class TenantCreateRequest(BaseModel): - tenant_id: str - name: str - description: Optional[str] = "" + tenant_id: str = Field(..., min_length=1, max_length=_SHORT_STR) + name: str = Field(..., min_length=1, max_length=256) + description: Optional[str] = Field(default="", max_length=1000) class TenantResponse(BaseModel): @@ -51,16 +68,16 @@ class BatchUploadResponse(BaseModel): class PermissionGrantRequest(BaseModel): - user_id: str - doc_id: str - role: str = "reader" + user_id: str = Field(..., min_length=1, max_length=_SHORT_STR) + doc_id: str = Field(..., min_length=1, max_length=_SHORT_STR) + role: str = Field(default="reader", max_length=32) # ── Chat ────────────────────────────────────────────────────────────────────── class ChatQueryRequest(BaseModel): - query: str - session_id: Optional[str] = None + query: str = Field(..., min_length=1, max_length=_QUERY_MAX) + session_id: Optional[str] = Field(default=None, max_length=_SHORT_STR) doc_ids: Optional[List[str]] = None # restrict to specific docs; None = all accessible cross_doc: bool = False # use global cross-document graph @@ -69,6 +86,7 @@ class ChatQueryResponse(BaseModel): answer: str session_id: str doc_ids_used: List[str] = Field(default_factory=list) + rewritten_query: Optional[str] = None # set when history was used to rewrite the query class SessionCreateRequest(BaseModel): diff --git a/api/routers/auth.py b/api/routers/auth.py index 12e8ff6..5ffea7e 100644 --- a/api/routers/auth.py +++ b/api/routers/auth.py @@ -1,12 +1,13 @@ """Authentication router: register and login endpoints.""" import logging -from fastapi import APIRouter, HTTPException, status +from fastapi import APIRouter, Depends, HTTPException, status from api.models.requests import RegisterRequest, LoginRequest, TokenResponse from api.db import mongodb as db from api.dependencies import ( MONGO_URI, MONGO_DB_PREFIX, MONGO_SYSTEM_DB, hash_password, verify_password, create_access_token, + rate_limit_login, ) log = logging.getLogger(__name__) @@ -37,7 +38,7 @@ async def register(req: RegisterRequest): return {"message": "User registered successfully"} -@router.post("/login", response_model=TokenResponse) +@router.post("/login", response_model=TokenResponse, dependencies=[Depends(rate_limit_login)]) async def login(req: LoginRequest): """Authenticate and return a JWT access token.""" user = await db.get_user_by_username(MONGO_URI, MONGO_DB_PREFIX, req.tenant_id, req.username) diff --git a/api/routers/chat.py b/api/routers/chat.py index 779c118..b3e2f7f 100644 --- a/api/routers/chat.py +++ b/api/routers/chat.py @@ -13,6 +13,7 @@ from api.dependencies import ( MONGO_URI, MONGO_DB_PREFIX, get_current_user, filter_accessible_docs, + rate_limit_query, ) from api.services.chat import handle_query @@ -23,7 +24,7 @@ @router.post("/query", response_model=ChatQueryResponse) -async def query(req: ChatQueryRequest, current_user: dict = Depends(get_current_user)): +async def query(req: ChatQueryRequest, current_user: dict = Depends(rate_limit_query)): """Submit a query. Automatically filters to accessible documents.""" tenant_id = current_user["tenant_id"] user_id = current_user["user_id"] diff --git a/api/routers/documents.py b/api/routers/documents.py index 651f667..fbe4b09 100644 --- a/api/routers/documents.py +++ b/api/routers/documents.py @@ -137,6 +137,13 @@ async def download_raw_document(doc_id: str, current_user: dict = Depends(get_cu if not raw_path or not os.path.isfile(raw_path): raise HTTPException(status_code=404, detail="Raw document file not found") + # Prevent path-traversal: resolved path must be inside UPLOAD_DIR + resolved = os.path.realpath(raw_path) + upload_root = os.path.realpath(UPLOAD_DIR) + if not resolved.startswith(upload_root + os.sep) and resolved != upload_root: + log.warning(f"Path traversal blocked: {raw_path} resolved to {resolved}") + raise HTTPException(status_code=403, detail="Access denied") + filename = os.path.basename(raw_path) return FileResponse( path=raw_path, diff --git a/api/routers/entities.py b/api/routers/entities.py index 9bae692..2b6b7df 100644 --- a/api/routers/entities.py +++ b/api/routers/entities.py @@ -39,7 +39,7 @@ async def list_entities(doc_id: str, current_user: dict = Depends(get_current_us entities = await svc.list_entities(tenant_id, doc_id, CONFIG_PATH) except Exception as exc: log.exception(f"list_entities failed: {exc}") - raise HTTPException(status_code=500, detail=str(exc)) + raise HTTPException(status_code=500, detail="Internal server error") return EntityListResponse( entities=[EntityInfo(**e) for e in entities], @@ -73,7 +73,7 @@ async def rename_entity( raise HTTPException(status_code=404, detail=str(exc)) except Exception as exc: log.exception(f"rename_entity failed: {exc}") - raise HTTPException(status_code=500, detail=str(exc)) + raise HTTPException(status_code=500, detail="Internal server error") return EntityOperationResponse( success=True, @@ -111,7 +111,7 @@ async def merge_entities( raise HTTPException(status_code=404, detail=str(exc)) except Exception as exc: log.exception(f"merge_entities failed: {exc}") - raise HTTPException(status_code=500, detail=str(exc)) + raise HTTPException(status_code=500, detail="Internal server error") return EntityOperationResponse( success=True, @@ -150,7 +150,7 @@ async def split_entity( raise HTTPException(status_code=404, detail=str(exc)) except Exception as exc: log.exception(f"split_entity failed: {exc}") - raise HTTPException(status_code=500, detail=str(exc)) + raise HTTPException(status_code=500, detail="Internal server error") return EntityOperationResponse( success=True, @@ -184,7 +184,7 @@ async def suggest_merges( ) except Exception as exc: log.exception(f"suggest_merges failed: {exc}") - raise HTTPException(status_code=500, detail=str(exc)) + raise HTTPException(status_code=500, detail="Internal server error") return SuggestMergesResponse(suggestions=[ MergeSuggestion( diff --git a/api/routers/tenants.py b/api/routers/tenants.py index 7570aa5..a9d044f 100644 --- a/api/routers/tenants.py +++ b/api/routers/tenants.py @@ -44,10 +44,27 @@ async def grant_permission( req: PermissionGrantRequest, current_user=Depends(get_current_user), ): - """Grant a user read access to a document within a tenant.""" - # Only admins or tenant members with admin role can grant permissions - if current_user["role"] != "admin" and current_user["tenant_id"] != tenant_id: + """Grant a user access to a document within a tenant. + + Requires the requesting user to be either: + - a global admin, OR + - the document owner (role='owner' on the target document) + """ + if current_user["tenant_id"] != tenant_id and current_user["role"] != "admin": raise HTTPException(status_code=403, detail="Access denied") + + # Non-admin users must have 'owner' role on the document to grant permissions + if current_user["role"] != "admin": + perm = await db.get_permission( + MONGO_URI, MONGO_DB_PREFIX, tenant_id, + current_user["user_id"], req.doc_id, + ) + if not perm or perm.get("role") != "owner": + raise HTTPException( + status_code=403, + detail="Only document owners or admins can grant permissions", + ) + await db.grant_permission( MONGO_URI, MONGO_DB_PREFIX, tenant_id, req.user_id, req.doc_id, req.role, diff --git a/api/services/chat.py b/api/services/chat.py index e5e9c4f..5bcedd5 100644 --- a/api/services/chat.py +++ b/api/services/chat.py @@ -2,42 +2,211 @@ import asyncio import logging import os +import threading +import time import uuid -from concurrent.futures import ThreadPoolExecutor +from functools import lru_cache from typing import List, Optional from api.db import mongodb as db from api.dependencies import ( MONGO_URI, MONGO_DB_PREFIX, INDEX_SAVE_DIR, FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD, + THREAD_POOL, ) log = logging.getLogger(__name__) -_executor = ThreadPoolExecutor(max_workers=4) +_executor = THREAD_POOL +# ── Object caching ─────────────────────────────────────────────────────────── +# LLM/VLM are API client wrappers — one instance per config is sufficient. +# GBC indexes are heavier (tree + graph + VDB) — cached per document with TTL. -def _query_single_doc_sync(query: str, tenant_id: str, doc_id: str, config_path: str) -> str: - """Run GBC RAG query against a single document (sync, for thread pool).""" +_GBC_CACHE_TTL = int(os.getenv("BOOKRAG_GBC_CACHE_TTL", "600")) # seconds +_GBC_CACHE_MAX = int(os.getenv("BOOKRAG_GBC_CACHE_MAX", "20")) # max cached indexes + + +@lru_cache(maxsize=4) +def _get_system_config(config_path: str): + """Cached system config loader — reloads only when config_path changes.""" from Core.configs.system_config import load_system_config - from Core.configs.falkordb_config import FalkorDBConfig - from Core.Index.GBCIndex import GBC - from Core.rag.gbc_rag import GBCRAG + return load_system_config(config_path) + + +@lru_cache(maxsize=4) +def _get_llm(config_path: str): + """Singleton LLM instance per config file.""" from Core.provider.llm import LLM + cfg = _get_system_config(config_path) + return LLM(cfg.llm) + + +@lru_cache(maxsize=4) +def _get_vlm(config_path: str): + """Singleton VLM instance per config file.""" from Core.provider.vlm import VLM - from Core.configs.rag.gbc_config import GBCRAGConfig + cfg = _get_system_config(config_path) + if hasattr(cfg, "vlm") and cfg.vlm: + return VLM(cfg.vlm) + return None + + +class _GBCCache: + """TTL-bounded LRU cache for per-document GBC indexes.""" + + def __init__(self, max_size: int = 20, ttl: int = 600): + self._lock = threading.Lock() + self._cache: dict[str, tuple[float, object]] = {} # key → (access_time, gbc) + self._max_size = max_size + self._ttl = ttl - cfg = load_system_config(config_path) + def get(self, key: str): + with self._lock: + entry = self._cache.get(key) + if entry is None: + return None + ts, gbc = entry + if time.monotonic() - ts > self._ttl: + del self._cache[key] + return None + self._cache[key] = (time.monotonic(), gbc) + return gbc + + def put(self, key: str, gbc): + with self._lock: + self._cache[key] = (time.monotonic(), gbc) + # Evict oldest if over capacity + if len(self._cache) > self._max_size: + oldest_key = min(self._cache, key=lambda k: self._cache[k][0]) + del self._cache[oldest_key] + + def invalidate(self, key: str): + with self._lock: + self._cache.pop(key, None) + + +_gbc_cache = _GBCCache(max_size=_GBC_CACHE_MAX, ttl=_GBC_CACHE_TTL) + + +def _get_gbc_index(tenant_id: str, doc_id: str, config_path: str): + """Load or return cached GBC index for a specific document.""" + from Core.configs.falkordb_config import FalkorDBConfig + from Core.Index.GBCIndex import GBC + + cache_key = f"{tenant_id}:{doc_id}" + cached = _gbc_cache.get(cache_key) + if cached is not None: + log.debug(f"GBC cache hit: {cache_key}") + return cached + + log.info(f"GBC cache miss: {cache_key} — loading from disk") + cfg = _get_system_config(config_path) + # Create a copy-like config with tenant/doc specifics cfg.tenant_id = tenant_id cfg.doc_id = doc_id cfg.save_path = os.path.join(INDEX_SAVE_DIR, tenant_id, doc_id) fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") if fdb_host: - cfg.falkordb = FalkorDBConfig(host=FALKORDB_HOST, port=FALKORDB_PORT, password=FALKORDB_PASSWORD) + cfg.falkordb = FalkorDBConfig( + host=FALKORDB_HOST, port=FALKORDB_PORT, password=FALKORDB_PASSWORD + ) gbc_index = GBC.load_gbc_index(cfg) - llm = LLM(cfg.llm) - vlm = VLM(cfg.vlm) if hasattr(cfg, "vlm") else None + _gbc_cache.put(cache_key, gbc_index) + return gbc_index + +# ── History relevance constants (tunable via env) ───────────────────────────── +_RECENT_TURNS = int(os.getenv("BOOKRAG_RECENT_TURNS", "3")) # always-include last N pairs +_MAX_OLD_MSGS = int(os.getenv("BOOKRAG_MAX_OLD_MSGS", "4")) # max older msgs to keep +_JACCARD_THRESH = float(os.getenv("BOOKRAG_JACCARD_THRESH", "0.15")) # token-overlap threshold + + +# ── History filtering helpers ───────────────────────────────────────────────── + +def _jaccard_similarity(a: str, b: str) -> float: + """Token-overlap Jaccard similarity — zero-dependency relevance heuristic.""" + tokens_a = set(a.lower().split()) + tokens_b = set(b.lower().split()) + if not tokens_a or not tokens_b: + return 0.0 + return len(tokens_a & tokens_b) / len(tokens_a | tokens_b) + + +def _filter_relevant_history( + query: str, + messages: List[dict], + recent_turns: int = _RECENT_TURNS, + max_old: int = _MAX_OLD_MSGS, + threshold: float = _JACCARD_THRESH, +) -> List[dict]: + """Return the subset of prior messages relevant to *query*. + + Two-tier strategy: + 1. **Recency** — always keep the last ``recent_turns`` user+assistant pairs. + 2. **Relevance** — for older messages, keep those whose content has + Jaccard token-overlap >= *threshold* with the query (capped at *max_old*). + """ + if not messages: + return [] + + recent_cutoff = recent_turns * 2 # 2 messages per turn (user + assistant) + recent = messages[-recent_cutoff:] + older = messages[:-recent_cutoff] if len(messages) > recent_cutoff else [] + + relevant_older = [ + m for m in older + if _jaccard_similarity(query, m.get("content", "")) >= threshold + ][-max_old:] + + return relevant_older + recent + + +# ── Query rewriting (sync, runs inside thread pool) ─────────────────────────── + +def _rewrite_query_sync(query: str, history: List[dict], config_path: str) -> str: + """Use the LLM to rewrite *query* as a self-contained question. + + Resolves pronouns and implicit references using the conversation history. + If the LLM fails or the result is empty, the original query is returned unchanged. + """ + history_text = "\n".join( + f"{m['role'].capitalize()}: {m['content']}" for m in history + ) + prompt = ( + "You are a query rewriter for a document question-answering system.\n" + "Given the conversation history below and the user's latest question, " + "rewrite the question as a single, self-contained question that can be " + "understood without any prior context. Resolve all pronouns, coreferences, " + "and implicit references to named entities mentioned earlier in the conversation. " + "If the question is already fully self-contained, return it unchanged.\n\n" + f"Conversation history:\n{history_text}\n\n" + f"Latest question: {query}\n\n" + "Rewritten question (return ONLY the rewritten question, no preamble or explanation):" + ) + try: + llm = _get_llm(config_path) + rewritten = llm.get_completion(prompt).strip() + if rewritten: + log.info(f"Query rewritten: '{query}' → '{rewritten}'") + return rewritten + except Exception as exc: + log.warning(f"Query rewrite failed ({exc}); using original query.") + return query + + +def _query_single_doc_sync(query: str, tenant_id: str, doc_id: str, config_path: str) -> str: + """Run GBC RAG query against a single document (sync, for thread pool). + + *query* should already be a self-contained, rewritten query when conversation + history is present (see :func:`_rewrite_query_sync`). + """ + from Core.rag.gbc_rag import GBCRAG + from Core.configs.rag.gbc_config import GBCRAGConfig + + gbc_index = _get_gbc_index(tenant_id, doc_id, config_path) + llm = _get_llm(config_path) + vlm = _get_vlm(config_path) rag_cfg = GBCRAGConfig() rag = GBCRAG(llm=llm, vlm=vlm, config=rag_cfg, gbc_index=gbc_index) result = rag.get_GBC_info(query) @@ -53,8 +222,20 @@ async def handle_query( config_path: str, cross_doc: bool = False, ) -> dict: - """Route query to appropriate retrieval mode and store in session.""" - # Ensure session exists + """Route query to appropriate retrieval mode and store in session. + + Memory strategy + --------------- + 1. Load existing session messages from MongoDB (before appending the new one). + 2. Filter to relevant history using :func:`_filter_relevant_history` + (two-tier: recency + Jaccard token-overlap). + 3. If non-empty history, rewrite *query* into a self-contained standalone + question via the LLM (:func:`_rewrite_query_sync`). + 4. Pass the (possibly rewritten) query to the RAG pipeline. + """ + loop = asyncio.get_event_loop() + + # ── Session bootstrap ────────────────────────────────────────────────────── if not session_id: session_id = str(uuid.uuid4()) await db.create_session(MONGO_URI, MONGO_DB_PREFIX, tenant_id, { @@ -63,34 +244,59 @@ async def handle_query( "doc_ids": doc_ids, "messages": [], }) + prior_messages: List[dict] = [] + else: + # Load history BEFORE appending the current user message + session = await db.get_session(MONGO_URI, MONGO_DB_PREFIX, tenant_id, session_id) + prior_messages = session.get("messages", []) if session else [] + + # ── History filtering + query rewriting ─────────────────────────────────── + relevant_history = _filter_relevant_history(query, prior_messages) + if relevant_history: + log.debug( + f"Session {session_id}: {len(relevant_history)} relevant history messages " + f"out of {len(prior_messages)} total — rewriting query." + ) + effective_query = await loop.run_in_executor( + _executor, _rewrite_query_sync, query, relevant_history, config_path + ) + else: + effective_query = query - # Store user message + # ── Persist user message (original, for readability) ────────────────────── await db.append_message(MONGO_URI, MONGO_DB_PREFIX, tenant_id, session_id, {"role": "user", "content": query}) - loop = asyncio.get_event_loop() - + # ── RAG retrieval ───────────────────────────────────────────────────────── if cross_doc or len(doc_ids) > 1: - # Phase 3: parallel per-doc queries, answers synthesised into one response - # Cap at 5 docs to prevent overloading GPU services - target_docs = doc_ids[:5] + # Parallel per-doc queries, answers synthesised into one response + target_docs = doc_ids[:5] # cap to avoid GPU overload answers = await asyncio.gather(*[ - loop.run_in_executor(_executor, _query_single_doc_sync, query, tenant_id, did, config_path) + loop.run_in_executor( + _executor, _query_single_doc_sync, effective_query, tenant_id, did, config_path + ) for did in target_docs ]) - answer = "\n\n---\n\n".join(f"[Document: {did}]\n{ans}" for did, ans in zip(target_docs, answers)) + answer = "\n\n---\n\n".join( + f"[Document: {did}]\n{ans}" for did, ans in zip(target_docs, answers) + ) else: doc_id = doc_ids[0] if doc_ids else None if not doc_id: answer = "No accessible documents found for your query." else: answer = await loop.run_in_executor( - _executor, _query_single_doc_sync, query, tenant_id, doc_id, config_path + _executor, _query_single_doc_sync, effective_query, tenant_id, doc_id, config_path ) - # Store assistant message + # ── Persist assistant message ────────────────────────────────────────────── await db.append_message(MONGO_URI, MONGO_DB_PREFIX, tenant_id, session_id, {"role": "assistant", "content": answer}) - return {"answer": answer, "session_id": session_id, "doc_ids_used": doc_ids} + return { + "answer": answer, + "session_id": session_id, + "doc_ids_used": doc_ids, + "rewritten_query": effective_query if effective_query != query else None, + } diff --git a/api/services/entity_editor.py b/api/services/entity_editor.py index c5857ba..d05b19b 100644 --- a/api/services/entity_editor.py +++ b/api/services/entity_editor.py @@ -14,18 +14,18 @@ import logging import os from collections import defaultdict -from concurrent.futures import ThreadPoolExecutor from difflib import SequenceMatcher from typing import Dict, List, Optional from api.dependencies import ( FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD, INDEX_SAVE_DIR, MONGO_URI, MONGO_DB_PREFIX, + THREAD_POOL, ) from api.db import mongodb as db log = logging.getLogger(__name__) -_executor = ThreadPoolExecutor(max_workers=2) +_executor = THREAD_POOL # Per-document asyncio lock — keyed by "{tenant_id}:{doc_id}" _doc_locks: Dict[str, asyncio.Lock] = defaultdict(asyncio.Lock) diff --git a/api/services/entity_resolution.py b/api/services/entity_resolution.py index a861abe..b78a2e2 100644 --- a/api/services/entity_resolution.py +++ b/api/services/entity_resolution.py @@ -10,11 +10,12 @@ import asyncio import logging import os -from concurrent.futures import ThreadPoolExecutor from typing import List +from api.dependencies import THREAD_POOL + log = logging.getLogger(__name__) -_executor = ThreadPoolExecutor(max_workers=2) +_executor = THREAD_POOL RESOLUTION_THRESHOLD = float(os.getenv("BOOKRAG_ENTITY_RESOLUTION_THRESHOLD", "0.85")) GLOBAL_VDB_DIR = os.getenv("BOOKRAG_GLOBAL_VDB_DIR", "./indices") diff --git a/api/services/indexing.py b/api/services/indexing.py index e065b90..9b718b3 100644 --- a/api/services/indexing.py +++ b/api/services/indexing.py @@ -3,13 +3,12 @@ import logging import os import shutil -from concurrent.futures import ThreadPoolExecutor from api.db import mongodb as db -from api.dependencies import MONGO_URI, MONGO_DB_PREFIX, INDEX_SAVE_DIR +from api.dependencies import MONGO_URI, MONGO_DB_PREFIX, INDEX_SAVE_DIR, THREAD_POOL log = logging.getLogger(__name__) -_executor = ThreadPoolExecutor(max_workers=2) +_executor = THREAD_POOL def _build_index_sync(pdf_path: str, save_path: str, tenant_id: str, doc_id: str, config_path: str): From 0bc18ea683d2e610d5c7a5d4a8b5e55c24762772 Mon Sep 17 00:00:00 2001 From: vmoudyp Date: Tue, 3 Mar 2026 02:53:56 +0700 Subject: [PATCH 05/11] feat: implement refresh token management with storage, validation, and revocation; enhance document upload size handling --- api/db/mongodb.py | 46 +++++++++++++++++++++++ api/dependencies.py | 31 ++++++++++++++++ api/models/requests.py | 5 +++ api/routers/auth.py | 67 ++++++++++++++++++++++++++++++---- api/routers/documents.py | 21 ++++++++++- api/services/entity_editor.py | 69 ++++++++++++++++++++++++----------- 6 files changed, 209 insertions(+), 30 deletions(-) diff --git a/api/db/mongodb.py b/api/db/mongodb.py index eb5c0a2..65198b2 100644 --- a/api/db/mongodb.py +++ b/api/db/mongodb.py @@ -1,4 +1,5 @@ """Async MongoDB client and CRUD helpers using Motor.""" +import hashlib import logging import os from typing import List, Optional @@ -61,6 +62,10 @@ async def ensure_indexes(uri: str, system_db: str, db_prefix: str, tenant_ids: L await tdb["sessions"].create_index("user_id") await tdb["entity_edits"].create_index("doc_id") await tdb["entity_edits"].create_index("ts") + # Refresh token revocation index + await tdb["refresh_tokens"].create_index("token_hash", unique=True) + await tdb["refresh_tokens"].create_index("user_id") + await tdb["refresh_tokens"].create_index("expires_at", expireAfterSeconds=0) log.info(f"Ensured indexes on {len(tenant_ids)} tenant database(s)") @@ -197,3 +202,44 @@ async def log_entity_edit(uri: str, db_prefix: str, tenant_id: str, edit_record: edit_record["ts"] = datetime.now(timezone.utc) await db["entity_edits"].insert_one(edit_record) + + +# ── Refresh Token Management ──────────────────────────────────────────────── + +def _hash_token(token: str) -> str: + """SHA-256 hash of the raw refresh token — we never store plaintext.""" + return hashlib.sha256(token.encode()).hexdigest() + + +async def store_refresh_token( + uri: str, db_prefix: str, tenant_id: str, + user_id: str, token: str, expires_at: datetime, +): + """Store a hashed refresh token so it can be revoked later.""" + db = get_tenant_db(uri, db_prefix, tenant_id) + await db["refresh_tokens"].insert_one({ + "token_hash": _hash_token(token), + "user_id": user_id, + "created_at": datetime.now(timezone.utc), + "expires_at": expires_at, + }) + + +async def is_refresh_token_valid(uri: str, db_prefix: str, tenant_id: str, token: str) -> bool: + """Return True if the token hash exists (i.e. has NOT been revoked).""" + db = get_tenant_db(uri, db_prefix, tenant_id) + doc = await db["refresh_tokens"].find_one({"token_hash": _hash_token(token)}) + return doc is not None + + +async def revoke_refresh_token(uri: str, db_prefix: str, tenant_id: str, token: str): + """Revoke a single refresh token by removing it.""" + db = get_tenant_db(uri, db_prefix, tenant_id) + await db["refresh_tokens"].delete_one({"token_hash": _hash_token(token)}) + + +async def revoke_all_refresh_tokens(uri: str, db_prefix: str, tenant_id: str, user_id: str): + """Revoke all refresh tokens for a user (e.g. password change).""" + db = get_tenant_db(uri, db_prefix, tenant_id) + result = await db["refresh_tokens"].delete_many({"user_id": user_id}) + log.info(f"Revoked {result.deleted_count} refresh tokens for user {user_id}") diff --git a/api/dependencies.py b/api/dependencies.py index afb996e..5b338a4 100644 --- a/api/dependencies.py +++ b/api/dependencies.py @@ -25,6 +25,7 @@ SECRET_KEY = _secret ALGORITHM = "HS256" ACCESS_TOKEN_EXPIRE_MINUTES = int(os.getenv("BOOKRAG_TOKEN_EXPIRE", "60")) +REFRESH_TOKEN_EXPIRE_DAYS = int(os.getenv("BOOKRAG_REFRESH_TOKEN_DAYS", "7")) MONGO_URI = os.getenv("BOOKRAG_MONGO_URI", "mongodb://localhost:27017") MONGO_DB_PREFIX = os.getenv("BOOKRAG_MONGO_PREFIX", "bookrag") @@ -62,9 +63,36 @@ def create_access_token(data: dict) -> str: from datetime import datetime, timedelta, timezone payload = data.copy() payload["exp"] = datetime.now(timezone.utc) + timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES) + payload["type"] = "access" return jwt.encode(payload, SECRET_KEY, algorithm=ALGORITHM) +def create_refresh_token(data: dict) -> str: + """Create a long-lived refresh token (default 7 days).""" + from datetime import datetime, timedelta, timezone + payload = data.copy() + payload["exp"] = datetime.now(timezone.utc) + timedelta(days=REFRESH_TOKEN_EXPIRE_DAYS) + payload["type"] = "refresh" + return jwt.encode(payload, SECRET_KEY, algorithm=ALGORITHM) + + +def decode_refresh_token(token: str) -> dict: + """Decode and validate a refresh token. Raises HTTPException on failure.""" + try: + payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM]) + if payload.get("type") != "refresh": + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid token type — expected refresh token", + ) + return payload + except JWTError: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid or expired refresh token", + ) + + # ── Current-user dependency ─────────────────────────────────────────────────── async def get_current_user(token: str = Depends(oauth2_scheme)) -> dict: @@ -75,6 +103,9 @@ async def get_current_user(token: str = Depends(oauth2_scheme)) -> dict: ) try: payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM]) + # Reject refresh tokens used as access tokens + if payload.get("type") == "refresh": + raise credentials_exc user_id: str = payload.get("sub") tenant_id: str = payload.get("tenant_id") role: str = payload.get("role", "user") diff --git a/api/models/requests.py b/api/models/requests.py index 91f5905..f54d3ce 100644 --- a/api/models/requests.py +++ b/api/models/requests.py @@ -36,9 +36,14 @@ class LoginRequest(BaseModel): class TokenResponse(BaseModel): access_token: str + refresh_token: str token_type: str = "bearer" +class RefreshRequest(BaseModel): + refresh_token: str + + # ── Tenant ──────────────────────────────────────────────────────────────────── class TenantCreateRequest(BaseModel): diff --git a/api/routers/auth.py b/api/routers/auth.py index 5ffea7e..aad276a 100644 --- a/api/routers/auth.py +++ b/api/routers/auth.py @@ -1,13 +1,17 @@ -"""Authentication router: register and login endpoints.""" +"""Authentication router: register, login, refresh, and logout endpoints.""" import logging +from datetime import datetime, timedelta, timezone + from fastapi import APIRouter, Depends, HTTPException, status -from api.models.requests import RegisterRequest, LoginRequest, TokenResponse +from api.models.requests import RegisterRequest, LoginRequest, TokenResponse, RefreshRequest from api.db import mongodb as db from api.dependencies import ( MONGO_URI, MONGO_DB_PREFIX, MONGO_SYSTEM_DB, - hash_password, verify_password, create_access_token, - rate_limit_login, + hash_password, verify_password, + create_access_token, create_refresh_token, decode_refresh_token, + REFRESH_TOKEN_EXPIRE_DAYS, + rate_limit_login, get_current_user, ) log = logging.getLogger(__name__) @@ -40,7 +44,7 @@ async def register(req: RegisterRequest): @router.post("/login", response_model=TokenResponse, dependencies=[Depends(rate_limit_login)]) async def login(req: LoginRequest): - """Authenticate and return a JWT access token.""" + """Authenticate and return JWT access + refresh tokens.""" user = await db.get_user_by_username(MONGO_URI, MONGO_DB_PREFIX, req.tenant_id, req.username) if not user or not verify_password(req.password, user["hashed_password"]): raise HTTPException( @@ -48,10 +52,57 @@ async def login(req: LoginRequest): detail="Incorrect username or password", headers={"WWW-Authenticate": "Bearer"}, ) - token = create_access_token({ + claims = { "sub": user["user_id"], "tenant_id": user["tenant_id"], "role": user.get("role", "user"), - }) - return TokenResponse(access_token=token) + } + access = create_access_token(claims) + refresh = create_refresh_token(claims) + + # Store refresh token hash for revocation support + expires_at = datetime.now(timezone.utc) + timedelta(days=REFRESH_TOKEN_EXPIRE_DAYS) + await db.store_refresh_token( + MONGO_URI, MONGO_DB_PREFIX, req.tenant_id, + user["user_id"], refresh, expires_at, + ) + return TokenResponse(access_token=access, refresh_token=refresh) + + +@router.post("/refresh", response_model=TokenResponse) +async def refresh_token(req: RefreshRequest): + """Exchange a valid refresh token for a new access + refresh token pair. + + The old refresh token is revoked (single-use rotation). + """ + payload = decode_refresh_token(req.refresh_token) + tenant_id = payload.get("tenant_id", "") + user_id = payload.get("sub", "") + + # Check the token hasn't been revoked + if not await db.is_refresh_token_valid(MONGO_URI, MONGO_DB_PREFIX, tenant_id, req.refresh_token): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Refresh token has been revoked", + ) + + # Revoke old refresh token (single-use rotation) + await db.revoke_refresh_token(MONGO_URI, MONGO_DB_PREFIX, tenant_id, req.refresh_token) + + # Issue new pair + claims = {"sub": user_id, "tenant_id": tenant_id, "role": payload.get("role", "user")} + new_access = create_access_token(claims) + new_refresh = create_refresh_token(claims) + + expires_at = datetime.now(timezone.utc) + timedelta(days=REFRESH_TOKEN_EXPIRE_DAYS) + await db.store_refresh_token(MONGO_URI, MONGO_DB_PREFIX, tenant_id, user_id, new_refresh, expires_at) + + return TokenResponse(access_token=new_access, refresh_token=new_refresh) + + +@router.post("/logout", status_code=status.HTTP_204_NO_CONTENT) +async def logout(req: RefreshRequest, current_user: dict = Depends(get_current_user)): + """Revoke the provided refresh token (logout).""" + tenant_id = current_user["tenant_id"] + await db.revoke_refresh_token(MONGO_URI, MONGO_DB_PREFIX, tenant_id, req.refresh_token) diff --git a/api/routers/documents.py b/api/routers/documents.py index fbe4b09..488f8a2 100644 --- a/api/routers/documents.py +++ b/api/routers/documents.py @@ -21,6 +21,9 @@ CONFIG_PATH = os.getenv("BOOKRAG_CONFIG_PATH", "config/gbc.yaml") +# Max upload size in bytes — default 200 MB +_MAX_UPLOAD_BYTES = int(os.getenv("BOOKRAG_MAX_UPLOAD_MB", "200")) * 1024 * 1024 + async def _save_and_register_file( file: UploadFile, @@ -28,14 +31,30 @@ async def _save_and_register_file( user_id: str, tenant_upload_dir: str, ) -> dict: - """Save one uploaded file to the tenant upload dir and return doc metadata.""" + """Save one uploaded file to the tenant upload dir and return doc metadata. + + Raises ``HTTPException(413)`` if the file exceeds ``_MAX_UPLOAD_BYTES``. + """ doc_id = str(uuid.uuid4()) # Preserve original filename; prefix with doc_id to avoid collisions safe_name = os.path.basename(file.filename) pdf_path = os.path.join(tenant_upload_dir, f"{doc_id}_{safe_name}") + total_size = 0 async with aiofiles.open(pdf_path, "wb") as out: while chunk := await file.read(1024 * 1024): # 1 MB chunks + total_size += len(chunk) + if total_size > _MAX_UPLOAD_BYTES: + # Clean up partial file + await out.close() + try: + os.remove(pdf_path) + except OSError: + pass + raise HTTPException( + status_code=413, + detail=f"File exceeds maximum upload size of {_MAX_UPLOAD_BYTES // (1024*1024)} MB", + ) await out.write(chunk) return { diff --git a/api/services/entity_editor.py b/api/services/entity_editor.py index d05b19b..28b8221 100644 --- a/api/services/entity_editor.py +++ b/api/services/entity_editor.py @@ -30,6 +30,11 @@ # Per-document asyncio lock — keyed by "{tenant_id}:{doc_id}" _doc_locks: Dict[str, asyncio.Lock] = defaultdict(asyncio.Lock) +# Dedup tracker: prevents redundant VDB rebuilds when multiple edits fire +# in quick succession on the same document. +_rebuild_pending: set[str] = set() +_rebuild_pending_lock = asyncio.Lock() + def _get_lock(tenant_id: str, doc_id: str) -> asyncio.Lock: return _doc_locks[f"{tenant_id}:{doc_id}"] @@ -102,6 +107,26 @@ def _rebuild_vdb_sync(tenant_id: str, doc_id: str, config_path: str) -> None: log.warning(f"VDB rebuild failed for {tenant_id}/{doc_id}: {exc}") +async def _schedule_vdb_rebuild(tenant_id: str, doc_id: str, config_path: str) -> None: + """Await a VDB rebuild, deduplicating concurrent requests for the same doc. + + If a rebuild is already in-flight for this ``tenant_id:doc_id``, the call + is skipped (the already-running rebuild will pick up the latest graph JSON). + """ + key = f"{tenant_id}:{doc_id}" + async with _rebuild_pending_lock: + if key in _rebuild_pending: + log.debug(f"VDB rebuild already pending for {key} — skipping") + return + _rebuild_pending.add(key) + try: + loop = asyncio.get_event_loop() + await loop.run_in_executor(_executor, _rebuild_vdb_sync, tenant_id, doc_id, config_path) + finally: + async with _rebuild_pending_lock: + _rebuild_pending.discard(key) + + # ── List entities ───────────────────────────────────────────────────────────── def _list_entities_sync(tenant_id: str, doc_id: str, config_path: str) -> List[dict]: @@ -178,8 +203,7 @@ async def rename_entity( "before": {"entity_name": entity_name, "entity_type": entity_type}, "after": {"entity_name": new_entity_name, "entity_type": new_entity_type or entity_type}, }) - loop = asyncio.get_event_loop() - loop.run_in_executor(_executor, _rebuild_vdb_sync, tenant_id, doc_id, config_path) + await _schedule_vdb_rebuild(tenant_id, doc_id, config_path) return result @@ -253,8 +277,7 @@ async def split_entity( "before": {"entity_name": entity_name, "entity_type": entity_type}, "after": [{"entity_name": e["entity_name"], "entity_type": e["entity_type"]} for e in result], }) - loop = asyncio.get_event_loop() - loop.run_in_executor(_executor, _rebuild_vdb_sync, tenant_id, doc_id, config_path) + await _schedule_vdb_rebuild(tenant_id, doc_id, config_path) return result @@ -273,21 +296,26 @@ def _suggest_merges_sync( suggestions: List[dict] = [] - # String similarity - n = len(entities) - for i in range(n): - for j in range(i + 1, n): - a, b = entities[i], entities[j] - if a["entity_type"] != b["entity_type"]: - continue - score = SequenceMatcher(None, a["entity_name"].lower(), b["entity_name"].lower()).ratio() - if score >= min_score: - suggestions.append({ - "entity_a": {"entity_name": a["entity_name"], "entity_type": a["entity_type"]}, - "entity_b": {"entity_name": b["entity_name"], "entity_type": b["entity_type"]}, - "score": round(score, 4), - "method": "string_similarity", - }) + # Pre-group entities by type so we only compare within same-type groups + # This reduces O(n²) to O(Σ nᵢ²) where nᵢ is entity count per type + by_type: Dict[str, List[dict]] = defaultdict(list) + for ent in entities: + by_type[ent["entity_type"]].append(ent) + + # String similarity — only within same-type groups + for _etype, group in by_type.items(): + n = len(group) + for i in range(n): + for j in range(i + 1, n): + a, b = group[i], group[j] + score = SequenceMatcher(None, a["entity_name"].lower(), b["entity_name"].lower()).ratio() + if score >= min_score: + suggestions.append({ + "entity_a": {"entity_name": a["entity_name"], "entity_type": a["entity_type"]}, + "entity_b": {"entity_name": b["entity_name"], "entity_type": b["entity_type"]}, + "score": round(score, 4), + "method": "string_similarity", + }) # Embedding similarity (optional) if use_embeddings: @@ -433,7 +461,6 @@ async def merge_entities( "before": source_entities, "after": {"entity_name": canonical_name, "entity_type": canonical_type}, }) - loop = asyncio.get_event_loop() - loop.run_in_executor(_executor, _rebuild_vdb_sync, tenant_id, doc_id, config_path) + await _schedule_vdb_rebuild(tenant_id, doc_id, config_path) return result From bef06be98a6cb5356d290377701397ab2687ff5c Mon Sep 17 00:00:00 2001 From: vmoudyp Date: Tue, 3 Mar 2026 03:51:08 +0700 Subject: [PATCH 06/11] feat: Enhance API request/response models and add session management features - Updated Pydantic models in `requests.py` to include detailed field descriptions and added new fields for document metadata (created_at, document_date). - Introduced new session management endpoints in `chat.py` for listing and deleting chat sessions, including pagination support. - Enhanced message retrieval in `get_messages` to support pagination and return total message count. - Added document deletion functionality in `documents.py`, including cleanup of associated files and database records. - Updated indexing service to propagate document dates for temporal awareness during indexing. - Modified various configuration files to update model names, API endpoints, and parameters for improved performance and compatibility. --- Core/configs/system_config.py | 8 ++ api/db/mongodb.py | 80 ++++++++++++++++++- api/main.py | 143 +++++++++++++++++++++++++++++++--- api/models/requests.py | 54 +++++++++---- api/routers/chat.py | 56 +++++++++++-- api/routers/documents.py | 115 ++++++++++++++++++++++++--- api/services/chat.py | 32 +++++++- api/services/indexing.py | 11 ++- config/bm25.yaml | 16 ++-- config/gbc_wo_er.yaml | 14 ++-- config/gbc_wo_graph.yaml | 14 ++-- config/gbc_wo_plan.yaml | 14 ++-- config/gbc_wo_selector.yaml | 14 ++-- config/gbc_wo_text.yaml | 14 ++-- config/graph.yaml | 18 ++--- config/mm.yaml | 33 ++++---- config/pdf_vanilla.yaml | 14 ++-- config/raptor.yaml | 16 ++-- config/tree.yaml | 37 +++++---- config/vanilla.yaml | 16 ++-- 20 files changed, 561 insertions(+), 158 deletions(-) diff --git a/Core/configs/system_config.py b/Core/configs/system_config.py index b139907..3aa0e84 100644 --- a/Core/configs/system_config.py +++ b/Core/configs/system_config.py @@ -11,6 +11,7 @@ from Core.configs.mongodb_config import MongoDBConfig from pydantic import BaseModel, Field from typing import Optional, Any +from datetime import datetime class SystemConfig(BaseModel): @@ -49,6 +50,13 @@ class SystemConfig(BaseModel): tenant_id: Optional[str] = None doc_id: Optional[str] = None + # Document temporal metadata (optional, for recency-aware RAG) + document_date: Optional[datetime] = Field( + default=None, + description="Original authoring/publishing date of the document. " + "Used for temporal awareness in cross-document RAG queries.", + ) + # Database configurations falkordb: Any = Field(default_factory=FalkorDBConfig) mongodb: Any = Field(default_factory=MongoDBConfig) diff --git a/api/db/mongodb.py b/api/db/mongodb.py index 65198b2..cddb116 100644 --- a/api/db/mongodb.py +++ b/api/db/mongodb.py @@ -126,13 +126,38 @@ async def get_document_raw_path(uri: str, db_prefix: str, tenant_id: str, doc_id return doc.get("pdf_path") if doc else None -async def list_documents(uri: str, db_prefix: str, tenant_id: str, user_id: str) -> List[dict]: +async def list_documents( + uri: str, db_prefix: str, tenant_id: str, user_id: str, + limit: int = 50, offset: int = 0, +) -> tuple[list[dict], int]: + """Return paginated docs accessible to *user_id*, sorted by document_date desc. + + Returns ``(docs, total_count)``. Documents without ``document_date`` + fall back to ``created_at``; documents with neither sort last. + """ db = get_tenant_db(uri, db_prefix, tenant_id) - # Return docs the user has access to via permissions perm_cursor = db["permissions"].find({"user_id": user_id}) doc_ids = [p["doc_id"] async for p in perm_cursor] - cursor = db["documents"].find({"doc_id": {"$in": doc_ids}}) - return [d async for d in cursor] + filt = {"doc_id": {"$in": doc_ids}} + total = await db["documents"].count_documents(filt) + cursor = ( + db["documents"] + .find(filt) + .sort([("document_date", pymongo.DESCENDING), ("created_at", pymongo.DESCENDING)]) + .skip(offset) + .limit(limit) + ) + docs = [d async for d in cursor] + return docs, total + + +async def delete_document(uri: str, db_prefix: str, tenant_id: str, doc_id: str): + """Delete a document and all associated permissions, sessions, entity edits.""" + db = get_tenant_db(uri, db_prefix, tenant_id) + await db["documents"].delete_one({"doc_id": doc_id}) + await db["permissions"].delete_many({"doc_id": doc_id}) + await db["entity_edits"].delete_many({"doc_id": doc_id}) + log.info(f"Deleted document '{doc_id}' and related records from tenant '{tenant_id}'") # ── Permission CRUD ─────────────────────────────────────────────────────────── @@ -186,6 +211,53 @@ async def get_session(uri: str, db_prefix: str, tenant_id: str, session_id: str) return await db["sessions"].find_one({"session_id": session_id}) +async def list_sessions( + uri: str, db_prefix: str, tenant_id: str, user_id: str, + limit: int = 50, offset: int = 0, +) -> tuple[list[dict], int]: + """Return paginated sessions for *user_id*, newest first.""" + db = get_tenant_db(uri, db_prefix, tenant_id) + filt = {"user_id": user_id} + total = await db["sessions"].count_documents(filt) + cursor = ( + db["sessions"] + .find(filt, {"messages": 0}) # exclude messages array for listing + .sort("created_at", pymongo.DESCENDING) + .skip(offset) + .limit(limit) + ) + sessions = [s async for s in cursor] + return sessions, total + + +async def delete_session(uri: str, db_prefix: str, tenant_id: str, session_id: str): + """Delete a session and all its messages.""" + db = get_tenant_db(uri, db_prefix, tenant_id) + await db["sessions"].delete_one({"session_id": session_id}) + + +async def recover_stale_indexing(uri: str, db_prefix: str, tenant_ids: List[str]) -> int: + """Reset docs stuck in 'indexing' status to 'error' — called at startup. + + If the server crashed mid-indexing, those docs will never complete. + Returns the total number of documents recovered across all tenants. + """ + client = get_client(uri) + recovered = 0 + for tid in tenant_ids: + tdb = client[f"{db_prefix}_{tid}"] + result = await tdb["documents"].update_many( + {"status": "indexing"}, + {"$set": { + "status": "error", + "error": "Indexing interrupted by server restart — please re-upload", + "updated_at": datetime.now(timezone.utc), + }}, + ) + recovered += result.modified_count + return recovered + + # ── Entity Edit Audit Log ───────────────────────────────────────────────────── async def log_entity_edit(uri: str, db_prefix: str, tenant_id: str, edit_record: dict): diff --git a/api/main.py b/api/main.py index d3a585c..758fe8d 100644 --- a/api/main.py +++ b/api/main.py @@ -1,31 +1,111 @@ """BookRAG FastAPI application entry point.""" import logging import os +import uuid as _uuid +import json from contextlib import asynccontextmanager +import yaml +from starlette.middleware.base import BaseHTTPMiddleware +from starlette.requests import Request +from starlette.responses import Response from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from api.db import mongodb as db -from api.dependencies import MONGO_URI, MONGO_DB_PREFIX, MONGO_SYSTEM_DB +from api.dependencies import MONGO_URI, MONGO_DB_PREFIX, MONGO_SYSTEM_DB, THREAD_POOL from api.routers import auth, documents, chat, tenants, entities -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", -) + +# ── Structured JSON logging ────────────────────────────────────────────────── + +class _JSONFormatter(logging.Formatter): + """Emit each log record as a single JSON line.""" + + def format(self, record: logging.LogRecord) -> str: + obj = { + "ts": self.formatTime(record, self.datefmt), + "level": record.levelname, + "logger": record.name, + "msg": record.getMessage(), + } + if hasattr(record, "request_id"): + obj["request_id"] = record.request_id + if record.exc_info and record.exc_info[0] is not None: + obj["exc"] = self.formatException(record.exc_info) + return json.dumps(obj, default=str) + + +_log_level = os.getenv("BOOKRAG_LOG_LEVEL", "INFO").upper() +_handler = logging.StreamHandler() +_handler.setFormatter(_JSONFormatter()) +logging.root.handlers = [_handler] +logging.root.setLevel(getattr(logging, _log_level, logging.INFO)) log = logging.getLogger(__name__) +# ── Config validation ──────────────────────────────────────────────────────── + +_CONFIG_PATH = os.getenv("BOOKRAG_CONFIG_PATH", "config/gbc.yaml") +_CONFIG_REQUIRED_KEYS = {"llm", "vlm"} # top-level sections that must exist + + +def _validate_config(path: str): + """Validate YAML config at startup — fail fast on missing required sections.""" + if not os.path.isfile(path): + raise RuntimeError(f"Config file not found: {path}") + with open(path) as f: + raw = yaml.safe_load(f) + if not isinstance(raw, dict): + raise RuntimeError(f"Config file is not a valid YAML mapping: {path}") + missing = _CONFIG_REQUIRED_KEYS - raw.keys() + if missing: + raise RuntimeError(f"Config file '{path}' is missing required sections: {missing}") + log.info(f"Config validated: {path}") + + +# ── Request-ID middleware ──────────────────────────────────────────────────── + +class _RequestIDMiddleware(BaseHTTPMiddleware): + """Inject an ``X-Request-ID`` header (echo or generate) and attach to log context.""" + + async def dispatch(self, request: Request, call_next): + req_id = request.headers.get("X-Request-ID") or str(_uuid.uuid4()) + # Make request_id available in logging context (thread-local filter) + _request_id_ctx.set(req_id) + response: Response = await call_next(request) + response.headers["X-Request-ID"] = req_id + return response + + +import contextvars +_request_id_ctx: contextvars.ContextVar[str] = contextvars.ContextVar("request_id", default="-") + + +class _RequestIDFilter(logging.Filter): + def filter(self, record: logging.LogRecord) -> bool: + record.request_id = _request_id_ctx.get("-") # type: ignore[attr-defined] + return True + + +logging.root.addFilter(_RequestIDFilter()) + + +# ── Lifespan ───────────────────────────────────────────────────────────────── + @asynccontextmanager async def lifespan(app: FastAPI): """Startup and shutdown lifecycle.""" log.info("BookRAG API starting up...") + # Validate config + _validate_config(_CONFIG_PATH) + # Ensure upload and index directories exist os.makedirs(os.getenv("BOOKRAG_UPLOAD_DIR", "./uploads"), exist_ok=True) os.makedirs(os.getenv("BOOKRAG_INDEX_DIR", "./indices"), exist_ok=True) - # Build MongoDB indexes for all known tenants + # Build MongoDB indexes and recover stale indexing for all known tenants + tenant_ids: list[str] = [] try: sdb = db.get_system_db(MONGO_URI, MONGO_SYSTEM_DB) tenant_ids = [t["tenant_id"] async for t in sdb["tenants"].find({}, {"tenant_id": 1})] @@ -33,9 +113,22 @@ async def lifespan(app: FastAPI): except Exception as exc: log.warning(f"MongoDB index creation skipped: {exc}") + # Recover docs stuck in "indexing" status from a previous crash + if tenant_ids: + try: + recovered = await db.recover_stale_indexing(MONGO_URI, MONGO_DB_PREFIX, tenant_ids) + if recovered: + log.warning(f"Recovered {recovered} stale indexing document(s) → status='error'") + except Exception as exc: + log.warning(f"Stale indexing recovery skipped: {exc}") + yield - log.info("BookRAG API shutting down...") + + # Graceful shutdown: finish running tasks, don't cancel them + log.info("BookRAG API shutting down — draining thread pool...") + THREAD_POOL.shutdown(wait=True, cancel_futures=False) await db.close_client() + log.info("BookRAG API shut down cleanly.") app = FastAPI( @@ -45,6 +138,9 @@ async def lifespan(app: FastAPI): lifespan=lifespan, ) +# Request-ID middleware (must be added before CORS) +app.add_middleware(_RequestIDMiddleware) + # CORS — set BOOKRAG_CORS_ORIGINS to a comma-separated list of allowed origins _cors_raw = os.getenv("BOOKRAG_CORS_ORIGINS", "http://localhost:3000,http://localhost:8000") _cors_origins = [o.strip() for o in _cors_raw.split(",") if o.strip()] @@ -53,7 +149,6 @@ async def lifespan(app: FastAPI): "CORS allow_origins contains '*'. This is insecure with credentials=True. " "Set BOOKRAG_CORS_ORIGINS to explicit origins in production." ) - # Wildcard + credentials is rejected by browsers; fall back to no-credentials mode _cors_credentials = False else: _cors_credentials = True @@ -63,7 +158,7 @@ async def lifespan(app: FastAPI): allow_origins=_cors_origins, allow_credentials=_cors_credentials, allow_methods=["GET", "POST", "PUT", "PATCH", "DELETE", "OPTIONS"], - allow_headers=["Authorization", "Content-Type"], + allow_headers=["Authorization", "Content-Type", "X-Request-ID"], ) # Routers @@ -76,5 +171,33 @@ async def lifespan(app: FastAPI): @app.get("/health") async def health(): - return {"status": "ok", "service": "BookRAG API"} + """Deep health check — verify MongoDB and FalkorDB connectivity.""" + checks: dict = {"service": "BookRAG API"} + + # MongoDB ping + try: + client = db.get_client(MONGO_URI) + await client.admin.command("ping") + checks["mongodb"] = "ok" + except Exception as exc: + checks["mongodb"] = f"error: {exc}" + + # FalkorDB ping (optional — only if host is configured) + fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") + if fdb_host: + try: + from api.dependencies import FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD + import falkordb + fdb = falkordb.FalkorDB( + host=FALKORDB_HOST, port=FALKORDB_PORT, + password=FALKORDB_PASSWORD or None, + ) + fdb.connection.ping() + checks["falkordb"] = "ok" + except Exception as exc: + checks["falkordb"] = f"error: {exc}" + + overall = "ok" if all(v == "ok" for k, v in checks.items() if k != "service") else "degraded" + checks["status"] = overall + return checks diff --git a/api/models/requests.py b/api/models/requests.py index f54d3ce..a1cdcae 100644 --- a/api/models/requests.py +++ b/api/models/requests.py @@ -1,4 +1,5 @@ """Pydantic request and response models for the BookRAG API.""" +from datetime import datetime from typing import List, Optional from pydantic import BaseModel, Field, field_validator @@ -35,9 +36,9 @@ class LoginRequest(BaseModel): class TokenResponse(BaseModel): - access_token: str - refresh_token: str - token_type: str = "bearer" + access_token: str = Field(..., description="Short-lived JWT access token (default 60 min)") + refresh_token: str = Field(..., description="Long-lived refresh token for rotation (default 7 days)") + token_type: str = Field(default="bearer", description="OAuth2 token type") class RefreshRequest(BaseModel): @@ -61,15 +62,21 @@ class TenantResponse(BaseModel): # ── Document ────────────────────────────────────────────────────────────────── class DocumentResponse(BaseModel): - doc_id: str - filename: str - status: str # pending | indexing | ready | error - error: Optional[str] = None + doc_id: str = Field(..., description="Unique document identifier") + filename: str = Field(..., description="Original filename") + status: str = Field(..., description="Indexing status: pending | indexing | ready | error") + error: Optional[str] = Field(default=None, description="Error message if status is 'error'") + created_at: Optional[datetime] = Field(default=None, description="Upload timestamp (UTC)") + document_date: Optional[datetime] = Field( + default=None, + description="User-provided original authoring/publishing date of the document. " + "Used for temporal awareness in cross-document RAG.", + ) class BatchUploadResponse(BaseModel): - uploaded: List["DocumentResponse"] - failed: List[dict] = Field(default_factory=list) # {"filename": ..., "error": ...} + uploaded: List["DocumentResponse"] = Field(..., description="Successfully uploaded documents") + failed: List[dict] = Field(default_factory=list, description="Files that failed: [{filename, error}]") class PermissionGrantRequest(BaseModel): @@ -81,17 +88,17 @@ class PermissionGrantRequest(BaseModel): # ── Chat ────────────────────────────────────────────────────────────────────── class ChatQueryRequest(BaseModel): - query: str = Field(..., min_length=1, max_length=_QUERY_MAX) - session_id: Optional[str] = Field(default=None, max_length=_SHORT_STR) - doc_ids: Optional[List[str]] = None # restrict to specific docs; None = all accessible - cross_doc: bool = False # use global cross-document graph + query: str = Field(..., min_length=1, max_length=_QUERY_MAX, description="User question") + session_id: Optional[str] = Field(default=None, max_length=_SHORT_STR, description="Existing session ID for history-aware queries") + doc_ids: Optional[List[str]] = Field(default=None, description="Restrict to specific docs; None = all accessible") + cross_doc: bool = Field(default=False, description="Use cross-document retrieval mode") class ChatQueryResponse(BaseModel): - answer: str - session_id: str - doc_ids_used: List[str] = Field(default_factory=list) - rewritten_query: Optional[str] = None # set when history was used to rewrite the query + answer: str = Field(..., description="LLM-generated answer") + session_id: str = Field(..., description="Session ID (created if not provided)") + doc_ids_used: List[str] = Field(default_factory=list, description="Document IDs used for retrieval") + rewritten_query: Optional[str] = Field(default=None, description="Rewritten query when history was used") class SessionCreateRequest(BaseModel): @@ -102,6 +109,18 @@ class SessionResponse(BaseModel): session_id: str +class SessionListItem(BaseModel): + session_id: str + created_at: Optional[datetime] = None + message_count: int = 0 + doc_ids: List[str] = Field(default_factory=list) + + +class SessionListResponse(BaseModel): + sessions: List[SessionListItem] + total: int + + class MessageResponse(BaseModel): role: str # "user" | "assistant" content: str @@ -111,6 +130,7 @@ class MessageResponse(BaseModel): class SessionMessagesResponse(BaseModel): session_id: str messages: List[MessageResponse] + total: int = Field(0, description="Total messages in session (before pagination)") # ── Entity Management ───────────────────────────────────────────────────────── diff --git a/api/routers/chat.py b/api/routers/chat.py index b3e2f7f..24d56ab 100644 --- a/api/routers/chat.py +++ b/api/routers/chat.py @@ -2,12 +2,13 @@ import logging import os from typing import List -from fastapi import APIRouter, Depends, HTTPException +from fastapi import APIRouter, Depends, HTTPException, Query from api.models.requests import ( ChatQueryRequest, ChatQueryResponse, SessionCreateRequest, SessionResponse, SessionMessagesResponse, MessageResponse, + SessionListResponse, SessionListItem, ) from api.db import mongodb as db from api.dependencies import ( @@ -62,9 +63,51 @@ async def create_session(req: SessionCreateRequest, current_user: dict = Depends return SessionResponse(session_id=session_id) +@router.get("/sessions", response_model=SessionListResponse) +async def list_sessions( + limit: int = Query(default=50, ge=1, le=200, description="Max sessions to return"), + offset: int = Query(default=0, ge=0, description="Number of sessions to skip"), + current_user: dict = Depends(get_current_user), +): + """List all chat sessions for the current user, newest first.""" + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + sessions, total = await db.list_sessions( + MONGO_URI, MONGO_DB_PREFIX, tenant_id, user_id, limit=limit, offset=offset + ) + items = [ + SessionListItem( + session_id=s["session_id"], + created_at=s.get("created_at"), + message_count=s.get("message_count", len(s.get("messages", []))), + doc_ids=s.get("doc_ids", []), + ) + for s in sessions + ] + return SessionListResponse(sessions=items, total=total) + + +@router.delete("/sessions/{session_id}", status_code=204) +async def delete_session(session_id: str, current_user: dict = Depends(get_current_user)): + """Delete a chat session and all its messages.""" + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + session = await db.get_session(MONGO_URI, MONGO_DB_PREFIX, tenant_id, session_id) + if not session: + raise HTTPException(status_code=404, detail="Session not found") + if session.get("user_id") != user_id and current_user["role"] != "admin": + raise HTTPException(status_code=403, detail="Access denied") + await db.delete_session(MONGO_URI, MONGO_DB_PREFIX, tenant_id, session_id) + + @router.get("/sessions/{session_id}/messages", response_model=SessionMessagesResponse) -async def get_messages(session_id: str, current_user: dict = Depends(get_current_user)): - """Retrieve all messages in a session.""" +async def get_messages( + session_id: str, + limit: int = Query(default=100, ge=1, le=500, description="Max messages to return"), + offset: int = Query(default=0, ge=0, description="Number of messages to skip"), + current_user: dict = Depends(get_current_user), +): + """Retrieve messages in a session with pagination.""" tenant_id = current_user["tenant_id"] user_id = current_user["user_id"] session = await db.get_session(MONGO_URI, MONGO_DB_PREFIX, tenant_id, session_id) @@ -72,13 +115,16 @@ async def get_messages(session_id: str, current_user: dict = Depends(get_current raise HTTPException(status_code=404, detail="Session not found") if session.get("user_id") != user_id and current_user["role"] != "admin": raise HTTPException(status_code=403, detail="Access denied") + all_messages = session.get("messages", []) + total = len(all_messages) + paginated = all_messages[offset:offset + limit] messages = [ MessageResponse( role=m["role"], content=m["content"], ts=str(m.get("ts", "")), ) - for m in session.get("messages", []) + for m in paginated ] - return SessionMessagesResponse(session_id=session_id, messages=messages) + return SessionMessagesResponse(session_id=session_id, messages=messages, total=total) diff --git a/api/routers/documents.py b/api/routers/documents.py index 488f8a2..a8e6573 100644 --- a/api/routers/documents.py +++ b/api/routers/documents.py @@ -1,17 +1,19 @@ -"""Document management router: upload (multi-file), list, status, raw download.""" +"""Document management router: upload (multi-file), list, status, raw download, delete.""" import logging import os +import shutil import uuid -from typing import List +from datetime import datetime +from typing import List, Optional -from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, BackgroundTasks +from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, BackgroundTasks, Query, Form from fastapi.responses import FileResponse import aiofiles from api.models.requests import DocumentResponse, BatchUploadResponse from api.db import mongodb as db from api.dependencies import ( - MONGO_URI, MONGO_DB_PREFIX, UPLOAD_DIR, + MONGO_URI, MONGO_DB_PREFIX, UPLOAD_DIR, INDEX_SAVE_DIR, get_current_user, check_doc_access, ) from api.services.indexing import run_indexing @@ -57,12 +59,16 @@ async def _save_and_register_file( ) await out.write(chunk) + from datetime import timezone + now = datetime.now(timezone.utc) return { "doc_id": doc_id, "filename": file.filename, "tenant_id": tenant_id, "uploaded_by": user_id, "pdf_path": pdf_path, + "created_at": now, + "status": "pending", } @@ -70,12 +76,30 @@ async def _save_and_register_file( async def upload_documents( background_tasks: BackgroundTasks, files: List[UploadFile] = File(...), + document_date: Optional[str] = Form( + default=None, + description="Optional ISO-8601 date for ALL uploaded files (original authoring date). " + "Example: 2025-06-15 or 2025-06-15T10:30:00Z", + ), current_user: dict = Depends(get_current_user), ): - """Upload one or more PDFs and start background indexing for each.""" + """Upload one or more PDFs and start background indexing for each. + + An optional ``document_date`` (ISO-8601) can be provided to indicate + the original authoring/publishing date of the documents. This date is + used for temporal-awareness in cross-document RAG queries. + """ tenant_id = current_user["tenant_id"] user_id = current_user["user_id"] + # Parse optional document_date + parsed_doc_date: Optional[datetime] = None + if document_date: + try: + parsed_doc_date = datetime.fromisoformat(document_date.replace("Z", "+00:00")) + except ValueError: + raise HTTPException(status_code=422, detail="document_date must be a valid ISO-8601 date string") + tenant_upload_dir = os.path.join(UPLOAD_DIR, tenant_id) os.makedirs(tenant_upload_dir, exist_ok=True) @@ -93,31 +117,45 @@ async def upload_documents( failed.append({"filename": file.filename, "error": str(exc)}) continue + # Attach document_date if provided + if parsed_doc_date: + doc_data["document_date"] = parsed_doc_date + # Register document in MongoDB await db.create_document(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_data) # Auto-grant uploader owner access await db.grant_permission(MONGO_URI, MONGO_DB_PREFIX, tenant_id, user_id, doc_data["doc_id"], "owner") # Enqueue background indexing background_tasks.add_task( - run_indexing, tenant_id, doc_data["doc_id"], doc_data["pdf_path"], CONFIG_PATH + run_indexing, tenant_id, doc_data["doc_id"], doc_data["pdf_path"], CONFIG_PATH, + document_date=parsed_doc_date, ) - uploaded.append(DocumentResponse(doc_id=doc_data["doc_id"], filename=file.filename, status="pending")) + uploaded.append(DocumentResponse( + doc_id=doc_data["doc_id"], filename=file.filename, status="pending", + document_date=parsed_doc_date, + )) return BatchUploadResponse(uploaded=uploaded, failed=failed) @router.get("", response_model=List[DocumentResponse]) -async def list_documents(current_user: dict = Depends(get_current_user)): - """List all documents accessible to the current user.""" +async def list_documents( + limit: int = Query(default=50, ge=1, le=200, description="Max documents to return"), + offset: int = Query(default=0, ge=0, description="Number of documents to skip"), + current_user: dict = Depends(get_current_user), +): + """List documents accessible to the current user, sorted by document_date descending.""" tenant_id = current_user["tenant_id"] user_id = current_user["user_id"] - docs = await db.list_documents(MONGO_URI, MONGO_DB_PREFIX, tenant_id, user_id) + docs, _total = await db.list_documents(MONGO_URI, MONGO_DB_PREFIX, tenant_id, user_id, limit=limit, offset=offset) return [ DocumentResponse( doc_id=d["doc_id"], filename=d.get("filename", ""), status=d.get("status", "unknown"), error=d.get("error"), + created_at=d.get("created_at"), + document_date=d.get("document_date"), ) for d in docs ] @@ -140,9 +178,66 @@ async def get_document_status(doc_id: str, current_user: dict = Depends(get_curr filename=doc.get("filename", ""), status=doc.get("status", "unknown"), error=doc.get("error"), + created_at=doc.get("created_at"), + document_date=doc.get("document_date"), ) +@router.delete("/{doc_id}", status_code=204) +async def delete_document(doc_id: str, current_user: dict = Depends(get_current_user)): + """Delete a document and all associated indexes, VDB data, and FalkorDB graph. + + Requires the requesting user to be the document owner or an admin. + """ + tenant_id = current_user["tenant_id"] + user_id = current_user["user_id"] + + # Only owner or admin can delete + if current_user["role"] != "admin": + perm = await db.get_permission(MONGO_URI, MONGO_DB_PREFIX, tenant_id, user_id, doc_id) + if not perm or perm.get("role") != "owner": + raise HTTPException(status_code=403, detail="Only document owners or admins can delete documents") + + doc = await db.get_document(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_id) + if not doc: + raise HTTPException(status_code=404, detail="Document not found") + + # Clean up filesystem: uploaded PDF + pdf_path = doc.get("pdf_path", "") + if pdf_path and os.path.isfile(pdf_path): + try: + os.remove(pdf_path) + except OSError: + log.warning(f"Could not remove uploaded PDF: {pdf_path}") + + # Clean up filesystem: index directory + index_dir = os.path.join(INDEX_SAVE_DIR, tenant_id, doc_id) + if os.path.isdir(index_dir): + try: + shutil.rmtree(index_dir) + except OSError: + log.warning(f"Could not remove index directory: {index_dir}") + + # Clean up FalkorDB graph (best-effort) + try: + from api.dependencies import FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD + if os.getenv("BOOKRAG_FALKORDB_HOST", ""): + import falkordb + fdb = falkordb.FalkorDB(host=FALKORDB_HOST, port=FALKORDB_PORT, password=FALKORDB_PASSWORD or None) + graph_name = f"bookrag:{tenant_id}:doc:{doc_id}" + try: + g = fdb.select_graph(graph_name) + g.delete() + log.info(f"Deleted FalkorDB graph '{graph_name}'") + except Exception: + pass # Graph may not exist + except Exception as exc: + log.warning(f"FalkorDB cleanup skipped: {exc}") + + # Clean up MongoDB records + await db.delete_document(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_id) + + @router.get("/{doc_id}/raw") async def download_raw_document(doc_id: str, current_user: dict = Depends(get_current_user)): """Stream back the original uploaded PDF file.""" diff --git a/api/services/chat.py b/api/services/chat.py index 5bcedd5..33bbda0 100644 --- a/api/services/chat.py +++ b/api/services/chat.py @@ -277,9 +277,35 @@ async def handle_query( ) for did in target_docs ]) - answer = "\n\n---\n\n".join( - f"[Document: {did}]\n{ans}" for did, ans in zip(target_docs, answers) - ) + + # ── Temporal awareness: fetch document dates for cross-doc synthesis ── + doc_dates: dict[str, str] = {} + try: + for did in target_docs: + doc_record = await db.get_document(MONGO_URI, MONGO_DB_PREFIX, tenant_id, did) + if doc_record: + ddate = doc_record.get("document_date") or doc_record.get("created_at") + if ddate: + doc_dates[did] = str(ddate)[:10] # YYYY-MM-DD + except Exception: + pass # Non-fatal: temporal info is best-effort + + # Build answer with temporal context + parts = [] + for did, ans in zip(target_docs, answers): + date_str = f" (dated {doc_dates[did]})" if did in doc_dates else "" + parts.append(f"[Document: {did}{date_str}]\n{ans}") + + if doc_dates: + # Prepend a temporal instruction for the combined answer + temporal_note = ( + "NOTE: The answers below come from multiple documents with different dates. " + "When documents contain contradictory or overlapping information, " + "prefer the information from the more recently dated document.\n\n" + ) + answer = temporal_note + "\n\n---\n\n".join(parts) + else: + answer = "\n\n---\n\n".join(parts) else: doc_id = doc_ids[0] if doc_ids else None if not doc_id: diff --git a/api/services/indexing.py b/api/services/indexing.py index 9b718b3..dd27d60 100644 --- a/api/services/indexing.py +++ b/api/services/indexing.py @@ -11,7 +11,10 @@ _executor = THREAD_POOL -def _build_index_sync(pdf_path: str, save_path: str, tenant_id: str, doc_id: str, config_path: str): +def _build_index_sync( + pdf_path: str, save_path: str, tenant_id: str, doc_id: str, + config_path: str, document_date=None, +): """Synchronous index build — runs in a thread pool.""" from Core.configs.system_config import load_system_config from Core.configs.falkordb_config import FalkorDBConfig @@ -22,6 +25,9 @@ def _build_index_sync(pdf_path: str, save_path: str, tenant_id: str, doc_id: str cfg.save_path = save_path cfg.tenant_id = tenant_id cfg.doc_id = doc_id + # Propagate document_date into the config for temporal awareness + if document_date is not None: + cfg.document_date = document_date # FalkorDB will be used if BOOKRAG_FALKORDB_HOST is set fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") if fdb_host: @@ -37,6 +43,7 @@ async def run_indexing( doc_id: str, pdf_path: str, config_path: str, + document_date=None, ): """Async wrapper: update status in MongoDB before/after indexing.""" save_path = os.path.join(INDEX_SAVE_DIR, tenant_id, doc_id) @@ -48,7 +55,7 @@ async def run_indexing( await loop.run_in_executor( _executor, _build_index_sync, - pdf_path, save_path, tenant_id, doc_id, config_path, + pdf_path, save_path, tenant_id, doc_id, config_path, document_date, ) await db.update_document_status(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_id, "ready") log.info(f"Indexing complete for doc '{doc_id}' in tenant '{tenant_id}'") diff --git a/config/bm25.yaml b/config/bm25.yaml index 445aa43..d67475f 100644 --- a/config/bm25.yaml +++ b/config/bm25.yaml @@ -1,24 +1,24 @@ -# configs/default.yaml +# config/bm25.yaml pdf_path: TODO save_path: TODO llm: - model_name: Qwen/Qwen3-8B-AWQ + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://10.26.1.21:8003/v1 + api_base: http://localhost:8003/v1 backend: openai - max_tokens: 5000 + max_tokens: 8000 temperature: 0.1 frequency_penalty: 0.0 presence_penalty: 0.0 - max_workers: 8 + max_workers: 4 vlm: - model_name: Qwen2-5-VL + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://localhost:8000/v1 + api_base: http://localhost:8003/v1 temperature: 0.1 max_tokens: 6000 backend: gpt @@ -43,7 +43,7 @@ graph: extractor_type: "llm" local_model_name: "en_core_web_sm" image_description_force: True - max_gleaning: 0 + max_gleaning: 1 refine_type: "advanced" embedding_config: model_name: Qwen3-Embedding-0.6B diff --git a/config/gbc_wo_er.yaml b/config/gbc_wo_er.yaml index a3261db..ec94a16 100644 --- a/config/gbc_wo_er.yaml +++ b/config/gbc_wo_er.yaml @@ -1,24 +1,24 @@ -# configs/default.yaml +# config/gbc_wo_er.yaml pdf_path: TODO save_path: TODO llm: - model_name: Qwen/Qwen3-8B-AWQ + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai api_base: http://localhost:8003/v1 backend: openai - max_tokens: 5000 + max_tokens: 8000 temperature: 0.1 frequency_penalty: 0.0 presence_penalty: 0.0 - max_workers: 8 + max_workers: 4 vlm: - model_name: Qwen2-5-VL + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://localhost:8000/v1 + api_base: http://localhost:8003/v1 temperature: 0.1 max_tokens: 6000 backend: gpt @@ -41,7 +41,7 @@ graph: extractor_type: "llm" local_model_name: "en_core_web_sm" image_description_force: True - max_gleaning: 0 + max_gleaning: 1 refine_type: "basic" embedding_config: model_name: Qwen3-Embedding-0.6B diff --git a/config/gbc_wo_graph.yaml b/config/gbc_wo_graph.yaml index a0f042c..49b325a 100644 --- a/config/gbc_wo_graph.yaml +++ b/config/gbc_wo_graph.yaml @@ -1,24 +1,24 @@ -# configs/default.yaml +# config/gbc_wo_graph.yaml pdf_path: TODO save_path: TODO llm: - model_name: Qwen/Qwen3-8B-AWQ + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai api_base: http://localhost:8003/v1 backend: openai - max_tokens: 5000 + max_tokens: 8000 temperature: 0.1 frequency_penalty: 0.0 presence_penalty: 0.0 - max_workers: 8 + max_workers: 4 vlm: - model_name: Qwen2-5-VL + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://localhost:8000/v1 + api_base: http://localhost:8003/v1 temperature: 0.1 max_tokens: 6000 backend: gpt @@ -41,7 +41,7 @@ graph: extractor_type: "llm" local_model_name: "en_core_web_sm" image_description_force: True - max_gleaning: 0 + max_gleaning: 1 refine_type: "advanced" embedding_config: model_name: Qwen3-Embedding-0.6B diff --git a/config/gbc_wo_plan.yaml b/config/gbc_wo_plan.yaml index aa41b64..dbec96c 100644 --- a/config/gbc_wo_plan.yaml +++ b/config/gbc_wo_plan.yaml @@ -1,24 +1,24 @@ -# configs/default.yaml +# config/gbc_wo_plan.yaml pdf_path: TODO save_path: TODO llm: - model_name: Qwen/Qwen3-8B-AWQ + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai api_base: http://localhost:8003/v1 backend: openai - max_tokens: 5000 + max_tokens: 8000 temperature: 0.1 frequency_penalty: 0.0 presence_penalty: 0.0 - max_workers: 8 + max_workers: 4 vlm: - model_name: Qwen2-5-VL + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://localhost:8000/v1 + api_base: http://localhost:8003/v1 temperature: 0.1 max_tokens: 6000 backend: gpt @@ -41,7 +41,7 @@ graph: extractor_type: "llm" local_model_name: "en_core_web_sm" image_description_force: True - max_gleaning: 0 + max_gleaning: 1 refine_type: "advanced" embedding_config: model_name: Qwen3-Embedding-0.6B diff --git a/config/gbc_wo_selector.yaml b/config/gbc_wo_selector.yaml index b6e1125..70b6e1c 100644 --- a/config/gbc_wo_selector.yaml +++ b/config/gbc_wo_selector.yaml @@ -1,24 +1,24 @@ -# configs/default.yaml +# config/gbc_wo_selector.yaml pdf_path: TODO save_path: TODO llm: - model_name: Qwen/Qwen3-8B-AWQ + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai api_base: http://localhost:8003/v1 backend: openai - max_tokens: 5000 + max_tokens: 8000 temperature: 0.1 frequency_penalty: 0.0 presence_penalty: 0.0 - max_workers: 8 + max_workers: 4 vlm: - model_name: Qwen2-5-VL + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://localhost:8000/v1 + api_base: http://localhost:8003/v1 temperature: 0.1 max_tokens: 6000 backend: gpt @@ -41,7 +41,7 @@ graph: extractor_type: "llm" local_model_name: "en_core_web_sm" image_description_force: True - max_gleaning: 0 + max_gleaning: 1 refine_type: "advanced" embedding_config: model_name: Qwen3-Embedding-0.6B diff --git a/config/gbc_wo_text.yaml b/config/gbc_wo_text.yaml index 3550c46..ca4c939 100644 --- a/config/gbc_wo_text.yaml +++ b/config/gbc_wo_text.yaml @@ -1,24 +1,24 @@ -# configs/default.yaml +# config/gbc_wo_text.yaml pdf_path: TODO save_path: TODO llm: - model_name: Qwen/Qwen3-8B-AWQ + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai api_base: http://localhost:8003/v1 backend: openai - max_tokens: 5000 + max_tokens: 8000 temperature: 0.1 frequency_penalty: 0.0 presence_penalty: 0.0 - max_workers: 8 + max_workers: 4 vlm: - model_name: Qwen2-5-VL + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://localhost:8000/v1 + api_base: http://localhost:8003/v1 temperature: 0.1 max_tokens: 6000 backend: gpt @@ -41,7 +41,7 @@ graph: extractor_type: "llm" local_model_name: "en_core_web_sm" image_description_force: True - max_gleaning: 0 + max_gleaning: 1 refine_type: "advanced" embedding_config: model_name: Qwen3-Embedding-0.6B diff --git a/config/graph.yaml b/config/graph.yaml index b659c61..cb3cd32 100644 --- a/config/graph.yaml +++ b/config/graph.yaml @@ -1,24 +1,24 @@ -# configs/default.yaml +# config/graph.yaml pdf_path: TODO save_path: TODO llm: - model_name: Qwen/Qwen3-8B-AWQ + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://10.26.1.21:8003/v1 + api_base: http://localhost:8003/v1 backend: openai - max_tokens: 5000 + max_tokens: 8000 temperature: 0.1 frequency_penalty: 0.0 presence_penalty: 0.0 - max_workers: 8 + max_workers: 4 vlm: - model_name: Qwen2-5-VL + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://localhost:8000/v1 + api_base: http://localhost:8003/v1 temperature: 0.1 max_tokens: 6000 backend: gpt @@ -41,7 +41,7 @@ graph: extractor_type: "llm" local_model_name: "en_core_web_sm" image_description_force: True - max_gleaning: 0 + max_gleaning: 1 refine_type: "advanced" embedding_config: model_name: Qwen3-Embedding-0.6B @@ -62,7 +62,7 @@ vdb: collection_name: "TreeVDB" embedding_config: model_name: Alibaba-NLP/gme-Qwen2-VL-2B-Instruct - device: "cuda:6" + device: "cuda:1" rag_force_reprocess: True diff --git a/config/mm.yaml b/config/mm.yaml index fab9c46..15aff2e 100644 --- a/config/mm.yaml +++ b/config/mm.yaml @@ -1,24 +1,24 @@ -# configs/default.yaml +# config/mm.yaml pdf_path: TODO save_path: TODO llm: - model_name: Qwen/Qwen3-8B-AWQ + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai api_base: http://localhost:8003/v1 backend: openai - max_tokens: 5000 + max_tokens: 8000 temperature: 0.1 frequency_penalty: 0.0 presence_penalty: 0.0 - max_workers: 8 + max_workers: 4 vlm: - model_name: Qwen2-5-VL + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://localhost:8000/v1 + api_base: http://localhost:8003/v1 temperature: 0.1 max_tokens: 6000 backend: gpt @@ -41,17 +41,20 @@ graph: extractor_type: "llm" local_model_name: "en_core_web_sm" image_description_force: True - max_gleaning: 0 + max_gleaning: 1 refine_type: "advanced" embedding_config: - model_name: Qwen/Qwen3-Embedding-0.6B - backend: local - max_length: 8192 - device: "cuda:7" + model_name: Qwen3-Embedding-0.6B + backend: openai + max_length: 4096 + device: "cuda:2" + api_base: "http://localhost:8007/v1" reranker_config: - model_name: Qwen/Qwen3-Reranker-4B - max_length: 8192 - device: "cuda:7" + model_name: Qwen3-Reranker-4B + max_length: 4096 + device: "cuda:2" + backend: vllm + api_base: "http://localhost:8011/v1" vdb: mm_embedding: True @@ -59,7 +62,7 @@ vdb: collection_name: "TreeVDB" embedding_config: model_name: Alibaba-NLP/gme-Qwen2-VL-2B-Instruct - device: "cuda:3" + device: "cuda:1" rag_force_reprocess: True rag: diff --git a/config/pdf_vanilla.yaml b/config/pdf_vanilla.yaml index 1d7edfb..98dfec8 100644 --- a/config/pdf_vanilla.yaml +++ b/config/pdf_vanilla.yaml @@ -1,24 +1,24 @@ -# configs/default.yaml +# config/pdf_vanilla.yaml pdf_path: TODO save_path: TODO llm: - model_name: Qwen/Qwen3-8B-AWQ + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai api_base: http://localhost:8003/v1 backend: openai - max_tokens: 5000 + max_tokens: 8000 temperature: 0.1 frequency_penalty: 0.0 presence_penalty: 0.0 - max_workers: 8 + max_workers: 4 vlm: - model_name: Qwen2-5-VL + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://localhost:8000/v1 + api_base: http://localhost:8003/v1 temperature: 0.1 max_tokens: 6000 backend: gpt @@ -43,7 +43,7 @@ graph: extractor_type: "llm" local_model_name: "en_core_web_sm" image_description_force: True - max_gleaning: 0 + max_gleaning: 1 refine_type: "advanced" embedding_config: model_name: Qwen3-Embedding-0.6B diff --git a/config/raptor.yaml b/config/raptor.yaml index c6730e4..e6158c1 100644 --- a/config/raptor.yaml +++ b/config/raptor.yaml @@ -1,24 +1,24 @@ -# configs/default.yaml +# config/raptor.yaml pdf_path: TODO save_path: TODO llm: - model_name: Qwen/Qwen3-8B-AWQ + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://10.26.1.21:8003/v1 + api_base: http://localhost:8003/v1 backend: openai - max_tokens: 5000 + max_tokens: 8000 temperature: 0.1 frequency_penalty: 0.0 presence_penalty: 0.0 - max_workers: 8 + max_workers: 4 vlm: - model_name: Qwen2-5-VL + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://localhost:8000/v1 + api_base: http://localhost:8003/v1 temperature: 0.1 max_tokens: 6000 backend: gpt @@ -43,7 +43,7 @@ graph: extractor_type: "llm" local_model_name: "en_core_web_sm" image_description_force: True - max_gleaning: 0 + max_gleaning: 1 refine_type: "advanced" embedding_config: model_name: Qwen3-Embedding-0.6B diff --git a/config/tree.yaml b/config/tree.yaml index 5b14745..a719d31 100644 --- a/config/tree.yaml +++ b/config/tree.yaml @@ -1,24 +1,24 @@ -# configs/default.yaml +# config/tree.yaml pdf_path: TODO save_path: TODO llm: - model_name: Qwen/Qwen3-8B-AWQ + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://10.26.1.21:8003/v1 + api_base: http://localhost:8003/v1 backend: openai - max_tokens: 5000 + max_tokens: 8000 temperature: 0.1 frequency_penalty: 0.0 presence_penalty: 0.0 - max_workers: 8 + max_workers: 4 vlm: - model_name: Qwen2-5-VL + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://localhost:8000/v1 + api_base: http://localhost:8003/v1 temperature: 0.1 max_tokens: 6000 backend: gpt @@ -41,25 +41,28 @@ graph: extractor_type: "llm" local_model_name: "en_core_web_sm" image_description_force: True - max_gleaning: 0 + max_gleaning: 1 refine_type: "advanced" embedding_config: - model_name: Qwen/Qwen3-Embedding-0.6B - backend: local - max_length: 8192 - device: "cuda:5" + model_name: Qwen3-Embedding-0.6B + backend: openai + max_length: 4096 + device: "cuda:2" + api_base: "http://localhost:8007/v1" reranker_config: - model_name: Qwen/Qwen3-Reranker-4B - max_length: 8192 - device: "cuda:5" + model_name: Qwen3-Reranker-4B + max_length: 4096 + device: "cuda:2" + backend: vllm + api_base: "http://localhost:8011/v1" vdb: mm_embedding: True - vdb_path: "Tree_vdb" + vdb_dir_name: "Tree_vdb" collection_name: "TreeVDB" embedding_config: model_name: Alibaba-NLP/gme-Qwen2-VL-2B-Instruct - device: "cuda:4" + device: "cuda:1" rag_force_reprocess: True rag: diff --git a/config/vanilla.yaml b/config/vanilla.yaml index 6816e67..7a83a1d 100644 --- a/config/vanilla.yaml +++ b/config/vanilla.yaml @@ -1,24 +1,24 @@ -# configs/default.yaml +# config/vanilla.yaml pdf_path: TODO save_path: TODO llm: - model_name: Qwen/Qwen3-8B-AWQ + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://10.26.1.21:8003/v1 + api_base: http://localhost:8003/v1 backend: openai - max_tokens: 5000 + max_tokens: 8000 temperature: 0.1 frequency_penalty: 0.0 presence_penalty: 0.0 - max_workers: 8 + max_workers: 4 vlm: - model_name: Qwen2-5-VL + model_name: Qwen/Qwen3.5-35B-A3B-AWQ api_key: openai - api_base: http://localhost:8000/v1 + api_base: http://localhost:8003/v1 temperature: 0.1 max_tokens: 6000 backend: gpt @@ -43,7 +43,7 @@ graph: extractor_type: "llm" local_model_name: "en_core_web_sm" image_description_force: True - max_gleaning: 0 + max_gleaning: 1 refine_type: "advanced" embedding_config: model_name: Qwen3-Embedding-0.6B From c11b888013e1ee5e2765d4849acfd283fe5c5551 Mon Sep 17 00:00:00 2001 From: vmoudyp Date: Tue, 3 Mar 2026 04:53:49 +0700 Subject: [PATCH 07/11] feat: Integrate language detection and legal heading detection features; enhance document processing with language-aware capabilities --- Core/configs/system_config.py | 11 ++ Core/pipelines/doc_tree_builder.py | 7 +- Core/pipelines/legal_heading_detector.py | 230 +++++++++++++++++++++++ Core/pipelines/pdf_refiner.py | 101 +++++----- api/models/requests.py | 5 + api/routers/documents.py | 15 ++ api/services/indexing.py | 7 +- tests/__init__.py | 1 + tests/test_legal_heading_detector.py | 167 ++++++++++++++++ tests/test_pdf_refiner_lang.py | 113 +++++++++++ 10 files changed, 609 insertions(+), 48 deletions(-) create mode 100644 Core/pipelines/legal_heading_detector.py create mode 100644 tests/__init__.py create mode 100644 tests/test_legal_heading_detector.py create mode 100644 tests/test_pdf_refiner_lang.py diff --git a/Core/configs/system_config.py b/Core/configs/system_config.py index 3aa0e84..8e7a7fc 100644 --- a/Core/configs/system_config.py +++ b/Core/configs/system_config.py @@ -50,6 +50,17 @@ class SystemConfig(BaseModel): tenant_id: Optional[str] = None doc_id: Optional[str] = None + # Document language hint (ISO 639-1). Used by the legal-heading + # detector, incomplete-paragraph heuristics, and other language-aware + # pipeline stages. Set to "auto" (default) for automatic detection + # from extracted text, or an explicit code like "en" or "id". + document_lang: Optional[str] = Field( + default="auto", + description="ISO 639-1 language code of the document content, or " + "'auto' for automatic detection. " + "Supported: auto, en, id, de, fr, es, pt, it, nl, th, zh, ja, ko, ar.", + ) + # Document temporal metadata (optional, for recency-aware RAG) document_date: Optional[datetime] = Field( default=None, diff --git a/Core/pipelines/doc_tree_builder.py b/Core/pipelines/doc_tree_builder.py index 9b2de76..1fe588e 100644 --- a/Core/pipelines/doc_tree_builder.py +++ b/Core/pipelines/doc_tree_builder.py @@ -2,6 +2,7 @@ from Core.pipelines.tree_node_builder import create_node_by_type from Core.pipelines.outline_extractor import extract_pdf_outline_in_chunks from Core.pipelines.pdf_refiner import pdf_info_refiner +from Core.pipelines.legal_heading_detector import detect_legal_headings, detect_document_language from Core.provider.extract_pdf_info import parse_doc, merge_middle_content from Core.pipelines.tree_node_summary import generate_tree_node_summary from Core.configs.system_config import SystemConfig @@ -151,7 +152,11 @@ def build_tree_from_pdf(cfg: SystemConfig, reforce: bool = False) -> DocumentTre llm = LLM(cfg.llm) vlm = VLM(cfg.vlm) if cfg.tree.use_vlm else None - pdf_list = pdf_info_refiner(pdf_list, llm) + lang = getattr(cfg, "document_lang", "auto") or "auto" + if lang == "auto": + lang = detect_document_language(pdf_list, fallback="en") + pdf_list = pdf_info_refiner(pdf_list, llm, lang=lang) + pdf_list = detect_legal_headings(pdf_list, lang=lang) title_outline = extract_pdf_outline_in_chunks(pdf_list, llm) tree_index = construct_tree_index( tree_index=tree_index, pdf_list=pdf_list, title_outline=title_outline diff --git a/Core/pipelines/legal_heading_detector.py b/Core/pipelines/legal_heading_detector.py new file mode 100644 index 0000000..6917f61 --- /dev/null +++ b/Core/pipelines/legal_heading_detector.py @@ -0,0 +1,230 @@ +""" +Legal heading detector — identifies structured headings in legal documents +using language-aware regex patterns. + +Supported languages: + - ``en`` — English (Article, Section, Chapter, Part, Clause, Schedule, …) + - ``id`` — Bahasa Indonesia (BAB, Bagian, Paragraf, Pasal, Ayat, …) + +The detector is intentionally conservative: it only promotes items whose +``text_level`` is ``-1`` (body text) and whose *entire trimmed text* matches +a known legal heading pattern. This avoids false-positives on sentences +that merely *mention* a legal keyword. +""" + +from __future__ import annotations + +import logging +import re +from typing import Dict, List, Optional, Tuple + +try: + from langdetect import detect as _langdetect_detect + from langdetect import DetectorFactory + + # Make langdetect deterministic + DetectorFactory.seed = 0 + _HAS_LANGDETECT = True +except ImportError: # pragma: no cover + _HAS_LANGDETECT = False + +log = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# Pattern registry +# --------------------------------------------------------------------------- +# Each entry is ``(compiled_regex, assigned_text_level)``. +# Lower ``text_level`` values indicate higher hierarchy levels so that the +# outline extractor can nest them correctly. +# +# ``text_level`` assignments (per language): +# 0 = top-level division (BAB / Chapter / Title / Part) +# 1 = section / Bagian +# 2 = sub-section / Paragraf +# 3 = article / Pasal / Section / Clause +# 4 = clause / verse / Ayat / sub-clause +# --------------------------------------------------------------------------- + +_NUM = r"(?:[0-9]+(?:\.[0-9]+)*|[IVXLCDM]+|[ivxlcdm]+)" +_ALPHA = r"[A-Za-z]" + +# ── English patterns ────────────────────────────────────────────────────── + +_EN_PATTERNS: List[Tuple[re.Pattern, int]] = [ + # Level 0 – top-level + (re.compile(rf"^(?:TITLE|Title)\s+{_NUM}\.?(?:\s.*)?$"), 0), + (re.compile(rf"^(?:PART|Part)\s+{_NUM}\.?(?:\s.*)?$"), 0), + (re.compile(rf"^(?:CHAPTER|Chapter)\s+{_NUM}\.?(?:\s.*)?$"), 0), + # Level 1 – section + (re.compile(rf"^(?:DIVISION|Division)\s+{_NUM}\.?(?:\s.*)?$"), 1), + (re.compile(rf"^(?:ARTICLE|Article)\s+{_NUM}\.?(?:\s.*)?$"), 1), + (re.compile(rf"^(?:SCHEDULE|Schedule)\s+{_NUM}\.?(?:\s.*)?$"), 1), + # Level 2 – sub-section + (re.compile(rf"^(?:SECTION|Section)\s+{_NUM}\.?(?:\s.*)?$"), 2), + (re.compile(rf"^(?:ANNEX|Annex)\s+{_ALPHA}\.?(?:\s.*)?$"), 2), + # Level 3 – clause + (re.compile(rf"^(?:CLAUSE|Clause)\s+{_NUM}\.?(?:\s.*)?$"), 3), + (re.compile(rf"^§\s*{_NUM}\.?(?:\s.*)?$"), 3), + # Level 4 – sub-clause + (re.compile(rf"^(?:SUB-?CLAUSE|Sub-?clause)\s+{_NUM}\.?(?:\s.*)?$"), 4), +] + +# ── Bahasa Indonesia patterns ──────────────────────────────────────────── + +_ID_PATTERNS: List[Tuple[re.Pattern, int]] = [ + # Level 0 – BAB (Chapter) + (re.compile(rf"^BAB\s+{_NUM}\.?(?:\s.*)?$", re.IGNORECASE), 0), + # Level 1 – Bagian (Part/Section) + (re.compile(rf"^Bagian\s+(?:Kesatu|Kedua|Ketiga|Keempat|Kelima|Keenam|Ketujuh|Kedelapan|Kesembilan|Kesepuluh|{_NUM})\.?(?:\s.*)?$", re.IGNORECASE), 1), + # Level 2 – Paragraf (Paragraph/Sub-section) + (re.compile(rf"^Paragraf\s+{_NUM}\.?(?:\s.*)?$", re.IGNORECASE), 2), + # Level 3 – Pasal (Article) + (re.compile(rf"^Pasal\s+{_NUM}\.?(?:\s.*)?$", re.IGNORECASE), 3), + # Level 4 – Ayat (Verse/Clause) — usually inline, rarely standalone + (re.compile(rf"^Ayat\s+\({_NUM}\)\.?(?:\s.*)?$", re.IGNORECASE), 4), +] + +# ── Language → patterns map ────────────────────────────────────────────── + +_LANG_PATTERNS: Dict[str, List[Tuple[re.Pattern, int]]] = { + "en": _EN_PATTERNS, + "id": _ID_PATTERNS, +} + + +def _match_heading( + text: str, patterns: List[Tuple[re.Pattern, int]] +) -> Optional[int]: + """Return the ``text_level`` if *text* matches any pattern, else ``None``.""" + stripped = text.strip() + if not stripped: + return None + for pat, level in patterns: + if pat.match(stripped): + return level + return None + + +def detect_legal_headings( + pdf_list: List[Optional[Dict]], + lang: str = "en", +) -> List[Optional[Dict]]: + """Scan *pdf_list* and promote body-text items that match legal heading + patterns to headings by setting their ``text_level``. + + Parameters + ---------- + pdf_list: + The pipeline's intermediate list of content dicts. + lang: + ISO 639-1 language code. Falls back to English patterns if the + requested language is not registered. + + Returns + ------- + The same *pdf_list* (mutated in-place) with matched items promoted. + """ + patterns = _LANG_PATTERNS.get(lang, _LANG_PATTERNS.get("en", [])) + if not patterns: + log.warning("No legal heading patterns for lang='%s'; skipping.", lang) + return pdf_list + + promoted = 0 + for content in pdf_list: + if content is None: + continue + # Only consider body-text items (text_level == -1 or absent) + if content.get("type") != "text": + continue + current_level = content.get("text_level", -1) + if current_level >= 0: + continue # already a heading — don't override parser + + text = content.get("text", "") + level = _match_heading(text, patterns) + if level is not None: + content["text_level"] = level + promoted += 1 + log.debug("Promoted to heading (level %d): %s", level, text[:80]) + + log.info( + "Legal heading detection (lang=%s): promoted %d items to headings.", + lang, + promoted, + ) + return pdf_list + + + +# --------------------------------------------------------------------------- +# Automatic language detection from extracted text +# --------------------------------------------------------------------------- +_SUPPORTED_LANGS = {"en", "id", "de", "fr", "es", "pt", "it", "nl", "th", "zh", "ja", "ko", "ar"} + + +def detect_document_language( + pdf_list: List[Optional[Dict]], + fallback: str = "en", + sample_chars: int = 2000, +) -> str: + """Detect the dominant language of the document from its extracted text. + + Collects the first *sample_chars* characters of body text from *pdf_list* + and runs ``langdetect`` on the sample. + + Parameters + ---------- + pdf_list: + The pipeline's intermediate list of content dicts. + fallback: + Language code to return when detection fails or ``langdetect`` is not + installed. + sample_chars: + Maximum number of characters to sample for detection. + + Returns + ------- + An ISO 639-1 language code (e.g. ``"en"``, ``"id"``). + """ + if not _HAS_LANGDETECT: + log.warning("langdetect is not installed; falling back to '%s'.", fallback) + return fallback + + # Collect body text (text_level == -1 or absent) + sample_parts: list[str] = [] + collected = 0 + for content in pdf_list: + if content is None: + continue + if content.get("type") != "text": + continue + if content.get("text_level", -1) >= 0: + continue # skip headings — they may be too short / formulaic + text = content.get("text", "").strip() + if not text: + continue + sample_parts.append(text) + collected += len(text) + if collected >= sample_chars: + break + + sample = " ".join(sample_parts) + if len(sample) < 20: + log.info("Not enough text for language detection; falling back to '%s'.", fallback) + return fallback + + try: + detected = _langdetect_detect(sample) + # langdetect may return sub-tags like "zh-cn"; normalise + lang = detected.split("-")[0].lower() + if lang not in _SUPPORTED_LANGS: + log.info( + "Detected language '%s' is not supported; falling back to '%s'.", + lang, fallback, + ) + return fallback + log.info("Auto-detected document language: '%s'.", lang) + return lang + except Exception as exc: + log.warning("Language detection failed (%s); falling back to '%s'.", exc, fallback) + return fallback \ No newline at end of file diff --git a/Core/pipelines/pdf_refiner.py b/Core/pipelines/pdf_refiner.py index a8a13af..d6a7065 100644 --- a/Core/pipelines/pdf_refiner.py +++ b/Core/pipelines/pdf_refiner.py @@ -16,73 +16,82 @@ log = logging.getLogger(__name__) -def is_likely_incomplete_paragraph(text: str) -> bool: +# --------------------------------------------------------------------------- +# Language-specific settings for incomplete-paragraph detection +# --------------------------------------------------------------------------- +_LANG_TERMINAL_PUNCTUATION = { + "en": r"[.!?]", + "id": r"[.!?]", # Bahasa Indonesia uses Latin punctuation + "de": r"[.!?]", + "fr": r"[.!?\u00BB]", # » can close a quote-sentence + "es": r"[.!?\u00BF\u00A1]", + "pt": r"[.!?]", + "it": r"[.!?]", + "nl": r"[.!?]", + "th": r"[\u0E2F\u0E46.!?]", # Thai ฯ, ๆ, plus Latin + "zh": r"[\u3002\uFF01\uFF1F.!?]", # 。！？ + "ja": r"[\u3002\uFF01\uFF1F.!?]", + "ko": r"[\uFF0E\uFF01\uFF1F.!?]", + "ar": r"[\u061F\u06D4.!?]", # ؟ ۔ +} + +_LANG_INCOMPLETE_ENDINGS: dict[str, set[str]] = { + "en": { + "and", "or", "but", "because", "although", "however", + "if", "while", "when", "to", "for", "in", "of", "with", + "on", "as", "at", "by", "from", "such", "the", "a", "an", + }, + "id": { + "dan", "atau", "tetapi", "namun", "karena", "sebab", + "jika", "apabila", "bahwa", "dengan", "untuk", "pada", + "dari", "oleh", "yang", "di", "ke", "se", "ini", "itu", + }, +} + + +def is_likely_incomplete_paragraph(text: str, lang: str = "en") -> bool: """ - Determine if an English paragraph is likely incomplete (truncated due to page/column breaks). + Determine if a paragraph is likely incomplete (truncated due to + page / column breaks). Language-aware. - :param text: input text to check - :return: bool, True if the paragraph is likely incomplete, False otherwise - e.g. "He said, "This method is the best." -> False (complete) - e.g. "The quick brown fox jumps over the lazy dog and" -> True (incomplete) + :param text: input text to check + :param lang: ISO 639-1 language code (default ``"en"``) + :return: ``True`` if the paragraph is likely incomplete """ if not text: return False # 空文本不是我们关心的“不完整段落” text = text.strip() - # Rule 1: Filter out very short strings. They are likely standalone titles/captions, not paragraphs to be merged. + # Rule 1: Filter out very short strings — likely titles/captions. if len(text.split()) < 5 or len(text) < 25: return False - # --- From here, we look for clear signals of INCOMPLETENESS --- - - # Rule 2: Ending with a hyphen is a very strong signal of incompleteness (a word was split). + # Rule 2: Ending with a hyphen → word was split across pages. if text.endswith("-"): return True - # Handles cases like "said he," or "he said." + # Strip trailing quotes so we can inspect the "real" ending. cleaned_text = re.sub(r"['\"]+$", "", text) - # Rule 3: Ending with a comma, colon, or semicolon is also a strong signal. + # Rule 3: Ending with comma / colon / semicolon → strong signal. if cleaned_text.endswith((",", ":", ";")): return True - # Rule 4: Not ending with a standard terminal punctuation mark. This is the most common case. - if not re.search(r"[.!?]$", cleaned_text): + # Rule 4: Not ending with terminal punctuation (language-aware). + terminal_re = _LANG_TERMINAL_PUNCTUATION.get(lang, r"[.!?]") + if not re.search(terminal_re + r"$", cleaned_text): return True - # Rule 5: Ending with a common connector word (even if mistakenly followed by a period). - # e.g., "The quick brown fox jumps over the lazy dog and." - incomplete_endings = { - "and", - "or", - "but", - "because", - "although", - "however", - "if", - "while", - "when", - "to", - "for", - "in", - "of", - "with", - "on", - "as", - "at", - "by", - "from", - "such", - "the", - "a", - "an", - } + # Rule 5: Ending with a connector word (language-aware). + incomplete_endings = _LANG_INCOMPLETE_ENDINGS.get( + lang, _LANG_INCOMPLETE_ENDINGS.get("en", set()) + ) last_word_match = re.findall(r"\b\w+\b", cleaned_text) if last_word_match and last_word_match[-1].lower() in incomplete_endings: return True - # If no "incomplete" signals were triggered, we assume it's complete. + # No "incomplete" signals triggered → assume complete. return False @@ -355,7 +364,7 @@ def merge_text_and_mark_invalid(prev_content: dict, merged_list: list[dict]): print(f"{prev_content['text']}") # Print first 100 chars for debug -def text_merger(pdf_list: list[Optional[str]], llm: LLM) -> list[Optional[str]]: +def text_merger(pdf_list: list[Optional[str]], llm: LLM, lang: str = "en") -> list[Optional[str]]: incomplete_paragraphs = [] # for循环的逻辑可以更清晰地组织 for content in pdf_list: @@ -368,7 +377,7 @@ def text_merger(pdf_list: list[Optional[str]], llm: LLM) -> list[Optional[str]]: # The logic is now direct: "if the paragraph is likely incomplete, add it." text = content.get("text", "") - if is_likely_incomplete_paragraph(text): + if is_likely_incomplete_paragraph(text, lang=lang): incomplete_paragraphs.append(content) if not incomplete_paragraphs: @@ -744,7 +753,7 @@ def truncate_ocr_error_refiner( return pdf_list -def pdf_info_refiner(pdf_list: list[Optional[str]], llm: LLM) -> list[Optional[str]]: +def pdf_info_refiner(pdf_list: list[Optional[str]], llm: LLM, lang: str = "en") -> list[Optional[str]]: # Heuristic refiner for "-" error in OCR pdf_list = dash_line_refiner(pdf_list) # Heuristic refiner for OCR Error @@ -754,7 +763,7 @@ def pdf_info_refiner(pdf_list: list[Optional[str]], llm: LLM) -> list[Optional[s pdf_list = enumerate_pdf_list(pdf_list) # Then we refine the PDF information by merging incomplete paragraphs and tables - pdf_list = text_merger(pdf_list, llm) + pdf_list = text_merger(pdf_list, llm, lang=lang) pdf_list = table_merger(pdf_list, llm) # After merging, we need to re-enumerate the pdf_list diff --git a/api/models/requests.py b/api/models/requests.py index a1cdcae..30abbd4 100644 --- a/api/models/requests.py +++ b/api/models/requests.py @@ -72,6 +72,11 @@ class DocumentResponse(BaseModel): description="User-provided original authoring/publishing date of the document. " "Used for temporal awareness in cross-document RAG.", ) + document_lang: Optional[str] = Field( + default=None, + description="ISO 639-1 language code (e.g. 'en', 'id') or 'auto' for auto-detection. " + "Used for legal heading detection and language-aware text processing.", + ) class BatchUploadResponse(BaseModel): diff --git a/api/routers/documents.py b/api/routers/documents.py index a8e6573..679b50b 100644 --- a/api/routers/documents.py +++ b/api/routers/documents.py @@ -81,6 +81,11 @@ async def upload_documents( description="Optional ISO-8601 date for ALL uploaded files (original authoring date). " "Example: 2025-06-15 or 2025-06-15T10:30:00Z", ), + document_lang: Optional[str] = Form( + default=None, + description="Optional ISO 639-1 language code (e.g. 'en', 'id') for ALL uploaded files. " + "Omit or set to 'auto' for automatic detection from extracted text.", + ), current_user: dict = Depends(get_current_user), ): """Upload one or more PDFs and start background indexing for each. @@ -88,6 +93,10 @@ async def upload_documents( An optional ``document_date`` (ISO-8601) can be provided to indicate the original authoring/publishing date of the documents. This date is used for temporal-awareness in cross-document RAG queries. + + An optional ``document_lang`` (ISO 639-1 code like ``en``, ``id``) can + be provided to hint the document language for legal heading detection + and text processing. Omit for automatic detection. """ tenant_id = current_user["tenant_id"] user_id = current_user["user_id"] @@ -121,6 +130,10 @@ async def upload_documents( if parsed_doc_date: doc_data["document_date"] = parsed_doc_date + # Attach document_lang if provided + if document_lang: + doc_data["document_lang"] = document_lang + # Register document in MongoDB await db.create_document(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_data) # Auto-grant uploader owner access @@ -129,10 +142,12 @@ async def upload_documents( background_tasks.add_task( run_indexing, tenant_id, doc_data["doc_id"], doc_data["pdf_path"], CONFIG_PATH, document_date=parsed_doc_date, + document_lang=document_lang, ) uploaded.append(DocumentResponse( doc_id=doc_data["doc_id"], filename=file.filename, status="pending", document_date=parsed_doc_date, + document_lang=document_lang, )) return BatchUploadResponse(uploaded=uploaded, failed=failed) diff --git a/api/services/indexing.py b/api/services/indexing.py index dd27d60..7f3105f 100644 --- a/api/services/indexing.py +++ b/api/services/indexing.py @@ -13,7 +13,7 @@ def _build_index_sync( pdf_path: str, save_path: str, tenant_id: str, doc_id: str, - config_path: str, document_date=None, + config_path: str, document_date=None, document_lang=None, ): """Synchronous index build — runs in a thread pool.""" from Core.configs.system_config import load_system_config @@ -28,6 +28,9 @@ def _build_index_sync( # Propagate document_date into the config for temporal awareness if document_date is not None: cfg.document_date = document_date + # Propagate document_lang into the config for language-aware processing + if document_lang is not None: + cfg.document_lang = document_lang # FalkorDB will be used if BOOKRAG_FALKORDB_HOST is set fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") if fdb_host: @@ -44,6 +47,7 @@ async def run_indexing( pdf_path: str, config_path: str, document_date=None, + document_lang=None, ): """Async wrapper: update status in MongoDB before/after indexing.""" save_path = os.path.join(INDEX_SAVE_DIR, tenant_id, doc_id) @@ -56,6 +60,7 @@ async def run_indexing( _executor, _build_index_sync, pdf_path, save_path, tenant_id, doc_id, config_path, document_date, + document_lang, ) await db.update_document_status(MONGO_URI, MONGO_DB_PREFIX, tenant_id, doc_id, "ready") log.info(f"Indexing complete for doc '{doc_id}' in tenant '{tenant_id}'") diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1 @@ + diff --git a/tests/test_legal_heading_detector.py b/tests/test_legal_heading_detector.py new file mode 100644 index 0000000..5d21e36 --- /dev/null +++ b/tests/test_legal_heading_detector.py @@ -0,0 +1,167 @@ +"""Tests for Core/pipelines/legal_heading_detector.py""" +import pytest + +from Core.pipelines.legal_heading_detector import ( + detect_legal_headings, + detect_document_language, + _match_heading, + _EN_PATTERNS, + _ID_PATTERNS, +) + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- +def _make_item(text: str, text_level: int = -1, item_type: str = "text"): + return {"type": item_type, "text": text, "text_level": text_level} + + +# --------------------------------------------------------------------------- +# English heading pattern matching +# --------------------------------------------------------------------------- +class TestEnglishPatterns: + @pytest.mark.parametrize("text,expected_level", [ + ("TITLE I", 0), + ("Title 1", 0), + ("PART IV", 0), + ("Part 2 Definitions", 0), + ("CHAPTER 3", 0), + ("Chapter III General Provisions", 0), + ("DIVISION 1", 1), + ("ARTICLE 5", 1), + ("Article 12 Obligations of the Parties", 1), + ("SCHEDULE 1", 1), + ("SECTION 4", 2), + ("Section 2.1 Scope", 2), # fails — no dot-numbers in _NUM + ("Annex A", 2), + ("CLAUSE 7", 3), + ("§ 12", 3), + ("§ 3 Definitions", 3), + ("SUB-CLAUSE 2", 4), + ("Sub-clause 1", 4), + ]) + def test_match(self, text, expected_level): + result = _match_heading(text, _EN_PATTERNS) + assert result == expected_level, f"Expected level {expected_level} for '{text}', got {result}" + + @pytest.mark.parametrize("text", [ + "This is a normal paragraph.", + "The Article discusses legal matters.", + "See Chapter 3 for more details.", + "", + "article", # no number + ]) + def test_no_match(self, text): + assert _match_heading(text, _EN_PATTERNS) is None + + +# --------------------------------------------------------------------------- +# Indonesian heading pattern matching +# --------------------------------------------------------------------------- +class TestIndonesianPatterns: + @pytest.mark.parametrize("text,expected_level", [ + ("BAB I", 0), + ("BAB IV KETENTUAN PERALIHAN", 0), + ("Bagian Kesatu Umum", 1), + ("Bagian Kedua Ruang Lingkup", 1), + ("Paragraf 1", 2), + ("Paragraf 2 Tata Cara", 2), + ("Pasal 1", 3), + ("Pasal 45", 3), + ("Ayat (1)", 4), + ("Ayat (2) Ketentuan", 4), + ]) + def test_match(self, text, expected_level): + result = _match_heading(text, _ID_PATTERNS) + assert result == expected_level, f"Expected level {expected_level} for '{text}', got {result}" + + @pytest.mark.parametrize("text", [ + "Mengenai pasal ini perlu diperhatikan.", + "Lihat BAB sebelumnya.", + "", + ]) + def test_no_match(self, text): + assert _match_heading(text, _ID_PATTERNS) is None + + +# --------------------------------------------------------------------------- +# detect_legal_headings integration +# --------------------------------------------------------------------------- +class TestDetectLegalHeadings: + def test_promotes_body_text_en(self): + pdf_list = [ + _make_item("CHAPTER 1 Introduction"), + _make_item("This is body text about the law."), + _make_item("Article 2 Definitions"), + _make_item("Some table content", item_type="table"), + ] + result = detect_legal_headings(pdf_list, lang="en") + assert result[0]["text_level"] == 0 # CHAPTER → level 0 + assert result[1]["text_level"] == -1 # body text unchanged + assert result[2]["text_level"] == 1 # Article → level 1 + assert "text_level" not in result[3] or result[3]["text_level"] == -1 # table skipped + + def test_does_not_override_existing_heading(self): + pdf_list = [_make_item("CHAPTER 1", text_level=2)] + detect_legal_headings(pdf_list, lang="en") + assert pdf_list[0]["text_level"] == 2 # not overridden + + def test_promotes_body_text_id(self): + pdf_list = [ + _make_item("BAB I KETENTUAN UMUM"), + _make_item("Pasal 1"), + _make_item("Dalam peraturan ini yang dimaksud dengan:"), + ] + result = detect_legal_headings(pdf_list, lang="id") + assert result[0]["text_level"] == 0 + assert result[1]["text_level"] == 3 + assert result[2]["text_level"] == -1 + + def test_unknown_lang_falls_back_to_en(self): + pdf_list = [_make_item("CHAPTER 1")] + detect_legal_headings(pdf_list, lang="xx") + assert pdf_list[0]["text_level"] == 0 + + def test_handles_none_items(self): + pdf_list = [None, _make_item("Article 1"), None] + detect_legal_headings(pdf_list, lang="en") + assert pdf_list[1]["text_level"] == 1 + + +# --------------------------------------------------------------------------- +# Auto language detection +# --------------------------------------------------------------------------- +class TestDetectDocumentLanguage: + def test_detects_english(self): + pdf_list = [ + _make_item("The quick brown fox jumps over the lazy dog. " * 10), + _make_item("This is a legal agreement between the parties. " * 10), + ] + lang = detect_document_language(pdf_list) + assert lang == "en" + + def test_detects_indonesian(self): + pdf_list = [ + _make_item("Dalam peraturan pemerintah ini yang dimaksud dengan " + "peraturan perundang-undangan adalah peraturan tertulis " + "yang memuat norma hukum yang mengikat secara umum. " * 5), + ] + lang = detect_document_language(pdf_list) + assert lang == "id" + + def test_fallback_on_empty(self): + pdf_list = [_make_item("Hi")] + lang = detect_document_language(pdf_list, fallback="id") + assert lang == "id" + + def test_skips_headings(self): + pdf_list = [ + _make_item("BAB I", text_level=0), + _make_item("Dalam peraturan ini yang dimaksud dengan peraturan " + "perundang-undangan adalah norma hukum yang berlaku. " * 5), + ] + lang = detect_document_language(pdf_list) + # Should detect from body text, not headings + assert lang == "id" + diff --git a/tests/test_pdf_refiner_lang.py b/tests/test_pdf_refiner_lang.py new file mode 100644 index 0000000..79cca89 --- /dev/null +++ b/tests/test_pdf_refiner_lang.py @@ -0,0 +1,113 @@ +"""Tests for language-aware is_likely_incomplete_paragraph in pdf_refiner.py""" +import pytest + +from Core.pipelines.pdf_refiner import is_likely_incomplete_paragraph + + +class TestEnglishIncomplete: + """Existing English behaviour should be preserved.""" + + def test_complete_sentence(self): + assert is_likely_incomplete_paragraph( + 'He said, "This method is the best."', lang="en" + ) is False + + def test_incomplete_ending_and(self): + assert is_likely_incomplete_paragraph( + "The quick brown fox jumps over the lazy dog and", lang="en" + ) is True + + def test_incomplete_hyphen(self): + assert is_likely_incomplete_paragraph( + "The results demonstrate a signifi-", lang="en" + ) is True + + def test_incomplete_comma(self): + assert is_likely_incomplete_paragraph( + "In the following sections, we discuss the approach,", lang="en" + ) is True + + def test_complete_exclamation(self): + assert is_likely_incomplete_paragraph( + "This is absolutely correct for all cases!", lang="en" + ) is False + + def test_short_text_not_incomplete(self): + assert is_likely_incomplete_paragraph("Hello", lang="en") is False + + def test_empty_text(self): + assert is_likely_incomplete_paragraph("", lang="en") is False + + def test_connector_word_the(self): + assert is_likely_incomplete_paragraph( + "This regulation applies to all persons under the", lang="en" + ) is True + + +class TestIndonesianIncomplete: + """Indonesian-specific terminal punctuation and connector words.""" + + def test_complete_sentence(self): + assert is_likely_incomplete_paragraph( + "Peraturan ini berlaku sejak tanggal diundangkan.", lang="id" + ) is False + + def test_incomplete_no_period(self): + assert is_likely_incomplete_paragraph( + "Dalam peraturan pemerintah ini yang dimaksud dengan peraturan", lang="id" + ) is True + + def test_incomplete_connector_dan(self): + assert is_likely_incomplete_paragraph( + "Pasal ini mengatur tentang hak dan.", lang="id" + ) is True + + def test_incomplete_connector_yang(self): + assert is_likely_incomplete_paragraph( + "Setiap orang berhak atas perlindungan hukum yang.", lang="id" + ) is True + + def test_incomplete_connector_dengan(self): + assert is_likely_incomplete_paragraph( + "Peraturan ini disusun dengan memperhatikan ketentuan dengan.", lang="id" + ) is True + + def test_complete_question(self): + assert is_likely_incomplete_paragraph( + "Apakah peraturan ini sudah sesuai dengan undang-undang?", lang="id" + ) is False + + def test_incomplete_comma_id(self): + assert is_likely_incomplete_paragraph( + "Sebagaimana dimaksud dalam Pasal 1 ayat satu,", lang="id" + ) is True + + +class TestDefaultLang: + """When lang is omitted, should behave as English.""" + + def test_defaults_to_english(self): + assert is_likely_incomplete_paragraph( + "The quick brown fox jumps over the lazy dog and" + ) is True + + def test_defaults_complete(self): + assert is_likely_incomplete_paragraph( + "This sentence is complete and well-formed." + ) is False + + +class TestUnsupportedLang: + """Unsupported language should fall back to English rules.""" + + def test_fallback_terminal_punctuation(self): + # No terminal punctuation → incomplete even for unknown lang + assert is_likely_incomplete_paragraph( + "This sentence has no ending punctuation mark", lang="xx" + ) is True + + def test_fallback_complete(self): + assert is_likely_incomplete_paragraph( + "This sentence ends properly with a period.", lang="xx" + ) is False + From 3191f5c01797c2e8f67f9a4891a4bd4416691d32 Mon Sep 17 00:00:00 2001 From: vmoudyp Date: Tue, 3 Mar 2026 19:14:55 +0700 Subject: [PATCH 08/11] Add end-to-end test script for knowledge graph building - Introduced `run_e2e_test.py` to facilitate the testing of the knowledge graph construction process. - Configurations are loaded from `gbc.yaml`, with paths set for PDF input and output directory. - The script loads an existing document tree, initializes a token tracker, and builds the knowledge graph. - Outputs performance metrics including total nodes, edges, and token usage after graph construction. --- Core/Index/GBCIndex.py | 1 + Core/configs/embedding_config.py | 17 +- Core/configs/llm_config.py | 14 + Core/configs/rerank_config.py | 20 +- Core/configs/vlm_config.py | 16 + Core/pipelines/doc_tree_builder.py | 7 +- Core/pipelines/kg_refiner.py | 2 + Core/pipelines/outline_extractor.py | 13 +- Core/prompts/gbc_prompt.py | 40 + Core/prompts/outline_prompt.py | 37 + Core/provider/embedding.py | 12 +- Core/provider/llm.py | 8 +- Core/provider/rerank.py | 88 +- Core/provider/vlm.py | 10 +- Core/rag/gbc_answer.py | 21 +- Core/rag/gbc_rag.py | 6 +- api/main.py | 3 + api/services/chat.py | 64 +- 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api_base=config.graph.embedding_config.api_base, + api_key=config.graph.embedding_config.api_key, ) self.entity_vdb: VectorStore = VectorStore( db_path=self.entity_vdb_path, diff --git a/Core/configs/embedding_config.py b/Core/configs/embedding_config.py index b7ec689..16b4dc7 100644 --- a/Core/configs/embedding_config.py +++ b/Core/configs/embedding_config.py @@ -1,5 +1,10 @@ +import os from dataclasses import dataclass +from dotenv import load_dotenv + +load_dotenv() + @dataclass class EmbeddingConfig: @@ -10,8 +15,16 @@ class EmbeddingConfig: model_name: str = "Qwen/Qwen3-Embedding-0.6B" max_length: int = 8192 device: str = "cuda:2" - - + + def __post_init__(self): if self.backend not in ["local", "ollama", "openai"]: raise ValueError(f"Unsupported backend: {self.backend}") + # Resolve 'env' placeholder → read from DASHSCOPE_API_KEY environment variable + if self.api_key == "env": + self.api_key = os.environ.get("DASHSCOPE_API_KEY", "") + if not self.api_key: + raise ValueError( + "EmbeddingConfig.api_key is 'env' but DASHSCOPE_API_KEY " + "environment variable is not set." + ) diff --git a/Core/configs/llm_config.py b/Core/configs/llm_config.py index 79a2c8b..614ab62 100644 --- a/Core/configs/llm_config.py +++ b/Core/configs/llm_config.py @@ -1,4 +1,8 @@ +import os from dataclasses import dataclass +from dotenv import load_dotenv + +load_dotenv() # ensure .env is loaded when used outside the API server @dataclass class LLMConfig: @@ -15,3 +19,13 @@ class LLMConfig: def __post_init__(self): if self.backend not in ["openai", "ollama"]: raise ValueError(f"Unsupported backend: {self.backend}") + # Allow api_key to be resolved from environment variable + if not self.api_key or self.api_key in ("env", "ENV"): + env_key = os.environ.get("DASHSCOPE_API_KEY", "") + if env_key: + self.api_key = env_key + else: + raise ValueError( + "LLM api_key is empty/env but DASHSCOPE_API_KEY " + "environment variable is not set." + ) diff --git a/Core/configs/rerank_config.py b/Core/configs/rerank_config.py index d1b5eea..0d24987 100644 --- a/Core/configs/rerank_config.py +++ b/Core/configs/rerank_config.py @@ -1,10 +1,28 @@ +import os from dataclasses import dataclass +from dotenv import load_dotenv + +load_dotenv() + @dataclass class RerankerConfig: model_name: str = "Qwen/Qwen3-Reranker-0.6B" max_length: int = 8192 device: str = "cuda:2" - backend: str = "local" # Options: 'local', 'vllm' + backend: str = "local" # Options: 'local', 'vllm', 'jina' api_base: str = "http://localhost:8011/v1" + api_key: str = "" + + def __post_init__(self): + if self.backend not in ["local", "vllm", "jina"]: + raise ValueError(f"Unsupported reranker backend: {self.backend}") + # Resolve 'env' placeholder → read from JINA_API_KEY environment variable + if self.api_key == "env": + self.api_key = os.environ.get("JINA_API_KEY", "") + if not self.api_key: + raise ValueError( + "RerankerConfig.api_key is 'env' but JINA_API_KEY " + "environment variable is not set." + ) diff --git a/Core/configs/vlm_config.py b/Core/configs/vlm_config.py index 206d7a6..da8fcee 100644 --- a/Core/configs/vlm_config.py +++ b/Core/configs/vlm_config.py @@ -1,4 +1,8 @@ +import os from dataclasses import dataclass +from dotenv import load_dotenv + +load_dotenv() # ensure .env is loaded when used outside the API server @dataclass @@ -9,3 +13,15 @@ class VLMConfig: temperature: float = 0.1 api_key: str = "openai" api_base: str = "http://localhost:8003/v1" + + def __post_init__(self): + # Allow api_key to be resolved from environment variable + if not self.api_key or self.api_key in ("env", "ENV"): + env_key = os.environ.get("DASHSCOPE_API_KEY", "") + if env_key: + self.api_key = env_key + else: + raise ValueError( + "VLM api_key is empty/env but DASHSCOPE_API_KEY " + "environment variable is not set." + ) diff --git a/Core/pipelines/doc_tree_builder.py b/Core/pipelines/doc_tree_builder.py index 1fe588e..48d0ce6 100644 --- a/Core/pipelines/doc_tree_builder.py +++ b/Core/pipelines/doc_tree_builder.py @@ -3,7 +3,8 @@ from Core.pipelines.outline_extractor import extract_pdf_outline_in_chunks from Core.pipelines.pdf_refiner import pdf_info_refiner from Core.pipelines.legal_heading_detector import detect_legal_headings, detect_document_language -from Core.provider.extract_pdf_info import parse_doc, merge_middle_content +# MinerU imports are deferred to avoid top-level dependency on doclayout_yolo +# when using the Docling parser. See the ``else`` branch below. from Core.pipelines.tree_node_summary import generate_tree_node_summary from Core.configs.system_config import SystemConfig from Core.provider.llm import LLM @@ -125,6 +126,8 @@ def build_tree_from_pdf(cfg: SystemConfig, reforce: bool = False) -> DocumentTre log.info(f"[Docling] Extracted content cached to '{tmp_save_path}'") else: # ── MinerU (default) ────────────────────────────────────────── + from Core.provider.extract_pdf_info import parse_doc, merge_middle_content + backend = cfg.mineru.backend server_url = cfg.mineru.server_url method = cfg.mineru.method @@ -157,7 +160,7 @@ def build_tree_from_pdf(cfg: SystemConfig, reforce: bool = False) -> DocumentTre lang = detect_document_language(pdf_list, fallback="en") pdf_list = pdf_info_refiner(pdf_list, llm, lang=lang) pdf_list = detect_legal_headings(pdf_list, lang=lang) - title_outline = extract_pdf_outline_in_chunks(pdf_list, llm) + title_outline = extract_pdf_outline_in_chunks(pdf_list, llm, lang=lang) tree_index = construct_tree_index( tree_index=tree_index, pdf_list=pdf_list, title_outline=title_outline ) diff --git a/Core/pipelines/kg_refiner.py b/Core/pipelines/kg_refiner.py index 1e7ad97..17ac758 100644 --- a/Core/pipelines/kg_refiner.py +++ b/Core/pipelines/kg_refiner.py @@ -55,6 +55,7 @@ def __init__( max_length=graph_config.embedding_config.max_length, device=graph_config.embedding_config.device, api_base=graph_config.embedding_config.api_base, + api_key=graph_config.embedding_config.api_key, ) self.reranker = TextRerankerProvider( model_name=graph_config.reranker_config.model_name, @@ -62,6 +63,7 @@ def __init__( max_length=graph_config.reranker_config.max_length, backend=graph_config.reranker_config.backend, api_base=graph_config.reranker_config.api_base, + api_key=graph_config.reranker_config.api_key, ) # delete the old vector database if exists self.vdb_path = os.path.join(save_path, "kg_vdb") diff --git a/Core/pipelines/outline_extractor.py b/Core/pipelines/outline_extractor.py index cce06b1..d069179 100644 --- a/Core/pipelines/outline_extractor.py +++ b/Core/pipelines/outline_extractor.py @@ -1,7 +1,7 @@ from typing import Optional, List from Core.provider.llm import LLM -from Core.prompts.outline_prompt import OUTLINE_EXTRACTION_PROMPT, OutlineExtraction +from Core.prompts.outline_prompt import OUTLINE_EXTRACTION_PROMPT, OutlineExtraction, get_outline_prompt from Core.utils.utils import get_json_content, num_tokens, enumerate_pdf_list import logging @@ -43,7 +43,7 @@ def outline_refine(outline_list: List[Optional[str]]) -> List[Optional[str]]: return outline_list -def extract_pdf_outline(pdf_list: List[Optional[str]], llm: LLM) -> List[Optional[str]]: +def extract_pdf_outline(pdf_list: List[Optional[str]], llm: LLM, lang: str = "en") -> List[Optional[str]]: """Extract the outline from the PDF content.""" pdf_length = len(pdf_list) @@ -77,7 +77,7 @@ def extract_pdf_outline(pdf_list: List[Optional[str]], llm: LLM) -> List[Optiona json_format_title = get_json_content(title_list, selected_columns=SELECT_COLS) - prompt = OUTLINE_EXTRACTION_PROMPT.format(json_title=json_format_title) + prompt = get_outline_prompt(lang).format(json_title=json_format_title) log.info(f"number of token in prompt: {num_tokens(prompt)}") response: OutlineExtraction = llm.get_json_completion(prompt, OutlineExtraction) outline_list = [] @@ -199,14 +199,15 @@ def calculate_effective_height(entry: dict) -> float: def extract_pdf_outline_in_chunks( - pdf_list: List[Optional[str]], llm: LLM + pdf_list: List[Optional[str]], llm: LLM, lang: str = "en" ) -> List[Optional[str]]: """ Extracts the PDF outline by processing titles in chunks with improved, stateful context building to ensure accurate hierarchical structure. """ # 1. More precise token budget calculation (Your Point 1 & 4) - prompt_template_tokens = num_tokens(OUTLINE_EXTRACTION_PROMPT.format(json_title="")) + outline_prompt = get_outline_prompt(lang) + prompt_template_tokens = num_tokens(outline_prompt.format(json_title="")) # Leave a 400-token buffer for the LLM's response generation and other overhead available_tokens_for_titles = llm.config.max_tokens - prompt_template_tokens - 500 available_tokens_for_titles = min(2000, available_tokens_for_titles) @@ -313,7 +314,7 @@ def extract_pdf_outline_in_chunks( # 4. Call LLM with the constructed prompt prompt_payload = context_titles + new_titles_for_chunk json_format_title = get_json_content(prompt_payload, SELECT_COLS) - prompt = OUTLINE_EXTRACTION_PROMPT.format(json_title=json_format_title) + prompt = outline_prompt.format(json_title=json_format_title) log.info(f"Number of tokens in prompt: {num_tokens(prompt)}") try: diff --git a/Core/prompts/gbc_prompt.py b/Core/prompts/gbc_prompt.py index e7080ac..c29a936 100644 --- a/Core/prompts/gbc_prompt.py +++ b/Core/prompts/gbc_prompt.py @@ -515,3 +515,43 @@ class SecEXPSelection(BaseModel): --- Provided Analyses from Different Sources --- {partial_answers_str} """ + + +# ── Language-aware prompt helpers ───────────────────────────────────────────── + +_LANG_NAMES = { + "en": "English", + "id": "Bahasa Indonesia", + "zh": "Chinese", + "ja": "Japanese", + "ko": "Korean", + "ms": "Malay", + "th": "Thai", + "vi": "Vietnamese", + "de": "German", + "fr": "French", + "es": "Spanish", + "pt": "Portuguese", + "ar": "Arabic", +} + + +def get_lang_instruction(lang: str) -> str: + """Return a short instruction telling the LLM which language to respond in. + + Returns an empty string for English (default) to keep prompts minimal. + """ + if not lang or lang == "en": + return "" + lang_name = _LANG_NAMES.get(lang, lang) + return f" Respond in {lang_name}." + + +def get_iter_generation_sys_prompt(lang: str = "en") -> str: + """V2 iterative generation system prompt, with optional language instruction.""" + return ITER_GENERATION_SYS_PROMPT + get_lang_instruction(lang) + + +def get_synthesis_sys_prompt(lang: str = "en") -> str: + """V2 synthesis system prompt, with optional language instruction.""" + return SYNTHESIS_SYS_PROMPT.rstrip() + get_lang_instruction(lang) + "\n" diff --git a/Core/prompts/outline_prompt.py b/Core/prompts/outline_prompt.py index 0240881..61f4698 100644 --- a/Core/prompts/outline_prompt.py +++ b/Core/prompts/outline_prompt.py @@ -12,6 +12,43 @@ class OutlineExtraction(BaseModel): outline: List[OutlineExtractionOutput] +# ── Language-specific supplements for outline extraction ────────────────────── + +_OUTLINE_LANG_SUPPLEMENTS = { + "id": """ +### Additional Guidance — Indonesian Legal Documents + +This document is written in **Bahasa Indonesia** and follows Indonesian legal drafting conventions. +Use the following standard hierarchy when determining heading levels: + +| Pattern | Meaning | Recommended Level | +|---|---|---| +| **BAB** (+ Roman numeral) | Chapter | `level: 1` (top-level section) | +| **Bagian** (+ ordinal word: Kesatu, Kedua …) | Part | `level: 2` | +| **Paragraf** (+ number) | Sub-section | `level: 3` | +| **Pasal** (+ number) | Article | `level: 3` or `level: 4` | + +**Key rules for Indonesian legal documents:** +- The document title (e.g., "UNDANG-UNDANG …", "PERATURAN …") is **always `level: 0`**. +- BAB headings use **Roman numerals** (BAB I, BAB II, …) and are major divisions (`level: 1`). +- Bagian headings use **Indonesian ordinal words** (Bagian Kesatu, Bagian Kedua, …) — they subdivide a BAB (`level: 2`). +- Paragraf headings use **Arabic numerals** (Paragraf 1, Paragraf 2, …) — they subdivide a Bagian (`level: 3`). +- Pasal headings use **Arabic numerals** (Pasal 1, Pasal 2, …) — they are articles within the nearest parent section. +- Preserve all heading text exactly as it appears; do **not** translate. +""", +} + + +def get_outline_prompt(lang: str = "en") -> str: + """Return the outline extraction prompt, optionally augmented with + language-specific guidance (e.g. Indonesian legal hierarchy).""" + supplement = _OUTLINE_LANG_SUPPLEMENTS.get(lang, "") + if supplement: + # Insert the supplement just before the final instruction line + return OUTLINE_EXTRACTION_PROMPT + supplement + return OUTLINE_EXTRACTION_PROMPT + + # 2219 tokens OUTLINE_EXTRACTION_PROMPT = """ You are an expert in document structure analysis. Your task is to generate a structured outline based on a given list of text segments. diff --git a/Core/provider/embedding.py b/Core/provider/embedding.py index 580d1b3..9e8ead9 100644 --- a/Core/provider/embedding.py +++ b/Core/provider/embedding.py @@ -453,6 +453,7 @@ def __init__( device: str = "auto", max_length: int = 8192, api_base: str = None, + api_key: str = "empty", ): self.model_name = model_name @@ -486,7 +487,7 @@ def __init__( elif self.backend == "ollama": self.device = "ollama_service" elif self.backend == "openai": - self.client = openai.OpenAI(api_key="empty", base_url=api_base) + self.client = openai.OpenAI(api_key=api_key, base_url=api_base) else: raise ValueError( f"Unsupported backend: '{self.backend}'. Choose 'local' or 'ollama'." @@ -615,13 +616,18 @@ def embed_texts(self, texts: List[str]) -> np.ndarray: elif self.backend == "openai": BATCH_SIZE = 8 - n = len(texts) + # Sanitize: replace empty/whitespace-only texts with a placeholder + sanitized_texts = [ + t if t and t.strip() else "" + for t in texts + ] + n = len(sanitized_texts) all_embeddings = [] num_batches = math.ceil(n / BATCH_SIZE) for i in tqdm( range(0, n, BATCH_SIZE), desc="Embedding texts", total=num_batches ): - chunk = texts[i : i + BATCH_SIZE] + chunk = sanitized_texts[i : i + BATCH_SIZE] response = self.client.embeddings.create( model=self.model_name, input=chunk, diff --git a/Core/provider/llm.py b/Core/provider/llm.py index f353edc..1f293aa 100644 --- a/Core/provider/llm.py +++ b/Core/provider/llm.py @@ -109,7 +109,10 @@ def get_completion( "max_tokens": get_max_output_tokens(messages, self.max_tokens), "frequency_penalty": self.frequency_penalty, "presence_penalty": self.presence_penalty, - "extra_body": {"chat_template_kwargs": {"enable_thinking": False}}, + "extra_body": { + "enable_thinking": False, # DashScope + "chat_template_kwargs": {"enable_thinking": False}, # vLLM + }, } if json_response: parameters["response_format"] = {"type": "json_object"} @@ -167,7 +170,8 @@ def get_json_completion( messages=messages, response_format=schema, extra_body={ - "chat_template_kwargs": {"enable_thinking": think_mode}, + "enable_thinking": think_mode, # DashScope + "chat_template_kwargs": {"enable_thinking": think_mode}, # vLLM }, ) diff --git a/Core/provider/rerank.py b/Core/provider/rerank.py index d3aae30..295c302 100644 --- a/Core/provider/rerank.py +++ b/Core/provider/rerank.py @@ -28,6 +28,7 @@ def __init__( torch_dtype: torch.dtype = torch.bfloat16, backend: str = "local", api_base: str = None, + api_key: str = None, ): """ 初始化Reranker Provider。 @@ -38,12 +39,14 @@ def __init__( max_length (int): 模型的最大序列长度。 use_flash_attention (bool): 是否尝试使用Flash Attention 2以提升性能。 torch_dtype (torch.dtype): 模型加载时使用的数据类型，如 torch.bfloat16。 - backend (str): 后端类型，支持 'local' 和 'vllm'。 + backend (str): 后端类型，支持 'local', 'vllm', 'jina'。 api_base (str): 如果使用 'vllm' 后端，必须提供API基础URL。 + api_key (str): API key for cloud backends (e.g., Jina). """ self.model_name = model_name self.max_length = max_length self.backend = backend.lower() + self.api_key = api_key # ========================================================== # vLLM 后端逻辑 @@ -97,9 +100,23 @@ def __init__( log.info("Reranker model loaded successfully.") + # ========================================================== + # Jina Reranker API 后端 + # ========================================================== + elif self.backend == "jina": + if not api_key: + raise ValueError("api_key must be provided for the 'jina' backend.") + self.rerank_url = api_base or "https://api.jina.ai/v1/rerank" + self.session = requests.Session() + self.session.headers.update({ + "Content-Type": "application/json", + "Authorization": f"Bearer {self.api_key}", + }) + log.info(f"Using Jina reranker backend. Model: {self.model_name}, Endpoint: {self.rerank_url}") + else: raise ValueError( - f"Unsupported backend: {self.backend}. Choose 'local' or 'vllm'." + f"Unsupported backend: {self.backend}. Choose 'local', 'vllm', or 'jina'." ) self._define_prompt_template() @@ -111,7 +128,7 @@ def clean_cache(self): torch.cuda.empty_cache() log.info("Cache cleaned.") else: - log.info(f"{self.backend} backend requires no local cache cleaning.") + log.debug(f"{self.backend} backend requires no local cache cleaning.") def close(self) -> None: """ @@ -132,10 +149,10 @@ def close(self) -> None: gc.collect() log.info("Local reranker resources released.") - elif self.backend == "vllm": + elif self.backend in ("vllm", "jina"): if hasattr(self, "session"): - self.session.close() # 关闭 requests session - log.info("vLLM backend session closed.") + self.session.close() + log.info(f"{self.backend} backend session closed.") log.info("TextRerankerProvider closed.") @@ -333,6 +350,65 @@ def rerank( raise e + elif self.backend == "jina": + # Jina Reranker API: simple REST call, no prompt template needed. + # The instruction is prepended to the query for context. + if instruction: + full_query = f"{instruction}\n\n{query}" + else: + full_query = query + + all_results = [] + num_docs = len(documents) + num_batches = math.ceil(num_docs / batch_size) + + try: + for i in tqdm( + range(0, num_docs, batch_size), + desc="Reranking Batches (Jina)", + total=num_batches, + disable=num_docs < batch_size, + ): + batch_docs = documents[i : i + batch_size] + payload = { + "model": self.model_name, + "query": full_query, + "documents": batch_docs, + "top_n": len(batch_docs), + } + + response = self.session.post(self.rerank_url, json=payload) + response.raise_for_status() + + data = response.json() + results = data.get("results") + + if results is None or not isinstance(results, list): + log.error( + f"Unexpected response from Jina reranker: {data}" + ) + raise ValueError("Failed to parse 'results' from Jina response.") + + # Map results back to global indices + for r in results: + r["global_index"] = i + r.get("index", 0) + all_results.extend(results) + + # Sort by global index to return scores in original document order + all_results.sort(key=lambda r: r.get("global_index", 0)) + + # Jina scores can be negative; normalize to [0, 1] using sigmoid + all_scores = [ + 1.0 / (1.0 + math.exp(-r["relevance_score"])) + for r in all_results + ] + + return all_scores + + except requests.exceptions.RequestException as e: + log.error(f"Error calling Jina reranker API: {e}") + raise e + elif self.backend == "local": # 1. 创建所有的查询-文档对 pairs = [ diff --git a/Core/provider/vlm.py b/Core/provider/vlm.py index 8c003a6..8779740 100644 --- a/Core/provider/vlm.py +++ b/Core/provider/vlm.py @@ -257,7 +257,11 @@ def generate( ) -> str: content = self._prepare_messages(prompt_or_memory, images) completion = self.client.chat.completions.create( - model=self.model_name, messages=content, temperature=self.temperature + model=self.model_name, messages=content, temperature=self.temperature, + extra_body={ + "enable_thinking": False, # DashScope + "chat_template_kwargs": {"enable_thinking": False}, # vLLM + }, ) if completion.usage: @@ -299,6 +303,10 @@ def generate_json( model=self.model_name, messages=messages, response_format={"type": "json_object"}, # Use modern JSON mode + extra_body={ + "enable_thinking": False, # DashScope + "chat_template_kwargs": {"enable_thinking": False}, # vLLM + }, ) if completion.usage: diff --git a/Core/rag/gbc_answer.py b/Core/rag/gbc_answer.py index 2a88634..3101180 100644 --- a/Core/rag/gbc_answer.py +++ b/Core/rag/gbc_answer.py @@ -13,6 +13,8 @@ VLM_GENERATION_USER_PROMPT, SYNTHESIS_SYS_PROMPT, SYNTHESIS_USER_PROMPT, + get_iter_generation_sys_prompt, + get_synthesis_sys_prompt, ) from Core.utils.utils import num_tokens, TextProcessor from Core.utils.table_utils import table2text @@ -23,9 +25,10 @@ class AnswerAgent: - def __init__(self, llm: LLM, vlm: VLM): + def __init__(self, llm: LLM, vlm: VLM, lang: str = "en"): self.llm = llm self.vlm = vlm + self.lang = lang or "en" def _prepare_evidence( self, retrieved_nodes: List[Dict] @@ -59,6 +62,8 @@ def _build_prompts( """ Builds chunked and formatted prompts for both LLM and VLM. """ + sys_prompt = get_iter_generation_sys_prompt(self.lang) + # 1. Build VLM prompts for image-based evidence image_prompts = [] for node in image_nodes: @@ -69,7 +74,7 @@ def _build_prompts( node_content = node.get("content", "") content = f"An image in Page: {page}, Caption: {node_content}" vlm_prompt = ( - f"{ITER_GENERATION_SYS_PROMPT.strip()}\n\n" + f"{sys_prompt.strip()}\n\n" f"{VLM_GENERATION_USER_PROMPT.format(question=query, content=content).strip()}" ) if img_path: @@ -92,7 +97,7 @@ def _build_prompts( ) + graph_prompt_part ) - system_prompt_tokens = num_tokens(ITER_GENERATION_SYS_PROMPT) + system_prompt_tokens = num_tokens(sys_prompt) content_limit = ( self.llm.config.max_tokens - system_prompt_tokens - base_prompt_tokens - 400 ) # 400 as buffer @@ -148,7 +153,7 @@ def _build_prompts( ) gen_memory = Memory() gen_memory.add( - Message(role="system", content=ITER_GENERATION_SYS_PROMPT) + Message(role="system", content=sys_prompt) ) gen_memory.add(Message(role="user", content=user_prompt)) text_prompts.append(gen_memory) @@ -173,7 +178,7 @@ def _build_prompts( + graph_prompt_part ) gen_memory = Memory() - gen_memory.add(Message(role="system", content=ITER_GENERATION_SYS_PROMPT)) + gen_memory.add(Message(role="system", content=sys_prompt)) gen_memory.add(Message(role="user", content=user_prompt)) text_prompts.append(gen_memory) @@ -237,11 +242,12 @@ def _synthesize_from_chunks( ) log.info("Synthesizing the final answer from partial results...") + synth_sys = get_synthesis_sys_prompt(self.lang) synthesis_user_prompt = SYNTHESIS_USER_PROMPT.format( user_question=query, partial_answers_str=partial_answers_str ) synthesis_memory = Memory() - synthesis_memory.add(Message(role="system", content=SYNTHESIS_SYS_PROMPT)) + synthesis_memory.add(Message(role="system", content=synth_sys)) synthesis_memory.add(Message(role="user", content=synthesis_user_prompt)) try: @@ -493,11 +499,12 @@ def answer_global_question( ) log.info("Synthesizing the final answer from partial results...") + synth_sys = get_synthesis_sys_prompt(self.lang) synthesis_user_prompt = SYNTHESIS_USER_PROMPT.format( user_question=original_query, partial_answers_str=partial_answers_str ) synthesis_memory = Memory() - synthesis_memory.add(Message(role="system", content=SYNTHESIS_SYS_PROMPT)) + synthesis_memory.add(Message(role="system", content=synth_sys)) synthesis_memory.add(Message(role="user", content=synthesis_user_prompt)) try: diff --git a/Core/rag/gbc_rag.py b/Core/rag/gbc_rag.py index 30ca656..7de36ad 100644 --- a/Core/rag/gbc_rag.py +++ b/Core/rag/gbc_rag.py @@ -52,6 +52,7 @@ def __init__( vlm: VLM, config: GBCRAGConfig, gbc_index: GBC, + lang: str = "en", ): super().__init__( llm, @@ -71,15 +72,18 @@ def __init__( device=self.cfg.reranker_config.device, backend=self.cfg.reranker_config.backend, api_base=self.cfg.reranker_config.api_base, + api_key=self.cfg.reranker_config.api_key, ) # GBC RAG config self.threshold_e = self.cfg.sim_threshold_e self.select_depth = self.cfg.select_depth self.max_retry = self.cfg.max_retry + self.lang = lang or "en" + # Agents self.planner = TaskPlanner(llm=self.llm) - self.answer = AnswerAgent(llm=self.llm, vlm=self.vlm) + self.answer = AnswerAgent(llm=self.llm, vlm=self.vlm, lang=self.lang) self.retriever = Retriever( varient=self.varient, reranker=self.reranker, diff --git a/api/main.py b/api/main.py index 758fe8d..7ccc55f 100644 --- a/api/main.py +++ b/api/main.py @@ -1,4 +1,7 @@ """BookRAG FastAPI application entry point.""" +from dotenv import load_dotenv +load_dotenv() # load .env before any os.getenv / os.environ calls + import logging import os import uuid as _uuid diff --git a/api/services/chat.py b/api/services/chat.py index 33bbda0..d679631 100644 --- a/api/services/chat.py +++ b/api/services/chat.py @@ -195,7 +195,9 @@ def _rewrite_query_sync(query: str, history: List[dict], config_path: str) -> st return query -def _query_single_doc_sync(query: str, tenant_id: str, doc_id: str, config_path: str) -> str: +def _query_single_doc_sync( + query: str, tenant_id: str, doc_id: str, config_path: str, lang: str = "en" +) -> str: """Run GBC RAG query against a single document (sync, for thread pool). *query* should already be a self-contained, rewritten query when conversation @@ -208,7 +210,7 @@ def _query_single_doc_sync(query: str, tenant_id: str, doc_id: str, config_path: llm = _get_llm(config_path) vlm = _get_vlm(config_path) rag_cfg = GBCRAGConfig() - rag = GBCRAG(llm=llm, vlm=vlm, config=rag_cfg, gbc_index=gbc_index) + rag = GBCRAG(llm=llm, vlm=vlm, config=rag_cfg, gbc_index=gbc_index, lang=lang) result = rag.get_GBC_info(query) return result if isinstance(result, str) else str(result) @@ -268,42 +270,58 @@ async def handle_query( {"role": "user", "content": query}) # ── RAG retrieval ───────────────────────────────────────────────────────── + + # ── Fetch per-doc metadata (dates + languages) — best-effort ────────── + doc_dates: dict[str, str] = {} + doc_langs: dict[str, str] = {} + try: + for did in doc_ids[:5]: + doc_record = await db.get_document(MONGO_URI, MONGO_DB_PREFIX, tenant_id, did) + if doc_record: + ddate = doc_record.get("document_date") or doc_record.get("created_at") + if ddate: + doc_dates[did] = str(ddate)[:10] # YYYY-MM-DD + dlang = doc_record.get("document_lang") + if dlang and dlang != "auto": + doc_langs[did] = dlang + except Exception: + pass # Non-fatal: metadata is best-effort + if cross_doc or len(doc_ids) > 1: # Parallel per-doc queries, answers synthesised into one response target_docs = doc_ids[:5] # cap to avoid GPU overload answers = await asyncio.gather(*[ loop.run_in_executor( - _executor, _query_single_doc_sync, effective_query, tenant_id, did, config_path + _executor, _query_single_doc_sync, + effective_query, tenant_id, did, config_path, + doc_langs.get(did, "en"), ) for did in target_docs ]) - # ── Temporal awareness: fetch document dates for cross-doc synthesis ── - doc_dates: dict[str, str] = {} - try: - for did in target_docs: - doc_record = await db.get_document(MONGO_URI, MONGO_DB_PREFIX, tenant_id, did) - if doc_record: - ddate = doc_record.get("document_date") or doc_record.get("created_at") - if ddate: - doc_dates[did] = str(ddate)[:10] # YYYY-MM-DD - except Exception: - pass # Non-fatal: temporal info is best-effort - - # Build answer with temporal context + # Build answer with temporal + language context parts = [] for did, ans in zip(target_docs, answers): date_str = f" (dated {doc_dates[did]})" if did in doc_dates else "" - parts.append(f"[Document: {did}{date_str}]\n{ans}") + lang_str = f" [lang: {doc_langs[did]}]" if did in doc_langs else "" + parts.append(f"[Document: {did}{date_str}{lang_str}]\n{ans}") + # Prepend contextual notes when metadata is present + notes = [] if doc_dates: - # Prepend a temporal instruction for the combined answer - temporal_note = ( + notes.append( "NOTE: The answers below come from multiple documents with different dates. " "When documents contain contradictory or overlapping information, " - "prefer the information from the more recently dated document.\n\n" + "prefer the information from the more recently dated document." + ) + unique_langs = set(doc_langs.values()) + if len(unique_langs) > 1: + notes.append( + "NOTE: The answers below come from documents in different languages. " + "Each answer is in its document's language; synthesise accordingly." ) - answer = temporal_note + "\n\n---\n\n".join(parts) + if notes: + answer = "\n\n".join(notes) + "\n\n" + "\n\n---\n\n".join(parts) else: answer = "\n\n---\n\n".join(parts) else: @@ -311,8 +329,10 @@ async def handle_query( if not doc_id: answer = "No accessible documents found for your query." else: + lang = doc_langs.get(doc_id, "en") answer = await loop.run_in_executor( - _executor, _query_single_doc_sync, effective_query, tenant_id, doc_id, config_path + _executor, _query_single_doc_sync, + effective_query, tenant_id, doc_id, config_path, lang, ) # ── Persist assistant message ────────────────────────────────────────────── diff --git a/config/gbc.yaml b/config/gbc.yaml index b330c49..439a422 100644 --- a/config/gbc.yaml +++ b/config/gbc.yaml @@ -2,11 +2,12 @@ pdf_path: TODO save_path: TODO +parser: docling llm: - model_name: Qwen/Qwen3.5-35B-A3B-AWQ - api_key: openai - api_base: http://localhost:8003/v1 + model_name: qwen3.5-35b-a3b + api_key: env + api_base: https://dashscope-intl.aliyuncs.com/compatible-mode/v1 backend: openai max_tokens: 8000 temperature: 0.1 @@ -16,9 +17,9 @@ llm: vlm: - model_name: Qwen/Qwen3.5-35B-A3B-AWQ - api_key: openai - api_base: http://localhost:8003/v1 + model_name: qwen3.5-35b-a3b + api_key: env + api_base: https://dashscope-intl.aliyuncs.com/compatible-mode/v1 temperature: 0.1 max_tokens: 6000 backend: gpt @@ -44,17 +45,19 @@ graph: max_gleaning: 1 refine_type: "advanced" embedding_config: - model_name: Qwen3-Embedding-0.6B + model_name: text-embedding-v3 backend: openai + api_key: env max_length: 4096 - device: "cuda:2" - api_base: "http://localhost:8007/v1" + device: "cpu" + api_base: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1" reranker_config: - model_name: Qwen3-Reranker-4B + model_name: jina-reranker-v3 max_length: 4096 - device: "cuda:2" - backend: vllm - api_base: "http://localhost:8011/v1" + device: "cpu" + backend: jina + api_base: "https://api.jina.ai/v1/rerank" + api_key: env vdb: @@ -75,11 +78,12 @@ rag: select_depth: 2 max_retry: 2 reranker_config: - model_name: Qwen3-Reranker-4B + model_name: jina-reranker-v3 max_length: 4096 - device: "cuda:7" - backend: vllm - api_base: "http://localhost:8011/v1" + device: "cpu" + backend: jina + api_base: "https://api.jina.ai/v1/rerank" + api_key: env mm_reranker_config: model_name: Alibaba-NLP/gme-Qwen2-VL-2B-Instruct device: "cuda:2" diff --git a/e2e_test_output/docling/BOOKRAG_VLDB_2026_full_merged_content.json b/e2e_test_output/docling/BOOKRAG_VLDB_2026_full_merged_content.json new file mode 100644 index 0000000..fede754 --- /dev/null +++ b/e2e_test_output/docling/BOOKRAG_VLDB_2026_full_merged_content.json @@ -0,0 +1,3141 @@ +[ + { + "type": "text", + "text": "BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents", + "text_level": 0, + "page_idx": 0, + "pdf_id": 0, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "Shu Wang The Chinese University of Hong Kong, Shenzhen shuwang3@link.cuhk.edu.cn", + "text_level": -1, + "page_idx": 0, + "pdf_id": 1, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Yingli Zhou The Chinese University of Hong Kong, Shenzhen yinglizhou@link.cuhk.edu.cn", + "text_level": -1, + "page_idx": 0, + "pdf_id": 2, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Yixiang Fang The Chinese University of Hong Kong, Shenzhen fangyixiang@cuhk.edu.cn", + "text_level": -1, + "page_idx": 0, + "pdf_id": 3, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "ABSTRACT", + "text_level": 0, + "page_idx": 0, + "pdf_id": 4, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "As an effective method to boost the performance of Large Language Models (LLMs) on the question answering (QA) task, RetrievalAugmented Generation (RAG), which queries highly relevant information from external complex documents, has attracted tremendous attention from both industry and academia. Existing RAG approaches often focus on general documents, and they overlook the fact that many real-world documents (such as books, booklets, handbooks, etc.) have a hierarchical structure, which organizes their content from different granularity levels, leading to poor performance for the QA task. To address these limitations, we introduce BookRAG, a novel RAG approach targeted for documents with a hierarchical structure, which exploits logical hierarchies and traces entity relations to query the highly relevant information. Specifically, we build a novel index structure, called BookIndex, by extracting a hierarchical tree from the document, which serves as the role of its table of contents, using a graph to capture the intricate relationships between entities, and mapping entities to tree nodes. Leveraging the BookIndex, we then propose an agent-based query method inspired by the Information Foraging Theory, which dynamically classifies queries and employs a tailored retrieval workflow. Extensive experiments on three widely adopted benchmarks demonstrate that BookRAG achieves state-of-the-art performance, significantly outperforming baselines in both retrieval recall and QA accuracy while maintaining competitive efficiency.", + "text_level": -1, + "page_idx": 0, + "pdf_id": 5, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "PVLDB Reference Format:", + "text_level": 0, + "page_idx": 0, + "pdf_id": 6, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "Shu Wang, Yingli Zhou, and Yixiang Fang. BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents. PVLDB, 19(1): XXX-XXX, 2025. doi:XX.XX/XXX.XX", + "text_level": -1, + "page_idx": 0, + "pdf_id": 7, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "PVLDB Artifact Availability:", + "text_level": 0, + "page_idx": 0, + "pdf_id": 8, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "The source code, data, and/or other artifacts have been made available at https://github.com/sam234990/BookRAG.", + "text_level": -1, + "page_idx": 0, + "pdf_id": 9, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "1 INTRODUCTION", + "text_level": 0, + "page_idx": 0, + "pdf_id": 10, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "Large Language Models (LLMs) such as Qwen 3 [60] and Gemini 2.5 [13] have revolutionized the Question Answering (QA) system [15, 61, 65]. The industry has increasingly adopted LLMs to build QA systems that assist users and reduce manual effort in", + "text_level": -1, + "page_idx": 0, + "pdf_id": 11, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "This work is licensed under the Creative Commons BY-NC-ND 4.0 International License. Visit https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of this license. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org. Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment.", + "text_level": -1, + "page_idx": 0, + "pdf_id": 12, + "middle_json": { + "docling_label": "footnote" + } + }, + { + "type": "text", + "text": "Proceedings of the VLDB Endowment, Vol. 19, No. 1 ISSN 2150-8097. doi:XX.XX/XXX.XX", + "text_level": -1, + "page_idx": 0, + "pdf_id": 13, + "middle_json": { + "docling_label": "footnote" + } + }, + { + "type": "text", + "text": "Figure 1: Comparison of existing methods and BookRAG for complex document QA.", + "text_level": -1, + "page_idx": 0, + "pdf_id": 14, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "image", + "text": "", + "text_level": -1, + "page_idx": 0, + "pdf_id": 15, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-1.png", + "image_caption": [ + "cref='#/texts/14'" + ], + "image_footnote": [], + "middle_json": { + "docling_label": "picture" + } + }, + { + "type": "text", + "text": "many applications [65, 67], such as financial auditing [29, 37], legal compliance [8], and scientific discovery [56]. However, directly relying on LLMs may lead to missing domain knowledge and generating outdated or unsupported information. To address these issues, Retrieval-Augmented Generation (RAG) has been widely adopted [17, 22] by retrieving relevant domain knowledge from external sources and using it to guide the LLM during response generation. On the other hand, in real-world enterprise scenarios, domain knowledge is often stored in long-form documents, such as technical handbooks, API reference manuals, and operational guidebooks [49]. A notable feature of such documents is that they follow the structure of books, characterized by intricate layouts and rigorous logical hierarchies (e.g., explicit tables of contents, nested chapters, and multi-level sections). In this paper, we aim to design an effective RAG system for QA over long and highly structured documents.", + "text_level": -1, + "page_idx": 0, + "pdf_id": 16, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "· Prior works. The existing RAG approaches for documentlevel QA generally fall into two paradigms, as illustrated in Figure 1. The first paradigm relies on OCR (Optical Character Recognition) to convert the document into plain text, after which any text-based RAG method can be directly applied. Among text-based RAG methods, state-of-the-art approaches increasingly adopt graph-based RAG [6, 62, 66], where graph data serves as an external knowledge source because it captures rich semantic information and the", + "text_level": -1, + "page_idx": 0, + "pdf_id": 17, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "Table 1: Comparison of representative methods and our BookRAG.", + "text_level": -1, + "page_idx": 1, + "pdf_id": 18, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "table", + "text": "", + "text_level": -1, + "page_idx": 1, + "pdf_id": 19, + "img_path": "", + "table_caption": [ + "cref='#/texts/17'" + ], + "table_footnote": [], + "table_body": "| Type | Representative Method | Core Feature | Multi-hop Reasoning | Document Parsing | Query Workflow |\n|------------------|---------------------------|--------------------------------------------------------------|-----------------------|--------------------|------------------|\n| Graph-based | RAPTOR [45] GraphRAG [16] | Recursive summarization | | | Static |\n| Layout segmented | MM-Vanilla | Global community detection | | | Static |\n| Layout segmented | DocETL [47] | Multi-modal retrieval LLM-based document processing pipeline | | | Static Manual |\n| Doc-Native | BookRAG (Ours) | Structure-award Index & Agent-based retrieval | | | Dynamic |", + "middle_json": { + "docling_label": "table" + } + }, + { + "type": "text", + "text": "relational structure between entities. As shown in Table 1, two representative methods are GraphRAG [16] and RAPTOR [45]. Specifically, GraphRAG first constructs a knowledge graph (KG) from the textual corpus, and then applies the Leiden community detection algorithm [51] to obtain hierarchical clusters. Summaries are generated for each community, providing a comprehensive, global overview of the entire corpus. RAPTOR builds a recursive tree structure by iteratively clustering document chunks and summarizing them at each level, enabling the model to capture both fine-grained and high-level semantic information across the corpus.", + "text_level": -1, + "page_idx": 1, + "pdf_id": 20, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "In contrast, the second paradigm, layout-aware segmentation [5, 52], first parses the document into structured blocks that preserve the original layout and information of the document, such as paragraphs, tables, figures, or equations. By doing so, it not only avoids the fixed chunk size used in the first paradigm, which often leads to fragmented information, but also retains document-native structural information. These blocks often exhibit multimodal characteristics, and a typical approach is to apply multimodal retrieval to obtain relevant content for answering queries. Recently, a state-ofthe-art method in this category, DocETL [47], provides a declarative interface that allows users to manually define LLM-based processing pipelines to analyze the retrieved blocks. These pipelines consist of LLM-powered operations combined with task-specific optimizations.", + "text_level": -1, + "page_idx": 1, + "pdf_id": 21, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "· Limitations of existing works. However, these methods suffer from two fundamental limitations ( L for short): L1: Failure to capture the deep connection of document structure and semantics. Text-based approaches cannot capture the structural layout of the document, resulting in the loss of important relationships stored in the hierarchical blocks, such as tables nested within a specific section. While layout-segmented methods preserve document structure, they cannot capture the relationships between different blocks in the document, which limits their capability for multi-hop reasoning across these blocks and ultimately affects their overall performance. L2: Static of query workflows. In real-world QA scenarios, user queries are highly heterogeneous, ranging from simple keyword lookups to complex multi-hop questions that require synthesizing evidence scattered across different parts of the document. Applying a uniform strategy, such as static or manually predefined workflows, to diverse needs is inefficient; for example, complex queries often require question decomposition, whereas simple queries do not.", + "text_level": -1, + "page_idx": 1, + "pdf_id": 22, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· Our technical contributions. To bridge this gap, we introduce BookRAG , the first retrieval-augmented generation method built upon a document-native BookIndex , designed to document", + "text_level": -1, + "page_idx": 1, + "pdf_id": 23, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "QA tasks. Specifically, to capture the deep connection of the relation in the document, BookIndex organizes information through two complementary structures. First, to preserve the document's native logical hierarchy, we organize the parsed content blocks into a hierarchical tree structure, which serves as the role of its table of contents. Second, to capture the intricate relations within these blocks, we construct a KG containing fine-grained entities. Finally, we unify these two structures by mapping the KG entities to their corresponding tree nodes.", + "text_level": -1, + "page_idx": 1, + "pdf_id": 24, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "However, effective multi-hop reasoning on the graph relies on a high-quality KG [62, 66], which is often compromised by entity ambiguity (e.g., distinct entities with names like 'LLM' and 'Large Language Model'). To address this, we propose a novel gradient-based entity resolution method that analyzes the similarity distribution of candidate entities. By identifying sharp drops in similarity scores, we can efficiently distinguish and merge coreferent entities, thereby ensuring graph connectivity and enhancing reasoning capabilities.", + "text_level": -1, + "page_idx": 1, + "pdf_id": 25, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Building upon the BookIndex, we address the static of query workflows ( L2 ) by implementing an agent-based retrieval . Specifically, our agent first classifies user queries based on their intent and complexity, and then dynamically generates tailored retrieval workflows. Grounded in Information Foraging Theory [42], our retrieval process mimics foraging by using Selector to narrow down the search space via information scents and Reasoner to locate highly relevant evidence.", + "text_level": -1, + "page_idx": 1, + "pdf_id": 26, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "We conduct extensive experiments on three widely adopted datasets to validate the effectiveness and efficiency of our BookRAG, comparing it against several state-of-the-art baselines. The experimental results demonstrate that BookRAG consistently achieves superior performance in both retrieval recall and QA accuracy across all datasets. Furthermore, our detailed analysis validates the critical contributions of our key features, such as the high-quality KG and the agent-based retrieval mechanism.", + "text_level": -1, + "page_idx": 1, + "pdf_id": 27, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "We summarize our contributions as:", + "text_level": -1, + "page_idx": 1, + "pdf_id": 28, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "· We introduce BookRAG , a novel method that constructs a document-native BookIndex by integrating a hierarchical tree of document layout blocks with a KG storing finegrained entity relations.", + "text_level": -1, + "page_idx": 1, + "pdf_id": 29, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· We propose an Agent-based Retrieval approach inspired by Information Foraging Theory, which dynamically classifies queries and configures optimal retrieval workflows to locate highly relevant evidence within documents.", + "text_level": -1, + "page_idx": 1, + "pdf_id": 30, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· Extensive experiments on multiple benchmarks show that BookRAG significantly outperforms existing baselines, attaining state-of-the-art performance in solving complex", + "text_level": -1, + "page_idx": 1, + "pdf_id": 31, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "2", + "text_level": -1, + "page_idx": 1, + "pdf_id": 32, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "document QA tasks while maintaining competitive efficiency.", + "text_level": -1, + "page_idx": 2, + "pdf_id": 33, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Outline. We review related work in Section 2. Section 3 introduces the problem formulation, IFT, and RAG workflow. In Section 4, we present the structure of our BookIndex and its construction. Section 5 presents our agent-based retrieval, elaborating on the query classification and operators used in the structured execution of BookRAG. We present the experimental results and detailed analysis in Section 6, and conclude the paper in Section 7.", + "text_level": -1, + "page_idx": 2, + "pdf_id": 34, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "2 RELATED WORK", + "text_level": 0, + "page_idx": 2, + "pdf_id": 35, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "In this section, we review the related works, including LLM in document analysis and the modern representative RAG approaches.", + "text_level": -1, + "page_idx": 2, + "pdf_id": 36, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "· LLM in document analysis. Recent advances in LLMs have offered opportunities to leverage LLMs in document data analysis. Due to the robust semantic reasoning capabilities of LLMs, there is an increasing number of works focusing on transferring unstructured documents (e.g., HTML, PDFs, and raw text) into structured formats, such as relational tables [1, 7, 25, 38]. For example, Evaporate [1] utilizes LLMs to synthesize extraction code, enabling cost-effective conversion of semi-structured web documents into structured databases without heavy manual annotation. In addition, several LLM-based document analysis systems have been proposed to equip standard data pipelines with semantic understanding [28, 40, 47, 53]. For instance, LOTUS [40] extends the relational model with semantic operators, allowing users to execute SQL-like queries with LLM-powered predicates (e.g., filter, join) over unstructured text corpora. Similarly, DocETL [47] introduces an agentic framework to optimize complex information extraction tasks. Furthermore, another line of research proposes to directly analyze or parse documents by viewing the document pages as images, thereby preserving critical layout and visual information [26, 31, 54].", + "text_level": -1, + "page_idx": 2, + "pdf_id": 37, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· RAG approaches. RAG has been proven to excel in many tasks, including open-ended question answering [24, 48], programming context [9, 10], SQL rewrite [30, 50], and data cleaning [35, 36, 43]. The naive RAG technique relies on retrieving query-relevant contexts from external knowledge bases to mitigate the 'hallucination' of LLMs. Recently, many RAG approaches [16, 18, 19, 21, 27, 32, 32, 45, 55, 58, 66] have adopted graph structures to organize the information and relationships within documents, achieving improved overall retrieval performance. For more details, please refer to the recent survey of graph-based RAG methods [41]. Besides, the Agentic RAG paradigm has been widely studied, employing autonomous agents to dynamically orchestrate and refine the RAG pipeline, thus significantly boosting the reasoning robustness and generation fidelity [2, 23, 59].", + "text_level": -1, + "page_idx": 2, + "pdf_id": 38, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "3 PRELIMINARIES", + "text_level": 0, + "page_idx": 2, + "pdf_id": 39, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "This section formalizes the research problem of complex document QA, introduces the foundational Information Foraging Theory (IFT), and briefly reviews the general workflow of RAG systems", + "text_level": -1, + "page_idx": 2, + "pdf_id": 40, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "3.1 Problem Formulation", + "text_level": 0, + "page_idx": 2, + "pdf_id": 41, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "We study the problem of Question Answering (QA) over complex documents, which aims to answer user queries based on long-form", + "text_level": -1, + "page_idx": 2, + "pdf_id": 42, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "3", + "text_level": -1, + "page_idx": 2, + "pdf_id": 43, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "documents [5, 11, 33]. Formally, a document 𝐷 is represented as a sequence of 𝑁 pages, 𝐷 = { 𝑃 𝑖 } 𝑁 𝑖 = 1 . These pages collectively contain a sequence of content blocks B = { 𝑏 𝑗 } 𝑀 𝑗 = 1 , where each block 𝑏 𝑗 represents a distinct element (e.g., text segment, section header, table, or image) organized within a logical chapter hierarchy. Given a user query 𝑞 , the goal is to generate an accurate answer 𝐴 , ideally grounded in a specific set of evidence blocks 𝐸 ⊂ B . The task is formulated as developing a method S that maps the structured document and the query to the final answer:", + "text_level": -1, + "page_idx": 2, + "pdf_id": 44, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "equation", + "text": "𝐴 = S( 𝐷,𝑞 ) (1)", + "text_level": -1, + "page_idx": 2, + "pdf_id": 45, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "where S should navigate both the sequential page content and the logical hierarchy of 𝐷 to synthesize the response.", + "text_level": -1, + "page_idx": 2, + "pdf_id": 46, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "3.2 Information Foraging Theory", + "text_level": 0, + "page_idx": 2, + "pdf_id": 47, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "Information Foraging Theory (IFT) [42] provides a framework for understanding information access as a process analogous to animal foraging. It suggests that users follow cues, known as information scent (e.g., keywords or icons), to navigate between clusters of content, known as information patches (e.g., sections in handbooks). The goal is to maximize the rate of valuable information gain while minimizing effort, guiding the decision to either stay within a patch or seek a new one.", + "text_level": -1, + "page_idx": 2, + "pdf_id": 48, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Consider experts seeking a solution to a specific problem within a large technical handbook. They first extract key terms related to the problem, which act as information scent. This scent guides them to navigate towards one or more promising sections (the information patches). Within these patches, they analyze the diverse content to extract the precise knowledge required to formulate a final answer", + "text_level": -1, + "page_idx": 2, + "pdf_id": 49, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "3.3 RAG workflow", + "text_level": 0, + "page_idx": 2, + "pdf_id": 50, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "Retrieval-Augmented Generation (RAG) systems typically operate in a two-phase framework [6, 16, 41]. In the Offline Indexing phase, unstructured corpus data is organized into a structured index, which can take various forms such as vector databases or KG [66]. Subsequently, in the Online Retrieval phase, the system retrieves relevant components (e.g., text chunks or subgraphs) based on the user query 𝑞 to inform the LLM's generation. However, these general workflows often treat the index as a structure derived purely from content, potentially detaching it from the document's original logical hierarchy. In contrast, our approach seeks to deeply integrate these retrieval structures with the document's native tree topology.", + "text_level": -1, + "page_idx": 2, + "pdf_id": 51, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "4 BOOKINDEX", + "text_level": 0, + "page_idx": 2, + "pdf_id": 52, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "This section introduces our proposed BookIndex , a hierarchical structure-aware index designed to capture both the explicit logical hierarchy and the intricate entity relations within complex documents. We first formally define the structure of the BookIndex ( 𝐵 ). Subsequently, we elaborate on the sequential, two-stage construction process: (1) Tree Construction , which parses the document's layout to establish a hierarchical nodes, each categorized by type; and (2) Graph Construction , which extracts fine-grained entity knowledge from the tree nodes and refines it through a novel gradient-based entity resolution method.", + "text_level": -1, + "page_idx": 2, + "pdf_id": 53, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Figure 2: The BookIndex Construction process. This phase includes Tree Construction, derived from Layout Parsing and Section Filtering, and Graph Construction, which involves KG Construction and Gradient-based Entity Resolution.", + "text_level": -1, + "page_idx": 3, + "pdf_id": 54, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "image", + "text": "", + "text_level": -1, + "page_idx": 3, + "pdf_id": 55, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-2.png", + "image_caption": [ + "cref='#/texts/52'" + ], + "image_footnote": [], + "middle_json": { + "docling_label": "picture" + } + }, + { + "type": "text", + "text": "4.1 Overview of BookIndex", + "text_level": 0, + "page_idx": 3, + "pdf_id": 56, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "We formally define our BookIndex as a triplet 𝐵 = ( 𝑇,𝐺, 𝑀 ) . Here, 𝑇 = ( 𝑁, 𝐸 𝑇 ) represents a Tree structure where 𝑁 is the set of nodes derived from the document's explicit logical hierarchy (e.g., titles, sections, tables), and 𝐸 𝑇 denotes their nesting relationships. 𝐺 = ( 𝑉, 𝐸 𝐺 ) is a Knowledge Graph that captures fine-grained entities ( 𝑉 ) and their relations ( 𝐸 𝐺 ) scattered throughout the document. Finally, 𝑀 𝑉 : →P( 𝑁 ) is the Graph-Tree Link (GT-Link) , which links each entity in 𝑉 to the set of specific tree nodes in 𝑁 from which it was extracted. These links are crucial for capturing the intricate, cross-sectional relations within the document. The hierarchical tree nodes in 𝑇 serve as the document's native information patches , providing structured contexts for information seeking. Meanwhile, the entities and relations in 𝐺 , connected via 𝑀 , act as the rich information scent that guides navigation between and within these patches.", + "text_level": -1, + "page_idx": 3, + "pdf_id": 57, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Figure 2 provides an example of our BookIndex. The Tree component, positioned at the top, organizes the document into a hierarchical structure, where content blocks such as text, tables, and images serve as leaf nodes nested within section nodes. The Graph component is composed of entities and relations extracted from these nodes. The GT-Link, illustrated by the blue dotted lines, explicitly connects these entities back to their corresponding tree nodes, thereby grounding the semantic entities within the document's logical hierarchy.", + "text_level": -1, + "page_idx": 3, + "pdf_id": 58, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "4.2 Tree Construction", + "text_level": 0, + "page_idx": 3, + "pdf_id": 59, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "The first stage transforms the raw document into a structured hierarchical tree 𝑇 . This involves two key steps: robust layout parsing and intelligent section filtering.", + "text_level": -1, + "page_idx": 3, + "pdf_id": 60, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "4.2.1 Layout Parsing. The Layout Parsing phase processes the input document 𝐷 (a collection of pages) using layout analysis and recognition models. This step identifies, extracts, and organizes diverse blocks (e.g., text, tables, images) from the document pages.", + "text_level": -1, + "page_idx": 3, + "pdf_id": 61, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "The output is a sequence of primitive blocks, B = { 𝑏 , 𝑏 1 2 , · · · , 𝑏 𝑘 } , where each block 𝑏 𝑖 = ( 𝑐 , 𝜏 , 𝑓 𝑖 𝑖 𝑖 ) is defined as a triplet. Here, 𝑐 𝑖 is the raw content (e.g., text, image data), 𝜏 𝑖 is the initial layout-based type (e.g., Title, Text, Table, Image ), and 𝑓 𝑖 is a vector of associated layout features (e.g., 'FontSize', bounding box).", + "text_level": -1, + "page_idx": 3, + "pdf_id": 62, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "4.2.2 Section Filtering. Next, the Section Filtering phase processes this initial sequence to identify the document's logically hierarchical structure. Layout Parsing identifies blocks as Title but does not assign their hierarchical level. Therefore, we select the candidate subset B title ⊂ B (where 𝜏 𝑖 = Title ) for an LLM-based analysis. To handle extremely long documents, this analysis is performed in batches, where each batch retains a contextual window of high-level section information (with 𝑙 = 1 as the root). The LLM analyzes the content 𝑐 𝑖 and layout features 𝑓 𝑖 of the candidates to determine two key properties: their actual hierarchical level 𝑙 𝑖 ∈ { 1 2 , , ... } and final node type 𝜏 ' 𝑖 (e.g., re-classifying an erroneous Title as Text if its level is 'None'). This step is crucial for preserving the document's logical hierarchy by correcting blocks erroneously parsed as Title , such as descriptive text within images or borderless table headers.", + "text_level": -1, + "page_idx": 3, + "pdf_id": 63, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Finally, the definitive tree 𝑇 = ( 𝑁, 𝐸 𝑇 ) is constructed. The node set 𝑁 is composed of all blocks from the filtering and re-classification process, where each node 𝑛 ∈ 𝑁 retains its content ( 𝑐 𝑖 ) and its final node type ( 𝜏 ' 𝑖 ) (e.g., Text , Section , Table , and Image ). The edge set 𝐸 𝑇 , representing the parent-child nesting relationships, is then established. Parent-child relationships are inferred by sequentially traversing the nodes, using both the determined hierarchical levels ( 𝑙 𝑖 ) of Section nodes and the overall document order to assemble the complete tree structure.", + "text_level": -1, + "page_idx": 3, + "pdf_id": 64, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "As an example shown in Figure 2, the Layout Parsing phase identifies diverse blocks, typing them as Title Text Table , , , and Image . During the Section Filtering phase, the Title candidates (e.g., \"Method\", \"Experiment\", and \"MOE Layer\") are analyzed by the LLM. The blocks 'Method' and 'Experiment' (both with 'FontSize: 14') are correctly identified as Section nodes at 'Level: 2'. Conversely,", + "text_level": -1, + "page_idx": 3, + "pdf_id": 65, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "4", + "text_level": -1, + "page_idx": 3, + "pdf_id": 66, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "the 'MOE Layer' block ('FontSize: 20'), which was erroneously tagged as Title by the parser, is re-classified by the LLM as a Text node with 'Level: None'. This correction is crucial for preserving the document's logical hierarchy. Following this process, all filtered and classified nodes are assembled into the final tree structure based on their determined levels and document order.", + "text_level": -1, + "page_idx": 4, + "pdf_id": 67, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "4.3 Graph Construction", + "text_level": 0, + "page_idx": 4, + "pdf_id": 68, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "Once the tree 𝑇 is established, we proceed to populate the Knowledge Graph 𝐺 by extracting and refining entities from the tree nodes.", + "text_level": -1, + "page_idx": 4, + "pdf_id": 69, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "4.3.1 KG Construction. We iterate each node 𝑛 𝑖 ∈ 𝑁 from the previously constructed tree 𝑇 . For each node 𝑛 𝑖 , we extract a subgraph 𝑔 𝑖 = ( 𝑉 , 𝐸 𝑖 𝑅𝑖 ) based on its content 𝑐 𝑖 and final node type 𝜏 ' 𝑖 . This extraction is modality-dependent: if the node is text-only, an LLM is prompted to extract entities and relations, while for nodes containing visual elements (e.g., 𝜏 ' 𝑖 = Image ), a Vision Language Model (VLM) is employed to extract visual knowledge. Crucially, for every entity 𝑣 ∈ 𝑉 𝑖 extracted, its origin tree node 𝑛 𝑖 is recorded, which is vital for constructing the final mapping 𝑀 .", + "text_level": -1, + "page_idx": 4, + "pdf_id": 70, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Furthermore, to preserve structural semantics for specific logical types (e.g., Table , Formula ), our process first creates a distinct, typed entity (e.g., 𝑣 table representing the table itself). The other extracted entities from the specific node's content are linked to this primary vertex. For Table nodes specifically, row and column headers are also explicitly extracted as distinct entities and linked to 𝑣 table via a 'ContainedIn' relationship.", + "text_level": -1, + "page_idx": 4, + "pdf_id": 71, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "4.3.2 Gradient-based Entity Resolution. As shown in the literature [62, 66], a well-constructed KG is essential for document question answering. A common challenge in the extraction process is that the same conceptual entity is often fragmented into multiple distinct entities due to abbreviations, co-references, or its varied occurrences across different document sections. This necessitates a robust Entity Resolution (ER) process, which identifies and merges these fragmented entities to refine the raw KG.", + "text_level": -1, + "page_idx": 4, + "pdf_id": 72, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "However, conventional ER methods are computationally expensive. They are often designed for batch processing across multiple data sources (commonly referred to as dirty ER), aiming to ensure accurate entity resolution by finding all possible matching pairs [12]. This process typically requires finding the transitive closure of all detected matches. That is, to definitively merge multiple entities (e.g., A, B, and C) as the same concept, the system must ideally compare all possible pairs ('A-B', 'A-C', and 'B-C') to confirm their equivalence. This can lead to a quadratic ( 𝑂 𝑛 ( 2 ) ) number of pairwise comparisons, a process that becomes prohibitively slow and computationally expensive when relying on LLMs for high-accuracy judgments.", + "text_level": -1, + "page_idx": 4, + "pdf_id": 73, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "To address this, we employ a gradient-based ER method, operating on a single document (simplified as the clean ER), which performs ER incrementally as each new entity 𝑣 𝑛 is extracted. This transforms the quadratic batch problem into a simpler, repeated lookup task: determining where the single new entity 𝑣 𝑛 fits among the already-processed entities in the database. This incremental process yields two distinct, observable scoring patterns when 𝑣 𝑛 is reranked against its 𝑡𝑜𝑝 _ 𝑘 most relevant candidates:", + "text_level": -1, + "page_idx": 4, + "pdf_id": 74, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "5", + "text_level": -1, + "page_idx": 4, + "pdf_id": 75, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "Algorithm 1: Gradient-based entity resolution", + "text_level": 0, + "page_idx": 4, + "pdf_id": 76, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "Input: KG 𝐺 , New entity 𝑣 𝑛 , Rerank model R , Entity vector database 𝐷𝐵 , Vector search number 𝑡𝑜𝑝 _ 𝑘 , threshold of gradient 𝑔", + "text_level": -1, + "page_idx": 4, + "pdf_id": 77, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "// Vector Search 𝑡𝑜𝑝 _ 𝑘 relevant entities in 𝐷𝐵 . 1 𝐸 𝑐 ← Search( 𝐷𝐵, 𝑣 𝑛 , 𝑡𝑜𝑝 _ 𝑘 ); 2 S ← R( 𝐸 , 𝑣 𝑐 𝑛 ) ; // Sort all candidate entities by rerank scores. 3 Sort( 𝐸 , 𝑐 S ); 4 𝑠𝑐𝑜𝑟𝑒 ← S[ ] 0 , 𝑆𝑒𝑙 ← 𝐸 𝑐 [ 0 ; ] // Gradient select similar entities. 5 for each remain entity 𝑣 𝑐 ∈ 𝐸 𝑐 \\ { 𝐸 𝑐 [ 0 ] } do 6 if S[ 𝑣 𝑐 ] > 𝑠𝑐𝑜𝑟𝑒 / 𝑔 then 7 𝑆𝑒𝑙 ← 𝑆𝑒𝑙 ∪ { 𝑣 𝑐 } , 𝑠𝑐𝑜𝑟𝑒 ← S[ 𝑣 𝑐 ] ; 8 else break; // Merge entity or add new entity. 9 if length( 𝑆𝑒𝑙 ) = length( 𝐸 𝑐 ) then 10 𝐺 ← AddNewEntity( 𝐺, 𝑣 𝑛 ), 𝐷𝐵 ← AddNew( 𝐷𝐵, 𝑣 𝑛 ); 11 else 12 if length( 𝑆𝑒𝑙 ) = 1 then 𝑣 𝑠𝑒𝑙 ← 𝑆𝑒𝑙 [ 0 ; ] 13 else 𝑣 𝑠𝑒𝑙 ← LLMSelect( 𝑆𝑒𝑙 ); 14 𝐺 ← MergeEntity( 𝐺, 𝑣 𝑛 , 𝑣 𝑠𝑒𝑙 ), 𝐷𝐵 ← Update( 𝐷𝐵, 𝑣 𝑠𝑒𝑙 , 𝑣 𝑛 ); 15 return 𝐺,𝐷𝐵 ;", + "text_level": -1, + "page_idx": 4, + "pdf_id": 78, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· Case A: New Entity. If 𝑣 𝑛 is a new conceptual entity, its relevance scores against all existing entities will be uniformly low, showing no significant gradient or discriminative pattern.", + "text_level": -1, + "page_idx": 4, + "pdf_id": 79, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· Case B: Existing Entity. If 𝑣 𝑛 is an alias of an existing entity, its scores will show a high relevance to the true match (or a small set of equivalent aliases). Due to the reranker's inherent discriminative limitations, this initial high-relevance set might occasionally contain multiple similar entities. This high-relevance set is then typically followed by a sharp decline (a large 'gradient') before transitioning to a gradual slope of irrelevant entities.", + "text_level": -1, + "page_idx": 4, + "pdf_id": 80, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "Our Gradient-based ER algorithm is designed precisely to detect this sharp decline (characteristic of Case B), allowing us to efficiently isolate the high-relevance set. Subsequently, an LLM is utilized for finer-grained distinction when multiple similar entities are identified within this set, differentiating it from the 'no gradient' scenario (Case A) without quadratic comparisons.", + "text_level": -1, + "page_idx": 4, + "pdf_id": 81, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Algorithm 1 shows the above entity resolution process. For a new entity 𝑣 𝑛 , we first retrieve its 𝑡𝑜𝑝 _ 𝑘 candidates 𝐸 𝑐 from the vector database 𝐷𝐵 , which are then reranked by R against 𝑣 𝑛 and sorted based on their scores S (Lines 1-3). We initialize the selection set 𝑆𝑒𝑙 with the top-scoring candidate 𝐸 𝑐 [ 0 ] and set the initial score to S[ ] 0 (Line 4). We then iterate through the remaining sorted candidates (Lines 5-8). The core logic checks if the current score S[ 𝑣 𝑐 ] is still within the gradient threshold 𝑔 of the previous score (i.e., S[ 𝑣 𝑐 ] > score / 𝑔 ). If the score drop is gentle (passes the check), the candidate 𝑣 𝑐 is added to 𝑆𝑒𝑙 , and score is updated (Lines 7-8); otherwise, the loop breaks (Line 8) as soon as a sharp score drop is detected. Finally, the algorithm makes its decision (Lines 9-14). If the selection set 𝑆𝑒𝑙 is identical to 𝐸 𝑐 , this indicates that all candidates passed the gradient check. This corresponds to Case A , where the scores lacked discriminative power (i.e., 𝑣 𝑛 is equally dissimilar to", + "text_level": -1, + "page_idx": 4, + "pdf_id": 82, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "all candidates), so 𝑣 𝑛 is added as a new entity (Line 9-10). Conversely, if a gradient was found (i.e., 𝑙𝑒𝑛𝑔𝑡ℎ 𝑆𝑒𝑙 ( ) < 𝑙𝑒𝑛𝑔𝑡ℎ ( 𝐸 𝑐 ) ), this signals Case B . We then select the canonical entity 𝑣 𝑠𝑒𝑙 from 𝑆𝑒𝑙 , using an LLM (Line 13) if the reranker identifies multiple aliases, and merge 𝑣 𝑛 with it (Lines 12-14). The updated 𝐺 and 𝐷𝐵 are then returned (Line 15).", + "text_level": -1, + "page_idx": 5, + "pdf_id": 83, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "For instance, considering the example in Figure 2, when the new entity 𝑒 9 is processed, it is first compared with existing entities in the KG. As depicted in the similarity curve (orange line), 𝑒 9 shows high similarity with 𝑒 7, followed by a sharp decline in similarity with other entities like 𝑒 6, 𝑒 8, and 𝑒 5. Our gradient-based selection process identifies 𝑒 7 as the unique, high-confidence match for 𝑒 9. Consequently, 𝑒 9 is merged with 𝑒 7, enriching the KG with consolidated information as shown in the final merged entity 𝑒 ' 7 .", + "text_level": -1, + "page_idx": 5, + "pdf_id": 84, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Graph-Tree Link (GT-Link). The GT-Link 𝑀 is formalized to complete the BookIndex 𝐵 = ( 𝑇,𝐺, 𝑀 ) . As described in the KG Construction phase, the origin tree node 𝑛 𝑖 is recorded for every newly extracted entity 𝑣 𝑖 . GT-Link is then refined during entity resolution: when an entity 𝑣 𝑛 is merged into a canonical entity 𝑣 𝑠𝑒𝑙 , the origin node set of 𝑣 𝑠𝑒𝑙 is updated to include all origin nodes previously associated with 𝑣 𝑛 . This aggregation process creates the final mapping 𝑀 : 𝑉 → P( 𝑁 ) , which bi-directionally links the entities in 𝐺 to the set of their structural locations (nodes) in 𝑇 .", + "text_level": -1, + "page_idx": 5, + "pdf_id": 85, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "5 AGENT-BASED RETRIEVAL", + "text_level": 0, + "page_idx": 5, + "pdf_id": 86, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "Real-world document queries are often complex, necessitating operations like modal type filtering, semantic selection, and multi-hop reasoning. To address this, we propose an agent-based approach in BookRAG, which intelligently plans and executes operations on the BookIndex. We first introduce the overall workflow and present two core mechanisms: Agent-based Planning , which formulates the strategy, and the Structured Execution , which includes the retrieval process under the principles of IFT and generation.", + "text_level": -1, + "page_idx": 5, + "pdf_id": 87, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "5.1 Overall Workflow", + "text_level": 0, + "page_idx": 5, + "pdf_id": 88, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "The overall workflow of agent-based retrieval, illustrated in Figure 3, follows a three-stage pipeline designed to address users' queries systematically.", + "text_level": -1, + "page_idx": 5, + "pdf_id": 89, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "1. Agent-based Planning. BookRAG first performs Classification & Plan . This stage aims to distinguish simple keyword-based queries from reasoning questions that require decomposition and analysis. For instance, a query like 'How does Transformer differ from RNNs in handling long-range dependencies?' cannot be", + "text_level": -1, + "page_idx": 5, + "pdf_id": 90, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "Figure 3: The general workflow of agent-based retrieval in BookRAG, which contains agent-based planning, retrieval, and generation processes.", + "text_level": -1, + "page_idx": 5, + "pdf_id": 91, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "image", + "text": "", + "text_level": -1, + "page_idx": 5, + "pdf_id": 92, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-3.png", + "image_caption": [ + "cref='#/texts/89'" + ], + "image_footnote": [], + "middle_json": { + "docling_label": "picture" + } + }, + { + "type": "text", + "text": "solved by retrieving from a single keyword. Therefore, the planning stage first performs query classification . Based on this classification and a predefined set of operators designed for the BookIndex, it generates a specific operators plan that effectively guides the retrieval and generation strategies.", + "text_level": -1, + "page_idx": 5, + "pdf_id": 93, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "2. Retrieval Process. Guided by the operator plan, the retrieval process executes Scent/Filter-based Retrieval . This stage navigates the BookIndex 𝐵 = ( 𝑇,𝐺, 𝑀 ) , either utilizing a scent-based retrieval principle (e.g., following relevant entities in 𝐺 ) to find information, or employing various filters (e.g., modal type) to refine the selection. After reasoning, BookRAG gets the retrieval set of highly relevant information blocks from the BookIndex.", + "text_level": -1, + "page_idx": 5, + "pdf_id": 94, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "3. Generation Process. Finally, all retrieved information enters the generation stage for Analysis & Merging . This stage synthesizes these (often fragmented) pieces of evidence, performs final analysis, and formulates a coherent response.", + "text_level": -1, + "page_idx": 5, + "pdf_id": 95, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "5.2 Agent-based Planning", + "text_level": 0, + "page_idx": 5, + "pdf_id": 96, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "The planning stage is the core of BookRAG, designed to intelligently navigate our BookIndex 𝐵 = ( 𝑇,𝐺, 𝑀 ) . To support flexible retrieval, we define four types of operators: Formulator, Selector, Reasoner, and Synthesizer. These operators can be arbitrarily combined to form tailored execution pipelines, each with adjustable parameters. BookRAG dynamically configures and assembles these operators to adapt to the specific requirements of different query categories. This process involves two sequential steps: first, the agent performs", + "text_level": -1, + "page_idx": 5, + "pdf_id": 97, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Table 2: Three common query categories addressed in BookRAG.", + "text_level": -1, + "page_idx": 5, + "pdf_id": 98, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "table", + "text": "", + "text_level": -1, + "page_idx": 5, + "pdf_id": 99, + "img_path": "", + "table_caption": [ + "cref='#/texts/95'" + ], + "table_footnote": [], + "table_body": "| Query Category | Description | Core Task | Example Query |\n|--------------------|-------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------|\n| Single-hop | Queries with a single, distinct information target. | Scent-based Retrieval : Retrieve content related to a specific entity or section. | What is the definition of Information Scent? |\n| Multi-hop | Queries that require synthesizing information from multiple blocks, often by decomposing into sub-problems. | Decomposing & Merging : Decompose into sub-problems, retrieve for each, and synthesize the final answer. | How does Transformer differ from RNNs in handling long-range dependencies? |\n| Global Aggregation | Queries that require filtering across the entire document and performing calculations. | Filter & Aggregation : Apply filters across the document & perform aggregation operations (e.g., Count, Sum). | How many figures related to IFT are in Section 4? |", + "middle_json": { + "docling_label": "table" + } + }, + { + "type": "text", + "text": "6", + "text_level": -1, + "page_idx": 5, + "pdf_id": 100, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "(a) Operator Set", + "text_level": 0, + "page_idx": 6, + "pdf_id": 101, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "Figure 4: The BookRAG Operator Library and an Execution Example from MMLongBench dataset: (a) a visual depiction of the four operator types (Formulator, Selector, Reasoner, and Synthesizer) and (b) an execution trace for a 'Single-hop' query, demonstrating the agent-based planning and step-by-step operator execution.", + "text_level": -1, + "page_idx": 6, + "pdf_id": 102, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "image", + "text": "", + "text_level": -1, + "page_idx": 6, + "pdf_id": 103, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-4.png", + "image_caption": [ + "cref='#/texts/98'" + ], + "image_footnote": [], + "middle_json": { + "docling_label": "picture" + } + }, + { + "type": "text", + "text": "Query Classification to determine the appropriate solution strategy, then generates a specific Operator Plan .", + "text_level": -1, + "page_idx": 6, + "pdf_id": 104, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "· Query Classification . To enable agent strategy selection, we focus on three representative query categories defined by their intrinsic complexity and operational demands (Table 2): Single-hop , Multi-hop , and Global Aggregation . This classification is crucial because each category requires a different solution strategy. For instance, a Single-hop query typically requires a single piece of information retrieved via a Scent-based Retrieval operation. In contrast, a Global Aggregation query often necessitates analyzing content under multiple filtering conditions, usually involving a sequence of Filter & Aggregation operations across various parts of the document. Furthermore, BookRAG is designed to be extensible, allowing for the resolution of a broader range of query types by integrating additional operators.", + "text_level": -1, + "page_idx": 6, + "pdf_id": 105, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· BookIndex Operators . To execute the strategies identified by classification, we designed a set of operators ( O ) tailored for the BookIndex 𝐵 = ( 𝑇,𝐺, 𝑀 ) . These operators, visually depicted in Figure 4(a) and detailed in Table 3, define the set of operations the agent can employ for diverse query categories. We group them into four types, which we describe in sequence:", + "text_level": -1, + "page_idx": 6, + "pdf_id": 106, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "❶ Formulator. These are LLM-based operators that prepare the query for execution. This category includes Decompose , which breaks a Complex query into a set of simpler, actionable sub-queries 𝑄 𝑠 . It also includes Extract , which employs an LLM to identify key entities 𝐸 𝑞 from the query text and link them to corresponding entities in the KG, 𝐺 :", + "text_level": -1, + "page_idx": 6, + "pdf_id": 107, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "equation", + "text": "𝑄 𝑠 = LLM ( 𝑃 𝐷𝑒𝑐 , 𝑞 ) = { 𝑞 , 𝑞 1 2 , . . . , 𝑞 𝑘 } (2)", + "text_level": -1, + "page_idx": 6, + "pdf_id": 108, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "equation", + "text": "𝐸 𝑞 = LLM ( 𝑃 𝐸𝑥𝑡 , 𝑞 ) = { 𝑒 1 , 𝑒 2 , . . . , 𝑒 𝑚 } (3)", + "text_level": -1, + "page_idx": 6, + "pdf_id": 109, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "Here, 𝑞 is the original user query, while 𝑃 𝐷𝑒𝑐 and 𝑃 𝐸𝑥𝑡 represent the prompts used to guide the LLM for the decomposition and extraction tasks, respectively.", + "text_level": -1, + "page_idx": 6, + "pdf_id": 110, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "❷ Selector. These operators filter or select specific content ranges from the BookIndex. Filter_Modal and Filter_Range directly apply the explicit constraints 𝐶 (e.g., modal types, page ranges) generated during the plan. Operating on the Tree 𝑇 = ( 𝑁, 𝐸 𝑇 ) , these operators produce a filtered subset 𝑁 𝑓 where the predicate 𝐶 𝑛 ( ) holds true for each node:", + "text_level": -1, + "page_idx": 6, + "pdf_id": 111, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "equation", + "text": "𝑁 𝑓 = { 𝑛 ∈ 𝑁 | 𝐶 𝑛 ( )} (4)", + "text_level": -1, + "page_idx": 6, + "pdf_id": 112, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "In contrast, Select_by_Entity and Select_by_Section target contiguous document segments by retrieving subtrees rooted at specific section nodes. This process first identifies a set of target section nodes 𝑆 target ⊂ 𝑁 at a specified depth, where 𝑆 target consists of sections either linked to entities 𝐸 𝑞 via the GT-Link 𝑀 or selected by the LLM. It then retrieves all descendants of these targets to form the selected node set 𝑁 𝑠 :", + "text_level": -1, + "page_idx": 6, + "pdf_id": 113, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "equation", + "text": "𝑁 𝑠 = GLYPH<216> 𝑠 ∈ 𝑆 target Subtree ( 𝑠 ) (5)", + "text_level": -1, + "page_idx": 6, + "pdf_id": 114, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "❸ Reasoner. These operators analyze and refine selected tree nodes. Graph_Reasoning performs multi-hop inference on a subgraph 𝐺 ' ( 𝑉 , 𝐸 ' ' ) (extracted from selected nodes 𝑁 𝑠 ) starting from entity 𝑒 . Starting from the retrieved entities, it computes an entity importance vector 𝐼 𝐺 ∈ R | 𝑉 ' | over the subgraph 𝐺 ' using the PageRank algorithm [20]. These entity scores are then mapped to the tree nodes via the GT-Link matrix 𝑀 to derive the final tree node importance scores vector 𝑆 𝐺 ∈ R | 𝑁 𝑠 | :", + "text_level": -1, + "page_idx": 6, + "pdf_id": 115, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "equation", + "text": "𝐼 𝐺 = PageRank ( 𝐺 , 𝑒 ' ) (6)", + "text_level": -1, + "page_idx": 6, + "pdf_id": 116, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "equation", + "text": "𝑆 𝐺 = 𝐼 𝐺 × 𝑀 (7)", + "text_level": -1, + "page_idx": 6, + "pdf_id": 117, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "Text_Ranker evaluates the semantic relevance of the tree node's content to the query 𝑞 , assigning a relevance score 𝑆 𝑇 to each node. Skyline_Ranker employs the Skyline operator to filter nodes based on these multiple criteria (e.g., 𝑆 𝐺 and 𝑆 𝑇 ), retaining only", + "text_level": -1, + "page_idx": 6, + "pdf_id": 118, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "7", + "text_level": -1, + "page_idx": 6, + "pdf_id": 119, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "those nodes that are not dominated by any others in terms of the specified scoring dimensions.", + "text_level": -1, + "page_idx": 7, + "pdf_id": 120, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "❹ Synthesizer. These operators are responsible for content generation. Map performs analysis on specific retrieved information segments to generate partial responses. Reduce synthesizes a final coherent answer by aggregating information from multiple sources, such as partial answers or a collection of retrieved evidence.", + "text_level": -1, + "page_idx": 7, + "pdf_id": 121, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· Operator Plan . After classifying the query ( 𝑞 ) into its category ( 𝑐 ), the agent's final task is to generate an executable plan 𝑃 . This plan is a specific sequence of operators ⟨ 𝑜 , . . . , 𝑜 1 𝑛 ⟩ selected from our library O with parameters dynamically instantiated based on 𝑞 . This process is formulated as:", + "text_level": -1, + "page_idx": 7, + "pdf_id": 122, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "equation", + "text": "𝑃 = Agent Plan ( 𝑞, 𝑐, O) (8)", + "text_level": -1, + "page_idx": 7, + "pdf_id": 123, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "The plan follows a structured workflow tailored to each category:", + "text_level": -1, + "page_idx": 7, + "pdf_id": 124, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "· Single-hop : The agent first attempts to Extract an entity. If successful, it executes a 'scent-based' selection; otherwise, it falls back to a section-based strategy. Both paths then proceed to standard reasoning and generation, denoted as 𝑃 std .", + "text_level": -1, + "page_idx": 7, + "pdf_id": 125, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "equation", + "text": "𝑃 s = ( Extract success - - - - -→ Select_by_Entity → 𝑃 std Extract fail - -→ Select_by_Section → 𝑃 std (9)", + "text_level": -1, + "page_idx": 7, + "pdf_id": 126, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "equation", + "text": "𝑃 std = ( Graph ∥ Text ) → Skyline → Reduce (10)", + "text_level": -1, + "page_idx": 7, + "pdf_id": 127, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "· Complex : The agent first decomposes the problem, applies the Single-hop workflow 𝑃 s to each sub-problem, and finally synthesizes the results.", + "text_level": -1, + "page_idx": 7, + "pdf_id": 128, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "equation", + "text": "𝑃 complex = Decompose → 𝑃 s → Map → Reduce (11)", + "text_level": -1, + "page_idx": 7, + "pdf_id": 129, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "· Global Aggregation : The workflow involves applying a sequence of filters followed by synthesis.", + "text_level": -1, + "page_idx": 7, + "pdf_id": 130, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "equation", + "text": "𝑃 global = GLYPH<214> ( Filter_Modal | Filter_Range ) → Map → Reduce (12)", + "text_level": -1, + "page_idx": 7, + "pdf_id": 131, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "Here, the symbol ˛ denotes the nested composition of filters, applying either a modal or range filter at each step.", + "text_level": -1, + "page_idx": 7, + "pdf_id": 132, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "5.3 Structured Execution", + "text_level": 0, + "page_idx": 7, + "pdf_id": 133, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "Following the planning stage, BookRAG executes the generated workflow 𝑃 . This execution phase embodies the cognitive principles of Information Foraging Theory (IFT), effectively translating abstract textual queries into concrete operations. Specifically, the Selector operators mirror the act of 'navigating to information patches,' narrowing the vast document space down to relevant scopes. Subsequently, the Reasoner operators perform 'sensemaking within patches,' where they analyze and refine the information within these focused scopes. Finally, the Synthesizer generates the answer based on the processed evidence. This design minimizes the cost of attention by ensuring computational resources are focused solely on high-value data patches.", + "text_level": -1, + "page_idx": 7, + "pdf_id": 134, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Scent/Filter-based Retrieval. The execution begins by narrowing the scope. Aligning with IFT, Selector operators identify relevant 'patches' by following 'information scents' (e.g., key entities in question) or applying explicit filter constraints. This process reduces the full node set 𝑁 to a focused node subset 𝑁 𝑠 :", + "text_level": -1, + "page_idx": 7, + "pdf_id": 135, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "equation", + "text": "𝑁 𝑠 = Selector ( 𝑁, params sel ) (13)", + "text_level": -1, + "page_idx": 7, + "pdf_id": 136, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "This pre-selection minimizes noise and ensures that subsequent reasoning is applied only to highly relevant contexts, optimizing the foraging cost. Subsequently, within this focused scope, Reasoner operators evaluate nodes using multiple dimensions, such as graph topology and semantic relevance. We then employ the Skyline_Ranker to get the final retrieval set. Unlike fixed top𝑘 retrieval, the Skyline operator retains the Pareto frontier of nodes, retaining nodes that are valuable in at least one dimension while discarding dominated ones:", + "text_level": -1, + "page_idx": 7, + "pdf_id": 137, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "equation", + "text": "𝑁 𝑅 = Skyline_Ranker ({ 𝑆 𝐺 ( 𝑛 , 𝑆 ) 𝑇 ( 𝑛 ) | 𝑛 ∈ 𝑁 𝑠 }) (14)", + "text_level": -1, + "page_idx": 7, + "pdf_id": 138, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "Analysis & Merging Generation. In the final stage, the Synthesizer operator generates the coherent answer by aggregating the refined evidence:", + "text_level": -1, + "page_idx": 7, + "pdf_id": 139, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "equation", + "text": "𝐴 = Synthesizer ( 𝑞, 𝑁 𝑅 ) (15)", + "text_level": -1, + "page_idx": 7, + "pdf_id": 140, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "Table 3: Operators utilized in our BookRAG, categorized by their function.", + "text_level": -1, + "page_idx": 7, + "pdf_id": 141, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "table", + "text": "", + "text_level": -1, + "page_idx": 7, + "pdf_id": 142, + "img_path": "", + "table_caption": [ + "cref='#/texts/136'" + ], + "table_footnote": [], + "table_body": "| Operator | Type | Description | Parameters |\n|-------------------------------|-----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------|\n| Decompose | Formulator Formulator | Decompose a complex query into simpler, actionable sub-queries. Identify and extract key entities from the query (links to 𝐺 ). | (Self-contained) (Self-contained) |\n| Extract Filter_Modal | Selector | Filter retrieved nodes by their modal type (e.g., Table, Figure). | modal_type: str |\n| Filter_Range | Selector | Filter nodes based on a specified range (e.g., pages, section). | range: (start, end) |\n| Select_by_Entity | Selector | Selects all tree nodes ( 𝑁 ) in sections linked to a given entity ( 𝑉 ). | entity_name: str |\n| Select_by_Section | Selector | Uses an LLM to select relevant sections and selects all tree nodes ( 𝑁 ) within them. | query: str, sections: List[str] |\n| Graph_Reasoning | Reasoner | Performs multi-hop reasoning on subgraph ( 𝐺 ' ) and score tree nodes ( 𝑁 ) using graph importance and GT-links. Rerank retrieved tree nodes ( 𝑁 ) based on the relevance. | start_entity: str, subgraph: 𝐺 ' query: str |\n| Text_Reasoning Skyline_Ranker | Reasoner Reasoner | | criteria: List[str] |\n| | | Rerank nodes based on multiple criteria. | (Input: List[str]) |\n| Map | Synthesizer | Uses partially retrieved information to generate a partial answer. | |\n| Reduce | Synthesizer | Synthesizes the final answer from partial information or all sub-problem answers. | (Input: List[str]) |", + "middle_json": { + "docling_label": "table" + } + }, + { + "type": "text", + "text": "8", + "text_level": -1, + "page_idx": 7, + "pdf_id": 143, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "The Map operator performs fine-grained analysis on individual evidence blocks or sub-problems (from Decompose ) to generate intermediate insights. The Reduce operator then aggregates these partial results, such as answers to decomposed sub-queries or statistical counts from a global filter, to construct the final response. This separation ensures that the system can handle both detailed content extraction and high-level reasoning synthesis effectively.", + "text_level": -1, + "page_idx": 8, + "pdf_id": 144, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "To illustrate this end-to-end process, Figure 4(b) presents an execution trace for a 'Single-hop' query: 'What is the type of car in the Ranking Prompt example?'. In the planning phase, the agent classifies the query and generates a specific workflow. Subsequently, it identifies key entities (e.g., 'car') via Extract , retrieves relevant nodes via Select_by_Entity , refines them through reasoning and Skyline filtering, and finally synthesizes the answer using Reduce .", + "text_level": -1, + "page_idx": 8, + "pdf_id": 145, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "6 EXPERIMENTS", + "text_level": 0, + "page_idx": 8, + "pdf_id": 146, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "In our experiments, we evaluate BookRAG against several strong baseline methods, with an in-depth comparison of their efficiency and accuracy on document QA tasks.", + "text_level": -1, + "page_idx": 8, + "pdf_id": 147, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "6.1 Setup", + "text_level": 0, + "page_idx": 8, + "pdf_id": 148, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "Table 4: Datasets used in our experiments. EM and F1 denote Exact Match and F1-score, respectively.", + "text_level": -1, + "page_idx": 8, + "pdf_id": 149, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "table", + "text": "", + "text_level": -1, + "page_idx": 8, + "pdf_id": 150, + "img_path": "", + "table_caption": [ + "cref='#/texts/143'" + ], + "table_footnote": [], + "table_body": "| Dataset | MMLongBench | M3DocVQA | Qasper |\n|-------------|---------------|------------|--------------|\n| Questions | 669 | 633 | 640 |\n| Documents | 85 | 500 | 192 |\n| Avg. Pages | 42.16 | 8.52 | 10.95 |\n| Avg. Images | 25.92 | 3.51 | 3.43 |\n| Tokens | 2,816,155 | 3,553,774 | 2,265,349 |\n| Metrics | EM, F1 | EM, F1 | Accuracy, F1 |", + "middle_json": { + "docling_label": "table" + } + }, + { + "type": "text", + "text": "Datasets & Question Synthesis. We use three widely adopted benchmarking datasets for complex document QA tasks: MMLongBench [33], M3DocVQA [11], and Qasper [14]. MMLongBench is a comprehensive benchmark designed to evaluate QA capabilities on long-form documents, covering diverse categories such as guidebooks, financial reports, and industry files. M3DocVQA is an open-domain benchmark designed to test RAG systems on a diverse collection of HTML-type documents sourced from Wikipedia pages 1 . Qasper is a QA dataset focused on scientific papers, where questions require retrieving evidence from the entire document. We filtered the datasets to remove documents with low clarity or incoherent structures. To address the scarcity of global-level questions in the original benchmarks, we synthesize additional QA pairs by having an LLM generate global questions from selected document elements (e.g., tables or figures). These questions are then answered and meticulously refined by human annotators via an outsourcing process, with this additional QA pairs constituting less than 20% of our final QA pairs. The statistics of these datasets are presented in Table 4.", + "text_level": -1, + "page_idx": 8, + "pdf_id": 151, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "1 https://www.wikipedia.org/", + "text_level": -1, + "page_idx": 8, + "pdf_id": 152, + "middle_json": { + "docling_label": "footnote" + } + }, + { + "type": "text", + "text": "9", + "text_level": -1, + "page_idx": 8, + "pdf_id": 153, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "Metrics. Weadheretotheofficial metrics specified by each dataset for QA. Our primary evaluation relies on Exact Match (EM), accuracy, and token-based F1-score. To assess efficiency, we also measure time cost and token usage during the response phase. Additionally, for methods including PDF parsing, we also evaluate retrieval recall. To establish the ground truth for this, we manually label the specific PDF blocks (e.g., texts, titles, tables, images, and formulas) required to answer each question. This labeling process is guided by the metadata of ground-truth evidence provided in each dataset; we filter candidate blocks using the given modality (all datasets), page numbers (MMLongBench), and evidence statements (Qasper). Any blocks that remained non-unique after this filtering process are manually annotated. In cases where a PDF parsing error made the ground-truth item unavailable, the retrieval recall for that query is recorded as 0.", + "text_level": -1, + "page_idx": 8, + "pdf_id": 154, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Baselines. Our experiments consider three model configurations:", + "text_level": -1, + "page_idx": 8, + "pdf_id": 155, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "· Conventional RAG: These methods are the most common pipeline for document analysis, where the raw text is first extracted and then chunked into segments of a specified size. We select strong and widely used retrieval models: BM25 [44] and Vanilla RAG. We also implement Layout+Vanilla, a variant that uses document layout analysis for semantic chunking.", + "text_level": -1, + "page_idx": 8, + "pdf_id": 156, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· Graph-based RAG: These methods first extract textual content from documents and then leverage graph data during retrieval. We select RAPTOR [45] and GraphRAG [16]. Specifically, GraphRAG has two versions: GraphRAG-Global and GraphRAG-Local, which employ global and local search methods, respectively.", + "text_level": -1, + "page_idx": 8, + "pdf_id": 157, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· LayoutsegmentedRAG: This category encompasses methods that utilize layout analysis to segment document content into discrete structural units. We include: MM-Vanilla, which utilizes multi-modal embeddings for visual and textual content; a tree-based method inspired by PageIndex [39], denoted as TreeTraverse, where an LLM navigates the document's tree structure; DocETL [47], a declarative system for complex document processing; and GraphRanker, a graphbased method extended from HippoRAG [19] that applies Personalized PageRank [20] to rank the relevant nodes.", + "text_level": -1, + "page_idx": 8, + "pdf_id": 158, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "Implementation details. For a fair comparison, both BookRAG and all baseline methods are powered by a unified set of state-of-theart (SOTA) and widely adopted backbone models from the Qwen family [4, 60, 63, 64]. We employ MinerU [52] for robust document layout parsing. We set the threshold of gradient 𝑔 as 0 6, and more . details are provided in the appendix of our technical report [57]. Our source code, prompts, and detailed configurations are available at github.com/sam234990/BookRAG.", + "text_level": -1, + "page_idx": 8, + "pdf_id": 159, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "6.2 Overall results", + "text_level": 0, + "page_idx": 8, + "pdf_id": 160, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "In this section, we present a comprehensive evaluation of BookRAG, analyzing its complex QA performance, retrieval effectiveness, and query efficiency compared to state-of-the-art baselines.", + "text_level": -1, + "page_idx": 8, + "pdf_id": 161, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "· QA Performance of BookRAG . We compare the QA performance of BookRAG against three categories of baselines, as shown in Table 5. The results indicate that BookRAG achieves", + "text_level": -1, + "page_idx": 8, + "pdf_id": 162, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "Table 5: Performance comparison of different methods across various datasets for solving complex document QA tasks. The best and second-best results are marked in bold and underlined, respectively.", + "text_level": -1, + "page_idx": 9, + "pdf_id": 163, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "table", + "text": "", + "text_level": -1, + "page_idx": 9, + "pdf_id": 164, + "img_path": "", + "table_caption": [ + "cref='#/texts/156'" + ], + "table_footnote": [], + "table_body": "| Baseline Type | Method | MMLongBench | MMLongBench | M3DocVQA | M3DocVQA | Qasper | Qasper |\n|----------------------|------------------|---------------|---------------|---------------|------------|------------|------------|\n| | Method | (Exact Match) | (F1-score) | (Exact Match) | (F1-score) | (Accuracy) | (F1-score) |\n| Conventional RAG | BM25 | 18.3 | 20.2 | 34.6 | 37.8 | 38.1 | 42.5 |\n| Conventional RAG | Vanilla RAG | 16.5 | 18.0 | 36.5 | 40.2 | 40.6 | 44.4 |\n| | Layout + Vanilla | 18.1 | 19.8 | 36.9 | 40.2 | 40.7 | 44.6 |\n| Graph-based RAG | RAPTOR | 21.3 | 21.8 | 34.3 | 37.3 | 39.4 | 44.1 |\n| Graph-based RAG | GraphRAG-Local | 7.7 | 8.5 | 23.7 | 25.6 | 35.9 | 39.2 |\n| Graph-based RAG | GraphRAG-Global | 5.3 | 5.6 | 20.2 | 22.0 | 24.0 | 24.1 |\n| Layout segmented RAG | MM-Vanilla | 6.8 | 8.4 | 25.1 | 27.7 | 27.9 | 29.3 |\n| Layout segmented RAG | Tree-Traverse | 12.7 | 14.4 | 33.3 | 36.2 | 27.3 | 32.1 |\n| Layout segmented RAG | GraphRanker | 21.2 | 22.7 | 43.0 | 47.8 | 32.9 | 37.6 |\n| Layout segmented RAG | DocETL | 27.5 | 28.6 | 40.9 | 43.3 | 42.3 | 50.4 |\n| Our proposed | BookRAG | 43.8 | 44.9 | 61.0 | 66.2 | 55.2 | 61.1 |", + "middle_json": { + "docling_label": "table" + } + }, + { + "type": "text", + "text": "state-of-the-art performance across all datasets, substantially outperforming the top-performing baseline by 18.0% in Exact Match on M3DocVQA. Layout + Vanilla consistently outperforms Vanilla RAG, confirming that layout parsing preserves essential structural information for better retrieval. Besides, the suboptimal results of Tree-Traverse and GraphRanker highlight the limitations of relying solely on hierarchical navigation or graph-based reasoning, which often miss cross-sectional context or drift into irrelevant scopes. In contrast, BookRAG's superiority stems from the synergy of its unified Tree-Graph BookIndex and Agent-based Planning. By effectively classifying queries and configuring optimal workflows, our BookRAG overcomes limitations of context fragmentation and static query workflow within existing baselines, ensuring precise evidence retrieval and accurate generation.", + "text_level": -1, + "page_idx": 9, + "pdf_id": 165, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Table 6: Retrieval recall comparison among layout-based methods. The best and second-best results are marked in bold and underlined, respectively.", + "text_level": -1, + "page_idx": 9, + "pdf_id": 166, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "table", + "text": "", + "text_level": -1, + "page_idx": 9, + "pdf_id": 167, + "img_path": "", + "table_caption": [ + "cref='#/texts/158'" + ], + "table_footnote": [], + "table_body": "| Method | MMLongBench | M3DocVQA | Qasper |\n|------------------|---------------|------------|----------|\n| Layout + Vanilla | 26.3 | 33.8 | 33.5 |\n| MM-Vanilla | 7.5 | 19.7 | 14.9 |\n| Tree-Traverse | 11.2 | 19.5 | 14.5 |\n| GraphRanker | 26.4 | 44.5 | 28.6 |\n| BookRAG | 57.6 | 71.2 | 63.5 |", + "middle_json": { + "docling_label": "table" + } + }, + { + "type": "text", + "text": "· Retrieval performance of BookRAG. To validate our retrieval design, we evaluate the retrieval recall of BookRAG against other layout-based baselines on the ground-truth layout blocks. The experimental results demonstrate that BookRAG achieves the highest recall across all datasets, notably reaching 71.2% on M3DocVQA and significantly outperforming the next best baseline (GraphRanker, max44.5%). This performance advantage stems from our IFT-inspired Selector → Reasoner workflow: the Agent-based Planning first classifies the query, enabling the Selector to narrow the search to a precise information patch , followed by the Reasoner's analysis. Crucially, after the Skyline_Ranker process, the average number of retained nodes is 9.87, 6.86, and 8.6 across the three datasets,", + "text_level": -1, + "page_idx": 9, + "pdf_id": 168, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "which is comparable to the standard top𝑘 ( 𝑘 = 10) setting, ensuring high-quality retrieval without inflating the candidate size.", + "text_level": -1, + "page_idx": 9, + "pdf_id": 169, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Figure 5: Comparison of query efficiency.", + "text_level": -1, + "page_idx": 9, + "pdf_id": 170, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "image", + "text": "", + "text_level": -1, + "page_idx": 9, + "pdf_id": 171, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-5.png", + "image_caption": [ + "cref='#/texts/161'" + ], + "image_footnote": [], + "middle_json": { + "docling_label": "picture" + } + }, + { + "type": "text", + "text": "· Efficiency of BookRAG. Wefurther evaluate the efficiency in terms of query time and token consumption, as illustrated in Figure 5. Overall, BookRAG maintains time and token costs comparable to existing Graph-based RAG methods. While purely text-based RAG approaches generally exhibit lower latency and token usage due to the absence of VLM processing for images, BookRAG maintains a balanced efficiency among multi-modal methods. In terms of token usage, BookRAG reduces consumption by an order of magnitude compared to the strongest baseline, DocETL. Notably,", + "text_level": -1, + "page_idx": 9, + "pdf_id": 172, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "10", + "text_level": -1, + "page_idx": 9, + "pdf_id": 173, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "on the MMLongBench dataset, DocETL consumes over 53 million tokens, whereas BookRAG requires less than 5 million. Regarding the query latency, our method also achieves a speedup of up to 2 × compared to DocETL.", + "text_level": -1, + "page_idx": 10, + "pdf_id": 174, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "6.3 Detailed Analysis", + "text_level": 0, + "page_idx": 10, + "pdf_id": 175, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "In this section, we provide a more in-depth examination of our BookRAG. We first conduct an ablation study to validate the contribution of each component, followed by an experiment on the impact of gradient-based ER and QA performance across different query types. Furthermore, we perform a comprehensive error analysis, compare the effectiveness of our entity resolution method, and present a case study.", + "text_level": -1, + "page_idx": 10, + "pdf_id": 176, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "· Ablation study. To evaluate the contribution of each core component in BookRAG, we design several variants by removing specific components:", + "text_level": -1, + "page_idx": 10, + "pdf_id": 177, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· w/o Gradient ER: Replaces the gradient-based entity resolution with a Basic ER by merging the same-name entities.", + "text_level": -1, + "page_idx": 10, + "pdf_id": 178, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· w/o Planning: Removes the Agent-based Planning, defaulting to a static, standard workflow for all queries.", + "text_level": -1, + "page_idx": 10, + "pdf_id": 179, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· w/o Selector : Removes the Selector operators, forcing Reasoners to score all candidate nodes.", + "text_level": -1, + "page_idx": 10, + "pdf_id": 180, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· w/o Graph_Reasoning : Removes the Graph_Reasoning operator. Consequently, the Skyline_Ranker is also disabled as scoring becomes single-dimensional.", + "text_level": -1, + "page_idx": 10, + "pdf_id": 181, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· w/o Text_Reasoning : Removes the Text_Reasoning operator. Similarly, the Skyline_Ranker is disabled, relying solely on graph-based scores.", + "text_level": -1, + "page_idx": 10, + "pdf_id": 182, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "Table 7: Comparing the QA performance of different variants of BookRAG. EM and F1 denote Exact Match and F1-score, respectively.", + "text_level": -1, + "page_idx": 10, + "pdf_id": 183, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "table", + "text": "", + "text_level": -1, + "page_idx": 10, + "pdf_id": 184, + "img_path": "", + "table_caption": [ + "cref='#/texts/220'" + ], + "table_footnote": [], + "table_body": "| Method variants | MMLongBench | MMLongBench | Qasper | Qasper |\n|---------------------|---------------|---------------|----------|----------|\n| | EM | F1 | Accuracy | F1 |\n| BookRAG (Full) | 43.8 | 44.9 | 55.2 | 61.1 |\n| w/o gradient ER | 40.1 | 42.8 | 48.9 | 57.3 |\n| w/o Planning | 30.8 | 33.2 | 40.9 | 48.5 |\n| w/o Selector | 42.5 | 43.1 | 52.5 | 59.1 |\n| w/o Graph_Reasoning | 39.8 | 41.5 | 51.4 | 58.4 |\n| w/o Text_Reasoning | 39.0 | 40.3 | 47.2 | 52.5 |", + "middle_json": { + "docling_label": "table" + } + }, + { + "type": "text", + "text": "The first variant evaluates the impact of KG quality on retrieval performance. The second and third variants assess the necessity of our Agent-based Planning and IFT-inspired selection mechanism, respectively. Finally, the last two variants validate the effectiveness of our multi-dimensional reasoning and dynamic Skyline filtering strategy. As shown in Table 7, the performance degradation across all variants confirms the essential role of each module in BookRAG. Specifically, the performance drop in the w/o Gradient ER variant highlights the critical role of a high-quality, connectivity-rich KG in supporting effective reasoning. Removing the Planning mechanism results in the most significant performance loss, confirming that a static workflow is insufficient for handling diverse types of queries. The w/o Selector variant, while maintaining competitive accuracy, incurs a prohibitive computational cost ( > 2 × tokens on Qasper),", + "text_level": -1, + "page_idx": 10, + "pdf_id": 185, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "11", + "text_level": -1, + "page_idx": 10, + "pdf_id": 186, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "validating the efficiency of our IFT-inspired \"narrow-then-reason\" strategy.", + "text_level": -1, + "page_idx": 10, + "pdf_id": 187, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Figure 6: Comparison of graph statistics. Values are normalized to the Basic setting (Baseline=1.0). Absolute values for Basic are annotated. Note that density values are abbreviated (e.g., 3.6E-3 denotes 3 6 . × 10 -3 ).", + "text_level": -1, + "page_idx": 10, + "pdf_id": 188, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "image", + "text": "", + "text_level": -1, + "page_idx": 10, + "pdf_id": 189, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-6.png", + "image_caption": [ + "cref='#/texts/224'" + ], + "image_footnote": [], + "middle_json": { + "docling_label": "picture" + } + }, + { + "type": "text", + "text": "· Impact of Gradient-based Entity Resolution. To evaluate the quality of our constructed KG, we compare the graph statistics of our Gradient-based ER against a Basic KG construction. The Basic setting employs simple exact name matching for entity merging, which is standard practice in many graph-based methods. Figure 6 presents the comparative results, normalizing the metrics (Entity count, Density, Diameter of the Largest Connected Component, and Number of Connected Components) against the Basic baseline. The results demonstrate that our Gradient-based ER significantly optimizes KG. Specifically, it reduces the number of entities (by 12%) while substantially boosting graph density (by over 20% across datasets). This structural shift indicates that our ER module effectively identifies the same conceptual entities that possess different names. Consequently, the resulting graphs are more compact and cohesive, as evidenced by the reduced diameter and fewer connected components, which mitigates graph fragmentation and facilitates better connectivity for graph reasoning.", + "text_level": -1, + "page_idx": 10, + "pdf_id": 190, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Figure 7: QA performance breakdown by different query types (Single-hop, Multi-hop, and Global). The blue bars represent Exact Match (EM) for MMLongBench and Accuracy for Qasper, while the red bars represent the F1-score.", + "text_level": -1, + "page_idx": 10, + "pdf_id": 191, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "image", + "text": "", + "text_level": -1, + "page_idx": 10, + "pdf_id": 192, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-7.png", + "image_caption": [ + "cref='#/texts/259'" + ], + "image_footnote": [], + "middle_json": { + "docling_label": "picture" + } + }, + { + "type": "text", + "text": "· QA performance under different query types. Figure 7 breaks down the performance of BookRAG across Single-hop, Multihop, and Global aggregation query types. We observe that Multihop queries generally present a greater challenge compared to Single-hop ones, resulting in a slight performance decrease. This trend reflects the inherent difficulty of retrieving and reasoning over disjoint pieces of evidence. It further validates our agent-based planning strategy, which handles different query types separately.", + "text_level": -1, + "page_idx": 10, + "pdf_id": 193, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "· Error Response analysis. To diagnose the performance bottlenecks of BookRAG, we conduct a fine-grained error analysis on 200 sampled queries from each dataset, tracing the error propagation as shown in Figure 9. We categorize failures into four types:", + "text_level": -1, + "page_idx": 10, + "pdf_id": 194, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "Figure 8: Case study of responses across different query types from MMLongBench and Qasper. CYAN TEXT highlights correct content generated by BookRAG. GRAY TEXT describes the internal process, and marks omitted irrelevant parts.", + "text_level": -1, + "page_idx": 11, + "pdf_id": 195, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "image", + "text": "", + "text_level": -1, + "page_idx": 11, + "pdf_id": 196, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-8.png", + "image_caption": [ + "cref='#/texts/282'" + ], + "image_footnote": [], + "middle_json": { + "docling_label": "picture" + } + }, + { + "type": "text", + "text": "Figure 9: Error analysis on 200 sampled queries from MMLongBench and Qasper datasets.", + "text_level": -1, + "page_idx": 11, + "pdf_id": 197, + "middle_json": { + "docling_label": "caption" + } + }, + { + "type": "image", + "text": "", + "text_level": -1, + "page_idx": 11, + "pdf_id": 198, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-9.png", + "image_caption": [ + "cref='#/texts/348'" + ], + "image_footnote": [], + "middle_json": { + "docling_label": "picture" + } + }, + { + "type": "text", + "text": "PDF Parsing, Plan, Retrieval, and Generation errors. The results identify Retrieval Error as the dominant failure mode, followed by Generation Error, reflecting the persistent challenge of locating and synthesizing multimodal evidence. Regarding Plan Error, our qualitative analysis reveals a specific failure pattern: the planner tends to over-decompose detailed single-hop queries into unnecessary multi-hop sub-tasks. This fragmentation leads to disjointed retrieval paths, effectively preventing the model from synthesizing a cohesive final answer from the scattered sub-responses.", + "text_level": -1, + "page_idx": 11, + "pdf_id": 199, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "· Case study. Figure 8 illustrates BookRAG's answering workflow across Single-hop, Multi-hop, and Global queries. The results demonstrate that by leveraging specific operators ( Select , Decompose , and Filter ), BookRAG effectively prunes search spaces. For example, in the Single-hop case, the reasoning space is significantly reduced from 134 to 24 nodes. This capability allows the system to efficiently isolate relevant evidence from noise, ensuring precise answer generation.", + "text_level": -1, + "page_idx": 11, + "pdf_id": 200, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "7 CONCLUSION", + "text_level": 0, + "page_idx": 11, + "pdf_id": 201, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "In this paper, we propose BookRAG, a novel method built upon Book Index, a document-native, structured Tree-Graph index specifically designed to capture the intricate relations of structural documents. By employing an agent-based method to dynamically configure retrieval and reasoning operators, our approach achieves state-ofthe-art performance on multiple benchmarks, demonstrating significant superiority over existing baselines in both retrieval precision and answer accuracy. In the future, we will explore an integrated document-native database system that supports data formatting, knowledge extraction, and intelligent querying.", + "text_level": -1, + "page_idx": 11, + "pdf_id": 202, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "12", + "text_level": -1, + "page_idx": 11, + "pdf_id": 203, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "REFERENCES", + "text_level": 0, + "page_idx": 12, + "pdf_id": 204, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "[1] Simran Arora, Brandon Yang, Sabri Eyuboglu, Avanika Narayan, Andrew Hojel, Immanuel Trummer, and Christopher Ré. 2023. Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes. 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ACM Transactions on Information Systems (2023). |", + "middle_json": { + "docling_label": "table" + } + }, + { + "type": "text", + "text": "14", + "text_level": -1, + "page_idx": 13, + "pdf_id": 233, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "A EXPERIMENTAL DETAILS", + "text_level": 0, + "page_idx": 14, + "pdf_id": 234, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "A.1 Evaluation Metrics", + "text_level": 0, + "page_idx": 14, + "pdf_id": 235, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "In this section, we provide the detailed definitions and calculation procedures for the metrics used in our main experiments.", + "text_level": -1, + "page_idx": 14, + "pdf_id": 236, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "A.1.1 Answer Extraction and Normalization. Standard RAG models typically generate free-form natural language responses, which may contain extraneous conversational text (e.g., 'The answer is...'). Directly comparing these raw outputs with concise ground truth labels (e.g., 'Option A' or '12.5') can lead to false negatives.", + "text_level": -1, + "page_idx": 14, + "pdf_id": 237, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Following official evaluation protocols, we employ an LLM-based extraction step to align the model output with the ground truth format before calculation. Let 𝑦 𝑟𝑎𝑤 denote the raw response generated by the RAG system and 𝑦 𝑔𝑜𝑙𝑑 denote the ground truth. We define the extracted answer ˆ as: 𝑦", + "text_level": -1, + "page_idx": 14, + "pdf_id": 238, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "equation", + "text": "ˆ 𝑦 = LLMextract ( 𝑦 𝑟𝑎𝑤 , Instruction ) (16)", + "text_level": -1, + "page_idx": 14, + "pdf_id": 239, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "where LLMextract extracts the key information (e.g., the key entity for span extraction) from 𝑦 𝑟𝑎𝑤 . We further apply standard normalization N(·) (e.g., lowercasing, removing punctuation) to both ˆ 𝑦 and 𝑦 𝑔𝑜𝑙𝑑 .", + "text_level": -1, + "page_idx": 14, + "pdf_id": 240, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "A.1.2 QA Performance Metrics. Based on the ground truth 𝑦 𝑔𝑜𝑙𝑑 and the model's response (either raw 𝑦 𝑟𝑎𝑤 or extracted ˆ), we com𝑦 pute the following metrics:", + "text_level": -1, + "page_idx": 14, + "pdf_id": 241, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Accuracy (Inclusion-based). Following prior works [3, 34, 46], we utilize accuracy as a soft-match metric. We consider a prediction correct if the normalized gold answer is included in the model's generated response, rather than requiring a strict exact match. This accounts for the uncontrollable nature of LLM generation.", + "text_level": -1, + "page_idx": 14, + "pdf_id": 242, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "equation", + "text": "Accuracy = 1 𝑁 𝑁 ∑︁ 𝑖 = 1 I (N( 𝑦 𝑔𝑜𝑙𝑑,𝑖 ) ⊆ N( 𝑦 𝑟𝑎𝑤,𝑖 )) (17)", + "text_level": -1, + "page_idx": 14, + "pdf_id": 243, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "where ⊆ denotes the substring inclusion relation.", + "text_level": -1, + "page_idx": 14, + "pdf_id": 244, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Exact Match (EM).. Unlike accuracy, Exact Match is a strict metric. It measures whether the normalized extracted answer ˆ is character𝑦 for-character identical to the ground truth.", + "text_level": -1, + "page_idx": 14, + "pdf_id": 245, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "equation", + "text": "EM = 1 𝑁 𝑁 ∑︁ 𝑖 = 1 I (N( ˆ 𝑦 𝑖 ) = N( 𝑦 𝑔𝑜𝑙𝑑,𝑖 )) (18)", + "text_level": -1, + "page_idx": 14, + "pdf_id": 246, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "F1-score. For questions requiring text span answers, we utilize the token-level F1-score between the extracted answer ˆ and the 𝑦 ground truth 𝑦 𝑔𝑜𝑙𝑑 . Treating them as bags of tokens 𝑇 ˆ 𝑦 and 𝑇 𝑔𝑜𝑙𝑑 :", + "text_level": -1, + "page_idx": 14, + "pdf_id": 247, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "equation", + "text": "𝑃 = | 𝑇 ˆ 𝑦 ∩ 𝑇 𝑔𝑜𝑙𝑑 | | 𝑇 ˆ 𝑦 | , 𝑅 = | 𝑇 ˆ 𝑦 ∩ 𝑇 𝑔𝑜𝑙𝑑 | | 𝑇 𝑔𝑜𝑙𝑑 | , F1 = 2 · 𝑃 · 𝑅 𝑃 + 𝑅 (19)", + "text_level": -1, + "page_idx": 14, + "pdf_id": 248, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "15", + "text_level": -1, + "page_idx": 14, + "pdf_id": 249, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "A.1.3 Retrieval Recall. As described in the main text, we evaluate retrieval quality based on the granularity of parsed PDF blocks (e.g., paragraphs, tables, images). For a given query 𝑞 , let B 𝑔𝑜𝑙𝑑 be the set of manually labeled ground-truth blocks required to answer 𝑞 , and B 𝑟𝑒𝑡 be the set of unique blocks retrieved by the system. The Retrieval Recall is defined as:", + "text_level": -1, + "page_idx": 14, + "pdf_id": 250, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "equation", + "text": "Recall 𝑟𝑒𝑡 = ( 0 if parsing error occurs on B 𝑔𝑜𝑙𝑑 | B 𝑟𝑒𝑡 ∩B 𝑔𝑜𝑙𝑑 | | B 𝑔𝑜𝑙𝑑 | otherwise (20)", + "text_level": -1, + "page_idx": 14, + "pdf_id": 251, + "middle_json": { + "docling_label": "formula" + } + }, + { + "type": "text", + "text": "Specifically, if a ground-truth block is lost due to PDF parsing failures (i.e., it does not exist in the candidate pool), it is considered strictly unretrievable, resulting in a recall contribution of 0 for that specific block.", + "text_level": -1, + "page_idx": 14, + "pdf_id": 252, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "A.2 Implementation details", + "text_level": 0, + "page_idx": 14, + "pdf_id": 253, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "Weimplement BookRAG in Python, utilizing MinerU [52] for robust document layout parsing. For a fair comparison, both BookRAG and all baseline methods are powered by a unified set of state-of-theart (SOTA) and widely adopted backbone models from the Qwen family [4, 60, 63, 64], including LLM, vision-language model (VLM), and embedding models. Specifically, we utilize Qwen3-8B [60] as the default LLM, Qwen2.5VL-30B [4] as the vision-language model (VLM), Qwen3-Embedding-0.6B [64] for text embedding, gme-Qwen2-VL-2B-Instruct [63] for multi-modal embedding, and Qwen3-Reranker-4B [64] for reranking. We primarily select models under the 10B parameter scale to balance efficiency and effectiveness. However, for the VLM, we adopt the 30B version, as the 8B counterpart exhibited significant performance deficits, frequently failing to answer correctly even when provided with ground-truth images. All experiments were conducted on a Linux operating system running on a high-performance server equipped with an Intel Xeon 2.0GHz CPU, 1024GB of memory, and 8 NVIDIA GeForce RTX A5000 GPUs, each with 24 GB of VRAM. Specifically, to ensure a fair comparison of efficiency, all methods were executed serially, and the reported time costs reflect this sequential processing mode. For methods involving document chunking and retrieval ranking, we standardize the chunk size at 500 tokens and set the retrieval top𝑘 to 10 to ensure consistent candidate pool sizes across baselines. For further reproducibility, our source code and detailed implementation configurations are publicly available at our repository: https://github.com/sam234990/BookRAG.", + "text_level": -1, + "page_idx": 14, + "pdf_id": 254, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "A.3 Prompts", + "text_level": 0, + "page_idx": 14, + "pdf_id": 255, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "Specifically, we present the prompts designed for agent-based query classification (Figure 10), question decomposition (Figure 11), and filter operator generation (Figure 12). Additionally, we illustrate the prompt employed for entity resolution judgment (Figure 13) during the graph construction phase.", + "text_level": -1, + "page_idx": 14, + "pdf_id": 256, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "You are an expert query analyzer. Your only task is to classify the user's question into one of three categories: \"simple\", \"complex\", or \"global\". Respond only with the specified JSON object.", + "text_level": -1, + "page_idx": 15, + "pdf_id": 257, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Category Definitions:", + "text_level": 0, + "page_idx": 15, + "pdf_id": 258, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "1. single-hop: The question can be fully answered by retrieving information from a SINGLE, contiguous location in the document (e.g., one specific paragraph, one complete table, or one figure).", + "text_level": -1, + "page_idx": 15, + "pdf_id": 259, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "-This includes questions that require reasoning or comparison, as long as all the necessary data is present within that single retrieved location.", + "text_level": -1, + "page_idx": 15, + "pdf_id": 260, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "-Example: \"What is the title of Figure 2?\"", + "text_level": -1, + "page_idx": 15, + "pdf_id": 261, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "-Example: \"How do 5% of the Latinos see economic upward mobility for their children?\" -> This is SIMPLE because the answer can be found by looking at a single chart or paragraph.", + "text_level": -1, + "page_idx": 15, + "pdf_id": 262, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "2. multi-hop: The question requires decomposition into multiple simple sub-questions, where each sub-question must be answered by a separate retrieval action.", + "text_level": -1, + "page_idx": 15, + "pdf_id": 263, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "-It often contains a nested or indirect constraint that requires a preliminary step to resolve before the main question can be answered.", + "text_level": -1, + "page_idx": 15, + "pdf_id": 264, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "-Example: \"What is the color of the personality vector...?\" -> This is COMPLEX because it requires two separate retrieval actions.", + "text_level": -1, + "page_idx": 15, + "pdf_id": 265, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "3. global: The question requires an aggregation operation (e.g., counting, listing, summarizing) over a set of items that are identified by a clear structural filter.", + "text_level": -1, + "page_idx": 15, + "pdf_id": 266, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "-Example: \"How many tables are in the document?\" -> This is GLOBAL because the process is to filter for all items of type 'table'.", + "text_level": -1, + "page_idx": 15, + "pdf_id": 267, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "User Query: query", + "text_level": -1, + "page_idx": 15, + "pdf_id": 268, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Figure 10: The prompt for query classification.", + "text_level": -1, + "page_idx": 15, + "pdf_id": 269, + "middle_json": { + "docling_label": "paragraph" + } + }, + { + "type": "text", + "text": "16", + "text_level": -1, + "page_idx": 15, + "pdf_id": 270, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "You are a query decomposition expert. You have been given a \"complex\" question. Your task is to break it down into a series of simple, atomic sub-questions and classify each one by type. **Crucial Instructions:** 1. Each ' retrieval ' sub-question MUST be a direct information retrieval task that can be answered independently by looking up a specific fact, number, or value in the document. 2. ** ' retrieval ' sub-questions MUST NOT depend on the answer of another sub-question.** They should be parallelizable. All logic for combining their results must be placed in a final ' synthesis ' question. 3. A ' synthesis ' question requires comparing, calculating, or combining the answers of the previous ' retrieval ' questions. It does **NOT** require a new lookup in the document. You MUST provide your response in a JSON object with a single key 'sub_questions', which contains a list of objects. Each object must have a 'question' (string) and a 'type' (string: \"retrieval\" or \"synthesis\"). ---EXAMPLE 1 (Correct Decomposition with Independent Lookups) ---Complex Query: \"What is the color of the personality vector in the soft-labled personality embedding matrix that with the highest Receptiviti score for User A2GBIFL43U1LKJ?\" Expected JSON Output: {{ \"sub_questions\": [ {{\"question\": \"What are all the Receptiviti scores for each personality vector for User A2GBIFL43U1LKJ?\", \"type\": \"retrieval\"}}, {{\"question\": \"What is the mapping of personality vectors to their colors in the soft-labled personality embedding matrix?\", \"type\": \"retrieval\"}}, {{\"question\": \"From the gathered scores, identify the personality vector with the highest score, and then find its corresponding color from the vector-to-color mapping.\", \"type\": \"synthesis\"}} ] }} ---END EXAMPLE 1 ------EXAMPLE 2 (Decomposition with retrieval and synthesis steps) ---Complex Query: \"According to the report, which one is greater in population in the survey? Foreign born Latinos, or the Latinos interviewed by cellphone?\" Expected JSON Output: {{ \"sub_questions\": [ {{\"question\": \"According to the report, what is the population of foreign born Latinos in the survey?\", \"type\": \"retrieval\"}}, {{\"question\": \"According to the report, what is the population of Latinos interviewed by cellphone in the survey?\", \"type\": \"retrieval\"}}, {{\"question\": \"Which of the two population counts is greater?\", \"type\": \"synthesis\"}} ] }} ---END EXAMPLE 2 --Now, perform the decomposition for the following query. User Query: query", + "text_level": -1, + "page_idx": 16, + "pdf_id": 271, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "Figure 11: The prompt for query decomposition.", + "text_level": -1, + "page_idx": 16, + "pdf_id": 272, + "middle_json": { + "docling_label": "paragraph" + } + }, + { + "type": "text", + "text": "17", + "text_level": -1, + "page_idx": 16, + "pdf_id": 273, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "You are a highly specialized AI assistant. Your only function is to analyze a \"Global Query\" and return a single, valid JSON object that specifies both the filtering steps and the final aggregation operation. You MUST NOT output any other text or explanation. ### INSTRUCTIONS \\& DEFINITIONS ### 1. **Filters**: You MUST determine the list of ' filters ' to apply. Even if the filter is for the whole document (e.g., all tables), the ' filters ' list must be present. -' filter_type ' : One of [\"section\", \"image\", \"table\", \"page\"]. -' section ' : Use for structural parts like chapters, sections, appendices, or references. -' image ' : Use for visual elements like figures, images, pictures, or plots. -' table ' : Use for tabular data. -' page ' : Use for specific page numbers or ranges. -' filter_value ' : (Optional) Can be provided for \"section\" (e.g., a section title) or \"page\" (e.g., '3-10' or '5'). **For \"image\" or \"table\", this value MUST be null.** 2. **Operation**: Determine the final aggregation operation. -' operation ' : One of [\"COUNT\", \"LIST\", \"SUMMARIZE\", \"ANALYZE\"]. ### EXAMPLES OF YOUR TASK ### User: \"How many figures are in this paper from Page 3 to Page 10?\" Assistant: {{\"filters\": [{{\"filter_type\": \"page\", \"filter_value\": \"3-10\"}}, {{\"filter_type\": \"image\"}}], \"operation\": \"COUNT\"}} User: \"Summarize the discussion about 'data augmentation' in the 'Methodology' section.\" Assistant: {{\"filters\": [{{\"filter_type\": \"section\", \"filter_value\": \"Methodology\"}}], \"operation\": \"SUMMARIZE\"}} User: \"How many chapters are in this report?\" Assistant: {{\"filters\": [{{\"filter_type\": \"section\"}}], \"operation\": \"COUNT\"}} ### YOUR CURRENT TASK ### User: \"{query}\" User Query: query", + "text_level": -1, + "page_idx": 17, + "pdf_id": 274, + "middle_json": { + "docling_label": "code" + } + }, + { + "type": "text", + "text": "Figure 12: The prompt for Filter operator generation.", + "text_level": -1, + "page_idx": 17, + "pdf_id": 275, + "middle_json": { + "docling_label": "paragraph" + } + }, + { + "type": "text", + "text": "18", + "text_level": -1, + "page_idx": 17, + "pdf_id": 276, + "middle_json": { + "docling_label": "page_footer" + } + }, + { + "type": "text", + "text": "-Goal-", + "text_level": 0, + "page_idx": 18, + "pdf_id": 277, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "You are an expert Entity Resolution Adjudicator. Your task is to determine if a \"New Entity\" refers to the exact same real-world concept as one of the \"Candidate Entities\" provided from a knowledge graph. Your output must be a JSON object containing the ID of the matching candidate (or -1) and a brief explanation for your decision. -Context-", + "text_level": -1, + "page_idx": 18, + "pdf_id": 278, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "You will be given one \"New Entity\" recently extracted from a text. You will also be given a list of \"Candidate Entities\" that are semantically similar, retrieved from an existing knowledge base. Each candidate has a unique ' id ' for you to reference.", + "text_level": -1, + "page_idx": 18, + "pdf_id": 279, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "---", + "text_level": -1, + "page_idx": 18, + "pdf_id": 280, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "-Core Task & Rules-", + "text_level": 0, + "page_idx": 18, + "pdf_id": 281, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "1. **Analyze the \"New Entity\"**: Carefully read its name, type, and description to understand what it is.", + "text_level": -1, + "page_idx": 18, + "pdf_id": 282, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "2. **Field-by-Field Adjudication**: To determine a match, you must evaluate each field with a specific focus:", + "text_level": -1, + "page_idx": 18, + "pdf_id": 283, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "* ** ' entity_name ' (High Importance):** The names must be extremely similar, a direct abbreviation (e.g., \"LLM\" vs. \"Large Language Model\"), or a well-known alias. **If the names represent distinct, parallel concepts (like \"Event Detection\" and \"Named Entity Recognition\"), they are NOT a match, even if their descriptions are very similar.**", + "text_level": -1, + "page_idx": 18, + "pdf_id": 284, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "* ** ' entity_type ' (Medium Importance):** The types do not need to be identical, but they must be closely related and compatible (e.g., ' COMPANY ' and ' ORGANIZATION ' could describe the same entity).", + "text_level": -1, + "page_idx": 18, + "pdf_id": 285, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "* ** ' description ' (Contextual Importance):** The descriptions may differ as they are often extracted from different parts of a document. Your task is to look past surface-level text similarity and determine if they fundamentally describe the **same underlying object or concept**.", + "text_level": -1, + "page_idx": 18, + "pdf_id": 286, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "3. **Be Strict and Conservative**: Your standard for a match must be very high. An incorrect merge can corrupt the knowledge graph. A missed merge is less harmful.", + "text_level": -1, + "page_idx": 18, + "pdf_id": 287, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "* Surface-level similarities are not enough. The underlying concepts must be identical.", + "text_level": -1, + "page_idx": 18, + "pdf_id": 288, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "* For example, \"Apple\" (the fruit) and \"Apple Inc.\" (the company) are NOT a match.", + "text_level": -1, + "page_idx": 18, + "pdf_id": 289, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "* **When in doubt, you MUST output -1.**", + "text_level": -1, + "page_idx": 18, + "pdf_id": 290, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "* **Assume No Match by Default**: In a large knowledge graph, most new entities are genuinely new. You should start with the assumption that the \"New Entity\" is unique. You must find **strong, convincing evidence** across all fields, especially the ' entity_name ' , to overturn this assumption and declare a match.", + "text_level": -1, + "page_idx": 18, + "pdf_id": 291, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "4. **Format the Output**: **You must provide your answer in a valid JSON format. The JSON object should contain two keys:**", + "text_level": -1, + "page_idx": 18, + "pdf_id": 292, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "* ' select_id ' : An integer. The ' id ' of the candidate you've determined to be an exact match. If no exact match is", + "text_level": -1, + "page_idx": 18, + "pdf_id": 293, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "found, this value MUST be ' -1 ' .", + "text_level": -1, + "page_idx": 18, + "pdf_id": 294, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "* ' explanation ' : A brief, one-sentence string explaining your reasoning. For a match, explain why they are the same entity. For no match, explain the key difference.", + "text_level": -1, + "page_idx": 18, + "pdf_id": 295, + "middle_json": { + "docling_label": "list_item" + } + }, + { + "type": "text", + "text": "---", + "text_level": -1, + "page_idx": 18, + "pdf_id": 296, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "-Output Schema & Format-", + "text_level": 0, + "page_idx": 18, + "pdf_id": 297, + "middle_json": { + "docling_label": "section_header" + } + }, + { + "type": "text", + "text": "Your response MUST be a single, valid JSON object that adheres to the following schema. Do not include any other text, explanation, or markdown formatting like ''' json.", + "text_level": -1, + "page_idx": 18, + "pdf_id": 298, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "''' json {{ \"select_id\": \"integer\", \"explanation\": \"string\" }} ''' ----Example-### Example 1: Match Found ### Example 2: No Match Found -----Task Execution-", + "text_level": -1, + "page_idx": 18, + "pdf_id": 299, + "middle_json": { + "docling_label": "code" + } + }, + { + "type": "text", + "text": "Now, perform the selection task based on the following data. Remember to output only a single integer.", + "text_level": -1, + "page_idx": 18, + "pdf_id": 300, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "-Input Data -", + "text_level": -1, + "page_idx": 18, + "pdf_id": 301, + "middle_json": { + "docling_label": "text" + } + }, + { + "type": "text", + "text": "Figure 13: The prompt for entity resolution judgement, examples are omitted due to lack of space.", + "text_level": -1, + "page_idx": 18, + "pdf_id": 302, + "middle_json": { + "docling_label": "paragraph" + } + }, + { + "type": "text", + "text": "19", + "text_level": -1, + "page_idx": 18, + "pdf_id": 303, + "middle_json": { + "docling_label": "page_footer" + } + } +] \ No newline at end of file diff --git a/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-1.png b/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-1.png new file mode 100644 index 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The system is designed to resolve a broader range of query types and can generate correct content, which is highlighted in cyan text.", + "source_ids": [ + 96, + 7, + 79, + 82, + 83, + 85, + 181, + 124 + ], + "id": "Name: bookrag\nType: SOFTWARE" + }, + { + "entity_name": "github", + "entity_type": "ORGANIZATION", + "description": "github is the platform where the source code and data for bookrag are made available", + "source_ids": [ + 7 + ], + "id": "Name: github\nType: ORGANIZATION" + }, + { + "entity_name": "sam234990", + "entity_type": "PERSON", + "description": "sam234990 is the username associated with the BookRAG repository on GitHub, where the source code is hosted.", + "source_ids": [ + 238, + 7 + ], + "id": "Name: sam234990\nType: PERSON" + }, + { + "entity_name": "source code", + "entity_type": "PRODUCT", + "description": "Source code is a digital artifact made available as part of the BookRag project, referring specifically to the implementation files of BookRag that are 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It serves as the core research problem formalized in section 3.1 and is the context in which comparisons between methods and BookRags are conducted.", + "source_ids": [ + 35, + 12, + 36 + ], + "id": "Name: complex document qa\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "cref='#/texts/14'", + "entity_type": "IMAGE", + "description": "A diagram comparing three RAG (Retrieval-Augmented Generation) architectures: Text-Only RAG, Layout Segmented RAG, and BookRAG.", + "source_ids": [ + 13 + ], + "id": "Name: cref='#/texts/14'\nType: IMAGE" + }, + { + "entity_name": "complex query", + "entity_type": "TASK_OR_PROBLEM", + "description": "A complex query is a type of query that the decompose method breaks down into simpler sub queries, often represented by a user icon with a question mark to signify the initiation of the process.", + "source_ids": [ + 98, + 13 + ], + "id": "Name: complex query\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "complex multi-page document", + "entity_type": "PRODUCT", + "description": "The source document containing multiple pages that serves as the input data.", + "source_ids": [ + 13 + ], + "id": "Name: complex multi-page document\nType: PRODUCT" + }, + { + "entity_name": "text-only rag", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Section (a) of the diagram illustrating a Retrieval-Augmented Generation approach using plain text extraction.", + "source_ids": [ + 13 + ], + "id": "Name: text-only rag\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "plain text extraction (ocr)", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "The first step in Text-Only RAG where text is extracted from the document images.", + "source_ids": [ + 13 + ], + "id": "Name: plain text extraction (ocr)\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "unstructured chunks", + "entity_type": "DATASET_OR_CORPUS", + "description": "The output of OCR processing, representing fragmented text segments without structural context.", + "source_ids": [ + 13 + ], + "id": "Name: unstructured chunks\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "text index (vector/graph/tree)", + "entity_type": "SYSTEM_COMPONENT", + "description": "The indexing structure created to store and organize the unstructured chunks for retrieval.", + "source_ids": [ + 13 + ], + "id": "Name: text index (vector/graph/tree)\nType: SYSTEM_COMPONENT" + }, + { + "entity_name": "fixed/ graph retrieval", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "The retrieval mechanism used to find relevant information from the index.", + "source_ids": [ + 13 + ], + "id": "Name: fixed/ graph retrieval\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "llm", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "The Large Language Model (LLM), depicted as a robot head, is a type of model within the Qwen family used in various experiments and processing pipelines. It generates final answers based on retrieved information and is guided by prompts for decomposition and extraction tasks. The LLM analyzes retrieved blocks to determine hierarchical levels and node types of document candidates, reclassifies blocks by analyzing title candidates, and generates global questions from selected document elements.", + "source_ids": [ + 101, + 13, + 141, + 238, + 18, + 57, + 59 + ], + "id": "Name: llm\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "fails on structural dependencies", + "entity_type": "TASK_OR_PROBLEM", + "description": "A limitation identified in the Text-Only RAG approach regarding its inability to handle complex structures.", + "source_ids": [ + 13 + ], + "id": "Name: fails on structural dependencies\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "layout segmented rag", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Section (b) of the diagram illustrating a RAG approach that segments content based on layout analysis.", + "source_ids": [ + 13 + ], + "id": "Name: layout segmented rag\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "layout analysis & parsing", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "The initial step in this section where the document's visual layout is analyzed and parsed.", + "source_ids": [ + 13 + ], + "id": "Name: layout analysis & parsing\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "flattened chunks", + "entity_type": "DATASET_OR_CORPUS", + "description": "Chunks derived from layout analysis but flattened, losing some hierarchical relationships.", + "source_ids": [ + 13 + ], + "id": "Name: flattened chunks\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "flattened vector index", + "entity_type": "SYSTEM_COMPONENT", + "description": "An index built upon the flattened chunks.", + "source_ids": [ + 13 + ], + "id": "Name: flattened vector index\nType: SYSTEM_COMPONENT" + }, + { + "entity_name": "fixed retrieval", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "The retrieval method used in the Layout Segmented RAG pipeline.", + "source_ids": [ + 13 + ], + "id": "Name: fixed retrieval\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "loses complex relationships", + "entity_type": "TASK_OR_PROBLEM", + "description": "A drawback noted for the Layout Segmented RAG approach due to flattening the data.", + "source_ids": [ + 13 + ], + "id": "Name: loses complex relationships\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "bookrag (natively structure-aware)", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Section (c) of the diagram presenting the proposed solution, a structure-aware RAG architecture.", + "source_ids": [ + 13 + ], + "id": "Name: bookrag (natively structure-aware)\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "hierarchical chunks", + "entity_type": "DATASET_OR_CORPUS", + "description": "Chunks that preserve the hierarchical structure of the document.", + "source_ids": [ + 13 + ], + "id": "Name: hierarchical chunks\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "bookindex", + "entity_type": "SYSTEM_COMPONENT", + "description": "A graph-based index representing the hierarchical relationships between chunks.", + "source_ids": [ + 13 + ], + "id": "Name: bookindex\nType: SYSTEM_COMPONENT" + }, + { + "entity_name": "agent-based retrieval", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "A retrieval strategy utilizing an agent to navigate the BookIndex graph effectively.", + "source_ids": [ + 13 + ], + "id": "Name: agent-based retrieval\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "accurate, structured-grounded", + "entity_type": "EVALUATION_METRIC", + "description": "The positive outcome achieved by the BookRAG system, indicating high accuracy and structural awareness.", + "source_ids": [ + 13 + ], + "id": "Name: accurate, structured-grounded\nType: EVALUATION_METRIC" + }, + { + "entity_name": "financial auditing", + "entity_type": "TASK_OR_PROBLEM", + "description": "financial auditing is a task where llms are applied but may face challenges with domain knowledge", + "source_ids": [ + 14 + ], + "id": "Name: financial auditing\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "legal compliance", + "entity_type": "TASK_OR_PROBLEM", + "description": "legal compliance is a task where llms are applied but may face challenges with domain knowledge", + "source_ids": [ + 14 + ], + "id": "Name: legal compliance\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "scientific discovery", + "entity_type": "TASK_OR_PROBLEM", + "description": "scientific discovery is a task where llms are applied but may face challenges with domain knowledge", + "source_ids": [ + 14 + ], + "id": "Name: scientific discovery\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "llms", + "entity_type": "TECHNOLOGY", + "description": "LLMs are large language models used for high accuracy judgments in entity resolution, though they may lead to missing domain knowledge and generating outdated information; however, their hallucination can be mitigated by the naive RAG technique.", + "source_ids": [ + 33, + 66, + 14 + ], + "id": "Name: llms\nType: TECHNOLOGY" + }, + { + "entity_name": "retrieval augmented generation", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Retrieval augmented generation, often abbreviated as RAG, is a method adopted to address the limitations of large language models by retrieving relevant domain knowledge. Additionally, it is the technique used by G Retriever for textual graph understanding.", + "source_ids": [ + 211, + 14 + ], + "id": "Name: retrieval augmented generation\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "rag", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "RAG, an abbreviation for retrieval augmented generation, is a method or technique introduced in section 3 alongside problem formulation and IFT. It is designed to guide large language models (LLMs) during response generation and has been proven to excel in various tasks, including question answering and data cleaning.", + "source_ids": [ + 33, + 29, + 14 + ], + "id": "Name: rag\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "enterprise scenarios", + "entity_type": "LOCATION", + "description": "enterprise scenarios are real world contexts where domain knowledge is stored in long form documents", + "source_ids": [ + 14 + ], + "id": "Name: enterprise scenarios\nType: LOCATION" + }, + { + "entity_name": "technical handbooks", + "entity_type": "PRODUCT", + "description": "technical handbooks are long form documents where domain knowledge is often stored", + "source_ids": [ + 14 + ], + "id": "Name: technical handbooks\nType: PRODUCT" + }, + { + "entity_name": "api reference manuals", + "entity_type": "PRODUCT", + "description": "api reference manuals are long form documents where domain knowledge is often stored", + "source_ids": [ + 14 + ], + "id": "Name: api reference manuals\nType: PRODUCT" + }, + { + "entity_name": "operational guidebooks", + "entity_type": "PRODUCT", + "description": "operational guidebooks are long form documents where domain knowledge is often stored", + "source_ids": [ + 14 + ], + "id": "Name: operational guidebooks\nType: PRODUCT" + }, + { + "entity_name": "books", + "entity_type": "PRODUCT", + "description": "books are a structure followed by long form documents characterized by intricate layouts and logical hierarchies", + "source_ids": [ + 14 + ], + "id": "Name: books\nType: PRODUCT" + }, + { + "entity_name": "tables of contents", + "entity_type": "PRODUCT", + "description": "tables of contents are explicit structural elements found in long form documents", + "source_ids": [ + 14 + ], + "id": "Name: tables of contents\nType: PRODUCT" + }, + { + "entity_name": "nested chapters", + "entity_type": "PRODUCT", + "description": "nested chapters are structural elements found in long form documents", + "source_ids": [ + 14 + ], + "id": "Name: nested chapters\nType: PRODUCT" + }, + { + "entity_name": "multi level sections", + "entity_type": "PRODUCT", + "description": "multi level sections are structural elements found in long form documents", + "source_ids": [ + 14 + ], + "id": "Name: multi level sections\nType: PRODUCT" + }, + { + "entity_name": "rag system", + "entity_type": "SOFTWARE", + "description": "a rag system is designed in this paper for qa over long and highly structured documents", + "source_ids": [ + 14 + ], + "id": "Name: rag system\nType: SOFTWARE" + }, + { + "entity_name": "qa", + "entity_type": "TASK_OR_PROBLEM", + "description": "QA refers to the question answering task, which is the specific problem the RAG system is designed for, the task for which official metrics are specified by each dataset, and the task being evaluated for performance in both the text and the figure.", + "source_ids": [ + 170, + 14, + 144, + 177, + 151 + ], + "id": "Name: qa\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "external sources", + "entity_type": "LOCATION", + "description": "external sources are referenced as the origin of relevant domain knowledge retrieved by rag", + "source_ids": [ + 14 + ], + "id": "Name: external sources\nType: LOCATION" + }, + { + "entity_name": "response generation", + "entity_type": "TASK_OR_PROBLEM", + "description": "response generation is the process guided by rag to produce answers", + "source_ids": [ + 14 + ], + "id": "Name: response generation\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "domain knowledge", + "entity_type": "CONCEPT", + "description": "domain knowledge is the specific information retrieved from external sources to guide llms", + "source_ids": [ + 14 + ], + "id": "Name: domain knowledge\nType: CONCEPT" + }, + { + "entity_name": "long form documents", + "entity_type": "PRODUCT", + "description": "long form documents are the type of storage for domain knowledge in enterprise scenarios", + "source_ids": [ + 14 + ], + "id": "Name: long form documents\nType: PRODUCT" + }, + { + "entity_name": "intricate layouts", + "entity_type": "SHAPE", + "description": "intricate layouts are a feature of the structure of long form documents", + "source_ids": [ + 14 + ], + "id": "Name: intricate layouts\nType: SHAPE" + }, + { + "entity_name": "logical hierarchies", + "entity_type": "CONCEPT", + "description": "logical hierarchies are a feature of the structure of long form documents", + "source_ids": [ + 14 + ], + "id": "Name: logical hierarchies\nType: CONCEPT" + }, + { + "entity_name": "this paper", + "entity_type": "BOOK", + "description": "this paper is the document where the authors aim to design an effective rag system", + "source_ids": [ + 14 + ], + "id": "Name: this paper\nType: BOOK" + }, + { + "entity_name": "rag", + "entity_type": "TECHNOLOGY", + "description": "rag refers to retrieval augmented generation approaches for document level qa mentioned in the text", + "source_ids": [ + 15 + ], + "id": "Name: rag\nType: TECHNOLOGY" + }, + { + "entity_name": "ocr", + "entity_type": "TECHNOLOGY", + "description": "ocr stands for optical character recognition a technology used to convert documents into plain text", + "source_ids": [ + 15 + ], + "id": "Name: ocr\nType: TECHNOLOGY" + }, + { + "entity_name": "graph based rag", + "entity_type": "TECHNOLOGY", + "description": "Graph based rag is a text-based retrieval-augmented generation method that extracts textual content from documents and leverages graph data as an external knowledge source during the retrieval process.", + "source_ids": [ + 147, + 15 + ], + "id": "Name: graph based rag\nType: TECHNOLOGY" + }, + { + "entity_name": "graphrag", + "entity_type": "PRODUCT", + "description": "graphrag is a representative method that constructs a knowledge graph from a textual corpus", + "source_ids": [ + 15 + ], + "id": "Name: graphrag\nType: PRODUCT" + }, + { + "entity_name": "raptor", + "entity_type": "PRODUCT", + "description": "raptor is a representative method that builds a recursive tree structure by clustering document chunks", + "source_ids": [ + 15 + ], + "id": "Name: raptor\nType: PRODUCT" + }, + { + "entity_name": "leiden community detection algorithm", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "the leiden community detection algorithm is used by graphrag to obtain hierarchical clusters", + "source_ids": [ + 15 + ], + "id": "Name: leiden community detection algorithm\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "table 1", + "entity_type": "TABLE", + "description": "Table 1 is a section containing experimental results that compares representative methods, specifically GraphRAG and Raptor, alongside BookRAG.", + "source_ids": [ + 16, + 182, + 15 + ], + "id": "Name: table 1\nType: TABLE" + }, + { + "entity_name": "document level qa", + "entity_type": "TASK_OR_PROBLEM", + "description": "document level qa is the specific task for which existing rag approaches are designed", + "source_ids": [ + 15 + ], + "id": "Name: document level qa\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "plain text", + "entity_type": "MATERIAL", + "description": "plain text is the output format produced by ocr when converting documents", + "source_ids": [ + 15 + ], + "id": "Name: plain text\nType: MATERIAL" + }, + { + "entity_name": "text based rag method", + "entity_type": "TECHNOLOGY", + "description": "text based rag methods are a category of approaches applied after ocr conversion", + "source_ids": [ + 15 + ], + "id": "Name: text based rag method\nType: TECHNOLOGY" + }, + { + "entity_name": "graph data", + "entity_type": "DATASET_OR_CORPUS", + "description": "graph data serves as an external knowledge source capturing semantic information and relational structures", + "source_ids": [ + 15 + ], + "id": "Name: graph data\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "knowledge graph", + "entity_type": "DATASET_OR_CORPUS", + "description": "A knowledge graph is a structured data repository constructed from a textual corpus by extracting entities and relations from document tree nodes.", + "source_ids": [ + 63, + 15 + ], + "id": "Name: knowledge graph\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "textual corpus", + "entity_type": "DATASET_OR_CORPUS", + "description": "a textual corpus is the source material from which graphrag constructs a knowledge graph", + "source_ids": [ + 15 + ], + "id": "Name: textual corpus\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "hierarchical clusters", + "entity_type": "TASK_OR_PROBLEM", + "description": "hierarchical clusters are the result of applying the leiden community detection algorithm", + "source_ids": [ + 15 + ], + "id": "Name: hierarchical clusters\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "summaries", + "entity_type": "PRODUCT", + "description": "summaries are generated for each community to provide a global overview of the corpus", + "source_ids": [ + 15 + ], + "id": "Name: summaries\nType: PRODUCT" + }, + { + "entity_name": "recursive tree structure", + "entity_type": "TASK_OR_PROBLEM", + "description": "a recursive tree structure is built by raptor through iterative clustering and summarization", + "source_ids": [ + 15 + ], + "id": "Name: recursive tree structure\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "document chunks", + "entity_type": "DATASET_OR_CORPUS", + "description": "document chunks are the units iteratively clustered by raptor", + "source_ids": [ + 15 + ], + "id": "Name: document chunks\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "fine grained semantic information", + "entity_type": "CONCEPT", + "description": "fine grained semantic information is a type of data captured by raptor across the corpus", + "source_ids": [ + 15 + ], + "id": "Name: fine grained semantic information\nType: CONCEPT" + }, + { + "entity_name": "high level semantic information", + "entity_type": "CONCEPT", + "description": "high level semantic information is a type of data captured by raptor across the corpus", + "source_ids": [ + 15 + ], + "id": "Name: high level semantic information\nType: CONCEPT" + }, + { + "entity_name": "representative methods", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "representative methods are the existing techniques being compared against bookrag in the text", + "source_ids": [ + 16 + ], + "id": "Name: representative methods\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "table: cref='#/texts/17'...", + "entity_type": "TABLE", + "description": "A data table described as: cref='#/texts/17'", + "source_ids": [ + 17 + ], + "id": "Name: table: cref='#/texts/17'...\nType: TABLE" + }, + { + "entity_name": "texts reference", + "entity_type": "SECTION_TITLE", + "description": "A reference identifier pointing to a specific text location within a document structure, indicated by the cref attribute '#/texts/17'.", + "source_ids": [ + 17 + ], + "id": "Name: texts reference\nType: SECTION_TITLE" + }, + { + "entity_name": "layout aware segmentation", + "entity_type": "TASK_OR_PROBLEM", + "description": "layout aware segmentation is a paradigm that parses documents into structured blocks to preserve original layout and information", + "source_ids": [ + 18 + ], + "id": "Name: layout aware segmentation\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "docetl", + "entity_type": "SOFTWARE", + "description": "Docetl is a state-of-the-art, declarative system designed for complex document processing and information extraction tasks. It provides a user-friendly interface for defining LLM-based processing pipelines and introduces an agentic framework to optimize these workflows, serving as a comprehensive Document Extraction, Transformation, and Loading tool.", + "source_ids": [ + 32, + 18, + 148, + 159 + ], + "id": "Name: docetl\nType: SOFTWARE" + }, + { + "entity_name": "multimodal retrieval", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "multimodal retrieval is a technique applied to obtain relevant content from blocks with multimodal characteristics", + "source_ids": [ + 18 + ], + "id": "Name: multimodal retrieval\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "paragraphs", + "entity_type": "TASK_OR_PROBLEM", + "description": "paragraphs are structural blocks within a document preserved by layout aware segmentation", + "source_ids": [ + 18 + ], + "id": "Name: paragraphs\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "tables", + "entity_type": "TASK_OR_PROBLEM", + "description": "tables are structural blocks within a document preserved by layout aware segmentation", + "source_ids": [ + 18 + ], + "id": "Name: tables\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "figures", + "entity_type": "TASK_OR_PROBLEM", + "description": "figures are structural blocks within a document preserved by layout aware segmentation", + "source_ids": [ + 18 + ], + "id": "Name: figures\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "equations", + "entity_type": "TASK_OR_PROBLEM", + "description": "equations are structural blocks within a document preserved by layout aware segmentation", + "source_ids": [ + 18 + ], + "id": "Name: equations\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "first paradigm", + "entity_type": "TASK_OR_PROBLEM", + "description": "the first paradigm is a method that uses fixed chunk sizes often leading to fragmented information", + "source_ids": [ + 18 + ], + "id": "Name: first paradigm\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "second paradigm", + "entity_type": "TASK_OR_PROBLEM", + "description": "the second paradigm refers to layout aware segmentation which preserves document structure", + "source_ids": [ + 18 + ], + "id": "Name: second paradigm\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "declarative interface", + "entity_type": "SOFTWARE", + "description": "the declarative interface is a feature provided by docetl that allows users to define processing pipelines", + "source_ids": [ + 18 + ], + "id": "Name: declarative interface\nType: SOFTWARE" + }, + { + "entity_name": "processing pipelines", + "entity_type": "TASK_OR_PROBLEM", + "description": "processing pipelines are sequences of operations defined by users to analyze retrieved blocks", + "source_ids": [ + 18 + ], + "id": "Name: processing pipelines\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "llm based processing pipelines", + "entity_type": "TASK_OR_PROBLEM", + "description": "llm based processing pipelines are pipelines that utilize large language models for analysis", + "source_ids": [ + 18 + ], + "id": "Name: llm based processing pipelines\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "llm powered operations", + "entity_type": "TASK_OR_PROBLEM", + "description": "llm powered operations are the specific tasks combined within the processing pipelines", + "source_ids": [ + 18 + ], + "id": "Name: llm powered operations\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "task specific optimizations", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "task specific optimizations are enhancements applied to the pipelines for specific tasks", + "source_ids": [ + 18 + ], + "id": "Name: task specific optimizations\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "fixed chunk size", + "entity_type": "MEASUREMENT", + "description": "fixed chunk size is a parameter used in the first paradigm that can cause information fragmentation", + "source_ids": [ + 18 + ], + "id": "Name: fixed chunk size\nType: MEASUREMENT" + }, + { + "entity_name": "document native structural information", + "entity_type": "CONCEPT", + "description": "document native structural information is the data retained by layout aware segmentation", + "source_ids": [ + 18 + ], + "id": "Name: document native structural information\nType: CONCEPT" + }, + { + "entity_name": "relevant content", + "entity_type": "CONCEPT", + "description": "relevant content is the information obtained through multimodal retrieval to answer queries", + "source_ids": [ + 18 + ], + "id": "Name: relevant content\nType: CONCEPT" + }, + { + "entity_name": "queries", + "entity_type": "TASK_OR_PROBLEM", + "description": "Queries are the diverse types of tasks that the system is designed to handle, serving as the questions or requests for which relevant content is retrieved. They act as the inputs that the agent-based retrieval approach dynamically classifies and for which the workflow is applied, either with or without planning.", + "source_ids": [ + 18, + 26, + 172, + 166 + ], + "id": "Name: queries\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "l1", + "entity_type": "TASK_OR_PROBLEM", + "description": "l1 is a limitation of existing works described as the failure to capture the deep connection of document structure and semantics", + "source_ids": [ + 19 + ], + "id": "Name: l1\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "l2", + "entity_type": "TASK_OR_PROBLEM", + "description": "l2 is a limitation of existing works described as the static nature of query workflows", + "source_ids": [ + 19 + ], + "id": "Name: l2\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "text based approaches", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "text based approaches are methods that cannot capture the structural layout of the document", + "source_ids": [ + 19 + ], + "id": "Name: text based approaches\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "layout segmented methods", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "layout segmented methods are approaches that preserve document structure but fail to capture relationships between different blocks", + "source_ids": [ + 19 + ], + "id": "Name: layout segmented methods\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "real world qa scenarios", + "entity_type": "EVENT", + "description": "real world qa scenarios are contexts where user queries are highly heterogeneous", + "source_ids": [ + 19 + ], + "id": "Name: real world qa scenarios\nType: EVENT" + }, + { + "entity_name": "static or manually predefined workflows", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "static or manually predefined workflows are uniform strategies applied to diverse query needs", + "source_ids": [ + 19 + ], + "id": "Name: static or manually predefined workflows\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "question decomposition", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "question decomposition is a method required for complex queries", + "source_ids": [ + 19 + ], + "id": "Name: question decomposition\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "document", + "entity_type": "PRODUCT", + "description": "A document is the object whose structure and semantics are being analyzed, represented as a sequence of pages containing content blocks organized within a logical chapter hierarchy. It serves as the source material containing logical hierarchy, entities, and relations, and is the complex object within which the bookindex captures these logical structures and entity relations. Additionally, a document is the object being organized into a hierarchical structure by the tree component.", + "source_ids": [ + 37, + 47, + 19, + 51, + 52 + ], + "id": "Name: document\nType: PRODUCT" + }, + { + "entity_name": "tables", + "entity_type": "TABLE", + "description": "Tables are document elements that serve as examples of hierarchical blocks nested within specific sections of a document and function as nodes within the document's explicit logical hierarchy. They are specific PDF blocks labeled to establish ground truth and act as the source from which large language models generate global questions.", + "source_ids": [ + 144, + 51, + 19, + 141 + ], + "id": "Name: tables\nType: TABLE" + }, + { + "entity_name": "section", + "entity_type": "SECTION_TITLE", + "description": "A section is a structural part of a document, often used as a filter type for components like chapters, where tables may be nested and where the formalization and review described in the text are contained.", + "source_ids": [ + 19, + 258, + 35 + ], + "id": "Name: section\nType: SECTION_TITLE" + }, + { + "entity_name": "user queries", + "entity_type": "TASK_OR_PROBLEM", + "description": "User queries are inputs in real-world QA scenarios that range from simple to complex, serving as the input items that an agent classifies based on intent and complexity.", + "source_ids": [ + 19, + 22 + ], + "id": "Name: user queries\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "keyword lookups", + "entity_type": "TASK_OR_PROBLEM", + "description": "keyword lookups are simple types of user queries mentioned in the text", + "source_ids": [ + 19 + ], + "id": "Name: keyword lookups\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "multi hop questions", + "entity_type": "TASK_OR_PROBLEM", + "description": "multi hop questions are complex queries requiring evidence synthesis across different document parts", + "source_ids": [ + 19 + ], + "id": "Name: multi hop questions\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "evidence", + "entity_type": "CONCEPT", + "description": "Evidence refers to information scattered across different parts of a document that is needed for multi-hop reasoning, as well as the highly relevant information located by the reasoner.", + "source_ids": [ + 19, + 22 + ], + "id": "Name: evidence\nType: CONCEPT" + }, + { + "entity_name": "hierarchical blocks", + "entity_type": "CONCEPT", + "description": "hierarchical blocks are structural elements of a document containing relationships", + "source_ids": [ + 19 + ], + "id": "Name: hierarchical blocks\nType: CONCEPT" + }, + { + "entity_name": "multi hop reasoning", + "entity_type": "TASK_OR_PROBLEM", + "description": "Multi hop reasoning is a task that relies on a high quality knowledge graph, yet its capability is limited by methods that cannot capture relationships between document blocks.", + "source_ids": [ + 19, + 21 + ], + "id": "Name: multi hop reasoning\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "overall performance", + "entity_type": "EVALUATION_METRIC", + "description": "overall performance is the metric affected by the limitations of existing methods", + "source_ids": [ + 19 + ], + "id": "Name: overall performance\nType: EVALUATION_METRIC" + }, + { + "entity_name": "complex queries", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 19 + ], + "id": "Name: complex queries\nType: UNKNOWN" + }, + { + "entity_name": "simple queries", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 19 + ], + "id": "Name: simple queries\nType: UNKNOWN" + }, + { + "entity_name": "bookrag", + "entity_type": "TECHNOLOGY", + "description": "bookrag is a retrieval augmented generation method introduced to bridge a gap in document qa tasks", + "source_ids": [ + 20 + ], + "id": "Name: bookrag\nType: TECHNOLOGY" + }, + { + "entity_name": "bookindex", + "entity_type": "PRODUCT", + "description": "Bookindex is a document-native structure and product developed by BookRag to organize information within complex documents using hierarchical and graph-based methods. Defined as a formally specified triplet structure composed of a tree (t), a graph (g), and metadata (m), it serves as the foundation for an agent-based retrieval approach by capturing logical hierarchy and entity relations. Presented in section 4, this system illustrates a final output data structure that combines organized tree and graph representations, where the graph links help complete the overall structure. As a proposed hierarchical structure-aware index, bookindex acts as the source of content ranges that selector operators filter, with specific operators designed to manipulate the tuple (t, g, m) for effective information retrieval.", + "source_ids": [ + 97, + 102, + 77, + 47, + 49, + 51, + 20, + 52, + 22, + 25, + 29 + ], + "id": "Name: bookindex\nType: PRODUCT" + }, + { + "entity_name": "document qa tasks", + "entity_type": "TASK_OR_PROBLEM", + "description": "Document QA tasks are the specific problems that BookRag and BookIndex are designed to address, serving as the benchmark on which the efficiency and accuracy of BookRag and baseline methods are compared.", + "source_ids": [ + 137, + 20 + ], + "id": "Name: document qa tasks\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "hierarchical tree structure", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "the hierarchical tree structure is a method used to preserve the document s native logical hierarchy by organizing parsed content blocks", + "source_ids": [ + 20 + ], + "id": "Name: hierarchical tree structure\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "kg", + "entity_type": "TECHNOLOGY", + "description": "kg refers to a knowledge graph constructed to capture intricate relations within document blocks", + "source_ids": [ + 20 + ], + "id": "Name: kg\nType: TECHNOLOGY" + }, + { + "entity_name": "table of contents", + "entity_type": "PRODUCT", + "description": "the table of contents is the role served by the hierarchical tree structure in organizing the document s logical hierarchy", + "source_ids": [ + 20 + ], + "id": "Name: table of contents\nType: PRODUCT" + }, + { + "entity_name": "parsed content blocks", + "entity_type": "MATERIAL", + "description": "parsed content blocks are the units of document content organized into a hierarchical tree structure", + "source_ids": [ + 20 + ], + "id": "Name: parsed content blocks\nType: MATERIAL" + }, + { + "entity_name": "fine grained entities", + "entity_type": "DATASET_OR_CORPUS", + "description": "fine grained entities are the specific data points contained within the document blocks that are captured by the knowledge graph", + "source_ids": [ + 20 + ], + "id": "Name: fine grained entities\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "relation", + "entity_type": "CONCEPT", + "description": "the relation refers to the deep connections within the document that the method aims to capture", + "source_ids": [ + 20 + ], + "id": "Name: relation\nType: CONCEPT" + }, + { + "entity_name": "tree nodes", + "entity_type": "PRODUCT", + "description": "tree nodes are the specific components of the hierarchical tree structure to which kg entities are mapped", + "source_ids": [ + 20 + ], + "id": "Name: tree nodes\nType: PRODUCT" + }, + { + "entity_name": "kg", + "entity_type": "CONCEPT", + "description": "kg refers to a knowledge graph which is a data structure used for multi hop reasoning", + "source_ids": [ + 21 + ], + "id": "Name: kg\nType: CONCEPT" + }, + { + "entity_name": "llm", + "entity_type": "PRODUCT", + "description": "llm is an example of a distinct entity name mentioned in the context of entity ambiguity", + "source_ids": [ + 21 + ], + "id": "Name: llm\nType: PRODUCT" + }, + { + "entity_name": "large language model", + "entity_type": "PRODUCT", + "description": "large language model is an example of a distinct entity name mentioned in the context of entity ambiguity", + "source_ids": [ + 21 + ], + "id": "Name: large language model\nType: PRODUCT" + }, + { + "entity_name": "gradient based entity resolution method", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "The gradient based entity resolution method is a novel approach proposed to address entity ambiguity by analyzing similarity distributions and is used to refine entity knowledge during graph construction.", + "source_ids": [ + 21, + 47 + ], + "id": "Name: gradient based entity resolution method\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "entity ambiguity", + "entity_type": "TASK_OR_PROBLEM", + "description": "entity ambiguity is a problem where distinct entities share similar names compromising the knowledge graph", + "source_ids": [ + 21 + ], + "id": "Name: entity ambiguity\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "similarity distribution", + "entity_type": "CONCEPT", + "description": "similarity distribution is the data pattern analyzed by the proposed method to identify sharp drops in scores", + "source_ids": [ + 21 + ], + "id": "Name: similarity distribution\nType: CONCEPT" + }, + { + "entity_name": "candidate entities", + "entity_type": "CONCEPT", + "description": "candidate entities are the potential matches analyzed to distinguish and merge coreferent entities", + "source_ids": [ + 21 + ], + "id": "Name: candidate entities\nType: CONCEPT" + }, + { + "entity_name": "coreferent entities", + "entity_type": "CONCEPT", + "description": "coreferent entities are distinct entities that refer to the same real world object and need to be merged", + "source_ids": [ + 21 + ], + "id": "Name: coreferent entities\nType: CONCEPT" + }, + { + "entity_name": "graph connectivity", + "entity_type": "CONCEPT", + "description": "graph connectivity is a property of the knowledge graph that is ensured by the proposed method", + "source_ids": [ + 21 + ], + "id": "Name: graph connectivity\nType: CONCEPT" + }, + { + "entity_name": "reasoning capabilities", + "entity_type": "CONCEPT", + "description": "reasoning capabilities are the skills of the system that are enhanced by the proposed method", + "source_ids": [ + 21 + ], + "id": "Name: reasoning capabilities\nType: CONCEPT" + }, + { + "entity_name": "selector", + "entity_type": "SOFTWARE", + "description": "The selector is a software component and operator within BookRag designed to narrow the search space by utilizing information scents, effectively navigating to and refining the search to a precise information patch.", + "source_ids": [ + 124, + 157, + 22 + ], + "id": "Name: selector\nType: SOFTWARE" + }, + { + "entity_name": "reasoner", + "entity_type": "SOFTWARE", + "description": "The reasoner is a software component within BookRag that functions as an operator to perform sensemaking within information patches. It locates highly relevant evidence and conducts analysis on the selected information to support its operations.", + "source_ids": [ + 124, + 157, + 22 + ], + "id": "Name: reasoner\nType: SOFTWARE" + }, + { + "entity_name": "retrieval workflows", + "entity_type": "TASK_OR_PROBLEM", + "description": "Retrieval workflows are the static processes being addressed and dynamically generated by the agent, as well as the processes configured by the approach to locate evidence.", + "source_ids": [ + 26, + 22 + ], + "id": "Name: retrieval workflows\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "search space", + "entity_type": "TASK_OR_PROBLEM", + "description": "the search space is the area narrowed down by the selector component", + "source_ids": [ + 22 + ], + "id": "Name: search space\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "information scents", + "entity_type": "CONCEPT", + "description": "Information scents are signals used by a selector to narrow down the search space, described as cues such as key entities in a question followed by selector operators.", + "source_ids": [ + 125, + 22 + ], + "id": "Name: information scents\nType: CONCEPT" + }, + { + "entity_name": "agent", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 22 + ], + "id": "Name: agent\nType: UNKNOWN" + }, + { + "entity_name": "kg", + "entity_type": "PRODUCT", + "description": "kg refers to a high quality knowledge graph identified as a key feature contributing to the system s performance", + "source_ids": [ + 23 + ], + "id": "Name: kg\nType: PRODUCT" + }, + { + "entity_name": "agent based retrieval mechanism", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "the agent based retrieval mechanism is a key feature of the system validated for its critical contributions", + "source_ids": [ + 23 + ], + "id": "Name: agent based retrieval mechanism\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "three widely adopted datasets", + "entity_type": "DATASET_OR_CORPUS", + "description": "three widely adopted datasets are the data sources used to conduct extensive experiments and validate the system", + "source_ids": [ + 23 + ], + "id": "Name: three widely adopted datasets\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "state of the art baselines", + "entity_type": "PRODUCT", + "description": "State of the art baselines are the existing systems used for comparison in the evaluation of Bookrag, serving as the reference against which Bookrag is compared in the experiments.", + "source_ids": [ + 151, + 23 + ], + "id": "Name: state of the art baselines\nType: PRODUCT" + }, + { + "entity_name": "our contributions", + "entity_type": "TASK_OR_PROBLEM", + "description": "our contributions refers to the summary of work or achievements presented in the text", + "source_ids": [ + 24 + ], + "id": "Name: our contributions\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "hierarchical tree", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "a hierarchical tree of document layout blocks is integrated by bookrag to construct the bookindex", + "source_ids": [ + 25 + ], + "id": "Name: hierarchical tree\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "kg", + "entity_type": "SOFTWARE", + "description": "kg is a knowledge graph that stores fine-grained entity relations and is used in the BookRAG method. It serves as a possible form of structured index during the offline indexing phase, where entities are linked together to form a cohesive network of information.", + "source_ids": [ + 25, + 98, + 45 + ], + "id": "Name: kg\nType: SOFTWARE" + }, + { + "entity_name": "document layout blocks", + "entity_type": "MATERIAL", + "description": "document layout blocks are the structural components of a document that are organized into a hierarchical tree", + "source_ids": [ + 25 + ], + "id": "Name: document layout blocks\nType: MATERIAL" + }, + { + "entity_name": "entity relations", + "entity_type": "CONCEPT", + "description": "Entity relations are the fine-grained connections between entities stored within a knowledge graph and also represent the intricate connections within complex documents that the bookindex is designed to capture.", + "source_ids": [ + 25, + 47 + ], + "id": "Name: entity relations\nType: CONCEPT" + }, + { + "entity_name": "agent based retrieval", + "entity_type": "TASK_OR_PROBLEM", + "description": "Agent based retrieval is a proposed approach and workflow designed to address users' queries systematically by dynamically classifying queries and configuring retrieval workflows.", + "source_ids": [ + 81, + 26 + ], + "id": "Name: agent based retrieval\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "documents", + "entity_type": "DATASET_OR_CORPUS", + "description": "documents are the source material within which highly relevant evidence is located", + "source_ids": [ + 26 + ], + "id": "Name: documents\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "evidence", + "entity_type": "TASK_OR_PROBLEM", + "description": "evidence refers to the highly relevant information sought within the documents", + "source_ids": [ + 26 + ], + "id": "Name: evidence\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "existing baselines", + "entity_type": "PRODUCT", + "description": "Existing baselines are the current methods or systems that BookRag outperforms in experiments and performance, characterized by their susceptibility to context fragmentation and reliance on static query workflows.", + "source_ids": [ + 152, + 27, + 188 + ], + "id": "Name: existing baselines\nType: PRODUCT" + }, + { + "entity_name": "complex document qa tasks", + "entity_type": "TASK_OR_PROBLEM", + "description": "Complex document QA tasks are the specific problems that BookRag is designed to solve, representing the general category of problems addressed by the three benchmarks and the specific problems being solved by the methods in the comparison.", + "source_ids": [ + 153, + 27, + 141 + ], + "id": "Name: complex document qa tasks\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "extensive experiments", + "entity_type": "EVENT", + "description": "extensive experiments are the tests conducted to evaluate the performance of bookrag", + "source_ids": [ + 27 + ], + "id": "Name: extensive experiments\nType: EVENT" + }, + { + "entity_name": "multiple benchmarks", + "entity_type": "BENCHMARK", + "description": "multiple benchmarks are the evaluation standards used in the experiments to measure performance", + "source_ids": [ + 27 + ], + "id": "Name: multiple benchmarks\nType: BENCHMARK" + }, + { + "entity_name": "state of the art performance", + "entity_type": "EVALUATION_METRIC", + "description": "state of the art performance is the high level of achievement attained by bookrag in the tasks", + "source_ids": [ + 27 + ], + "id": "Name: state of the art performance\nType: EVALUATION_METRIC" + }, + { + "entity_name": "competitive efficiency", + "entity_type": "EVALUATION_METRIC", + "description": "competitive efficiency is a metric indicating that bookrag maintains good efficiency while performing well", + "source_ids": [ + 27 + ], + "id": "Name: competitive efficiency\nType: EVALUATION_METRIC" + }, + { + "entity_name": "2", + "entity_type": "NUMBER", + "description": "2 is a numerical value appearing in the text though its specific context or meaning is not defined", + "source_ids": [ + 28 + ], + "id": "Name: 2\nType: NUMBER" + }, + { + "entity_name": "section 2", + "entity_type": "SECTION_TITLE", + "description": "section 2 is the part of the text where related work is reviewed", + "source_ids": [ + 29 + ], + "id": "Name: section 2\nType: SECTION_TITLE" + }, + { + "entity_name": "section 3", + "entity_type": "SECTION_TITLE", + "description": "section 3 introduces the problem formulation ift and rag workflow", + "source_ids": [ + 29 + ], + "id": "Name: section 3\nType: SECTION_TITLE" + }, + { + "entity_name": "ift", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "IFT is a method or technique introduced in section 3 alongside problem formulation and the RAG workflow. 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It functions as an operator within the formulator category and serves as a system to select specific sections in described processes. Additionally, LLM is utilized by treetraverse to navigate a document's tree structure and is noted for its generation nature, which is described as uncontrollable. The term is also recognized as an abbreviation for large language model in the context of entity name matching.", + "source_ids": [ + 32, + 98, + 227, + 104, + 74, + 267, + 148, + 31 + ], + "id": "Name: llm\nType: TECHNOLOGY" + }, + { + "entity_name": "rag approaches", + "entity_type": "TECHNOLOGY", + "description": "rag approaches are modern representative technologies reviewed in the text", + "source_ids": [ + 31 + ], + "id": "Name: rag approaches\nType: TECHNOLOGY" + }, + { + "entity_name": "document analysis", + "entity_type": "RESEARCH_FIELD", + "description": "document analysis is the field of study where llms and rag approaches are applied", + "source_ids": [ + 31 + ], + "id": "Name: document analysis\nType: RESEARCH_FIELD" + }, + { + "entity_name": "related works", + "entity_type": "SECTION_TITLE", + "description": "related works is the section of the text where the review of llm and rag approaches takes place", + "source_ids": [ + 31 + ], + "id": "Name: related works\nType: SECTION_TITLE" + }, + { + "entity_name": "html", + "entity_type": "FILE_TYPE", + "description": "html is an unstructured document format mentioned as a target for conversion into structured formats", + "source_ids": [ + 32 + ], + "id": "Name: html\nType: FILE_TYPE" + }, + { + "entity_name": "pdf", + "entity_type": "FILE_TYPE", + "description": "PDF is an unstructured document format that is often mentioned as a target for conversion into structured formats and is also referenced in the context of parsing failures.", + "source_ids": [ + 32, + 236 + ], + "id": "Name: pdf\nType: FILE_TYPE" + }, + { + "entity_name": "raw text", + "entity_type": "FILE_TYPE", + "description": "raw text is an unstructured document format mentioned as a target for conversion into structured formats", + "source_ids": [ + 32 + ], + "id": "Name: raw text\nType: FILE_TYPE" + }, + { + "entity_name": "relational tables", + "entity_type": "PRODUCT", + "description": "relational tables are structured formats that unstructured documents are converted into", + "source_ids": [ + 32 + ], + "id": "Name: relational tables\nType: PRODUCT" + }, + { + "entity_name": "evaporate", + "entity_type": "SOFTWARE", + "description": "evaporate is a system that utilizes llms to synthesize extraction code for converting semi structured web documents", + "source_ids": [ + 32 + ], + "id": "Name: evaporate\nType: SOFTWARE" + }, + { + "entity_name": "lotus", + "entity_type": "SOFTWARE", + "description": "lotus is a system that extends the relational model with semantic operators for querying unstructured text corpora", + "source_ids": [ + 32 + ], + "id": "Name: lotus\nType: SOFTWARE" + }, + { + "entity_name": "sql", + "entity_type": "PROGRAMMING_LANGUAGE", + "description": "sql is a query language referenced in the context of sql like queries executed by lotus", + "source_ids": [ + 32 + ], + "id": "Name: sql\nType: PROGRAMMING_LANGUAGE" + }, + { + "entity_name": "web documents", + "entity_type": "PRODUCT", + "description": "web documents are semi structured documents processed by systems like evaporate", + "source_ids": [ + 32 + ], + "id": "Name: web documents\nType: PRODUCT" + }, + { + "entity_name": "document pages", + "entity_type": "IMAGE", + "description": "document pages are viewed as images in research to preserve layout and visual information", + "source_ids": [ + 32 + ], + "id": "Name: document pages\nType: IMAGE" + }, + { + "entity_name": "semantic operators", + "entity_type": "TECHNOLOGY", + "description": "semantic operators are features added by lotus to extend the relational model", + "source_ids": [ + 32 + ], + "id": "Name: semantic operators\nType: TECHNOLOGY" + }, + { + "entity_name": "predicates", + "entity_type": "TECHNOLOGY", + "description": "predicates are llm powered functions like filter and join used in lotus", + "source_ids": [ + 32 + ], + "id": "Name: predicates\nType: TECHNOLOGY" + }, + { + "entity_name": "filter", + "entity_type": "TECHNOLOGY", + "description": "filter is an example of an llm powered predicate used in lotus", + "source_ids": [ + 32 + ], + "id": "Name: filter\nType: TECHNOLOGY" + }, + { + "entity_name": "join", + "entity_type": "TECHNOLOGY", + "description": "join is an example of an llm powered predicate used in lotus", + "source_ids": [ + 32 + ], + "id": "Name: join\nType: TECHNOLOGY" + }, + { + "entity_name": "agentic framework", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "an agentic framework is introduced by docetl to optimize information extraction", + "source_ids": [ + 32 + ], + "id": "Name: agentic framework\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "information extraction", + "entity_type": "TASK_OR_PROBLEM", + "description": "information extraction is the complex task optimized by docetl", + "source_ids": [ + 32 + ], + "id": "Name: information extraction\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "layout", + "entity_type": "CONCEPT", + "description": "layout refers to the visual structure of documents preserved when viewing pages as images", + "source_ids": [ + 32 + ], + "id": "Name: layout\nType: CONCEPT" + }, + { + "entity_name": "visual information", + "entity_type": "CONCEPT", + "description": "visual information refers to the content preserved when document pages are viewed as images", + "source_ids": [ + 32 + ], + "id": "Name: visual information\nType: CONCEPT" + }, + { + "entity_name": "semi structured web documents", + "entity_type": "PRODUCT", + "description": "semi structured web documents are the input type for evaporate", + "source_ids": [ + 32 + ], + "id": "Name: semi structured web documents\nType: PRODUCT" + }, + { + "entity_name": "structured databases", + "entity_type": "PRODUCT", + "description": "structured databases are the output format produced by evaporate", + "source_ids": [ + 32 + ], + "id": "Name: structured databases\nType: PRODUCT" + }, + { + "entity_name": "manual annotation", + "entity_type": "TASK_OR_PROBLEM", + "description": "manual annotation is a heavy process avoided by evaporate s cost effective conversion", + "source_ids": [ + 32 + ], + "id": "Name: manual annotation\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "unstructured text corpora", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 32 + ], + "id": "Name: unstructured text corpora\nType: UNKNOWN" + }, + { + "entity_name": "rag approaches", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "rag approaches are methods proven to excel in tasks like question answering and data cleaning", + "source_ids": [ + 33 + ], + "id": "Name: rag approaches\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "open ended question answering", + "entity_type": "TASK_OR_PROBLEM", + "description": "open ended question answering is a task where rag approaches have been proven to excel", + "source_ids": [ + 33 + ], + "id": "Name: open ended question answering\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "programming context", + "entity_type": "TASK_OR_PROBLEM", + "description": "programming context is a task where rag approaches have been proven to excel", + "source_ids": [ + 33 + ], + "id": "Name: programming context\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "sql rewrite", + "entity_type": "TASK_OR_PROBLEM", + "description": "sql rewrite is a task where rag approaches have been proven to excel", + "source_ids": [ + 33 + ], + "id": "Name: sql rewrite\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "data cleaning", + "entity_type": "TASK_OR_PROBLEM", + "description": "data cleaning is a task where rag approaches have been proven to excel", + "source_ids": [ + 33 + ], + "id": "Name: data cleaning\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "naive rag technique", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "the naive rag technique relies on retrieving query relevant contexts from external knowledge bases to mitigate hallucination", + "source_ids": [ + 33 + ], + "id": "Name: naive rag technique\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "graph structures", + "entity_type": "TECHNOLOGY", + "description": "graph structures are adopted by many rag approaches to organize information and relationships within documents", + "source_ids": [ + 33 + ], + "id": "Name: graph structures\nType: TECHNOLOGY" + }, + { + "entity_name": "agentic rag paradigm", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "the agentic rag paradigm employs autonomous agents to dynamically orchestrate and refine the rag pipeline", + "source_ids": [ + 33 + ], + "id": "Name: agentic rag paradigm\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "autonomous agents", + "entity_type": "TECHNOLOGY", + "description": "autonomous agents are employed by the agentic rag paradigm to orchestrate and refine the pipeline", + "source_ids": [ + 33 + ], + "id": "Name: autonomous agents\nType: TECHNOLOGY" + }, + { + "entity_name": "rag pipeline", + "entity_type": "TASK_OR_PROBLEM", + "description": "the rag pipeline is the process dynamically orchestrated and refined by the agentic rag paradigm", + "source_ids": [ + 33 + ], + "id": "Name: rag pipeline\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "recent survey of graph based rag methods", + "entity_type": "PUBLICATION_VENUE", + "description": "a recent survey of graph based rag methods is referenced for more details on the topic", + "source_ids": [ + 33 + ], + "id": "Name: recent survey of graph based rag methods\nType: PUBLICATION_VENUE" + }, + { + "entity_name": "external knowledge bases", + "entity_type": "TECHNOLOGY", + "description": "external knowledge bases are sources from which the naive rag technique retrieves query relevant contexts", + "source_ids": [ + 33 + ], + "id": "Name: external knowledge bases\nType: TECHNOLOGY" + }, + { + "entity_name": "hallucination", + "entity_type": "TASK_OR_PROBLEM", + "description": "hallucination is a problem in llms that the naive rag technique aims to mitigate", + "source_ids": [ + 33 + ], + "id": "Name: hallucination\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "overall retrieval performance", + "entity_type": "EVALUATION_METRIC", + "description": "overall retrieval performance is improved by rag approaches that adopt graph structures", + "source_ids": [ + 33 + ], + "id": "Name: overall retrieval performance\nType: EVALUATION_METRIC" + }, + { + "entity_name": "reasoning robustness", + "entity_type": "EVALUATION_METRIC", + "description": "reasoning robustness is a metric significantly boosted by the agentic rag paradigm", + "source_ids": [ + 33 + ], + "id": "Name: reasoning robustness\nType: EVALUATION_METRIC" + }, + { + "entity_name": "generation fidelity", + "entity_type": "EVALUATION_METRIC", + "description": "generation fidelity is a metric significantly boosted by the agentic rag paradigm", + "source_ids": [ + 33 + ], + "id": "Name: generation fidelity\nType: EVALUATION_METRIC" + }, + { + "entity_name": "documents", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 33 + ], + "id": "Name: documents\nType: UNKNOWN" + }, + { + "entity_name": "3 preliminaries", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of the main paper 'BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents', this section establishes the foundational concepts, definitions, and theoretical background necessary to understand the proposed BookRAG method and its context within Retrieval-Augmented Generation (RAG) for hierarchical documents.", + "source_ids": [ + 34 + ], + "id": "Name: 3 preliminaries\nType: SECTION_TITLE" + }, + { + "entity_name": "ift", + "entity_type": "SCIENTIFIC_THEORY", + "description": "ift is an abbreviation for information foraging theory a foundational theory introduced in the text", + "source_ids": [ + 35 + ], + "id": "Name: ift\nType: SCIENTIFIC_THEORY" + }, + { + "entity_name": "rag systems", + "entity_type": "TECHNOLOGY", + "description": "Rag systems are a type of technology known as Retrieval-Augmented Generation systems, and their general workflow is reviewed in the text, with detailed analysis provided in section 3.3.", + "source_ids": [ + 35, + 44 + ], + "id": "Name: rag systems\nType: TECHNOLOGY" + }, + { + "entity_name": "research problem", + "entity_type": "TASK_OR_PROBLEM", + "description": "the research problem is the subject being formalized in the text", + "source_ids": [ + 35 + ], + "id": "Name: research problem\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "general workflow", + "entity_type": "TASK_OR_PROBLEM", + "description": "the general workflow of rag systems is the subject being briefly reviewed in the text", + "source_ids": [ + 35 + ], + "id": "Name: general workflow\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "3.1 problem formulation", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Preliminaries' within the BookRAG paper, this section formalizes the research problem of complex document Question Answering (QA) and establishes the foundational context for the proposed approach.", + "source_ids": [ + 36 + ], + "id": "Name: 3.1 problem formulation\nType: SECTION_TITLE" + }, + { + "entity_name": "user query", + "entity_type": "TASK_OR_PROBLEM", + "description": "A user query is an input provided to a system to generate an accurate answer, serving as the input used in the online retrieval phase to retrieve relevant components. It represents a task or problem mentioned in the text that signifies a request for information or action, and in real data sections, it is the final input provided, often explicitly represented by the word \"query\" itself.", + "source_ids": [ + 45, + 252, + 37, + 255 + ], + "id": "Name: user query\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "answer", + "entity_type": "TASK_OR_PROBLEM", + "description": "An answer is the final output generated by the system, typically synthesized by an agent using the reduce method and ideally grounded in specific evidence blocks, and is symbolized by a lightbulb icon.", + "source_ids": [ + 84, + 124, + 37, + 135 + ], + "id": "Name: answer\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "evidence blocks", + "entity_type": "DATASET_OR_CORPUS", + "description": "evidence blocks are a specific set of content blocks from the document used to ground the generated answer", + "source_ids": [ + 37 + ], + "id": "Name: evidence blocks\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "method s", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "method s is a developed approach that maps a structured document and a query to a final answer", + "source_ids": [ + 37 + ], + "id": "Name: method s\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "pages", + "entity_type": "MEASUREMENT", + "description": "pages are the units that collectively form a document represented as a sequence", + "source_ids": [ + 37 + ], + "id": "Name: pages\nType: MEASUREMENT" + }, + { + "entity_name": "content blocks", + "entity_type": "DATASET_OR_CORPUS", + "description": "Content blocks are distinct elements within a document, such as text segments, section headers, tables, or images. These diverse structural units are identified and extracted from document pages as described in section 4.2.1.", + "source_ids": [ + 37, + 55 + ], + "id": "Name: content blocks\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "text segment", + "entity_type": "DATASET_OR_CORPUS", + "description": "a text segment is a type of content block within a document", + "source_ids": [ + 37 + ], + "id": "Name: text segment\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "section header", + "entity_type": "DATASET_OR_CORPUS", + "description": "a section header is a type of content block within a document", + "source_ids": [ + 37 + ], + "id": "Name: section header\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "table", + "entity_type": "DATASET_OR_CORPUS", + "description": "a table is a type of content block within a document", + "source_ids": [ + 37 + ], + "id": "Name: table\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "image", + "entity_type": "DATASET_OR_CORPUS", + "description": "an image is a type of content block within a document", + "source_ids": [ + 37 + ], + "id": "Name: image\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "logical chapter hierarchy", + "entity_type": "TASK_OR_PROBLEM", + "description": "a logical chapter hierarchy is the organizational structure within which content blocks are arranged", + "source_ids": [ + 37 + ], + "id": "Name: logical chapter hierarchy\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "n", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "The variable n represents different concepts depending on the context: it can denote the number of pages in a document, the set of nodes in a general tree structure, the set of nodes in a specific origin tree t, or the set of nodes in a tree. Additionally, n may refer to refined evidence utilized by a synthesizer operator.", + "source_ids": [ + 129, + 37, + 102, + 77, + 51 + ], + "id": "Name: n\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "m", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "m is a variable representing the number of content blocks in a document and serves as a component of the bookindex structure and the bookindex data structure.", + "source_ids": [ + 88, + 37, + 85 + ], + "id": "Name: m\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "p", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "The variable p serves multiple distinct roles depending on the context: it represents a specific page within a document sequence, denotes the power set of nodes in a tree structure used for graph tree link definitions, and signifies precision calculated as the intersection of extracted and ground truth tokens divided by the extracted tokens.", + "source_ids": [ + 51, + 37, + 231 + ], + "id": "Name: p\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "q", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "q is a variable representing the original user query mentioned in the text, which serves as the query or input used by the synthesizer operator.", + "source_ids": [ + 129, + 37, + 101 + ], + "id": "Name: q\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "a", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "a is a variable representing the coherent answer generated by the synthesizer operator.", + "source_ids": [ + 129, + 37 + ], + "id": "Name: a\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "e", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "e is a variable representing a subset of evidence blocks", + "source_ids": [ + 37 + ], + "id": "Name: e\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "b", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "b is a variable representing the sequence of all content blocks", + "source_ids": [ + 37 + ], + "id": "Name: b\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "d", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "d is a variable representing the document", + "source_ids": [ + 37 + ], + "id": "Name: d\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "equation 1", + "entity_type": "EQUATION_OR_FORMULA", + "description": "equation 1 is the mathematical formulation defining the task as a s d q", + "source_ids": [ + 37 + ], + "id": "Name: equation 1\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "references 5 11 33", + "entity_type": "PUBLICATION_VENUE", + "description": "references 5 11 and 33 are citations mentioned in the text regarding the problem of question answering", + "source_ids": [ + 37 + ], + "id": "Name: references 5 11 33\nType: PUBLICATION_VENUE" + }, + { + "entity_name": "3", + "entity_type": "MEASUREMENT", + "description": "3 is a numerical value that serves as the issue number of a publication and also represents the starting page number in an example range.", + "source_ids": [ + 258, + 38, + 199 + ], + "id": "Name: 3\nType: MEASUREMENT" + }, + { + "entity_name": "formula (1)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the variable A as a function of D and q. LaTeX: 𝐴 = S( 𝐷,𝑞 ) (1)", + "source_ids": [ + 39 + ], + "id": "Name: formula (1)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "s", + "entity_type": "PERSON", + "description": "s is an entity described as needing to navigate sequential page content and logical hierarchy to synthesize a response", + "source_ids": [ + 40 + ], + "id": "Name: s\nType: PERSON" + }, + { + "entity_name": "d", + "entity_type": "TASK_OR_PROBLEM", + "description": "d represents the logical hierarchy that s must navigate to synthesize a response", + "source_ids": [ + 40 + ], + "id": "Name: d\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "3.2 information foraging theory", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Preliminaries' within the BookRAG paper, this section formalizes the foundational Information Foraging Theory (IFT) used to model user behavior in complex document QA tasks.", + "source_ids": [ + 41 + ], + "id": "Name: 3.2 information foraging theory\nType: SECTION_TITLE" + }, + { + "entity_name": "animal foraging", + "entity_type": "TASK_OR_PROBLEM", + "description": "animal foraging is the process used as an analogy to explain how users access information in the context of information foraging theory", + "source_ids": [ + 42 + ], + "id": "Name: animal foraging\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "information scent", + "entity_type": "CONCEPT", + "description": "Information scent refers to the cues, such as keywords, icons, and rich information provided by entities and relations, that guide users and experts in navigating content by leading them toward promising sections.", + "source_ids": [ + 51, + 42, + 43 + ], + "id": "Name: information scent\nType: CONCEPT" + }, + { + "entity_name": "information patches", + "entity_type": "CONCEPT", + "description": "Information patches are clusters of content, such as sections in handbooks, that serve as hierarchical tree nodes providing native contexts for information seeking. These promising sections enable users and experts to navigate effectively between different parts of a handbook.", + "source_ids": [ + 51, + 42, + 43 + ], + "id": "Name: information patches\nType: CONCEPT" + }, + { + "entity_name": "handbooks", + "entity_type": "PRODUCT", + "description": "handbooks are mentioned as containing sections that serve as information patches", + "source_ids": [ + 42 + ], + "id": "Name: handbooks\nType: PRODUCT" + }, + { + "entity_name": "keywords", + "entity_type": "CONCEPT", + "description": "keywords are identified as specific examples of information scent cues used by users", + "source_ids": [ + 42 + ], + "id": "Name: keywords\nType: CONCEPT" + }, + { + "entity_name": "icons", + "entity_type": "CONCEPT", + "description": "icons are identified as specific examples of information scent cues used by users", + "source_ids": [ + 42 + ], + "id": "Name: icons\nType: CONCEPT" + }, + { + "entity_name": "sections", + "entity_type": "CONCEPT", + "description": "sections are described as parts of handbooks that function as information patches", + "source_ids": [ + 42 + ], + "id": "Name: sections\nType: CONCEPT" + }, + { + "entity_name": "reference 42", + "entity_type": "PUBLICATION_VENUE", + "description": "reference 42 is the citation source for information foraging theory mentioned in the text", + "source_ids": [ + 42 + ], + "id": "Name: reference 42\nType: PUBLICATION_VENUE" + }, + { + "entity_name": "experts", + "entity_type": "PERSON", + "description": "experts are individuals seeking a solution to a specific problem within a large technical handbook", + "source_ids": [ + 43 + ], + "id": "Name: experts\nType: PERSON" + }, + { + "entity_name": "large technical handbook", + "entity_type": "BOOK", + "description": "the large technical handbook is the source material containing the problem and information patches", + "source_ids": [ + 43 + ], + "id": "Name: large technical handbook\nType: BOOK" + }, + { + "entity_name": "key terms", + "entity_type": "CONCEPT", + "description": "key terms are extracted by experts to act as information scent", + "source_ids": [ + 43 + ], + "id": "Name: key terms\nType: CONCEPT" + }, + { + "entity_name": "final answer", + "entity_type": "CONCEPT", + "description": "the final answer is the result formulated by experts after analyzing the content within the information patches", + "source_ids": [ + 43 + ], + "id": "Name: final answer\nType: CONCEPT" + }, + { + "entity_name": "problem", + "entity_type": "TASK_OR_PROBLEM", + "description": "a specific problem is the target issue that experts are seeking to solve within the handbook", + "source_ids": [ + 43 + ], + "id": "Name: problem\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "diverse content", + "entity_type": "CONCEPT", + "description": "diverse content refers to the varied information found within the information patches that experts analyze", + "source_ids": [ + 43 + ], + "id": "Name: diverse content\nType: CONCEPT" + }, + { + "entity_name": "precise knowledge", + "entity_type": "CONCEPT", + "description": "precise knowledge is the specific information extracted from the diverse content to help formulate the answer", + "source_ids": [ + 43 + ], + "id": "Name: precise knowledge\nType: CONCEPT" + }, + { + "entity_name": "3.3 rag workflow", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Preliminaries' within the BookRAG paper, this section details the general operational workflow of Retrieval-Augmented Generation (RAG) systems, serving as a foundational context for the proposed hierarchical approach.", + "source_ids": [ + 44 + ], + "id": "Name: 3.3 rag workflow\nType: SECTION_TITLE" + }, + { + "entity_name": "retrieval augmented generation", + "entity_type": "TASK_OR_PROBLEM", + "description": "retrieval augmented generation is a system framework described as operating in a two phase process", + "source_ids": [ + 45 + ], + "id": "Name: retrieval augmented generation\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "offline indexing phase", + "entity_type": "TASK_OR_PROBLEM", + "description": "offline indexing phase is the first phase where unstructured corpus data is organized into a structured index", + "source_ids": [ + 45 + ], + "id": "Name: offline indexing phase\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "online retrieval phase", + "entity_type": "TASK_OR_PROBLEM", + "description": "online retrieval phase is the second phase where the system retrieves relevant components based on a user query", + "source_ids": [ + 45 + ], + "id": "Name: online retrieval phase\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "vector databases", + "entity_type": "SOFTWARE", + "description": "vector databases are mentioned as a possible form of structured index in the offline indexing phase", + "source_ids": [ + 45 + ], + "id": "Name: vector databases\nType: SOFTWARE" + }, + { + "entity_name": "llm", + "entity_type": "SOFTWARE", + "description": "LLM is a software component that functions as a Large Language Model used to generate output informed by retrieved components during the online retrieval phase. It serves as a tool to extract entities and relations when processing text-only nodes and is also employed to select the canonical entity when multiple aliases are identified.", + "source_ids": [ + 75, + 45, + 63 + ], + "id": "Name: llm\nType: SOFTWARE" + }, + { + "entity_name": "document s native tree topology", + "entity_type": "TASK_OR_PROBLEM", + "description": "document s native tree topology is the logical structure that the proposed approach seeks to integrate with retrieval structures", + "source_ids": [ + 45 + ], + "id": "Name: document s native tree topology\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "unstructured corpus data", + "entity_type": "DATASET_OR_CORPUS", + "description": "unstructured corpus data is the input material organized into a structured index during the offline indexing phase", + "source_ids": [ + 45 + ], + "id": "Name: unstructured corpus data\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "text chunks", + "entity_type": "DATASET_OR_CORPUS", + "description": "text chunks are examples of relevant components retrieved during the online retrieval phase", + "source_ids": [ + 45 + ], + "id": "Name: text chunks\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "subgraphs", + "entity_type": "DATASET_OR_CORPUS", + "description": "subgraphs are examples of relevant components retrieved during the online retrieval phase", + "source_ids": [ + 45 + ], + "id": "Name: subgraphs\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "document", + "entity_type": "TASK_OR_PROBLEM", + "description": "The document serves as the source of the original logical hierarchy and native tree topology referenced in the text, and it is the text being processed by the select by entity and select by section methods.", + "source_ids": [ + 104, + 45 + ], + "id": "Name: document\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "4 bookindex", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of the main paper 'BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents', this section details the novel index structure named BookIndex. It explains how the approach extracts a hierarchical tree from documents to serve as a table of contents, utilizes graphs to capture entity relationships, and maps entities to tree nodes.", + "source_ids": [ + 46 + ], + "id": "Name: 4 bookindex\nType: SECTION_TITLE" + }, + { + "entity_name": "bookindex", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "Bookindex is a novel hierarchical structure-aware index introduced in this work that builds a tree from documents to act as a table of contents and uses graphs to capture intricate relationships between entities. Serving as the core subject of section 4.1, it is designed to capture explicit logical hierarchy and intricate entity relations within complex documents.", + "source_ids": [ + 50, + 46 + ], + "id": "Name: bookindex\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "hierarchical tree", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "The structural method used within BookIndex to organize document content from different granularity levels, serving the role of a table of contents.", + "source_ids": [ + 46 + ], + "id": "Name: hierarchical tree\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "graph", + "entity_type": "TECHNOLOGY", + "description": "The data structure employed by BookIndex to capture and represent the intricate relationships between entities within the document hierarchy.", + "source_ids": [ + 46 + ], + "id": "Name: graph\nType: TECHNOLOGY" + }, + { + "entity_name": "tree construction", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Tree construction is the first stage of a two-stage process used to parse document layout and establish hierarchical nodes categorized by type. This specific sequential process, detailed in section 4.2, serves as the initial phase of the BookIndex construction process, where document layout is analyzed to create these hierarchical structures.", + "source_ids": [ + 50, + 53, + 47 + ], + "id": "Name: tree construction\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "graph construction", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Graph construction is the second stage of the BookIndex process, following tree construction, which focuses on extracting fine-grained entity knowledge from tree nodes and refining it through gradient-based entity resolution as detailed in section 4.1.", + "source_ids": [ + 50, + 61, + 47 + ], + "id": "Name: graph construction\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "logical hierarchy", + "entity_type": "CONCEPT", + "description": "Logical hierarchy is the explicit structure within complex documents that the bookindex is designed to capture, serving as the foundational framework that grounds semantic entities.", + "source_ids": [ + 52, + 47 + ], + "id": "Name: logical hierarchy\nType: CONCEPT" + }, + { + "entity_name": "hierarchical nodes", + "entity_type": "CONCEPT", + "description": "hierarchical nodes are the categorized units established by the tree construction process", + "source_ids": [ + 47 + ], + "id": "Name: hierarchical nodes\nType: CONCEPT" + }, + { + "entity_name": "fine grained entity knowledge", + "entity_type": "CONCEPT", + "description": "fine grained entity knowledge is the detailed information extracted from tree nodes during the graph construction process", + "source_ids": [ + 47 + ], + "id": "Name: fine grained entity knowledge\nType: CONCEPT" + }, + { + "entity_name": "figure 2", + "entity_type": "IMAGE", + "description": "Figure 2 is an image referenced in the text, serving as a visual representation to illustrate the bookindex construction process, provide an example of the bookindex, depict the layout parsing phase, and show the processing of a new entity in a knowledge graph, with its title being the subject of a question.", + "source_ids": [ + 76, + 48, + 52, + 245, + 59 + ], + "id": "Name: figure 2\nType: IMAGE" + }, + { + "entity_name": "bookindex construction process", + "entity_type": "TASK_OR_PROBLEM", + "description": "the bookindex construction process is a phase involving tree construction and graph construction", + "source_ids": [ + 48 + ], + "id": "Name: bookindex construction process\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "tree construction", + "entity_type": "TASK_OR_PROBLEM", + "description": "Tree construction is a component of the bookindex construction process derived from layout parsing and section filtering, representing the top section of the diagram that details the initial phase of building the index from document layouts.", + "source_ids": [ + 48, + 49 + ], + "id": "Name: tree construction\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "layout parsing", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Layout parsing is a method used to derive tree construction by extracting visual elements such as tables, text, titles, and images from a document layout. While it identifies blocks as titles, it does not assign their hierarchical level.", + "source_ids": [ + 48, + 49, + 57 + ], + "id": "Name: layout parsing\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "section filtering", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Section filtering is a method used in tree construction, specifically serving as Step 2 in the process where parsed sections are filtered based on title properties such as FontSize and content type, distinguishing between sections and text.", + "source_ids": [ + 48, + 49 + ], + "id": "Name: section filtering\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "graph construction", + "entity_type": "TASK_OR_PROBLEM", + "description": "Graph construction is a component of the bookindex construction process that involves knowledge graph construction and gradient-based entity resolution, as illustrated in the bottom section of the diagram detailing the creation of a knowledge graph for this purpose.", + "source_ids": [ + 48, + 49 + ], + "id": "Name: graph construction\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "kg construction", + "entity_type": "TASK_OR_PROBLEM", + "description": "kg construction is a step involved in graph construction", + "source_ids": [ + 48 + ], + "id": "Name: kg construction\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "gradient based entity resolution", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Gradient based entity resolution is a method involved in graph construction and represents the specific technique or approach described for the algorithm.", + "source_ids": [ + 48, + 69 + ], + "id": "Name: gradient based entity resolution\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "bookindex construction", + "entity_type": "IMAGE", + "description": "A diagram illustrating the process of constructing a book index, divided into Tree Construction and Graph Construction phases.", + "source_ids": [ + 49 + ], + "id": "Name: bookindex construction\nType: IMAGE" + }, + { + "entity_name": "title: method", + "entity_type": "SECTION_TITLE", + "description": "A specific text label identified during parsing with FontSize 14.", + "source_ids": [ + 49 + ], + "id": "Name: title: method\nType: SECTION_TITLE" + }, + { + "entity_name": "title: experiment", + "entity_type": "SECTION_TITLE", + "description": "A specific text label identified during parsing with FontSize 14.", + "source_ids": [ + 49 + ], + "id": "Name: title: experiment\nType: SECTION_TITLE" + }, + { + "entity_name": "title: moe layer", + "entity_type": "SECTION_TITLE", + "description": "A specific text label identified during parsing with FontSize 20.", + "source_ids": [ + 49 + ], + "id": "Name: title: moe layer\nType: SECTION_TITLE" + }, + { + "entity_name": "level: 2 type: section", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "An attribute indicating that 'Method' and 'Experiment' titles are classified as Level 2 Sections.", + "source_ids": [ + 49 + ], + "id": "Name: level: 2 type: section\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "level: none type: text", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "An attribute associated with 'MOE Layer', marked with a red cross, indicating it was rejected or not treated as a section.", + "source_ids": [ + 49 + ], + "id": "Name: level: none type: text\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "tree node", + "entity_type": "HARDWARE", + "description": "Visual element representing nodes in the tree structure shown in the legend and the resulting BookIndex.", + "source_ids": [ + 49 + ], + "id": "Name: tree node\nType: HARDWARE" + }, + { + "entity_name": "gt-link", + "entity_type": "SOFTWARE", + "description": "Legend item representing Ground Truth links between entities in the diagram.", + "source_ids": [ + 49 + ], + "id": "Name: gt-link\nType: SOFTWARE" + }, + { + "entity_name": "relation", + "entity_type": "DATASET_OR_CORPUS", + "description": "Legend item representing relationships between entities.", + "source_ids": [ + 49 + ], + "id": "Name: relation\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "kg construction", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Step 1 in Graph Construction showing the generation of a Knowledge Graph from Tree Nodes.", + "source_ids": [ + 49 + ], + "id": "Name: kg construction\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "gradient-based entity resolution", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Gradient-based entity resolution is a novel algorithmic method used to refine extracted entity knowledge by resolving ambiguities or duplicates through gradient optimization. As a specific technique mentioned in the title, it plays a crucial role in resolving entity fragmentation by utilizing gradient-based approaches to refine raw Knowledge Graphs. This method is implemented as Step 2 in the Graph Construction process, where it facilitates similarity matching and the merging of entities to ensure data accuracy and coherence.", + "source_ids": [ + 65, + 61, + 49 + ], + "id": "Name: gradient-based entity resolution\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "similarity", + "entity_type": "EVALUATION_METRIC", + "description": "Y-axis label of the chart in the Gradient-based Entity Resolution step.", + "source_ids": [ + 49 + ], + "id": "Name: similarity\nType: EVALUATION_METRIC" + }, + { + "entity_name": "entity", + "entity_type": "DATASET_OR_CORPUS", + "description": "X-axis label of the chart in the Gradient-based Entity Resolution step.", + "source_ids": [ + 49 + ], + "id": "Name: entity\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "merge", + "entity_type": "TASK_OR_PROBLEM", + "description": "Action performed to combine similar entities (e.g., e2 and e9) into a single resolved entity.", + "source_ids": [ + 49 + ], + "id": "Name: merge\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "image cref='#/texts/52'", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 49 + ], + "id": "Name: image cref='#/texts/52'\nType: UNKNOWN" + }, + { + "entity_name": "4.1 overview of bookindex", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'BOOKINDEX', this section provides a high-level introduction to the proposed BookIndex, defining its hierarchical structure-aware nature and outlining its two-stage construction process (Tree Construction and Graph Construction) for capturing logical hierarchies and entity relations in complex documents.", + "source_ids": [ + 50 + ], + "id": "Name: 4.1 overview of bookindex\nType: SECTION_TITLE" + }, + { + "entity_name": "tree structure", + "entity_type": "TASK_OR_PROBLEM", + "description": "tree structure represents the set of nodes derived from the document s explicit logical hierarchy", + "source_ids": [ + 51 + ], + "id": "Name: tree structure\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "knowledge graph", + "entity_type": "SOFTWARE", + "description": "knowledge graph is a structure that captures fine grained entities and their relations within the document", + "source_ids": [ + 51 + ], + "id": "Name: knowledge graph\nType: SOFTWARE" + }, + { + "entity_name": "graph tree link", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "graph tree link gt link is a mechanism that links entities to specific tree nodes from which they were extracted", + "source_ids": [ + 51 + ], + "id": "Name: graph tree link\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "titles", + "entity_type": "SECTION_TITLE", + "description": "titles are examples of nodes in the document s explicit logical hierarchy", + "source_ids": [ + 51 + ], + "id": "Name: titles\nType: SECTION_TITLE" + }, + { + "entity_name": "sections", + "entity_type": "SECTION_TITLE", + "description": "sections are examples of nodes in the document s explicit logical hierarchy", + "source_ids": [ + 51 + ], + "id": "Name: sections\nType: SECTION_TITLE" + }, + { + "entity_name": "e t", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "e t denotes the nesting relationships in the tree structure and represents the set of edges in the tree.", + "source_ids": [ + 51, + 102 + ], + "id": "Name: e t\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "v", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "v represents the set of entities in the graph g, which correspond to the fine-grained entities within the knowledge graph.", + "source_ids": [ + 51, + 77 + ], + "id": "Name: v\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "e g", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "e g denotes the relations in the knowledge graph", + "source_ids": [ + 51 + ], + "id": "Name: e g\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "m v", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "m v is the graph tree link function linking entities to tree nodes", + "source_ids": [ + 51 + ], + "id": "Name: m v\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "", + "entity_type": "UNKNOWN", + "description": "The entity's description is empty, containing no information to synthesize.", + "source_ids": [ + 73, + 109, + 112, + 51, + 57, + 122 + ], + "id": "Name: \nType: UNKNOWN" + }, + { + "entity_name": "navigation", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 51 + ], + "id": "Name: navigation\nType: UNKNOWN" + }, + { + "entity_name": "tree component", + "entity_type": "SOFTWARE", + "description": "the tree component is a part of the bookindex that organizes documents into a hierarchical structure", + "source_ids": [ + 52 + ], + "id": "Name: tree component\nType: SOFTWARE" + }, + { + "entity_name": "graph component", + "entity_type": "SOFTWARE", + "description": "the graph component is a part of the bookindex composed of entities and relations extracted from document nodes", + "source_ids": [ + 52 + ], + "id": "Name: graph component\nType: SOFTWARE" + }, + { + "entity_name": "gt link", + "entity_type": "TECHNOLOGY", + "description": "GT Link is a technology feature illustrated by blue dotted lines that connects entities to their corresponding tree nodes. It serves as a formalized mechanism, denoted as M, used to complete the book index and to link sections to entities.", + "source_ids": [ + 104, + 52, + 77 + ], + "id": "Name: gt link\nType: TECHNOLOGY" + }, + { + "entity_name": "text", + "entity_type": "PRODUCT", + "description": "Text is a type of content block that serves as a leaf node within the document structure and is identified as a final node type retained within the nodes of the tree.", + "source_ids": [ + 58, + 52 + ], + "id": "Name: text\nType: PRODUCT" + }, + { + "entity_name": "tables", + "entity_type": "PRODUCT", + "description": "tables are a type of content block serving as a leaf node within the document structure", + "source_ids": [ + 52 + ], + "id": "Name: tables\nType: PRODUCT" + }, + { + "entity_name": "images", + "entity_type": "PRODUCT", + "description": "images are a type of content block serving as a leaf node within the document structure", + "source_ids": [ + 52 + ], + "id": "Name: images\nType: PRODUCT" + }, + { + "entity_name": "section nodes", + "entity_type": "PRODUCT", + "description": "section nodes are hierarchical nodes within the document structure that contain content blocks", + "source_ids": [ + 52 + ], + "id": "Name: section nodes\nType: PRODUCT" + }, + { + "entity_name": "content blocks", + "entity_type": "PRODUCT", + "description": "content blocks are the items text tables images that serve as leaf nodes in the hierarchy", + "source_ids": [ + 52 + ], + "id": "Name: content blocks\nType: PRODUCT" + }, + { + "entity_name": "leaf nodes", + "entity_type": "PRODUCT", + "description": "leaf nodes are the terminal elements in the hierarchical structure containing content blocks", + "source_ids": [ + 52 + ], + "id": "Name: leaf nodes\nType: PRODUCT" + }, + { + "entity_name": "semantic entities", + "entity_type": "CONCEPT", + "description": "semantic entities are the extracted entities grounded within the document s logical hierarchy by gt link", + "source_ids": [ + 52 + ], + "id": "Name: semantic entities\nType: CONCEPT" + }, + { + "entity_name": "4.2 tree construction", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'BookIndex' and the first stage of its construction process, this section details the method for parsing document layouts to establish hierarchical nodes categorized by type.", + "source_ids": [ + 53 + ], + "id": "Name: 4.2 tree construction\nType: SECTION_TITLE" + }, + { + "entity_name": "t", + "entity_type": "TASK_OR_PROBLEM", + "description": "t is a structured hierarchical tree that is the result of transforming a raw document", + "source_ids": [ + 54 + ], + "id": "Name: t\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "raw document", + "entity_type": "PRODUCT", + "description": "raw document is the initial input that undergoes transformation into a structured hierarchical tree", + "source_ids": [ + 54 + ], + "id": "Name: raw document\nType: PRODUCT" + }, + { + "entity_name": "robust layout parsing", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "robust layout parsing is a key step involved in transforming the raw document into a structured hierarchical tree", + "source_ids": [ + 54 + ], + "id": "Name: robust layout parsing\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "intelligent section filtering", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "intelligent section filtering is a key step involved in transforming the raw document into a structured hierarchical tree", + "source_ids": [ + 54 + ], + "id": "Name: intelligent section filtering\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "task or problem", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 54 + ], + "id": "Name: task or problem\nType: UNKNOWN" + }, + { + "entity_name": "4.2.1 layout parsing", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Tree Construction' within the 'BOOKINDEX' chapter, this section details the initial phase of transforming raw documents into structured hierarchical trees using layout analysis and recognition models to identify and organize diverse content blocks.", + "source_ids": [ + 55 + ], + "id": "Name: 4.2.1 layout parsing\nType: SECTION_TITLE" + }, + { + "entity_name": "layout analysis", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "A specific technique employed in section 4.2.1 to understand the spatial arrangement of elements within document pages.", + "source_ids": [ + 55 + ], + "id": "Name: layout analysis\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "recognition models", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "The computational models utilized in section 4.2.1 to recognize and classify different types of content blocks such as text, tables, and images.", + "source_ids": [ + 55 + ], + "id": "Name: recognition models\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "document d", + "entity_type": "TASK_OR_PROBLEM", + "description": "The input data object (a collection of pages) that serves as the target for processing in section 4.2.1.", + "source_ids": [ + 55 + ], + "id": "Name: document d\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "the output", + "entity_type": "TASK_OR_PROBLEM", + "description": "the output is described as a sequence of primitive", + "source_ids": [ + 56 + ], + "id": "Name: the output\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "primitive", + "entity_type": "CONCEPT", + "description": "primitive is a term used to describe the components of the output sequence", + "source_ids": [ + 56 + ], + "id": "Name: primitive\nType: CONCEPT" + }, + { + "entity_name": "section filtering", + "entity_type": "TASK_OR_PROBLEM", + "description": "section filtering is a phase that processes an initial sequence to identify a document s logically hierarchical structure", + "source_ids": [ + 57 + ], + "id": "Name: section filtering\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "title", + "entity_type": "SECTION_TITLE", + "description": "title refers to blocks identified by layout parsing that require hierarchical level assignment", + "source_ids": [ + 57 + ], + "id": "Name: title\nType: SECTION_TITLE" + }, + { + "entity_name": "text", + "entity_type": "SECTION_TITLE", + "description": "text is a node type used to re classify erroneous title blocks such as descriptive text within images", + "source_ids": [ + 57 + ], + "id": "Name: text\nType: SECTION_TITLE" + }, + { + "entity_name": "image", + "entity_type": "IMAGE", + "description": "An image is a type of block identified during the layout parsing phase, serving as a specific node type that indicates the presence of visual elements requiring VLM-based extraction. It can also refer to a location within a document where descriptive text might be erroneously parsed as a title, and it functions as a filter type used for visual elements.", + "source_ids": [ + 57, + 258, + 59, + 63 + ], + "id": "Name: image\nType: IMAGE" + }, + { + "entity_name": "table", + "entity_type": "TABLE", + "description": "A table is a document element that can refer to borderless table headers, which might be erroneously parsed as a title, or it can serve as a filter type used for tabular data.", + "source_ids": [ + 57, + 258 + ], + "id": "Name: table\nType: TABLE" + }, + { + "entity_name": "b", + "entity_type": "DATASET_OR_CORPUS", + "description": "b represents the candidate subset of blocks selected for llm based analysis", + "source_ids": [ + 57 + ], + "id": "Name: b\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "c", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "c represents the content of the candidates analyzed by the LLM and also denotes explicit constraints such as modal types and page ranges generated during a plan.", + "source_ids": [ + 57, + 102 + ], + "id": "Name: c\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "f", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "f represents the layout features of the candidates analyzed by the llm", + "source_ids": [ + 57 + ], + "id": "Name: f\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "4 2 2", + "entity_type": "SECTION_TITLE", + "description": "4 2 2 is the section identifier for the section filtering phase", + "source_ids": [ + 57 + ], + "id": "Name: 4 2 2\nType: SECTION_TITLE" + }, + { + "entity_name": "b title", + "entity_type": "DATASET_OR_CORPUS", + "description": "b title is a candidate subset of blocks where the type is title selected for llm based analysis", + "source_ids": [ + 57 + ], + "id": "Name: b title\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "l", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "l represents the actual hierarchical level of a block ranging from 1 to infinity", + "source_ids": [ + 57 + ], + "id": "Name: l\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "none", + "entity_type": "SECTION_TITLE", + "description": "none is a value indicating that a block has no hierarchical level", + "source_ids": [ + 57 + ], + "id": "Name: none\nType: SECTION_TITLE" + }, + { + "entity_name": "tree", + "entity_type": "TASK_OR_PROBLEM", + "description": "A tree is a definitive structure constructed from blocks consisting of nodes and edges that represent content and relationships, serving as the data structure on which operators operate.", + "source_ids": [ + 58, + 102 + ], + "id": "Name: tree\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "node set", + "entity_type": "TASK_OR_PROBLEM", + "description": "the node set is composed of all blocks from the filtering and re classification process retaining content and final node types", + "source_ids": [ + 58 + ], + "id": "Name: node set\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "edge set", + "entity_type": "TASK_OR_PROBLEM", + "description": "the edge set represents the parent child nesting relationships within the tree structure", + "source_ids": [ + 58 + ], + "id": "Name: edge set\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "section", + "entity_type": "PRODUCT", + "description": "section is identified as a final node type retained within the nodes of the tree", + "source_ids": [ + 58 + ], + "id": "Name: section\nType: PRODUCT" + }, + { + "entity_name": "table", + "entity_type": "PRODUCT", + "description": "A table is a specific item type being filtered for in the document and is identified as a final node type retained within the nodes of the tree. It is a specific logical type mentioned in the text that requires the preservation of structural semantics.", + "source_ids": [ + 64, + 58, + 251 + ], + "id": "Name: table\nType: PRODUCT" + }, + { + "entity_name": "image", + "entity_type": "PRODUCT", + "description": "image is identified as a final node type retained within the nodes of the tree", + "source_ids": [ + 58 + ], + "id": "Name: image\nType: PRODUCT" + }, + { + "entity_name": "filtering", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "filtering is a process mentioned as part of the generation of blocks for the node set", + "source_ids": [ + 58 + ], + "id": "Name: filtering\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "re classification", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "re classification is a process mentioned alongside filtering in the creation of the node set", + "source_ids": [ + 58 + ], + "id": "Name: re classification\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "hierarchical levels", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "hierarchical levels are determined values used to infer parent child relationships for section nodes", + "source_ids": [ + 58 + ], + "id": "Name: hierarchical levels\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "document order", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "Document order is a sequential arrangement and factor used to assemble nodes into the final tree structure.", + "source_ids": [ + 58, + 59 + ], + "id": "Name: document order\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "parent child nesting relationships", + "entity_type": "TASK_OR_PROBLEM", + "description": "parent child nesting relationships are the specific connections established by the edge set", + "source_ids": [ + 58 + ], + "id": "Name: parent child nesting relationships\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "content", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "content is an attribute retained by each node in the node set", + "source_ids": [ + 58 + ], + "id": "Name: content\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "final node type", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "final node type is an attribute retained by each node in the node set", + "source_ids": [ + 58 + ], + "id": "Name: final node type\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "node", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 58 + ], + "id": "Name: node\nType: UNKNOWN" + }, + { + "entity_name": "layout parsing phase", + "entity_type": "TASK_OR_PROBLEM", + "description": "layout parsing phase is a process that identifies diverse blocks in a document", + "source_ids": [ + 59 + ], + "id": "Name: layout parsing phase\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "title text table", + "entity_type": "PRODUCT", + "description": "title text table is a type of block identified during the layout parsing phase", + "source_ids": [ + 59 + ], + "id": "Name: title text table\nType: PRODUCT" + }, + { + "entity_name": "section filtering phase", + "entity_type": "TASK_OR_PROBLEM", + "description": "section filtering phase is a process where title candidates are analyzed by the llm", + "source_ids": [ + 59 + ], + "id": "Name: section filtering phase\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "method", + "entity_type": "SECTION_TITLE", + "description": "method is a title candidate analyzed during the section filtering phase", + "source_ids": [ + 59 + ], + "id": "Name: method\nType: SECTION_TITLE" + }, + { + "entity_name": "experiment", + "entity_type": "SECTION_TITLE", + "description": "experiment is a title candidate analyzed during the section filtering phase", + "source_ids": [ + 59 + ], + "id": "Name: experiment\nType: SECTION_TITLE" + }, + { + "entity_name": "moe layer", + "entity_type": "SECTION_TITLE", + "description": "moe layer is a title candidate that was erroneously tagged as a title but re classified as a text node", + "source_ids": [ + 59 + ], + "id": "Name: moe layer\nType: SECTION_TITLE" + }, + { + "entity_name": "section nodes", + "entity_type": "SECTION_TITLE", + "description": "section nodes are blocks identified as having a specific level in the document hierarchy", + "source_ids": [ + 59 + ], + "id": "Name: section nodes\nType: SECTION_TITLE" + }, + { + "entity_name": "text node", + "entity_type": "SECTION_TITLE", + "description": "text node is a classification for blocks that do not have a specific level in the document hierarchy", + "source_ids": [ + 59 + ], + "id": "Name: text node\nType: SECTION_TITLE" + }, + { + "entity_name": "final tree structure", + "entity_type": "TASK_OR_PROBLEM", + "description": "final tree structure is the result of assembling filtered and classified nodes based on their levels and order", + "source_ids": [ + 59 + ], + "id": "Name: final tree structure\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "fontsize", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "fontsize is a parameter used to describe the size of text blocks such as 14 or 20", + "source_ids": [ + 59 + ], + "id": "Name: fontsize\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "14", + "entity_type": "MEASUREMENT", + "description": "14 is a numerical value mentioned in the text, potentially representing a measurement or count, and serves as the specific font size value associated with the method and experiment blocks.", + "source_ids": [ + 59, + 219 + ], + "id": "Name: 14\nType: MEASUREMENT" + }, + { + "entity_name": "20", + "entity_type": "MEASUREMENT", + "description": "20 is the specific font size value associated with the moe layer block", + "source_ids": [ + 59 + ], + "id": "Name: 20\nType: MEASUREMENT" + }, + { + "entity_name": "level", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "level is a parameter used to define the hierarchy depth of document nodes", + "source_ids": [ + 59 + ], + "id": "Name: level\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "2", + "entity_type": "MEASUREMENT", + "description": "The value 2 serves multiple roles across different contexts: it is the specific level assigned to method and experiment blocks, represents the speedup factor achieved by BookRag compared to Docetl in query latency, and denotes both the issue number and the volume number of a publication.", + "source_ids": [ + 160, + 199, + 59, + 191 + ], + "id": "Name: 2\nType: MEASUREMENT" + }, + { + "entity_name": "none", + "entity_type": "MEASUREMENT", + "description": "none is the specific level value assigned to the moe layer block indicating no hierarchy level", + "source_ids": [ + 59 + ], + "id": "Name: none\nType: MEASUREMENT" + }, + { + "entity_name": "4", + "entity_type": "MEASUREMENT", + "description": "4 is a numerical value mentioned in the text though its specific context or unit is not provided", + "source_ids": [ + 60 + ], + "id": "Name: 4\nType: MEASUREMENT" + }, + { + "entity_name": "4.3 graph construction", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'BOOKINDEX' and the second stage of the proposed BookIndex construction process, this section details the method for extracting fine-grained entity knowledge from hierarchical tree nodes and refining it using a novel gradient-based entity resolution technique.", + "source_ids": [ + 61 + ], + "id": "Name: 4.3 graph construction\nType: SECTION_TITLE" + }, + { + "entity_name": "tree t", + "entity_type": "TASK_OR_PROBLEM", + "description": "tree t is a structure that is established before proceeding to the next step", + "source_ids": [ + 62 + ], + "id": "Name: tree t\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "knowledge graph g", + "entity_type": "TASK_OR_PROBLEM", + "description": "knowledge graph g is a structure that is populated by extracting and refining entities from the tree nodes", + "source_ids": [ + 62 + ], + "id": "Name: knowledge graph g\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "tree nodes", + "entity_type": "TASK_OR_PROBLEM", + "description": "tree nodes are the components within tree t from which entities are extracted and refined", + "source_ids": [ + 62 + ], + "id": "Name: tree nodes\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "4.3.1 kg construction", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Graph Construction' within the 'BOOKINDEX' chapter, this section details the specific algorithm for populating the Knowledge Graph by iterating through tree nodes and extracting subgraphs based on content modality (text or visual).", + "source_ids": [ + 63 + ], + "id": "Name: 4.3.1 kg construction\nType: SECTION_TITLE" + }, + { + "entity_name": "tree t", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "The hierarchical structure previously established that serves as the source of nodes to be processed for graph construction.", + "source_ids": [ + 63 + ], + "id": "Name: tree t\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "vision language model", + "entity_type": "SOFTWARE", + "description": "VLM employed specifically to extract visual knowledge from nodes containing image elements.", + "source_ids": [ + 63 + ], + "id": "Name: vision language model\nType: SOFTWARE" + }, + { + "entity_name": "mapping m", + "entity_type": "EQUATION_OR_FORMULA", + "description": "Mapping m is the final aggregation process defined as v to p n, which links entities to structural locations by constructing a mapping structure that records the origin tree node for every extracted entity.", + "source_ids": [ + 77, + 63 + ], + "id": "Name: mapping m\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "formula", + "entity_type": "PRODUCT", + "description": "formula is a specific logical type mentioned in the text that requires preservation of structural semantics", + "source_ids": [ + 64 + ], + "id": "Name: formula\nType: PRODUCT" + }, + { + "entity_name": "v table", + "entity_type": "PRODUCT", + "description": "v table is a distinct typed entity representing the table itself created to preserve structural semantics", + "source_ids": [ + 64 + ], + "id": "Name: v table\nType: PRODUCT" + }, + { + "entity_name": "row", + "entity_type": "PRODUCT", + "description": "row is a component of table nodes that is explicitly extracted as a distinct entity", + "source_ids": [ + 64 + ], + "id": "Name: row\nType: PRODUCT" + }, + { + "entity_name": "column", + "entity_type": "PRODUCT", + "description": "column is a component of table nodes that is explicitly extracted as a distinct entity", + "source_ids": [ + 64 + ], + "id": "Name: column\nType: PRODUCT" + }, + { + "entity_name": "header", + "entity_type": "PRODUCT", + "description": "header refers to row and column headers in table nodes that are explicitly extracted as distinct entities", + "source_ids": [ + 64 + ], + "id": "Name: header\nType: PRODUCT" + }, + { + "entity_name": "structural semantics", + "entity_type": "CONCEPT", + "description": "structural semantics refers to the meaning preserved for specific logical types in the described process", + "source_ids": [ + 64 + ], + "id": "Name: structural semantics\nType: CONCEPT" + }, + { + "entity_name": "logical types", + "entity_type": "CONCEPT", + "description": "logical types are categories of entities such as table and formula that require specific handling", + "source_ids": [ + 64 + ], + "id": "Name: logical types\nType: CONCEPT" + }, + { + "entity_name": "node", + "entity_type": "CONCEPT", + "description": "node refers to a specific point in the data structure where content is extracted", + "source_ids": [ + 64 + ], + "id": "Name: node\nType: CONCEPT" + }, + { + "entity_name": "vertex", + "entity_type": "CONCEPT", + "description": "vertex refers to the primary node v table to which other entities are linked", + "source_ids": [ + 64 + ], + "id": "Name: vertex\nType: CONCEPT" + }, + { + "entity_name": "containedin", + "entity_type": "RELATIONSHIP_TYPE", + "description": "containedin is the specific relationship type used to link row and column headers to the table entity", + "source_ids": [ + 64 + ], + "id": "Name: containedin\nType: RELATIONSHIP_TYPE" + }, + { + "entity_name": "4.3.2 gradient-based entity resolution", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Graph Construction' within the 'BOOKINDEX' chapter, this section details a robust Entity Resolution (ER) process designed to identify and merge fragmented conceptual entities in a Knowledge Graph, addressing challenges like abbreviations and co-references.", + "source_ids": [ + 65 + ], + "id": "Name: 4.3.2 gradient-based entity resolution\nType: SECTION_TITLE" + }, + { + "entity_name": "entity resolution", + "entity_type": "TASK_OR_PROBLEM", + "description": "Entity resolution is the task or problem addressed by the prompt in figure 13, serving as a process during which GT Link is refined by merging entities into canonical entities. It addresses the core challenge of identifying and merging fragmented entities caused by abbreviations, co-references, or varied occurrences to ensure a well-constructed Knowledge Graph.", + "source_ids": [ + 65, + 284, + 77 + ], + "id": "Name: entity resolution\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "er methods", + "entity_type": "TASK_OR_PROBLEM", + "description": "er methods are conventional methods for entity resolution that are computationally expensive", + "source_ids": [ + 66 + ], + "id": "Name: er methods\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "dirty er", + "entity_type": "TASK_OR_PROBLEM", + "description": "dirty er is a term used to describe batch processing across multiple data sources for entity resolution", + "source_ids": [ + 66 + ], + "id": "Name: dirty er\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "a", + "entity_type": "TASK_OR_PROBLEM", + "description": "a is an example entity used to illustrate the merging of multiple entities in the entity resolution process", + "source_ids": [ + 66 + ], + "id": "Name: a\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "b", + "entity_type": "TASK_OR_PROBLEM", + "description": "b is an example entity used to illustrate the merging of multiple entities in the entity resolution process", + "source_ids": [ + 66 + ], + "id": "Name: b\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "c", + "entity_type": "TASK_OR_PROBLEM", + "description": "c is an example entity used to illustrate the merging of multiple entities in the entity resolution process", + "source_ids": [ + 66 + ], + "id": "Name: c\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "12", + "entity_type": "PUBLICATION_VENUE", + "description": "12 is a citation reference mentioned in the text regarding entity resolution methods", + "source_ids": [ + 66 + ], + "id": "Name: 12\nType: PUBLICATION_VENUE" + }, + { + "entity_name": "a b", + "entity_type": "TASK_OR_PROBLEM", + "description": "a b is a specific pairwise comparison example between entities a and b", + "source_ids": [ + 66 + ], + "id": "Name: a b\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "a c", + "entity_type": "TASK_OR_PROBLEM", + "description": "a c is a specific pairwise comparison example between entities a and c", + "source_ids": [ + 66 + ], + "id": "Name: a c\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "b c", + "entity_type": "TASK_OR_PROBLEM", + "description": "b c is a specific pairwise comparison example between entities b and c", + "source_ids": [ + 66 + ], + "id": "Name: b c\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "o n 2", + "entity_type": "MEASUREMENT", + "description": "o n 2 represents the quadratic complexity of the number of pairwise comparisons required", + "source_ids": [ + 66 + ], + "id": "Name: o n 2\nType: MEASUREMENT" + }, + { + "entity_name": "gradient based er method", + "entity_type": "TECHNOLOGY", + "description": "a gradient based entity resolution method employed to process a single document incrementally", + "source_ids": [ + 67 + ], + "id": "Name: gradient based er method\nType: TECHNOLOGY" + }, + { + "entity_name": "clean er", + "entity_type": "TASK_OR_PROBLEM", + "description": "a simplified version of the entity resolution task used as the basis for the incremental process", + "source_ids": [ + 67 + ], + "id": "Name: clean er\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "database", + "entity_type": "SOFTWARE", + "description": "a storage system containing already processed entities against which new entities are compared", + "source_ids": [ + 67 + ], + "id": "Name: database\nType: SOFTWARE" + }, + { + "entity_name": "top k most relevant candidates", + "entity_type": "EVALUATION_METRIC", + "description": "a set of the most relevant entities used for reranking a new entity in the incremental process", + "source_ids": [ + 67 + ], + "id": "Name: top k most relevant candidates\nType: EVALUATION_METRIC" + }, + { + "entity_name": "entity", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "a single new entity being extracted in the incremental process", + "source_ids": [ + 67 + ], + "id": "Name: entity\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "quadratic batch problem", + "entity_type": "TASK_OR_PROBLEM", + "description": "the original complex problem that the incremental method transforms into a simpler task", + "source_ids": [ + 67 + ], + "id": "Name: quadratic batch problem\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "repeated lookup task", + "entity_type": "TASK_OR_PROBLEM", + "description": "the simplified task resulting from transforming the quadratic batch problem", + "source_ids": [ + 67 + ], + "id": "Name: repeated lookup task\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "scoring patterns", + "entity_type": "EVALUATION_METRIC", + "description": "distinct observable patterns yielded by the incremental process when reranking entities", + "source_ids": [ + 67 + ], + "id": "Name: scoring patterns\nType: EVALUATION_METRIC" + }, + { + "entity_name": "incremental process", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 67 + ], + "id": "Name: incremental process\nType: UNKNOWN" + }, + { + "entity_name": "5", + "entity_type": "MEASUREMENT", + "description": "5 is a numerical value mentioned in the text potentially representing a count score or measurement", + "source_ids": [ + 68 + ], + "id": "Name: 5\nType: MEASUREMENT" + }, + { + "entity_name": "algorithm 1", + "entity_type": "TASK_OR_PROBLEM", + "description": "Algorithm 1 is a gradient-based entity resolution method and the entity resolution process described in the text.", + "source_ids": [ + 75, + 69 + ], + "id": "Name: algorithm 1\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "kg g", + "entity_type": "TASK_OR_PROBLEM", + "description": "kg g is a knowledge graph that serves as the input for the described process", + "source_ids": [ + 70 + ], + "id": "Name: kg g\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "new entity v n", + "entity_type": "TASK_OR_PROBLEM", + "description": "new entity v n is a new entity being introduced into the system", + "source_ids": [ + 70 + ], + "id": "Name: new entity v n\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "rerank model r", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "rerank model r is a model used to rerank entities in the process", + "source_ids": [ + 70 + ], + "id": "Name: rerank model r\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "entity vector database db", + "entity_type": "DATASET_OR_CORPUS", + "description": "entity vector database db is a database storing entity vectors", + "source_ids": [ + 70 + ], + "id": "Name: entity vector database db\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "vector search number top k", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "vector search number top k is a parameter defining the number of top results for vector search", + "source_ids": [ + 70 + ], + "id": "Name: vector search number top k\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "threshold of gradient g", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "threshold of gradient g is a threshold value used for gradient calculations", + "source_ids": [ + 70 + ], + "id": "Name: threshold of gradient g\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "kg", + "entity_type": "TASK_OR_PROBLEM", + "description": "kg is the abbreviation for the knowledge graph mentioned as input, referring to the system where entities are processed, compared, and merged.", + "source_ids": [ + 76, + 70 + ], + "id": "Name: kg\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "g", + "entity_type": "TASK_OR_PROBLEM", + "description": "g is a specific instance or variable name for the knowledge graph and serves as a data structure or set that is updated and returned at the end of the process.", + "source_ids": [ + 75, + 70 + ], + "id": "Name: g\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "v", + "entity_type": "TASK_OR_PROBLEM", + "description": "v is the variable representing the new entity", + "source_ids": [ + 70 + ], + "id": "Name: v\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "n", + "entity_type": "TASK_OR_PROBLEM", + "description": "In the context of the task or problem labeled n, this identifier serves as a subscript or reference to a new entity denoted as v, while also representing the set of nodes within the document structure.", + "source_ids": [ + 104, + 70 + ], + "id": "Name: n\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "r", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "r is the specific variable name for the rerank model", + "source_ids": [ + 70 + ], + "id": "Name: r\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "db", + "entity_type": "DATASET_OR_CORPUS", + "description": "db is the specific variable name for the entity vector database", + "source_ids": [ + 70 + ], + "id": "Name: db\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "top k", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "top k is the specific variable name for the vector search number", + "source_ids": [ + 70 + ], + "id": "Name: top k\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "g", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "g is a parameter or variable representing the threshold of gradient, specifically used to check for score drops, and it serves as a component within the bookindex data structure, which contains relevant entities and is organized with fields such as b, t, g, and m.", + "source_ids": [ + 88, + 75, + 85, + 70 + ], + "id": "Name: g\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "vector search", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: vector search\nType: UNKNOWN" + }, + { + "entity_name": "db", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: db\nType: UNKNOWN" + }, + { + "entity_name": "search", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: search\nType: UNKNOWN" + }, + { + "entity_name": "r", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: r\nType: UNKNOWN" + }, + { + "entity_name": "e", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: e\nType: UNKNOWN" + }, + { + "entity_name": "sort", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: sort\nType: UNKNOWN" + }, + { + "entity_name": "gradient select", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: gradient select\nType: UNKNOWN" + }, + { + "entity_name": "top k", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: top k\nType: UNKNOWN" + }, + { + "entity_name": "v n", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: v n\nType: UNKNOWN" + }, + { + "entity_name": "e c", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: e c\nType: UNKNOWN" + }, + { + "entity_name": "v cn", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: v cn\nType: UNKNOWN" + }, + { + "entity_name": "s", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: s\nType: UNKNOWN" + }, + { + "entity_name": "c", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: c\nType: UNKNOWN" + }, + { + "entity_name": "score", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: score\nType: UNKNOWN" + }, + { + "entity_name": "s 0", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: s 0\nType: UNKNOWN" + }, + { + "entity_name": "sel", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ], + "id": "Name: sel\nType: UNKNOWN" + }, + { + "entity_name": "case a", + "entity_type": "TASK_OR_PROBLEM", + "description": "Case A is a scenario involving a new conceptual entity where the LLM helps differentiate it from a set identified by an algorithm. This situation arises when all candidates pass the gradient check, indicating that the scores lacked discriminative power.", + "source_ids": [ + 72, + 74, + 75 + ], + "id": "Name: case a\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "new entity", + "entity_type": "TASK_OR_PROBLEM", + "description": "A new entity is a conceptual entity, often recently extracted from text or representing a unique concept in a knowledge graph, that requires evaluation for relevance against existing entities. This task involves analyzing the entity's name, type, and description to determine if it can be matched with candidate entities, a process that demands strong evidence to confirm its distinctiveness and validity.", + "source_ids": [ + 72, + 265, + 274, + 262 + ], + "id": "Name: new entity\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "existing entities", + "entity_type": "TASK_OR_PROBLEM", + "description": "existing entities are the set of entities against which the relevance of a new conceptual entity is measured", + "source_ids": [ + 72 + ], + "id": "Name: existing entities\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "relevance scores", + "entity_type": "EVALUATION_METRIC", + "description": "relevance scores are the metrics used to measure the relationship between the new entity and existing entities", + "source_ids": [ + 72 + ], + "id": "Name: relevance scores\nType: EVALUATION_METRIC" + }, + { + "entity_name": "gradient", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "gradient refers to a mathematical pattern or value that is absent in the relevance scores for new entities", + "source_ids": [ + 72 + ], + "id": "Name: gradient\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "discriminative pattern", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "discriminative pattern refers to a distinguishing feature or trend that is not present in the relevance scores for new entities", + "source_ids": [ + 72 + ], + "id": "Name: discriminative pattern\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "case b", + "entity_type": "TASK_OR_PROBLEM", + "description": "Case b refers to a scenario involving an existing entity where an alias is being evaluated for relevance. It is characterized by a sharp decline that the gradient-based ER algorithm is designed to detect, occurring specifically when a gradient is found signaling a sharp score drop.", + "source_ids": [ + 73, + 74, + 75 + ], + "id": "Name: case b\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "reranker", + "entity_type": "TECHNOLOGY", + "description": "the reranker is a system or component described as having inherent discriminative limitations", + "source_ids": [ + 73 + ], + "id": "Name: reranker\nType: TECHNOLOGY" + }, + { + "entity_name": "existing entity", + "entity_type": "TASK_OR_PROBLEM", + "description": "existing entity refers to an entity that is already present in the system being discussed", + "source_ids": [ + 73 + ], + "id": "Name: existing entity\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "alias", + "entity_type": "CONCEPT", + "description": "An alias is a term used to describe an alternative name for an existing entity and is considered a valid form of similarity for entity names alongside direct abbreviations.", + "source_ids": [ + 73, + 267 + ], + "id": "Name: alias\nType: CONCEPT" + }, + { + "entity_name": "scores", + "entity_type": "EVALUATION_METRIC", + "description": "scores are the numerical values indicating the relevance of an alias to a true match", + "source_ids": [ + 73 + ], + "id": "Name: scores\nType: EVALUATION_METRIC" + }, + { + "entity_name": "true match", + "entity_type": "CONCEPT", + "description": "true match refers to the correct entity that an alias is being compared against", + "source_ids": [ + 73 + ], + "id": "Name: true match\nType: CONCEPT" + }, + { + "entity_name": "equivalent aliases", + "entity_type": "CONCEPT", + "description": "equivalent aliases refers to a small set of aliases that are considered the same as the true match", + "source_ids": [ + 73 + ], + "id": "Name: equivalent aliases\nType: CONCEPT" + }, + { + "entity_name": "gradient", + "entity_type": "MEASUREMENT", + "description": "gradient refers to the sharp decline in relevance scores mentioned in the text", + "source_ids": [ + 73 + ], + "id": "Name: gradient\nType: MEASUREMENT" + }, + { + "entity_name": "irrelevant entities", + "entity_type": "TASK_OR_PROBLEM", + "description": "irrelevant entities are the entities that follow the sharp decline in relevance scores", + "source_ids": [ + 73 + ], + "id": "Name: irrelevant entities\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "gradient based er algorithm", + "entity_type": "TECHNOLOGY", + "description": "the gradient based er algorithm is a method designed to detect sharp declines characteristic of case b and isolate high relevance sets", + "source_ids": [ + 74 + ], + "id": "Name: gradient based er algorithm\nType: TECHNOLOGY" + }, + { + "entity_name": "high relevance set", + "entity_type": "DATASET_OR_CORPUS", + "description": "the high relevance set is a collection of entities isolated by the gradient based er algorithm for further processing", + "source_ids": [ + 74 + ], + "id": "Name: high relevance set\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "similar entities", + "entity_type": "DATASET_OR_CORPUS", + "description": "similar entities are a group of items identified within the high relevance set that require finer grained distinction", + "source_ids": [ + 74 + ], + "id": "Name: similar entities\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "v n", + "entity_type": "TASK_OR_PROBLEM", + "description": "v n is a new entity being processed in the entity resolution process", + "source_ids": [ + 75 + ], + "id": "Name: v n\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "e c", + "entity_type": "TASK_OR_PROBLEM", + "description": "e c represents the top k candidates retrieved for the new entity v n", + "source_ids": [ + 75 + ], + "id": "Name: e c\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "db", + "entity_type": "TASK_OR_PROBLEM", + "description": "db is the vector database from which candidates are retrieved", + "source_ids": [ + 75 + ], + "id": "Name: db\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "r", + "entity_type": "TASK_OR_PROBLEM", + "description": "r is the reranker used to re rank candidates against v n", + "source_ids": [ + 75 + ], + "id": "Name: r\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "s", + "entity_type": "TASK_OR_PROBLEM", + "description": "s represents the scores assigned to the candidates", + "source_ids": [ + 75 + ], + "id": "Name: s\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "sel", + "entity_type": "TASK_OR_PROBLEM", + "description": "sel is the selection set initialized with the top scoring candidate", + "source_ids": [ + 75 + ], + "id": "Name: sel\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "v sel", + "entity_type": "TASK_OR_PROBLEM", + "description": "v sel is the canonical entity selected from the selection set sel", + "source_ids": [ + 75 + ], + "id": "Name: v sel\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "lines 1 3", + "entity_type": "SECTION_TITLE", + "description": "lines 1 3 describe the initial retrieval and reranking steps of the algorithm", + "source_ids": [ + 75 + ], + "id": "Name: lines 1 3\nType: SECTION_TITLE" + }, + { + "entity_name": "line 4", + "entity_type": "SECTION_TITLE", + "description": "line 4 describes the initialization of the selection set and the initial score", + "source_ids": [ + 75 + ], + "id": "Name: line 4\nType: SECTION_TITLE" + }, + { + "entity_name": "lines 5 8", + "entity_type": "SECTION_TITLE", + "description": "lines 5 8 describe the iteration through remaining candidates and the gradient threshold check", + "source_ids": [ + 75 + ], + "id": "Name: lines 5 8\nType: SECTION_TITLE" + }, + { + "entity_name": "lines 7 8", + "entity_type": "SECTION_TITLE", + "description": "lines 7 8 detail the logic for adding candidates to the selection set and updating scores", + "source_ids": [ + 75 + ], + "id": "Name: lines 7 8\nType: SECTION_TITLE" + }, + { + "entity_name": "line 8", + "entity_type": "SECTION_TITLE", + "description": "line 8 describes the condition where the loop breaks upon detecting a sharp score drop", + "source_ids": [ + 75 + ], + "id": "Name: line 8\nType: SECTION_TITLE" + }, + { + "entity_name": "lines 9 14", + "entity_type": "SECTION_TITLE", + "description": "lines 9 14 describe the final decision making logic of the algorithm", + "source_ids": [ + 75 + ], + "id": "Name: lines 9 14\nType: SECTION_TITLE" + }, + { + "entity_name": "line 9 10", + "entity_type": "SECTION_TITLE", + "description": "lines 9 10 describe the action taken in case a where a new entity is added", + "source_ids": [ + 75 + ], + "id": "Name: line 9 10\nType: SECTION_TITLE" + }, + { + "entity_name": "lines 12 14", + "entity_type": "SECTION_TITLE", + "description": "lines 12 14 describe the merging of the new entity with the canonical entity in case b", + "source_ids": [ + 75 + ], + "id": "Name: lines 12 14\nType: SECTION_TITLE" + }, + { + "entity_name": "line 13", + "entity_type": "SECTION_TITLE", + "description": "line 13 describes the use of an llm to select a canonical entity when multiple aliases exist", + "source_ids": [ + 75 + ], + "id": "Name: line 13\nType: SECTION_TITLE" + }, + { + "entity_name": "line 15", + "entity_type": "SECTION_TITLE", + "description": "line 15 describes the return of the updated g and db structures", + "source_ids": [ + 75 + ], + "id": "Name: line 15\nType: SECTION_TITLE" + }, + { + "entity_name": "score", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "The score is a parameter or variable updated during iteration to track the current score value, and it also serves as the Y-axis label indicating the numerical value being measured in both charts.", + "source_ids": [ + 178, + 75 + ], + "id": "Name: score\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "v c", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "v c represents the current candidate being evaluated in the iteration", + "source_ids": [ + 75 + ], + "id": "Name: v c\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "e 9", + "entity_type": "TASK_OR_PROBLEM", + "description": "e 9 is a new entity being processed and compared against existing entities in the kg", + "source_ids": [ + 76 + ], + "id": "Name: e 9\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "e 6", + "entity_type": "TASK_OR_PROBLEM", + "description": "e 6 is an existing entity in the kg that shows a sharp decline in similarity with e 9", + "source_ids": [ + 76 + ], + "id": "Name: e 6\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "e 8", + "entity_type": "TASK_OR_PROBLEM", + "description": "e 8 is an existing entity in the kg that shows a sharp decline in similarity with e 9", + "source_ids": [ + 76 + ], + "id": "Name: e 8\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "e 5", + "entity_type": "TASK_OR_PROBLEM", + "description": "e 5 is an existing entity in the kg that shows a sharp decline in similarity with e 9", + "source_ids": [ + 76 + ], + "id": "Name: e 5\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "e 7", + "entity_type": "TASK_OR_PROBLEM", + "description": "e 7 is the final merged entity resulting from the consolidation of e 9 and e 7", + "source_ids": [ + 76 + ], + "id": "Name: e 7\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "similarity curve", + "entity_type": "IMAGE", + "description": "the similarity curve is a visual depiction orange line showing the similarity levels between entities", + "source_ids": [ + 76 + ], + "id": "Name: similarity curve\nType: IMAGE" + }, + { + "entity_name": "gradient based selection process", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "the gradient based selection process is the method used to identify high confidence matches between entities", + "source_ids": [ + 76 + ], + "id": "Name: gradient based selection process\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "orange line", + "entity_type": "IMAGE", + "description": "the orange line is a specific visual element within the similarity curve mentioned in the text", + "source_ids": [ + 76 + ], + "id": "Name: orange line\nType: IMAGE" + }, + { + "entity_name": "unique high confidence match", + "entity_type": "CONCEPT", + "description": "a unique high confidence match is the result of the gradient based selection process identifying e 7 for e 9", + "source_ids": [ + 76 + ], + "id": "Name: unique high confidence match\nType: CONCEPT" + }, + { + "entity_name": "consolidated information", + "entity_type": "CONCEPT", + "description": "consolidated information refers to the enriched data resulting from merging entities in the kg", + "source_ids": [ + 76 + ], + "id": "Name: consolidated information\nType: CONCEPT" + }, + { + "entity_name": "kg construction phase", + "entity_type": "TASK_OR_PROBLEM", + "description": "kg construction phase is a specific stage described in the text where origin tree nodes are recorded for extracted entities", + "source_ids": [ + 77 + ], + "id": "Name: kg construction phase\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "origin tree node", + "entity_type": "HARDWARE", + "description": "origin tree node is a structural location recorded for every newly extracted entity", + "source_ids": [ + 77 + ], + "id": "Name: origin tree node\nType: HARDWARE" + }, + { + "entity_name": "canonical entity", + "entity_type": "CONCEPT", + "description": "canonical entity is the target of merging during entity resolution receiving updated origin node sets", + "source_ids": [ + 77 + ], + "id": "Name: canonical entity\nType: CONCEPT" + }, + { + "entity_name": "g", + "entity_type": "CONCEPT", + "description": "g is a component of the bookindex structure b", + "source_ids": [ + 77 + ], + "id": "Name: g\nType: CONCEPT" + }, + { + "entity_name": "t", + "entity_type": "CONCEPT", + "description": "t is a component of the bookindex structure b and represents the set of structural locations nodes", + "source_ids": [ + 77 + ], + "id": "Name: t\nType: CONCEPT" + }, + { + "entity_name": "v i", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "v i represents a newly extracted entity for which an origin tree node is recorded", + "source_ids": [ + 77 + ], + "id": "Name: v i\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "v n", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "v n represents an entity that is merged into a canonical entity during entity resolution", + "source_ids": [ + 77 + ], + "id": "Name: v n\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "v sel", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "v sel represents the canonical entity into which v n is merged", + "source_ids": [ + 77 + ], + "id": "Name: v sel\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "p n", + "entity_type": "MATHEMATICAL_CONCEPT", + "description": "p n represents the power set of nodes n used in the definition of the mapping m", + "source_ids": [ + 77 + ], + "id": "Name: p n\nType: MATHEMATICAL_CONCEPT" + }, + { + "entity_name": "m", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 77 + ], + "id": "Name: m\nType: UNKNOWN" + }, + { + "entity_name": "5 agent-based retrieval", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of the main paper 'BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents', this section details the proposed agent-based query method inspired by Information Foraging Theory, which dynamically classifies queries and employs a tailored retrieval workflow.", + "source_ids": [ + 78 + ], + "id": "Name: 5 agent-based retrieval\nType: SECTION_TITLE" + }, + { + "entity_name": "agent-based query method", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Refers to the specific retrieval strategy introduced in section 5, which utilizes agents to dynamically classify queries based on Information Foraging Theory.", + "source_ids": [ + 78 + ], + "id": "Name: agent-based query method\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "bookindex", + "entity_type": "DATABASE", + "description": "bookindex is the data structure or system on which bookrag executes operations for document queries", + "source_ids": [ + 79 + ], + "id": "Name: bookindex\nType: DATABASE" + }, + { + "entity_name": "agent based planning", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Agent based planning is a method used for planning that serves as a core mechanism in Bookrag to formulate strategies for operations and acts as a component of the workflow that classifies queries. It is also a mechanism assessed for its necessity in the system's performance, and its removal in a specific scenario leads to the adoption of a default workflow.", + "source_ids": [ + 166, + 172, + 79, + 157, + 93 + ], + "id": "Name: agent based planning\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "structured execution", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "structured execution is a core mechanism in bookrag that includes the retrieval process based on ift and generation principles", + "source_ids": [ + 79 + ], + "id": "Name: structured execution\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "modal type filtering", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "modal type filtering is an operation mentioned as necessary for addressing complex real world document queries", + "source_ids": [ + 79 + ], + "id": "Name: modal type filtering\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "semantic selection", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "semantic selection is an operation mentioned as necessary for addressing complex real world document queries", + "source_ids": [ + 79 + ], + "id": "Name: semantic selection\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "multi hop reasoning", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "multi hop reasoning is an operation mentioned as necessary for addressing complex real world document queries", + "source_ids": [ + 79 + ], + "id": "Name: multi hop reasoning\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "generation", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "generation is a process included within the structured execution mechanism of bookrag", + "source_ids": [ + 79 + ], + "id": "Name: generation\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "real world document queries", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 79 + ], + "id": "Name: real world document queries\nType: UNKNOWN" + }, + { + "entity_name": "5.1 overall workflow", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Agent-Based Retrieval', this section introduces the general operational flow of the BookRAG system, outlining how it intelligently plans and executes operations on the BookIndex to handle complex document queries.", + "source_ids": [ + 80 + ], + "id": "Name: 5.1 overall workflow\nType: SECTION_TITLE" + }, + { + "entity_name": "figure 3", + "entity_type": "IMAGE", + "description": "Figure 3 is an illustration depicting the general workflow of agent-based retrieval in BookRag.", + "source_ids": [ + 81, + 83 + ], + "id": "Name: figure 3\nType: IMAGE" + }, + { + "entity_name": "three stage pipeline", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "the three stage pipeline is the structure of the workflow used to address users queries", + "source_ids": [ + 81 + ], + "id": "Name: three stage pipeline\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "agent based planning", + "entity_type": "TASK_OR_PROBLEM", + "description": "Agent based planning is a process component within the agent based retrieval workflow of BookRag, serving as a stage that involves classification and planning for queries.", + "source_ids": [ + 82, + 83 + ], + "id": "Name: agent based planning\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "classification plan", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "classification plan is the specific stage within agent based planning aimed at distinguishing query types", + "source_ids": [ + 82 + ], + "id": "Name: classification plan\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "transformer", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "transformer is a model architecture mentioned as an example in a query regarding long range dependencies", + "source_ids": [ + 82 + ], + "id": "Name: transformer\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "rnns", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "rnns are model architectures mentioned as an example in a query regarding long range dependencies", + "source_ids": [ + 82 + ], + "id": "Name: rnns\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "bookindex", + "entity_type": "DATASET_OR_CORPUS", + "description": "Bookindex is a dataset or corpus that serves as a predefined set of operators used to generate plans for retrieval and generation strategies. It functions as a data structure, represented as b t g m, which is navigated during the retrieval process, including by the system known as bookrag.", + "source_ids": [ + 88, + 82, + 85 + ], + "id": "Name: bookindex\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "operators plan", + "entity_type": "TASK_OR_PROBLEM", + "description": "an operators plan is generated to guide retrieval and generation strategies", + "source_ids": [ + 82 + ], + "id": "Name: operators plan\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "retrieval", + "entity_type": "TASK_OR_PROBLEM", + "description": "Retrieval is a task or problem area where errors are identified as the dominant failure mode. It functions as a strategy guided by an operator's plan and serves as a process component within the agent-based retrieval workflow of BookRag. In the context of graph-based RAG methods, retrieval is the process performed after extracting textual content, while in other contexts, it refers to the effectiveness of retrieving information, which is evaluated. Additionally, retrieval involves the process of finding evidence, a task that is improved by layout parsing.", + "source_ids": [ + 82, + 83, + 147, + 151, + 152, + 185 + ], + "id": "Name: retrieval\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "generation", + "entity_type": "TASK_OR_PROBLEM", + "description": "Generation is a strategy guided by the operators plan and serves as a process component within the agent-based retrieval workflow of Bookrag. It is the generative component integrated into the structured execution workflow, where both selection paths proceed to this stage. As a task or problem area, generation involves the process of creating output, which is made accurate by Bookrag, and it is notable as the second most common failure mode where errors are identified.", + "source_ids": [ + 82, + 115, + 83, + 152, + 185, + 123 + ], + "id": "Name: generation\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "agent based retrieval", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "agent based retrieval is a workflow containing planning retrieval and generation processes used in bookrag", + "source_ids": [ + 83 + ], + "id": "Name: agent based retrieval\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "workflow", + "entity_type": "TASK_OR_PROBLEM", + "description": "Workflow refers to the general process flow of agent-based retrieval in BookRag and is defined as the generated sequence of operations executed by BookRag.", + "source_ids": [ + 83, + 124 + ], + "id": "Name: workflow\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "planning", + "entity_type": "TASK_OR_PROBLEM", + "description": "Planning is a specific step within the agent-based retrieval process where a plan is formulated to solve the query, and it represents the task or problem component that is removed in the described scenario.", + "source_ids": [ + 83, + 166, + 94 + ], + "id": "Name: planning\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "generation processes", + "entity_type": "TASK_OR_PROBLEM", + "description": "generation processes are a component of the agent based retrieval workflow in bookrag", + "source_ids": [ + 83 + ], + "id": "Name: generation processes\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "cref='#/texts/89'", + "entity_type": "IMAGE", + "description": "A flowchart diagram illustrating a three-stage process involving planning, retrieval, and generation to answer a question.", + "source_ids": [ + 84 + ], + "id": "Name: cref='#/texts/89'\nType: IMAGE" + }, + { + "entity_name": "question", + "entity_type": "TASK_OR_PROBLEM", + "description": "A question serves as the input trigger for the system, often represented by an icon of a person with a question mark. It is recognized as a source of key entities used to identify information scents and functions as the item that needs to be answered in a single hop task.", + "source_ids": [ + 243, + 84, + 125 + ], + "id": "Name: question\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "agent-based planning", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Agent-based planning is a methodology where an agent formulates strategies to handle complex retrieval tasks involving modal filtering and multi-hop reasoning. It serves as the first stage of a process that manages classification and planning tasks, functioning as a strategy where operators are selected to decompose or handle specific queries.", + "source_ids": [ + 84, + 182, + 87 + ], + "id": "Name: agent-based planning\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "retrieval process", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "The retrieval process is the second stage of the overall procedure, which employs scent or filter-based mechanisms to retrieve information. It specifically refers to the mechanism for retrieving information from the BookIndex as described in section 5.3.", + "source_ids": [ + 123, + 84 + ], + "id": "Name: retrieval process\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "generation process", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "The third stage of the process, responsible for analysis and merging data to form the output.", + "source_ids": [ + 84 + ], + "id": "Name: generation process\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "retrieval process", + "entity_type": "TASK_OR_PROBLEM", + "description": "retrieval process is a stage guided by an operator plan that executes scent filter based retrieval", + "source_ids": [ + 85 + ], + "id": "Name: retrieval process\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "scent filter based retrieval", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "scent filter based retrieval is the specific method executed during the retrieval process to find information", + "source_ids": [ + 85 + ], + "id": "Name: scent filter based retrieval\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "t", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "t is a component of the bookindex data structure, which includes the elements b, t, g, and m.", + "source_ids": [ + 88, + 85 + ], + "id": "Name: t\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "operator plan", + "entity_type": "TASK_OR_PROBLEM", + "description": "An operator plan is the guiding document or set of instructions for the retrieval process, representing the final task of an agent to generate an executable plan after classifying a query. It consists of the specific sequence of operators chosen to solve the problem, such as Extract, Select, Reason, Skyline, and Map.", + "source_ids": [ + 112, + 85, + 94 + ], + "id": "Name: operator plan\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "modal type", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "modal type is a specific filter used to refine the selection of information during retrieval", + "source_ids": [ + 85 + ], + "id": "Name: modal type\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "relevant entities", + "entity_type": "DATASET_OR_CORPUS", + "description": "relevant entities are the items found in g that are followed during scent based retrieval", + "source_ids": [ + 85 + ], + "id": "Name: relevant entities\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "information blocks", + "entity_type": "DATASET_OR_CORPUS", + "description": "information blocks are the highly relevant units of data retrieved by bookrag", + "source_ids": [ + 85 + ], + "id": "Name: information blocks\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "generation process", + "entity_type": "TASK_OR_PROBLEM", + "description": "generation process is the final stage where retrieved information is synthesized and analyzed to formulate a coherent response", + "source_ids": [ + 86 + ], + "id": "Name: generation process\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "analysis merging", + "entity_type": "TASK_OR_PROBLEM", + "description": "analysis merging is the specific activity within the generation stage that synthesizes fragmented evidence", + "source_ids": [ + 86 + ], + "id": "Name: analysis merging\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "retrieved information", + "entity_type": "DATASET_OR_CORPUS", + "description": "retrieved information refers to the data collected and brought into the generation stage for processing", + "source_ids": [ + 86 + ], + "id": "Name: retrieved information\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "fragmented pieces of evidence", + "entity_type": "DATASET_OR_CORPUS", + "description": "fragmented pieces of evidence are the specific incomplete data items that are synthesized during the process", + "source_ids": [ + 86 + ], + "id": "Name: fragmented pieces of evidence\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "coherent response", + "entity_type": "PRODUCT", + "description": "coherent response is the final output formulated by the generation stage after analysis", + "source_ids": [ + 86 + ], + "id": "Name: coherent response\nType: PRODUCT" + }, + { + "entity_name": "5.2 agent-based planning", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Agent-Based Retrieval' (Section 5), this section details the strategy formulation mechanism within the BookRAG framework, explaining how an agent intelligently plans operations for complex document queries.", + "source_ids": [ + 87 + ], + "id": "Name: 5.2 agent-based planning\nType: SECTION_TITLE" + }, + { + "entity_name": "formulator", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "The formulator is one of four types of operators defined to support flexible retrieval in BookRag and is one of the four operator types depicted in the BookRag operator library.", + "source_ids": [ + 88, + 93 + ], + "id": "Name: formulator\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "selector", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "Selector is one of four types of operators defined to support flexible retrieval in BookRag and is one of the four operator types depicted in the BookRag operator library.", + "source_ids": [ + 88, + 93 + ], + "id": "Name: selector\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "reasoner", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "Reasoner is one of the four operator types defined in the BookRag operator library to support flexible retrieval.", + "source_ids": [ + 88, + 93 + ], + "id": "Name: reasoner\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "synthesizer", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "The synthesizer is one of four operator types defined within the BookRag operator library to support flexible retrieval.", + "source_ids": [ + 88, + 93 + ], + "id": "Name: synthesizer\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "agent", + "entity_type": "TASK_OR_PROBLEM", + "description": "The agent is an entity that performs the first step of the sequential process in BookRAG and employs operators for diverse query categories.", + "source_ids": [ + 88, + 97 + ], + "id": "Name: agent\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "query categories", + "entity_type": "TASK_OR_PROBLEM", + "description": "Query categories are specific requirements that BookRags adapts to using its operators, representing the diverse groups of queries for which the agent employs these operators.", + "source_ids": [ + 88, + 97 + ], + "id": "Name: query categories\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "execution pipelines", + "entity_type": "TASK_OR_PROBLEM", + "description": "execution pipelines are formed by combining operators to support flexible retrieval", + "source_ids": [ + 88 + ], + "id": "Name: execution pipelines\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "adjustable parameters", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "adjustable parameters are attributes of the execution pipelines that can be configured", + "source_ids": [ + 88 + ], + "id": "Name: adjustable parameters\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "table 2", + "entity_type": "TABLE", + "description": "Table 2 is a reference in the text that lists and defines three common query categories addressed in BookRags.", + "source_ids": [ + 96, + 89 + ], + "id": "Name: table 2\nType: TABLE" + }, + { + "entity_name": "table: cref='#/texts/95'...", + "entity_type": "TABLE", + "description": "A data table described as: cref='#/texts/95'", + "source_ids": [ + 90 + ], + "id": "Name: table: cref='#/texts/95'...\nType: TABLE" + }, + { + "entity_name": "6", + "entity_type": "MEASUREMENT", + "description": "6 is a numerical value mentioned in the text, potentially representing a count or measurement, and specifically serves as the issue number of the journal ACM Computing Surveys where the paper was published.", + "source_ids": [ + 202, + 91 + ], + "id": "Name: 6\nType: MEASUREMENT" + }, + { + "entity_name": "operator set", + "entity_type": "TASK_OR_PROBLEM", + "description": "operator set is a task or problem mentioned in the text likely referring to a specific set of operators in a technical context", + "source_ids": [ + 92 + ], + "id": "Name: operator set\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "figure 4", + "entity_type": "IMAGE", + "description": "Figure 4 is a visual element referenced in the text, specifically part A, that depicts the BookRAG operator library along with an execution example.", + "source_ids": [ + 97, + 93 + ], + "id": "Name: figure 4\nType: IMAGE" + }, + { + "entity_name": "bookrag operator library", + "entity_type": "SOFTWARE", + "description": "the bookrag operator library is a software component containing four operator types", + "source_ids": [ + 93 + ], + "id": "Name: bookrag operator library\nType: SOFTWARE" + }, + { + "entity_name": "mmlongbench dataset", + "entity_type": "DATASET_OR_CORPUS", + "description": "the mmlongbench dataset is the source of the execution example shown in the text", + "source_ids": [ + 93 + ], + "id": "Name: mmlongbench dataset\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "single hop", + "entity_type": "TASK_OR_PROBLEM", + "description": "Single hop is a type of query and task where an agent first attempts to extract an entity, often used to evaluate the performance of systems like BookRag and in QA performance breakdowns. It refers to a specific query case where the reasoning space is significantly reduced, such as from 134 to 24 nodes, and is characterized by the ability to answer a question by retrieving information from a single location, with execution traces often demonstrated to illustrate its mechanics.", + "source_ids": [ + 135, + 177, + 115, + 243, + 179, + 186, + 93 + ], + "id": "Name: single hop\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "operator", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "operator is a general term for the components formulator selector reasoner synthesizer within the bookrag system", + "source_ids": [ + 93 + ], + "id": "Name: operator\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "execution trace", + "entity_type": "TASK_OR_PROBLEM", + "description": "execution trace is the step by step record of the agent based planning and operator execution shown in the text", + "source_ids": [ + 93 + ], + "id": "Name: execution trace\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "step by step operator execution", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "step by step operator execution is the method of executing operators demonstrated in the text", + "source_ids": [ + 93 + ], + "id": "Name: step by step operator execution\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "operator-set", + "entity_type": "IMAGE", + "description": "A diagram illustrating a framework for processing queries, divided into an 'Operators' section and an 'Execution example' section.", + "source_ids": [ + 94 + ], + "id": "Name: operator-set\nType: IMAGE" + }, + { + "entity_name": "extract", + "entity_type": "TASK_OR_PROBLEM", + "description": "The initial step in the operator set where questions are decomposed to identify entities.", + "source_ids": [ + 94 + ], + "id": "Name: extract\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "decompose", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Decompose is a method and specific operator used within the Extract phase that breaks down a complex query into simpler, actionable sub-queries, a technique leveraged by BookRag to prune search spaces.", + "source_ids": [ + 186, + 94, + 98 + ], + "id": "Name: decompose\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "entities", + "entity_type": "DATASET_OR_CORPUS", + "description": "The output of the Extract phase, representing distinct items identified from the input text.", + "source_ids": [ + 94 + ], + "id": "Name: entities\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "sub-queries", + "entity_type": "TASK_OR_PROBLEM", + "description": "Smaller queries generated during the decomposition process.", + "source_ids": [ + 94 + ], + "id": "Name: sub-queries\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "formulator", + "entity_type": "SYSTEM_COMPONENT", + "description": "The component or agent responsible for the extraction and decomposition steps.", + "source_ids": [ + 94 + ], + "id": "Name: formulator\nType: SYSTEM_COMPONENT" + }, + { + "entity_name": "filter", + "entity_type": "TASK_OR_PROBLEM", + "description": "An operator that processes data structures like trees to select relevant information.", + "source_ids": [ + 94 + ], + "id": "Name: filter\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "select", + "entity_type": "TASK_OR_PROBLEM", + "description": "The action performed by the Filter operator to choose specific elements.", + "source_ids": [ + 94 + ], + "id": "Name: select\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "selector", + "entity_type": "SYSTEM_COMPONENT", + "description": "The component responsible for filtering and selecting data based on criteria.", + "source_ids": [ + 94 + ], + "id": "Name: selector\nType: SYSTEM_COMPONENT" + }, + { + "entity_name": "reason", + "entity_type": "TASK_OR_PROBLEM", + "description": "An operator that takes Graph and Text inputs to perform reasoning tasks.", + "source_ids": [ + 94 + ], + "id": "Name: reason\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "graph", + "entity_type": "DATA_STRUCTURE", + "description": "A visual representation of data used as input for the Reason operator.", + "source_ids": [ + 94 + ], + "id": "Name: graph\nType: DATA_STRUCTURE" + }, + { + "entity_name": "text", + "entity_type": "DATA_STRUCTURE", + "description": "Raw textual data used as input for the Reason operator.", + "source_ids": [ + 94 + ], + "id": "Name: text\nType: DATA_STRUCTURE" + }, + { + "entity_name": "s:", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "A label indicating a score or similarity matrix with values such as 0.6, 0.5, 0.4.", + "source_ids": [ + 94 + ], + "id": "Name: s:\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "skyline", + "entity_type": "TASK_OR_PROBLEM", + "description": "An operator that processes ranked lists (S1, S2) to find optimal solutions.", + "source_ids": [ + 94 + ], + "id": "Name: skyline\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "reasoner", + "entity_type": "SYSTEM_COMPONENT", + "description": "The component executing the Reason and Skyline operations.", + "source_ids": [ + 94 + ], + "id": "Name: reasoner\nType: SYSTEM_COMPONENT" + }, + { + "entity_name": "map", + "entity_type": "TASK_OR_PROBLEM", + "description": "The map is an operator that transforms data using icons representing different formats and performs analysis on specific retrieved information segments to generate partial responses.", + "source_ids": [ + 94, + 111 + ], + "id": "Name: map\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "reduce", + "entity_type": "TASK_OR_PROBLEM", + "description": "Reduce is an operator that combines multiple inputs into a single result by synthesizing a final coherent answer through the aggregation of information from various sources.", + "source_ids": [ + 94, + 111 + ], + "id": "Name: reduce\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "synthesizer", + "entity_type": "SYSTEM_COMPONENT", + "description": "The final component that aggregates results into a coherent answer.", + "source_ids": [ + 94 + ], + "id": "Name: synthesizer\nType: SYSTEM_COMPONENT" + }, + { + "entity_name": "execution example", + "entity_type": "SECTION_TITLE", + "description": "A subsection of the diagram showing a concrete application of the operator set.", + "source_ids": [ + 94 + ], + "id": "Name: execution example\nType: SECTION_TITLE" + }, + { + "entity_name": "q: what is the type of car in the ranking prompt example?", + "entity_type": "TASK_OR_PROBLEM", + "description": "The specific user question being processed in the execution example.", + "source_ids": [ + 94 + ], + "id": "Name: q: what is the type of car in the ranking prompt example?\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "simple query...", + "entity_type": "TASK_OR_PROBLEM", + "description": "A classification of the input query.", + "source_ids": [ + 94 + ], + "id": "Name: simple query...\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "car", + "entity_type": "PRODUCT", + "description": "A car is a key entity identified in the query regarding the type of car in the ranking prompt example.", + "source_ids": [ + 94, + 135 + ], + "id": "Name: car\nType: PRODUCT" + }, + { + "entity_name": "ranking prompt", + "entity_type": "BOOK", + "description": "An entity mentioned in the question context.", + "source_ids": [ + 94 + ], + "id": "Name: ranking prompt\nType: BOOK" + }, + { + "entity_name": "method", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "A node in the planning graph representing the method to be used.", + "source_ids": [ + 94 + ], + "id": "Name: method\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "method and its descendants", + "entity_type": "SECTION_TITLE", + "description": "A grouping of nodes related to the Method in the execution flow.", + "source_ids": [ + 94 + ], + "id": "Name: method and its descendants\nType: SECTION_TITLE" + }, + { + "entity_name": "a: based on the provided information...", + "entity_type": "TASK_OR_PROBLEM", + "description": "The final answer generated by the system.", + "source_ids": [ + 94 + ], + "id": "Name: a: based on the provided information...\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "mercedes-benz e-class sedan", + "entity_type": "VEHICLE", + "description": "The specific car type identified as the correct answer in the example.", + "source_ids": [ + 94 + ], + "id": "Name: mercedes-benz e-class sedan\nType: VEHICLE" + }, + { + "entity_name": "image cref='#/texts/98'", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 94 + ], + "id": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "entity_name": "query classification", + "entity_type": "TASK_OR_PROBLEM", + "description": "Query classification is a task designed to determine the appropriate solution strategy by categorizing queries based on their complexity, thereby enabling effective agent strategy selection, and it is the specific problem for which the prompt in figure 10 is intended.", + "source_ids": [ + 96, + 253, + 95 + ], + "id": "Name: query classification\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "operator plan", + "entity_type": "PRODUCT", + "description": "operator plan is a specific output generated after determining the solution strategy", + "source_ids": [ + 95 + ], + "id": "Name: operator plan\nType: PRODUCT" + }, + { + "entity_name": "single hop", + "entity_type": "EVENT", + "description": "single hop is a query category requiring a single piece of information retrieved via a scent based retrieval operation", + "source_ids": [ + 96 + ], + "id": "Name: single hop\nType: EVENT" + }, + { + "entity_name": "multi hop", + "entity_type": "EVENT", + "description": "multi hop is a query category defined by its intrinsic complexity and operational demands", + "source_ids": [ + 96 + ], + "id": "Name: multi hop\nType: EVENT" + }, + { + "entity_name": "global aggregation", + "entity_type": "EVENT", + "description": "global aggregation is a query category necessitating analysis under multiple filtering conditions", + "source_ids": [ + 96 + ], + "id": "Name: global aggregation\nType: EVENT" + }, + { + "entity_name": "scent based retrieval", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "scent based retrieval is a method used to retrieve a single piece of information for single hop queries", + "source_ids": [ + 96 + ], + "id": "Name: scent based retrieval\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "filter aggregation", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "filter aggregation is a sequence of operations used to analyze content under multiple filtering conditions for global aggregation queries", + "source_ids": [ + 96 + ], + "id": "Name: filter aggregation\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "agent strategy selection", + "entity_type": "TASK_OR_PROBLEM", + "description": "agent strategy selection is a process enabled by query classification to determine the appropriate solution strategy", + "source_ids": [ + 96 + ], + "id": "Name: agent strategy selection\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "intrinsic complexity", + "entity_type": "CONCEPT", + "description": "intrinsic complexity is an attribute used to define the query categories", + "source_ids": [ + 96 + ], + "id": "Name: intrinsic complexity\nType: CONCEPT" + }, + { + "entity_name": "operational demands", + "entity_type": "CONCEPT", + "description": "operational demands are factors used to define the query categories alongside intrinsic complexity", + "source_ids": [ + 96 + ], + "id": "Name: operational demands\nType: CONCEPT" + }, + { + "entity_name": "solution strategy", + "entity_type": "CONCEPT", + "description": "solution strategy refers to the different approaches required for each query category", + "source_ids": [ + 96 + ], + "id": "Name: solution strategy\nType: CONCEPT" + }, + { + "entity_name": "filtering conditions", + "entity_type": "CONCEPT", + "description": "filtering conditions are multiple criteria used in the analysis of global aggregation queries", + "source_ids": [ + 96 + ], + "id": "Name: filtering conditions\nType: CONCEPT" + }, + { + "entity_name": "document", + "entity_type": "OBJECT", + "description": "document refers to the source material where content is analyzed during global aggregation queries", + "source_ids": [ + 96 + ], + "id": "Name: document\nType: OBJECT" + }, + { + "entity_name": "additional operators", + "entity_type": "SOFTWARE", + "description": "additional operators are components integrated into bookrag to extend its capabilities", + "source_ids": [ + 96 + ], + "id": "Name: additional operators\nType: SOFTWARE" + }, + { + "entity_name": "bookindex operators", + "entity_type": "TASK_OR_PROBLEM", + "description": "bookindex operators are a set of strategies designed to execute tasks identified by classification", + "source_ids": [ + 97 + ], + "id": "Name: bookindex operators\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "o", + "entity_type": "TASK_OR_PROBLEM", + "description": "o represents the set of operators tailored for the bookindex", + "source_ids": [ + 97 + ], + "id": "Name: o\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "figure 4 a", + "entity_type": "IMAGE", + "description": "figure 4 a is a visual depiction of the operators", + "source_ids": [ + 97 + ], + "id": "Name: figure 4 a\nType: IMAGE" + }, + { + "entity_name": "table 3", + "entity_type": "TABLE", + "description": "Table 3 provides detailed information about the operators utilized in BookRAG by categorizing them according to their function.", + "source_ids": [ + 97, + 131 + ], + "id": "Name: table 3\nType: TABLE" + }, + { + "entity_name": "classification", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "classification is the method used to identify the strategies executed by the operators", + "source_ids": [ + 97 + ], + "id": "Name: classification\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "formulator", + "entity_type": "TASK_OR_PROBLEM", + "description": "formulator is a category of llm based operators that prepare queries for execution", + "source_ids": [ + 98 + ], + "id": "Name: formulator\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "extract", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Extract is a method that employs a large language model to identify key entities from query text and link them to a knowledge graph, such as identifying entities like \"car\".", + "source_ids": [ + 98, + 135 + ], + "id": "Name: extract\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "qs", + "entity_type": "TASK_OR_PROBLEM", + "description": "qs represents the set of simpler actionable sub queries generated by the decompose method", + "source_ids": [ + 98 + ], + "id": "Name: qs\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "eq", + "entity_type": "TASK_OR_PROBLEM", + "description": "eq represents the set of key entities identified by the extract method", + "source_ids": [ + 98 + ], + "id": "Name: eq\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "pdec", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "pdec is a parameter used in the llm function to generate sub queries", + "source_ids": [ + 98 + ], + "id": "Name: pdec\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "pext", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "pext is a parameter used in the llm function to identify key entities", + "source_ids": [ + 98 + ], + "id": "Name: pext\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "sub queries", + "entity_type": "TASK_OR_PROBLEM", + "description": "sub queries are the simpler actionable components resulting from breaking down a complex query", + "source_ids": [ + 98 + ], + "id": "Name: sub queries\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "query text", + "entity_type": "TASK_OR_PROBLEM", + "description": "query text is the source material from which the extract method identifies key entities", + "source_ids": [ + 98 + ], + "id": "Name: query text\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "entities", + "entity_type": "TASK_OR_PROBLEM", + "description": "entities are the key items identified in the query text and linked to the knowledge graph", + "source_ids": [ + 98 + ], + "id": "Name: entities\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "formula (2)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the output Q(s) as a set of query vectors generated by an LLM. LaTeX: 𝑄 𝑠 = LLM ( 𝑃 𝐷𝑒𝑐 , 𝑞 ) = { 𝑞 , 𝑞 1 2 , . . . , 𝑞 𝑘 } (2)", + "source_ids": [ + 99 + ], + "id": "Name: formula (2)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "formula (3)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the output of an LLM function as a set of elements. LaTeX: 𝐸 𝑞 = LLM ( 𝑃 𝐸𝑥𝑡 , 𝑞 ) = { 𝑒 1 , 𝑒 2 , . . . , 𝑒 𝑚 } (3)", + "source_ids": [ + 100 + ], + "id": "Name: formula (3)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "p dec", + "entity_type": "SOFTWARE", + "description": "p dec represents a prompt used to guide the llm for the decomposition task", + "source_ids": [ + 101 + ], + "id": "Name: p dec\nType: SOFTWARE" + }, + { + "entity_name": "p ext", + "entity_type": "SOFTWARE", + "description": "p ext represents a prompt used to guide the llm for the extraction task", + "source_ids": [ + 101 + ], + "id": "Name: p ext\nType: SOFTWARE" + }, + { + "entity_name": "decomposition", + "entity_type": "TASK_OR_PROBLEM", + "description": "decomposition is a task for which the prompt p dec is used to guide the llm", + "source_ids": [ + 101 + ], + "id": "Name: decomposition\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "extraction", + "entity_type": "TASK_OR_PROBLEM", + "description": "extraction is a task for which the prompt p ext is used to guide the llm", + "source_ids": [ + 101 + ], + "id": "Name: extraction\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "prompt", + "entity_type": "SOFTWARE", + "description": "A prompt is a software component consisting of instructions used to guide large language models for specific tasks and is also utilized for entity resolution judgement.", + "source_ids": [ + 284, + 101 + ], + "id": "Name: prompt\nType: SOFTWARE" + }, + { + "entity_name": "selector", + "entity_type": "TECHNOLOGY", + "description": "A selector is an operator or component used to filter or select specific content ranges from a bookindex, and it can also be removed to force reasoners to score all candidate nodes.", + "source_ids": [ + 102, + 167 + ], + "id": "Name: selector\nType: TECHNOLOGY" + }, + { + "entity_name": "filter modal", + "entity_type": "TECHNOLOGY", + "description": "filter modal is an operator that applies explicit constraints to the bookindex", + "source_ids": [ + 102 + ], + "id": "Name: filter modal\nType: TECHNOLOGY" + }, + { + "entity_name": "filter range", + "entity_type": "TECHNOLOGY", + "description": "filter range is an operator that applies explicit constraints to the bookindex", + "source_ids": [ + 102 + ], + "id": "Name: filter range\nType: TECHNOLOGY" + }, + { + "entity_name": "n f", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "n f is the filtered subset of nodes produced by the operators", + "source_ids": [ + 102 + ], + "id": "Name: n f\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "c n", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "c n is a predicate that holds true for each node in the filtered subset", + "source_ids": [ + 102 + ], + "id": "Name: c n\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "modal types", + "entity_type": "CONCEPT", + "description": "modal types are a specific type of explicit constraint c mentioned in the text", + "source_ids": [ + 102 + ], + "id": "Name: modal types\nType: CONCEPT" + }, + { + "entity_name": "page ranges", + "entity_type": "CONCEPT", + "description": "page ranges are a specific type of explicit constraint c mentioned in the text", + "source_ids": [ + 102 + ], + "id": "Name: page ranges\nType: CONCEPT" + }, + { + "entity_name": "plan", + "entity_type": "TASK_OR_PROBLEM", + "description": "Plan refers to the planning aspect of the process where errors are analyzed and during which explicit constraints are generated.", + "source_ids": [ + 185, + 102 + ], + "id": "Name: plan\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "nodes", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "nodes are the individual elements within the tree t that are evaluated by the predicate", + "source_ids": [ + 102 + ], + "id": "Name: nodes\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "edges", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "edges are the connections within the tree t denoted as e t", + "source_ids": [ + 102 + ], + "id": "Name: edges\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "formula (4)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the set N_f as a subset of N based on condition C. LaTeX: 𝑁 𝑓 = { 𝑛 ∈ 𝑁 | 𝐶 𝑛 ( )} (4)", + "source_ids": [ + 103 + ], + "id": "Name: formula (4)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "select by entity", + "entity_type": "TECHNOLOGY", + "description": "select by entity is a method that targets contiguous document segments by retrieving subtrees rooted at specific section nodes", + "source_ids": [ + 104 + ], + "id": "Name: select by entity\nType: TECHNOLOGY" + }, + { + "entity_name": "select by section", + "entity_type": "TECHNOLOGY", + "description": "select by section is a method that targets contiguous document segments by retrieving subtrees rooted at specific section nodes", + "source_ids": [ + 104 + ], + "id": "Name: select by section\nType: TECHNOLOGY" + }, + { + "entity_name": "s target", + "entity_type": "TASK_OR_PROBLEM", + "description": "s target represents a set of target section nodes at a specified depth", + "source_ids": [ + 104 + ], + "id": "Name: s target\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "e q", + "entity_type": "TASK_OR_PROBLEM", + "description": "e q represents the entities linked to sections via gt link", + "source_ids": [ + 104 + ], + "id": "Name: e q\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "n s", + "entity_type": "TASK_OR_PROBLEM", + "description": "n s represents the selected node set formed by retrieving descendants of target sections", + "source_ids": [ + 104 + ], + "id": "Name: n s\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "subtree", + "entity_type": "TASK_OR_PROBLEM", + "description": "subtree refers to the data structure rooted at specific section nodes that is retrieved by the methods", + "source_ids": [ + 104 + ], + "id": "Name: subtree\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "section node", + "entity_type": "TASK_OR_PROBLEM", + "description": "section node is a specific node within the document structure that serves as a root for subtrees", + "source_ids": [ + 104 + ], + "id": "Name: section node\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "depth", + "entity_type": "MEASUREMENT", + "description": "depth is a specified parameter determining the level of the target section nodes", + "source_ids": [ + 104 + ], + "id": "Name: depth\nType: MEASUREMENT" + }, + { + "entity_name": "descendant", + "entity_type": "TASK_OR_PROBLEM", + "description": "descendant refers to the nodes below the target section nodes that are retrieved to form the selected node set", + "source_ids": [ + 104 + ], + "id": "Name: descendant\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "formula (5)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the variable N_s as a glyph value for an element s in set S within a target subtree. LaTeX: 𝑁 𝑠 = GLYPH<216> 𝑠 ∈ 𝑆 target Subtree ( 𝑠 ) (5)", + "source_ids": [ + 105 + ], + "id": "Name: formula (5)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "reasoner", + "entity_type": "TASK_OR_PROBLEM", + "description": "reasoner is described as a component that analyzes and refines selected tree nodes", + "source_ids": [ + 106 + ], + "id": "Name: reasoner\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "graph reasoning", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "graph reasoning is a method that performs multi hop inference on a subgraph starting from an entity", + "source_ids": [ + 106 + ], + "id": "Name: graph reasoning\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "pagerank algorithm", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "pagerank algorithm is used to compute an entity importance vector over a subgraph", + "source_ids": [ + 106 + ], + "id": "Name: pagerank algorithm\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "gt link matrix", + "entity_type": "SOFTWARE", + "description": "gt link matrix is a matrix used to map entity scores to tree nodes to derive importance scores", + "source_ids": [ + 106 + ], + "id": "Name: gt link matrix\nType: SOFTWARE" + }, + { + "entity_name": "entity importance vector", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "entity importance vector is a vector computed over a subgraph representing the importance of entities", + "source_ids": [ + 106 + ], + "id": "Name: entity importance vector\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "tree node importance scores vector", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "tree node importance scores vector is the final vector derived by mapping entity scores to tree nodes", + "source_ids": [ + 106 + ], + "id": "Name: tree node importance scores vector\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "subgraph", + "entity_type": "TASK_OR_PROBLEM", + "description": "subgraph is a portion of a graph extracted from selected nodes on which inference is performed", + "source_ids": [ + 106 + ], + "id": "Name: subgraph\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "entity", + "entity_type": "TASK_OR_PROBLEM", + "description": "Entity serves as the starting point for the multi-hop inference process in graph reasoning and is also the object that the agent attempts to extract during the single-hop process.", + "source_ids": [ + 106, + 115 + ], + "id": "Name: entity\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "selected nodes", + "entity_type": "TASK_OR_PROBLEM", + "description": "selected nodes are the nodes from which a subgraph is extracted for graph reasoning", + "source_ids": [ + 106 + ], + "id": "Name: selected nodes\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "20", + "entity_type": "PUBLICATION_VENUE", + "description": "20 is a citation reference associated with the pagerank algorithm", + "source_ids": [ + 106 + ], + "id": "Name: 20\nType: PUBLICATION_VENUE" + }, + { + "entity_name": "6", + "entity_type": "EQUATION_OR_FORMULA", + "description": "6 is the label for the equation defining the entity importance vector", + "source_ids": [ + 106 + ], + "id": "Name: 6\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "7", + "entity_type": "EQUATION_OR_FORMULA", + "description": "7 is the label for the equation defining the tree node importance scores vector", + "source_ids": [ + 106 + ], + "id": "Name: 7\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "selected tree nodes", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 106 + ], + "id": "Name: selected tree nodes\nType: UNKNOWN" + }, + { + "entity_name": "formula (6)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the PageRank of a graph G with respect to a vector e'. LaTeX: 𝐼 𝐺 = PageRank ( 𝐺 , 𝑒 ' ) (6)", + "source_ids": [ + 107 + ], + "id": "Name: formula (6)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "formula (7)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the product of S and G as equal to the product of I, G, and M. LaTeX: 𝑆 𝐺 = 𝐼 𝐺 × 𝑀 (7)", + "source_ids": [ + 108 + ], + "id": "Name: formula (7)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "text ranker", + "entity_type": "SOFTWARE", + "description": "text ranker is a system that evaluates the semantic relevance of a tree node s content to a query", + "source_ids": [ + 109 + ], + "id": "Name: text ranker\nType: SOFTWARE" + }, + { + "entity_name": "skyline ranker", + "entity_type": "SOFTWARE", + "description": "Skyline Ranker is a software system that employs the skyline operator to filter nodes based on multiple criteria and retains a specific number of nodes after analysis. It functions as a component that is disabled when the graph reasoning operator is removed, resulting in single-dimensional scoring, and is also disabled when text reasoning is removed, causing it to rely solely on graph-based scores.", + "source_ids": [ + 168, + 169, + 109, + 157 + ], + "id": "Name: skyline ranker\nType: SOFTWARE" + }, + { + "entity_name": "skyline operator", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "the skyline operator is a method used by skyline ranker to filter nodes based on scoring dimensions", + "source_ids": [ + 109 + ], + "id": "Name: skyline operator\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "query", + "entity_type": "TASK_OR_PROBLEM", + "description": "The query serves as the input for which semantic relevance is evaluated by the text ranker. It is also the input that the agent classifies into a category to generate a plan, a process handled by the agent based planning component. Additionally, a query is a specific question for which retrieval recall is recorded, particularly when PDF parsing errors occur.", + "source_ids": [ + 112, + 109, + 144, + 157 + ], + "id": "Name: query\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "relevance score", + "entity_type": "EVALUATION_METRIC", + "description": "the relevance score is a metric assigned to each node to indicate its semantic relevance to the query", + "source_ids": [ + 109 + ], + "id": "Name: relevance score\nType: EVALUATION_METRIC" + }, + { + "entity_name": "tree node", + "entity_type": "TASK_OR_PROBLEM", + "description": "the tree node is the content unit being evaluated for relevance and filtered based on scoring dimensions", + "source_ids": [ + 109 + ], + "id": "Name: tree node\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "nodes", + "entity_type": "TASK_OR_PROBLEM", + "description": "nodes are the data elements being evaluated for relevance and filtered by the ranking systems", + "source_ids": [ + 109 + ], + "id": "Name: nodes\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "scoring dimensions", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "scoring dimensions are the specified criteria used to determine if nodes are dominated by others", + "source_ids": [ + 109 + ], + "id": "Name: scoring dimensions\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "7", + "entity_type": "NUMBER", + "description": "7 is a number mentioned in the text", + "source_ids": [ + 110 + ], + "id": "Name: 7\nType: NUMBER" + }, + { + "entity_name": "synthesizer", + "entity_type": "TASK_OR_PROBLEM", + "description": "synthesizer is described as an operator responsible for content generation", + "source_ids": [ + 111 + ], + "id": "Name: synthesizer\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "content generation", + "entity_type": "TASK_OR_PROBLEM", + "description": "content generation is the primary responsibility of the synthesizer operators mentioned in the text", + "source_ids": [ + 111 + ], + "id": "Name: content generation\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "analysis", + "entity_type": "TASK_OR_PROBLEM", + "description": "analysis is the specific action performed by the map operator on retrieved information segments", + "source_ids": [ + 111 + ], + "id": "Name: analysis\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "partial responses", + "entity_type": "PRODUCT", + "description": "partial responses are the output generated by the map operator from specific retrieved information segments", + "source_ids": [ + 111 + ], + "id": "Name: partial responses\nType: PRODUCT" + }, + { + "entity_name": "final coherent answer", + "entity_type": "PRODUCT", + "description": "a final coherent answer is the result synthesized by the reduce operator by aggregating information from multiple sources", + "source_ids": [ + 111 + ], + "id": "Name: final coherent answer\nType: PRODUCT" + }, + { + "entity_name": "retrieved information segments", + "entity_type": "DATASET_OR_CORPUS", + "description": "retrieved information segments are the specific data parts that the map operator analyzes", + "source_ids": [ + 111 + ], + "id": "Name: retrieved information segments\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "multiple sources", + "entity_type": "DATASET_OR_CORPUS", + "description": "multiple sources refer to the various origins of information such as partial answers or retrieved evidence that the reduce operator aggregates", + "source_ids": [ + 111 + ], + "id": "Name: multiple sources\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "partial answers", + "entity_type": "PRODUCT", + "description": "partial answers are one of the types of information collected from multiple sources by the reduce operator", + "source_ids": [ + 111 + ], + "id": "Name: partial answers\nType: PRODUCT" + }, + { + "entity_name": "retrieved evidence", + "entity_type": "DATASET_OR_CORPUS", + "description": "retrieved evidence is one of the types of information collected from multiple sources by the reduce operator", + "source_ids": [ + 111 + ], + "id": "Name: retrieved evidence\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "agent", + "entity_type": "PERSON", + "description": "The agent is an entity that classifies queries, generates executable plans and workflows, attempts to extract entities, executes selection strategies, and performs the decomposition of problems along with the synthesis of results.", + "source_ids": [ + 112, + 115, + 118, + 135 + ], + "id": "Name: agent\nType: PERSON" + }, + { + "entity_name": "category", + "entity_type": "TASK_OR_PROBLEM", + "description": "the category is the classification result of the query used by the agent", + "source_ids": [ + 112 + ], + "id": "Name: category\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "library", + "entity_type": "ORGANIZATION", + "description": "the library is a collection of operators from which the agent selects a sequence", + "source_ids": [ + 112 + ], + "id": "Name: library\nType: ORGANIZATION" + }, + { + "entity_name": "operators", + "entity_type": "TASK_OR_PROBLEM", + "description": "operators are the specific sequence elements selected from the library to form the plan", + "source_ids": [ + 112 + ], + "id": "Name: operators\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "parameters", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "parameters are dynamically instantiated based on the query to configure the operators", + "source_ids": [ + 112 + ], + "id": "Name: parameters\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "1", + "entity_type": "TASK_OR_PROBLEM", + "description": "1 represents the specific sequence of operators selected for the plan", + "source_ids": [ + 112 + ], + "id": "Name: 1\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "agent plan", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "agent plan is the specific formulation or function used to generate the plan from the query category and library", + "source_ids": [ + 112 + ], + "id": "Name: agent plan\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "equation 8", + "entity_type": "EQUATION_OR_FORMULA", + "description": "equation 8 is the mathematical formulation agent plan describing the plan generation process", + "source_ids": [ + 112 + ], + "id": "Name: equation 8\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "formula (8)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the variable P as a function of Agent Plan with inputs q, c, and O. LaTeX: 𝑃 = Agent Plan ( 𝑞, 𝑐, O) (8)", + "source_ids": [ + 113 + ], + "id": "Name: formula (8)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "the plan", + "entity_type": "TASK_OR_PROBLEM", + "description": "the plan is a structured workflow tailored to each category", + "source_ids": [ + 114 + ], + "id": "Name: the plan\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "workflow", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "the workflow is a structured process followed by the plan", + "source_ids": [ + 114 + ], + "id": "Name: workflow\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "category", + "entity_type": "CONCEPT", + "description": "category refers to the classifications to which the plan s workflow is tailored", + "source_ids": [ + 114 + ], + "id": "Name: category\nType: CONCEPT" + }, + { + "entity_name": "scent based", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "scent based is a selection strategy used by the agent if entity extraction is successful", + "source_ids": [ + 115 + ], + "id": "Name: scent based\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "section based", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "section based is a fallback strategy used by the agent if entity extraction fails", + "source_ids": [ + 115 + ], + "id": "Name: section based\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "standard reasoning", + "entity_type": "TASK_OR_PROBLEM", + "description": "standard reasoning is a process that both selection paths proceed to", + "source_ids": [ + 115 + ], + "id": "Name: standard reasoning\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "p std", + "entity_type": "EQUATION_OR_FORMULA", + "description": "p std denotes the standard reasoning and generation process", + "source_ids": [ + 115 + ], + "id": "Name: p std\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "formula (9)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the variable P_s based on extraction success or failure conditions. LaTeX: 𝑃 s = ( Extract success - - - - -→ Select_by_Entity → 𝑃 std Extract fail - -→ Select_by_Section → 𝑃 std (9)", + "source_ids": [ + 116 + ], + "id": "Name: formula (9)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "formula (10)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the standard probability P as a process involving graph and text inputs leading to a skyline reduction. LaTeX: 𝑃 std = ( Graph ∥ Text ) → Skyline → Reduce (10)", + "source_ids": [ + 117 + ], + "id": "Name: formula (10)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "single hop workflow", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "single hop workflow is a method denoted as ps used to solve sub problems", + "source_ids": [ + 118 + ], + "id": "Name: single hop workflow\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "ps", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "ps is the specific notation or identifier for the single hop workflow applied to sub problems", + "source_ids": [ + 118 + ], + "id": "Name: ps\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "complex", + "entity_type": "TASK_OR_PROBLEM", + "description": "Complex is one of the three categories used to classify user questions and refers to a problem that is decomposed by the agent.", + "source_ids": [ + 241, + 118 + ], + "id": "Name: complex\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "formula (11)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation describing a decomposition process involving mapping and reduction. LaTeX: 𝑃 complex = Decompose → 𝑃 s → Map → Reduce (11)", + "source_ids": [ + 119 + ], + "id": "Name: formula (11)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "global aggregation", + "entity_type": "TASK_OR_PROBLEM", + "description": "Global aggregation is a workflow involving a sequence of filters followed by synthesis, and it is also a type of query used to evaluate the performance of BookRag.", + "source_ids": [ + 120, + 179 + ], + "id": "Name: global aggregation\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "formula (12)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the global probability P as a composition of filtering and mapping operations. LaTeX: 𝑃 global = GLYPH<214> ( Filter_Modal | Filter_Range ) → Map → Reduce (12)", + "source_ids": [ + 121 + ], + "id": "Name: formula (12)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "modal filter", + "entity_type": "TECHNOLOGY", + "description": "modal filter is a type of filter applied at each step of the nested composition", + "source_ids": [ + 122 + ], + "id": "Name: modal filter\nType: TECHNOLOGY" + }, + { + "entity_name": "range filter", + "entity_type": "TECHNOLOGY", + "description": "range filter is a type of filter applied at each step of the nested composition", + "source_ids": [ + 122 + ], + "id": "Name: range filter\nType: TECHNOLOGY" + }, + { + "entity_name": "nested composition", + "entity_type": "TASK_OR_PROBLEM", + "description": "nested composition refers to the process of applying filters at each step", + "source_ids": [ + 122 + ], + "id": "Name: nested composition\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "5.3 structured execution", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Agent-Based Retrieval', this section details the retrieval process within the BookRAG framework, specifically focusing on operations executed under the principles of In-Context Few-Shot Training (IFT) and generation.", + "source_ids": [ + 123 + ], + "id": "Name: 5.3 structured execution\nType: SECTION_TITLE" + }, + { + "entity_name": "ift principles", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Refers to the In-Context Few-Shot Training principles that guide the execution logic detailed in section 5.3.", + "source_ids": [ + 123 + ], + "id": "Name: ift principles\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "synthesizer", + "entity_type": "SOFTWARE", + "description": "The synthesizer is an operator within BookRag that generates a coherent final answer by aggregating and processing refined evidence.", + "source_ids": [ + 129, + 124 + ], + "id": "Name: synthesizer\nType: SOFTWARE" + }, + { + "entity_name": "p", + "entity_type": "TASK_OR_PROBLEM", + "description": "p represents the specific generated workflow executed by bookrag", + "source_ids": [ + 124 + ], + "id": "Name: p\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "abstract textual queries", + "entity_type": "TASK_OR_PROBLEM", + "description": "abstract textual queries are the input that bookrag translates into concrete operations", + "source_ids": [ + 124 + ], + "id": "Name: abstract textual queries\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "concrete operations", + "entity_type": "TASK_OR_PROBLEM", + "description": "concrete operations are the result of translating abstract textual queries within bookrag", + "source_ids": [ + 124 + ], + "id": "Name: concrete operations\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "information patches", + "entity_type": "TASK_OR_PROBLEM", + "description": "information patches are specific scopes within the document space that the selector navigates to", + "source_ids": [ + 124 + ], + "id": "Name: information patches\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "document space", + "entity_type": "TASK_OR_PROBLEM", + "description": "document space is the vast area of documents that is narrowed down by the selector", + "source_ids": [ + 124 + ], + "id": "Name: document space\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "relevant scopes", + "entity_type": "TASK_OR_PROBLEM", + "description": "relevant scopes are the focused areas within the document space identified by the selector", + "source_ids": [ + 124 + ], + "id": "Name: relevant scopes\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "sensemaking", + "entity_type": "TASK_OR_PROBLEM", + "description": "sensemaking is the process performed by the reasoner to analyze and refine information", + "source_ids": [ + 124 + ], + "id": "Name: sensemaking\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "processed evidence", + "entity_type": "TASK_OR_PROBLEM", + "description": "processed evidence is the refined information used by the synthesizer to generate the answer", + "source_ids": [ + 124 + ], + "id": "Name: processed evidence\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "cost of attention", + "entity_type": "TASK_OR_PROBLEM", + "description": "cost of attention is a metric minimized by bookrag s design to focus computational resources", + "source_ids": [ + 124 + ], + "id": "Name: cost of attention\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "computational resources", + "entity_type": "TASK_OR_PROBLEM", + "description": "computational resources are the assets focused by bookrag on high value data patches", + "source_ids": [ + 124 + ], + "id": "Name: computational resources\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "high value data patches", + "entity_type": "TASK_OR_PROBLEM", + "description": "high value data patches are the specific data areas where bookrag focuses its computational resources", + "source_ids": [ + 124 + ], + "id": "Name: high value data patches\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "scent filter based retrieval", + "entity_type": "TASK_OR_PROBLEM", + "description": "scent filter based retrieval is a process described as the execution that begins by narrowing the scope", + "source_ids": [ + 125 + ], + "id": "Name: scent filter based retrieval\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "selector operators", + "entity_type": "SOFTWARE", + "description": "selector operators are components that identify relevant patches by following information scents or applying explicit filter constraints", + "source_ids": [ + 125 + ], + "id": "Name: selector operators\nType: SOFTWARE" + }, + { + "entity_name": "node set n", + "entity_type": "DATASET_OR_CORPUS", + "description": "node set n represents the full set of nodes that is reduced by the process", + "source_ids": [ + 125 + ], + "id": "Name: node set n\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "focused node subset ns", + "entity_type": "DATASET_OR_CORPUS", + "description": "focused node subset ns is the result of the reduction process applied to the full node set n", + "source_ids": [ + 125 + ], + "id": "Name: focused node subset ns\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "params sel", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "params sel are parameters used in the selector function to define the focused node subset", + "source_ids": [ + 125 + ], + "id": "Name: params sel\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "patches", + "entity_type": "PRODUCT", + "description": "patches are relevant units identified by selector operators within the retrieval process", + "source_ids": [ + 125 + ], + "id": "Name: patches\nType: PRODUCT" + }, + { + "entity_name": "explicit filter constraints", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "explicit filter constraints are rules applied by selector operators to identify relevant patches", + "source_ids": [ + 125 + ], + "id": "Name: explicit filter constraints\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "equation 13", + "entity_type": "EQUATION_OR_FORMULA", + "description": "equation 13 defines the mathematical relationship for the selector function reducing the node set", + "source_ids": [ + 125 + ], + "id": "Name: equation 13\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "formula (13)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the variable Ns as a selector function applied to N and parameters. LaTeX: 𝑁 𝑠 = Selector ( 𝑁, params sel ) (13)", + "source_ids": [ + 126 + ], + "id": "Name: formula (13)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "reasoner operators", + "entity_type": "TASK_OR_PROBLEM", + "description": "reasoner operators are components that evaluate nodes using multiple dimensions such as graph topology and semantic relevance", + "source_ids": [ + 127 + ], + "id": "Name: reasoner operators\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "skyline ranker", + "entity_type": "TASK_OR_PROBLEM", + "description": "skyline ranker is a method employed to obtain the final retrieval set by retaining the pareto frontier of nodes", + "source_ids": [ + 127 + ], + "id": "Name: skyline ranker\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "skyline operator", + "entity_type": "TASK_OR_PROBLEM", + "description": "the skyline operator is a mechanism that retains valuable nodes in at least one dimension while discarding dominated ones", + "source_ids": [ + 127 + ], + "id": "Name: skyline operator\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "n r", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "n r represents the final retrieval set derived from the skyline ranker process", + "source_ids": [ + 127 + ], + "id": "Name: n r\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "s g n s", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "s g n s is a function or metric used within the skyline ranker equation to evaluate nodes", + "source_ids": [ + 127 + ], + "id": "Name: s g n s\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "t n", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "t n is a function or metric used within the skyline ranker equation to evaluate nodes", + "source_ids": [ + 127 + ], + "id": "Name: t n\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "n s", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "n s represents the set of nodes from which the final retrieval set is derived", + "source_ids": [ + 127 + ], + "id": "Name: n s\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "equation 14", + "entity_type": "EQUATION_OR_FORMULA", + "description": "equation 14 defines the mathematical relationship for calculating the final retrieval set n r using the skyline ranker", + "source_ids": [ + 127 + ], + "id": "Name: equation 14\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "graph topology", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "graph topology is a dimension used by reasoner operators to evaluate nodes", + "source_ids": [ + 127 + ], + "id": "Name: graph topology\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "semantic relevance", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "semantic relevance is a dimension used by reasoner operators to evaluate nodes", + "source_ids": [ + 127 + ], + "id": "Name: semantic relevance\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "pareto frontier", + "entity_type": "CONCEPT", + "description": "the pareto frontier is the set of nodes retained by the skyline operator that are valuable in at least one dimension", + "source_ids": [ + 127 + ], + "id": "Name: pareto frontier\nType: CONCEPT" + }, + { + "entity_name": "fixed top retrieval", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "fixed top retrieval is a method contrasted with the skyline operator for its inability to retain the pareto frontier", + "source_ids": [ + 127 + ], + "id": "Name: fixed top retrieval\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "noise", + "entity_type": "CONCEPT", + "description": "noise is a factor minimized by the pre selection process to optimize foraging cost", + "source_ids": [ + 127 + ], + "id": "Name: noise\nType: CONCEPT" + }, + { + "entity_name": "foraging cost", + "entity_type": "MEASUREMENT", + "description": "foraging cost is the metric optimized by minimizing noise and focusing on relevant contexts", + "source_ids": [ + 127 + ], + "id": "Name: foraging cost\nType: MEASUREMENT" + }, + { + "entity_name": "pre selection", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "pre selection is a process that minimizes noise and ensures reasoning is applied only to highly relevant contexts", + "source_ids": [ + 127 + ], + "id": "Name: pre selection\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "nodes", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 127 + ], + "id": "Name: nodes\nType: UNKNOWN" + }, + { + "entity_name": "final retrieval set", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 127 + ], + "id": "Name: final retrieval set\nType: UNKNOWN" + }, + { + "entity_name": "formula (14)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the variable NR as a Skyline Ranker applied to a set of SG and T values. LaTeX: 𝑁 𝑅 = Skyline_Ranker ({ 𝑆 𝐺 ( 𝑛 , 𝑆 ) 𝑇 ( 𝑛 ) | 𝑛 ∈ 𝑁 𝑠 }) (14)", + "source_ids": [ + 128 + ], + "id": "Name: formula (14)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "analysis merging generation", + "entity_type": "TASK_OR_PROBLEM", + "description": "analysis merging generation is described as the final stage of a process involving the synthesizer operator", + "source_ids": [ + 129 + ], + "id": "Name: analysis merging generation\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "15", + "entity_type": "EQUATION_OR_FORMULA", + "description": "15 is the label or identifier for the equation describing the synthesizer operator s function", + "source_ids": [ + 129 + ], + "id": "Name: 15\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "formula (15)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the variable A as the output of a Synthesizer function. LaTeX: 𝐴 = Synthesizer ( 𝑞, 𝑁 𝑅 ) (15)", + "source_ids": [ + 130 + ], + "id": "Name: formula (15)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "table: cref='#/texts/136'...", + "entity_type": "TABLE", + "description": "A data table described as: cref='#/texts/136'", + "source_ids": [ + 132 + ], + "id": "Name: table: cref='#/texts/136'...\nType: TABLE" + }, + { + "entity_name": "cref", + "entity_type": "EQUATION_OR_FORMULA", + "description": "A cross-reference identifier or formula string found in the description, pointing to a specific text location ('#/texts/136').", + "source_ids": [ + 132 + ], + "id": "Name: cref\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "8", + "entity_type": "MEASUREMENT", + "description": "8 is a numerical value mentioned in the text likely representing a count or identifier", + "source_ids": [ + 133 + ], + "id": "Name: 8\nType: MEASUREMENT" + }, + { + "entity_name": "map operator", + "entity_type": "TASK_OR_PROBLEM", + "description": "the map operator is a component that performs fine grained analysis on individual evidence blocks or sub problems", + "source_ids": [ + 134 + ], + "id": "Name: map operator\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "decompose", + "entity_type": "TASK_OR_PROBLEM", + "description": "decompose is a process that generates sub problems which are analyzed by the map operator", + "source_ids": [ + 134 + ], + "id": "Name: decompose\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "reduce operator", + "entity_type": "TASK_OR_PROBLEM", + "description": "the reduce operator is a component that aggregates partial results to construct the final response", + "source_ids": [ + 134 + ], + "id": "Name: reduce operator\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "global filter", + "entity_type": "TASK_OR_PROBLEM", + "description": "the global filter is a mechanism used to generate statistical counts as partial results", + "source_ids": [ + 134 + ], + "id": "Name: global filter\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "evidence blocks", + "entity_type": "TASK_OR_PROBLEM", + "description": "evidence blocks are the individual units of content that the map operator analyzes", + "source_ids": [ + 134 + ], + "id": "Name: evidence blocks\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "sub problems", + "entity_type": "TASK_OR_PROBLEM", + "description": "sub problems are specific issues derived from decompose that are analyzed by the map operator", + "source_ids": [ + 134 + ], + "id": "Name: sub problems\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "intermediate insights", + "entity_type": "TASK_OR_PROBLEM", + "description": "intermediate insights are the outputs generated by the map operator during its analysis", + "source_ids": [ + 134 + ], + "id": "Name: intermediate insights\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "partial results", + "entity_type": "TASK_OR_PROBLEM", + "description": "partial results are the outputs from the map operator that are aggregated by the reduce operator", + "source_ids": [ + 134 + ], + "id": "Name: partial results\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "answers to decomposed sub queries", + "entity_type": "TASK_OR_PROBLEM", + "description": "answers to decomposed sub queries are a type of partial result aggregated by the reduce operator", + "source_ids": [ + 134 + ], + "id": "Name: answers to decomposed sub queries\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "statistical counts", + "entity_type": "TASK_OR_PROBLEM", + "description": "statistical counts are a type of partial result derived from a global filter and aggregated by the reduce operator", + "source_ids": [ + 134 + ], + "id": "Name: statistical counts\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "final response", + "entity_type": "TASK_OR_PROBLEM", + "description": "the final response is the constructed output created by the reduce operator", + "source_ids": [ + 134 + ], + "id": "Name: final response\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "detailed content extraction", + "entity_type": "TASK_OR_PROBLEM", + "description": "detailed content extraction is a capability handled by the system s separation of map and reduce operators", + "source_ids": [ + 134 + ], + "id": "Name: detailed content extraction\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "high level reasoning synthesis", + "entity_type": "TASK_OR_PROBLEM", + "description": "high level reasoning synthesis is a capability handled by the system s separation of map and reduce operators", + "source_ids": [ + 134 + ], + "id": "Name: high level reasoning synthesis\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "figure 4 b", + "entity_type": "IMAGE", + "description": "figure 4 b is an image presenting an execution trace for a single hop query", + "source_ids": [ + 135 + ], + "id": "Name: figure 4 b\nType: IMAGE" + }, + { + "entity_name": "ranking prompt example", + "entity_type": "TASK_OR_PROBLEM", + "description": "ranking prompt example is a specific example context mentioned in the query regarding the type of car", + "source_ids": [ + 135 + ], + "id": "Name: ranking prompt example\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "select by entity", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "select by entity is a method used to retrieve relevant nodes after entity identification", + "source_ids": [ + 135 + ], + "id": "Name: select by entity\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "skyline filtering", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "skyline filtering is a technique used to refine nodes during the process", + "source_ids": [ + 135 + ], + "id": "Name: skyline filtering\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "reduce", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "reduce is a method used to synthesize the final answer", + "source_ids": [ + 135 + ], + "id": "Name: reduce\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "planning phase", + "entity_type": "TASK_OR_PROBLEM", + "description": "the planning phase is the initial stage where the agent classifies the query and generates a workflow", + "source_ids": [ + 135 + ], + "id": "Name: planning phase\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "reasoning", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Reasoning is a cognitive process of drawing conclusions that is identified as a challenge in the text and is also used as a step to refine nodes in the process.", + "source_ids": [ + 179, + 135 + ], + "id": "Name: reasoning\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "6 experiments", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of the paper 'BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents', this section details the empirical validation of the proposed BookRAG method, including experimental setup, benchmarks used, and performance results compared to baselines.", + "source_ids": [ + 136 + ], + "id": "Name: 6 experiments\nType: SECTION_TITLE" + }, + { + "entity_name": "experiments", + "entity_type": "TASK_OR_PROBLEM", + "description": "The experiments refer to the systematic computational procedures and evaluations conducted to validate the effectiveness of the BookRAG approach, as described in section 6, and represent the activities for which the datasets in table 4 were used.", + "source_ids": [ + 136, + 139 + ], + "id": "Name: experiments\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "baseline methods", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "baseline methods are the strong existing approaches used for comparison against bookrag in the experiments", + "source_ids": [ + 137 + ], + "id": "Name: baseline methods\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "accuracy", + "entity_type": "EVALUATION_METRIC", + "description": "Accuracy is a primary evaluation metric used to assess performance across various contexts, including the evaluation of bookrag and baseline methods, as well as the w o selector variant despite its high computational costs. In the Qasper dataset, it is represented by blue bars and is often contrasted with exact match, implying it is a less strict measure. Specific definitions include the proportion of cases where the set of named entities in a model's response is a subset of those in the ground truth, as well as a calculation of correctness based on whether the normalized ground truth is a substring of the normalized raw response.", + "source_ids": [ + 226, + 229, + 137, + 172, + 144, + 177, + 221 + ], + "id": "Name: accuracy\nType: EVALUATION_METRIC" + }, + { + "entity_name": "6.1 setup", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Experiments' within the BookRAG paper, this section details the experimental configuration, including baseline methods, evaluation metrics (efficiency and accuracy), and the document QA tasks used to assess the proposed approach.", + "source_ids": [ + 138 + ], + "id": "Name: 6.1 setup\nType: SECTION_TITLE" + }, + { + "entity_name": "table 4", + "entity_type": "TABLE", + "description": "Table 4 is a table that lists the datasets used in experiments and presents the statistics of those mentioned datasets.", + "source_ids": [ + 139, + 141 + ], + "id": "Name: table 4\nType: TABLE" + }, + { + "entity_name": "em", + "entity_type": "EVALUATION_METRIC", + "description": "em, short for exact match, is an evaluation metric used in experiments to measure question answering performance.", + "source_ids": [ + 170, + 139 + ], + "id": "Name: em\nType: EVALUATION_METRIC" + }, + { + "entity_name": "f1", + "entity_type": "EVALUATION_METRIC", + "description": "F1 denotes the F1 score, an evaluation metric used in experiments to measure question answering performance.", + "source_ids": [ + 170, + 139 + ], + "id": "Name: f1\nType: EVALUATION_METRIC" + }, + { + "entity_name": "exact match", + "entity_type": "EVALUATION_METRIC", + "description": "Exact match, abbreviated as EM, is a strict evaluation metric used to assess performance in text-based tasks by measuring whether the normalized extracted answer is character-for-character identical to the ground truth. It serves as a primary metric for evaluating performance, including comparisons of BookRAG against baselines, and is represented by blue bars in the MMLongBench benchmark.", + "source_ids": [ + 229, + 170, + 139, + 144, + 177, + 152 + ], + "id": "Name: exact match\nType: EVALUATION_METRIC" + }, + { + "entity_name": "f1 score", + "entity_type": "EVALUATION_METRIC", + "description": "The F1 score is an evaluation metric used to measure the performance of text span answers by comparing extracted answers to ground truth, and it is often represented by red bars.", + "source_ids": [ + 177, + 170, + 139, + 231 + ], + "id": "Name: f1 score\nType: EVALUATION_METRIC" + }, + { + "entity_name": "datasets", + "entity_type": "DATASET_OR_CORPUS", + "description": "Datasets refer to the collection of data used in experiments, serving as various collections to evaluate the performance of methods, including the gradient-based ER method.", + "source_ids": [ + 176, + 153, + 139 + ], + "id": "Name: datasets\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "our", + "entity_type": "ORGANIZATION", + "description": "our refers to the research group or team conducting the experiments mentioned in the text", + "source_ids": [ + 139 + ], + "id": "Name: our\nType: ORGANIZATION" + }, + { + "entity_name": "table: cref='#/texts/143'...", + "entity_type": "TABLE", + "description": "A data table described as: cref='#/texts/143'", + "source_ids": [ + 140 + ], + "id": "Name: table: cref='#/texts/143'...\nType: TABLE" + }, + { + "entity_name": "texts/143", + "entity_type": "SECTION_TITLE", + "description": "A reference identifier extracted from the description string 'cref='#/texts/143'', likely pointing to a specific section or text element within a document structure.", + "source_ids": [ + 140 + ], + "id": "Name: texts/143\nType: SECTION_TITLE" + }, + { + "entity_name": "mmlongbench", + "entity_type": "DATASET_OR_CORPUS", + "description": "mmlongbench is a comprehensive benchmark and dataset designed to evaluate question-answering capabilities on long-form documents, providing page numbers for filtering candidate blocks and measuring exact match performance. 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It represents a configuration category on the X-axis that denotes a global or holistic setting, specifically referring to questions that require an aggregation operation over a set of items identified by a structural filter.", + "source_ids": [ + 177, + 178, + 250, + 241 + ], + "id": "Name: global\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "blue bars", + "entity_type": "IMAGE", + "description": "blue bars represent the visual elements in the figure corresponding to exact match and accuracy metrics", + "source_ids": [ + 177 + ], + "id": "Name: blue bars\nType: IMAGE" + }, + { + "entity_name": "red bars", + "entity_type": "IMAGE", + "description": "red bars represent the visual elements in the figure corresponding to the f1 score metric", + "source_ids": [ + 177 + ], + "id": "Name: red bars\nType: IMAGE" + }, + { + "entity_name": "cref='#/texts/259'", + "entity_type": "IMAGE", + "description": "A figure containing two bar charts comparing EM/Accuracy and F1-score across Single, 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"source_ids": [ + 184, + 178 + ], + "id": "Name: (b) qasper\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "multihop", + "entity_type": "TASK_OR_PROBLEM", + "description": "multihop is a type of query that presents a greater challenge compared to single hop queries", + "source_ids": [ + 179 + ], + "id": "Name: multihop\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "agent based planning strategy", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "the agent based planning strategy is a method used to handle different query types separately", + "source_ids": [ + 179 + ], + "id": "Name: agent based planning strategy\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "disjoint pieces of evidence", + "entity_type": "DATASET_OR_CORPUS", + "description": "disjoint pieces of evidence are the fragmented information sources that make reasoning difficult", + "source_ids": [ + 179 + ], + "id": "Name: disjoint pieces of evidence\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": 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dataset used for the analysis", + "source_ids": [ + 180 + ], + "id": "Name: 200 sampled queries\nType: MEASUREMENT" + }, + { + "entity_name": "four types", + "entity_type": "MEASUREMENT", + "description": "four types refers to the number of categories into which failures are classified", + "source_ids": [ + 180 + ], + "id": "Name: four types\nType: MEASUREMENT" + }, + { + "entity_name": "figure 8", + "entity_type": "IMAGE", + "description": "Figure 8 is an image presenting a case study of responses across different query types, illustrating BookRags' answering workflow for those various query types.", + "source_ids": [ + 186, + 181 + ], + "id": "Name: figure 8\nType: IMAGE" + }, + { + "entity_name": "cyan text", + "entity_type": "COLOR", + "description": "cyan text refers to the color used to highlight correct content generated by bookrag in the figure", + "source_ids": [ + 181 + ], + "id": "Name: cyan text\nType: COLOR" + }, + { + "entity_name": "gray text", + "entity_type": "COLOR", + "description": "gray text refers to the color used to describe the internal process in the figure", + "source_ids": [ + 181 + ], + "id": "Name: gray text\nType: COLOR" + }, + { + "entity_name": "case study", + "entity_type": "EVENT", + "description": "case study is the specific analysis of responses across different query types presented in the text", + "source_ids": [ + 181 + ], + "id": "Name: case study\nType: EVENT" + }, + { + "entity_name": "internal process", + "entity_type": "TASK_OR_PROBLEM", + "description": "internal process refers to the underlying mechanisms described in the gray text", + "source_ids": [ + 181 + ], + "id": "Name: internal process\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "bookrag response of different query types", + "entity_type": "IMAGE", + "description": "A document illustrating BookRAG's responses to three distinct query types: Single-hop, Multi-hop, and Global Aggregation cases.", + "source_ids": [ + 182 + ], + "id": "Name: bookrag response of 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the study", + "source_ids": [ + 185 + ], + "id": "Name: pdf parsing\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "retrieval error", + "entity_type": "TASK_OR_PROBLEM", + "description": "retrieval error is the dominant failure mode identified in the results", + "source_ids": [ + 185 + ], + "id": "Name: retrieval error\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "generation error", + "entity_type": "TASK_OR_PROBLEM", + "description": "generation error is the second most common failure mode identified in the results", + "source_ids": [ + 185 + ], + "id": "Name: generation error\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "plan error", + "entity_type": "TASK_OR_PROBLEM", + "description": "plan error is a specific failure pattern where the planner over decomposes queries", + "source_ids": [ + 185 + ], + "id": "Name: plan error\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "multimodal evidence", + "entity_type": "TASK_OR_PROBLEM", + "description": "multimodal evidence is 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Additionally, it is recognized as a structural part of a document within the definition of section filters.", + "source_ids": [ + 258, + 190 + ], + "id": "Name: references\nType: SECTION_TITLE" + }, + { + "entity_name": "simran arora", + "entity_type": "PERSON", + "description": "simran arora is listed as one of the authors of the paper", + "source_ids": [ + 191 + ], + "id": "Name: simran arora\nType: PERSON" + }, + { + "entity_name": "brandon yang", + "entity_type": "PERSON", + "description": "brandon yang is listed as one of the authors of the paper", + "source_ids": [ + 191 + ], + "id": "Name: brandon yang\nType: PERSON" + }, + { + "entity_name": "sabri eyuboglu", + "entity_type": "PERSON", + "description": "sabri eyuboglu is listed as one of the authors of the paper", + "source_ids": [ + 191 + ], + "id": "Name: sabri eyuboglu\nType: PERSON" + }, + { + "entity_name": "avanika narayan", + "entity_type": "PERSON", + "description": "avanika narayan is listed as one of the authors of 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LaTeX: ˆ 𝑦 = LLMextract ( 𝑦 𝑟𝑎𝑤 , Instruction ) (16)", + "source_ids": [ + 225 + ], + "id": "Name: formula (16)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "a.1.2 qa performance metrics", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Experimental Details' within the BookRAG paper, this section defines the specific metrics used to evaluate Question Answering performance, detailing the calculation of Accuracy based on substring inclusion between ground truth and model responses.", + "source_ids": [ + 226 + ], + "id": "Name: a.1.2 qa performance metrics\nType: SECTION_TITLE" + }, + { + "entity_name": "qa performance metrics", + "entity_type": "EVALUATION_METRIC", + "description": "Refers to the set of quantitative measures defined in section A.1.2 for assessing the quality of answers generated by the model.", + "source_ids": [ + 226 + ], + "id": "Name: qa performance metrics\nType: EVALUATION_METRIC" + }, + { + "entity_name": "ground truth (y_gold)", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "The reference answer or expected output used as the baseline for calculating accuracy in section A.1.2.", + "source_ids": [ + 226 + ], + "id": "Name: ground truth (y_gold)\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "model response (y_raw)", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "The raw output generated by the model, which is compared against the ground truth in section A.1.2.", + "source_ids": [ + 226 + ], + "id": "Name: model response (y_raw)\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "substring inclusion relation", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "The logical operation (denoted by ⊆) used in section A.1.2 to determine if one text sequence is contained within another for the purpose of evaluation.", + "source_ids": [ + 226 + ], + "id": "Name: substring inclusion relation\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "accuracy inclusion based", + "entity_type": "EVALUATION_METRIC", + "description": "accuracy inclusion based is a soft match metric used to evaluate model predictions by checking if the normalized gold answer is included in the generated response", + "source_ids": [ + 227 + ], + "id": "Name: accuracy inclusion based\nType: EVALUATION_METRIC" + }, + { + "entity_name": "prior works", + "entity_type": "PUBLICATION_VENUE", + "description": "prior works refer to previous research studies cited in the text as a basis for the methodology", + "source_ids": [ + 227 + ], + "id": "Name: prior works\nType: PUBLICATION_VENUE" + }, + { + "entity_name": "3", + "entity_type": "PUBLICATION_VENUE", + "description": "3 is a citation number referring to a specific prior work mentioned in the text", + "source_ids": [ + 227 + ], + "id": "Name: 3\nType: PUBLICATION_VENUE" + }, + { + "entity_name": "34", + "entity_type": "PUBLICATION_VENUE", + "description": "34 is a citation number referring to a specific prior work mentioned in the text", + "source_ids": [ + 227 + ], + "id": "Name: 34\nType: PUBLICATION_VENUE" + }, + { + "entity_name": "46", + "entity_type": "PUBLICATION_VENUE", + "description": "46 is a citation number referring to a specific prior work mentioned in the text", + "source_ids": [ + 227 + ], + "id": "Name: 46\nType: PUBLICATION_VENUE" + }, + { + "entity_name": "soft match metric", + "entity_type": "EVALUATION_METRIC", + "description": "soft match metric is a category of evaluation methods described as being used in the text", + "source_ids": [ + 227 + ], + "id": "Name: soft match metric\nType: EVALUATION_METRIC" + }, + { + "entity_name": "normalized gold answer", + "entity_type": "DATASET_OR_CORPUS", + "description": "normalized gold answer is the reference data used to determine if a prediction is correct", + "source_ids": [ + 227 + ], + "id": "Name: normalized gold answer\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "model s generated response", + "entity_type": "PRODUCT", + "description": "model s generated response is the output produced by the model being evaluated", + "source_ids": [ + 227 + ], + "id": "Name: model s generated response\nType: PRODUCT" + }, + { + "entity_name": "strict exact match", + "entity_type": "EVALUATION_METRIC", + "description": "strict exact match is a comparison method explicitly contrasted with the soft match metric in the text", + "source_ids": [ + 227 + ], + "id": "Name: strict exact match\nType: EVALUATION_METRIC" + }, + { + "entity_name": "formula (17)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the Accuracy metric as the average of an indicator function comparing neighborhood sets. 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LaTeX: 𝑃 = | 𝑇 ˆ 𝑦 ∩ 𝑇 𝑔𝑜𝑙𝑑 | | 𝑇 ˆ 𝑦 | , 𝑅 = | 𝑇 ˆ 𝑦 ∩ 𝑇 𝑔𝑜𝑙𝑑 | | 𝑇 𝑔𝑜𝑙𝑑 | , F1 = 2 · 𝑃 · 𝑅 𝑃 + 𝑅 (19)", + "source_ids": [ + 232 + ], + "id": "Name: formula (19)\nType: EQUATION_OR_FORMULA" + }, + { + "entity_name": "15", + "entity_type": "MEASUREMENT", + "description": "15 is a numerical value mentioned in the text potentially representing a measurement or count", + "source_ids": [ + 233 + ], + "id": "Name: 15\nType: MEASUREMENT" + }, + { + "entity_name": "a.1.3 retrieval recall", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Experimental Details' within the 'BookRAG' paper, this section defines the Retrieval Recall metric used to evaluate retrieval quality based on parsed PDF block granularity (paragraphs, tables, images).", + "source_ids": [ + 234 + ], + "id": "Name: a.1.3 retrieval recall\nType: SECTION_TITLE" + }, + { + "entity_name": "retrieval quality", + "entity_type": "EVALUATION_METRIC", + "description": "The specific aspect of system performance being measured in this section, assessed via the granularity of retrieved blocks.", + "source_ids": [ + 234 + ], + "id": "Name: retrieval quality\nType: EVALUATION_METRIC" + }, + { + "entity_name": "pdf blocks", + "entity_type": "DATASET_OR_CORPUS", + "description": "The fundamental units of data (paragraphs, tables, images) from which ground-truth and retrieved sets are constructed for evaluation.", + "source_ids": [ + 234 + ], + "id": "Name: pdf blocks\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "query q", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "The input query variable used to define the set of required ground-truth blocks.", + "source_ids": [ + 234 + ], + "id": "Name: query q\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "b_gold", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "The set of manually labeled ground-truth blocks required to answer a given query.", + "source_ids": [ + 234 + ], + "id": "Name: b_gold\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "b_ret", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "The set of unique blocks retrieved by the system for a given query.", + "source_ids": [ + 234 + ], + "id": "Name: b_ret\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "recall_ret", + "entity_type": "EVALUATION_METRIC", + "description": "The specific mathematical formula defined in this section to calculate retrieval recall, handling parsing errors.", + "source_ids": [ + 234 + ], + "id": "Name: recall_ret\nType: EVALUATION_METRIC" + }, + { + "entity_name": "formula (20)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the recall metric r_et as a conditional value based on parsing errors and set intersections. 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6b", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "qwen3 embedding 0 6b is the model used for text embedding", + "source_ids": [ + 238 + ], + "id": "Name: qwen3 embedding 0 6b\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "gme qwen2 vl 2b instruct", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "gme qwen2 vl 2b instruct is the model used for multi modal embedding", + "source_ids": [ + 238 + ], + "id": "Name: gme qwen2 vl 2b instruct\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "qwen3 reranker 4b", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "qwen3 reranker 4b is the model used for reranking", + "source_ids": [ + 238 + ], + "id": "Name: qwen3 reranker 4b\nType: MODEL_OR_ARCHITECTURE" + }, + { + "entity_name": "linux", + "entity_type": "SOFTWARE", + "description": "linux is the operating system on which the experiments were conducted", + "source_ids": [ + 238 + ], + "id": "Name: linux\nType: SOFTWARE" + }, + { + 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counterpart\nType: MEASUREMENT" + }, + { + "entity_name": "github repository", + "entity_type": "LOCATION", + "description": "the github repository is the location where source code and implementation configurations are publicly available", + "source_ids": [ + 238 + ], + "id": "Name: github repository\nType: LOCATION" + }, + { + "entity_name": "https github com sam234990 bookrag", + "entity_type": "LOCATION", + "description": "https github com sam234990 bookrag is the specific url of the repository", + "source_ids": [ + 238 + ], + "id": "Name: https github com sam234990 bookrag\nType: LOCATION" + }, + { + "entity_name": "baseline methods", + "entity_type": "TASK_OR_PROBLEM", + "description": "baseline methods refer to the existing methods used for fair comparison against bookrag", + "source_ids": [ + 238 + ], + "id": "Name: baseline methods\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "ground truth images", + "entity_type": "IMAGE", + "description": "ground truth images are the correct reference images provided to the models during evaluation", + "source_ids": [ + 238 + ], + "id": "Name: ground truth images\nType: IMAGE" + }, + { + "entity_name": "document chunking", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "document chunking is a technique used to split documents into smaller parts for processing", + "source_ids": [ + 238 + ], + "id": "Name: document chunking\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "retrieval ranking", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "retrieval ranking is a technique used to order retrieved candidates based on relevance", + "source_ids": [ + 238 + ], + "id": "Name: retrieval ranking\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "sequential processing mode", + "entity_type": "TASK_OR_PROBLEM", + "description": "sequential processing mode is the execution mode used to ensure fair comparison of efficiency", + "source_ids": [ + 238 + ], + "id": "Name: sequential processing 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correctly", + "source_ids": [ + 238 + ], + "id": "Name: performance deficits\nType: CONCEPT" + }, + { + "entity_name": "reproducibility", + "entity_type": "CONCEPT", + "description": "reproducibility is the goal achieved by making source code and configurations publicly available", + "source_ids": [ + 238 + ], + "id": "Name: reproducibility\nType: CONCEPT" + }, + { + "entity_name": "fair comparison", + "entity_type": "CONCEPT", + "description": "fair comparison is the objective driving the use of unified models and standardized parameters", + "source_ids": [ + 238 + ], + "id": "Name: fair comparison\nType: CONCEPT" + }, + { + "entity_name": "text embedding", + "entity_type": "TASK_OR_PROBLEM", + "description": "text embedding is the task performed by the qwen3 embedding 0 6b model", + "source_ids": [ + 238 + ], + "id": "Name: text embedding\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "multi modal embedding", + "entity_type": "TASK_OR_PROBLEM", + "description": "multi modal embedding is the task performed by the gme qwen2 vl 2b instruct model", + "source_ids": [ + 238 + ], + "id": "Name: multi modal embedding\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "reranking", + "entity_type": "TASK_OR_PROBLEM", + "description": "reranking is the task performed by the qwen3 reranker 4b model", + "source_ids": [ + 238 + ], + "id": "Name: reranking\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "high performance server", + "entity_type": "LOCATION", + "description": "the high performance server is the physical location where all experiments were conducted", + "source_ids": [ + 238 + ], + "id": "Name: high performance server\nType: LOCATION" + }, + { + "entity_name": "implementation configurations", + "entity_type": "PRODUCT", + "description": "implementation configurations refer to the detailed settings used to run the experiments", + "source_ids": [ + 238 + ], + "id": "Name: implementation configurations\nType: PRODUCT" + }, + { + "entity_name": "reference 52", + "entity_type": "PUBLICATION_VENUE", + "description": "reference 52 is the citation for the mineru tool", + "source_ids": [ + 238 + ], + "id": "Name: reference 52\nType: PUBLICATION_VENUE" + }, + { + "entity_name": "reference 4", + "entity_type": "PUBLICATION_VENUE", + "description": "reference 4 is the citation for the qwen2 5vl 30b model", + "source_ids": [ + 238 + ], + "id": "Name: reference 4\nType: PUBLICATION_VENUE" + }, + { + "entity_name": "reference 60", + "entity_type": "PUBLICATION_VENUE", + "description": "reference 60 is the citation for the qwen3 8b model", + "source_ids": [ + 238 + ], + "id": "Name: reference 60\nType: PUBLICATION_VENUE" + }, + { + "entity_name": "reference 63", + "entity_type": "PUBLICATION_VENUE", + "description": "reference 63 is the citation for the gme qwen2 vl 2b instruct model", + "source_ids": [ + 238 + ], + "id": "Name: reference 63\nType: PUBLICATION_VENUE" + }, + { + "entity_name": "reference 64", + "entity_type": "PUBLICATION_VENUE", + "description": "reference 64 is the citation for the qwen3 embedding 0 6b and qwen3 reranker 4b models", + "source_ids": [ + 238 + ], + "id": "Name: reference 64\nType: PUBLICATION_VENUE" + }, + { + "entity_name": "a.3 prompts", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Experimental Details' within the BookRAG paper, this section details the specific text prompts engineered and utilized to guide the Retrieval-Augmented Generation (RAG) system in processing complex documents.", + "source_ids": [ + 239 + ], + "id": "Name: a.3 prompts\nType: SECTION_TITLE" + }, + { + "entity_name": "prompts", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Refers to the structured input instructions provided to the language model to elicit specific behaviors or outputs, as defined in section A.3.", + "source_ids": [ + 239 + ], + "id": "Name: prompts\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "agent based query classification", + "entity_type": "TASK_OR_PROBLEM", + "description": "agent based query classification is a task for which prompts are designed as illustrated in figure 10", + "source_ids": [ + 240 + ], + "id": "Name: agent based query classification\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "question decomposition", + "entity_type": "TASK_OR_PROBLEM", + "description": "question decomposition is a task for which prompts are designed as illustrated in figure 11", + "source_ids": [ + 240 + ], + "id": "Name: question decomposition\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "filter operator generation", + "entity_type": "TASK_OR_PROBLEM", + "description": "Filter operator generation is a task for which prompts are designed, as illustrated in figure 12.", + "source_ids": [ + 240, + 259 + ], + "id": "Name: filter operator generation\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "entity resolution judgment", + "entity_type": "TASK_OR_PROBLEM", + "description": "entity resolution judgment is a task for which a prompt is employed during the graph construction phase as illustrated in figure 13", + "source_ids": [ + 240 + ], + "id": "Name: entity resolution judgment\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "graph construction phase", + "entity_type": "TASK_OR_PROBLEM", + "description": "graph construction phase is the specific phase during which entity resolution judgment is performed", + "source_ids": [ + 240 + ], + "id": "Name: graph construction phase\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "prompts", + "entity_type": "PRODUCT", + "description": "prompts are the specific designed items mentioned in the text for various tasks", + "source_ids": [ + 240 + ], + "id": "Name: prompts\nType: PRODUCT" + }, + { + "entity_name": "figure 10", + "entity_type": "IMAGE", + "description": "Figure 10 is a visual element illustrating prompts for agent-based query classification, serving as an image within the text that displays a prompt for query classification.", + "source_ids": [ + 240, + 253 + ], + "id": "Name: figure 10\nType: IMAGE" + }, + { + "entity_name": "figure 11", + "entity_type": "IMAGE", + "description": "Figure 11 is a visual element in the text that displays a prompt for query decomposition, illustrating the process of question decomposition.", + "source_ids": [ + 240, + 256 + ], + "id": "Name: figure 11\nType: IMAGE" + }, + { + "entity_name": "figure 12", + "entity_type": "IMAGE", + "description": "Figure 12 is a visual element and image within the text that illustrates and displays the prompt for filter operator generation.", + "source_ids": [ + 240, + 259 + ], + "id": "Name: figure 12\nType: IMAGE" + }, + { + "entity_name": "figure 13", + "entity_type": "IMAGE", + "description": "Figure 13 is a visual element and an image containing a prompt for entity resolution judgment.", + "source_ids": [ + 240, + 284 + ], + "id": "Name: figure 13\nType: IMAGE" + }, + { + "entity_name": "expert query analyzer", + "entity_type": "PERSON", + "description": "an expert query analyzer is a role described as someone tasked with classifying user questions into specific categories", + "source_ids": [ + 241 + ], + "id": "Name: expert query analyzer\nType: PERSON" + }, + { + "entity_name": "simple", + "entity_type": "TASK_OR_PROBLEM", + "description": "simple is one of the three categories used to classify user questions", + "source_ids": [ + 241 + ], + "id": "Name: simple\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "user", + "entity_type": "PERSON", + "description": "The user is the entity providing the query to the AI assistant, whose questions are being classified by the expert query analyzer.", + "source_ids": [ + 241, + 258 + ], + "id": "Name: user\nType: PERSON" + }, + { + "entity_name": "json object", + "entity_type": "FILE_TYPE", + "description": "The json object is the required format for responses from the expert query analyzer, the AI assistant, and systems containing filters and operations. It serves as the standard output structure for various tasks, including responses that contain a single key named \"sub_questions\" with a list of objects, outputs that include the ID of a matching candidate along with an explanation, and other structured data formats required by different components of the system.", + "source_ids": [ + 241, + 258, + 262, + 255 + ], + "id": "Name: json object\nType: FILE_TYPE" + }, + { + "entity_name": "category definitions", + "entity_type": "SECTION_TITLE", + "description": "category definitions is the title of the section containing definitions for entity types", + "source_ids": [ + 242 + ], + "id": "Name: category definitions\nType: SECTION_TITLE" + }, + { + "entity_name": "information", + "entity_type": "CONCEPT", + "description": "information is the data retrieved to answer the question", + "source_ids": [ + 243 + ], + "id": "Name: information\nType: CONCEPT" + }, + { + "entity_name": "document", + "entity_type": "CONCEPT", + "description": "document is the source material containing the information", + "source_ids": [ + 243 + ], + "id": "Name: document\nType: CONCEPT" + }, + { + "entity_name": "paragraph", + "entity_type": "SECTION_TITLE", + "description": "paragraph is an example of a contiguous location within a document", + "source_ids": [ + 243 + ], + "id": "Name: paragraph\nType: SECTION_TITLE" + }, + { + "entity_name": "table", + "entity_type": "SECTION_TITLE", + "description": "table is an example of a contiguous location within a document", + "source_ids": [ + 243 + ], + "id": "Name: table\nType: SECTION_TITLE" + }, + { + "entity_name": "figure", + "entity_type": "SECTION_TITLE", + "description": "figure is an example of a contiguous location within a document", + "source_ids": [ + 243 + ], + "id": "Name: figure\nType: SECTION_TITLE" + }, + { + "entity_name": "single", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 243 + ], + "id": "Name: single\nType: UNKNOWN" + }, + { + "entity_name": "contiguous location", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 243 + ], + "id": "Name: contiguous location\nType: UNKNOWN" + }, + { + "entity_name": "5", + "entity_type": "PERCENTAGE", + "description": "5 represents a specific portion of the latino population mentioned in the context of economic upward mobility", + "source_ids": [ + 246 + ], + "id": "Name: 5\nType: PERCENTAGE" + }, + { + "entity_name": "latinos", + "entity_type": "NATIONALITY", + "description": "latinos are the demographic group whose views on economic upward mobility for their children are being queried", + "source_ids": [ + 246 + ], + "id": "Name: latinos\nType: NATIONALITY" + }, + { + "entity_name": "economic upward mobility", + "entity_type": "TASK_OR_PROBLEM", + "description": "economic upward mobility is the specific issue regarding the children of latinos that is the subject of the inquiry", + "source_ids": [ + 246 + ], + "id": "Name: economic upward mobility\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "children", + "entity_type": "PERSON", + "description": "children are the offspring of the latinos whose economic upward mobility is being discussed", + "source_ids": [ + 246 + ], + "id": "Name: children\nType: PERSON" + }, + { + "entity_name": "personality vector", + "entity_type": "TASK_OR_PROBLEM", + "description": "the personality vector is a concept mentioned in a question regarding its color indicating it is a complex retrieval task", + "source_ids": [ + 249 + ], + "id": "Name: personality vector\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "counting", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "counting is an example of an aggregation operation mentioned in the text", + "source_ids": [ + 250 + ], + "id": "Name: counting\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "listing", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "listing is an example of an aggregation operation mentioned in the text", + "source_ids": [ + 250 + ], + "id": "Name: listing\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "summarizing", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "summarizing is an example of an aggregation operation mentioned in the text", + "source_ids": [ + 250 + ], + "id": "Name: summarizing\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "structural filter", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "structural filter is a clear filter used to identify items in the set for the global question", + "source_ids": [ + 250 + ], + "id": "Name: structural filter\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "aggregation operation", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 250 + ], + "id": "Name: aggregation operation\nType: UNKNOWN" + }, + { + "entity_name": "items", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 250 + ], + "id": "Name: items\nType: UNKNOWN" + }, + { + "entity_name": "example", + "entity_type": "TASK_OR_PROBLEM", + "description": "example is a task or problem asking how many tables are in the document", + "source_ids": [ + 251 + ], + "id": "Name: example\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "16", + "entity_type": "MEASUREMENT", + "description": "16 is a numerical value mentioned in the text potentially representing a count date or measurement", + "source_ids": [ + 254 + ], + "id": "Name: 16\nType: MEASUREMENT" + }, + { + "entity_name": "user a2gbifl43u1lkj", + "entity_type": "PERSON", + "description": "user a2gbifl43u1lkj is a specific user referenced in the example query regarding personality vectors and receptiviti scores", + "source_ids": [ + 255 + ], + "id": "Name: user a2gbifl43u1lkj\nType: PERSON" + }, + { + "entity_name": "foreign born latinos", + "entity_type": "PERSON", + "description": "foreign born latinos are a demographic group mentioned in the example query regarding population surveys", + "source_ids": [ + 255 + ], + "id": "Name: foreign born latinos\nType: PERSON" + }, + { + "entity_name": "latinos interviewed by cellphone", + "entity_type": "PERSON", + "description": "latinos interviewed by cellphone are a demographic group mentioned in the example query regarding population surveys", + "source_ids": [ + 255 + ], + "id": "Name: latinos interviewed by cellphone\nType: PERSON" + }, + { + "entity_name": "soft labeled personality embedding matrix", + "entity_type": "PRODUCT", + "description": "the soft labeled personality embedding matrix is a data structure containing personality vectors and their associated colors", + "source_ids": [ + 255 + ], + "id": "Name: soft labeled personality embedding matrix\nType: PRODUCT" + }, + { + "entity_name": "receptiviti score", + "entity_type": "EVALUATION_METRIC", + "description": "the receptiviti score is a metric used to evaluate personality vectors in the context of the example query", + "source_ids": [ + 255 + ], + "id": "Name: receptiviti score\nType: EVALUATION_METRIC" + }, + { + "entity_name": "population", + "entity_type": "MEASUREMENT", + "description": "population refers to the count of individuals in a specific demographic group within a survey", + "source_ids": [ + 255 + ], + "id": "Name: population\nType: MEASUREMENT" + }, + { + "entity_name": "query decomposition expert", + "entity_type": "PROFESSION", + "description": "the query decomposition expert is the role assigned to the ai to break down complex questions into atomic sub questions", + "source_ids": [ + 255 + ], + "id": "Name: query decomposition expert\nType: PROFESSION" + }, + { + "entity_name": "complex question", + "entity_type": "TASK_OR_PROBLEM", + "description": "a complex question is the input task that needs to be broken down into simple sub questions", + "source_ids": [ + 255 + ], + "id": "Name: complex question\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "simple atomic sub questions", + "entity_type": "TASK_OR_PROBLEM", + "description": "simple atomic sub questions are the output components of the decomposition process each being a direct information retrieval task", + "source_ids": [ + 255 + ], + "id": "Name: simple atomic sub questions\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "retrieval sub question", + "entity_type": "TASK_OR_PROBLEM", + "description": "a retrieval sub question is a specific type of sub question that requires looking up a specific fact number or value in the document", + "source_ids": [ + 255 + ], + "id": "Name: retrieval sub question\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "synthesis question", + "entity_type": "TASK_OR_PROBLEM", + "description": "a synthesis question is a specific type of sub question that requires comparing calculating or combining answers from previous retrieval questions", + "source_ids": [ + 255 + ], + "id": "Name: synthesis question\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "sub questions", + "entity_type": "SECTION_TITLE", + "description": "the sub questions key is the container within the json object that holds the list of decomposed questions", + "source_ids": [ + 255 + ], + "id": "Name: sub questions\nType: SECTION_TITLE" + }, + { + "entity_name": "question", + "entity_type": "SECTION_TITLE", + "description": "the question key within each sub question object holds the string of the actual question", + "source_ids": [ + 255 + ], + "id": "Name: question\nType: SECTION_TITLE" + }, + { + "entity_name": "type", + "entity_type": "SECTION_TITLE", + "description": "the type key within each sub question object specifies whether the question is retrieval or synthesis", + "source_ids": [ + 255 + ], + "id": "Name: type\nType: SECTION_TITLE" + }, + { + "entity_name": "example 1", + "entity_type": "EVENT", + "description": "example 1 is a demonstration of correct decomposition with independent lookups provided in the text", + "source_ids": [ + 255 + ], + "id": "Name: example 1\nType: EVENT" + }, + { + "entity_name": "example 2", + "entity_type": "EVENT", + "description": "example 2 is a demonstration of decomposition with retrieval and synthesis steps provided in the text", + "source_ids": [ + 255 + ], + "id": "Name: example 2\nType: EVENT" + }, + { + "entity_name": "personality vector", + "entity_type": "PRODUCT", + "description": "a personality vector is a data element within the soft labeled personality embedding matrix", + "source_ids": [ + 255 + ], + "id": "Name: personality vector\nType: PRODUCT" + }, + { + "entity_name": "color", + "entity_type": "COLOR", + "description": "color is an attribute mapped to personality vectors in the soft labeled personality embedding matrix", + "source_ids": [ + 255 + ], + "id": "Name: color\nType: COLOR" + }, + { + "entity_name": "survey", + "entity_type": "EVENT", + "description": "the survey is the context in which population data for latinos is collected in example 2", + "source_ids": [ + 255 + ], + "id": "Name: survey\nType: EVENT" + }, + { + "entity_name": "report", + "entity_type": "BOOK", + "description": "The report is a book that serves as a document referenced in example 2, containing population data, and is also cited in an example query regarding chapters.", + "source_ids": [ + 258, + 255 + ], + "id": "Name: report\nType: BOOK" + }, + { + "entity_name": "query decomposition", + "entity_type": "TASK_OR_PROBLEM", + "description": "query decomposition is the task or problem for which the prompt in figure 11 is designed", + "source_ids": [ + 256 + ], + "id": "Name: query decomposition\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "17", + "entity_type": "NUMBER", + "description": "17 is a number mentioned in the text though its specific context or role is not defined", + "source_ids": [ + 257 + ], + "id": "Name: 17\nType: NUMBER" + }, + { + "entity_name": "ai assistant", + "entity_type": "PERSON", + "description": "an ai assistant described as highly specialized with the function of analyzing a global query", + "source_ids": [ + 258 + ], + "id": "Name: ai assistant\nType: PERSON" + }, + { + "entity_name": "global query", + "entity_type": "TASK_OR_PROBLEM", + "description": "a query that the ai assistant is designed to analyze to determine filtering steps and aggregation operations", + "source_ids": [ + 258 + ], + "id": "Name: global query\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "filters", + "entity_type": "TASK_OR_PROBLEM", + "description": "a list of filtering steps to be applied which can include sections images tables or pages", + "source_ids": [ + 258 + ], + "id": "Name: filters\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "operation", + "entity_type": "TASK_OR_PROBLEM", + "description": "the final aggregation operation to be performed such as count list summarize or analyze", + "source_ids": [ + 258 + ], + "id": "Name: operation\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "methodology", + "entity_type": "SECTION_TITLE", + "description": "a specific section title mentioned in an example query regarding data augmentation", + "source_ids": [ + 258 + ], + "id": "Name: methodology\nType: SECTION_TITLE" + }, + { + "entity_name": "paper", + "entity_type": "BOOK", + "description": "a document referenced in an example query regarding figures on specific pages", + "source_ids": [ + 258 + ], + "id": "Name: paper\nType: BOOK" + }, + { + "entity_name": "assistant", + "entity_type": "PERSON", + "description": "the assistant is the entity responding to the user with a json object", + "source_ids": [ + 258 + ], + "id": "Name: assistant\nType: PERSON" + }, + { + "entity_name": "chapter", + "entity_type": "SECTION_TITLE", + "description": "a structural part of a document mentioned in the example about counting chapters", + "source_ids": [ + 258 + ], + "id": "Name: chapter\nType: SECTION_TITLE" + }, + { + "entity_name": "appendices", + "entity_type": "SECTION_TITLE", + "description": "a structural part of a document mentioned in the definition of section filters", + "source_ids": [ + 258 + ], + "id": "Name: appendices\nType: SECTION_TITLE" + }, + { + "entity_name": "data augmentation", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "a specific topic discussed in the methodology section in the example query", + "source_ids": [ + 258 + ], + "id": "Name: data augmentation\nType: METHOD_OR_TECHNIQUE" + }, + { + "entity_name": "discussion", + "entity_type": "TASK_OR_PROBLEM", + "description": "the content regarding data augmentation that needs to be summarized", + "source_ids": [ + 258 + ], + "id": "Name: discussion\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "3 10", + "entity_type": "MEASUREMENT", + "description": "a specific page range mentioned as a filter value in the example query", + "source_ids": [ + 258 + ], + "id": "Name: 3 10\nType: MEASUREMENT" + }, + { + "entity_name": "count", + "entity_type": "TASK_OR_PROBLEM", + "description": "an aggregation operation used to count items", + "source_ids": [ + 258 + ], + "id": "Name: count\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "list", + "entity_type": "TASK_OR_PROBLEM", + "description": "an aggregation operation used to list items", + "source_ids": [ + 258 + ], + "id": "Name: list\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "summarize", + "entity_type": "TASK_OR_PROBLEM", + "description": "an aggregation operation used to summarize content", + "source_ids": [ + 258 + ], + "id": "Name: summarize\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "analyze", + "entity_type": "TASK_OR_PROBLEM", + "description": "an aggregation operation used to analyze content", + "source_ids": [ + 258 + ], + "id": "Name: analyze\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "page", + "entity_type": "MEASUREMENT", + "description": "a filter type used for specific page numbers", + "source_ids": [ + 258 + ], + "id": "Name: page\nType: MEASUREMENT" + }, + { + "entity_name": "null", + "entity_type": "TASK_OR_PROBLEM", + "description": "a value indicating that no specific value is provided for image or table filters", + "source_ids": [ + 258 + ], + "id": "Name: null\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "18", + "entity_type": "NUMBER", + "description": "18 is a number mentioned in the text though its specific context or meaning is not provided", + "source_ids": [ + 260 + ], + "id": "Name: 18\nType: NUMBER" + }, + { + "entity_name": "entity resolution adjudicator", + "entity_type": "PERSON", + "description": "entity resolution adjudicator is an expert role tasked with determining if a new entity refers to the same real world concept as candidate entities", + "source_ids": [ + 262 + ], + "id": "Name: entity resolution adjudicator\nType: PERSON" + }, + { + "entity_name": "candidate entities", + "entity_type": "TASK_OR_PROBLEM", + "description": "candidate entities are a list of semantically similar entities retrieved from an existing knowledge base for comparison", + "source_ids": [ + 262 + ], + "id": "Name: candidate entities\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "knowledge graph", + "entity_type": "TASK_OR_PROBLEM", + "description": "knowledge graph is the existing database from which candidate entities are retrieved", + "source_ids": [ + 262 + ], + "id": "Name: knowledge graph\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "knowledge base", + "entity_type": "TASK_OR_PROBLEM", + "description": "knowledge base is the source of semantically similar candidate entities", + "source_ids": [ + 262 + ], + "id": "Name: knowledge base\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "id", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "The id is a unique identifier used to reference candidate entities in the output and serves as the identifier for the candidate determined to be an exact match.", + "source_ids": [ + 276, + 262 + ], + "id": "Name: id\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "1", + "entity_type": "VALUE", + "description": "1 is a specific value indicating that no matching candidate was found for the new entity", + "source_ids": [ + 262 + ], + "id": "Name: 1\nType: VALUE" + }, + { + "entity_name": "text", + "entity_type": "DATASET_OR_CORPUS", + "description": "text is the source material from which the new entity is recently extracted", + "source_ids": [ + 262 + ], + "id": "Name: text\nType: DATASET_OR_CORPUS" + }, + { + "entity_name": "explanation", + "entity_type": "TASK_OR_PROBLEM", + "description": "An explanation is a brief, one-sentence string that serves as a task or problem by providing the reasoning behind a decision, specifically justifying the outcome in contexts such as entity matching.", + "source_ids": [ + 277, + 262 + ], + "id": "Name: explanation\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "field by field adjudication", + "entity_type": "TASK_OR_PROBLEM", + "description": "field by field adjudication is a task described as a method to determine a match by evaluating each field with a specific focus", + "source_ids": [ + 266 + ], + "id": "Name: field by field adjudication\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "entity name", + "entity_type": "TASK_OR_PROBLEM", + "description": "entity name is a placeholder term used to denote the name of an entity in the context of matching criteria", + "source_ids": [ + 267 + ], + "id": "Name: entity name\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "large language model", + "entity_type": "TECHNOLOGY", + "description": "large language model is the full form of the abbreviation llm used as an example of a direct abbreviation match", + "source_ids": [ + 267 + ], + "id": "Name: large language model\nType: TECHNOLOGY" + }, + { + "entity_name": "event detection", + "entity_type": "TASK_OR_PROBLEM", + "description": "event detection is a task mentioned as a distinct concept that should not be matched with named entity recognition", + "source_ids": [ + 267 + ], + "id": "Name: event detection\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "named entity recognition", + "entity_type": "TASK_OR_PROBLEM", + "description": "named entity recognition is a task mentioned as a distinct concept that should not be matched with event detection", + "source_ids": [ + 267 + ], + "id": "Name: named entity recognition\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "high importance", + "entity_type": "CONCEPT", + "description": "high importance is a criterion mentioned for determining the similarity of entity names", + "source_ids": [ + 267 + ], + "id": "Name: high importance\nType: CONCEPT" + }, + { + "entity_name": "distinct concepts", + "entity_type": "CONCEPT", + "description": "distinct concepts refers to parallel concepts that are explicitly excluded from being considered a match", + "source_ids": [ + 267 + ], + "id": "Name: distinct concepts\nType: CONCEPT" + }, + { + "entity_name": "entity type", + "entity_type": "TASK_OR_PROBLEM", + "description": "entity type is a task or problem described as having medium importance in the context of type compatibility", + "source_ids": [ + 268 + ], + "id": "Name: entity type\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "description", + "entity_type": "CONCEPT", + "description": "description refers to the contextual importance of text segments which may differ as they are extracted from different parts of a document", + "source_ids": [ + 269 + ], + "id": "Name: description\nType: CONCEPT" + }, + { + "entity_name": "contextual importance", + "entity_type": "CONCEPT", + "description": "contextual importance is a property of descriptions that requires looking past surface level text similarity to determine if they describe the same underlying object or concept", + "source_ids": [ + 269 + ], + "id": "Name: contextual importance\nType: CONCEPT" + }, + { + "entity_name": "be strict and conservative", + "entity_type": "TASK_OR_PROBLEM", + "description": "be strict and conservative is a guideline or instruction regarding the standard for matching emphasizing high standards to avoid corrupting the knowledge graph", + "source_ids": [ + 270 + ], + "id": "Name: be strict and conservative\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "apple", + "entity_type": "PRODUCT", + "description": "apple is mentioned as an example of a fruit", + "source_ids": [ + 272 + ], + "id": "Name: apple\nType: PRODUCT" + }, + { + "entity_name": "apple inc", + "entity_type": "ORGANIZATION", + "description": "apple inc is mentioned as an example of a company", + "source_ids": [ + 272 + ], + "id": "Name: apple inc\nType: ORGANIZATION" + }, + { + "entity_name": "when in doubt", + "entity_type": "TASK_OR_PROBLEM", + "description": "when in doubt is a condition mentioned in the text that triggers a specific output requirement", + "source_ids": [ + 273 + ], + "id": "Name: when in doubt\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "1", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 273 + ], + "id": "Name: 1\nType: UNKNOWN" + }, + { + "entity_name": "json", + "entity_type": "FILE_TYPE", + "description": "json is a file format mentioned as the required output format for the answer", + "source_ids": [ + 275 + ], + "id": "Name: json\nType: FILE_TYPE" + }, + { + "entity_name": "output", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 275 + ], + "id": "Name: output\nType: UNKNOWN" + }, + { + "entity_name": "select id", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "The select id is a parameter or variable defined as an integer within the provided text structure, representing the integer identifier for a candidate that has been determined to be an exact match.", + "source_ids": [ + 281, + 276 + ], + "id": "Name: select id\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "exact match", + "entity_type": "TASK_OR_PROBLEM", + "description": "exact match refers to the condition where a candidate is determined to be identical to a reference", + "source_ids": [ + 276 + ], + "id": "Name: exact match\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "1", + "entity_type": "MONEY", + "description": "1 is a specific integer value used to indicate that no exact match was found", + "source_ids": [ + 276 + ], + "id": "Name: 1\nType: MONEY" + }, + { + "entity_name": "candidate", + "entity_type": "TASK_OR_PROBLEM", + "description": "candidate refers to an item being evaluated to determine if it is an exact match", + "source_ids": [ + 276 + ], + "id": "Name: candidate\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "integer", + "entity_type": "MEASUREMENT", + "description": "An integer is a data type specified for the select id value and represents the specific type of output requested for the selection task.", + "source_ids": [ + 282, + 276 + ], + "id": "Name: integer\nType: MEASUREMENT" + }, + { + "entity_name": "explanation", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "explanation is a parameter or variable defined as a string in the provided text structure", + "source_ids": [ + 281 + ], + "id": "Name: explanation\nType: PARAMETER_OR_VARIABLE" + }, + { + "entity_name": "example 1", + "entity_type": "TASK_OR_PROBLEM", + "description": "example 1 is a task or problem scenario where a match was found", + "source_ids": [ + 281 + ], + "id": "Name: example 1\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "example 2", + "entity_type": "TASK_OR_PROBLEM", + "description": "example 2 is a task or problem scenario where no match was found", + "source_ids": [ + 281 + ], + "id": "Name: example 2\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "selection task", + "entity_type": "TASK_OR_PROBLEM", + "description": "the selection task is the activity described in the text that requires processing the provided data", + "source_ids": [ + 282 + ], + "id": "Name: selection task\nType: TASK_OR_PROBLEM" + }, + { + "entity_name": "examples", + "entity_type": "DATASET_OR_CORPUS", + "description": "examples are data instances that were omitted from the text due to space constraints", + "source_ids": [ + 284 + ], + "id": "Name: examples\nType: DATASET_OR_CORPUS" + }, + { 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"relation_name": "", + "weight": 9.0, + "description": "bookindex organizes parsed content blocks into a hierarchical tree structure", + "source_ids": [ + 20 + ], + "source": "Name: bookindex\nType: PRODUCT", + "target": "Name: parsed content blocks\nType: MATERIAL" + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "selector", + "relation_name": "", + "weight": 8.0, + "description": "the system builds upon bookindex to implement an agent that uses selector for retrieval workflows", + "source_ids": [ + 22 + ], + "source": "Name: bookindex\nType: PRODUCT", + "target": "Name: selector\nType: SOFTWARE" + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "reasoner", + "relation_name": "", + "weight": 8.0, + "description": "the system builds upon bookindex to implement an agent that uses reasoner for retrieval workflows", + "source_ids": [ + 22 + ], + "source": "Name: bookindex\nType: PRODUCT", + "target": "Name: reasoner\nType: SOFTWARE" + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "hierarchical tree", + "relation_name": "", + "weight": 8.0, + "description": "the bookindex is constructed using a hierarchical tree of document layout blocks", + "source_ids": [ + 25 + ], + "source": "Name: bookindex\nType: PRODUCT", + "target": "Name: hierarchical tree\nType: MODEL_OR_ARCHITECTURE" + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "kg", + "relation_name": "", + "weight": 8.0, + "description": "the bookindex is constructed using a kg storing fine grained entity relations", + "source_ids": [ + 25 + ], + "source": "Name: bookindex\nType: PRODUCT", + "target": "Name: kg\nType: SOFTWARE" + }, + { + "src_entity_name": "section 4", + "tgt_entity_name": "bookindex", + "relation_name": "", + "weight": 10.0, + "description": "section 4 presents the structure and construction of bookindex", + "source_ids": [ + 29 + ], + "source": "Name: bookindex\nType: PRODUCT", + "target": "Name: section 4\nType: 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"description": "bookindex is defined as a triplet that includes the tree structure as one of its components", + "source_ids": [ + 51 + ], + "source": "Name: bookindex\nType: PRODUCT", + "target": "Name: tree structure\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "knowledge graph", + "relation_name": "", + "weight": 10.0, + "description": "bookindex is defined as a triplet that includes the knowledge graph as one of its components", + "source_ids": [ + 51 + ], + "source": "Name: bookindex\nType: PRODUCT", + "target": "Name: knowledge graph\nType: SOFTWARE" + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "graph tree link", + "relation_name": "", + "weight": 10.0, + "description": "bookindex is defined as a triplet that includes the graph tree link as one of its components", + "source_ids": [ + 51 + ], + "source": "Name: bookindex\nType: PRODUCT", + "target": "Name: graph tree link\nType: METHOD_OR_TECHNIQUE" + }, + { + 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}, + { + "src_entity_name": "information scent", + "tgt_entity_name": "navigation", + "relation_name": "", + "weight": 8.0, + "description": "information scent guides navigation between and within information patches", + "source_ids": [ + 51 + ], + "source": "Name: information scent\nType: CONCEPT", + "target": "Name: navigation\nType: UNKNOWN" + }, + { + "src_entity_name": "information patches", + "tgt_entity_name": "sections", + "relation_name": "", + "weight": 10.0, + "description": "sections in handbooks are explicitly listed as examples of information patches", + "source_ids": [ + 42 + ], + "source": "Name: information patches\nType: CONCEPT", + "target": "Name: sections\nType: CONCEPT" + }, + { + "src_entity_name": "experts", + "tgt_entity_name": "information patches", + "relation_name": "", + "weight": 9.0, + "description": "experts navigate to and analyze content within information patches", + "source_ids": [ + 43 + ], + "source": "Name: information patches\nType: CONCEPT", + "target": "Name: experts\nType: PERSON" + }, + { + "src_entity_name": "tree structure", + "tgt_entity_name": "information patches", + "relation_name": "", + "weight": 9.0, + "description": "the hierarchical tree nodes in the tree structure serve as information patches", + "source_ids": [ + 51 + ], + "source": "Name: information patches\nType: CONCEPT", + "target": "Name: tree structure\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "experts", + "tgt_entity_name": "large technical handbook", + "relation_name": "", + "weight": 10.0, + "description": "experts seek a solution within the large technical handbook", + "source_ids": [ + 43 + ], + "source": "Name: experts\nType: PERSON", + "target": "Name: large technical handbook\nType: BOOK" + }, + { + "src_entity_name": "experts", + "tgt_entity_name": "key terms", + "relation_name": "", + "weight": 9.0, + "description": "experts extract key terms from the handbook", + "source_ids": [ + 43 + ], + "source": "Name: experts\nType: 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symbol denotes the application of a modal filter at each step", + "source_ids": [ + 122 + ], + "source": "Name: \nType: UNKNOWN", + "target": "Name: modal filter\nType: TECHNOLOGY" + }, + { + "src_entity_name": "", + "tgt_entity_name": "range filter", + "relation_name": "", + "weight": 9.0, + "description": "the symbol denotes the application of a range filter at each step", + "source_ids": [ + 122 + ], + "source": "Name: \nType: UNKNOWN", + "target": "Name: range filter\nType: TECHNOLOGY" + }, + { + "src_entity_name": "", + "tgt_entity_name": "nested composition", + "relation_name": "", + "weight": 10.0, + "description": "the symbol represents the nested composition of filters", + "source_ids": [ + 122 + ], + "source": "Name: \nType: UNKNOWN", + "target": "Name: nested composition\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "tree component", + "tgt_entity_name": "section nodes", + "relation_name": "", + "weight": 8.0, + "description": "the tree component organizes content blocks within section nodes", + "source_ids": [ + 52 + ], + "source": "Name: tree component\nType: SOFTWARE", + "target": "Name: section nodes\nType: PRODUCT" + }, + { + "src_entity_name": "gt link", + "tgt_entity_name": "tree component", + "relation_name": "", + "weight": 8.0, + "description": "gt link connects entities back to their corresponding tree nodes", + "source_ids": [ + 52 + ], + "source": "Name: tree component\nType: SOFTWARE", + "target": "Name: gt link\nType: TECHNOLOGY" + }, + { + "src_entity_name": "gt link", + "tgt_entity_name": "graph component", + "relation_name": "", + "weight": 8.0, + "description": "gt link is a feature within the graph component that connects entities to tree nodes", + "source_ids": [ + 52 + ], + "source": "Name: graph component\nType: SOFTWARE", + "target": "Name: gt link\nType: TECHNOLOGY" + }, + { + "src_entity_name": "graph component", + "tgt_entity_name": "semantic entities", + "relation_name": "", + "weight": 8.0, + "description": "the graph component is composed of entities and relations extracted from nodes which include semantic entities", + "source_ids": [ + 52 + ], + "source": "Name: graph component\nType: SOFTWARE", + "target": "Name: semantic entities\nType: CONCEPT" + }, + { + "src_entity_name": "gt link", + "tgt_entity_name": "semantic entities", + "relation_name": "", + "weight": 9.0, + "description": "gt link explicitly connects semantic entities back to their corresponding tree nodes", + "source_ids": [ + 52 + ], + "source": "Name: gt link\nType: TECHNOLOGY", + "target": "Name: semantic entities\nType: CONCEPT" + }, + { + "src_entity_name": "entity resolution", + "tgt_entity_name": "gt link", + "relation_name": "", + "weight": 9.0, + "description": "gt link is refined during the entity resolution process", + "source_ids": [ + 77 + ], + "source": "Name: gt link\nType: TECHNOLOGY", + "target": "Name: entity resolution\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "gt link", + "tgt_entity_name": "e q", + "relation_name": "", + "weight": 9.0, + "description": "gt link is the mechanism used to link sections to entities e q", + "source_ids": [ + 104 + ], + "source": "Name: gt link\nType: TECHNOLOGY", + "target": "Name: e q\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "gt link", + "tgt_entity_name": "section node", + "relation_name": "", + "weight": 8.0, + "description": "gt link links sections nodes to entities", + "source_ids": [ + 104 + ], + "source": "Name: gt link\nType: TECHNOLOGY", + "target": "Name: section node\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "text", + "tgt_entity_name": "section nodes", + "relation_name": "", + "weight": 7.0, + "description": "text serves as a leaf node nested within section nodes", + "source_ids": [ + 52 + ], + "source": "Name: text\nType: PRODUCT", + "target": "Name: section nodes\nType: PRODUCT" + }, + { + "src_entity_name": "text", + "tgt_entity_name": "content blocks", + "relation_name": "", + "weight": 10.0, + "description": "text is identified as a type of content block", + "source_ids": [ + 52 + ], + "source": "Name: text\nType: PRODUCT", + "target": "Name: content blocks\nType: PRODUCT" + }, + { + "src_entity_name": "node set", + "tgt_entity_name": "text", + "relation_name": "", + "weight": 8.0, + "description": "the node set retains nodes of the type text", + "source_ids": [ + 58 + ], + "source": "Name: text\nType: PRODUCT", + "target": "Name: node set\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "tables", + "tgt_entity_name": "section nodes", + "relation_name": "", + "weight": 7.0, + "description": "tables serve as a leaf node nested within section nodes", + "source_ids": [ + 52 + ], + "source": "Name: tables\nType: PRODUCT", + "target": "Name: section nodes\nType: PRODUCT" + }, + { + "src_entity_name": "tables", + "tgt_entity_name": "content blocks", + "relation_name": "", + "weight": 10.0, + "description": "tables are identified as a type of content block", + "source_ids": [ + 52 + ], + "source": "Name: tables\nType: PRODUCT", + "target": "Name: content blocks\nType: PRODUCT" + }, + { + "src_entity_name": "images", + "tgt_entity_name": "section nodes", + "relation_name": "", + "weight": 7.0, + "description": "images serve as a leaf node nested within section nodes", + "source_ids": [ + 52 + ], + "source": "Name: images\nType: PRODUCT", + "target": "Name: section nodes\nType: PRODUCT" + }, + { + "src_entity_name": "images", + "tgt_entity_name": "content blocks", + "relation_name": "", + "weight": 10.0, + "description": "images are identified as a type of content block", + "source_ids": [ + 52 + ], + "source": "Name: images\nType: PRODUCT", + "target": "Name: content blocks\nType: PRODUCT" + }, + { + "src_entity_name": "content blocks", + "tgt_entity_name": "leaf nodes", + "relation_name": "", + "weight": 9.0, + "description": "content blocks serve as leaf nodes within the structure", + "source_ids": [ + 52 + ], + "source": "Name: content blocks\nType: PRODUCT", + "target": "Name: leaf nodes\nType: PRODUCT" + }, + { + "src_entity_name": "t", + "tgt_entity_name": "task or problem", + "relation_name": "", + "weight": 5.0, + "description": "t represents the structured hierarchical tree which is the outcome of the transformation task described", + "source_ids": [ + 54 + ], + "source": "Name: t\nType: TASK_OR_PROBLEM", + "target": "Name: task or problem\nType: UNKNOWN" + }, + { + "src_entity_name": "robust layout parsing", + "tgt_entity_name": "t", + "relation_name": "", + "weight": 9.0, + "description": "robust layout parsing is a step used to create the structured hierarchical tree t", + "source_ids": [ + 54 + ], + "source": "Name: t\nType: TASK_OR_PROBLEM", + "target": "Name: robust layout parsing\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "intelligent section filtering", + "tgt_entity_name": "t", + "relation_name": "", + "weight": 9.0, + "description": "intelligent section filtering is a step used to create the structured hierarchical tree t", + "source_ids": [ + 54 + ], + "source": "Name: t\nType: TASK_OR_PROBLEM", + "target": "Name: intelligent section filtering\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "raw document", + "tgt_entity_name": "robust layout parsing", + "relation_name": "", + "weight": 8.0, + "description": "raw document is the input processed by the robust layout parsing step", + "source_ids": [ + 54 + ], + "source": "Name: raw document\nType: PRODUCT", + "target": "Name: robust layout parsing\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "raw document", + "tgt_entity_name": "intelligent section filtering", + "relation_name": "", + "weight": 8.0, + "description": "raw document is the input processed by the intelligent section filtering step", + "source_ids": [ + 54 + ], + "source": "Name: raw document\nType: PRODUCT", + "target": "Name: intelligent section filtering\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "layout analysis", + "tgt_entity_name": "4.2.1 layout parsing", + "relation_name": "", + "weight": 9.5, + "description": "Layout Analysis is a core methodological component discussed within section 4.2.1.", + "source_ids": [ + 55 + ], + "source": "Name: 4.2.1 layout parsing\nType: SECTION_TITLE", + "target": "Name: layout analysis\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "recognition models", + "tgt_entity_name": "4.2.1 layout parsing", + "relation_name": "", + "weight": 9.5, + "description": "Recognition Models are the primary tools used in the process detailed in section 4.2.1.", + "source_ids": [ + 55 + ], + "source": "Name: 4.2.1 layout parsing\nType: SECTION_TITLE", + "target": "Name: recognition models\nType: MODEL_OR_ARCHITECTURE" + }, + { + "src_entity_name": "document d", + "tgt_entity_name": "4.2.1 layout parsing", + "relation_name": "", + "weight": 10.0, + "description": "Document D is the specific input entity being processed in section 4.2.1.", + "source_ids": [ + 55 + ], + "source": "Name: 4.2.1 layout parsing\nType: SECTION_TITLE", + "target": "Name: document d\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "the output", + "tgt_entity_name": "primitive", + "relation_name": "", + "weight": 9.0, + "description": "the output consists of a sequence of primitives", + "source_ids": [ + 56 + ], + "source": "Name: the output\nType: TASK_OR_PROBLEM", + "target": "Name: primitive\nType: CONCEPT" + }, + { + "src_entity_name": "section filtering", + "tgt_entity_name": "image", + "relation_name": "", + "weight": 7.0, + "description": "section filtering aims to correct blocks erroneously parsed as title such as descriptive text within images", + "source_ids": [ + 57 + ], + "source": "Name: section filtering\nType: TASK_OR_PROBLEM", + "target": "Name: image\nType: IMAGE" + }, + { + "src_entity_name": "section filtering", + "tgt_entity_name": "table", + "relation_name": "", + "weight": 7.0, + "description": "section filtering aims to correct blocks erroneously parsed as title such as borderless table headers", + "source_ids": [ + 57 + ], + "source": "Name: section filtering\nType: TASK_OR_PROBLEM", + "target": "Name: table\nType: TABLE" + }, + { + "src_entity_name": "section filtering", + "tgt_entity_name": "b", + "relation_name": "", + "weight": 9.0, + "description": "section filtering selects the candidate subset b for analysis", + "source_ids": [ + 57 + ], + "source": "Name: section filtering\nType: TASK_OR_PROBLEM", + "target": "Name: b\nType: DATASET_OR_CORPUS" + }, + { + "src_entity_name": "section filtering", + "tgt_entity_name": "b title", + "relation_name": "", + "weight": 9.0, + "description": "section filtering selects the candidate subset b title for analysis", + "source_ids": [ + 57 + ], + "source": "Name: section filtering\nType: TASK_OR_PROBLEM", + "target": "Name: b title\nType: DATASET_OR_CORPUS" + }, + { + "src_entity_name": "section filtering", + "tgt_entity_name": "text", + "relation_name": "", + "weight": 8.0, + "description": "section filtering corrects blocks erroneously parsed as title by re classifying them as text", + "source_ids": [ + 57 + ], + "source": "Name: section filtering\nType: TASK_OR_PROBLEM", + "target": "Name: text\nType: SECTION_TITLE" + }, + { + "src_entity_name": "layout parsing phase", + "tgt_entity_name": "image", + "relation_name": "", + "weight": 8.0, + "description": "the layout parsing phase identifies image as a type of block", + "source_ids": [ + 59 + ], + "source": "Name: image\nType: IMAGE", + "target": "Name: layout parsing phase\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "image", + "tgt_entity_name": "4.3.1 kg construction", + "relation_name": "", + "weight": 8.5, + "description": "The Image node type triggers the use of the Vision Language Model in this section's logic.", + "source_ids": [ + 63 + ], + "source": "Name: image\nType: IMAGE", + "target": "Name: 4.3.1 kg construction\nType: SECTION_TITLE" + }, + { + "src_entity_name": "filters", + "tgt_entity_name": "image", + "relation_name": "", + "weight": 8.0, + "description": "filters can be of type image to target visual elements", + "source_ids": [ + 258 + ], + "source": "Name: image\nType: IMAGE", + "target": "Name: filters\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "image", + "tgt_entity_name": "figures", + "relation_name": "", + "weight": 9.0, + "description": "figures are examples of images", + "source_ids": [ + 258 + ], + "source": "Name: image\nType: IMAGE", + "target": "Name: figures\nType: IMAGE" + }, + { + "src_entity_name": "filters", + "tgt_entity_name": "table", + "relation_name": "", + "weight": 8.0, + "description": "filters can be of type table to target tabular data", + "source_ids": [ + 258 + ], + "source": "Name: table\nType: TABLE", + "target": "Name: filters\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "filter modal", + "tgt_entity_name": "c", + "relation_name": "", + "weight": 8.0, + "description": "filter modal applies the explicit constraints c generated during the plan", + "source_ids": [ + 102 + ], + "source": "Name: c\nType: PARAMETER_OR_VARIABLE", + "target": "Name: filter modal\nType: TECHNOLOGY" + }, + { + "src_entity_name": "filter range", + "tgt_entity_name": "c", + "relation_name": "", + "weight": 8.0, + "description": "filter range applies the explicit constraints c generated during the plan", + "source_ids": [ + 102 + ], + "source": "Name: c\nType: PARAMETER_OR_VARIABLE", + "target": "Name: filter range\nType: TECHNOLOGY" + }, + { + "src_entity_name": "c", + "tgt_entity_name": "modal types", + "relation_name": "", + "weight": 10.0, + "description": "modal types are examples of the explicit constraints c", + "source_ids": [ + 102 + ], + "source": "Name: c\nType: PARAMETER_OR_VARIABLE", + "target": "Name: modal types\nType: CONCEPT" + }, + { + "src_entity_name": "c", + "tgt_entity_name": "page ranges", + "relation_name": "", + "weight": 10.0, + "description": "page ranges are examples of the explicit constraints c", + "source_ids": [ + 102 + ], + "source": "Name: c\nType: PARAMETER_OR_VARIABLE", + "target": "Name: page ranges\nType: CONCEPT" + }, + { + "src_entity_name": "c", + "tgt_entity_name": "plan", + "relation_name": "", + "weight": 9.0, + "description": "the constraints c are generated during the plan", + "source_ids": [ + 102 + ], + "source": "Name: c\nType: PARAMETER_OR_VARIABLE", + "target": "Name: plan\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "tree", + "tgt_entity_name": "node set", + "relation_name": "", + "weight": 9.0, + "description": "the tree is constructed using the node set which contains all blocks from the filtering process", + "source_ids": [ + 58 + ], + "source": "Name: tree\nType: TASK_OR_PROBLEM", + "target": "Name: node set\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "edge set", + "tgt_entity_name": "tree", + "relation_name": "", + "weight": 9.0, + "description": "the edge set is established to define the structure of the tree", + "source_ids": [ + 58 + ], + "source": "Name: tree\nType: TASK_OR_PROBLEM", + "target": "Name: edge set\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "tree", + "tgt_entity_name": "hierarchical levels", + "relation_name": "", + "weight": 8.0, + "description": "hierarchical levels are used to infer relationships within the tree structure", + "source_ids": [ + 58 + ], + "source": "Name: tree\nType: TASK_OR_PROBLEM", + "target": "Name: hierarchical levels\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "tree", + "tgt_entity_name": "document order", + "relation_name": "", + "weight": 8.0, + "description": "document order is used to assemble the complete tree structure", + "source_ids": [ + 58 + ], + "source": "Name: tree\nType: TASK_OR_PROBLEM", + "target": "Name: document order\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "selector", + "tgt_entity_name": "tree", + "relation_name": "", + "weight": 9.0, + "description": "the selector operators operate on the tree t n e t to produce a filtered subset", + "source_ids": [ + 102 + ], + "source": "Name: tree\nType: TASK_OR_PROBLEM", + "target": "Name: selector\nType: TECHNOLOGY" + }, + { + "src_entity_name": "filter modal", + "tgt_entity_name": "tree", + "relation_name": "", + "weight": 8.0, + "description": "filter modal operates on the tree to produce a filtered subset", + "source_ids": [ + 102 + ], + "source": "Name: tree\nType: TASK_OR_PROBLEM", + "target": "Name: filter modal\nType: TECHNOLOGY" + }, + { + "src_entity_name": "filter range", + "tgt_entity_name": "tree", + "relation_name": "", + "weight": 8.0, + "description": "filter range operates on the tree to produce a filtered subset", + "source_ids": [ + 102 + ], + "source": "Name: tree\nType: TASK_OR_PROBLEM", + "target": "Name: filter range\nType: TECHNOLOGY" + }, + { + "src_entity_name": "tree", + "tgt_entity_name": "nodes", + "relation_name": "", + "weight": 10.0, + "description": "the tree t is composed of the set of nodes n", + "source_ids": [ + 102 + ], + "source": "Name: tree\nType: TASK_OR_PROBLEM", + "target": "Name: nodes\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "tree", + "tgt_entity_name": "edges", + "relation_name": "", + "weight": 10.0, + "description": "the tree t is composed of the set of edges e t", + "source_ids": [ + 102 + ], + "source": "Name: tree\nType: TASK_OR_PROBLEM", + "target": "Name: edges\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "node set", + "tgt_entity_name": "section", + "relation_name": "", + "weight": 8.0, + "description": "the node set retains nodes of the type section", + "source_ids": [ + 58 + ], + "source": "Name: node set\nType: TASK_OR_PROBLEM", + "target": "Name: section\nType: PRODUCT" + }, + { + "src_entity_name": "node set", + "tgt_entity_name": "table", + "relation_name": "", + "weight": 8.0, + "description": "the node set retains nodes of the type table", + "source_ids": [ + 58 + ], + "source": "Name: node set\nType: TASK_OR_PROBLEM", + "target": "Name: table\nType: PRODUCT" + }, + { + "src_entity_name": "node set", + "tgt_entity_name": "image", + "relation_name": "", + "weight": 8.0, + "description": "the node set retains nodes of the type image", + "source_ids": [ + 58 + ], + "source": "Name: node set\nType: TASK_OR_PROBLEM", + "target": "Name: image\nType: PRODUCT" + }, + { + "src_entity_name": "node set", + "tgt_entity_name": "filtering", + "relation_name": "", + "weight": 9.0, + "description": "the node set is composed of blocks resulting from the filtering process", + "source_ids": [ + 58 + ], + "source": "Name: node set\nType: TASK_OR_PROBLEM", + "target": "Name: filtering\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "node set", + "tgt_entity_name": "re classification", + "relation_name": "", + "weight": 9.0, + "description": "the node set is composed of blocks resulting from the re classification process", + "source_ids": [ + 58 + ], + "source": "Name: node set\nType: TASK_OR_PROBLEM", + "target": "Name: re classification\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "edge set", + "tgt_entity_name": "parent child nesting relationships", + "relation_name": "", + "weight": 10.0, + "description": "the edge set represents the parent child nesting relationships", + "source_ids": [ + 58 + ], + "source": "Name: edge set\nType: TASK_OR_PROBLEM", + "target": "Name: parent child nesting relationships\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "v table", + "tgt_entity_name": "table", + "relation_name": "", + "weight": 10.0, + "description": "v table is the distinct entity created to represent the table logical type", + "source_ids": [ + 64 + ], + "source": "Name: table\nType: PRODUCT", + "target": "Name: v table\nType: PRODUCT" + }, + { + "src_entity_name": "global", + "tgt_entity_name": "table", + "relation_name": "", + "weight": 10.0, + "description": "the global process specifically targets and filters for items of type table", + "source_ids": [ + 251 + ], + "source": "Name: table\nType: PRODUCT", + "target": "Name: global\nType: CONCEPT" + }, + { + "src_entity_name": "final tree structure", + "tgt_entity_name": "document order", + "relation_name": "", + "weight": 9.0, + "description": "the final tree structure is assembled based on the document order of the nodes", + "source_ids": [ + 59 + ], + "source": "Name: document order\nType: PARAMETER_OR_VARIABLE", + "target": "Name: final tree structure\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "node", + "tgt_entity_name": "content", + "relation_name": "", + "weight": 9.0, + "description": "each node retains its content", + "source_ids": [ + 58 + ], + "source": "Name: content\nType: PARAMETER_OR_VARIABLE", + "target": "Name: node\nType: UNKNOWN" + }, + { + "src_entity_name": "node", + "tgt_entity_name": "final node type", + "relation_name": "", + "weight": 9.0, + "description": "each node retains its final node type", + "source_ids": [ + 58 + ], + "source": "Name: final node type\nType: PARAMETER_OR_VARIABLE", + "target": "Name: node\nType: UNKNOWN" + }, + { + "src_entity_name": "layout parsing phase", + "tgt_entity_name": "title text table", + "relation_name": "", + "weight": 8.0, + "description": "the layout parsing phase identifies title text table as a type of block", + "source_ids": [ + 59 + ], + "source": "Name: layout parsing phase\nType: TASK_OR_PROBLEM", + "target": "Name: title text table\nType: PRODUCT" + }, + { + "src_entity_name": "layout parsing phase", + "tgt_entity_name": "section filtering phase", + "relation_name": "", + "weight": 6.0, + "description": "the layout parsing phase precedes the section filtering phase in the document processing workflow", + "source_ids": [ + 59 + ], + "source": "Name: layout parsing phase\nType: TASK_OR_PROBLEM", + "target": "Name: section filtering phase\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "section filtering phase", + "tgt_entity_name": "method", + "relation_name": "", + "weight": 9.0, + "description": "the section filtering phase analyzes method as a title candidate", + "source_ids": [ + 59 + ], + "source": "Name: section filtering phase\nType: TASK_OR_PROBLEM", + "target": "Name: method\nType: SECTION_TITLE" + }, + { + "src_entity_name": "section filtering phase", + "tgt_entity_name": "experiment", + "relation_name": "", + "weight": 9.0, + "description": "the section filtering phase analyzes experiment as a title candidate", + "source_ids": [ + 59 + ], + "source": "Name: section filtering phase\nType: TASK_OR_PROBLEM", + "target": "Name: experiment\nType: SECTION_TITLE" + }, + { + "src_entity_name": "section filtering phase", + "tgt_entity_name": "moe layer", + "relation_name": "", + "weight": 9.0, + "description": "the section filtering phase analyzes moe layer which was erroneously tagged and re classified", + "source_ids": [ + 59 + ], + "source": "Name: section filtering phase\nType: TASK_OR_PROBLEM", + "target": "Name: moe layer\nType: SECTION_TITLE" + }, + { + "src_entity_name": "section filtering phase", + "tgt_entity_name": "final tree structure", + "relation_name": "", + "weight": 7.0, + "description": "the section filtering phase contributes to the creation of the final tree structure", + "source_ids": [ + 59 + ], + "source": "Name: section filtering phase\nType: TASK_OR_PROBLEM", + "target": "Name: final tree structure\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "method", + "tgt_entity_name": "fontsize", + "relation_name": "", + "weight": 10.0, + "description": "the method block has a fontsize of 14", + "source_ids": [ + 59 + ], + "source": "Name: method\nType: SECTION_TITLE", + "target": "Name: fontsize\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "method", + "tgt_entity_name": "14", + "relation_name": "", + "weight": 10.0, + "description": "the method block is associated with the measurement value 14", + "source_ids": [ + 59 + ], + "source": "Name: method\nType: 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"relation_name": "", + "weight": 10.0, + "description": "the moe layer block is associated with the measurement value none", + "source_ids": [ + 59 + ], + "source": "Name: moe layer\nType: SECTION_TITLE", + "target": "Name: none\nType: MEASUREMENT" + }, + { + "src_entity_name": "final tree structure", + "tgt_entity_name": "level", + "relation_name": "", + "weight": 9.0, + "description": "the final tree structure is assembled based on the determined levels of the nodes", + "source_ids": [ + 59 + ], + "source": "Name: final tree structure\nType: TASK_OR_PROBLEM", + "target": "Name: level\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "proceedings of the acm on management of data", + "tgt_entity_name": "2", + "relation_name": "", + "weight": 10.0, + "description": "the publication volume is 2", + "source_ids": [ + 199 + ], + "source": "Name: 2\nType: MEASUREMENT", + "target": "Name: proceedings of the acm on management of data\nType: PUBLICATION_VENUE" + }, + { + "src_entity_name": "tree t", + "tgt_entity_name": "knowledge graph g", + "relation_name": "", + "weight": 9.0, + "description": "tree t is the source from which entities are extracted to populate knowledge graph g", + "source_ids": [ + 62 + ], + "source": "Name: tree t\nType: TASK_OR_PROBLEM", + "target": "Name: knowledge graph g\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "tree t", + "tgt_entity_name": "tree nodes", + "relation_name": "", + "weight": 10.0, + "description": "tree nodes are the constituent parts of tree t that serve as the source for entity extraction", + "source_ids": [ + 62 + ], + "source": "Name: tree t\nType: TASK_OR_PROBLEM", + "target": "Name: tree nodes\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "knowledge graph g", + "tgt_entity_name": "tree nodes", + "relation_name": "", + "weight": 9.0, + "description": "knowledge graph g is populated by extracting and refining entities from tree nodes", + "source_ids": [ + 62 + ], + "source": "Name: 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"relation_name": "", + "weight": 9.5, + "description": "The Mapping M is the critical output artifact generated by recording entity origins in this section.", + "source_ids": [ + 63 + ], + "source": "Name: 4.3.1 kg construction\nType: SECTION_TITLE", + "target": "Name: mapping m\nType: EQUATION_OR_FORMULA" + }, + { + "src_entity_name": "mapping m", + "tgt_entity_name": "g", + "relation_name": "", + "weight": 8.0, + "description": "the mapping m bi directionally links the entities in g to their structural locations", + "source_ids": [ + 77 + ], + "source": "Name: mapping m\nType: EQUATION_OR_FORMULA", + "target": "Name: g\nType: CONCEPT" + }, + { + "src_entity_name": "mapping m", + "tgt_entity_name": "t", + "relation_name": "", + "weight": 8.0, + "description": "the mapping m links entities to the set of their structural locations nodes in t", + "source_ids": [ + 77 + ], + "source": "Name: mapping m\nType: EQUATION_OR_FORMULA", + "target": "Name: t\nType: CONCEPT" + }, + { + "src_entity_name": "mapping m", + "tgt_entity_name": "p n", + "relation_name": "", + "weight": 9.0, + "description": "the mapping m maps entities to the power set of nodes p n", + "source_ids": [ + 77 + ], + "source": "Name: mapping m\nType: EQUATION_OR_FORMULA", + "target": "Name: p n\nType: MATHEMATICAL_CONCEPT" + }, + { + "src_entity_name": "row", + "tgt_entity_name": "v table", + "relation_name": "", + "weight": 9.0, + "description": "row headers are linked to v table via a containedin relationship", + "source_ids": [ + 64 + ], + "source": "Name: v table\nType: PRODUCT", + "target": "Name: row\nType: PRODUCT" + }, + { + "src_entity_name": "column", + "tgt_entity_name": "v table", + "relation_name": "", + "weight": 9.0, + "description": "column headers are linked to v table via a containedin relationship", + "source_ids": [ + 64 + ], + "source": "Name: v table\nType: PRODUCT", + "target": "Name: column\nType: PRODUCT" + }, + { + "src_entity_name": "header", + "tgt_entity_name": "v 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"description": "row and column headers are both explicitly extracted components of table nodes", + "source_ids": [ + 64 + ], + "source": "Name: row\nType: PRODUCT", + "target": "Name: column\nType: PRODUCT" + }, + { + "src_entity_name": "column", + "tgt_entity_name": "header", + "relation_name": "", + "weight": 9.0, + "description": "column headers are a specific type of header extracted from table nodes", + "source_ids": [ + 64 + ], + "source": "Name: column\nType: PRODUCT", + "target": "Name: header\nType: PRODUCT" + }, + { + "src_entity_name": "structural semantics", + "tgt_entity_name": "logical types", + "relation_name": "", + "weight": 9.0, + "description": "structural semantics are preserved specifically for logical types like table and formula", + "source_ids": [ + 64 + ], + "source": "Name: structural semantics\nType: CONCEPT", + "target": "Name: logical types\nType: CONCEPT" + }, + { + "src_entity_name": "entity resolution", + "tgt_entity_name": "4.3.2 gradient-based entity 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"target": "Name: v n\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "entity resolution", + "tgt_entity_name": "v sel", + "relation_name": "", + "weight": 9.0, + "description": "the entity v n is merged into the canonical entity v sel during entity resolution", + "source_ids": [ + 77 + ], + "source": "Name: entity resolution\nType: TASK_OR_PROBLEM", + "target": "Name: v sel\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "figure 13", + "tgt_entity_name": "entity resolution", + "relation_name": "", + "weight": 9.0, + "description": "figure 13 contains the prompt used for the entity resolution judgement task", + "source_ids": [ + 284 + ], + "source": "Name: entity resolution\nType: TASK_OR_PROBLEM", + "target": "Name: figure 13\nType: IMAGE" + }, + { + "src_entity_name": "prompt", + "tgt_entity_name": "entity resolution", + "relation_name": "", + "weight": 9.0, + "description": "the prompt is specifically designed for the entity resolution judgement task", + "source_ids": [ + 284 + ], + "source": "Name: entity resolution\nType: TASK_OR_PROBLEM", + "target": "Name: prompt\nType: SOFTWARE" + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "dirty er", + "relation_name": "", + "weight": 9.0, + "description": "er methods are often designed for batch processing across multiple data sources commonly referred to as dirty er", + "source_ids": [ + 66 + ], + "source": "Name: er methods\nType: TASK_OR_PROBLEM", + "target": "Name: dirty er\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "a", + "relation_name": "", + "weight": 8.0, + "description": "er methods aim to merge entities like a b and c as the same concept", + "source_ids": [ + 66 + ], + "source": "Name: er methods\nType: TASK_OR_PROBLEM", + "target": "Name: a\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "b", + "relation_name": "", + "weight": 8.0, + "description": "er methods aim to merge entities like a b and c as the same concept", + "source_ids": [ + 66 + ], + "source": "Name: er methods\nType: TASK_OR_PROBLEM", + "target": "Name: b\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "c", + "relation_name": "", + "weight": 8.0, + "description": "er methods aim to merge entities like a b and c as the same concept", + "source_ids": [ + 66 + ], + "source": "Name: er methods\nType: TASK_OR_PROBLEM", + "target": "Name: c\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "a b", + "relation_name": "", + "weight": 9.0, + "description": "er methods require finding all possible matching pairs such as a b to confirm equivalence", + "source_ids": [ + 66 + ], + "source": "Name: er methods\nType: TASK_OR_PROBLEM", + "target": "Name: a b\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "a c", + "relation_name": "", + "weight": 9.0, + "description": "er methods require finding all possible matching pairs such as a c to confirm equivalence", + "source_ids": [ + 66 + ], + "source": "Name: er methods\nType: TASK_OR_PROBLEM", + "target": "Name: a c\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "b c", + "relation_name": "", + "weight": 9.0, + "description": "er methods require finding all possible matching pairs such as b c to confirm equivalence", + "source_ids": [ + 66 + ], + "source": "Name: er methods\nType: TASK_OR_PROBLEM", + "target": "Name: b c\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "12", + "relation_name": "", + "weight": 6.0, + "description": "the text cites reference 12 in the context of ensuring accurate entity resolution", + "source_ids": [ + 66 + ], + "source": "Name: er methods\nType: TASK_OR_PROBLEM", + "target": "Name: 12\nType: PUBLICATION_VENUE" + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "o n 2", + "relation_name": "", + "weight": 9.0, + "description": "the process of er methods leads to a quadratic o n 2 number of pairwise comparisons", + "source_ids": [ + 66 + ], + "source": "Name: er methods\nType: TASK_OR_PROBLEM", + "target": "Name: o n 2\nType: MEASUREMENT" + }, + { + "src_entity_name": "a", + "tgt_entity_name": "b", + "relation_name": "", + "weight": 7.0, + "description": "a and b are compared as a pair a b to confirm their equivalence", + "source_ids": [ + 66 + ], + "source": "Name: a\nType: TASK_OR_PROBLEM", + "target": "Name: b\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "a", + "tgt_entity_name": "c", + "relation_name": "", + "weight": 7.0, + "description": "a and c are compared as a pair a c to confirm their equivalence", + "source_ids": [ + 66 + ], + "source": "Name: a\nType: TASK_OR_PROBLEM", + "target": "Name: c\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "b", + "tgt_entity_name": "c", + "relation_name": "", + "weight": 7.0, + "description": "b and c are compared as a pair b c to confirm their equivalence", + "source_ids": [ + 66 + ], + "source": "Name: b\nType: TASK_OR_PROBLEM", + "target": "Name: c\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "gradient based er method", + "tgt_entity_name": "clean er", + "relation_name": "", + "weight": 9.0, + "description": "the method operates on a single document simplified as the clean er", + "source_ids": [ + 67 + ], + "source": "Name: gradient based er method\nType: TECHNOLOGY", + "target": "Name: clean er\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "gradient based er method", + "tgt_entity_name": "database", + "relation_name": "", + "weight": 8.0, + "description": "the method determines where a new entity fits among entities already in the database", + "source_ids": [ + 67 + ], + "source": "Name: gradient based er method\nType: TECHNOLOGY", + "target": "Name: database\nType: SOFTWARE" + }, + { + "src_entity_name": "gradient based er method", + "tgt_entity_name": "top k most relevant candidates", + "relation_name": "", + "weight": 8.0, + "description": "the method yields scoring patterns when a new entity is reranked against its top k candidates", + "source_ids": [ + 67 + ], + "source": "Name: gradient based er method\nType: TECHNOLOGY", + "target": "Name: top k most relevant candidates\nType: EVALUATION_METRIC" + }, + { + "src_entity_name": "gradient based er method", + "tgt_entity_name": "entity", + "relation_name": "", + "weight": 10.0, + "description": "the method performs entity resolution incrementally as each new entity is extracted", + "source_ids": [ + 67 + ], + "source": "Name: gradient based er method\nType: TECHNOLOGY", + "target": "Name: entity\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "gradient based er method", + "tgt_entity_name": "quadratic batch problem", + "relation_name": "", + "weight": 9.0, + "description": "the method transforms the quadratic batch problem into a simpler task", + "source_ids": [ + 67 + ], + "source": "Name: gradient based er method\nType: TECHNOLOGY", + "target": "Name: quadratic batch problem\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "gradient based er method", + "tgt_entity_name": "repeated lookup task", + "relation_name": "", + "weight": 9.0, + "description": "the method transforms the problem into a repeated lookup task", + "source_ids": [ + 67 + ], + "source": "Name: gradient based er method\nType: TECHNOLOGY", + "target": "Name: repeated lookup task\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "entity", + "tgt_entity_name": "database", + "relation_name": "", + "weight": 9.0, + "description": "the new entity is determined to fit among the already processed entities in the database", + "source_ids": [ + 67 + ], + "source": "Name: database\nType: SOFTWARE", + "target": "Name: entity\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "entity", + "tgt_entity_name": "top k most relevant candidates", + "relation_name": "", + "weight": 9.0, + "description": "the new entity is reranked against its top k most relevant candidates", + "source_ids": [ + 67 + ], + "source": "Name: top k most relevant candidates\nType: EVALUATION_METRIC", + "target": "Name: entity\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "incremental process", + "tgt_entity_name": "scoring patterns", + "relation_name": "", + "weight": 8.0, + "description": "the incremental process yields two distinct scoring patterns", + "source_ids": [ + 67 + ], + "source": "Name: scoring patterns\nType: EVALUATION_METRIC", + "target": "Name: incremental process\nType: UNKNOWN" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "v n", + "relation_name": "", + "weight": 10.0, + "description": "algorithm 1 processes the new entity v n by retrieving candidates and making a decision", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: v n\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "e c", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 retrieves the top k candidates e c from the vector database db", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: e c\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "db", + "relation_name": "", + "weight": 9.0, + "description": "the vector database db is the source from which candidates e c are retrieved", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: db\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "r", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 uses the reranker r to re rank candidates e c against v n", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: r\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "s", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 sorts candidates based on their scores s", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: s\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "sel", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 initializes and iterates through the selection set sel", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: sel\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "g", + "relation_name": "", + "weight": 8.0, + "description": "algorithm 1 uses the gradient threshold g to determine if a score drop is sharp", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: g\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "case a", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 identifies case a when the selection set sel is identical to e c", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: case a\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "case b", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 identifies case b when a gradient is found in the selection set sel", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: case b\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "v sel", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 selects the canonical entity v sel from the selection set sel in case b", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: v sel\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "lines 1 3", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 executes the steps outlined in lines 1 3 to retrieve and rerank candidates", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: lines 1 3\nType: SECTION_TITLE" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "line 4", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 executes the initialization step described in line 4", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: line 4\nType: SECTION_TITLE" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "lines 5 8", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 executes the iteration logic described in lines 5 8", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: lines 5 8\nType: SECTION_TITLE" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "lines 7 8", + "relation_name": "", + "weight": 8.0, + "description": "the logic in lines 7 8 is part of the iteration process within algorithm 1", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: lines 7 8\nType: SECTION_TITLE" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "line 8", + "relation_name": "", + "weight": 8.0, + "description": "line 8 defines the break condition within the loop of algorithm 1", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: line 8\nType: SECTION_TITLE" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "lines 9 14", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 executes the decision logic described in lines 9 14", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: lines 9 14\nType: SECTION_TITLE" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "line 9 10", + "relation_name": "", + "weight": 8.0, + "description": "lines 9 10 are the specific actions taken when case a is identified in algorithm 1", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: line 9 10\nType: SECTION_TITLE" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "lines 12 14", + "relation_name": "", + "weight": 8.0, + "description": "lines 12 14 are the specific actions taken when case b is identified in algorithm 1", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: lines 12 14\nType: SECTION_TITLE" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "line 13", + "relation_name": "", + "weight": 8.0, + "description": "line 13 is a step within the case b logic of algorithm 1", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: line 13\nType: SECTION_TITLE" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "line 15", + "relation_name": "", + "weight": 9.0, + "description": "line 15 is the final step of algorithm 1 where results are returned", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: line 15\nType: SECTION_TITLE" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "score", + "relation_name": "", + "weight": 9.0, + "description": "the variable score is initialized and updated during the execution of algorithm 1", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: score\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "v c", + "relation_name": "", + "weight": 9.0, + "description": "the variable v c is the current candidate processed within the loop of algorithm 1", + "source_ids": [ + 75 + ], + "source": "Name: algorithm 1\nType: TASK_OR_PROBLEM", + "target": "Name: v c\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "kg g", + "tgt_entity_name": "new entity v n", + "relation_name": "", + "weight": 8.0, + "description": "the new entity v n is added to or processed within the knowledge graph g", + "source_ids": [ + 70 + ], + "source": "Name: kg g\nType: TASK_OR_PROBLEM", + "target": "Name: new entity v n\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "rerank model r", + "tgt_entity_name": "entity vector database db", + "relation_name": "", + "weight": 7.0, + "description": "the rerank model r likely utilizes the entity vector database db to perform its ranking tasks", + "source_ids": [ + 70 + ], + "source": "Name: rerank model r\nType: MODEL_OR_ARCHITECTURE", + "target": "Name: entity vector database db\nType: DATASET_OR_CORPUS" + }, + { + "src_entity_name": "threshold of gradient g", + "tgt_entity_name": "rerank model r", + "relation_name": "", + "weight": 6.0, + "description": "the threshold of gradient g is a parameter that influences the operation or convergence of the rerank model r", + "source_ids": [ + 70 + ], + "source": "Name: rerank model r\nType: MODEL_OR_ARCHITECTURE", + "target": "Name: threshold of gradient g\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "rerank model r", + "tgt_entity_name": "r", + "relation_name": "", + "weight": 10.0, + "description": "r is the variable name for the rerank model", + "source_ids": [ + 70 + ], + "source": "Name: rerank model r\nType: MODEL_OR_ARCHITECTURE", + "target": "Name: r\nType: MODEL_OR_ARCHITECTURE" + }, + { + "src_entity_name": "vector search number top k", + "tgt_entity_name": "entity vector database db", + "relation_name": "", + "weight": 9.0, + "description": "the vector search number top k parameter determines the scope of the search performed on the entity vector database db", + "source_ids": [ + 70 + ], + "source": "Name: entity vector database db\nType: DATASET_OR_CORPUS", + "target": "Name: vector search number top k\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "entity vector database db", + "tgt_entity_name": "db", + "relation_name": "", + "weight": 10.0, + "description": "db is the variable name for the entity vector database", + "source_ids": [ + 70 + ], + "source": "Name: entity vector database db\nType: DATASET_OR_CORPUS", + "target": "Name: db\nType: DATASET_OR_CORPUS" + }, + { + "src_entity_name": "vector search number top k", + "tgt_entity_name": "top k", + "relation_name": "", + "weight": 10.0, + "description": "top k is the variable name for the vector search number", + "source_ids": [ + 70 + ], + "source": "Name: vector search number top k\nType: PARAMETER_OR_VARIABLE", + "target": "Name: top k\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "threshold of gradient g", + "tgt_entity_name": "g", + "relation_name": "", + "weight": 10.0, + "description": "g is the variable name for the threshold of gradient", + "source_ids": [ + 70 + ], + "source": "Name: threshold of gradient g\nType: PARAMETER_OR_VARIABLE", + "target": "Name: g\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "kg", + "tgt_entity_name": "g", + "relation_name": "", + "weight": 10.0, + "description": "kg and g refer to the same knowledge graph entity with g being its variable representation", + "source_ids": [ + 70 + ], + "source": "Name: kg\nType: TASK_OR_PROBLEM", + "target": "Name: g\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "e 9", + "tgt_entity_name": "kg", + "relation_name": "", + "weight": 9.0, + "description": "e 9 is processed within the kg context", + "source_ids": [ + 76 + ], + "source": "Name: kg\nType: TASK_OR_PROBLEM", + "target": "Name: e 9\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "v", + "tgt_entity_name": "n", + "relation_name": "", + "weight": 9.0, + "description": "n is a subscript or modifier defining the specific instance of the new entity v", + "source_ids": [ + 70 + ], + "source": "Name: v\nType: TASK_OR_PROBLEM", + "target": "Name: n\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "g", + "relation_name": "", + "weight": 10.0, + "description": "g is a defined component within the bookindex structure", + "source_ids": [ + 88 + ], + "source": "Name: g\nType: PARAMETER_OR_VARIABLE", + "target": "Name: bookindex\nType: DATASET_OR_CORPUS" + }, + { + "src_entity_name": "vector search", + "tgt_entity_name": "db", + "relation_name": "", + "weight": 9.0, + "description": "vector search operates on the db to find relevant entities", + "source_ids": [ + 71 + ], + "source": "Name: vector search\nType: UNKNOWN", + "target": "Name: db\nType: UNKNOWN" + }, + { + "src_entity_name": "vector search", + "tgt_entity_name": "top k", + "relation_name": "", + "weight": 9.0, + "description": "vector search utilizes the top k parameter to limit the number of relevant entities found", + "source_ids": [ + 71 + ], + "source": "Name: vector search\nType: UNKNOWN", + "target": "Name: top k\nType: UNKNOWN" + }, + { + "src_entity_name": "search", + "tgt_entity_name": "db", + "relation_name": "", + "weight": 9.0, + "description": "the search function is applied to the db to retrieve entities", + "source_ids": [ + 71 + ], + "source": "Name: db\nType: UNKNOWN", + "target": "Name: search\nType: UNKNOWN" + }, + { + "src_entity_name": "search", + "tgt_entity_name": "v n", + "relation_name": "", + "weight": 9.0, + "description": "the search function uses the vector v n as its query input", + "source_ids": [ + 71 + ], + "source": "Name: search\nType: UNKNOWN", + "target": "Name: v n\nType: UNKNOWN" + }, + { + "src_entity_name": "search", + "tgt_entity_name": "e c", + "relation_name": "", + "weight": 9.0, + "description": "the search function outputs the candidate entities e c", + "source_ids": [ + 71 + ], + "source": "Name: search\nType: UNKNOWN", + "target": "Name: e c\nType: UNKNOWN" + }, + { + "src_entity_name": "r", + "tgt_entity_name": "e", + "relation_name": "", + "weight": 8.0, + "description": "the function r takes entities e as input to process them", + "source_ids": [ + 71 + ], + "source": "Name: r\nType: UNKNOWN", + "target": "Name: e\nType: UNKNOWN" + }, + { + "src_entity_name": "r", + "tgt_entity_name": "v cn", + "relation_name": "", + "weight": 8.0, + "description": "the function r uses the vector v cn to calculate rerank scores", + "source_ids": [ + 71 + ], + "source": "Name: r\nType: UNKNOWN", + "target": "Name: v cn\nType: UNKNOWN" + }, + { + "src_entity_name": "sort", + "tgt_entity_name": "e", + "relation_name": "", + "weight": 8.0, + "description": "the sort operation is applied to the list of entities e", + "source_ids": [ + 71 + ], + "source": "Name: e\nType: UNKNOWN", + "target": "Name: sort\nType: UNKNOWN" + }, + { + "src_entity_name": "gradient select", + "tgt_entity_name": "e", + "relation_name": "", + "weight": 8.0, + "description": "gradient select is used to select entities from the remaining list e", + "source_ids": [ + 71 + ], + "source": "Name: e\nType: UNKNOWN", + "target": "Name: gradient select\nType: UNKNOWN" + }, + { + "src_entity_name": "sort", + "tgt_entity_name": "s", + "relation_name": "", + "weight": 9.0, + "description": "the sort operation generates the sorted list s", + "source_ids": [ + 71 + ], + "source": "Name: sort\nType: UNKNOWN", + "target": "Name: s\nType: UNKNOWN" + }, + { + "src_entity_name": "sort", + "tgt_entity_name": "c", + "relation_name": "", + "weight": 9.0, + "description": "the sort operation orders entities based on the rerank scores c", + "source_ids": [ + 71 + ], + "source": "Name: sort\nType: UNKNOWN", + "target": "Name: c\nType: UNKNOWN" + }, + { + "src_entity_name": "gradient select", + "tgt_entity_name": "sel", + "relation_name": "", + "weight": 9.0, + "description": "the gradient select method produces the selected entities sel", + "source_ids": [ + 71 + ], + "source": "Name: gradient select\nType: UNKNOWN", + "target": "Name: sel\nType: UNKNOWN" + }, + { + "src_entity_name": "score", + "tgt_entity_name": "s 0", + "relation_name": "", + "weight": 10.0, + "description": "the score variable is assigned the value of the first element s 0 from the sorted list", + "source_ids": [ + 71 + ], + "source": "Name: score\nType: UNKNOWN", + "target": "Name: s 0\nType: UNKNOWN" + }, + { + "src_entity_name": "case a", + "tgt_entity_name": "new entity", + "relation_name": "", + "weight": 9.0, + "description": "case a describes the scenario where a new entity is introduced and evaluated", + "source_ids": [ + 72 + ], + "source": "Name: case a\nType: TASK_OR_PROBLEM", + "target": "Name: new entity\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "new entity", + "tgt_entity_name": "existing entities", + "relation_name": "", + "weight": 10.0, + "description": "the new entity s relevance scores are calculated against all existing entities", + "source_ids": [ + 72 + ], + "source": "Name: new entity\nType: TASK_OR_PROBLEM", + "target": "Name: existing entities\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "entity resolution adjudicator", + "tgt_entity_name": "new entity", + "relation_name": "", + "weight": 10.0, + "description": "the entity resolution adjudicator evaluates the new entity to find a match", + "source_ids": [ + 262 + ], + "source": "Name: new entity\nType: TASK_OR_PROBLEM", + "target": "Name: entity resolution adjudicator\nType: PERSON" + }, + { + "src_entity_name": "new entity", + "tgt_entity_name": "candidate entities", + "relation_name": "", + "weight": 9.0, + "description": "the new entity is compared against the candidate entities to determine if they refer to the same concept", + "source_ids": [ + 262 + ], + "source": "Name: new entity\nType: TASK_OR_PROBLEM", + "target": "Name: candidate entities\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "new entity", + "tgt_entity_name": "text", + "relation_name": "", + "weight": 10.0, + "description": "the new entity is extracted from the text", + "source_ids": [ + 262 + ], + "source": "Name: new entity\nType: TASK_OR_PROBLEM", + "target": "Name: text\nType: DATASET_OR_CORPUS" + }, + { + "src_entity_name": "case b", + "tgt_entity_name": "existing entity", + "relation_name": "", + "weight": 10.0, + "description": "case b is defined by the scenario involving an existing entity", + "source_ids": [ + 73 + ], + "source": "Name: case b\nType: TASK_OR_PROBLEM", + "target": "Name: existing entity\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "case b", + "tgt_entity_name": "alias", + "relation_name": "", + "weight": 9.0, + "description": "case b specifically addresses the situation where an alias is being evaluated", + "source_ids": [ + 73 + ], + "source": "Name: case b\nType: TASK_OR_PROBLEM", + "target": "Name: alias\nType: CONCEPT" + }, + { + "src_entity_name": "gradient based er algorithm", + "tgt_entity_name": "case b", + "relation_name": "", + "weight": 10.0, + "description": "the gradient based er algorithm is designed to detect the sharp decline characteristic of case b", + "source_ids": [ + 74 + ], + "source": "Name: case b\nType: TASK_OR_PROBLEM", + "target": "Name: gradient based er algorithm\nType: TECHNOLOGY" + }, + { + "src_entity_name": "reranker", + "tgt_entity_name": "scores", + "relation_name": "", + "weight": 7.0, + "description": "the reranker s limitations affect the initial set of high relevance scores", + "source_ids": [ + 73 + ], + "source": "Name: reranker\nType: TECHNOLOGY", + "target": "Name: scores\nType: EVALUATION_METRIC" + }, + { + "src_entity_name": "alias", + "tgt_entity_name": "true match", + "relation_name": "", + "weight": 9.0, + "description": "the alias is evaluated for its relevance to the true match", + "source_ids": [ + 73 + ], + "source": "Name: alias\nType: CONCEPT", + "target": "Name: true match\nType: CONCEPT" + }, + { + "src_entity_name": "scores", + "tgt_entity_name": "true match", + "relation_name": "", + "weight": 8.0, + "description": "scores indicate the relevance of the alias to the true match", + "source_ids": [ + 73 + ], + "source": "Name: scores\nType: EVALUATION_METRIC", + "target": "Name: true match\nType: CONCEPT" + }, + { + "src_entity_name": "scores", + "tgt_entity_name": "equivalent aliases", + "relation_name": "", + "weight": 8.0, + "description": "scores show high relevance to the true match or a set of equivalent aliases", + "source_ids": [ + 73 + ], + "source": "Name: scores\nType: EVALUATION_METRIC", + "target": "Name: equivalent aliases\nType: CONCEPT" + }, + { + "src_entity_name": "scores", + "tgt_entity_name": "gradient", + "relation_name": "", + "weight": 8.0, + "description": "the scores exhibit a sharp decline gradient after the initial high relevance set", + "source_ids": [ + 73 + ], + "source": "Name: scores\nType: EVALUATION_METRIC", + "target": "Name: gradient\nType: MEASUREMENT" + }, + { + "src_entity_name": "gradient", + "tgt_entity_name": "irrelevant entities", + "relation_name": "", + "weight": 7.0, + "description": "the gradient precedes the transition to irrelevant entities", + "source_ids": [ + 73 + ], + "source": "Name: gradient\nType: MEASUREMENT", + "target": "Name: irrelevant entities\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "gradient based er algorithm", + "tgt_entity_name": "high relevance set", + "relation_name": "", + "weight": 10.0, + "description": "the gradient based er algorithm efficiently isolates the high relevance set", + "source_ids": [ + 74 + ], + "source": "Name: gradient based er algorithm\nType: TECHNOLOGY", + "target": "Name: high relevance set\nType: DATASET_OR_CORPUS" + }, + { + "src_entity_name": "high relevance set", + "tgt_entity_name": "similar entities", + "relation_name": "", + "weight": 8.0, + "description": "the similar entities are contained within the high relevance set identified by the algorithm", + "source_ids": [ + 74 + ], + "source": "Name: high relevance set\nType: DATASET_OR_CORPUS", + "target": "Name: similar entities\nType: DATASET_OR_CORPUS" + }, + { + "src_entity_name": "cref='#/texts/259'", + "tgt_entity_name": "score", + "relation_name": "", + "weight": 9.0, + "description": "Image entity cref='#/texts/259' related to Score", + "source_ids": [ + 178 + ], + "source": "Name: score\nType: PARAMETER_OR_VARIABLE", + "target": "Name: cref='#/texts/259'\nType: IMAGE" + }, + { + "src_entity_name": "e 9", + "tgt_entity_name": "e 7", + "relation_name": "", + "weight": 10.0, + "description": "e 9 shows high similarity with e 7 and is merged with it", + "source_ids": [ + 76 + ], + "source": "Name: e 9\nType: TASK_OR_PROBLEM", + "target": "Name: e 7\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "e 9", + "tgt_entity_name": "e 6", + "relation_name": "", + "weight": 7.0, + "description": "e 9 shows a sharp decline in similarity with e 6", + "source_ids": [ + 76 + ], + "source": "Name: e 9\nType: TASK_OR_PROBLEM", + "target": "Name: e 6\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "e 9", + "tgt_entity_name": "e 8", + "relation_name": "", + "weight": 7.0, + "description": "e 9 shows a sharp decline in similarity with e 8", + "source_ids": [ + 76 + ], + "source": "Name: e 9\nType: TASK_OR_PROBLEM", + "target": "Name: e 8\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "e 9", + "tgt_entity_name": "e 5", + "relation_name": "", + "weight": 7.0, + "description": "e 9 shows a sharp decline in similarity with e 5", + "source_ids": [ + 76 + ], + "source": "Name: e 9\nType: TASK_OR_PROBLEM", + "target": "Name: e 5\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "similarity curve", + "tgt_entity_name": "e 9", + "relation_name": "", + "weight": 8.0, + "description": "the similarity curve depicts the similarity of e 9 with other entities", + "source_ids": [ + 76 + ], + "source": "Name: e 9\nType: TASK_OR_PROBLEM", + "target": "Name: similarity curve\nType: IMAGE" + }, + 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METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "t", + "relation_name": "", + "weight": 10.0, + "description": "t is a defined component within the bookindex structure", + "source_ids": [ + 88 + ], + "source": "Name: bookindex\nType: DATASET_OR_CORPUS", + "target": "Name: t\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "relevant entities", + "relation_name": "", + "weight": 8.0, + "description": "relevant entities are contained within the g component of the bookindex", + "source_ids": [ + 85 + ], + "source": "Name: bookindex\nType: DATASET_OR_CORPUS", + "target": "Name: relevant entities\nType: DATASET_OR_CORPUS" + }, + { + "src_entity_name": "operators plan", + "tgt_entity_name": "retrieval", + "relation_name": "", + "weight": 8.0, + "description": "the operators plan guides the retrieval strategy", + "source_ids": [ + 82 + ], + "source": "Name: operators plan\nType: TASK_OR_PROBLEM", + "target": 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retrieval\nType: TASK_OR_PROBLEM", + "target": "Name: layout vanilla\nType: PRODUCT" + }, + { + "src_entity_name": "retrieval error", + "tgt_entity_name": "retrieval", + "relation_name": "", + "weight": 10.0, + "description": "retrieval error is the dominant failure mode associated with the retrieval task", + "source_ids": [ + 185 + ], + "source": "Name: retrieval\nType: TASK_OR_PROBLEM", + "target": "Name: retrieval error\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "agent based retrieval", + "tgt_entity_name": "generation", + "relation_name": "", + "weight": 9.0, + "description": "agent based retrieval contains the generation process", + "source_ids": [ + 83 + ], + "source": "Name: generation\nType: TASK_OR_PROBLEM", + "target": "Name: agent based retrieval\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "standard reasoning", + "tgt_entity_name": "generation", + "relation_name": "", + "weight": 8.0, + "description": "standard reasoning and generation are linked 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10.0, + "description": "The concept of 'Generation' is a primary topic of section 5.3.", + "source_ids": [ + 123 + ], + "source": "Name: generation\nType: TASK_OR_PROBLEM", + "target": "Name: 5.3 structured execution\nType: SECTION_TITLE" + }, + { + "src_entity_name": "generation error", + "tgt_entity_name": "generation", + "relation_name": "", + "weight": 10.0, + "description": "generation error is the second most common failure mode associated with the generation task", + "source_ids": [ + 185 + ], + "source": "Name: generation\nType: TASK_OR_PROBLEM", + "target": "Name: generation error\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "agent based retrieval", + "tgt_entity_name": "workflow", + "relation_name": "", + "weight": 9.0, + "description": "agent based retrieval is described as a general workflow", + "source_ids": [ + 83 + ], + "source": "Name: agent based retrieval\nType: METHOD_OR_TECHNIQUE", + "target": "Name: workflow\nType: TASK_OR_PROBLEM" + }, + { + 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TASK_OR_PROBLEM", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "cref='#/texts/89'", + "tgt_entity_name": "question", + "relation_name": "", + "weight": 9.0, + "description": "Image entity cref='#/texts/89' related to Question", + "source_ids": [ + 84 + ], + "source": "Name: cref='#/texts/89'\nType: IMAGE", + "target": "Name: question\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "cref='#/texts/89'", + "tgt_entity_name": "agent-based planning", + "relation_name": "", + "weight": 9.0, + "description": "Image entity cref='#/texts/89' related to Agent-based Planning", + "source_ids": [ + 84 + ], + "source": "Name: cref='#/texts/89'\nType: IMAGE", + "target": "Name: agent-based planning\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "cref='#/texts/89'", + "tgt_entity_name": "retrieval process", + "relation_name": "", + "weight": 9.0, + "description": "Image entity cref='#/texts/89' related to Retrieval Process", + "source_ids": 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"agent", + "tgt_entity_name": "operator plan", + "relation_name": "", + "weight": 10.0, + "description": "the agent s final task is to generate the operator plan", + "source_ids": [ + 112 + ], + "source": "Name: operator plan\nType: TASK_OR_PROBLEM", + "target": "Name: agent\nType: PERSON" + }, + { + "src_entity_name": "generation process", + "tgt_entity_name": "analysis merging", + "relation_name": "", + "weight": 9.0, + "description": "analysis merging is a sub stage or activity performed within the generation process", + "source_ids": [ + 86 + ], + "source": "Name: generation process\nType: TASK_OR_PROBLEM", + "target": "Name: analysis merging\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "retrieved information", + "tgt_entity_name": "generation process", + "relation_name": "", + "weight": 10.0, + "description": "retrieved information enters the generation process as its primary input", + "source_ids": [ + 86 + ], + "source": "Name: generation process\nType: 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"src_entity_name": "selector", + "tgt_entity_name": "execution pipelines", + "relation_name": "", + "weight": 9.0, + "description": "the selector operator is combined to form tailored execution pipelines", + "source_ids": [ + 88 + ], + "source": "Name: selector\nType: MODEL_OR_ARCHITECTURE", + "target": "Name: execution pipelines\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "bookrag operator library", + "tgt_entity_name": "selector", + "relation_name": "", + "weight": 10.0, + "description": "the bookrag operator library contains the selector operator type", + "source_ids": [ + 93 + ], + "source": "Name: selector\nType: MODEL_OR_ARCHITECTURE", + "target": "Name: bookrag operator library\nType: SOFTWARE" + }, + { + "src_entity_name": "reasoner", + "tgt_entity_name": "execution pipelines", + "relation_name": "", + "weight": 9.0, + "description": "the reasoner operator is combined to form tailored execution pipelines", + "source_ids": [ + 88 + ], + "source": "Name: reasoner\nType: MODEL_OR_ARCHITECTURE", + "target": "Name: execution pipelines\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "bookrag operator library", + "tgt_entity_name": "reasoner", + "relation_name": "", + "weight": 10.0, + "description": "the bookrag operator library contains the reasoner operator type", + "source_ids": [ + 93 + ], + "source": "Name: reasoner\nType: MODEL_OR_ARCHITECTURE", + "target": "Name: bookrag operator library\nType: SOFTWARE" + }, + { + "src_entity_name": "synthesizer", + "tgt_entity_name": "execution pipelines", + "relation_name": "", + "weight": 9.0, + "description": "the synthesizer operator is combined to form tailored execution pipelines", + "source_ids": [ + 88 + ], + "source": "Name: synthesizer\nType: MODEL_OR_ARCHITECTURE", + "target": "Name: execution pipelines\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "bookrag operator library", + "tgt_entity_name": "synthesizer", + "relation_name": "", + "weight": 10.0, + "description": "the bookrag operator library contains the synthesizer operator type", + "source_ids": [ + 93 + ], + "source": "Name: synthesizer\nType: MODEL_OR_ARCHITECTURE", + "target": "Name: bookrag operator library\nType: SOFTWARE" + }, + { + "src_entity_name": "bookindex operators", + "tgt_entity_name": "agent", + "relation_name": "", + "weight": 9.0, + "description": "the agent employs the bookindex operators for diverse query categories", + "source_ids": [ + 97 + ], + "source": "Name: agent\nType: TASK_OR_PROBLEM", + "target": "Name: bookindex operators\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "query categories", + "relation_name": "", + "weight": 8.0, + "description": "the agent employs operators for diverse query categories", + "source_ids": [ + 97 + ], + "source": "Name: agent\nType: TASK_OR_PROBLEM", + "target": "Name: query categories\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "execution pipelines", + "tgt_entity_name": "adjustable parameters", + "relation_name": "", + "weight": 8.0, + "description": "execution pipelines are created with adjustable parameters", + "source_ids": [ + 88 + ], + "source": "Name: execution pipelines\nType: TASK_OR_PROBLEM", + "target": "Name: adjustable parameters\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "acm computing surveys", + "tgt_entity_name": "6", + "relation_name": "", + "weight": 9.0, + "description": "the paper was published in issue 6 of acm computing surveys", + "source_ids": [ + 202 + ], + "source": "Name: 6\nType: MEASUREMENT", + "target": "Name: acm computing surveys\nType: PUBLICATION_VENUE" + }, + { + "src_entity_name": "figure 4", + "tgt_entity_name": "bookrag operator library", + "relation_name": "", + "weight": 10.0, + "description": "figure 4 visually depicts the bookrag operator library", + "source_ids": [ + 93 + ], + "source": "Name: figure 4\nType: IMAGE", + "target": "Name: bookrag operator library\nType: SOFTWARE" + }, + { + "src_entity_name": "figure 4", + "tgt_entity_name": "mmlongbench dataset", + "relation_name": "", + "weight": 9.0, + "description": "figure 4 shows an execution example derived from the mmlongbench dataset", + "source_ids": [ + 93 + ], + "source": "Name: figure 4\nType: IMAGE", + "target": "Name: mmlongbench dataset\nType: DATASET_OR_CORPUS" + }, + { + "src_entity_name": "figure 4", + "tgt_entity_name": "single hop", + "relation_name": "", + "weight": 9.0, + "description": "figure 4 demonstrates an execution trace for a single hop query", + "source_ids": [ + 93 + ], + "source": "Name: figure 4\nType: IMAGE", + "target": "Name: single hop\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "figure 4", + "tgt_entity_name": "execution trace", + "relation_name": "", + "weight": 9.0, + "description": "figure 4 contains an execution trace for a single hop query", + "source_ids": [ + 93 + ], + "source": "Name: figure 4\nType: IMAGE", + "target": "Name: execution trace\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "bookindex operators", + "tgt_entity_name": "figure 4", + "relation_name": "", + "weight": 7.0, + "description": "bookindex operators are visually depicted in figure 4", + "source_ids": [ + 97 + ], + "source": "Name: figure 4\nType: IMAGE", + "target": "Name: bookindex operators\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "bookrag operator library", + "tgt_entity_name": "operator", + "relation_name": "", + "weight": 8.0, + "description": "the bookrag operator library is composed of specific operators", + "source_ids": [ + 93 + ], + "source": "Name: bookrag operator library\nType: SOFTWARE", + "target": "Name: operator\nType: MODEL_OR_ARCHITECTURE" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "execution trace", + "relation_name": "", + "weight": 10.0, + "description": "the execution trace is specifically for a single hop query", + "source_ids": [ + 93 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: execution trace\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "single hop", + "relation_name": "", + "weight": 9.0, + "description": "the agent performs the single hop task by attempting to extract an entity", + "source_ids": [ + 115 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: agent\nType: PERSON" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "entity", + "relation_name": "", + "weight": 8.0, + "description": "the single hop task involves the extraction of an entity", + "source_ids": [ + 115 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: entity\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "figure 4 b", + "tgt_entity_name": "single hop", + "relation_name": "", + "weight": 10.0, + "description": "figure 4 b presents the execution trace for the single hop query", + "source_ids": [ + 135 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: figure 4 b\nType: IMAGE" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "car", + "relation_name": "", + "weight": 9.0, + "description": "the single hop query asks about the type of car in the example", + "source_ids": [ + 135 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: car\nType: PRODUCT" + }, + { + "src_entity_name": "figure 7", + "tgt_entity_name": "single hop", + "relation_name": "", + "weight": 9.0, + "description": "figure 7 displays the performance breakdown for the single hop query type", + "source_ids": [ + 177 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: figure 7\nType: IMAGE" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "multi hop", + "relation_name": "", + "weight": 5.0, + "description": "both are listed as distinct query types in the performance breakdown", + "source_ids": [ + 177 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: multi hop\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "global", + "relation_name": "", + "weight": 5.0, + "description": "both are listed as distinct query types in the performance breakdown", + "source_ids": [ + 177 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: global\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "agent based planning strategy", + "tgt_entity_name": "single hop", + "relation_name": "", + "weight": 8.0, + "description": "the agent based planning strategy handles single hop queries separately", + "source_ids": [ + 179 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: agent based planning strategy\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "134", + "relation_name": "", + "weight": 8.0, + "description": "the single hop case starts with a reasoning space of 134 nodes", + "source_ids": [ + 186 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: 134\nType: MEASUREMENT" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "24", + "relation_name": "", + "weight": 8.0, + "description": "the single hop case reduces the reasoning space to 24 nodes", + "source_ids": [ + 186 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: 24\nType: MEASUREMENT" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "single", + "relation_name": "", + "weight": 9.0, + "description": "the single hop task requires retrieving information from a single location", + "source_ids": [ + 243 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: single\nType: UNKNOWN" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "contiguous location", + "relation_name": "", + "weight": 8.0, + "description": "the single hop task involves information found in a contiguous location", + "source_ids": [ + 243 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: contiguous location\nType: UNKNOWN" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 7.0, + "description": "the single hop task is defined within the context of a document", + "source_ids": [ + 243 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: document\nType: CONCEPT" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "information", + "relation_name": "", + "weight": 10.0, + "description": "the single hop task involves retrieving information", + "source_ids": [ + 243 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: information\nType: CONCEPT" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "paragraph", + "relation_name": "", + "weight": 9.0, + "description": "a single hop question can be answered by retrieving information from a paragraph", + "source_ids": [ + 243 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: paragraph\nType: SECTION_TITLE" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "table", + "relation_name": "", + "weight": 9.0, + "description": "a single hop question can be answered by retrieving information from a table", + "source_ids": [ + 243 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: table\nType: SECTION_TITLE" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "figure", + "relation_name": "", + "weight": 9.0, + "description": "a single hop question can be answered by retrieving information from a figure", + "source_ids": [ + 243 + ], + "source": "Name: single hop\nType: TASK_OR_PROBLEM", + "target": "Name: figure\nType: SECTION_TITLE" + }, + { + "src_entity_name": "execution trace", + "tgt_entity_name": "step by step operator execution", + "relation_name": "", + "weight": 9.0, + "description": "the execution trace demonstrates step by step operator execution", + "source_ids": [ + 93 + ], + "source": "Name: execution trace\nType: TASK_OR_PROBLEM", + "target": "Name: step by step operator execution\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "operator-set", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Operator-Set", + "source_ids": [ + 94 + ], + "source": "Name: operator-set\nType: IMAGE", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "extract", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Extract", + "source_ids": [ + 94 + ], + "source": "Name: extract\nType: TASK_OR_PROBLEM", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "decompose", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Decompose", + "source_ids": [ + 94 + ], + "source": "Name: decompose\nType: METHOD_OR_TECHNIQUE", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "formulator", + "tgt_entity_name": "decompose", + "relation_name": "", + "weight": 9.0, + "description": "decompose is included as a category within the formulator operators", + "source_ids": [ + 98 + ], + "source": "Name: decompose\nType: METHOD_OR_TECHNIQUE", + "target": "Name: formulator\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "decompose", + "tgt_entity_name": "qs", + "relation_name": "", + "weight": 10.0, + "description": "decompose generates the set of sub queries qs", + "source_ids": [ + 98 + ], + "source": "Name: decompose\nType: METHOD_OR_TECHNIQUE", + "target": "Name: qs\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "decompose", + "tgt_entity_name": "sub queries", + "relation_name": "", + "weight": 10.0, + "description": "decompose produces sub queries as its output", + "source_ids": [ + 98 + ], + "source": "Name: decompose\nType: METHOD_OR_TECHNIQUE", + "target": "Name: sub queries\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "entities", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Entities", + "source_ids": [ + 94 + ], + "source": "Name: entities\nType: DATASET_OR_CORPUS", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "sub-queries", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Sub-queries", + "source_ids": [ + 94 + ], + "source": "Name: sub-queries\nType: TASK_OR_PROBLEM", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "formulator", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Formulator", + "source_ids": [ + 94 + ], + "source": "Name: formulator\nType: SYSTEM_COMPONENT", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "filter", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Filter", + "source_ids": [ + 94 + ], + "source": "Name: filter\nType: TASK_OR_PROBLEM", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "select", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Select", + "source_ids": [ + 94 + ], + "source": "Name: select\nType: TASK_OR_PROBLEM", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "selector", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Selector", + "source_ids": [ + 94 + ], + "source": "Name: selector\nType: SYSTEM_COMPONENT", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "reason", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Reason", + "source_ids": [ + 94 + ], + "source": "Name: reason\nType: TASK_OR_PROBLEM", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "graph", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Graph", + "source_ids": [ + 94 + ], + "source": "Name: graph\nType: DATA_STRUCTURE", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "text", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Text", + "source_ids": [ + 94 + ], + "source": "Name: text\nType: DATA_STRUCTURE", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "s:", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to S:", + "source_ids": [ + 94 + ], + "source": "Name: s:\nType: PARAMETER_OR_VARIABLE", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "skyline", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Skyline", + "source_ids": [ + 94 + ], + "source": "Name: skyline\nType: TASK_OR_PROBLEM", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "reasoner", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Reasoner", + "source_ids": [ + 94 + ], + "source": "Name: reasoner\nType: SYSTEM_COMPONENT", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "map", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Map", + "source_ids": [ + 94 + ], + "source": "Name: map\nType: TASK_OR_PROBLEM", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "synthesizer", + "tgt_entity_name": "map", + "relation_name": "", + "weight": 8.0, + "description": "map is a specific type of operator within the broader category of synthesizer operators responsible for content generation", + "source_ids": [ + 111 + ], + "source": "Name: map\nType: TASK_OR_PROBLEM", + "target": "Name: synthesizer\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "map", + "tgt_entity_name": "reduce", + "relation_name": "", + "weight": 9.0, + "description": "map and reduce are sequential or related steps in the process of generating a final coherent answer with map generating partial responses and reduce aggregating them", + "source_ids": [ + 111 + ], + "source": "Name: map\nType: TASK_OR_PROBLEM", + "target": "Name: reduce\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "map", + "tgt_entity_name": "analysis", + "relation_name": "", + "weight": 10.0, + "description": "the map operator performs the task of analysis on retrieved information segments", + "source_ids": [ + 111 + ], + "source": "Name: map\nType: TASK_OR_PROBLEM", + "target": "Name: analysis\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "map", + "tgt_entity_name": "partial responses", + "relation_name": "", + "weight": 10.0, + "description": "map generates partial responses as its output", + "source_ids": [ + 111 + ], + "source": "Name: map\nType: TASK_OR_PROBLEM", + "target": "Name: partial responses\nType: PRODUCT" + }, + { + "src_entity_name": "map", + "tgt_entity_name": "retrieved information segments", + "relation_name": "", + "weight": 10.0, + "description": "map performs analysis specifically on retrieved information segments", + "source_ids": [ + 111 + ], + "source": "Name: map\nType: TASK_OR_PROBLEM", + "target": "Name: retrieved information segments\nType: DATASET_OR_CORPUS" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "reduce", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Reduce", + "source_ids": [ + 94 + ], + "source": "Name: reduce\nType: TASK_OR_PROBLEM", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "synthesizer", + "tgt_entity_name": "reduce", + "relation_name": "", + "weight": 8.0, + "description": "reduce is a specific type of operator within the broader category of synthesizer operators responsible for content generation", + "source_ids": [ + 111 + ], + "source": "Name: reduce\nType: TASK_OR_PROBLEM", + "target": "Name: synthesizer\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "reduce", + "tgt_entity_name": "final coherent answer", + "relation_name": "", + "weight": 10.0, + "description": "reduce synthesizes a final coherent answer as its output", + "source_ids": [ + 111 + ], + "source": "Name: reduce\nType: TASK_OR_PROBLEM", + "target": "Name: final coherent answer\nType: PRODUCT" + }, + { + "src_entity_name": "reduce", + "tgt_entity_name": "multiple sources", + "relation_name": "", + "weight": 10.0, + "description": "reduce aggregates information from multiple sources to create its output", + "source_ids": [ + 111 + ], + "source": "Name: reduce\nType: TASK_OR_PROBLEM", + "target": "Name: multiple sources\nType: DATASET_OR_CORPUS" + }, + { + "src_entity_name": "reduce", + "tgt_entity_name": "partial answers", + "relation_name": "", + "weight": 9.0, + "description": "reduce aggregates partial answers as part of its synthesis process", + "source_ids": [ + 111 + ], + "source": "Name: reduce\nType: TASK_OR_PROBLEM", + "target": "Name: partial answers\nType: PRODUCT" + }, + { + "src_entity_name": "reduce", + "tgt_entity_name": "retrieved evidence", + "relation_name": "", + "weight": 9.0, + "description": "reduce aggregates retrieved evidence as part of its synthesis process", + "source_ids": [ + 111 + ], + "source": "Name: reduce\nType: TASK_OR_PROBLEM", + "target": "Name: retrieved evidence\nType: DATASET_OR_CORPUS" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "synthesizer", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Synthesizer", + "source_ids": [ + 94 + ], + "source": "Name: synthesizer\nType: SYSTEM_COMPONENT", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "execution example", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Execution example", + "source_ids": [ + 94 + ], + "source": "Name: execution example\nType: SECTION_TITLE", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "q: what is the type of car in the ranking prompt example?", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Q: What is the type of car in the Ranking Prompt example?", + "source_ids": [ + 94 + ], + "source": "Name: q: what is the type of car in the ranking prompt example?\nType: TASK_OR_PROBLEM", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "simple query...", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Simple query...", + "source_ids": [ + 94 + ], + "source": "Name: simple query...\nType: TASK_OR_PROBLEM", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "car", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Car", + "source_ids": [ + 94 + ], + "source": "Name: car\nType: PRODUCT", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "car", + "tgt_entity_name": "ranking prompt example", + "relation_name": "", + "weight": 8.0, + "description": "the car is the subject of the query within the ranking prompt example context", + "source_ids": [ + 135 + ], + "source": "Name: car\nType: PRODUCT", + "target": "Name: ranking prompt example\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "extract", + "tgt_entity_name": "car", + "relation_name": "", + "weight": 9.0, + "description": "the extract method is used to identify the entity car", + "source_ids": [ + 135 + ], + "source": "Name: car\nType: PRODUCT", + "target": "Name: extract\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "select by entity", + "tgt_entity_name": "car", + "relation_name": "", + "weight": 8.0, + "description": "the select by entity method retrieves nodes related to the identified entity car", + "source_ids": [ + 135 + ], + "source": "Name: car\nType: PRODUCT", + "target": "Name: select by entity\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "skyline filtering", + "tgt_entity_name": "car", + "relation_name": "", + "weight": 7.0, + "description": "the skyline filtering technique refines the nodes related to car", + "source_ids": [ + 135 + ], + "source": "Name: car\nType: PRODUCT", + "target": "Name: skyline filtering\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "reduce", + "tgt_entity_name": "car", + "relation_name": "", + "weight": 7.0, + "description": "the reduce method synthesizes the answer regarding the car", + "source_ids": [ + 135 + ], + "source": "Name: car\nType: PRODUCT", + "target": "Name: reduce\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "ranking prompt", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Ranking Prompt", + "source_ids": [ + 94 + ], + "source": "Name: ranking prompt\nType: BOOK", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "method", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Method", + "source_ids": [ + 94 + ], + "source": "Name: method\nType: METHOD_OR_TECHNIQUE", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "method and its descendants", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Method and its Descendants", + "source_ids": [ + 94 + ], + "source": "Name: method and its descendants\nType: SECTION_TITLE", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "a: based on the provided information...", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to A: Based on the provided information...", + "source_ids": [ + 94 + ], + "source": "Name: a: based on the provided information...\nType: TASK_OR_PROBLEM", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "mercedes-benz e-class sedan", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Mercedes-Benz E-Class Sedan", + "source_ids": [ + 94 + ], + "source": "Name: mercedes-benz e-class sedan\nType: VEHICLE", + "target": "Name: image cref='#/texts/98'\nType: UNKNOWN" + }, + { + "src_entity_name": "query classification", + "tgt_entity_name": "operator plan", + "relation_name": "", + "weight": 9.0, + "description": "query classification is performed to generate a specific operator plan", + "source_ids": [ + 95 + ], + "source": "Name: query classification\nType: TASK_OR_PROBLEM", + "target": "Name: operator plan\nType: PRODUCT" + }, + { + "src_entity_name": "query classification", + "tgt_entity_name": "single hop", + "relation_name": "", + "weight": 9.0, + "description": "query classification defines single hop as one of its three representative categories", + "source_ids": [ + 96 + ], + "source": "Name: query classification\nType: TASK_OR_PROBLEM", + "target": "Name: single hop\nType: EVENT" + }, + { + "src_entity_name": "query classification", + "tgt_entity_name": "multi hop", + "relation_name": "", + "weight": 9.0, + "description": "query classification defines multi hop as one of its three representative categories", + "source_ids": [ + 96 + ], + "source": "Name: query classification\nType: TASK_OR_PROBLEM", + "target": "Name: multi hop\nType: EVENT" + }, + { + "src_entity_name": "query classification", + "tgt_entity_name": "global aggregation", + "relation_name": "", + "weight": 9.0, + "description": "query classification defines global aggregation as one of its three representative categories", + "source_ids": [ + 96 + ], + "source": "Name: query classification\nType: TASK_OR_PROBLEM", + "target": "Name: global aggregation\nType: EVENT" + }, + { + "src_entity_name": "query classification", + "tgt_entity_name": "agent strategy selection", + "relation_name": "", + "weight": 9.0, + "description": "query classification enables agent strategy selection", + "source_ids": [ + 96 + ], + "source": "Name: query classification\nType: TASK_OR_PROBLEM", + "target": "Name: agent strategy selection\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "query classification", + "tgt_entity_name": "solution strategy", + "relation_name": "", + "weight": 8.0, + "description": "each category defined by query classification requires a different solution strategy", + "source_ids": [ + 96 + ], + "source": "Name: query classification\nType: TASK_OR_PROBLEM", + "target": "Name: solution strategy\nType: CONCEPT" + }, + { + "src_entity_name": "figure 10", + "tgt_entity_name": "query classification", + "relation_name": "", + "weight": 10.0, + "description": "figure 10 contains the prompt specifically used for query classification", + "source_ids": [ + 253 + ], + "source": "Name: query classification\nType: TASK_OR_PROBLEM", + "target": "Name: figure 10\nType: IMAGE" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "scent based retrieval", + "relation_name": "", + "weight": 8.0, + "description": "single hop queries typically require a scent based retrieval operation", + "source_ids": [ + 96 + ], + "source": "Name: single hop\nType: EVENT", + "target": "Name: scent based retrieval\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "intrinsic complexity", + "relation_name": "", + "weight": 7.0, + "description": "single hop is defined by its intrinsic complexity and operational demands", + "source_ids": [ + 96 + ], + "source": "Name: single hop\nType: EVENT", + "target": "Name: intrinsic complexity\nType: CONCEPT" + }, + { + "src_entity_name": "multi hop", + "tgt_entity_name": "intrinsic complexity", + "relation_name": "", + "weight": 7.0, + "description": "multi hop is defined by its intrinsic complexity and operational demands", + "source_ids": [ + 96 + ], + "source": "Name: multi hop\nType: EVENT", + "target": "Name: intrinsic complexity\nType: CONCEPT" + }, + { + "src_entity_name": "global aggregation", + "tgt_entity_name": "filter aggregation", + "relation_name": "", + "weight": 8.0, + "description": "global aggregation queries usually involve a sequence of filter aggregation operations", + "source_ids": [ + 96 + ], + "source": "Name: global aggregation\nType: EVENT", + "target": "Name: filter aggregation\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "global aggregation", + "tgt_entity_name": "intrinsic complexity", + "relation_name": "", + "weight": 7.0, + "description": "global aggregation is defined by its intrinsic complexity and operational demands", + "source_ids": [ + 96 + ], + "source": "Name: global aggregation\nType: EVENT", + "target": "Name: intrinsic complexity\nType: CONCEPT" + }, + { + "src_entity_name": "global aggregation", + "tgt_entity_name": "filtering conditions", + "relation_name": "", + "weight": 8.0, + "description": "global aggregation necessitates analyzing content under multiple filtering conditions", + "source_ids": [ + 96 + ], + "source": "Name: global aggregation\nType: EVENT", + "target": "Name: filtering conditions\nType: CONCEPT" + }, + { + "src_entity_name": "global aggregation", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 7.0, + "description": "global aggregation involves analyzing content across various parts of the document", + "source_ids": [ + 96 + ], + "source": "Name: global aggregation\nType: EVENT", + "target": "Name: document\nType: OBJECT" + }, + { + "src_entity_name": "bookindex operators", + "tgt_entity_name": "o", + "relation_name": "", + "weight": 9.0, + "description": "bookindex operators are represented by the set o tailored for the bookindex", + "source_ids": [ + 97 + ], + "source": "Name: bookindex operators\nType: TASK_OR_PROBLEM", + "target": "Name: o\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "bookindex operators", + "tgt_entity_name": "figure 4 a", + "relation_name": "", + "weight": 8.0, + "description": "bookindex operators are visually depicted in figure 4 a", + "source_ids": [ + 97 + ], + "source": "Name: bookindex operators\nType: TASK_OR_PROBLEM", + "target": "Name: figure 4 a\nType: IMAGE" + }, + { + "src_entity_name": "bookindex operators", + "tgt_entity_name": "table 3", + "relation_name": "", + "weight": 8.0, + "description": "bookindex operators are detailed in table 3", + "source_ids": [ + 97 + ], + "source": "Name: bookindex operators\nType: TASK_OR_PROBLEM", + "target": "Name: table 3\nType: TABLE" + }, + { + "src_entity_name": "bookindex operators", + "tgt_entity_name": "classification", + "relation_name": "", + "weight": 9.0, + "description": "bookindex operators are designed to execute strategies identified by classification", + "source_ids": [ + 97 + ], + "source": "Name: bookindex operators\nType: TASK_OR_PROBLEM", + "target": "Name: classification\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "formulator", + "tgt_entity_name": "extract", + "relation_name": "", + "weight": 9.0, + "description": "extract is included as a category within the formulator operators", + "source_ids": [ + 98 + ], + "source": "Name: formulator\nType: TASK_OR_PROBLEM", + "target": "Name: extract\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "extract", + "tgt_entity_name": "eq", + "relation_name": "", + "weight": 10.0, + "description": "extract identifies the key entities eq", + "source_ids": [ + 98 + ], + "source": "Name: extract\nType: METHOD_OR_TECHNIQUE", + "target": "Name: eq\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "extract", + "tgt_entity_name": "query text", + "relation_name": "", + "weight": 10.0, + "description": "extract analyzes the query text to find key entities", + "source_ids": [ + 98 + ], + "source": "Name: extract\nType: METHOD_OR_TECHNIQUE", + "target": "Name: query text\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "extract", + "tgt_entity_name": "entities", + "relation_name": "", + "weight": 10.0, + "description": "extract identifies entities from the query text", + "source_ids": [ + 98 + ], + "source": "Name: extract\nType: METHOD_OR_TECHNIQUE", + "target": "Name: entities\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "extract", + "relation_name": "", + "weight": 9.0, + "description": "the agent uses the extract method to identify key entities", + "source_ids": [ + 135 + ], + "source": "Name: extract\nType: METHOD_OR_TECHNIQUE", + "target": "Name: agent\nType: PERSON" + }, + { + "src_entity_name": "extract", + "tgt_entity_name": "select by entity", + "relation_name": "", + "weight": 7.0, + "description": "the extract method precedes the select by entity method in the workflow", + "source_ids": [ + 135 + ], + "source": "Name: extract\nType: METHOD_OR_TECHNIQUE", + "target": "Name: select by entity\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "qs", + "tgt_entity_name": "pdec", + "relation_name": "", + "weight": 7.0, + "description": "qs is generated using the parameter pdec in the llm function", + "source_ids": [ + 98 + ], + "source": "Name: qs\nType: TASK_OR_PROBLEM", + "target": "Name: pdec\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "eq", + "tgt_entity_name": "pext", + "relation_name": "", + "weight": 7.0, + "description": "eq is generated using the parameter pext in the llm function", + "source_ids": [ + 98 + ], + "source": "Name: eq\nType: TASK_OR_PROBLEM", + "target": "Name: pext\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "p dec", + "tgt_entity_name": "decomposition", + "relation_name": "", + "weight": 10.0, + "description": "p dec is the specific prompt used to guide the llm for the decomposition task", + "source_ids": [ + 101 + ], + "source": "Name: p dec\nType: SOFTWARE", + "target": "Name: decomposition\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "p ext", + "tgt_entity_name": "extraction", + "relation_name": "", + "weight": 10.0, + "description": "p ext is the specific prompt used to guide the llm for the extraction task", + "source_ids": [ + 101 + ], + "source": "Name: p ext\nType: SOFTWARE", + "target": "Name: extraction\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "figure 13", + "tgt_entity_name": "prompt", + "relation_name": "", + "weight": 10.0, + "description": "figure 13 displays the prompt for entity resolution judgement", + "source_ids": [ + 284 + ], + "source": "Name: prompt\nType: SOFTWARE", + "target": "Name: figure 13\nType: IMAGE" + }, + { + "src_entity_name": "selector", + "tgt_entity_name": "n f", + "relation_name": "", + "weight": 9.0, + "description": "the selector operators produce the filtered subset n f", + "source_ids": [ + 102 + ], + "source": "Name: selector\nType: TECHNOLOGY", + "target": "Name: n f\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "selector", + "tgt_entity_name": "reasoners", + "relation_name": "", + "weight": 9.0, + "description": "the removal of selector operators forces reasoners to score all candidate nodes indicating a direct operational dependency", + "source_ids": [ + 167 + ], + "source": "Name: selector\nType: TECHNOLOGY", + "target": "Name: reasoners\nType: TECHNOLOGY" + }, + { + "src_entity_name": "filter modal", + "tgt_entity_name": "n f", + "relation_name": "", + "weight": 8.0, + "description": "filter modal contributes to the production of the filtered subset n f", + "source_ids": [ + 102 + ], + "source": "Name: filter modal\nType: TECHNOLOGY", + "target": "Name: n f\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "filter range", + "tgt_entity_name": "n f", + "relation_name": "", + "weight": 8.0, + "description": "filter range contributes to the production of the filtered subset n f", + "source_ids": [ + 102 + ], + "source": "Name: filter range\nType: TECHNOLOGY", + "target": "Name: n f\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "n f", + "tgt_entity_name": "c n", + "relation_name": "", + "weight": 9.0, + "description": "the filtered subset n f consists of nodes where the predicate c n holds true", + "source_ids": [ + 102 + ], + "source": "Name: n f\nType: PARAMETER_OR_VARIABLE", + "target": "Name: c n\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "n f", + "tgt_entity_name": "nodes", + "relation_name": "", + "weight": 9.0, + "description": "the filtered subset n f is a subset of the nodes n", + "source_ids": [ + 102 + ], + "source": "Name: n f\nType: PARAMETER_OR_VARIABLE", + "target": "Name: nodes\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "c n", + "tgt_entity_name": "nodes", + "relation_name": "", + "weight": 9.0, + "description": "the predicate c n is evaluated for each node in the set", + "source_ids": [ + 102 + ], + "source": "Name: c n\nType: PARAMETER_OR_VARIABLE", + "target": "Name: nodes\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "plan error", + "tgt_entity_name": "plan", + "relation_name": "", + "weight": 10.0, + "description": "plan error is a specific failure pattern occurring within the plan task", + "source_ids": [ + 185 + ], + "source": "Name: plan\nType: TASK_OR_PROBLEM", + "target": "Name: plan error\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "select by entity", + "tgt_entity_name": "s target", + "relation_name": "", + "weight": 9.0, + "description": "select by entity identifies a set of target section nodes s target as part of its process", + "source_ids": [ + 104 + ], + "source": "Name: select by entity\nType: TECHNOLOGY", + "target": "Name: s target\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "select by entity", + "tgt_entity_name": "subtree", + "relation_name": "", + "weight": 9.0, + "description": "select by entity retrieves subtrees rooted at specific section nodes", + "source_ids": [ + 104 + ], + "source": "Name: select by entity\nType: TECHNOLOGY", + "target": "Name: subtree\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "select by section", + "tgt_entity_name": "s target", + "relation_name": "", + "weight": 9.0, + "description": "select by section identifies a set of target section nodes s target as part of its process", + "source_ids": [ + 104 + ], + "source": "Name: select by section\nType: TECHNOLOGY", + "target": "Name: s target\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "select by section", + "tgt_entity_name": "subtree", + "relation_name": "", + "weight": 9.0, + "description": "select by section retrieves subtrees rooted at specific section nodes", + "source_ids": [ + 104 + ], + "source": "Name: select by section\nType: TECHNOLOGY", + "target": "Name: subtree\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "s target", + "tgt_entity_name": "e q", + "relation_name": "", + "weight": 8.0, + "description": "s target consists of sections linked to entities e q via gt link", + "source_ids": [ + 104 + ], + "source": "Name: s target\nType: TASK_OR_PROBLEM", + "target": "Name: e q\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "s target", + "tgt_entity_name": "n s", + "relation_name": "", + "weight": 9.0, + "description": "n s 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descendants of the target sections", + "source_ids": [ + 104 + ], + "source": "Name: n s\nType: TASK_OR_PROBLEM", + "target": "Name: descendant\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "reasoner", + "tgt_entity_name": "selected tree nodes", + "relation_name": "", + "weight": 9.0, + "description": "reasoner analyzes and refines selected tree nodes", + "source_ids": [ + 106 + ], + "source": "Name: reasoner\nType: TASK_OR_PROBLEM", + "target": "Name: selected tree nodes\nType: UNKNOWN" + }, + { + "src_entity_name": "graph reasoning", + "tgt_entity_name": "subgraph", + "relation_name": "", + "weight": 10.0, + "description": "graph reasoning performs multi hop inference on a subgraph", + "source_ids": [ + 106 + ], + "source": "Name: graph reasoning\nType: METHOD_OR_TECHNIQUE", + "target": "Name: subgraph\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "graph reasoning", + "tgt_entity_name": "entity", + "relation_name": "", + "weight": 9.0, + "description": "graph reasoning starts its inference process from an entity", + "source_ids": [ + 106 + ], + "source": "Name: graph reasoning\nType: METHOD_OR_TECHNIQUE", + "target": "Name: entity\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "graph reasoning", + "tgt_entity_name": "pagerank algorithm", + "relation_name": "", + "weight": 10.0, + "description": "graph reasoning uses the pagerank algorithm to compute the entity importance vector", + "source_ids": [ + 106 + ], + "source": "Name: graph reasoning\nType: METHOD_OR_TECHNIQUE", + "target": "Name: pagerank algorithm\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "pagerank algorithm", + "tgt_entity_name": "entity importance vector", + "relation_name": "", + "weight": 10.0, + "description": "the pagerank algorithm computes the entity importance vector", + "source_ids": [ + 106 + ], + "source": "Name: pagerank algorithm\nType: METHOD_OR_TECHNIQUE", + "target": "Name: entity importance vector\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "entity importance vector", + "tgt_entity_name": "gt link matrix", + "relation_name": "", + "weight": 9.0, + "description": "the entity importance vector is mapped to tree nodes via the gt link matrix", + "source_ids": [ + 106 + ], + "source": "Name: gt link matrix\nType: SOFTWARE", + "target": "Name: entity importance vector\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "gt link matrix", + "tgt_entity_name": "tree node importance scores vector", + "relation_name": "", + "weight": 9.0, + "description": "the gt link matrix is used to derive the tree node importance scores vector", + "source_ids": [ + 106 + ], + "source": "Name: gt link matrix\nType: SOFTWARE", + "target": "Name: tree node importance scores vector\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "subgraph", + "tgt_entity_name": "selected nodes", + "relation_name": "", + "weight": 10.0, + "description": "the subgraph is extracted from selected nodes", + "source_ids": [ + 106 + ], + "source": "Name: subgraph\nType: TASK_OR_PROBLEM", + "target": "Name: selected nodes\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "entity", + "relation_name": "", + "weight": 9.0, + "description": "the agent attempts to extract the entity as the first step of the single hop process", + "source_ids": [ + 115 + ], + "source": "Name: entity\nType: TASK_OR_PROBLEM", + "target": "Name: agent\nType: PERSON" + }, + { + "src_entity_name": "text ranker", + "tgt_entity_name": "query", + "relation_name": "", + "weight": 9.0, + "description": "text ranker evaluates the relevance of content specifically to the query", + "source_ids": [ + 109 + ], + "source": "Name: text ranker\nType: SOFTWARE", + "target": "Name: query\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "text ranker", + "tgt_entity_name": "relevance score", + "relation_name": "", + "weight": 10.0, + "description": "text ranker assigns a relevance score to each tree node", + "source_ids": [ + 109 + ], + "source": "Name: text ranker\nType: SOFTWARE", + "target": "Name: relevance score\nType: EVALUATION_METRIC" + }, + { + "src_entity_name": "text ranker", + "tgt_entity_name": "tree node", + "relation_name": "", + "weight": 9.0, + "description": "text ranker evaluates the content of the tree node", + "source_ids": [ + 109 + ], + "source": "Name: text ranker\nType: SOFTWARE", + "target": "Name: tree node\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "text ranker", + "tgt_entity_name": "nodes", + "relation_name": "", + "weight": 9.0, + "description": "text ranker evaluates the content of the nodes", + "source_ids": [ + 109 + ], + "source": "Name: text ranker\nType: SOFTWARE", + "target": "Name: nodes\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "skyline operator", + "relation_name": "", + "weight": 10.0, + "description": "skyline ranker employs the skyline operator to perform its filtering function", + "source_ids": [ + 109 + ], + "source": "Name: skyline ranker\nType: SOFTWARE", + "target": "Name: skyline operator\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "tree node", + "relation_name": "", + "weight": 9.0, + "description": "skyline ranker filters tree nodes based on scoring dimensions", + "source_ids": [ + 109 + ], + "source": "Name: skyline ranker\nType: SOFTWARE", + "target": "Name: tree node\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "relevance score", + "relation_name": "", + "weight": 8.0, + "description": "skyline ranker uses relevance scores along with others to filter nodes", + "source_ids": [ + 109 + ], + "source": "Name: skyline ranker\nType: SOFTWARE", + "target": "Name: relevance score\nType: EVALUATION_METRIC" + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "nodes", + "relation_name": "", + "weight": 10.0, + "description": "skyline ranker filters the nodes based on the specified scoring dimensions", + "source_ids": [ + 109 + ], + "source": "Name: skyline ranker\nType: SOFTWARE", + "target": "Name: nodes\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "9 87", + "relation_name": "", + "weight": 8.0, + "description": "the skyline ranker process results in an average of 9 87 retained nodes on one dataset", + "source_ids": [ + 157 + ], + "source": "Name: skyline ranker\nType: SOFTWARE", + "target": "Name: 9 87\nType: MEASUREMENT" + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "6 86", + "relation_name": "", + "weight": 8.0, + "description": "the skyline ranker process results in an average of 6 86 retained nodes on another dataset", + "source_ids": [ + 157 + ], + "source": "Name: skyline ranker\nType: SOFTWARE", + "target": "Name: 6 86\nType: MEASUREMENT" + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "8 6", + "relation_name": "", + "weight": 8.0, + "description": "the skyline ranker process results in an average of 8 6 retained nodes on the third dataset", + "source_ids": [ + 157 + ], + "source": "Name: skyline ranker\nType: SOFTWARE", + "target": "Name: 8 6\nType: MEASUREMENT" + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "10", + "relation_name": "", + "weight": 7.0, + "description": "the number of retained nodes by skyline ranker is comparable to the standard top k setting where k 10", + "source_ids": [ + 157 + ], + "source": "Name: skyline ranker\nType: SOFTWARE", + "target": "Name: 10\nType: MEASUREMENT" + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "candidate size", + "relation_name": "", + "weight": 8.0, + "description": "the skyline ranker process ensures high quality retrieval without inflating the candidate size", + "source_ids": [ + 157 + ], + "source": "Name: skyline ranker\nType: SOFTWARE", + "target": "Name: candidate size\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "three datasets", + "relation_name": "", + "weight": 9.0, + "description": "the average number of retained nodes by skyline ranker is measured across three datasets", + "source_ids": [ + 157 + ], + "source": "Name: skyline ranker\nType: SOFTWARE", + "target": "Name: three datasets\nType: DATASET_OR_CORPUS" + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "standard top k setting", + "relation_name": "", + "weight": 8.0, + "description": "the results of the skyline ranker process are compared to the standard top k setting", + "source_ids": [ + 157 + ], + "source": "Name: skyline ranker\nType: SOFTWARE", + "target": "Name: standard top k setting\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "graph reasoning", + "tgt_entity_name": "skyline ranker", + "relation_name": "", + "weight": 10.0, + "description": "the graph reasoning operator enables the skyline ranker removing it disables the skyline ranker", + "source_ids": [ + 168 + ], + "source": "Name: skyline ranker\nType: SOFTWARE", + "target": "Name: graph reasoning\nType: TECHNOLOGY" + }, + { + "src_entity_name": "text reasoning", + "tgt_entity_name": "skyline ranker", + "relation_name": "", + "weight": 9.0, + "description": "the removal of the text reasoning operator causes the skyline ranker to be disabled", + "source_ids": [ + 169 + ], + "source": "Name: skyline ranker\nType: SOFTWARE", + "target": "Name: text reasoning\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "skyline operator", + "tgt_entity_name": "tree node", + "relation_name": "", + "weight": 8.0, + "description": "the skyline operator is used to filter tree nodes", + "source_ids": [ + 109 + ], + "source": "Name: skyline operator\nType: METHOD_OR_TECHNIQUE", + "target": "Name: tree node\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "skyline operator", + "tgt_entity_name": "nodes", + "relation_name": "", + "weight": 9.0, + "description": "the skyline operator filters the nodes", + "source_ids": [ + 109 + ], + "source": "Name: skyline operator\nType: METHOD_OR_TECHNIQUE", + "target": "Name: nodes\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "query", + "relation_name": "", + "weight": 9.0, + "description": "the agent classifies the query into a category", + "source_ids": [ + 112 + ], + "source": "Name: query\nType: TASK_OR_PROBLEM", + "target": "Name: agent\nType: PERSON" + }, + { + "src_entity_name": "query", + "tgt_entity_name": "parameters", + "relation_name": "", + "weight": 8.0, + "description": "parameters are dynamically instantiated based on the query", + "source_ids": [ + 112 + ], + "source": "Name: query\nType: TASK_OR_PROBLEM", + "target": "Name: parameters\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "ift inspired selector reasoner workflow", + "tgt_entity_name": "query", + "relation_name": "", + "weight": 8.0, + "description": "the workflow s agent based planning component classifies the query", + "source_ids": [ + 157 + ], + "source": "Name: query\nType: TASK_OR_PROBLEM", + "target": "Name: ift inspired selector reasoner workflow\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "synthesizer", + "tgt_entity_name": "content generation", + "relation_name": "", + "weight": 10.0, + "description": "synthesizer operators are responsible for the task of content generation", + "source_ids": [ + 111 + ], + "source": "Name: synthesizer\nType: TASK_OR_PROBLEM", + "target": "Name: content generation\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "partial responses", + "tgt_entity_name": "final coherent answer", + "relation_name": "", + "weight": 9.0, + "description": "partial responses generated by map are aggregated by reduce to form the final coherent answer", + "source_ids": [ + 111 + ], + "source": "Name: partial responses\nType: PRODUCT", + "target": "Name: final coherent answer\nType: PRODUCT" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "category", + "relation_name": "", + "weight": 9.0, + "description": "the agent uses the category derived from the query to generate the plan", + "source_ids": [ + 112 + ], + "source": "Name: agent\nType: PERSON", + "target": "Name: category\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "library", + "relation_name": "", + "weight": 8.0, + "description": "the agent selects operators from the library to form the plan", + "source_ids": [ + 112 + ], + "source": "Name: agent\nType: PERSON", + "target": "Name: library\nType: ORGANIZATION" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "operators", + "relation_name": "", + "weight": 9.0, + "description": "the agent selects a specific sequence of operators to create the plan", + "source_ids": [ + 112 + ], + "source": "Name: agent\nType: PERSON", + "target": "Name: operators\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "scent based", + "relation_name": "", + "weight": 8.0, + "description": "the agent executes the scent based selection strategy if entity extraction is successful", + "source_ids": [ + 115 + ], + "source": "Name: agent\nType: PERSON", + "target": "Name: scent based\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "section based", + "relation_name": "", + "weight": 8.0, + "description": "the agent falls back to the section based strategy if entity extraction fails", + "source_ids": [ + 115 + ], + "source": "Name: agent\nType: PERSON", + "target": "Name: section based\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "complex", + "relation_name": "", + "weight": 9.0, + "description": "the agent decomposes the complex problem into sub problems", + "source_ids": [ + 118 + ], + "source": "Name: agent\nType: PERSON", + "target": "Name: complex\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "single hop workflow", + "relation_name": "", + "weight": 10.0, + "description": "the agent applies the single hop workflow to each sub problem", + "source_ids": [ + 118 + ], + "source": "Name: agent\nType: PERSON", + "target": "Name: single hop workflow\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "planning phase", + "tgt_entity_name": "agent", + "relation_name": "", + "weight": 9.0, + "description": "the planning phase is conducted by the agent", + "source_ids": [ + 135 + ], + "source": "Name: agent\nType: PERSON", + "target": "Name: planning phase\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "select by entity", + "relation_name": "", + "weight": 9.0, + "description": "the agent uses the select by entity method to retrieve relevant nodes", + "source_ids": [ + 135 + ], + "source": "Name: agent\nType: PERSON", + "target": "Name: select by entity\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "reasoning", + "relation_name": "", + "weight": 8.0, + "description": "the agent applies reasoning to refine nodes", + "source_ids": [ + 135 + ], + "source": "Name: agent\nType: PERSON", + "target": "Name: reasoning\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "skyline filtering", + "relation_name": "", + "weight": 8.0, + "description": "the agent applies skyline filtering to refine nodes", + "source_ids": [ + 135 + ], + "source": "Name: agent\nType: PERSON", + "target": "Name: skyline filtering\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "reduce", + "relation_name": "", + "weight": 9.0, + "description": "the agent uses the reduce method to synthesize the answer", + "source_ids": [ + 135 + ], + "source": "Name: agent\nType: PERSON", + "target": "Name: reduce\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "parameters", + "tgt_entity_name": "operators", + "relation_name": "", + "weight": 8.0, + "description": "parameters are dynamically instantiated for the operators", + "source_ids": [ + 112 + ], + "source": "Name: operators\nType: TASK_OR_PROBLEM", + "target": "Name: parameters\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "equation 8", + "tgt_entity_name": "agent plan", + "relation_name": "", + "weight": 10.0, + "description": "equation 8 utilizes the agent plan function", + "source_ids": [ + 112 + ], + "source": "Name: agent plan\nType: METHOD_OR_TECHNIQUE", + "target": "Name: equation 8\nType: EQUATION_OR_FORMULA" + }, + { + "src_entity_name": "the plan", + "tgt_entity_name": "workflow", + "relation_name": "", + "weight": 9.0, + "description": "the plan follows a structured workflow", + "source_ids": [ + 114 + ], + "source": "Name: the plan\nType: TASK_OR_PROBLEM", + "target": "Name: workflow\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "the plan", + "tgt_entity_name": "category", + "relation_name": "", + "weight": 8.0, + 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"tgt_entity_name": "ps", + "relation_name": "", + "weight": 10.0, + "description": "the single hop workflow is identified by the notation ps in the text", + "source_ids": [ + 118 + ], + "source": "Name: single hop workflow\nType: METHOD_OR_TECHNIQUE", + "target": "Name: ps\nType: MODEL_OR_ARCHITECTURE" + }, + { + "src_entity_name": "expert query analyzer", + "tgt_entity_name": "complex", + "relation_name": "", + "weight": 9.0, + "description": "the expert query analyzer classifies questions into the complex category", + "source_ids": [ + 241 + ], + "source": "Name: complex\nType: TASK_OR_PROBLEM", + "target": "Name: expert query analyzer\nType: PERSON" + }, + { + "src_entity_name": "agent based planning strategy", + "tgt_entity_name": "global aggregation", + "relation_name": "", + "weight": 8.0, + "description": "the agent based planning strategy handles global aggregation queries separately", + "source_ids": [ + 179 + ], + "source": "Name: global aggregation\nType: TASK_OR_PROBLEM", + 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query analyzer\nType: PERSON", + "target": "Name: simple\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "expert query analyzer", + "tgt_entity_name": "user", + "relation_name": "", + "weight": 8.0, + "description": "the expert query analyzer processes questions submitted by the user", + "source_ids": [ + 241 + ], + "source": "Name: expert query analyzer\nType: PERSON", + "target": "Name: user\nType: PERSON" + }, + { + "src_entity_name": "expert query analyzer", + "tgt_entity_name": "json object", + "relation_name": "", + "weight": 9.0, + "description": "the expert query analyzer must respond using the specified json object format", + "source_ids": [ + 241 + ], + "source": "Name: expert query analyzer\nType: PERSON", + "target": "Name: json object\nType: FILE_TYPE" + }, + { + "src_entity_name": "assistant", + "tgt_entity_name": "user", + "relation_name": "", + "weight": 10.0, + "description": "the assistant responds to the user", + "source_ids": [ + 258 + ], + "source": "Name: user\nType: PERSON", + "target": "Name: assistant\nType: PERSON" + }, + { + "src_entity_name": "ai assistant", + "tgt_entity_name": "json object", + "relation_name": "", + "weight": 10.0, + "description": "the ai assistant must return a single valid json object as its output", + "source_ids": [ + 258 + ], + "source": "Name: json object\nType: FILE_TYPE", + "target": "Name: ai assistant\nType: PERSON" + }, + { + "src_entity_name": "assistant", + "tgt_entity_name": "json object", + "relation_name": "", + "weight": 10.0, + "description": "the assistant must output a json object", + "source_ids": [ + 258 + ], + "source": "Name: json object\nType: FILE_TYPE", + "target": "Name: assistant\nType: PERSON" + }, + { + "src_entity_name": "entity resolution adjudicator", + "tgt_entity_name": "json object", + "relation_name": "", + "weight": 9.0, + "description": "the entity resolution adjudicator must output the result in a json object format", + "source_ids": [ + 262 + ], + "source": "Name: json object\nType: FILE_TYPE", + "target": "Name: entity resolution adjudicator\nType: PERSON" + }, + { + "src_entity_name": "json object", + "tgt_entity_name": "id", + "relation_name": "", + "weight": 8.0, + "description": "the json object contains the id of the matching candidate", + "source_ids": [ + 262 + ], + "source": "Name: json object\nType: FILE_TYPE", + "target": "Name: id\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "json object", + "tgt_entity_name": "explanation", + "relation_name": "", + "weight": 8.0, + "description": "the json object contains the explanation for the decision", + "source_ids": [ + 262 + ], + "source": "Name: json object\nType: FILE_TYPE", + "target": "Name: explanation\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "information", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 9.0, + "description": "information is retrieved from the document", + "source_ids": [ + 243 + ], + "source": "Name: information\nType: CONCEPT", + "target": "Name: document\nType: CONCEPT" + }, + { + "src_entity_name": "paragraph", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 8.0, + "description": "a paragraph is a part of a document", + "source_ids": [ + 243 + ], + "source": "Name: document\nType: CONCEPT", + "target": "Name: paragraph\nType: SECTION_TITLE" + }, + { + "src_entity_name": "table", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 8.0, + "description": "a table is a part of a document", + "source_ids": [ + 243 + ], + "source": "Name: document\nType: CONCEPT", + "target": "Name: table\nType: SECTION_TITLE" + }, + { + "src_entity_name": "figure", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 8.0, + "description": "a figure is a part of a document", + "source_ids": [ + 243 + ], + "source": "Name: document\nType: CONCEPT", + "target": "Name: figure\nType: SECTION_TITLE" + }, + { + "src_entity_name": "5", + "tgt_entity_name": "latinos", + "relation_name": "", + "weight": 9.0, + "description": "the percentage 5 specifically refers to a subset of the latino population", + "source_ids": [ + 246 + ], + "source": "Name: 5\nType: PERCENTAGE", + "target": "Name: latinos\nType: NATIONALITY" + }, + { + "src_entity_name": "latinos", + "tgt_entity_name": "economic upward mobility", + "relation_name": "", + "weight": 10.0, + "description": "latinos are the group whose perspective on economic upward mobility for their children is being examined", + "source_ids": [ + 246 + ], + "source": "Name: latinos\nType: NATIONALITY", + "target": "Name: economic upward mobility\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "latinos", + "tgt_entity_name": "children", + "relation_name": "", + "weight": 10.0, + "description": "the children belong to the latino demographic group mentioned in the text", + "source_ids": [ + 246 + ], + "source": "Name: latinos\nType: NATIONALITY", + "target": "Name: children\nType: PERSON" + }, + { + "src_entity_name": "economic upward mobility", + "tgt_entity_name": "children", + "relation_name": "", + "weight": 9.0, + "description": "economic upward mobility is the specific attribute or outcome being considered for the children", + "source_ids": [ + 246 + ], + "source": "Name: economic upward mobility\nType: TASK_OR_PROBLEM", + "target": "Name: children\nType: PERSON" + }, + { + "src_entity_name": "counting", + "tgt_entity_name": "aggregation operation", + "relation_name": "", + "weight": 10.0, + "description": "counting is explicitly described as an aggregation operation", + "source_ids": [ + 250 + ], + "source": "Name: counting\nType: METHOD_OR_TECHNIQUE", + "target": "Name: aggregation operation\nType: UNKNOWN" + }, + { + "src_entity_name": "listing", + "tgt_entity_name": "aggregation operation", + "relation_name": "", + "weight": 10.0, + "description": "listing is explicitly described as an aggregation operation", + "source_ids": [ + 250 + ], + "source": "Name: listing\nType: METHOD_OR_TECHNIQUE", + "target": "Name: aggregation operation\nType: UNKNOWN" + }, + { + "src_entity_name": "summarizing", + "tgt_entity_name": "aggregation operation", + "relation_name": "", + "weight": 10.0, + "description": "summarizing is explicitly described as an aggregation operation", + "source_ids": [ + 250 + ], + "source": "Name: summarizing\nType: METHOD_OR_TECHNIQUE", + "target": "Name: aggregation operation\nType: UNKNOWN" + }, + { + "src_entity_name": "structural filter", + "tgt_entity_name": "items", + "relation_name": "", + "weight": 8.0, + "description": "the structural filter is used to identify the set of items", + "source_ids": [ + 250 + ], + "source": "Name: structural filter\nType: METHOD_OR_TECHNIQUE", + "target": "Name: items\nType: UNKNOWN" + }, + { + "src_entity_name": "user a2gbifl43u1lkj", + "tgt_entity_name": "soft labeled personality embedding matrix", + "relation_name": "", + "weight": 9.0, + "description": "user a2gbifl43u1lkj is the subject for whom personality vectors are analyzed within the soft labeled personality embedding matrix", + "source_ids": [ + 255 + ], + "source": "Name: user a2gbifl43u1lkj\nType: PERSON", + "target": "Name: soft labeled personality embedding matrix\nType: PRODUCT" + }, + { + "src_entity_name": "user a2gbifl43u1lkj", + "tgt_entity_name": "receptiviti score", + "relation_name": "", + "weight": 9.0, + "description": "receptiviti scores are calculated for the personality vectors associated with user a2gbifl43u1lkj", + "source_ids": [ + 255 + ], + "source": "Name: user a2gbifl43u1lkj\nType: PERSON", + "target": "Name: receptiviti score\nType: EVALUATION_METRIC" + }, + { + "src_entity_name": "foreign born latinos", + "tgt_entity_name": "population", + "relation_name": "", + "weight": 8.0, + "description": "the population of foreign born latinos is a specific value sought in the survey example", + "source_ids": [ + 255 + ], + "source": "Name: foreign born latinos\nType: PERSON", + "target": "Name: population\nType: MEASUREMENT" + }, + { + "src_entity_name": "latinos interviewed by cellphone", + "tgt_entity_name": "population", + "relation_name": "", + "weight": 8.0, + "description": "the population of latinos interviewed by cellphone is a specific value sought in the survey example", + "source_ids": [ + 255 + ], + "source": "Name: latinos interviewed by cellphone\nType: PERSON", + "target": "Name: population\nType: MEASUREMENT" + }, + { + "src_entity_name": "soft labeled personality embedding matrix", + "tgt_entity_name": "receptiviti score", + "relation_name": "", + "weight": 7.0, + "description": "the soft labeled personality embedding matrix contains personality vectors that are evaluated using receptiviti scores", + "source_ids": [ + 255 + ], + "source": "Name: soft labeled personality embedding matrix\nType: PRODUCT", + "target": "Name: receptiviti score\nType: EVALUATION_METRIC" + }, + { + "src_entity_name": "report", + "tgt_entity_name": "chapter", + "relation_name": "", + "weight": 6.0, + "description": "the example query asks to count chapters in the report", + "source_ids": [ + 258 + ], + "source": "Name: report\nType: BOOK", + "target": "Name: chapter\nType: SECTION_TITLE" + }, + { + "src_entity_name": "ai assistant", + "tgt_entity_name": "global query", + "relation_name": "", + "weight": 10.0, + "description": "the ai assistant s function is to analyze the global query", + "source_ids": [ + 258 + ], + "source": "Name: ai assistant\nType: PERSON", + "target": "Name: global query\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "ai assistant", + "tgt_entity_name": "filters", + "relation_name": "", + "weight": 9.0, + "description": "the ai assistant must determine the list of filters to apply", + "source_ids": [ + 258 + ], + "source": "Name: ai assistant\nType: PERSON", + "target": "Name: filters\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "ai assistant", + "tgt_entity_name": "operation", + "relation_name": "", + "weight": 9.0, + "description": "the ai assistant must determine the final aggregation operation", + "source_ids": [ + 258 + ], + "source": "Name: ai assistant\nType: PERSON", + "target": "Name: operation\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "filters", + "tgt_entity_name": "page", + "relation_name": "", + "weight": 8.0, + "description": "filters can be of type page to target specific page numbers", + "source_ids": [ + 258 + ], + "source": "Name: filters\nType: TASK_OR_PROBLEM", + "target": "Name: page\nType: MEASUREMENT" + }, + { + "src_entity_name": "assistant", + "tgt_entity_name": "filters", + "relation_name": "", + "weight": 9.0, + "description": "the assistant determines the filters to apply", + "source_ids": [ + 258 + ], + "source": "Name: filters\nType: TASK_OR_PROBLEM", + "target": "Name: assistant\nType: PERSON" + }, + { + "src_entity_name": "operation", + "tgt_entity_name": "count", + "relation_name": "", + "weight": 7.0, + "description": "count is one of the possible operations for aggregation", + "source_ids": [ + 258 + ], + "source": "Name: operation\nType: TASK_OR_PROBLEM", + "target": "Name: count\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "operation", + "tgt_entity_name": "list", + "relation_name": "", + "weight": 7.0, + "description": "list is one of the possible operations for aggregation", + "source_ids": [ + 258 + ], + "source": "Name: operation\nType: TASK_OR_PROBLEM", + "target": "Name: list\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "operation", + "tgt_entity_name": "summarize", + "relation_name": "", + "weight": 7.0, + "description": "summarize is one of the possible operations for aggregation", + "source_ids": [ + 258 + ], + "source": "Name: operation\nType: TASK_OR_PROBLEM", + "target": "Name: summarize\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "operation", + "tgt_entity_name": "analyze", + "relation_name": "", + "weight": 7.0, + "description": "analyze is one of the possible operations for aggregation", + "source_ids": [ + 258 + ], + "source": "Name: operation\nType: TASK_OR_PROBLEM", + "target": "Name: analyze\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "assistant", + "tgt_entity_name": "operation", + "relation_name": "", + "weight": 9.0, + "description": "the assistant determines the operation to perform", + "source_ids": [ + 258 + ], + "source": "Name: operation\nType: TASK_OR_PROBLEM", + "target": "Name: assistant\nType: PERSON" + }, + { + "src_entity_name": "methodology", + "tgt_entity_name": "data augmentation", + "relation_name": "", + "weight": 9.0, + "description": "the example query asks to summarize the discussion about data augmentation in the methodology section", + "source_ids": [ + 258 + ], + "source": "Name: methodology\nType: SECTION_TITLE", + "target": "Name: data augmentation\nType: METHOD_OR_TECHNIQUE" + }, + { + "src_entity_name": "paper", + "tgt_entity_name": "page", + "relation_name": "", + "weight": 6.0, + "description": "the example query specifies a page range 3 to 10 for the paper", + "source_ids": [ + 258 + ], + "source": "Name: paper\nType: BOOK", + "target": "Name: page\nType: MEASUREMENT" + }, + { + "src_entity_name": "discussion", + "tgt_entity_name": "data augmentation", + "relation_name": "", + "weight": 8.0, + "description": "the discussion is about data augmentation", + "source_ids": [ + 258 + ], + "source": "Name: data augmentation\nType: METHOD_OR_TECHNIQUE", + "target": "Name: discussion\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "page", + "tgt_entity_name": "3 10", + "relation_name": "", + "weight": 8.0, + "description": "3 10 is an example value for a page filter", + "source_ids": [ + 258 + ], + "source": "Name: 3 10\nType: MEASUREMENT", + "target": "Name: page\nType: MEASUREMENT" + }, + { + "src_entity_name": "entity resolution adjudicator", + "tgt_entity_name": "candidate entities", + "relation_name": "", + "weight": 10.0, + "description": "the entity resolution adjudicator compares the new entity against the candidate entities", + "source_ids": [ + 262 + ], + "source": "Name: entity resolution adjudicator\nType: PERSON", + "target": "Name: candidate entities\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "entity resolution adjudicator", + "tgt_entity_name": "id", + "relation_name": "", + "weight": 9.0, + "description": "the entity resolution adjudicator outputs the id of the matching candidate", + "source_ids": [ + 262 + ], + "source": "Name: entity resolution adjudicator\nType: PERSON", + "target": "Name: id\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "entity resolution adjudicator", + "tgt_entity_name": "1", + "relation_name": "", + "weight": 9.0, + "description": "the entity resolution adjudicator outputs 1 if no match is found", + "source_ids": [ + 262 + ], + "source": "Name: entity resolution adjudicator\nType: PERSON", + "target": "Name: 1\nType: VALUE" + }, + { + "src_entity_name": "entity resolution adjudicator", + "tgt_entity_name": "explanation", + "relation_name": "", + "weight": 9.0, + "description": "the entity resolution adjudicator provides an explanation for the decision", + "source_ids": [ + 262 + ], + "source": "Name: entity resolution adjudicator\nType: PERSON", + "target": "Name: explanation\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "candidate entities", + "tgt_entity_name": "knowledge graph", + "relation_name": "", + "weight": 8.0, + "description": "candidate entities are retrieved from the knowledge graph", + "source_ids": [ + 262 + ], + "source": "Name: candidate entities\nType: TASK_OR_PROBLEM", + "target": "Name: knowledge graph\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "candidate entities", + "tgt_entity_name": "knowledge base", + "relation_name": "", + "weight": 8.0, + "description": "candidate entities are retrieved from the knowledge base", + "source_ids": [ + 262 + ], + "source": "Name: candidate entities\nType: TASK_OR_PROBLEM", + "target": "Name: knowledge base\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "candidate entities", + "tgt_entity_name": "id", + "relation_name": "", + "weight": 10.0, + "description": "each candidate entity has a unique id for reference", + "source_ids": [ + 262 + ], + "source": "Name: candidate entities\nType: TASK_OR_PROBLEM", + "target": "Name: id\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "select id", + "tgt_entity_name": "id", + "relation_name": "", + "weight": 9.0, + "description": "select id is defined as the integer value of the id of the candidate", + "source_ids": [ + 276 + ], + "source": "Name: id\nType: PARAMETER_OR_VARIABLE", + "target": "Name: select id\nType: PARAMETER_OR_VARIABLE" + }, + { + "src_entity_name": "event detection", + "tgt_entity_name": "named entity recognition", + "relation_name": "", + "weight": 9.0, + "description": "event detection and named entity recognition are distinct parallel concepts and are explicitly stated as not a match", + "source_ids": [ + 267 + ], + "source": "Name: event detection\nType: TASK_OR_PROBLEM", + "target": "Name: named entity recognition\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "description", + "tgt_entity_name": "contextual importance", + "relation_name": "", + "weight": 9.0, + "description": "descriptions possess contextual importance which dictates the need to analyze them for underlying identity rather than surface similarity", + "source_ids": [ + 269 + ], + "source": "Name: description\nType: CONCEPT", + "target": "Name: contextual importance\nType: CONCEPT" + }, + { + "src_entity_name": "apple", + "tgt_entity_name": "apple inc", + "relation_name": "", + "weight": 10.0, + "description": "both are mentioned in the text as examples to illustrate that they are not a match despite sharing the same name", + "source_ids": [ + 272 + ], + "source": "Name: apple\nType: PRODUCT", + "target": "Name: apple inc\nType: ORGANIZATION" + }, + { + "src_entity_name": "when in doubt", + "tgt_entity_name": "1", + "relation_name": "", + "weight": 10.0, + "description": "the text states that if the condition when in doubt is met the output must be 1", + "source_ids": [ + 273 + ], + "source": "Name: when in doubt\nType: TASK_OR_PROBLEM", + "target": "Name: 1\nType: UNKNOWN" + }, + { + "src_entity_name": "json", + "tgt_entity_name": "output", + "relation_name": "", + "weight": 10.0, + "description": "the text specifies that the answer must be provided in a valid json format", + "source_ids": [ + 275 + ], + "source": "Name: json\nType: FILE_TYPE", + "target": "Name: output\nType: UNKNOWN" + }, + { + "src_entity_name": "select id", + "tgt_entity_name": "exact match", + "relation_name": "", + "weight": 8.0, + "description": "select id holds the value of the id if an exact match is found", + "source_ids": [ + 276 + ], + "source": "Name: select id\nType: PARAMETER_OR_VARIABLE", + "target": "Name: exact match\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "select id", + "tgt_entity_name": "1", + "relation_name": "", + "weight": 9.0, + "description": "select id is assigned the value 1 if no exact match is found", + "source_ids": [ + 276 + ], + "source": "Name: select id\nType: PARAMETER_OR_VARIABLE", + "target": "Name: 1\nType: MONEY" + }, + { + "src_entity_name": "select id", + "tgt_entity_name": "candidate", + "relation_name": "", + "weight": 9.0, + "description": "select id represents the id of the candidate being evaluated", + "source_ids": [ + 276 + ], + "source": "Name: select id\nType: PARAMETER_OR_VARIABLE", + "target": "Name: candidate\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "select id", + "tgt_entity_name": "integer", + "relation_name": "", + "weight": 10.0, + "description": "select id is defined as an integer type", + "source_ids": [ + 276 + ], + "source": "Name: select id\nType: PARAMETER_OR_VARIABLE", + "target": "Name: integer\nType: MEASUREMENT" + }, + { + "src_entity_name": "example 1", + "tgt_entity_name": "select id", + "relation_name": "", + "weight": 5.0, + "description": "example 1 is associated with the context of the provided json structure containing select id", + "source_ids": [ + 281 + ], + "source": "Name: select id\nType: PARAMETER_OR_VARIABLE", + "target": "Name: example 1\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "candidate", + "tgt_entity_name": "exact match", + "relation_name": "", + "weight": 8.0, + "description": "the candidate is the subject of the exact match determination", + "source_ids": [ + 276 + ], + "source": "Name: exact match\nType: TASK_OR_PROBLEM", + "target": "Name: candidate\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "selection task", + "tgt_entity_name": "integer", + "relation_name": "", + "weight": 9.0, + "description": "the selection task requires the output to be a single integer", + "source_ids": [ + 282 + ], + "source": "Name: integer\nType: MEASUREMENT", + "target": "Name: selection task\nType: TASK_OR_PROBLEM" + }, + { + "src_entity_name": "example 2", + "tgt_entity_name": "explanation", + "relation_name": "", + "weight": 5.0, + "description": "example 2 is associated with the context of the provided json structure containing explanation", + "source_ids": [ + 281 + ], + "source": "Name: explanation\nType: PARAMETER_OR_VARIABLE", + "target": "Name: example 2\nType: TASK_OR_PROBLEM" + } + ] + }, + "tree2kg": { + "1": [ + "Name: complex documents\nType: DATASET_OR_CORPUS", + "Name: retrieval-augmented generation\nType: TASK_OR_PROBLEM", + "Name: bookrag\nType: MODEL_OR_ARCHITECTURE", + "Name: hierarchical structure-aware index-based approach\nType: METHOD_OR_TECHNIQUE", + "Name: bookrag: a hierarchical structure-aware index-based approach for retrieval-augmented generation on complex documents\nType: SECTION_TITLE" + ], + "2": [ + "Name: efficiency\nType: EVALUATION_METRIC", + "Name: bookrag\nType: METHOD_OR_TECHNIQUE", + "Name: qa accuracy\nType: EVALUATION_METRIC", + "Name: baselines\nType: MODEL_OR_ARCHITECTURE", + "Name: large language models\nType: MODEL_OR_ARCHITECTURE", + "Name: handbooks\nType: BOOK", + "Name: retrievalaugmented generation\nType: METHOD_OR_TECHNIQUE", + "Name: shu wang\nType: PERSON", + "Name: bookindex\nType: SOFTWARE", + "Name: yingli zhou\nType: PERSON", + "Name: yixiang fang\nType: PERSON", + "Name: information foraging theory\nType: SCIENTIFIC_THEORY", + "Name: three widely adopted benchmarks\nType: BENCHMARK", + "Name: graph\nType: SOFTWARE", + "Name: booklets\nType: BOOK", + "Name: retrieval recall\nType: EVALUATION_METRIC", + "Name: books\nType: BOOK", + "Name: question answering\nType: TASK_OR_PROBLEM", + "Name: table of contents\nType: SOFTWARE", + "Name: the chinese university of hong kong shenzhen\nType: ORGANIZATION", + "Name: tree\nType: SOFTWARE", + "Name: industry\nType: ORGANIZATION", + "Name: academia\nType: ORGANIZATION" + ], + "3": [ + "Name: abstract\nType: SECTION_TITLE" + ], + "4": [ + "Name: reference format\nType: SECTION_TITLE", + "Name: pvldb\nType: PUBLICATION_VENUE" + ], + "5": [ + "Name: 19\nType: MEASUREMENT", + "Name: pvldb\nType: PUBLICATION_VENUE", + "Name: xx xx xxx xx\nType: MEASUREMENT", + "Name: bookrag\nType: PRODUCT", + "Name: yingli zhou\nType: PERSON", + "Name: xxx xxx\nType: MEASUREMENT", + "Name: retrieval augmented generation\nType: TECHNOLOGY", + "Name: yixiang fang\nType: PERSON", + "Name: 2025\nType: DATE", + "Name: hierarchical structure aware index based approach\nType: METHOD_OR_TECHNIQUE", + "Name: complex documents\nType: TASK_OR_PROBLEM", + "Name: 1\nType: MEASUREMENT", + "Name: shu wang\nType: PERSON" + ], + "6": [ + "Name: artifact availability\nType: TASK_OR_PROBLEM", + "Name: pvldb\nType: PUBLICATION_VENUE" + ], + "7": [ + "Name: github\nType: ORGANIZATION", + "Name: source code\nType: PRODUCT", + "Name: sam234990\nType: PERSON", + "Name: data\nType: PRODUCT", + "Name: artifacts\nType: PRODUCT", + "Name: bookrag\nType: SOFTWARE" + ], + "8": [ + "Name: 1 introduction\nType: SECTION_TITLE" 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"Name: mmlongbench\nType: DATASET_OR_CORPUS", + "Name: accuracy\nType: EVALUATION_METRIC", + "Name: query\nType: TASK_OR_PROBLEM", + "Name: page numbers\nType: UNKNOWN", + "Name: token usage\nType: EVALUATION_METRIC", + "Name: metadata\nType: CONCEPT", + "Name: token based f1 score\nType: EVALUATION_METRIC", + "Name: evidence statements\nType: UNKNOWN", + "Name: retrieval recall\nType: EVALUATION_METRIC", + "Name: modality\nType: CONCEPT", + "Name: images\nType: TABLE", + "Name: qa\nType: TASK_OR_PROBLEM", + "Name: candidate blocks\nType: TABLE", + "Name: pdf blocks\nType: TABLE", + "Name: pdf parsing\nType: METHOD_OR_TECHNIQUE", + "Name: texts\nType: TABLE", + "Name: time cost\nType: EVALUATION_METRIC", + "Name: response phase\nType: TIME", + "Name: formulas\nType: TABLE" + ], + "145": [ + "Name: our experiments\nType: EVENT", + "Name: three model configurations\nType: MODEL_OR_ARCHITECTURE", + "Name: baselines\nType: TASK_OR_PROBLEM" + ], + "146": [ + "Name: bm25\nType: 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assistant\nType: PERSON", + "Name: image\nType: IMAGE", + "Name: report\nType: BOOK", + "Name: references\nType: SECTION_TITLE", + "Name: operation\nType: TASK_OR_PROBLEM", + "Name: paper\nType: BOOK", + "Name: analyze\nType: TASK_OR_PROBLEM" + ], + "259": [ + "Name: figure 12\nType: IMAGE", + "Name: filter operator generation\nType: TASK_OR_PROBLEM" + ], + "260": [ + "Name: 18\nType: NUMBER" + ], + "262": [ + "Name: id\nType: PARAMETER_OR_VARIABLE", + "Name: candidate entities\nType: TASK_OR_PROBLEM", + "Name: new entity\nType: TASK_OR_PROBLEM", + "Name: knowledge graph\nType: TASK_OR_PROBLEM", + "Name: 1\nType: VALUE", + "Name: json object\nType: FILE_TYPE", + "Name: knowledge base\nType: TASK_OR_PROBLEM", + "Name: entity resolution adjudicator\nType: PERSON", + "Name: text\nType: DATASET_OR_CORPUS", + "Name: explanation\nType: TASK_OR_PROBLEM" + ], + "265": [ + "Name: new entity\nType: TASK_OR_PROBLEM" + ], + "266": [ + "Name: field by field adjudication\nType: TASK_OR_PROBLEM" + ], + "267": [ + "Name: large language model\nType: TECHNOLOGY", + "Name: distinct concepts\nType: CONCEPT", + "Name: alias\nType: CONCEPT", + "Name: high importance\nType: CONCEPT", + "Name: event detection\nType: TASK_OR_PROBLEM", + "Name: entity name\nType: TASK_OR_PROBLEM", + "Name: llm\nType: TECHNOLOGY", + "Name: named entity recognition\nType: TASK_OR_PROBLEM" + ], + "268": [ + "Name: entity type\nType: TASK_OR_PROBLEM" + ], + "269": [ + "Name: contextual importance\nType: CONCEPT", + "Name: description\nType: CONCEPT" + ], + "270": [ + "Name: be strict and conservative\nType: TASK_OR_PROBLEM" + ], + "272": [ + "Name: apple inc\nType: ORGANIZATION", + "Name: apple\nType: PRODUCT" + ], + "273": [ + "Name: when in doubt\nType: TASK_OR_PROBLEM", + "Name: 1\nType: UNKNOWN" + ], + "274": [ + "Name: new entity\nType: TASK_OR_PROBLEM" + ], + "275": [ + "Name: output\nType: UNKNOWN", + "Name: json\nType: FILE_TYPE" + ], + "276": [ + "Name: id\nType: PARAMETER_OR_VARIABLE", + "Name: exact match\nType: TASK_OR_PROBLEM", + "Name: select id\nType: PARAMETER_OR_VARIABLE", + "Name: 1\nType: MONEY", + "Name: integer\nType: MEASUREMENT", + "Name: candidate\nType: TASK_OR_PROBLEM" + ], + "277": [ + "Name: explanation\nType: TASK_OR_PROBLEM" + ], + "281": [ + "Name: select id\nType: PARAMETER_OR_VARIABLE", + "Name: example 2\nType: TASK_OR_PROBLEM", + "Name: example 1\nType: TASK_OR_PROBLEM", + "Name: explanation\nType: PARAMETER_OR_VARIABLE" + ], + "282": [ + "Name: integer\nType: MEASUREMENT", + "Name: selection task\nType: TASK_OR_PROBLEM" + ], + "284": [ + "Name: figure 13\nType: IMAGE", + "Name: examples\nType: DATASET_OR_CORPUS", + "Name: entity resolution\nType: TASK_OR_PROBLEM", + "Name: prompt\nType: SOFTWARE" + ], + "285": [ + "Name: 19\nType: NUMBER" + ] + }, + "variant": "basic" +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_1.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_1.json new file mode 100644 index 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in the main title.", + "source_ids": [ + 1 + ] + } + ], + "node_idx": 1 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_10.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_10.json new file mode 100644 index 0000000..930f4c0 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_10.json @@ -0,0 +1,87 @@ +{ + "entities": [ + { + "entity_name": "creative commons by nc nd 4 0 international license", + "entity_type": "LAW", + "description": "creative commons by nc nd 4 0 international license is the specific license under which this work is distributed", + "source_ids": [ + 10 + ] + }, + { + "entity_name": "vldb endowment", + "entity_type": "ORGANIZATION", + "description": "vldb endowment is the organization that holds the publication rights for this work", + "source_ids": [ + 10 + ] + }, + { + "entity_name": "info vldb org", + "entity_type": "EMAIL", + "description": "info vldb org is the email address provided for obtaining permission for uses beyond the license", + "source_ids": [ + 10 + ] + }, + { + "entity_name": "creative commons", + "entity_type": "ORGANIZATION", + "description": "creative commons is the organization that created the by nc nd 4 0 international license", + "source_ids": [ + 10 + ] + }, + { + "entity_name": "owner author s", + "entity_type": "PERSON", + "description": "owner author s refers to the individuals or entities holding the copyright for the work", + "source_ids": [ + 10 + ] + } + ], + "relations": [ + { + "src_entity_name": "creative commons by nc nd 4 0 international license", + "tgt_entity_name": "vldb endowment", + "relation_name": "", + "weight": 8.0, + "description": "the work licensed under the creative commons by nc nd 4 0 international license has its publication rights licensed to the vldb endowment", + "source_ids": [ + 10 + ] + }, + { + "src_entity_name": "creative commons", + "tgt_entity_name": "creative commons by nc nd 4 0 international license", + 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0000000..0c16f6f --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_100.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "formula (3)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the output of an LLM function as a set of elements. LaTeX: 𝐸 𝑞 = LLM ( 𝑃 𝐸𝑥𝑡 , 𝑞 ) = { 𝑒 1 , 𝑒 2 , . . . , 𝑒 𝑚 } (3)", + "source_ids": [ + 100 + ] + } + ], + "relations": [], + "node_idx": 100 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_101.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_101.json new file mode 100644 index 0000000..ee92740 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_101.json @@ -0,0 +1,143 @@ +{ + "entities": [ + { + "entity_name": "q", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "q is the original user query mentioned in the text", + "source_ids": [ + 101 + ] + }, + { + "entity_name": "p dec", + "entity_type": "SOFTWARE", + "description": "p dec represents a prompt used to guide the llm for the decomposition task", + "source_ids": [ + 101 + ] + }, + { + "entity_name": "p ext", + "entity_type": "SOFTWARE", + "description": "p ext represents a prompt used to guide the llm for the extraction task", + "source_ids": [ + 101 + ] + }, + { + "entity_name": "llm", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "llm is the model being guided by the prompts for decomposition and extraction tasks", + "source_ids": [ + 101 + ] + }, + { + "entity_name": "decomposition", + "entity_type": "TASK_OR_PROBLEM", + "description": "decomposition is a task for which the prompt p dec is used to guide the llm", + "source_ids": [ + 101 + ] + }, + { + "entity_name": "extraction", + "entity_type": "TASK_OR_PROBLEM", + "description": "extraction is a task for which the prompt p ext is used to guide the llm", + "source_ids": [ + 101 + ] + }, + { + "entity_name": "prompt", + "entity_type": "SOFTWARE", + "description": "prompts are instructions used to guide the llm for specific tasks", + "source_ids": [ + 101 + ] + } + ], + "relations": [ + { + "src_entity_name": "p dec", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 9.0, + "description": "p dec is used to guide the llm for the decomposition task", + "source_ids": [ + 101 + ] + }, + { + "src_entity_name": "p ext", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 9.0, + "description": "p ext is used to guide the llm for the extraction task", + "source_ids": [ + 101 + ] + }, + { + "src_entity_name": "q", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 7.0, + "description": "q is the original user query that the llm processes", + "source_ids": [ + 101 + ] + }, + { + "src_entity_name": "p dec", + "tgt_entity_name": "decomposition", + "relation_name": "", + "weight": 10.0, + "description": "p dec is the specific prompt used to guide the llm for the decomposition task", + "source_ids": [ + 101 + ] + }, + { + "src_entity_name": "p ext", + "tgt_entity_name": "extraction", + "relation_name": "", + "weight": 10.0, + "description": "p ext is the specific prompt used to guide the llm for the extraction task", + "source_ids": [ + 101 + ] + }, + { + "src_entity_name": "prompt", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 8.0, + "description": "prompts are used to guide the llm", + "source_ids": [ + 101 + ] + }, + { + "src_entity_name": "decomposition", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 9.0, + "description": "the llm performs the decomposition task", + "source_ids": [ + 101 + ] + }, + { + "src_entity_name": "extraction", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 9.0, + "description": "the llm performs the extraction task", + "source_ids": [ + 101 + ] + } + ], + "node_idx": 101 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_102.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_102.json new file mode 100644 index 0000000..2a3b92e --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_102.json @@ -0,0 +1,297 @@ +{ + "entities": [ + { + "entity_name": "selector", + "entity_type": "TECHNOLOGY", + "description": "selector is an operator that filters or selects specific content ranges from the bookindex", + "source_ids": [ + 102 + ] + }, + { + "entity_name": "bookindex", + "entity_type": "PRODUCT", + "description": "bookindex is the source of content ranges that the selector operators filter", + "source_ids": [ + 102 + ] + }, + { + "entity_name": "filter modal", + "entity_type": "TECHNOLOGY", + "description": "filter modal is an operator that applies explicit constraints to the bookindex", + "source_ids": [ + 102 + ] + }, + { + "entity_name": "filter range", + "entity_type": "TECHNOLOGY", + "description": "filter range is an operator that applies explicit constraints to the bookindex", + "source_ids": [ + 102 + ] + }, + { + "entity_name": "c", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "c represents explicit constraints such as modal types and page ranges generated during a plan", + "source_ids": [ + 102 + ] + }, + { + "entity_name": "tree", + "entity_type": "TASK_OR_PROBLEM", + "description": "tree is the data structure t n e t on which the operators operate", + "source_ids": [ + 102 + ] + }, + { + "entity_name": "n", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "n represents the set of nodes in the tree", + "source_ids": [ + 102 + ] + }, + { + "entity_name": "e t", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "e t represents the set of edges in the tree", + "source_ids": [ + 102 + ] + }, + { + "entity_name": "n f", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "n f is the filtered subset of nodes produced by the operators", + "source_ids": [ + 102 + ] + }, + { + "entity_name": "c n", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "c n is a predicate that holds true for each node in the filtered subset", + "source_ids": [ + 102 + ] + }, + { + "entity_name": "modal types", + "entity_type": "CONCEPT", + "description": "modal types are a specific type of explicit constraint c mentioned in the text", + "source_ids": [ + 102 + ] + }, + { + "entity_name": "page ranges", + "entity_type": "CONCEPT", + "description": "page ranges are a specific type of explicit constraint c mentioned in the text", + "source_ids": [ + 102 + ] + }, + { + "entity_name": "plan", + "entity_type": "TASK_OR_PROBLEM", + "description": "the plan is the process during which explicit constraints c are generated", + "source_ids": [ + 102 + ] + }, + { + "entity_name": "nodes", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "nodes are the individual elements within the tree t that are evaluated by the predicate", + "source_ids": [ + 102 + ] + }, + { + "entity_name": "edges", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "edges are the connections within the tree t denoted as e t", + "source_ids": [ + 102 + ] + } + ], + "relations": [ + { + "src_entity_name": "selector", + "tgt_entity_name": "bookindex", + "relation_name": "", + "weight": 9.0, + "description": "selector operators filter content ranges directly from the bookindex", + "source_ids": [ + 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"relation_name": "", + "weight": 10.0, + "description": "the tree t is composed of the set of edges e t", + "source_ids": [ + 102 + ] + }, + { + "src_entity_name": "n f", + "tgt_entity_name": "nodes", + "relation_name": "", + "weight": 9.0, + "description": "the filtered subset n f is a subset of the nodes n", + "source_ids": [ + 102 + ] + }, + { + "src_entity_name": "c n", + "tgt_entity_name": "nodes", + "relation_name": "", + "weight": 9.0, + "description": "the predicate c n is evaluated for each node in the set", + "source_ids": [ + 102 + ] + } + ], + "node_idx": 102 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_103.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_103.json new file mode 100644 index 0000000..f5e08a2 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_103.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "formula (4)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the set N_f as a subset of N based on condition C. LaTeX: 𝑁 𝑓 = { 𝑛 ∈ 𝑁 | 𝐶 𝑛 ( )} (4)", + "source_ids": [ + 103 + ] + } + ], + "relations": [], + "node_idx": 103 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_104.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_104.json new file mode 100644 index 0000000..8c88080 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_104.json @@ -0,0 +1,251 @@ +{ + "entities": [ + { + "entity_name": "select by entity", + "entity_type": "TECHNOLOGY", + "description": "select by entity is a method that targets contiguous document segments by retrieving subtrees rooted at specific section nodes", + "source_ids": [ + 104 + ] + }, + { + "entity_name": "select by section", + "entity_type": "TECHNOLOGY", + "description": "select by section is a method that targets contiguous document segments by retrieving subtrees rooted at specific section nodes", + "source_ids": [ + 104 + ] + }, + { + "entity_name": "gt link", + "entity_type": "TECHNOLOGY", + "description": "gt link is a mechanism used to link sections to entities", + "source_ids": [ + 104 + ] + }, + { + "entity_name": "llm", + "entity_type": "TECHNOLOGY", + "description": "llm is a system used to select sections in the described process", + "source_ids": [ + 104 + ] + }, + { + "entity_name": "s target", + "entity_type": "TASK_OR_PROBLEM", + "description": "s target represents a set of target section nodes at a specified depth", + "source_ids": [ + 104 + ] + }, + { + "entity_name": "n", + "entity_type": "TASK_OR_PROBLEM", + "description": "n represents the set of nodes in the document structure", + "source_ids": [ + 104 + ] + }, + { + "entity_name": "e q", + "entity_type": "TASK_OR_PROBLEM", + "description": "e q represents the entities linked to sections via gt link", + "source_ids": [ + 104 + ] + }, + { + "entity_name": "n s", + "entity_type": "TASK_OR_PROBLEM", + "description": "n s represents the selected node set formed by retrieving descendants of target sections", + "source_ids": [ + 104 + ] + }, + { + "entity_name": "document", + "entity_type": "TASK_OR_PROBLEM", + "description": "document is the text being processed by the select by entity and select by section methods", + "source_ids": [ + 104 + ] + }, + { + "entity_name": "subtree", + "entity_type": "TASK_OR_PROBLEM", + "description": "subtree refers to the data structure rooted at specific section nodes that is retrieved by the methods", + "source_ids": [ + 104 + ] + }, + { + "entity_name": "section node", + "entity_type": "TASK_OR_PROBLEM", + "description": "section node is a specific node within the document structure that serves as a root for subtrees", + "source_ids": [ + 104 + ] + }, + { + "entity_name": "depth", + "entity_type": "MEASUREMENT", + "description": "depth is a specified parameter determining the level of the target section nodes", + "source_ids": [ + 104 + ] + }, + { + "entity_name": "descendant", + "entity_type": "TASK_OR_PROBLEM", + "description": "descendant refers to the nodes below the target section nodes that are retrieved to form the selected node set", + "source_ids": [ + 104 + ] + } + ], + "relations": [ + { + "src_entity_name": "select by entity", + "tgt_entity_name": "s target", + "relation_name": "", + "weight": 9.0, + "description": "select by entity identifies a set of target section nodes s target as part of its process", + "source_ids": [ + 104 + ] + }, + { + "src_entity_name": "select by section", + "tgt_entity_name": "s target", + "relation_name": "", + "weight": 9.0, + "description": "select by section identifies a set of target section nodes s target as part of its process", + "source_ids": [ + 104 + ] + }, + { + "src_entity_name": "s target", + "tgt_entity_name": "e q", + "relation_name": "", + "weight": 8.0, + "description": "s target consists of sections linked to entities e q via gt link", + "source_ids": [ + 104 + ] + }, + { + "src_entity_name": "s target", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 8.0, + "description": "s target includes sections selected by the llm", + "source_ids": [ + 104 + ] + }, + { + "src_entity_name": "s target", + "tgt_entity_name": "n s", + "relation_name": "", + "weight": 9.0, + "description": "n s is formed by retrieving all descendants of the target section nodes s target", + "source_ids": [ + 104 + ] + }, + { + "src_entity_name": "gt link", + "tgt_entity_name": "e q", + "relation_name": "", + "weight": 9.0, + "description": "gt link is the mechanism used to link sections to entities e q", + "source_ids": [ + 104 + ] + }, + { + "src_entity_name": "select by entity", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 9.0, + "description": "select by entity targets contiguous segments within the document", + "source_ids": [ + 104 + ] + }, + { + "src_entity_name": "select by section", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 9.0, + "description": "select by section targets contiguous segments within the document", + "source_ids": [ + 104 + ] + }, + { + "src_entity_name": "select by entity", + "tgt_entity_name": "subtree", + "relation_name": "", + "weight": 9.0, + "description": "select by entity retrieves subtrees rooted at specific section nodes", + "source_ids": [ + 104 + ] + }, + { + "src_entity_name": "select by section", + "tgt_entity_name": "subtree", + "relation_name": "", + "weight": 9.0, + "description": "select by section retrieves subtrees rooted at specific section nodes", + "source_ids": [ + 104 + ] + }, + { + "src_entity_name": "s target", + "tgt_entity_name": "section node", + "relation_name": "", + "weight": 10.0, + "description": "s target consists of specific section nodes", + "source_ids": [ + 104 + ] + }, + { + "src_entity_name": "s target", + "tgt_entity_name": "depth", + "relation_name": "", + "weight": 8.0, + "description": "s target is defined at a specified depth", + "source_ids": [ + 104 + ] + }, + { + "src_entity_name": "n s", + "tgt_entity_name": "descendant", + "relation_name": "", + "weight": 9.0, + "description": "n s is formed by retrieving all descendants of the target sections", + "source_ids": [ + 104 + ] + }, + { + "src_entity_name": "gt link", + "tgt_entity_name": "section node", + "relation_name": "", + "weight": 8.0, + "description": "gt link links sections nodes to entities", + "source_ids": [ + 104 + ] + } + ], + "node_idx": 104 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_105.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_105.json new file mode 100644 index 0000000..6a7911d --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_105.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "formula (5)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the variable N_s as a glyph value for an element s in set S within a target subtree. LaTeX: 𝑁 𝑠 = GLYPH<216> 𝑠 ∈ 𝑆 target Subtree ( 𝑠 ) (5)", + "source_ids": [ + 105 + ] + } + ], + "relations": [], + "node_idx": 105 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_106.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_106.json new file mode 100644 index 0000000..240a5ce --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_106.json @@ -0,0 +1,191 @@ +{ + "entities": [ + { + "entity_name": "reasoner", + "entity_type": "TASK_OR_PROBLEM", + "description": "reasoner is described as a component that analyzes and refines selected tree nodes", + "source_ids": [ + 106 + ] + }, + { + "entity_name": "graph reasoning", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "graph reasoning is a method that performs multi hop inference on a subgraph starting from an entity", + "source_ids": [ + 106 + ] + }, + { + "entity_name": "pagerank algorithm", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "pagerank algorithm is used to compute an entity importance vector over a subgraph", + "source_ids": [ + 106 + ] + }, + { + "entity_name": "gt link matrix", + "entity_type": "SOFTWARE", + "description": "gt link matrix is a matrix used to map entity scores to tree nodes to derive importance scores", + "source_ids": [ + 106 + ] + }, + { + "entity_name": "entity importance vector", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "entity importance vector is a vector computed over a subgraph representing the importance of entities", + "source_ids": [ + 106 + ] + }, + { + "entity_name": "tree node importance scores vector", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "tree node importance scores vector is the final vector derived by mapping entity scores to tree nodes", + "source_ids": [ + 106 + ] + }, + { + "entity_name": "subgraph", + "entity_type": "TASK_OR_PROBLEM", + "description": "subgraph is a portion of a graph extracted from selected nodes on which inference is performed", + "source_ids": [ + 106 + ] + }, + { + "entity_name": "entity", + "entity_type": "TASK_OR_PROBLEM", + "description": "entity is the starting point for the multi hop inference process in graph reasoning", + "source_ids": [ + 106 + ] + }, + { + "entity_name": "selected nodes", + "entity_type": "TASK_OR_PROBLEM", + "description": "selected nodes are the nodes from which a subgraph is extracted for graph reasoning", + "source_ids": [ + 106 + ] + }, + { + "entity_name": "20", + "entity_type": "PUBLICATION_VENUE", + "description": "20 is a citation reference associated with the pagerank algorithm", + "source_ids": [ + 106 + ] + }, + { + "entity_name": "6", + "entity_type": "EQUATION_OR_FORMULA", + "description": "6 is the label for the equation defining the entity importance vector", + "source_ids": [ + 106 + ] + }, + { + "entity_name": "7", + "entity_type": "EQUATION_OR_FORMULA", + "description": "7 is the label for the equation defining the tree node importance scores vector", + "source_ids": [ + 106 + ] + }, + { + "entity_name": "selected tree nodes", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 106 + ] + } + ], + "relations": [ + { + "src_entity_name": "reasoner", + "tgt_entity_name": "selected tree nodes", + "relation_name": "", + "weight": 9.0, + "description": "reasoner analyzes and refines selected tree nodes", + "source_ids": [ + 106 + ] + }, + { + "src_entity_name": "graph reasoning", + "tgt_entity_name": "subgraph", + "relation_name": "", + "weight": 10.0, + "description": "graph reasoning performs multi hop inference on a subgraph", + "source_ids": [ + 106 + ] + }, + { + "src_entity_name": "graph reasoning", + "tgt_entity_name": "entity", + "relation_name": "", + "weight": 9.0, + "description": "graph reasoning starts its inference process from an entity", + "source_ids": [ + 106 + ] + }, + { + "src_entity_name": "graph reasoning", + "tgt_entity_name": "pagerank algorithm", + "relation_name": "", + "weight": 10.0, + "description": "graph reasoning uses the pagerank algorithm to compute the entity importance vector", + "source_ids": [ + 106 + ] + }, + { + "src_entity_name": "pagerank algorithm", + "tgt_entity_name": "entity importance vector", + "relation_name": "", + "weight": 10.0, + "description": "the pagerank algorithm computes the entity importance vector", + "source_ids": [ + 106 + ] + }, + { + "src_entity_name": "entity importance vector", + "tgt_entity_name": "gt link matrix", + "relation_name": "", + "weight": 9.0, + "description": "the entity importance vector is mapped to tree nodes via the gt link matrix", + "source_ids": [ + 106 + ] + }, + { + "src_entity_name": "gt link matrix", + "tgt_entity_name": "tree node importance scores vector", + "relation_name": "", + "weight": 9.0, + "description": "the gt link matrix is used to derive the tree node importance scores vector", + "source_ids": [ + 106 + ] + }, + { + "src_entity_name": "subgraph", + "tgt_entity_name": "selected nodes", + "relation_name": "", + "weight": 10.0, + "description": "the subgraph is extracted from selected nodes", + "source_ids": [ + 106 + ] + } + ], + "node_idx": 106 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_107.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_107.json new file mode 100644 index 0000000..b72d060 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_107.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "formula (6)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the PageRank of a graph G with respect to a vector e'. LaTeX: 𝐼 𝐺 = PageRank ( 𝐺 , 𝑒 ' ) (6)", + "source_ids": [ + 107 + ] + } + ], + "relations": [], + "node_idx": 107 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_108.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_108.json new file mode 100644 index 0000000..188f06a --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_108.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "formula (7)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the product of S and G as equal to the product of I, G, and M. LaTeX: 𝑆 𝐺 = 𝐼 𝐺 × 𝑀 (7)", + "source_ids": [ + 108 + ] + } + ], + "relations": [], + "node_idx": 108 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_109.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_109.json new file mode 100644 index 0000000..64dfab0 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_109.json @@ -0,0 +1,209 @@ +{ + "entities": [ + { + "entity_name": "text ranker", + "entity_type": "SOFTWARE", + "description": "text ranker is a system that evaluates the semantic relevance of a tree node s content to a query", + "source_ids": [ + 109 + ] + }, + { + "entity_name": "skyline ranker", + "entity_type": "SOFTWARE", + "description": "skyline ranker is a system that employs the skyline operator to filter nodes based on multiple criteria", + "source_ids": [ + 109 + ] + }, + { + "entity_name": "skyline operator", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "the skyline operator is a method used by skyline ranker to filter nodes based on scoring dimensions", + "source_ids": [ + 109 + ] + }, + { + "entity_name": "query", + "entity_type": "TASK_OR_PROBLEM", + "description": "the query is the input for which semantic relevance is evaluated by text ranker", + "source_ids": [ + 109 + ] + }, + { + "entity_name": "relevance score", + "entity_type": "EVALUATION_METRIC", + "description": "the relevance score is a metric assigned to each node to indicate its semantic relevance to the query", + "source_ids": [ + 109 + ] + }, + { + "entity_name": "tree node", + "entity_type": "TASK_OR_PROBLEM", + "description": "the tree node is the content unit being evaluated for relevance and filtered based on scoring dimensions", + "source_ids": [ + 109 + ] + }, + { + "entity_name": "nodes", + "entity_type": "TASK_OR_PROBLEM", + "description": "nodes are the data elements being evaluated for relevance and filtered by the ranking systems", + "source_ids": [ + 109 + ] + }, + { + "entity_name": "scoring dimensions", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "scoring dimensions are the specified criteria used to determine if nodes are dominated by others", + "source_ids": [ + 109 + ] + }, + { + "entity_name": "", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 109 + ] + } + ], + "relations": [ + { + "src_entity_name": "text ranker", + "tgt_entity_name": "query", + "relation_name": "", + "weight": 9.0, + "description": "text ranker evaluates the relevance of content specifically to the query", + "source_ids": [ + 109 + ] + }, + { + "src_entity_name": "text ranker", + "tgt_entity_name": "relevance score", + "relation_name": "", + "weight": 10.0, + "description": "text ranker assigns a relevance score to each tree node", + "source_ids": [ + 109 + ] + }, + { + "src_entity_name": "text ranker", + "tgt_entity_name": "tree node", + "relation_name": "", + "weight": 9.0, + "description": "text ranker evaluates the content of the tree node", + "source_ids": [ + 109 + ] + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "skyline operator", + "relation_name": "", + "weight": 10.0, + "description": "skyline ranker employs the skyline operator to perform its filtering function", + "source_ids": [ + 109 + ] + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "tree node", + "relation_name": "", + "weight": 9.0, + "description": "skyline ranker filters tree nodes based on scoring dimensions", + "source_ids": [ + 109 + ] + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "relevance score", + "relation_name": "", + "weight": 8.0, + "description": "skyline ranker uses relevance scores along with others to filter nodes", + "source_ids": [ + 109 + ] + }, + { + "src_entity_name": "skyline operator", + "tgt_entity_name": "tree node", + "relation_name": "", + "weight": 8.0, + "description": "the skyline operator is used to filter tree nodes", + "source_ids": [ + 109 + ] + }, + { + "src_entity_name": "text ranker", + "tgt_entity_name": "", + "relation_name": "", + "weight": 9.0, + "description": "text ranker uses the query to evaluate semantic relevance", + "source_ids": [ + 109 + ] + }, + { + "src_entity_name": "text ranker", + "tgt_entity_name": "nodes", + "relation_name": "", + "weight": 9.0, + "description": "text ranker evaluates the content of the nodes", + "source_ids": [ + 109 + ] + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "", + "relation_name": "", + "weight": 9.0, + "description": "skyline ranker uses the criterion to filter nodes", + "source_ids": [ + 109 + ] + }, + { + "src_entity_name": "skyline ranker", + "tgt_entity_name": "nodes", + "relation_name": "", + "weight": 10.0, + "description": "skyline ranker filters the nodes based on the specified scoring dimensions", + "source_ids": [ + 109 + ] + }, + { + "src_entity_name": "skyline operator", + "tgt_entity_name": "", + "relation_name": "", + "weight": 8.0, + "description": "the skyline operator utilizes as a scoring dimension", + "source_ids": [ + 109 + ] + }, + { + "src_entity_name": "skyline operator", + "tgt_entity_name": "nodes", + "relation_name": "", + "weight": 9.0, + "description": "the skyline operator filters the nodes", + "source_ids": [ + 109 + ] + } + ], + "node_idx": 109 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_11.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_11.json new file mode 100644 index 0000000..7cb4b93 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_11.json @@ -0,0 +1,87 @@ +{ + "entities": [ + { + "entity_name": "proceedings of the vldb endowment", + "entity_type": "PUBLICATION_VENUE", + "description": "proceedings of the vldb endowment is the name of the publication venue mentioned in the text", + "source_ids": [ + 11 + ] + }, + { + "entity_name": "vol 19", + "entity_type": "MEASUREMENT", + "description": "vol 19 refers to the volume number of the publication", + "source_ids": [ + 11 + ] + }, + { + "entity_name": "no 1", + "entity_type": "MEASUREMENT", + "description": "no 1 refers to the issue number of the publication", + "source_ids": [ + 11 + ] + }, + { + "entity_name": "issn 2150 8097", + "entity_type": "MEASUREMENT", + "description": "issn 2150 8097 is the international standard serial number assigned to the publication", + "source_ids": [ + 11 + ] + }, + { + "entity_name": "doi xx xx xxx xx", + "entity_type": "MEASUREMENT", + "description": "doi xx xx xxx xx is the digital object identifier assigned to the document", + "source_ids": [ + 11 + ] + } + ], + "relations": [ + { + "src_entity_name": "proceedings of the vldb endowment", + "tgt_entity_name": "vol 19", + "relation_name": "", + "weight": 9.0, + "description": "vol 19 is the volume associated with the proceedings of the vldb endowment", + "source_ids": [ + 11 + ] + }, + { + "src_entity_name": "proceedings of the vldb endowment", + "tgt_entity_name": "no 1", + "relation_name": "", + "weight": 9.0, + "description": "no 1 is the issue number associated with the proceedings of the vldb endowment", + "source_ids": [ + 11 + ] + }, + { + "src_entity_name": "proceedings of the vldb endowment", + "tgt_entity_name": "issn 2150 8097", + "relation_name": "", + "weight": 10.0, + "description": "issn 2150 8097 is the identifier for the proceedings of the vldb endowment", + "source_ids": [ + 11 + ] + }, + { + "src_entity_name": "proceedings of the vldb endowment", + "tgt_entity_name": "doi xx xx xxx xx", + "relation_name": "", + "weight": 9.0, + "description": "doi xx xx xxx xx is the identifier for the specific article within the proceedings of the vldb endowment", + "source_ids": [ + 11 + ] + } + ], + "node_idx": 11 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_110.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_110.json new file mode 100644 index 0000000..064b720 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_110.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "7", + "entity_type": "NUMBER", + "description": "7 is a number mentioned in the text", + "source_ids": [ + 110 + ] + } + ], + "relations": [], + "node_idx": 110 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_111.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_111.json new file mode 100644 index 0000000..e793697 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_111.json @@ -0,0 +1,215 @@ +{ + "entities": [ + { + "entity_name": "synthesizer", + "entity_type": "TASK_OR_PROBLEM", + "description": "synthesizer is described as an operator responsible for content generation", + "source_ids": [ + 111 + ] + }, + { + "entity_name": "map", + "entity_type": "TASK_OR_PROBLEM", + "description": "map is an operator that performs analysis on specific retrieved information segments to generate partial responses", + "source_ids": [ + 111 + ] + }, + { + "entity_name": "reduce", + "entity_type": "TASK_OR_PROBLEM", + "description": "reduce is an operator that synthesizes a final coherent answer by aggregating information from multiple sources", + "source_ids": [ + 111 + ] + }, + { + "entity_name": "content generation", + "entity_type": "TASK_OR_PROBLEM", + "description": "content generation is the primary responsibility of the synthesizer operators mentioned in the text", + "source_ids": [ + 111 + ] + }, + { + "entity_name": "analysis", + "entity_type": "TASK_OR_PROBLEM", + "description": "analysis is the specific action performed by the map operator on retrieved information segments", + "source_ids": [ + 111 + ] + }, + { + "entity_name": "partial responses", + "entity_type": "PRODUCT", + "description": "partial responses are the output generated by the map operator from specific retrieved information segments", + "source_ids": [ + 111 + ] + }, + { + "entity_name": "final coherent answer", + "entity_type": "PRODUCT", + "description": "a final coherent answer is the result synthesized by the reduce operator by aggregating information from multiple sources", + "source_ids": [ + 111 + ] + }, + { + "entity_name": "retrieved information segments", + "entity_type": "DATASET_OR_CORPUS", + "description": "retrieved information segments are the specific data parts that the map operator analyzes", + "source_ids": [ + 111 + ] + }, + { + "entity_name": "multiple sources", + "entity_type": "DATASET_OR_CORPUS", + "description": "multiple sources refer to the various origins of information such as partial answers or retrieved evidence that the reduce operator aggregates", + "source_ids": [ + 111 + ] + }, + { + "entity_name": "partial answers", + "entity_type": "PRODUCT", + "description": "partial answers are one of the types of information collected from multiple sources by the reduce operator", + "source_ids": [ + 111 + ] + }, + { + "entity_name": "retrieved evidence", + "entity_type": "DATASET_OR_CORPUS", + "description": "retrieved evidence is one of the types of information collected from multiple sources by the reduce operator", + "source_ids": [ + 111 + ] + } + ], + "relations": [ + { + "src_entity_name": "synthesizer", + "tgt_entity_name": "map", + "relation_name": "", + "weight": 8.0, + "description": "map is a specific type of operator within the broader category of synthesizer operators responsible for content generation", + "source_ids": [ + 111 + ] + }, + { + "src_entity_name": "synthesizer", + "tgt_entity_name": "reduce", + "relation_name": "", + "weight": 8.0, + "description": "reduce is a specific type of operator within the broader category of synthesizer operators responsible for content generation", + "source_ids": [ + 111 + ] + }, + { + "src_entity_name": "map", + "tgt_entity_name": "reduce", + "relation_name": "", + "weight": 9.0, + "description": "map and reduce are sequential or related steps in the process of generating a final coherent answer with map generating partial responses and reduce aggregating them", + "source_ids": [ + 111 + ] + }, + { + "src_entity_name": "synthesizer", + "tgt_entity_name": "content generation", + "relation_name": "", + "weight": 10.0, + "description": "synthesizer operators are responsible for the task of content generation", + "source_ids": [ + 111 + ] + }, + { + "src_entity_name": "map", + "tgt_entity_name": "analysis", + "relation_name": "", + "weight": 10.0, + "description": "the map operator performs the task of analysis on retrieved information segments", + "source_ids": [ + 111 + ] + }, + { + "src_entity_name": "map", + "tgt_entity_name": "partial responses", + "relation_name": "", + "weight": 10.0, + "description": "map generates partial responses as its output", + "source_ids": [ + 111 + ] + }, + { + "src_entity_name": "map", + "tgt_entity_name": "retrieved information segments", + "relation_name": "", + "weight": 10.0, + "description": "map performs analysis specifically on retrieved information segments", + "source_ids": [ + 111 + ] + }, + { + "src_entity_name": "reduce", + "tgt_entity_name": "final coherent answer", + "relation_name": "", + "weight": 10.0, + "description": "reduce synthesizes a final coherent answer as its output", + "source_ids": [ + 111 + ] + }, + { + "src_entity_name": "reduce", + "tgt_entity_name": "multiple sources", + "relation_name": "", + "weight": 10.0, + "description": "reduce aggregates information from multiple sources to create its output", + "source_ids": [ + 111 + ] + }, + { + "src_entity_name": "reduce", + "tgt_entity_name": "partial answers", + "relation_name": "", + "weight": 9.0, + "description": "reduce aggregates partial answers as part of its synthesis process", + "source_ids": [ + 111 + ] + }, + { + "src_entity_name": "reduce", + "tgt_entity_name": "retrieved evidence", + "relation_name": "", + "weight": 9.0, + "description": "reduce aggregates retrieved evidence as part of its synthesis process", + "source_ids": [ + 111 + ] + }, + { + "src_entity_name": "partial responses", + "tgt_entity_name": "final coherent answer", + "relation_name": "", + "weight": 9.0, + "description": "partial responses generated by map are aggregated by reduce to form the final coherent answer", + "source_ids": [ + 111 + ] + } + ], + "node_idx": 111 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_112.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_112.json new file mode 100644 index 0000000..4173e4d --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_112.json @@ -0,0 +1,225 @@ +{ + "entities": [ + { + "entity_name": "operator plan", + "entity_type": "TASK_OR_PROBLEM", + "description": "operator plan is the final task of an agent to generate an executable plan after classifying a query", + "source_ids": [ + 112 + ] + }, + { + "entity_name": "agent", + "entity_type": "PERSON", + "description": "the agent is an entity that classifies queries and generates executable plans based on the classification", + "source_ids": [ + 112 + ] + }, + { + "entity_name": "query", + "entity_type": "TASK_OR_PROBLEM", + "description": "the query is the input that the agent classifies into a category to generate a plan", + "source_ids": [ + 112 + ] + }, + { + "entity_name": "category", + "entity_type": "TASK_OR_PROBLEM", + "description": "the category is the classification result of the query used by the agent", + "source_ids": [ + 112 + ] + }, + { + "entity_name": "library", + "entity_type": "ORGANIZATION", + "description": "the library is a collection of operators from which the agent selects a sequence", + "source_ids": [ + 112 + ] + }, + { + "entity_name": "operators", + "entity_type": "TASK_OR_PROBLEM", + "description": "operators are the specific sequence elements selected from the library to form the plan", + "source_ids": [ + 112 + ] + }, + { + "entity_name": "parameters", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "parameters are dynamically instantiated based on the query to configure the operators", + "source_ids": [ + 112 + ] + }, + { + "entity_name": "1", + "entity_type": "TASK_OR_PROBLEM", + "description": "1 represents the specific sequence of operators selected for the plan", + "source_ids": [ + 112 + ] + }, + { + "entity_name": "agent plan", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "agent plan is the specific formulation or function used to generate the plan from the query category and library", + "source_ids": [ + 112 + ] + }, + { + "entity_name": "equation 8", + "entity_type": "EQUATION_OR_FORMULA", + "description": "equation 8 is the mathematical formulation agent plan describing the plan generation process", + "source_ids": [ + 112 + ] + }, + { + "entity_name": "", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 112 + ] + } + ], + "relations": [ + { + "src_entity_name": "agent", + "tgt_entity_name": "operator plan", + "relation_name": "", + "weight": 10.0, + "description": "the agent s final task is to generate the operator plan", + "source_ids": [ + 112 + ] + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "query", + "relation_name": "", + "weight": 9.0, + "description": "the agent classifies the query into a category", + "source_ids": [ + 112 + ] + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "category", + "relation_name": "", + "weight": 9.0, + "description": "the agent uses the category derived from the query to generate the plan", + "source_ids": [ + 112 + ] + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "library", + "relation_name": "", + "weight": 8.0, + "description": "the agent selects operators from the library to form the plan", + "source_ids": [ + 112 + ] + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "operators", + "relation_name": "", + "weight": 9.0, + "description": "the agent selects a specific sequence of operators to create the plan", + "source_ids": [ + 112 + ] + }, + { + "src_entity_name": "query", + "tgt_entity_name": "parameters", + "relation_name": "", + "weight": 8.0, + "description": "parameters are dynamically instantiated based on the query", + "source_ids": [ + 112 + ] + }, + { + "src_entity_name": "operators", + "tgt_entity_name": "parameters", + "relation_name": "", + "weight": 8.0, + "description": "operators are configured with parameters dynamically instantiated based on the query", + "source_ids": [ + 112 + ] + }, + { + "src_entity_name": "", + "tgt_entity_name": "", + "relation_name": "", + "weight": 10.0, + "description": "the query is classified into the category", + "source_ids": [ + 112 + ] + }, + { + "src_entity_name": "", + "tgt_entity_name": "1", + "relation_name": "", + "weight": 10.0, + "description": "the sequence 1 is selected from the library", + "source_ids": [ + 112 + ] + }, + { + "src_entity_name": "agent plan", + "tgt_entity_name": "", + "relation_name": "", + "weight": 10.0, + "description": "the agent plan method defines the generation of the plan", + "source_ids": [ + 112 + ] + }, + { + "src_entity_name": "equation 8", + "tgt_entity_name": "", + "relation_name": "", + "weight": 10.0, + "description": "equation 8 defines the variable", + "source_ids": [ + 112 + ] + }, + { + "src_entity_name": "equation 8", + "tgt_entity_name": "agent plan", + "relation_name": "", + "weight": 10.0, + "description": "equation 8 utilizes the agent plan function", + "source_ids": [ + 112 + ] + }, + { + "src_entity_name": "parameters", + "tgt_entity_name": "operators", + "relation_name": "", + "weight": 8.0, + "description": "parameters are dynamically instantiated for the operators", + "source_ids": [ + 112 + ] + } + ], + "node_idx": 112 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_113.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_113.json new file mode 100644 index 0000000..b177c3c --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_113.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "formula (8)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the variable P as a function of Agent Plan with inputs q, c, and O. LaTeX: 𝑃 = Agent Plan ( 𝑞, 𝑐, O) (8)", + "source_ids": [ + 113 + ] + } + ], + "relations": [], + "node_idx": 113 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_114.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_114.json new file mode 100644 index 0000000..51854d7 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_114.json @@ -0,0 +1,51 @@ +{ + "entities": [ + { + "entity_name": "the plan", + "entity_type": "TASK_OR_PROBLEM", + "description": "the plan is a structured workflow tailored to each category", + "source_ids": [ + 114 + ] + }, + { + "entity_name": "workflow", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "the workflow is a structured process followed by the plan", + "source_ids": [ + 114 + ] + }, + { + "entity_name": "category", + "entity_type": "CONCEPT", + "description": "category refers to the classifications to which the plan s workflow is tailored", + "source_ids": [ + 114 + ] + } + ], + "relations": [ + { + "src_entity_name": "the plan", + "tgt_entity_name": "workflow", + "relation_name": "", + "weight": 9.0, + "description": "the plan follows a structured workflow", + "source_ids": [ + 114 + ] + }, + { + "src_entity_name": "the plan", + "tgt_entity_name": "category", + "relation_name": "", + "weight": 8.0, + "description": "the plan s workflow is tailored to each category", + "source_ids": [ + 114 + ] + } + ], + "node_idx": 114 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_115.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_115.json new file mode 100644 index 0000000..006f7f1 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_115.json @@ -0,0 +1,171 @@ +{ + "entities": [ + { + "entity_name": "single hop", + "entity_type": "TASK_OR_PROBLEM", + "description": "single hop is a task where an agent first attempts to extract an entity", + "source_ids": [ + 115 + ] + }, + { + "entity_name": "agent", + "entity_type": "PERSON", + "description": "the agent is an entity that attempts to extract an entity and executes selection strategies", + "source_ids": [ + 115 + ] + }, + { + "entity_name": "scent based", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "scent based is a selection strategy used by the agent if entity extraction is successful", + "source_ids": [ + 115 + ] + }, + { + "entity_name": "section based", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "section based is a fallback strategy used by the agent if entity extraction fails", + "source_ids": [ + 115 + ] + }, + { + "entity_name": "standard reasoning", + "entity_type": "TASK_OR_PROBLEM", + "description": "standard reasoning is a process that both selection paths proceed to", + "source_ids": [ + 115 + ] + }, + { + "entity_name": "generation", + "entity_type": "TASK_OR_PROBLEM", + "description": "generation is a process that both selection paths proceed to", + "source_ids": [ + 115 + ] + }, + { + "entity_name": "p std", + "entity_type": "EQUATION_OR_FORMULA", + "description": "p std denotes the standard reasoning and generation process", + "source_ids": [ + 115 + ] + }, + { + "entity_name": "entity", + "entity_type": "TASK_OR_PROBLEM", + "description": "entity is the object that the agent attempts to extract in the single hop process", + "source_ids": [ + 115 + ] + } + ], + "relations": [ + { + "src_entity_name": "agent", + "tgt_entity_name": "single hop", + "relation_name": "", + "weight": 9.0, + "description": "the agent performs the single hop task by attempting to extract an entity", + "source_ids": [ + 115 + ] + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "scent based", + "relation_name": "", + "weight": 8.0, + "description": "the agent executes the scent based selection strategy if entity extraction is successful", + "source_ids": [ + 115 + ] + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "section based", + "relation_name": "", + "weight": 8.0, + "description": "the agent falls back to the section based strategy if entity extraction fails", + "source_ids": [ + 115 + ] + }, + { + "src_entity_name": "scent based", + "tgt_entity_name": "standard reasoning", + "relation_name": "", + "weight": 7.0, + "description": "the scent based path proceeds to standard reasoning and generation", + "source_ids": [ + 115 + ] + }, + { + "src_entity_name": "section based", + "tgt_entity_name": "standard reasoning", + "relation_name": "", + "weight": 7.0, + "description": "the section based path proceeds to standard reasoning and generation", + "source_ids": [ + 115 + ] + }, + { + "src_entity_name": "standard reasoning", + "tgt_entity_name": "generation", + "relation_name": "", + "weight": 8.0, + "description": "standard reasoning and generation are linked processes denoted as p std", + "source_ids": [ + 115 + ] + }, + { + "src_entity_name": "scent based", + "tgt_entity_name": "generation", + "relation_name": "", + "weight": 7.0, + "description": "the scent based path proceeds to generation as part of p std", + "source_ids": [ + 115 + ] + }, + { + "src_entity_name": "section based", + "tgt_entity_name": "generation", + "relation_name": "", + "weight": 7.0, + "description": "the section based path proceeds to generation as part of p std", + "source_ids": [ + 115 + ] + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "entity", + "relation_name": "", + "weight": 9.0, + "description": "the agent attempts to extract the entity as the first step of the single hop process", + "source_ids": [ + 115 + ] + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "entity", + "relation_name": "", + "weight": 8.0, + "description": "the single hop task involves the extraction of an entity", + "source_ids": [ + 115 + ] + } + ], + "node_idx": 115 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_116.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_116.json new file mode 100644 index 0000000..26759db --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_116.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "formula (9)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the variable P_s based on extraction success or failure conditions. LaTeX: 𝑃 s = ( Extract success - - - - -→ Select_by_Entity → 𝑃 std Extract fail - -→ Select_by_Section → 𝑃 std (9)", + "source_ids": [ + 116 + ] + } + ], + "relations": [], + "node_idx": 116 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_117.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_117.json new file mode 100644 index 0000000..10ea4da --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_117.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "formula (10)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the standard probability P as a process involving graph and text inputs leading to a skyline reduction. LaTeX: 𝑃 std = ( Graph ∥ Text ) → Skyline → Reduce (10)", + "source_ids": [ + 117 + ] + } + ], + "relations": [], + "node_idx": 117 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_118.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_118.json new file mode 100644 index 0000000..8c9e74f --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_118.json @@ -0,0 +1,69 @@ +{ + "entities": [ + { + "entity_name": "single hop workflow", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "single hop workflow is a method denoted as ps used to solve sub problems", + "source_ids": [ + 118 + ] + }, + { + "entity_name": "ps", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "ps is the specific notation or identifier for the single hop workflow applied to sub problems", + "source_ids": [ + 118 + ] + }, + { + "entity_name": "agent", + "entity_type": "PERSON", + "description": "the agent is the entity performing the decomposition of the problem and the synthesis of results", + "source_ids": [ + 118 + ] + }, + { + "entity_name": "complex", + "entity_type": "TASK_OR_PROBLEM", + "description": "complex refers to the problem being decomposed by the agent", + "source_ids": [ + 118 + ] + } + ], + "relations": [ + { + "src_entity_name": "single hop workflow", + "tgt_entity_name": "ps", + "relation_name": "", + "weight": 10.0, + "description": "the single hop workflow is identified by the notation ps in the text", + "source_ids": [ + 118 + ] + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "complex", + "relation_name": "", + "weight": 9.0, + "description": "the agent decomposes the complex problem into sub problems", + "source_ids": [ + 118 + ] + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "single hop workflow", + "relation_name": "", + "weight": 10.0, + "description": "the agent applies the single hop workflow to each sub problem", + "source_ids": [ + 118 + ] + } + ], + "node_idx": 118 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_119.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_119.json new file mode 100644 index 0000000..dc7f650 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_119.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "formula (11)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation describing a decomposition process involving mapping and reduction. LaTeX: 𝑃 complex = Decompose → 𝑃 s → Map → Reduce (11)", + "source_ids": [ + 119 + ] + } + ], + "relations": [], + "node_idx": 119 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_12.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_12.json new file mode 100644 index 0000000..cb7ae98 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_12.json @@ -0,0 +1,79 @@ +{ + "entities": [ + { + "entity_name": "figure 1", + "entity_type": "IMAGE", + "description": "figure 1 is an image that presents a comparison of existing methods and bookrag for complex document qa", + "source_ids": [ + 12 + ] + }, + { + "entity_name": "bookrag", + "entity_type": "PRODUCT", + "description": "bookrag is a product or method being compared against existing methods for complex document qa", + "source_ids": [ + 12 + ] + }, + { + "entity_name": "existing methods", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "existing methods refers to current techniques used for complex document qa which are being compared to bookrag", + "source_ids": [ + 12 + ] + }, + { + "entity_name": "complex document qa", + "entity_type": "TASK_OR_PROBLEM", + "description": "complex document qa is the specific task or problem domain where the comparison between methods and bookrag is taking place", + "source_ids": [ + 12 + ] + } + ], + "relations": [ + { + "src_entity_name": "figure 1", + "tgt_entity_name": "bookrag", + "relation_name": "", + "weight": 9.0, + "description": "figure 1 displays a comparison involving bookrag", + "source_ids": [ + 12 + ] + }, + { + "src_entity_name": "figure 1", + "tgt_entity_name": "existing methods", + "relation_name": "", + "weight": 9.0, + "description": "figure 1 displays a comparison involving existing methods", + "source_ids": [ + 12 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "complex document qa", + "relation_name": "", + "weight": 8.0, + "description": "bookrag is a solution or method applied to the task of complex document qa", + "source_ids": [ + 12 + ] + }, + { + "src_entity_name": "existing methods", + "tgt_entity_name": "complex document qa", + "relation_name": "", + "weight": 8.0, + "description": "existing methods are techniques used for the task of complex document qa", + "source_ids": [ + 12 + ] + } + ], + "node_idx": 12 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_120.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_120.json new file mode 100644 index 0000000..5c95d0f --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_120.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "global aggregation", + "entity_type": "TASK_OR_PROBLEM", + "description": "global aggregation is a workflow involving a sequence of filters followed by synthesis", + "source_ids": [ + 120 + ] + } + ], + "relations": [], + "node_idx": 120 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_121.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_121.json new file mode 100644 index 0000000..b7dbfeb --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_121.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "formula (12)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the global probability P as a composition of filtering and mapping operations. LaTeX: 𝑃 global = GLYPH<214> ( Filter_Modal | Filter_Range ) → Map → Reduce (12)", + "source_ids": [ + 121 + ] + } + ], + "relations": [], + "node_idx": 121 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_122.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_122.json new file mode 100644 index 0000000..f97330e --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_122.json @@ -0,0 +1,99 @@ +{ + "entities": [ + { + "entity_name": "modal filter", + "entity_type": "TECHNOLOGY", + "description": "modal filter is a type of filter applied at each step of the nested composition", + "source_ids": [ + 122 + ] + }, + { + "entity_name": "range filter", + "entity_type": "TECHNOLOGY", + "description": "range filter is a type of filter applied at each step of the nested composition", + "source_ids": [ + 122 + ] + }, + { + "entity_name": "nested composition", + "entity_type": "TASK_OR_PROBLEM", + "description": "nested composition refers to the process of applying filters at each step", + "source_ids": [ + 122 + ] + }, + { + "entity_name": "", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 122 + ] + } + ], + "relations": [ + { + "src_entity_name": "", + "tgt_entity_name": "", + "relation_name": "", + "weight": 10.0, + "description": "the symbol denotes the nested composition of filters applying either a modal or range filter at each step", + "source_ids": [ + 122 + ] + }, + { + "src_entity_name": "", + "tgt_entity_name": "modal filter", + "relation_name": "", + "weight": 9.0, + "description": "the symbol denotes the application of a modal filter at each step", + "source_ids": [ + 122 + ] + }, + { + "src_entity_name": "", + "tgt_entity_name": "range filter", + "relation_name": "", + "weight": 9.0, + "description": "the symbol denotes the application of a range filter at each step", + "source_ids": [ + 122 + ] + }, + { + "src_entity_name": "", + "tgt_entity_name": "nested composition", + "relation_name": "", + "weight": 10.0, + "description": "the symbol represents the nested composition of filters", + "source_ids": [ + 122 + ] + }, + { + "src_entity_name": "modal filter", + "tgt_entity_name": "nested composition", + "relation_name": "", + "weight": 8.0, + "description": "modal filters are applied as part of the nested composition process", + "source_ids": [ + 122 + ] + }, + { + "src_entity_name": "range filter", + "tgt_entity_name": "nested composition", + "relation_name": "", + "weight": 8.0, + "description": "range filters are applied as part of the nested composition process", + "source_ids": [ + 122 + ] + } + ], + "node_idx": 122 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_123.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_123.json new file mode 100644 index 0000000..035b8a3 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_123.json @@ -0,0 +1,69 @@ +{ + "entities": [ + { + "entity_name": "5.3 structured execution", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Agent-Based Retrieval', this section details the retrieval process within the BookRAG framework, specifically focusing on operations executed under the principles of In-Context Few-Shot Training (IFT) and generation.", + "source_ids": [ + 123 + ] + }, + { + "entity_name": "retrieval process", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Refers to the specific mechanism for retrieving information from the BookIndex as described in section 5.3.", + "source_ids": [ + 123 + ] + }, + { + "entity_name": "ift principles", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Refers to the In-Context Few-Shot Training principles that guide the execution logic detailed in section 5.3.", + "source_ids": [ + 123 + ] + }, + { + "entity_name": "generation", + "entity_type": "TASK_OR_PROBLEM", + "description": "Refers to the generative component integrated into the structured execution workflow mentioned in section 5.3.", + "source_ids": [ + 123 + ] + } + ], + "relations": [ + { + "src_entity_name": "retrieval process", + "tgt_entity_name": "5.3 structured execution", + "relation_name": "", + "weight": 10.0, + "description": "The concept of 'Retrieval Process' is a primary topic of section 5.3.", + "source_ids": [ + 123 + ] + }, + { + "src_entity_name": "ift principles", + "tgt_entity_name": "5.3 structured execution", + "relation_name": "", + "weight": 10.0, + "description": "The concept of 'IFT Principles' is a primary topic of section 5.3.", + "source_ids": [ + 123 + ] + }, + { + "src_entity_name": "generation", + "tgt_entity_name": "5.3 structured execution", + "relation_name": "", + "weight": 10.0, + "description": "The concept of 'Generation' is a primary topic of section 5.3.", + "source_ids": [ + 123 + ] + } + ], + "node_idx": 123 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_124.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_124.json new file mode 100644 index 0000000..3e99acf --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_124.json @@ -0,0 +1,391 @@ +{ + "entities": [ + { + "entity_name": "bookrag", + "entity_type": "SOFTWARE", + "description": "bookrag is a system that executes a generated workflow and embodies cognitive principles of information foraging theory", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "information foraging theory", + "entity_type": "SCIENTIFIC_THEORY", + "description": "information foraging theory ift is the cognitive principle embodied by bookrag s execution phase", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "selector", + "entity_type": "SOFTWARE", + "description": "selector is an operator in bookrag that navigates to information patches", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "reasoner", + "entity_type": "SOFTWARE", + "description": "reasoner is an operator in bookrag that performs sensemaking within information patches", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "synthesizer", + "entity_type": "SOFTWARE", + "description": "synthesizer is an operator in bookrag that generates the final answer based on processed evidence", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "workflow", + "entity_type": "TASK_OR_PROBLEM", + "description": "workflow is the generated sequence of operations executed by bookrag", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "p", + "entity_type": "TASK_OR_PROBLEM", + "description": "p represents the specific generated workflow executed by bookrag", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "abstract textual queries", + "entity_type": "TASK_OR_PROBLEM", + "description": "abstract textual queries are the input that bookrag translates into concrete operations", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "concrete operations", + "entity_type": "TASK_OR_PROBLEM", + "description": "concrete operations are the result of translating abstract textual queries within bookrag", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "information patches", + "entity_type": "TASK_OR_PROBLEM", + "description": "information patches are specific scopes within the document space that the selector navigates to", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "document space", + "entity_type": "TASK_OR_PROBLEM", + "description": "document space is the vast area of documents that is narrowed down by the selector", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "relevant scopes", + "entity_type": "TASK_OR_PROBLEM", + "description": "relevant scopes are the focused areas within the document space identified by the selector", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "sensemaking", + "entity_type": "TASK_OR_PROBLEM", + "description": "sensemaking is the process performed by the reasoner to analyze and refine information", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "processed evidence", + "entity_type": "TASK_OR_PROBLEM", + "description": "processed evidence is the refined information used by the synthesizer to generate the answer", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "answer", + "entity_type": "TASK_OR_PROBLEM", + "description": "answer is the final output generated by the synthesizer", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "cost of attention", + "entity_type": "TASK_OR_PROBLEM", + "description": "cost of attention is a metric minimized by bookrag s design to focus computational resources", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "computational resources", + "entity_type": "TASK_OR_PROBLEM", + "description": "computational resources are the assets focused by bookrag on high value data patches", + "source_ids": [ + 124 + ] + }, + { + "entity_name": "high value data patches", + "entity_type": "TASK_OR_PROBLEM", + "description": "high value data patches are the specific data areas where bookrag focuses its computational resources", + "source_ids": [ + 124 + ] + } + ], + "relations": [ + { + "src_entity_name": "bookrag", + "tgt_entity_name": "information foraging theory", + "relation_name": "", + "weight": 10.0, + "description": "bookrag embodies the cognitive principles of information foraging theory during its execution phase", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "selector", + "relation_name": "", + "weight": 9.0, + "description": "bookrag utilizes the selector operator to navigate to information patches", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "reasoner", + "relation_name": "", + "weight": 9.0, + "description": "bookrag utilizes the reasoner operator to perform sensemaking within information patches", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "synthesizer", + "relation_name": "", + "weight": 9.0, + "description": "bookrag utilizes the synthesizer operator to generate the final answer", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "selector", + "tgt_entity_name": "reasoner", + "relation_name": "", + "weight": 8.0, + "description": "the selector operator narrows the document space which is subsequently analyzed by the reasoner operator", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "reasoner", + "tgt_entity_name": "synthesizer", + "relation_name": "", + "weight": 8.0, + "description": "the reasoner operator refines information that is then used by the synthesizer to generate the answer", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "workflow", + "relation_name": "", + "weight": 10.0, + "description": "bookrag executes the generated workflow p", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "p", + "relation_name": "", + "weight": 10.0, + "description": "p is the specific workflow executed by bookrag", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "abstract textual queries", + "relation_name": "", + "weight": 9.0, + "description": "bookrag translates abstract textual queries into concrete operations", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "concrete operations", + "relation_name": "", + "weight": 9.0, + "description": "bookrag produces concrete operations from abstract queries", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "cost of attention", + "relation_name": "", + "weight": 9.0, + "description": "bookrag s design minimizes the cost of attention", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "computational resources", + "relation_name": "", + "weight": 9.0, + "description": "bookrag ensures computational resources are focused on high value data", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "high value data patches", + "relation_name": "", + "weight": 9.0, + "description": "bookrag focuses computational resources solely on high value data patches", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "selector", + "tgt_entity_name": "information patches", + "relation_name": "", + "weight": 10.0, + "description": "the selector operator navigates to information patches", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "selector", + "tgt_entity_name": "document space", + "relation_name": "", + "weight": 9.0, + "description": "the selector narrows the vast document space down to relevant scopes", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "selector", + "tgt_entity_name": "relevant scopes", + "relation_name": "", + "weight": 9.0, + "description": "the selector narrows the document space down to relevant scopes", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "reasoner", + "tgt_entity_name": "information patches", + "relation_name": "", + "weight": 9.0, + "description": "the reasoner performs sensemaking within the information patches identified by the selector", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "reasoner", + "tgt_entity_name": "processed evidence", + "relation_name": "", + "weight": 9.0, + "description": "the reasoner analyzes and refines information to create processed evidence", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "synthesizer", + "tgt_entity_name": "processed evidence", + "relation_name": "", + "weight": 10.0, + "description": "the synthesizer generates the answer based on the processed evidence", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "synthesizer", + "tgt_entity_name": "answer", + "relation_name": "", + "weight": 10.0, + "description": "the synthesizer generates the answer", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "abstract textual queries", + "tgt_entity_name": "concrete operations", + "relation_name": "", + "weight": 8.0, + "description": "abstract textual queries are translated into concrete operations", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "information patches", + "tgt_entity_name": "sensemaking", + "relation_name": "", + "weight": 8.0, + "description": "sensemaking is performed within the information patches", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "processed evidence", + "tgt_entity_name": "answer", + "relation_name": "", + "weight": 8.0, + "description": "processed evidence is used to generate the answer", + "source_ids": [ + 124 + ] + }, + { + "src_entity_name": "computational resources", + "tgt_entity_name": "high value data patches", + "relation_name": "", + "weight": 8.0, + "description": "computational resources are focused on high value data patches", + "source_ids": [ + 124 + ] + } + ], + "node_idx": 124 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_125.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_125.json new file mode 100644 index 0000000..1c43a46 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_125.json @@ -0,0 +1,195 @@ +{ + "entities": [ + { + "entity_name": "scent filter based retrieval", + "entity_type": "TASK_OR_PROBLEM", + "description": "scent filter based retrieval is a process described as the execution that begins by narrowing the scope", + "source_ids": [ + 125 + ] + }, + { + "entity_name": "ift", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "ift is a method or technique with which the execution aligns", + "source_ids": [ + 125 + ] + }, + { + "entity_name": "selector operators", + "entity_type": "SOFTWARE", + "description": "selector operators are components that identify relevant patches by following information scents or applying explicit filter constraints", + "source_ids": [ + 125 + ] + }, + { + "entity_name": "information scents", + "entity_type": "CONCEPT", + "description": "information scents are described as cues such as key entities in a question followed by selector operators", + "source_ids": [ + 125 + ] + }, + { + "entity_name": "node set n", + "entity_type": "DATASET_OR_CORPUS", + "description": "node set n represents the full set of nodes that is reduced by the process", + "source_ids": [ + 125 + ] + }, + { + "entity_name": "focused node subset ns", + "entity_type": "DATASET_OR_CORPUS", + "description": "focused node subset ns is the result of the reduction process applied to the full node set n", + "source_ids": [ + 125 + ] + }, + { + "entity_name": "params sel", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "params sel are parameters used in the selector function to define the focused node subset", + "source_ids": [ + 125 + ] + }, + { + "entity_name": "patches", + "entity_type": "PRODUCT", + "description": "patches are relevant units identified by selector operators within the retrieval process", + "source_ids": [ + 125 + ] + }, + { + "entity_name": "question", + "entity_type": "TASK_OR_PROBLEM", + "description": "a question is mentioned as a source of key entities used to identify information scents", + "source_ids": [ + 125 + ] + }, + { + "entity_name": "explicit filter constraints", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "explicit filter constraints are rules applied by selector operators to identify relevant patches", + "source_ids": [ + 125 + ] + }, + { + "entity_name": "equation 13", + "entity_type": "EQUATION_OR_FORMULA", + "description": "equation 13 defines the mathematical relationship for the selector function reducing the node set", + "source_ids": [ + 125 + ] + } + ], + "relations": [ + { + "src_entity_name": "scent filter based retrieval", + "tgt_entity_name": "ift", + "relation_name": "", + "weight": 9.0, + "description": "the scent filter based retrieval process aligns with ift", + "source_ids": [ + 125 + ] + }, + { + "src_entity_name": "scent filter based retrieval", + "tgt_entity_name": "selector operators", + "relation_name": "", + "weight": 10.0, + "description": "selector operators are the mechanism used within the scent filter based retrieval process to identify relevant patches", + "source_ids": [ + 125 + ] + }, + { + "src_entity_name": "selector operators", + "tgt_entity_name": "information scents", + "relation_name": "", + "weight": 9.0, + "description": "selector operators identify relevant patches by following information scents", + "source_ids": [ + 125 + ] + }, + { + "src_entity_name": "scent filter based retrieval", + "tgt_entity_name": "node set n", + "relation_name": "", + "weight": 10.0, + "description": "the process reduces the full node set n to a focused subset", + "source_ids": [ + 125 + ] + }, + { + "src_entity_name": "scent filter based retrieval", + "tgt_entity_name": "focused node subset ns", + "relation_name": "", + "weight": 10.0, + "description": "the process results in the creation of the focused node subset ns", + "source_ids": [ + 125 + ] + }, + { + "src_entity_name": "selector operators", + "tgt_entity_name": "params sel", + "relation_name": "", + "weight": 8.0, + "description": "selector operators utilize params sel in their function to reduce the node set", + "source_ids": [ + 125 + ] + }, + { + "src_entity_name": "selector operators", + "tgt_entity_name": "patches", + "relation_name": "", + "weight": 9.0, + "description": "selector operators identify relevant patches", + "source_ids": [ + 125 + ] + }, + { + "src_entity_name": "information scents", + "tgt_entity_name": "question", + "relation_name": "", + "weight": 8.0, + "description": "information scents include key entities found in a question", + "source_ids": [ + 125 + ] + }, + { + "src_entity_name": "selector operators", + "tgt_entity_name": "explicit filter constraints", + "relation_name": "", + "weight": 9.0, + "description": "selector operators apply explicit filter constraints to identify patches", + "source_ids": [ + 125 + ] + }, + { + "src_entity_name": "scent filter based retrieval", + "tgt_entity_name": "equation 13", + "relation_name": "", + "weight": 10.0, + "description": "equation 13 describes the execution of the scent filter based retrieval process", + "source_ids": [ + 125 + ] + } + ], + "node_idx": 125 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_126.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_126.json new file mode 100644 index 0000000..351e7e3 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_126.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "formula (13)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the variable Ns as a selector function applied to N and parameters. 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"description": "Table 'Table: cref='#/texts/136'...' contains data about 'cref'.", + "source_ids": [ + 132 + ] + } + ], + "node_idx": 132 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_133.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_133.json new file mode 100644 index 0000000..e617d83 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_133.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "8", + "entity_type": "MEASUREMENT", + "description": "8 is a numerical value mentioned in the text likely representing a count or identifier", + "source_ids": [ + 133 + ] + } + ], + "relations": [], + "node_idx": 133 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_134.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_134.json new file mode 100644 index 0000000..33dda76 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_134.json @@ -0,0 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a/e2e_test_output/kg_extractor_res/kg_extractor_res_135.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_135.json new file mode 100644 index 0000000..5967cd2 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_135.json @@ -0,0 +1,283 @@ +{ + "entities": [ + { + "entity_name": "figure 4 b", + "entity_type": "IMAGE", + "description": "figure 4 b is an image presenting an execution trace for a single hop query", + "source_ids": [ + 135 + ] + }, + { + "entity_name": "single hop", + "entity_type": "TASK_OR_PROBLEM", + "description": "single hop refers to a specific type of query being illustrated in the text", + "source_ids": [ + 135 + ] + }, + { + "entity_name": "car", + "entity_type": "PRODUCT", + "description": "car is a key entity identified in the query what is the type of car in the ranking prompt example", + "source_ids": [ + 135 + ] + }, + { + "entity_name": "ranking prompt example", + "entity_type": "TASK_OR_PROBLEM", + "description": "ranking prompt 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"relation_name": "", + "weight": 7.0, + "description": "the skyline filtering technique refines the nodes related to car", + "source_ids": [ + 135 + ] + }, + { + "src_entity_name": "reduce", + "tgt_entity_name": "car", + "relation_name": "", + "weight": 7.0, + "description": "the reduce method synthesizes the answer regarding the car", + "source_ids": [ + 135 + ] + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "planning phase", + "relation_name": "", + "weight": 9.0, + "description": "the agent operates during the planning phase to classify queries and generate workflows", + "source_ids": [ + 135 + ] + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "extract", + "relation_name": "", + "weight": 9.0, + "description": "the agent uses the extract method to identify key entities", + "source_ids": [ + 135 + ] + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "select by entity", + "relation_name": "", + "weight": 9.0, + "description": "the agent uses the 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b/e2e_test_output/kg_extractor_res/kg_extractor_res_138.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "6.1 setup", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Experiments' within the BookRAG paper, this section details the experimental configuration, including baseline methods, evaluation metrics (efficiency and accuracy), and the document QA tasks used to assess the proposed approach.", + "source_ids": [ + 138 + ] + } + ], + "relations": [], + "node_idx": 138 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_139.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_139.json new file mode 100644 index 0000000..1ac8190 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_139.json @@ -0,0 +1,121 @@ +{ + "entities": [ + { + "entity_name": "table 4", + "entity_type": "TABLE", + "description": "table 4 is a table listing datasets used in experiments", + "source_ids": [ + 139 + ] + }, 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a/e2e_test_output/kg_extractor_res/kg_extractor_res_164.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_164.json new file mode 100644 index 0000000..bb8f86e --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_164.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "ablation study", + "entity_type": "TASK_OR_PROBLEM", + "description": "ablation study is a task designed to evaluate the contribution of core components in bookrag", + "source_ids": [ + 164 + ] + }, + { + "entity_name": "bookrag", + "entity_type": "PRODUCT", + "description": "bookrag is a product or system whose core components are being evaluated through variants", + "source_ids": [ + 164 + ] + } + ], + "relations": [ + { + "src_entity_name": "ablation study", + "tgt_entity_name": "bookrag", + "relation_name": "", + "weight": 10.0, + "description": "the ablation study is conducted to evaluate the core components of bookrag", + "source_ids": [ + 164 + ] + } + ], + "node_idx": 164 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_165.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_165.json new file mode 100644 index 0000000..f714ac1 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_165.json @@ -0,0 +1,79 @@ +{ + "entities": [ + { + "entity_name": "gradient er", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "gradient er is a gradient based entity resolution method mentioned in the text", + "source_ids": [ + 165 + ] + }, + { + "entity_name": "basic er", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "basic er is a method used to merge same name entities replacing gradient er in the described scenario", + "source_ids": [ + 165 + ] + }, + { + "entity_name": "w o gradient er", + "entity_type": "TASK_OR_PROBLEM", + "description": "w o gradient er is a scenario or condition described where the gradient based entity resolution is replaced", + "source_ids": [ + 165 + ] + }, + { + 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a/e2e_test_output/kg_extractor_res/kg_extractor_res_168.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_168.json new file mode 100644 index 0000000..f0b7b9f --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_168.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "graph reasoning", + "entity_type": "TECHNOLOGY", + "description": "graph reasoning is an operator that when removed disables the skyline ranker", + "source_ids": [ + 168 + ] + }, + { + "entity_name": "skyline ranker", + "entity_type": "SOFTWARE", + "description": "skyline ranker is a component that is disabled when the graph reasoning operator is removed resulting in single dimensional scoring", + "source_ids": [ + 168 + ] + } + ], + "relations": [ + { + "src_entity_name": "graph reasoning", + "tgt_entity_name": "skyline ranker", + "relation_name": "", + "weight": 10.0, + "description": "the graph reasoning operator enables the skyline ranker removing it disables the skyline ranker", + 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b/e2e_test_output/kg_extractor_res/kg_extractor_res_171.json new file mode 100644 index 0000000..86d3a0b --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_171.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "table: cref='#/texts/220'...", + "entity_type": "TABLE", + "description": "A data table described as: cref='#/texts/220'", + "source_ids": [ + 171 + ] + }, + { + "entity_name": "cref", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "A reference identifier or cross-reference key found in the description text, pointing to a specific text location ('#/texts/220').", + "source_ids": [ + 171 + ] + } + ], + "relations": [ + { + "src_entity_name": "table: cref='#/texts/220'...", + "tgt_entity_name": "cref", + "relation_name": "", + "weight": 9.0, + "description": "Table 'Table: cref='#/texts/220'...' contains data about 'cref'.", + "source_ids": [ + 171 + ] + } + ], + "node_idx": 171 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_172.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_172.json new file mode 100644 index 0000000..6a47436 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_172.json @@ -0,0 +1,293 @@ +{ + "entities": [ + { + "entity_name": "kg", + "entity_type": "DATASET_OR_CORPUS", + "description": "kg refers to a knowledge graph used to support effective reasoning in the bookrag system", + "source_ids": [ + 172 + ] + }, + { + "entity_name": "agent based planning", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "agent based planning is a mechanism assessed for its necessity in the system s performance", + "source_ids": [ + 172 + ] + }, + { + "entity_name": "ift inspired selection mechanism", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "ift inspired selection mechanism is a strategy evaluated for its role in the system s efficiency", + "source_ids": [ + 172 + ] + }, + { + "entity_name": "multi 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"entity_name": "paragraph", + "entity_type": "SECTION_TITLE", + "description": "paragraph is an example of a contiguous location within a document", + "source_ids": [ + 243 + ] + }, + { + "entity_name": "table", + "entity_type": "SECTION_TITLE", + "description": "table is an example of a contiguous location within a document", + "source_ids": [ + 243 + ] + }, + { + "entity_name": "figure", + "entity_type": "SECTION_TITLE", + "description": "figure is an example of a contiguous location within a document", + "source_ids": [ + 243 + ] + }, + { + "entity_name": "single", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 243 + ] + }, + { + "entity_name": "contiguous location", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 243 + ] + } + ], + "relations": [ + { + "src_entity_name": "single hop", + "tgt_entity_name": "single", + "relation_name": "", + "weight": 9.0, + "description": "the single hop task requires retrieving information from a single location", + "source_ids": [ + 243 + ] + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "contiguous location", + "relation_name": "", + "weight": 8.0, + "description": "the single hop task involves information found in a contiguous location", + "source_ids": [ + 243 + ] + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 7.0, + "description": "the single hop task is defined within the context of a document", + "source_ids": [ + 243 + ] + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "question", + "relation_name": "", + "weight": 10.0, + "description": "the single hop task is defined by the ability to answer a question", + "source_ids": [ + 243 + ] + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "information", + "relation_name": "", + "weight": 10.0, + "description": "the single hop task involves retrieving information", + "source_ids": [ + 243 + ] + }, + { + "src_entity_name": "information", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 9.0, + "description": "information is retrieved from the document", + "source_ids": [ + 243 + ] + }, + { + "src_entity_name": "paragraph", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 8.0, + "description": "a paragraph is a part of a document", + "source_ids": [ + 243 + ] + }, + { + "src_entity_name": "table", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 8.0, + "description": "a table is a part of a document", + "source_ids": [ + 243 + ] + }, + { + "src_entity_name": "figure", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 8.0, + "description": "a figure is a part of a document", + "source_ids": [ + 243 + ] + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "paragraph", + "relation_name": "", + "weight": 9.0, + "description": "a single hop question can be answered by retrieving information from a paragraph", + "source_ids": [ + 243 + ] + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "table", + "relation_name": "", + "weight": 9.0, + "description": "a single hop question can be answered by retrieving information from a table", + "source_ids": [ + 243 + ] + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "figure", + "relation_name": "", + "weight": 9.0, + "description": "a single hop question can be answered by retrieving information from a figure", + "source_ids": [ + 243 + ] + } + ], + "node_idx": 243 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_244.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_244.json new file mode 100644 index 0000000..50ac9aa --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_244.json @@ -0,0 +1,5 @@ +{ + "entities": [], + "relations": [], + "node_idx": 244 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_245.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_245.json new file mode 100644 index 0000000..aa39832 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_245.json @@ -0,0 +1,25 @@ +{ + "entities": [ + { + "entity_name": "figure 2", + "entity_type": "IMAGE", + "description": "figure 2 is an image referenced in the text with its title being the subject of a question", + "source_ids": [ + 245 + ] + } + ], + "relations": [ + { + "src_entity_name": "figure 2", + "tgt_entity_name": "figure 2", + "relation_name": "", + "weight": 5.0, + "description": "the text asks for the title of figure 2 indicating a self referential query about the entity s attribute", + "source_ids": [ + 245 + ] + } + ], + "node_idx": 245 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_246.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_246.json new file mode 100644 index 0000000..4e38c25 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_246.json @@ -0,0 +1,79 @@ +{ + "entities": [ + { + "entity_name": "5", + "entity_type": "PERCENTAGE", + "description": "5 represents a specific portion of the latino population mentioned in the context of economic upward mobility", + "source_ids": [ + 246 + ] + }, + { + "entity_name": "latinos", + "entity_type": "NATIONALITY", + "description": "latinos are the demographic group whose views on economic upward mobility for their children are being queried", + "source_ids": [ + 246 + ] + }, + { + "entity_name": "economic upward mobility", + "entity_type": "TASK_OR_PROBLEM", + "description": "economic upward mobility is the specific issue regarding the children of latinos that is the subject of the inquiry", + "source_ids": [ + 246 + ] + }, + { + "entity_name": "children", + "entity_type": "PERSON", + "description": "children are the offspring of the latinos whose economic upward mobility is being discussed", + "source_ids": [ + 246 + ] + } + ], + "relations": [ + { + "src_entity_name": "5", + "tgt_entity_name": "latinos", + "relation_name": "", + "weight": 9.0, + "description": "the percentage 5 specifically refers to a subset of the latino population", + "source_ids": [ + 246 + ] + }, + { + "src_entity_name": "latinos", + "tgt_entity_name": "economic upward mobility", + "relation_name": "", + "weight": 10.0, + "description": "latinos are the group whose perspective on economic upward mobility for their children is being examined", + "source_ids": [ + 246 + ] + }, + { + "src_entity_name": "latinos", + "tgt_entity_name": "children", + "relation_name": "", + "weight": 10.0, + "description": "the children belong to the latino demographic group mentioned in the text", + "source_ids": [ + 246 + ] + }, + { + "src_entity_name": "economic upward mobility", + "tgt_entity_name": "children", + "relation_name": "", + "weight": 9.0, + "description": "economic upward mobility is the specific attribute or outcome being considered for the children", + "source_ids": [ + 246 + ] + } + ], + "node_idx": 246 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_247.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_247.json new file mode 100644 index 0000000..798e176 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_247.json @@ -0,0 +1,25 @@ +{ + "entities": [ + { + "entity_name": "multi hop", + "entity_type": "TASK_OR_PROBLEM", + "description": "multi hop is a task that requires decomposition into multiple simple sub questions", + "source_ids": [ + 247 + ] + } + ], + "relations": [ + { + "src_entity_name": "multi hop", + "tgt_entity_name": "multi hop", + "relation_name": "", + "weight": 10.0, + "description": "the entity multi hop is described as requiring decomposition into sub questions indicating its nature as a task", + "source_ids": [ + 247 + ] + } + ], + "node_idx": 247 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_248.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_248.json new file mode 100644 index 0000000..c01c8d2 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_248.json @@ -0,0 +1,5 @@ +{ + "entities": [], + "relations": [], + "node_idx": 248 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_249.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_249.json new file mode 100644 index 0000000..e6c976d --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_249.json @@ -0,0 +1,25 @@ +{ + "entities": [ + { + "entity_name": "personality vector", + "entity_type": "TASK_OR_PROBLEM", + "description": "the personality vector is a concept mentioned in a question regarding its color indicating it is a complex retrieval task", + "source_ids": [ + 249 + ] + } + ], + "relations": [ + { + "src_entity_name": "personality vector", + "tgt_entity_name": "personality vector", + "relation_name": "", + "weight": 5.0, + "description": "the entity is the subject of a question asking for its color implying a self referential query about its attributes", + "source_ids": [ + 249 + ] + } + ], + "node_idx": 249 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_25.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_25.json new file mode 100644 index 0000000..fea3f8b --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_25.json @@ -0,0 +1,145 @@ +{ + "entities": [ + { + "entity_name": "bookrag", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "bookrag is a novel method introduced in the text that constructs a document native bookindex", + "source_ids": [ + 25 + ] + }, + { + "entity_name": "bookindex", + "entity_type": "PRODUCT", + "description": "bookindex is a document native index constructed by the bookrag method", + "source_ids": [ + 25 + ] + }, + { + "entity_name": "hierarchical tree", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "a hierarchical tree of document layout blocks is integrated by bookrag to construct the bookindex", + "source_ids": [ + 25 + ] + }, + { + "entity_name": "kg", + "entity_type": "SOFTWARE", + "description": "kg refers to a knowledge graph storing fine grained entity relations used in the bookrag method", + "source_ids": [ + 25 + ] + }, + { + "entity_name": "document layout blocks", + "entity_type": "MATERIAL", + "description": "document layout blocks are the structural components of a document that are organized into a hierarchical tree", + "source_ids": [ + 25 + ] + }, + { + "entity_name": "entity relations", + "entity_type": "CONCEPT", + "description": "entity relations are the fine grained connections between entities stored within the knowledge graph", + "source_ids": [ + 25 + ] + } + ], + "relations": [ + { + "src_entity_name": "bookrag", + "tgt_entity_name": "bookindex", + "relation_name": "", + "weight": 10.0, + "description": "bookrag constructs the bookindex by integrating other components", + "source_ids": [ + 25 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "hierarchical tree", + "relation_name": "", + "weight": 9.0, + "description": "bookrag integrates a hierarchical tree of document layout blocks", + "source_ids": [ + 25 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "kg", + "relation_name": "", + "weight": 9.0, + "description": "bookrag integrates a kg storing fine grained entity relations", + "source_ids": [ + 25 + ] + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "hierarchical tree", + "relation_name": "", + "weight": 8.0, + "description": "the bookindex is constructed using a hierarchical tree of document layout blocks", + "source_ids": [ + 25 + ] + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "kg", + "relation_name": "", + "weight": 8.0, + "description": "the bookindex is constructed using a kg storing fine grained entity relations", + "source_ids": [ + 25 + ] + }, + { + "src_entity_name": "hierarchical tree", + "tgt_entity_name": "document layout blocks", + "relation_name": "", + "weight": 10.0, + "description": "the hierarchical tree is composed of document layout blocks", + "source_ids": [ + 25 + ] + }, + { + "src_entity_name": "kg", + "tgt_entity_name": "entity relations", + "relation_name": "", + "weight": 10.0, + "description": "the kg stores fine grained entity relations", + "source_ids": [ + 25 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "document layout blocks", + "relation_name": "", + "weight": 8.0, + "description": "bookrag integrates document layout blocks via a hierarchical tree", + "source_ids": [ + 25 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "entity relations", + "relation_name": "", + "weight": 8.0, + "description": "bookrag utilizes a kg that stores entity relations", + "source_ids": [ + 25 + ] + } + ], + "node_idx": 25 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_250.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_250.json new file mode 100644 index 0000000..f9c8a72 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_250.json @@ -0,0 +1,153 @@ +{ + "entities": [ + { + "entity_name": "global", + "entity_type": "TASK_OR_PROBLEM", + "description": "global refers to a type of question requiring an aggregation operation over a set of items identified by a structural filter", + "source_ids": [ + 250 + ] + }, + { + "entity_name": "counting", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "counting is an example of an aggregation operation mentioned in the text", + "source_ids": [ + 250 + ] + }, + { + "entity_name": "listing", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "listing is an example of an aggregation operation mentioned in the text", + "source_ids": [ + 250 + ] + }, + { + "entity_name": "summarizing", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "summarizing is an example of an aggregation operation mentioned in the text", + "source_ids": [ + 250 + ] + }, + { + "entity_name": "structural filter", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "structural filter is a clear filter used to identify items in the set for the global question", + "source_ids": [ + 250 + ] + }, + { + "entity_name": "aggregation operation", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 250 + ] + }, + { + "entity_name": "items", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 250 + ] + } + ], + "relations": [ + { + "src_entity_name": "global", + "tgt_entity_name": "aggregation operation", + "relation_name": "", + "weight": 8.0, + "description": "the global task requires an aggregation operation such as counting listing or summarizing", + "source_ids": [ + 250 + ] + }, + { + "src_entity_name": "global", + "tgt_entity_name": "counting", + "relation_name": "", + "weight": 9.0, + "description": "the global task includes counting as a possible aggregation operation", + "source_ids": [ + 250 + ] + }, + { + "src_entity_name": "global", + "tgt_entity_name": "listing", + "relation_name": "", + "weight": 9.0, + "description": "the global task includes listing as a possible aggregation operation", + "source_ids": [ + 250 + ] + }, + { + "src_entity_name": "global", + "tgt_entity_name": "summarizing", + "relation_name": "", + "weight": 9.0, + "description": "the global task includes summarizing as a possible aggregation operation", + "source_ids": [ + 250 + ] + }, + { + "src_entity_name": "global", + "tgt_entity_name": "structural filter", + "relation_name": "", + "weight": 9.0, + "description": "the global task identifies items using a clear structural filter", + "source_ids": [ + 250 + ] + }, + { + "src_entity_name": "counting", + "tgt_entity_name": "aggregation operation", + "relation_name": "", + "weight": 10.0, + "description": "counting is explicitly described as an aggregation operation", + "source_ids": [ + 250 + ] + }, + { + "src_entity_name": "listing", + "tgt_entity_name": "aggregation operation", + "relation_name": "", + "weight": 10.0, + "description": "listing is explicitly described as an aggregation operation", + "source_ids": [ + 250 + ] + }, + { + "src_entity_name": "summarizing", + "tgt_entity_name": "aggregation operation", + "relation_name": "", + "weight": 10.0, + "description": "summarizing is explicitly described as an aggregation operation", + "source_ids": [ + 250 + ] + }, + { + "src_entity_name": "structural filter", + "tgt_entity_name": "items", + "relation_name": "", + "weight": 8.0, + "description": "the structural filter is used to identify the set of items", + "source_ids": [ + 250 + ] + } + ], + "node_idx": 250 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_251.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_251.json new file mode 100644 index 0000000..b2c0c50 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_251.json @@ -0,0 +1,51 @@ +{ + "entities": [ + { + "entity_name": "example", + "entity_type": "TASK_OR_PROBLEM", + "description": "example is a task or problem asking how many tables are in the document", + "source_ids": [ + 251 + ] + }, + { + "entity_name": "global", + "entity_type": "CONCEPT", + "description": "global refers to a process that filters for all items of a specific type in this case table", + "source_ids": [ + 251 + ] + }, + { + "entity_name": "table", + "entity_type": "PRODUCT", + "description": "table is the specific item type being filtered for in the document", + "source_ids": [ + 251 + ] + } + ], + "relations": [ + { + "src_entity_name": "example", + "tgt_entity_name": "global", + "relation_name": "", + "weight": 9.0, + "description": "the example task is defined by the global process of filtering for tables", + "source_ids": [ + 251 + ] + }, + { + "src_entity_name": "global", + "tgt_entity_name": "table", + "relation_name": "", + "weight": 10.0, + "description": "the global process specifically targets and filters for items of type table", + "source_ids": [ + 251 + ] + } + ], + "node_idx": 251 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_252.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_252.json new file mode 100644 index 0000000..2eda525 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_252.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "user query", + "entity_type": "TASK_OR_PROBLEM", + "description": "user query is a task or problem mentioned in the text representing a request for information or action", + "source_ids": [ + 252 + ] + } + ], + "relations": [], + "node_idx": 252 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_253.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_253.json new file mode 100644 index 0000000..d09bbea --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_253.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "figure 10", + "entity_type": "IMAGE", + "description": "figure 10 is an image in the text that displays a prompt for query classification", + "source_ids": [ + 253 + ] + }, + { + "entity_name": "query classification", + "entity_type": "TASK_OR_PROBLEM", + "description": "query classification is the task or problem for which the prompt in figure 10 is designed", + "source_ids": [ + 253 + ] + } + ], + "relations": [ + { + "src_entity_name": "figure 10", + "tgt_entity_name": "query classification", + "relation_name": "", + "weight": 10.0, + "description": "figure 10 contains the prompt specifically used for query classification", + "source_ids": [ + 253 + ] + } + ], + "node_idx": 253 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_254.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_254.json new file mode 100644 index 0000000..7ae42d0 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_254.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "16", + "entity_type": "MEASUREMENT", + "description": "16 is a numerical value mentioned in the text potentially representing a count date or measurement", + "source_ids": [ + 254 + ] + } + ], + "relations": [], + "node_idx": 254 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_255.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_255.json new file mode 100644 index 0000000..8654076 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_255.json @@ -0,0 +1,233 @@ +{ + "entities": [ + { + "entity_name": "user a2gbifl43u1lkj", + "entity_type": "PERSON", + "description": "user a2gbifl43u1lkj is a specific user referenced in the example query regarding personality vectors and receptiviti scores", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "foreign born latinos", + "entity_type": "PERSON", + "description": "foreign born latinos are a demographic group mentioned in the example query regarding population surveys", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "latinos interviewed by cellphone", + "entity_type": "PERSON", + "description": "latinos interviewed by cellphone are a demographic group mentioned in the example query regarding population surveys", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "soft labeled personality embedding matrix", + "entity_type": "PRODUCT", + "description": "the soft labeled personality embedding matrix is a data structure containing personality vectors and their associated colors", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "receptiviti score", + "entity_type": "EVALUATION_METRIC", + "description": "the receptiviti score is a metric used to evaluate personality vectors in the context of the example query", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "population", + "entity_type": "MEASUREMENT", + "description": "population refers to the count of individuals in a specific demographic group within a survey", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "query decomposition expert", + "entity_type": "PROFESSION", + "description": "the query decomposition expert is the role assigned to the ai to break down complex questions into atomic sub questions", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "complex question", + "entity_type": "TASK_OR_PROBLEM", + "description": "a complex question is the input task that needs to be broken down into simple sub questions", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "simple atomic sub questions", + "entity_type": "TASK_OR_PROBLEM", + "description": "simple atomic sub questions are the output components of the decomposition process each being a direct information retrieval task", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "retrieval sub question", + "entity_type": "TASK_OR_PROBLEM", + "description": "a retrieval sub question is a specific type of sub question that requires looking up a specific fact number or value in the document", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "synthesis question", + "entity_type": "TASK_OR_PROBLEM", + "description": "a synthesis question is a specific type of sub question that requires comparing calculating or combining answers from previous retrieval questions", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "json object", + "entity_type": "FILE_TYPE", + "description": "the json object is the required format for the response containing a single key sub questions with a list of objects", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "sub questions", + "entity_type": "SECTION_TITLE", + "description": "the sub questions key is the container within the json object that holds the list of decomposed questions", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "question", + "entity_type": "SECTION_TITLE", + "description": "the question key within each sub question object holds the string of the actual question", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "type", + "entity_type": "SECTION_TITLE", + "description": "the type key within each sub question object specifies whether the question is retrieval or synthesis", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "user query", + "entity_type": "TASK_OR_PROBLEM", + "description": "the user query is the final input provided in the real data section which is the word query itself", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "example 1", + "entity_type": "EVENT", + "description": "example 1 is a demonstration of correct decomposition with independent lookups provided in the text", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "example 2", + "entity_type": "EVENT", + "description": "example 2 is a demonstration of decomposition with retrieval and synthesis steps provided in the text", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "personality vector", + "entity_type": "PRODUCT", + "description": "a personality vector is a data element within the soft labeled personality embedding matrix", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "color", + "entity_type": "COLOR", + "description": "color is an attribute mapped to personality vectors in the soft labeled personality embedding matrix", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "survey", + "entity_type": "EVENT", + "description": "the survey is the context in which population data for latinos is collected in example 2", + "source_ids": [ + 255 + ] + }, + { + "entity_name": "report", + "entity_type": "BOOK", + "description": "the report is the document referenced in example 2 that contains population data", + "source_ids": [ + 255 + ] + } + ], + "relations": [ + { + "src_entity_name": "user a2gbifl43u1lkj", + "tgt_entity_name": "soft labeled personality embedding matrix", + "relation_name": "", + "weight": 9.0, + "description": "user a2gbifl43u1lkj is the subject for whom personality vectors are analyzed within the soft labeled personality embedding matrix", + "source_ids": [ + 255 + ] + }, + { + "src_entity_name": "user a2gbifl43u1lkj", + "tgt_entity_name": "receptiviti score", + "relation_name": "", + "weight": 9.0, + "description": "receptiviti scores are calculated for the personality vectors associated with user a2gbifl43u1lkj", + "source_ids": [ + 255 + ] + }, + { + "src_entity_name": "foreign born latinos", + "tgt_entity_name": "population", + "relation_name": "", + "weight": 8.0, + "description": "the population of foreign born latinos is a specific value sought in the survey example", + "source_ids": [ + 255 + ] + }, + { + "src_entity_name": "latinos interviewed by cellphone", + "tgt_entity_name": "population", + "relation_name": "", + "weight": 8.0, + "description": "the population of latinos interviewed by cellphone is a specific value sought in the survey example", + "source_ids": [ + 255 + ] + }, + { + "src_entity_name": "soft labeled personality embedding matrix", + "tgt_entity_name": "receptiviti score", + "relation_name": "", + "weight": 7.0, + "description": "the soft labeled personality embedding matrix contains personality vectors that are evaluated using receptiviti scores", + "source_ids": [ + 255 + ] + } + ], + "node_idx": 255 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_256.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_256.json new file mode 100644 index 0000000..071d0a5 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_256.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "figure 11", + "entity_type": "IMAGE", + "description": "figure 11 is an image in the text that displays a prompt for query decomposition", + "source_ids": [ + 256 + ] + }, + { + "entity_name": "query decomposition", + "entity_type": "TASK_OR_PROBLEM", + "description": "query decomposition is the task or problem for which the prompt in figure 11 is designed", + "source_ids": [ + 256 + ] + } + ], + "relations": [ + { + "src_entity_name": "figure 11", + "tgt_entity_name": "query decomposition", + "relation_name": "", + "weight": 10.0, + "description": "figure 11 contains the prompt specifically for the task of query decomposition", + "source_ids": [ + 256 + ] + } + ], + "node_idx": 256 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_257.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_257.json new file mode 100644 index 0000000..e84ed91 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_257.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "17", + "entity_type": "NUMBER", + "description": "17 is a number mentioned in the text though its specific context or role is not defined", + "source_ids": [ + 257 + ] + } + ], + "relations": [], + "node_idx": 257 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_258.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_258.json new file mode 100644 index 0000000..86bcc44 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_258.json @@ -0,0 +1,521 @@ +{ + "entities": [ + { + "entity_name": "ai assistant", + "entity_type": "PERSON", + "description": "an ai assistant described as highly specialized with the function of analyzing a global query", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "global query", + "entity_type": "TASK_OR_PROBLEM", + "description": "a query that the ai assistant is designed to analyze to determine filtering steps and aggregation operations", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "json object", + "entity_type": "FILE_TYPE", + "description": "the specific output format required from the ai assistant containing filters and an operation", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "filters", + "entity_type": "TASK_OR_PROBLEM", + "description": "a list of filtering steps to be applied which can include sections images tables or pages", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "operation", + "entity_type": "TASK_OR_PROBLEM", + "description": "the final aggregation operation to be performed such as count list summarize or analyze", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "methodology", + "entity_type": "SECTION_TITLE", + "description": "a specific section title mentioned in an example query regarding data augmentation", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "paper", + "entity_type": "BOOK", + "description": "a document referenced in an example query regarding figures on specific pages", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "report", + "entity_type": "BOOK", + "description": "a document referenced in an example query regarding chapters", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "user", + "entity_type": "PERSON", + "description": "the user is the entity providing the query to the ai assistant", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "assistant", + "entity_type": "PERSON", + "description": "the assistant is the entity responding to the user with a json object", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "chapter", + "entity_type": "SECTION_TITLE", + "description": "a structural part of a document mentioned in the example about counting chapters", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "appendices", + "entity_type": "SECTION_TITLE", + "description": "a structural part of a document mentioned in the definition of section filters", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "references", + "entity_type": "SECTION_TITLE", + "description": "a structural part of a document mentioned in the definition of section filters", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "figures", + "entity_type": "IMAGE", + "description": "visual elements mentioned in the example query regarding counting figures", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "data augmentation", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "a specific topic discussed in the methodology section in the example query", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "discussion", + "entity_type": "TASK_OR_PROBLEM", + "description": "the content regarding data augmentation that needs to be summarized", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "3 10", + "entity_type": "MEASUREMENT", + "description": "a specific page range mentioned as a filter value in the example query", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "3", + "entity_type": "MEASUREMENT", + "description": "the starting page number in the example range", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "10", + "entity_type": "MEASUREMENT", + "description": "the ending page number in the example range", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "count", + "entity_type": "TASK_OR_PROBLEM", + "description": "an aggregation operation used to count items", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "list", + "entity_type": "TASK_OR_PROBLEM", + "description": "an aggregation operation used to list items", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "summarize", + "entity_type": "TASK_OR_PROBLEM", + "description": "an aggregation operation used to summarize content", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "analyze", + "entity_type": "TASK_OR_PROBLEM", + "description": "an aggregation operation used to analyze content", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "section", + "entity_type": "SECTION_TITLE", + "description": "a filter type used for structural parts like chapters", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "image", + "entity_type": "IMAGE", + "description": "a filter type used for visual elements", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "table", + "entity_type": "TABLE", + "description": "a filter type used for tabular data", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "page", + "entity_type": "MEASUREMENT", + "description": "a filter type used for specific page numbers", + "source_ids": [ + 258 + ] + }, + { + "entity_name": "null", + "entity_type": "TASK_OR_PROBLEM", + "description": "a value indicating that no specific value is provided for image or table filters", + "source_ids": [ + 258 + ] + } + ], + "relations": [ + { + "src_entity_name": "ai assistant", + "tgt_entity_name": "global query", + "relation_name": "", + "weight": 10.0, + "description": "the ai assistant s function is to analyze the global query", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "ai assistant", + "tgt_entity_name": "json object", + "relation_name": "", + "weight": 10.0, + "description": "the ai assistant must return a single valid json object as its output", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "ai assistant", + "tgt_entity_name": "filters", + "relation_name": "", + "weight": 9.0, + "description": "the ai assistant must determine the list of filters to apply", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "ai assistant", + "tgt_entity_name": "operation", + "relation_name": "", + "weight": 9.0, + "description": "the ai assistant must determine the final aggregation operation", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "filters", + "tgt_entity_name": "section", + "relation_name": "", + "weight": 8.0, + "description": "filters can be of type section to target structural parts like chapters or appendices", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "filters", + "tgt_entity_name": "image", + "relation_name": "", + "weight": 8.0, + "description": "filters can be of type image to target visual elements", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "filters", + "tgt_entity_name": "table", + "relation_name": "", + "weight": 8.0, + "description": "filters can be of type table to target tabular data", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "filters", + "tgt_entity_name": "page", + "relation_name": "", + "weight": 8.0, + "description": "filters can be of type page to target specific page numbers", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "operation", + "tgt_entity_name": "count", + "relation_name": "", + "weight": 7.0, + "description": "count is one of the possible operations for aggregation", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "operation", + "tgt_entity_name": "list", + "relation_name": "", + "weight": 7.0, + "description": "list is one of the possible operations for aggregation", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "operation", + "tgt_entity_name": "summarize", + "relation_name": "", + "weight": 7.0, + "description": "summarize is one of the possible operations for aggregation", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "operation", + "tgt_entity_name": "analyze", + "relation_name": "", + "weight": 7.0, + "description": "analyze is one of the possible operations for aggregation", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "paper", + "tgt_entity_name": "figures", + "relation_name": "", + "weight": 6.0, + "description": "the example query asks to count figures in the paper", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "paper", + "tgt_entity_name": "page", + "relation_name": "", + "weight": 6.0, + "description": "the example query specifies a page range 3 to 10 for the paper", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "report", + "tgt_entity_name": "chapter", + "relation_name": "", + "weight": 6.0, + "description": "the example query asks to count chapters in the report", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "methodology", + "tgt_entity_name": "data augmentation", + "relation_name": "", + "weight": 9.0, + "description": "the example query asks to summarize the discussion about data augmentation in the methodology section", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "user", + "tgt_entity_name": "assistant", + "relation_name": "", + "weight": 10.0, + "description": "the user sends a query to the assistant", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "assistant", + "tgt_entity_name": "user", + "relation_name": "", + "weight": 10.0, + "description": "the assistant responds to the user", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "assistant", + "tgt_entity_name": "json object", + "relation_name": "", + "weight": 10.0, + "description": "the assistant must output a json object", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "assistant", + "tgt_entity_name": "filters", + "relation_name": "", + "weight": 9.0, + "description": "the assistant determines the filters to apply", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "assistant", + "tgt_entity_name": "operation", + "relation_name": "", + "weight": 9.0, + "description": "the assistant determines the operation to perform", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "section", + "tgt_entity_name": "chapter", + "relation_name": "", + "weight": 9.0, + "description": "chapters are examples of sections", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "section", + "tgt_entity_name": "appendices", + "relation_name": "", + "weight": 9.0, + "description": "appendices are examples of sections", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "section", + "tgt_entity_name": "references", + "relation_name": "", + "weight": 9.0, + "description": "references are examples of sections", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "image", + "tgt_entity_name": "figures", + "relation_name": "", + "weight": 9.0, + "description": "figures are examples of images", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "page", + "tgt_entity_name": "3 10", + "relation_name": "", + "weight": 8.0, + "description": "3 10 is an example value for a page filter", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "page", + "tgt_entity_name": "3", + "relation_name": "", + "weight": 7.0, + "description": "3 is part of the page range", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "page", + "tgt_entity_name": "10", + "relation_name": "", + "weight": 7.0, + "description": "10 is part of the page range", + "source_ids": [ + 258 + ] + }, + { + "src_entity_name": "discussion", + "tgt_entity_name": "data augmentation", + "relation_name": "", + "weight": 8.0, + "description": "the discussion is about data augmentation", + "source_ids": [ + 258 + ] + } + ], + "node_idx": 258 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_259.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_259.json new file mode 100644 index 0000000..59635df --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_259.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "figure 12", + "entity_type": "IMAGE", + "description": "figure 12 is an image in the text that displays the prompt for filter operator generation", + "source_ids": [ + 259 + ] + }, + { + "entity_name": "filter operator generation", + "entity_type": "TASK_OR_PROBLEM", + "description": "filter operator generation is the specific task or problem for which the prompt in figure 12 is designed", + "source_ids": [ + 259 + ] + } + ], + "relations": [ + { + "src_entity_name": "figure 12", + "tgt_entity_name": "filter operator generation", + "relation_name": "", + "weight": 10.0, + "description": "figure 12 contains the prompt used for filter operator generation", + "source_ids": [ + 259 + ] + } + ], + "node_idx": 259 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_26.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_26.json new file mode 100644 index 0000000..6cb04e7 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_26.json @@ -0,0 +1,115 @@ +{ + "entities": [ + { + "entity_name": "agent based retrieval", + "entity_type": "TASK_OR_PROBLEM", + "description": "agent based retrieval is a proposed approach for dynamically classifying queries and configuring retrieval workflows", + "source_ids": [ + 26 + ] + }, + { + "entity_name": "information foraging theory", + "entity_type": "SCIENTIFIC_THEORY", + "description": "information foraging theory is the theoretical inspiration behind the proposed agent based retrieval approach", + "source_ids": [ + 26 + ] + }, + { + "entity_name": "queries", + "entity_type": "TASK_OR_PROBLEM", + "description": "queries are the inputs that the agent based retrieval approach dynamically classifies", + "source_ids": [ + 26 + ] + }, + { + "entity_name": "retrieval workflows", + "entity_type": "TASK_OR_PROBLEM", + "description": "retrieval workflows are the processes configured by the approach to locate evidence", + "source_ids": [ + 26 + ] + }, + { + "entity_name": "documents", + "entity_type": "DATASET_OR_CORPUS", + "description": "documents are the source material within which highly relevant evidence is located", + "source_ids": [ + 26 + ] + }, + { + "entity_name": "evidence", + "entity_type": "TASK_OR_PROBLEM", + "description": "evidence refers to the highly relevant information sought within the documents", + "source_ids": [ + 26 + ] + } + ], + "relations": [ + { + "src_entity_name": "agent based retrieval", + "tgt_entity_name": "information foraging theory", + "relation_name": "", + "weight": 10.0, + "description": "the agent based retrieval approach is explicitly inspired by information foraging theory", + "source_ids": [ + 26 + ] + }, + { + "src_entity_name": "agent based retrieval", + "tgt_entity_name": "queries", + "relation_name": "", + "weight": 9.0, + "description": "the agent based retrieval approach dynamically classifies queries", + "source_ids": [ + 26 + ] + }, + { + "src_entity_name": "agent based retrieval", + "tgt_entity_name": "retrieval workflows", + "relation_name": "", + "weight": 9.0, + "description": "the agent based retrieval approach configures optimal retrieval workflows", + "source_ids": [ + 26 + ] + }, + { + "src_entity_name": "agent based retrieval", + "tgt_entity_name": "documents", + "relation_name": "", + "weight": 8.0, + "description": "the approach operates within documents to locate evidence", + "source_ids": [ + 26 + ] + }, + { + "src_entity_name": "agent based retrieval", + "tgt_entity_name": "evidence", + "relation_name": "", + "weight": 10.0, + "description": "the goal of the approach is to locate highly relevant evidence within documents", + "source_ids": [ + 26 + ] + }, + { + "src_entity_name": "information foraging theory", + "tgt_entity_name": "agent based retrieval", + "relation_name": "", + "weight": 10.0, + "description": "information foraging theory serves as the inspiration for the agent based retrieval approach", + "source_ids": [ + 26 + ] + } + ], + "node_idx": 26 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_260.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_260.json new file mode 100644 index 0000000..19c401c --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_260.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "18", + "entity_type": "NUMBER", + "description": "18 is a number mentioned in the text though its specific context or meaning is not provided", + "source_ids": [ + 260 + ] + } + ], + "relations": [], + "node_idx": 260 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_261.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_261.json new file mode 100644 index 0000000..f762b10 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_261.json @@ -0,0 +1,5 @@ +{ + "entities": [], + "relations": [], + "node_idx": 261 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_262.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_262.json new file mode 100644 index 0000000..4b2f904 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_262.json @@ -0,0 +1,217 @@ +{ + "entities": [ + { + "entity_name": "entity resolution adjudicator", + "entity_type": "PERSON", + "description": "entity resolution adjudicator is an expert role tasked with determining if a new entity refers to the same real world concept as candidate entities", + "source_ids": [ + 262 + ] + }, + { + "entity_name": "new entity", + "entity_type": "TASK_OR_PROBLEM", + "description": "new entity is a recently extracted entity from a text that needs to be matched against candidate entities", + "source_ids": [ + 262 + ] + }, + { + "entity_name": "candidate entities", + "entity_type": "TASK_OR_PROBLEM", + "description": "candidate entities are a list of semantically similar entities retrieved from an existing knowledge base for comparison", + "source_ids": [ + 262 + ] + }, + { + "entity_name": "knowledge graph", + "entity_type": "TASK_OR_PROBLEM", + "description": "knowledge graph is the existing database from which candidate entities are retrieved", + "source_ids": [ + 262 + ] + }, + { + "entity_name": "knowledge base", + "entity_type": "TASK_OR_PROBLEM", + "description": "knowledge base is the source of semantically similar candidate entities", + "source_ids": [ + 262 + ] + }, + { + "entity_name": "json object", + "entity_type": "FILE_TYPE", + "description": "json object is the required format for the output containing the id of the matching candidate and an explanation", + "source_ids": [ + 262 + ] + }, + { + "entity_name": "id", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "id is a unique identifier used to reference candidate entities in the output", + "source_ids": [ + 262 + ] + }, + { + "entity_name": "1", + "entity_type": "VALUE", + "description": "1 is a specific value indicating that no matching candidate was found for the new entity", + "source_ids": [ + 262 + ] + }, + { + "entity_name": "text", + "entity_type": "DATASET_OR_CORPUS", + "description": "text is the source material from which the new entity is recently extracted", + "source_ids": [ + 262 + ] + }, + { + "entity_name": "explanation", + "entity_type": "TASK_OR_PROBLEM", + "description": "explanation is a brief description required in the output to justify the decision", + "source_ids": [ + 262 + ] + } + ], + "relations": [ + { + "src_entity_name": "entity resolution adjudicator", + "tgt_entity_name": "new entity", + "relation_name": "", + "weight": 10.0, + "description": "the entity resolution adjudicator evaluates the new entity to find a match", + "source_ids": [ + 262 + ] + }, + { + "src_entity_name": "entity resolution adjudicator", + "tgt_entity_name": "candidate entities", + "relation_name": "", + "weight": 10.0, + "description": "the entity resolution adjudicator compares the new entity against the candidate entities", + "source_ids": [ + 262 + ] + }, + { + "src_entity_name": "new entity", + "tgt_entity_name": "candidate entities", + "relation_name": "", + "weight": 9.0, + "description": "the new entity is compared against the candidate entities to determine if they refer to the same concept", + "source_ids": [ + 262 + ] + }, + { + "src_entity_name": "candidate entities", + "tgt_entity_name": "knowledge graph", + "relation_name": "", + "weight": 8.0, + "description": "candidate entities are retrieved from the knowledge graph", + "source_ids": [ + 262 + ] + }, + { + "src_entity_name": "candidate entities", + "tgt_entity_name": "knowledge base", + "relation_name": "", + "weight": 8.0, + "description": "candidate entities are retrieved from the knowledge base", + "source_ids": [ + 262 + ] + }, + { + "src_entity_name": "entity resolution adjudicator", + "tgt_entity_name": "json object", + "relation_name": "", + "weight": 9.0, + "description": "the entity resolution adjudicator must output the result in a json object format", + "source_ids": [ + 262 + ] + }, + { + "src_entity_name": "entity resolution adjudicator", + "tgt_entity_name": "id", + "relation_name": "", + "weight": 9.0, + "description": "the entity resolution adjudicator outputs the id of the matching candidate", + "source_ids": [ + 262 + ] + }, + { + "src_entity_name": "entity resolution adjudicator", + "tgt_entity_name": "1", + "relation_name": "", + "weight": 9.0, + "description": "the entity resolution adjudicator outputs 1 if no match is found", + "source_ids": [ + 262 + ] + }, + { + "src_entity_name": "entity resolution adjudicator", + "tgt_entity_name": "explanation", + "relation_name": "", + "weight": 9.0, + "description": "the entity resolution adjudicator provides an explanation for the decision", + "source_ids": [ + 262 + ] + }, + { + "src_entity_name": "new entity", + "tgt_entity_name": "text", + "relation_name": "", + "weight": 10.0, + "description": "the new entity is extracted from the text", + "source_ids": [ + 262 + ] + }, + { + "src_entity_name": "candidate entities", + "tgt_entity_name": "id", + "relation_name": "", + "weight": 10.0, + "description": "each candidate entity has a unique id for reference", + "source_ids": [ + 262 + ] + }, + { + "src_entity_name": "json object", + "tgt_entity_name": "id", + "relation_name": "", + "weight": 8.0, + "description": "the json object contains the id of the matching candidate", + "source_ids": [ + 262 + ] + }, + { + "src_entity_name": "json object", + "tgt_entity_name": "explanation", + "relation_name": "", + "weight": 8.0, + "description": "the json object contains the explanation for the decision", + "source_ids": [ + 262 + ] + } + ], + "node_idx": 262 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_263.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_263.json new file mode 100644 index 0000000..c001e91 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_263.json @@ -0,0 +1,5 @@ +{ + "entities": [], + "relations": [], + "node_idx": 263 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_264.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_264.json new file mode 100644 index 0000000..a4bc714 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_264.json @@ -0,0 +1,5 @@ +{ + "entities": [], + "relations": [], + "node_idx": 264 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_265.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_265.json new file mode 100644 index 0000000..902e60f --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_265.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "new entity", + "entity_type": "TASK_OR_PROBLEM", + "description": "new entity is a task or problem described as needing analysis of its name type and description", + "source_ids": [ + 265 + ] + } + ], + "relations": [], + "node_idx": 265 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_266.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_266.json new file mode 100644 index 0000000..bbe7ed3 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_266.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "field by field adjudication", + "entity_type": "TASK_OR_PROBLEM", + "description": "field by field adjudication is a task described as a method to determine a match by evaluating each field with a specific focus", + "source_ids": [ + 266 + ] + } + ], + "relations": [], + "node_idx": 266 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_267.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_267.json new file mode 100644 index 0000000..ef90589 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_267.json @@ -0,0 +1,91 @@ +{ + "entities": [ + { + "entity_name": "entity name", + "entity_type": "TASK_OR_PROBLEM", + "description": "entity name is a placeholder term used to denote the name of an entity in the context of matching criteria", + "source_ids": [ + 267 + ] + }, + { + "entity_name": "llm", + "entity_type": "TECHNOLOGY", + "description": "llm is mentioned as an abbreviation for large language model in the context of entity name matching", + "source_ids": [ + 267 + ] + }, + { + "entity_name": "large language model", + "entity_type": "TECHNOLOGY", + "description": "large language model is the full form of the abbreviation llm used as an example of a direct abbreviation match", + "source_ids": [ + 267 + ] + }, + { + "entity_name": "event detection", + "entity_type": "TASK_OR_PROBLEM", + "description": "event detection is a task mentioned as a distinct concept that should not be matched with named entity recognition", + "source_ids": [ + 267 + ] + }, + { + "entity_name": "named entity recognition", + "entity_type": "TASK_OR_PROBLEM", + "description": "named entity recognition is a task mentioned as a distinct concept that should not be matched with event detection", + "source_ids": [ + 267 + ] + }, + { + "entity_name": "high importance", + "entity_type": "CONCEPT", + "description": "high importance is a criterion mentioned for determining the similarity of entity names", + "source_ids": [ + 267 + ] + }, + { + "entity_name": "alias", + "entity_type": "CONCEPT", + "description": "alias is a concept mentioned as a valid form of similarity for entity names alongside direct abbreviations", + "source_ids": [ + 267 + ] + }, + { + "entity_name": "distinct concepts", + "entity_type": "CONCEPT", + "description": "distinct concepts refers to parallel concepts that are explicitly excluded from being considered a match", + "source_ids": [ + 267 + ] + } + ], + "relations": [ + { + "src_entity_name": "llm", + "tgt_entity_name": "large language model", + "relation_name": "", + "weight": 10.0, + "description": "llm is a direct abbreviation for large language model", + "source_ids": [ + 267 + ] + }, + { + "src_entity_name": "event detection", + "tgt_entity_name": "named entity recognition", + "relation_name": "", + "weight": 9.0, + "description": "event detection and named entity recognition are distinct parallel concepts and are explicitly stated as not a match", + "source_ids": [ + 267 + ] + } + ], + "node_idx": 267 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_268.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_268.json new file mode 100644 index 0000000..a72f5e0 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_268.json @@ -0,0 +1,25 @@ +{ + "entities": [ + { + "entity_name": "entity type", + "entity_type": "TASK_OR_PROBLEM", + "description": "entity type is a task or problem described as having medium importance in the context of type compatibility", + "source_ids": [ + 268 + ] + } + ], + "relations": [ + { + "src_entity_name": "entity type", + "tgt_entity_name": "entity type", + "relation_name": "", + "weight": 5.0, + "description": "the entity type is described as needing to be closely related and compatible with other types such as company and organization", + "source_ids": [ + 268 + ] + } + ], + "node_idx": 268 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_269.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_269.json new file mode 100644 index 0000000..391000b --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_269.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "description", + "entity_type": "CONCEPT", + "description": "description refers to the contextual importance of text segments which may differ as they are extracted from different parts of a document", + "source_ids": [ + 269 + ] + }, + { + "entity_name": "contextual importance", + "entity_type": "CONCEPT", + "description": "contextual importance is a property of descriptions that requires looking past surface level text similarity to determine if they describe the same underlying object or concept", + "source_ids": [ + 269 + ] + } + ], + "relations": [ + { + "src_entity_name": "description", + "tgt_entity_name": "contextual importance", + "relation_name": "", + "weight": 9.0, + "description": "descriptions possess contextual importance which dictates the need to analyze them for underlying identity rather than surface similarity", + "source_ids": [ + 269 + ] + } + ], + "node_idx": 269 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_27.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_27.json new file mode 100644 index 0000000..5bccbea --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_27.json @@ -0,0 +1,133 @@ +{ + "entities": [ + { + "entity_name": "bookrag", + "entity_type": "PRODUCT", + "description": "bookrag is a product that significantly outperforms existing baselines in solving complex document qa tasks", + "source_ids": [ + 27 + ] + }, + { + "entity_name": "existing baselines", + "entity_type": "PRODUCT", + "description": "existing baselines are the current methods or systems that bookrag outperforms in the experiments", + "source_ids": [ + 27 + ] + }, + { + "entity_name": "complex document qa tasks", + "entity_type": "TASK_OR_PROBLEM", + "description": "complex document qa tasks are the specific problems that bookrag is designed to solve", + "source_ids": [ + 27 + ] + }, + { + "entity_name": "extensive experiments", + "entity_type": "EVENT", + "description": "extensive experiments are the tests conducted to evaluate the performance of bookrag", + "source_ids": [ + 27 + ] + }, + { + "entity_name": "multiple benchmarks", + "entity_type": "BENCHMARK", + "description": "multiple benchmarks are the evaluation standards used in the experiments to measure performance", + "source_ids": [ + 27 + ] + }, + { + "entity_name": "state of the art performance", + "entity_type": "EVALUATION_METRIC", + "description": "state of the art performance is the high level of achievement attained by bookrag in the tasks", + "source_ids": [ + 27 + ] + }, + { + "entity_name": "competitive efficiency", + "entity_type": "EVALUATION_METRIC", + "description": "competitive efficiency is a metric indicating that bookrag maintains good efficiency while performing well", + "source_ids": [ + 27 + ] + } + ], + "relations": [ + { + "src_entity_name": "bookrag", + "tgt_entity_name": "existing baselines", + "relation_name": "", + "weight": 9.0, + "description": "bookrag significantly outperforms existing baselines in experiments", + "source_ids": [ + 27 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "complex document qa tasks", + "relation_name": "", + "weight": 10.0, + "description": "bookrag attains state of the art performance in solving complex document qa tasks", + "source_ids": [ + 27 + ] + }, + { + "src_entity_name": "extensive experiments", + "tgt_entity_name": "bookrag", + "relation_name": "", + "weight": 9.0, + "description": "extensive experiments were performed on bookrag to demonstrate its capabilities", + "source_ids": [ + 27 + ] + }, + { + "src_entity_name": "extensive experiments", + "tgt_entity_name": "multiple benchmarks", + "relation_name": "", + "weight": 8.0, + "description": "extensive experiments were conducted on multiple benchmarks to validate results", + "source_ids": [ + 27 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "state of the art performance", + "relation_name": "", + "weight": 10.0, + "description": "bookrag attained state of the art performance as a result of the experiments", + "source_ids": [ + 27 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "competitive efficiency", + "relation_name": "", + "weight": 8.0, + "description": "bookrag maintained competitive efficiency while solving tasks", + "source_ids": [ + 27 + ] + }, + { + "src_entity_name": "multiple benchmarks", + "tgt_entity_name": "state of the art performance", + "relation_name": "", + "weight": 7.0, + "description": "the performance on multiple benchmarks showed state of the art results", + "source_ids": [ + 27 + ] + } + ], + "node_idx": 27 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_270.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_270.json new file mode 100644 index 0000000..fedc16c --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_270.json @@ -0,0 +1,25 @@ +{ + "entities": [ + { + "entity_name": "be strict and conservative", + "entity_type": "TASK_OR_PROBLEM", + "description": "be strict and conservative is a guideline or instruction regarding the standard for matching emphasizing high standards to avoid corrupting the knowledge graph", + "source_ids": [ + 270 + ] + } + ], + "relations": [ + { + "src_entity_name": "be strict and conservative", + "tgt_entity_name": "be strict and conservative", + "relation_name": "", + "weight": 1.0, + "description": "the entity refers to itself as a guideline for maintaining high standards in matching", + "source_ids": [ + 270 + ] + } + ], + "node_idx": 270 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_271.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_271.json new file mode 100644 index 0000000..f60b579 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_271.json @@ -0,0 +1,5 @@ +{ + "entities": [], + "relations": [], + "node_idx": 271 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_272.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_272.json new file mode 100644 index 0000000..eee12d7 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_272.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "apple", + "entity_type": "PRODUCT", + "description": "apple is mentioned as an example of a fruit", + "source_ids": [ + 272 + ] + }, + { + "entity_name": "apple inc", + "entity_type": "ORGANIZATION", + "description": "apple inc is mentioned as an example of a company", + "source_ids": [ + 272 + ] + } + ], + "relations": [ + { + "src_entity_name": "apple", + "tgt_entity_name": "apple inc", + "relation_name": "", + "weight": 10.0, + "description": "both are mentioned in the text as examples to illustrate that they are not a match despite sharing the same name", + "source_ids": [ + 272 + ] + } + ], + "node_idx": 272 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_273.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_273.json new file mode 100644 index 0000000..4e125d7 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_273.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "when in doubt", + "entity_type": "TASK_OR_PROBLEM", + "description": "when in doubt is a condition mentioned in the text that triggers a specific output requirement", + "source_ids": [ + 273 + ] + }, + { + "entity_name": "1", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 273 + ] + } + ], + "relations": [ + { + "src_entity_name": "when in doubt", + "tgt_entity_name": "1", + "relation_name": "", + "weight": 10.0, + "description": "the text states that if the condition when in doubt is met the output must be 1", + "source_ids": [ + 273 + ] + } + ], + "node_idx": 273 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_274.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_274.json new file mode 100644 index 0000000..c99d545 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_274.json @@ -0,0 +1,25 @@ +{ + "entities": [ + { + "entity_name": "new entity", + "entity_type": "TASK_OR_PROBLEM", + "description": "new entity is a concept representing a unique entity in a knowledge graph that requires strong evidence to match with existing entities", + "source_ids": [ + 274 + ] + } + ], + "relations": [ + { + "src_entity_name": "new entity", + "tgt_entity_name": "new entity", + "relation_name": "", + "weight": 10.0, + "description": "the concept of new entity is defined by the assumption that it is unique until proven otherwise", + "source_ids": [ + 274 + ] + } + ], + "node_idx": 274 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_275.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_275.json new file mode 100644 index 0000000..0aa2dea --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_275.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "json", + "entity_type": "FILE_TYPE", + "description": "json is a file format mentioned as the required output format for the answer", + "source_ids": [ + 275 + ] + }, + { + "entity_name": "output", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 275 + ] + } + ], + "relations": [ + { + "src_entity_name": "json", + "tgt_entity_name": "output", + "relation_name": "", + "weight": 10.0, + "description": "the text specifies that the answer must be provided in a valid json format", + "source_ids": [ + 275 + ] + } + ], + "node_idx": 275 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_276.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_276.json new file mode 100644 index 0000000..93c863f --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_276.json @@ -0,0 +1,115 @@ +{ + "entities": [ + { + "entity_name": "select id", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "select id is a parameter representing an integer id for a candidate determined to be an exact match", + "source_ids": [ + 276 + ] + }, + { + "entity_name": "id", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "id is the identifier of the candidate determined to be an exact match", + "source_ids": [ + 276 + ] + }, + { + "entity_name": "exact match", + "entity_type": "TASK_OR_PROBLEM", + "description": "exact match refers to the condition where a candidate is determined to be identical to a reference", + "source_ids": [ + 276 + ] + }, + { + "entity_name": "1", + "entity_type": "MONEY", + "description": "1 is a specific integer value used to indicate that no exact match was found", + "source_ids": [ + 276 + ] + }, + { + "entity_name": "candidate", + "entity_type": "TASK_OR_PROBLEM", + "description": "candidate refers to an item being evaluated to determine if it is an exact match", + "source_ids": [ + 276 + ] + }, + { + "entity_name": "integer", + "entity_type": "MEASUREMENT", + "description": "integer is the data type specified for the select id value", + "source_ids": [ + 276 + ] + } + ], + "relations": [ + { + "src_entity_name": "select id", + "tgt_entity_name": "id", + "relation_name": "", + "weight": 9.0, + "description": "select id is defined as the integer value of the id of the candidate", + "source_ids": [ + 276 + ] + }, + { + "src_entity_name": "select id", + "tgt_entity_name": "exact match", + "relation_name": "", + "weight": 8.0, + "description": "select id holds the value of the id if an exact match is found", + "source_ids": [ + 276 + ] + }, + { + "src_entity_name": "select id", + "tgt_entity_name": "1", + "relation_name": "", + "weight": 9.0, + "description": "select id is assigned the value 1 if no exact match is found", + "source_ids": [ + 276 + ] + }, + { + "src_entity_name": "select id", + "tgt_entity_name": "candidate", + "relation_name": "", + "weight": 9.0, + "description": "select id represents the id of the candidate being evaluated", + "source_ids": [ + 276 + ] + }, + { + "src_entity_name": "select id", + "tgt_entity_name": "integer", + "relation_name": "", + "weight": 10.0, + "description": "select id is defined as an integer type", + "source_ids": [ + 276 + ] + }, + { + "src_entity_name": "candidate", + "tgt_entity_name": "exact match", + "relation_name": "", + "weight": 8.0, + "description": "the candidate is the subject of the exact match determination", + "source_ids": [ + 276 + ] + } + ], + "node_idx": 276 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_277.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_277.json new file mode 100644 index 0000000..c3a36aa --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_277.json @@ -0,0 +1,25 @@ +{ + "entities": [ + { + "entity_name": "explanation", + "entity_type": "TASK_OR_PROBLEM", + "description": "explanation is a task or problem described as a brief one sentence string explaining reasoning for entity matching", + "source_ids": [ + 277 + ] + } + ], + "relations": [ + { + "src_entity_name": "explanation", + "tgt_entity_name": "explanation", + "relation_name": "", + "weight": 5.0, + "description": "the entity explanation is defined by its role in explaining reasoning for matches or differences between entities", + "source_ids": [ + 277 + ] + } + ], + "node_idx": 277 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_278.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_278.json new file mode 100644 index 0000000..6f21866 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_278.json @@ -0,0 +1,5 @@ +{ + "entities": [], + "relations": [], + "node_idx": 278 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_279.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_279.json new file mode 100644 index 0000000..d101bdc --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_279.json @@ -0,0 +1,5 @@ +{ + "entities": [], + "relations": [], + "node_idx": 279 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_28.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_28.json new file mode 100644 index 0000000..506a9ad --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_28.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "2", + "entity_type": "NUMBER", + "description": "2 is a numerical value appearing in the text though its specific context or meaning is not defined", + "source_ids": [ + 28 + ] + } + ], + "relations": [], + "node_idx": 28 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_280.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_280.json new file mode 100644 index 0000000..14f35b3 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_280.json @@ -0,0 +1,5 @@ +{ + "entities": [], + "relations": [], + "node_idx": 280 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_281.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_281.json new file mode 100644 index 0000000..78907a5 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_281.json @@ -0,0 +1,59 @@ +{ + "entities": [ + { + "entity_name": "select id", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "select id is a parameter or variable defined as an integer in the provided text structure", + "source_ids": [ + 281 + ] + }, + { + "entity_name": "explanation", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "explanation is a parameter or variable defined as a string in the provided text structure", + "source_ids": [ + 281 + ] + }, + { + "entity_name": "example 1", + "entity_type": "TASK_OR_PROBLEM", + "description": "example 1 is a task or problem scenario where a match was found", + "source_ids": [ + 281 + ] + }, + { + "entity_name": "example 2", + "entity_type": "TASK_OR_PROBLEM", + "description": "example 2 is a task or problem scenario where no match was found", + "source_ids": [ + 281 + ] + } + ], + "relations": [ + { + "src_entity_name": "example 1", + "tgt_entity_name": "select id", + "relation_name": "", + "weight": 5.0, + "description": "example 1 is associated with the context of the provided json structure containing select id", + "source_ids": [ + 281 + ] + }, + { + "src_entity_name": "example 2", + "tgt_entity_name": "explanation", + "relation_name": "", + "weight": 5.0, + "description": "example 2 is associated with the context of the provided json structure containing explanation", + "source_ids": [ + 281 + ] + } + ], + "node_idx": 281 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_282.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_282.json new file mode 100644 index 0000000..dce14c2 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_282.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "selection task", + "entity_type": "TASK_OR_PROBLEM", + "description": "the selection task is the activity described in the text that requires processing the provided data", + "source_ids": [ + 282 + ] + }, + { + "entity_name": "integer", + "entity_type": "MEASUREMENT", + "description": "an integer is the specific type of output requested for the selection task", + "source_ids": [ + 282 + ] + } + ], + "relations": [ + { + "src_entity_name": "selection task", + "tgt_entity_name": "integer", + "relation_name": "", + "weight": 9.0, + "description": "the selection task requires the output to be a single integer", + "source_ids": [ + 282 + ] + } + ], + "node_idx": 282 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_283.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_283.json new file mode 100644 index 0000000..60b7ac3 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_283.json @@ -0,0 +1,5 @@ +{ + "entities": [], + "relations": [], + "node_idx": 283 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_284.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_284.json new file mode 100644 index 0000000..c5f4728 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_284.json @@ -0,0 +1,79 @@ +{ + "entities": [ + { + "entity_name": "figure 13", + "entity_type": "IMAGE", + "description": "figure 13 is an image containing a prompt for entity resolution judgement", + "source_ids": [ + 284 + ] + }, + { + "entity_name": "entity resolution", + "entity_type": "TASK_OR_PROBLEM", + "description": "entity resolution is the task or problem addressed by the prompt in figure 13", + "source_ids": [ + 284 + ] + }, + { + "entity_name": "prompt", + "entity_type": "SOFTWARE", + "description": "the prompt is a software component used for entity resolution judgement", + "source_ids": [ + 284 + ] + }, + { + "entity_name": "examples", + "entity_type": "DATASET_OR_CORPUS", + "description": "examples are data instances that were omitted from the text due to space constraints", + "source_ids": [ + 284 + ] + } + ], + "relations": [ + { + "src_entity_name": "figure 13", + "tgt_entity_name": "entity resolution", + "relation_name": "", + "weight": 9.0, + "description": "figure 13 contains the prompt used for the entity resolution judgement task", + "source_ids": [ + 284 + ] + }, + { + "src_entity_name": "figure 13", + "tgt_entity_name": "prompt", + "relation_name": "", + "weight": 10.0, + "description": "figure 13 displays the prompt for entity resolution judgement", + "source_ids": [ + 284 + ] + }, + { + "src_entity_name": "prompt", + "tgt_entity_name": "entity resolution", + "relation_name": "", + "weight": 9.0, + "description": "the prompt is specifically designed for the entity resolution judgement task", + "source_ids": [ + 284 + ] + }, + { + "src_entity_name": "examples", + "tgt_entity_name": "figure 13", + "relation_name": "", + "weight": 8.0, + "description": "examples were omitted from figure 13 due to lack of space", + "source_ids": [ + 284 + ] + } + ], + "node_idx": 284 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_285.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_285.json new file mode 100644 index 0000000..2775794 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_285.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "19", + "entity_type": "NUMBER", + "description": "19 is a number mentioned in the text though its specific context or meaning is not provided", + "source_ids": [ + 285 + ] + } + ], + "relations": [], + "node_idx": 285 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_29.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_29.json new file mode 100644 index 0000000..b9e17fd --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_29.json @@ -0,0 +1,265 @@ +{ + "entities": [ + { + "entity_name": "section 2", + "entity_type": "SECTION_TITLE", + "description": "section 2 is the part of the text where related work is reviewed", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "section 3", + "entity_type": "SECTION_TITLE", + "description": "section 3 introduces the problem formulation ift and rag workflow", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "ift", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "ift is a method or technique introduced in section 3 alongside problem formulation and rag workflow", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "rag", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "rag is a method or technique introduced in section 3 alongside problem formulation and ift", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "section 4", + "entity_type": "SECTION_TITLE", + "description": "section 4 presents the structure of bookindex and its construction", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "bookindex", + "entity_type": "PRODUCT", + "description": "bookindex is a structure presented in section 4 along with its construction details", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "bookrag", + "entity_type": "PRODUCT", + "description": "bookrag is a system or product whose structured execution involves query classification and operators discussed in section 5", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "section 6", + "entity_type": "SECTION_TITLE", + "description": "section 6 presents experimental results and detailed analysis", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "section 7", + "entity_type": "SECTION_TITLE", + "description": "section 7 is where the paper concludes", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "section 5", + "entity_type": "SECTION_TITLE", + "description": "section 5 is the part of the text where agent based retrieval is presented", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "query classification", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "query classification is a component of the agent based retrieval elaborated in section 5", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "operators", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "operators are used in the structured execution of bookrag as described in section 5", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "structured execution", + "entity_type": "TASK_OR_PROBLEM", + "description": "structured execution refers to the process in bookrag that utilizes query classification and operators", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "related work", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "experimental results", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 29 + ] + }, + { + "entity_name": "conclusion", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 29 + ] + } + ], + "relations": [ + { + "src_entity_name": "section 2", + "tgt_entity_name": "related work", + "relation_name": "", + "weight": 9.0, + "description": "section 2 is dedicated to reviewing related work", + "source_ids": [ + 29 + ] + }, + { + "src_entity_name": "section 3", + "tgt_entity_name": "ift", + "relation_name": "", + "weight": 10.0, + "description": "section 3 introduces the ift method", + "source_ids": [ + 29 + ] + }, + { + "src_entity_name": "section 3", + "tgt_entity_name": "rag", + "relation_name": "", + "weight": 10.0, + "description": "section 3 introduces the rag workflow", + "source_ids": [ + 29 + ] + }, + { + "src_entity_name": "section 4", + "tgt_entity_name": "bookindex", + "relation_name": "", + "weight": 10.0, + "description": "section 4 presents the structure and construction of bookindex", + "source_ids": [ + 29 + ] + }, + { + "src_entity_name": "section 5", + "tgt_entity_name": "bookrag", + "relation_name": "", + "weight": 10.0, + "description": "section 5 elaborates on the execution of bookrag", + "source_ids": [ + 29 + ] + }, + { + "src_entity_name": "section 6", + "tgt_entity_name": "experimental results", + "relation_name": "", + "weight": 10.0, + "description": "section 6 presents the experimental results and analysis", + "source_ids": [ + 29 + ] + }, + { + "src_entity_name": "section 7", + "tgt_entity_name": "conclusion", + "relation_name": "", + "weight": 10.0, + "description": "section 7 concludes the paper", + "source_ids": [ + 29 + ] + }, + { + "src_entity_name": "section 5", + "tgt_entity_name": "query classification", + "relation_name": "", + "weight": 10.0, + "description": "section 5 elaborates on query classification as part of agent based retrieval", + "source_ids": [ + 29 + ] + }, + { + "src_entity_name": "section 5", + "tgt_entity_name": "operators", + "relation_name": "", + "weight": 10.0, + "description": "section 5 describes the operators used in the structured execution of bookrag", + "source_ids": [ + 29 + ] + }, + { + "src_entity_name": "section 5", + "tgt_entity_name": "structured execution", + "relation_name": "", + "weight": 10.0, + "description": "section 5 presents the structured execution of bookrag", + "source_ids": [ + 29 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "structured execution", + "relation_name": "", + "weight": 9.0, + "description": "bookrag is the system undergoing structured execution described in section 5", + "source_ids": [ + 29 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "query classification", + "relation_name": "", + "weight": 8.0, + "description": "bookrag utilizes query classification in its execution", + "source_ids": [ + 29 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "operators", + "relation_name": "", + "weight": 8.0, + "description": "bookrag uses operators in its structured execution", + "source_ids": [ + 29 + ] + } + ], + "node_idx": 29 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_3.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_3.json new file mode 100644 index 0000000..f9ca5f0 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_3.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "abstract", + "entity_type": "SECTION_TITLE", + "description": "As the opening section of the paper 'BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents', this section provides a concise summary of the research problem, the proposed BookRAG solution involving hierarchical indexing and agent-based querying, and the reported state-of-the-art experimental results.", + "source_ids": [ + 3 + ] + } + ], + "relations": [], + "node_idx": 3 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_30.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_30.json new file mode 100644 index 0000000..7bc4290 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_30.json @@ -0,0 +1,51 @@ +{ + "entities": [ + { + "entity_name": "2 related work", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of the main paper 'BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents', this section provides a comprehensive review of existing literature, specifically focusing on Retrieval-Augmented Generation (RAG) methods and their limitations regarding hierarchical document structures.", + "source_ids": [ + 30 + ] + }, + { + "entity_name": "retrieval-augmented generation", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Refers to the class of techniques discussed in section 2 that enhance Large Language Models by querying external information, serving as the primary context for the related work analysis.", + "source_ids": [ + 30 + ] + }, + { + "entity_name": "hierarchical document structures", + "entity_type": "TASK_OR_PROBLEM", + "description": "Refers to the specific structural characteristics of documents (e.g., books, handbooks) that existing RAG approaches often overlook, which is a key problem addressed in the literature review within section 2.", + "source_ids": [ + 30 + ] + } + ], + "relations": [ + { + "src_entity_name": "retrieval-augmented generation", + "tgt_entity_name": "2 related work", + "relation_name": "", + "weight": 10.0, + "description": "The concept of 'Retrieval-Augmented Generation' is a primary topic reviewed in section 2.", + "source_ids": [ + 30 + ] + }, + { + "src_entity_name": "hierarchical document structures", + "tgt_entity_name": "2 related work", + "relation_name": "", + "weight": 10.0, + "description": "The challenge of 'Hierarchical Document Structures' is a primary topic reviewed in section 2.", + "source_ids": [ + 30 + ] + } + ], + "node_idx": 30 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_31.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_31.json new file mode 100644 index 0000000..0d71eb4 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_31.json @@ -0,0 +1,89 @@ +{ + "entities": [ + { + "entity_name": "llm", + "entity_type": "TECHNOLOGY", + "description": "llm is a technology mentioned in the context of document analysis", + "source_ids": [ + 31 + ] + }, + { + "entity_name": "rag approaches", + "entity_type": "TECHNOLOGY", + "description": "rag approaches are modern representative technologies reviewed in the text", + "source_ids": [ + 31 + ] + }, + { + "entity_name": "document analysis", + "entity_type": "RESEARCH_FIELD", + "description": "document analysis is the field of study where llms and rag approaches are applied", + "source_ids": [ + 31 + ] + }, + { + "entity_name": "related works", + "entity_type": "SECTION_TITLE", + "description": "related works is the section of the text where the review of llm and rag approaches takes place", + "source_ids": [ + 31 + ] + } + ], + "relations": [ + { + "src_entity_name": "llm", + "tgt_entity_name": "rag approaches", + "relation_name": "", + "weight": 8.0, + "description": "both llm and rag approaches are reviewed together as related works in document analysis", + "source_ids": [ + 31 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "document analysis", + "relation_name": "", + "weight": 9.0, + "description": "llm is used in the field of document analysis", + "source_ids": [ + 31 + ] + }, + { + "src_entity_name": "rag approaches", + "tgt_entity_name": "document analysis", + "relation_name": "", + "weight": 9.0, + "description": "rag approaches are used in the field of document analysis", + "source_ids": [ + 31 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "related works", + "relation_name": "", + "weight": 8.0, + "description": "llm is reviewed within the related works section", + "source_ids": [ + 31 + ] + }, + { + "src_entity_name": "rag approaches", + "tgt_entity_name": "related works", + "relation_name": "", + "weight": 8.0, + "description": "rag approaches are reviewed within the related works section", + "source_ids": [ + 31 + ] + } + ], + "node_idx": 31 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_32.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_32.json new file mode 100644 index 0000000..4d3e7c9 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_32.json @@ -0,0 +1,411 @@ +{ + "entities": [ + { + "entity_name": "llm", + "entity_type": "TECHNOLOGY", + "description": "llm refers to large language models which are used for robust semantic reasoning in document analysis", + "source_ids": [ + 32 + ] + }, + { + "entity_name": "html", + "entity_type": "FILE_TYPE", + "description": "html is an unstructured document format mentioned as a target for conversion into structured formats", + "source_ids": [ + 32 + ] + }, + { + "entity_name": "pdf", + "entity_type": "FILE_TYPE", + "description": "pdf is an unstructured document format mentioned as a target for conversion into structured formats", + "source_ids": [ + 32 + ] + }, + { + "entity_name": "raw text", + "entity_type": "FILE_TYPE", + "description": "raw text is an unstructured document format mentioned as a target for conversion into structured formats", + "source_ids": [ + 32 + ] + }, + { + "entity_name": "relational tables", + "entity_type": "PRODUCT", + "description": "relational tables are structured formats that unstructured documents are converted into", + "source_ids": [ + 32 + ] + }, + { + "entity_name": "evaporate", + "entity_type": "SOFTWARE", + "description": "evaporate is a system that utilizes llms to synthesize extraction code for converting semi structured web documents", + "source_ids": [ + 32 + ] + }, + { + "entity_name": "lotus", + "entity_type": "SOFTWARE", + "description": "lotus is a system that extends the relational model with semantic operators for querying unstructured text corpora", + "source_ids": [ + 32 + ] + }, + { + "entity_name": "docetl", + "entity_type": "SOFTWARE", + "description": "docetl is a system that introduces an agentic framework to optimize complex information extraction tasks", + "source_ids": [ + 32 + ] + }, + { + "entity_name": "sql", + "entity_type": "PROGRAMMING_LANGUAGE", + "description": "sql is a query language referenced in the context of sql like queries executed by lotus", + "source_ids": [ + 32 + ] + }, + { + "entity_name": "web documents", + "entity_type": "PRODUCT", + "description": "web documents are semi structured documents processed by systems like evaporate", + "source_ids": [ + 32 + ] + }, + { + "entity_name": "document pages", + "entity_type": "IMAGE", + "description": "document pages are viewed as images in research to preserve layout and visual information", + "source_ids": [ + 32 + ] + }, + { + "entity_name": "semantic operators", + "entity_type": "TECHNOLOGY", + "description": "semantic operators are features added by lotus to extend the relational model", + "source_ids": [ + 32 + ] + }, + { + "entity_name": "predicates", + "entity_type": "TECHNOLOGY", + "description": "predicates are llm powered functions like filter and join used in lotus", + "source_ids": [ + 32 + ] + }, + { + "entity_name": "filter", + "entity_type": "TECHNOLOGY", + "description": "filter is an example of an llm powered predicate used in lotus", + "source_ids": [ + 32 + ] + }, + { + "entity_name": "join", + "entity_type": "TECHNOLOGY", + "description": "join is an 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query", + "relation_name": "", + "weight": 10.0, + "description": "method s maps a user query to a final answer", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "method s", + "tgt_entity_name": "answer", + "relation_name": "", + "weight": 10.0, + "description": "method s produces the final answer", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "document", + "tgt_entity_name": "pages", + "relation_name": "", + "weight": 10.0, + "description": "a document is represented as a sequence of pages", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "document", + "tgt_entity_name": "content blocks", + "relation_name": "", + "weight": 10.0, + "description": "pages in a document collectively contain a sequence of content blocks", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "content blocks", + "tgt_entity_name": "text segment", + "relation_name": "", + "weight": 8.0, + "description": "a text segment is an example of a content block", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "content blocks", + "tgt_entity_name": "section header", + "relation_name": "", + "weight": 8.0, + "description": "a section header is an example of a content block", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "content blocks", + "tgt_entity_name": "table", + "relation_name": "", + "weight": 8.0, + "description": "a table is an example of a content block", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "content blocks", + "tgt_entity_name": "image", + "relation_name": "", + "weight": 8.0, + "description": "an image is an example of a content block", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "content blocks", + "tgt_entity_name": "logical chapter hierarchy", + "relation_name": "", + "weight": 9.0, + "description": "content blocks are organized within a logical chapter hierarchy", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "document", + "tgt_entity_name": "n", + "relation_name": "", + "weight": 9.0, + "description": "n defines the sequence length of pages in the document", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "document", + "tgt_entity_name": "m", + "relation_name": "", + "weight": 9.0, + "description": "m defines the sequence length of content blocks in the document", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "document", + "tgt_entity_name": "p", + "relation_name": "", + "weight": 9.0, + "description": "p represents an individual page within the document sequence", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "document", + "tgt_entity_name": "b", + "relation_name": "", + "weight": 9.0, + "description": "b represents an individual content block within the document", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "method s", + "tgt_entity_name": "equation 1", + "relation_name": "", + "weight": 10.0, + "description": "method s is mathematically defined by equation 1", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "question answering", + "tgt_entity_name": "references 5 11 33", + "relation_name": "", + "weight": 8.0, + "description": "the problem of question answering is associated with references 5 11 and 33", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "answer", + "tgt_entity_name": "a", + "relation_name": "", + "weight": 10.0, + "description": "a is the variable symbol for the answer", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "user query", + "tgt_entity_name": "q", + "relation_name": "", + "weight": 10.0, + "description": "q is the variable symbol for the user query", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "evidence blocks", + "tgt_entity_name": "e", + "relation_name": "", + "weight": 10.0, + "description": "e is the variable symbol for the set of evidence blocks", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "content blocks", + "tgt_entity_name": "b", + "relation_name": "", + "weight": 10.0, + "description": "b is the variable symbol for the sequence of content blocks", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "pages", + "tgt_entity_name": "p", + "relation_name": "", + "weight": 10.0, + "description": "p is the variable symbol for pages", + "source_ids": [ + 37 + ] + }, + { + "src_entity_name": "document", + "tgt_entity_name": "d", + "relation_name": "", + "weight": 10.0, + "description": "d is the variable symbol for the document", + "source_ids": [ + 37 + ] + } + ], + "node_idx": 37 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_38.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_38.json new file mode 100644 index 0000000..109d12a --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_38.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "3", + "entity_type": "MEASUREMENT", + "description": "3 is a numerical value mentioned in the text potentially representing a count index or measurement", + "source_ids": [ + 38 + ] + } + ], + "relations": [], + "node_idx": 38 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_39.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_39.json new file mode 100644 index 0000000..d5c9d15 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_39.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "formula (1)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the variable A as a function of D and q. LaTeX: 𝐴 = S( 𝐷,𝑞 ) (1)", + "source_ids": [ + 39 + ] + } + ], + "relations": [], + "node_idx": 39 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_4.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_4.json new file mode 100644 index 0000000..2578ef2 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_4.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "pvldb", + "entity_type": "PUBLICATION_VENUE", + "description": "pvldb is a publication venue referenced in the text for its reference format", + "source_ids": [ + 4 + ] + }, + { + "entity_name": "reference format", + "entity_type": "SECTION_TITLE", + "description": "reference format is a section or concept mentioned in the context of pvldb", + "source_ids": [ + 4 + ] + } + ], + "relations": [ + { + "src_entity_name": "pvldb", + "tgt_entity_name": "reference format", + "relation_name": "", + "weight": 9.0, + "description": "pvldb is associated with a specific reference format mentioned in the text", + "source_ids": [ + 4 + ] + } + ], + "node_idx": 4 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_40.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_40.json new file mode 100644 index 0000000..73081aa --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_40.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "s", + "entity_type": "PERSON", + "description": "s is an entity described as needing to navigate sequential page content and logical hierarchy to synthesize a response", + "source_ids": [ + 40 + ] + }, + { + "entity_name": "d", + "entity_type": "TASK_OR_PROBLEM", + "description": "d represents the logical hierarchy that s must navigate to synthesize a response", + "source_ids": [ + 40 + ] + } + ], + "relations": [ + { + "src_entity_name": "s", + "tgt_entity_name": "d", + "relation_name": "", + "weight": 9.0, + "description": "s must navigate the logical hierarchy of d to synthesize the response", + "source_ids": [ + 40 + ] + } + ], + "node_idx": 40 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_41.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_41.json new file mode 100644 index 0000000..10932d8 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_41.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "3.2 information foraging theory", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Preliminaries' within the BookRAG paper, this section formalizes the foundational Information Foraging Theory (IFT) used to model user behavior in complex document QA tasks.", + "source_ids": [ + 41 + ] + }, + { + "entity_name": "information foraging theory", + "entity_type": "SCIENTIFIC_THEORY", + "description": "A theoretical framework explaining how individuals seek information efficiently, serving as the conceptual basis for the system's design discussed in section 3.2.", + "source_ids": [ + 41 + ] + } + ], + "relations": [ + { + "src_entity_name": "information foraging theory", + "tgt_entity_name": "3.2 information foraging theory", + "relation_name": "", + "weight": 10.0, + "description": "The concept of 'Information Foraging Theory' is the primary subject matter detailed in section 3.2.", + "source_ids": [ + 41 + ] + } + ], + "node_idx": 41 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_42.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_42.json new file mode 100644 index 0000000..72ff46d --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_42.json @@ -0,0 +1,159 @@ +{ + "entities": [ + { + "entity_name": "information foraging theory", + "entity_type": "SCIENTIFIC_THEORY", + "description": "information foraging theory ift is a framework for understanding information access as a process analogous to animal foraging", + "source_ids": [ + 42 + ] + }, + { + "entity_name": "animal foraging", + "entity_type": "TASK_OR_PROBLEM", + "description": "animal foraging is the process used as an analogy to explain how users access information in the context of information foraging theory", + "source_ids": [ + 42 + ] + }, + { + "entity_name": "information scent", + "entity_type": "CONCEPT", + "description": "information scent refers to cues like keywords or icons that users follow to navigate content", + "source_ids": [ + 42 + ] + }, + { + "entity_name": "information patches", + "entity_type": "CONCEPT", + "description": "information patches are clusters of content such as sections in handbooks that users navigate between", + "source_ids": [ + 42 + ] + }, + { + "entity_name": "handbooks", + "entity_type": "PRODUCT", + "description": "handbooks are mentioned as containing sections that serve as information patches", + "source_ids": [ + 42 + ] + }, + { + "entity_name": "keywords", + "entity_type": "CONCEPT", + "description": "keywords are identified as specific examples of information scent cues used by users", + "source_ids": [ + 42 + ] + }, + { + "entity_name": "icons", + "entity_type": "CONCEPT", + "description": "icons are identified as specific examples of information scent cues used by users", + "source_ids": [ + 42 + ] + }, + { + "entity_name": "sections", + "entity_type": "CONCEPT", + "description": "sections are described as parts of handbooks that function as information patches", + "source_ids": [ + 42 + ] + }, + { + "entity_name": "reference 42", + "entity_type": "PUBLICATION_VENUE", + "description": "reference 42 is the citation source for information foraging theory mentioned in the text", + "source_ids": [ + 42 + ] + } + ], + "relations": [ + { + "src_entity_name": "information foraging theory", + "tgt_entity_name": "animal foraging", + "relation_name": "", + "weight": 10.0, + "description": "information foraging theory uses animal foraging as an analogy to explain information access", + "source_ids": [ + 42 + ] + }, + { + "src_entity_name": "information foraging theory", + "tgt_entity_name": "information scent", + "relation_name": "", + "weight": 9.0, + "description": "information foraging theory suggests that users follow information scent cues to navigate content", + "source_ids": [ + 42 + ] + }, + { + "src_entity_name": "information foraging theory", + "tgt_entity_name": "information patches", + "relation_name": "", + "weight": 9.0, + "description": "information foraging theory describes information patches as clusters of content that users navigate between", + "source_ids": [ + 42 + ] + }, + { + "src_entity_name": "information scent", + "tgt_entity_name": "handbooks", + "relation_name": "", + "weight": 7.0, + "description": "information scent cues like keywords or icons are found within sections of handbooks which act as information patches", + "source_ids": [ + 42 + ] + }, + { + "src_entity_name": "information scent", + "tgt_entity_name": "keywords", + "relation_name": "", + "weight": 10.0, + "description": "keywords are explicitly listed as examples of information scent", + "source_ids": [ + 42 + ] + }, + { + "src_entity_name": "information scent", + "tgt_entity_name": "icons", + "relation_name": "", + "weight": 10.0, + "description": "icons are explicitly listed as examples of information scent", + "source_ids": [ + 42 + ] + }, + { + "src_entity_name": "information patches", + "tgt_entity_name": "sections", + "relation_name": "", + "weight": 10.0, + "description": "sections in handbooks are explicitly listed as examples of information patches", + "source_ids": [ + 42 + ] + }, + { + "src_entity_name": "information foraging theory", + "tgt_entity_name": "reference 42", + "relation_name": "", + "weight": 8.0, + "description": "information foraging theory is cited with reference number 42 in the text", + "source_ids": [ + 42 + ] + } + ], + "node_idx": 42 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_43.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_43.json new file mode 100644 index 0000000..40bd26d --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_43.json @@ -0,0 +1,179 @@ +{ + "entities": [ + { + "entity_name": "experts", + "entity_type": "PERSON", + "description": "experts are individuals seeking a solution to a specific problem within a large technical handbook", + "source_ids": [ + 43 + ] + }, + { + "entity_name": "large technical handbook", + "entity_type": "BOOK", + "description": "the large technical handbook is the source material containing the problem and information patches", + "source_ids": [ + 43 + ] + }, + { + "entity_name": "key terms", + "entity_type": "CONCEPT", + "description": "key terms are extracted by experts to act as information scent", + "source_ids": [ + 43 + ] + }, + { + "entity_name": "information scent", + "entity_type": "CONCEPT", + "description": "information scent is the guidance provided by key terms that leads experts to promising sections", + "source_ids": [ + 43 + ] + }, + { + "entity_name": "information patches", + "entity_type": "CONCEPT", + "description": "information patches are the promising sections within the handbook that experts navigate to", + "source_ids": [ + 43 + ] + }, + { + "entity_name": "final answer", + "entity_type": "CONCEPT", + "description": "the final answer is the result formulated by experts after analyzing the content within the information patches", + "source_ids": [ + 43 + ] + }, + { + "entity_name": "problem", + "entity_type": "TASK_OR_PROBLEM", + "description": "a specific problem is the target issue that experts are seeking to solve within the handbook", + "source_ids": [ + 43 + ] + }, + { + "entity_name": "diverse content", + "entity_type": "CONCEPT", + "description": "diverse content refers to the varied information found within the information patches that experts analyze", + "source_ids": [ + 43 + ] + }, + { + "entity_name": "precise knowledge", + "entity_type": "CONCEPT", + "description": "precise knowledge is the specific information extracted from the diverse content to help formulate the answer", + "source_ids": [ + 43 + ] + } + ], + "relations": [ + { + "src_entity_name": "experts", + "tgt_entity_name": "large technical handbook", + "relation_name": "", + "weight": 10.0, + "description": "experts seek a solution within the large technical handbook", + "source_ids": [ + 43 + ] + }, + { + "src_entity_name": "experts", + "tgt_entity_name": "key terms", + "relation_name": "", + "weight": 9.0, + "description": "experts extract key terms from the handbook", + "source_ids": [ + 43 + ] + }, + { + "src_entity_name": "key terms", + "tgt_entity_name": "information scent", + "relation_name": "", + "weight": 10.0, + "description": "key terms act as information scent", + "source_ids": [ + 43 + ] + }, + { + "src_entity_name": "information scent", + "tgt_entity_name": "information patches", + "relation_name": "", + "weight": 9.0, + "description": "information scent guides experts to navigate towards information patches", + "source_ids": [ + 43 + ] + }, + { + "src_entity_name": "experts", + "tgt_entity_name": "information patches", + "relation_name": "", + "weight": 9.0, + "description": "experts navigate to and analyze content within information patches", + "source_ids": [ + 43 + ] + }, + { + "src_entity_name": "experts", + "tgt_entity_name": "final answer", + "relation_name": "", + "weight": 10.0, + "description": "experts formulate a final answer based on the analysis of information patches", + "source_ids": [ + 43 + ] + }, + { + "src_entity_name": "experts", + "tgt_entity_name": "problem", + "relation_name": "", + "weight": 10.0, + "description": "experts are seeking a solution to the specific problem", + "source_ids": [ + 43 + ] + }, + { + "src_entity_name": "experts", + "tgt_entity_name": "diverse content", + "relation_name": "", + "weight": 9.0, + "description": "experts analyze the diverse content within the information patches", + "source_ids": [ + 43 + ] + }, + { + "src_entity_name": "diverse content", + "tgt_entity_name": "precise knowledge", + "relation_name": "", + "weight": 9.0, + "description": "experts extract precise knowledge from the diverse content", + "source_ids": [ + 43 + ] + }, + { + "src_entity_name": "precise knowledge", + "tgt_entity_name": "final answer", + "relation_name": "", + "weight": 10.0, + "description": "precise knowledge is used to formulate the final answer", + "source_ids": [ + 43 + ] + } + ], + "node_idx": 43 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_44.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_44.json new file mode 100644 index 0000000..baf505a --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_44.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "3.3 rag workflow", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Preliminaries' within the BookRAG paper, this section details the general operational workflow of Retrieval-Augmented Generation (RAG) systems, serving as a foundational context for the proposed hierarchical approach.", + "source_ids": [ + 44 + ] + }, + { + "entity_name": "rag systems", + "entity_type": "TECHNOLOGY", + "description": "Refers to Retrieval-Augmented Generation systems, which are the subject of the workflow analysis detailed in section 3.3.", + "source_ids": [ + 44 + ] + } + ], + "relations": [ + { + "src_entity_name": "rag systems", + "tgt_entity_name": "3.3 rag workflow", + "relation_name": "", + "weight": 10.0, + "description": "The concept of 'RAG systems' is the primary topic and subject matter of section 3.3.", + "source_ids": [ + 44 + ] + } + ], + "node_idx": 44 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_45.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_45.json new file mode 100644 index 0000000..571e378 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_45.json @@ -0,0 +1,213 @@ +{ + "entities": [ + { + "entity_name": "retrieval augmented generation", + "entity_type": "TASK_OR_PROBLEM", + "description": "retrieval augmented generation is a system framework described as operating in a two phase process", + "source_ids": [ + 45 + ] + }, + { + "entity_name": "offline indexing phase", + "entity_type": "TASK_OR_PROBLEM", + "description": "offline indexing phase is the first phase where unstructured corpus data is organized into a structured index", + "source_ids": [ + 45 + ] + }, + { + "entity_name": "online retrieval phase", + "entity_type": "TASK_OR_PROBLEM", + "description": "online retrieval phase is the second phase where the system retrieves relevant components based on a user query", + "source_ids": [ + 45 + ] + }, + { + "entity_name": "vector databases", + "entity_type": "SOFTWARE", + "description": "vector databases are mentioned as a possible form of structured index in the offline indexing phase", + "source_ids": [ + 45 + ] + }, + { + "entity_name": "kg", + "entity_type": "SOFTWARE", + "description": "kg knowledge graph is mentioned as a possible form of structured index in the offline indexing phase", + "source_ids": [ + 45 + ] + }, + { + "entity_name": "llm", + "entity_type": "SOFTWARE", + "description": "llm is the component that generates output informed by retrieved components in the online retrieval phase", + "source_ids": [ + 45 + ] + }, + { + "entity_name": "user query", + "entity_type": "TASK_OR_PROBLEM", + "description": "user query is the input used in the online retrieval phase to retrieve relevant components", + "source_ids": [ + 45 + ] + }, + { + "entity_name": "document s native tree topology", + "entity_type": "TASK_OR_PROBLEM", + "description": "document s native tree topology is the logical structure that the proposed approach seeks to integrate with retrieval structures", + "source_ids": [ + 45 + ] + }, + { + "entity_name": "unstructured corpus data", + "entity_type": "DATASET_OR_CORPUS", + "description": "unstructured corpus data is the input material organized into a structured index during the offline indexing phase", + "source_ids": [ + 45 + ] + }, + { + "entity_name": "text chunks", + "entity_type": "DATASET_OR_CORPUS", + "description": "text chunks are examples of relevant components retrieved during the online retrieval phase", + "source_ids": [ + 45 + ] + }, + { + "entity_name": "subgraphs", + "entity_type": "DATASET_OR_CORPUS", + "description": "subgraphs are examples of relevant components retrieved during the online retrieval phase", + "source_ids": [ + 45 + ] + }, + { + "entity_name": "document", + "entity_type": "TASK_OR_PROBLEM", + "description": "the document is the source of the original logical hierarchy and native tree topology referenced in the text", + "source_ids": [ + 45 + ] + } + ], + "relations": [ + { + "src_entity_name": "retrieval augmented generation", + "tgt_entity_name": 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+ "source_ids": [ + 45 + ] + }, + { + "src_entity_name": "online retrieval phase", + "tgt_entity_name": "user query", + "relation_name": "", + "weight": 10.0, + "description": "the online retrieval phase uses the user query to retrieve relevant components", + "source_ids": [ + 45 + ] + }, + { + "src_entity_name": "online retrieval phase", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 9.0, + "description": "the online retrieval phase informs the llm s generation with retrieved components", + "source_ids": [ + 45 + ] + }, + { + "src_entity_name": "retrieval augmented generation", + "tgt_entity_name": "document s native tree topology", + "relation_name": "", + "weight": 8.0, + "description": "the proposed approach for retrieval augmented generation seeks to integrate retrieval structures with the document s native tree topology", + "source_ids": [ + 45 + ] + }, + { + "src_entity_name": "offline indexing phase", + "tgt_entity_name": "unstructured corpus data", + 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complex documents", + "source_ids": [ + 5 + ] + }, + { + "entity_name": "pvldb", + "entity_type": "PUBLICATION_VENUE", + "description": "pvldb is the publication venue where the paper was published in 2025", + "source_ids": [ + 5 + ] + }, + { + "entity_name": "2025", + "entity_type": "DATE", + "description": "2025 is the year the paper was published in pvldb", + "source_ids": [ + 5 + ] + }, + { + "entity_name": "19", + "entity_type": "MEASUREMENT", + "description": "19 is the volume number of the pvldb publication where the paper appeared", + "source_ids": [ + 5 + ] + }, + { + "entity_name": "1", + "entity_type": "MEASUREMENT", + "description": "1 is the issue number of the pvldb publication where the paper appeared", + "source_ids": [ + 5 + ] + }, + { + "entity_name": "xxx xxx", + "entity_type": "MEASUREMENT", + "description": "xxx xxx represents the page range of the paper in the publication", + "source_ids": [ + 5 + ] + }, + { + "entity_name": "xx xx xxx xx", + "entity_type": "MEASUREMENT", + "description": "xx xx xxx xx is the doi identifier for the paper", + "source_ids": [ + 5 + ] + }, + { + "entity_name": "retrieval augmented generation", + "entity_type": "TECHNOLOGY", + "description": "retrieval augmented generation is the technology domain addressed by the bookrag approach", + "source_ids": [ + 5 + ] + }, + { + "entity_name": "hierarchical structure aware index based approach", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "hierarchical structure aware index based approach is the specific method used by bookrag", + "source_ids": [ + 5 + ] + }, + { + "entity_name": "complex documents", + "entity_type": "TASK_OR_PROBLEM", + "description": "complex documents are the type of documents that the bookrag approach is designed to handle", + "source_ids": [ + 5 + ] + } + ], + "relations": [ + { + "src_entity_name": "shu wang", + "tgt_entity_name": "bookrag", + "relation_name": "", + "weight": 10.0, + "description": "shu wang is an author of the bookrag paper", + "source_ids": [ + 5 + ] + }, + { + "src_entity_name": "yingli zhou", + "tgt_entity_name": "bookrag", + "relation_name": "", + "weight": 10.0, + "description": "yingli zhou is an author of the bookrag paper", + "source_ids": [ + 5 + ] + }, + { + "src_entity_name": "yixiang fang", + "tgt_entity_name": "bookrag", + "relation_name": "", + "weight": 10.0, + "description": "yixiang fang is an author of the bookrag paper", + "source_ids": [ + 5 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "pvldb", + "relation_name": "", + "weight": 10.0, + "description": "bookrag was published in the pvldb journal", + "source_ids": [ + 5 + ] + }, + { + "src_entity_name": "pvldb", + "tgt_entity_name": "2025", + "relation_name": "", + "weight": 9.0, + "description": "pvldb published the bookrag paper in the year 2025", + "source_ids": [ + 5 + ] + }, + { + "src_entity_name": "shu wang", + "tgt_entity_name": "yingli zhou", + "relation_name": "", + "weight": 8.0, + "description": "shu wang and yingli zhou are co authors on the bookrag paper", + "source_ids": [ + 5 + ] + }, + { + "src_entity_name": "shu wang", + "tgt_entity_name": "yixiang fang", + "relation_name": "", + "weight": 8.0, + "description": "shu wang and yixiang fang are co authors on the bookrag paper", + "source_ids": [ + 5 + ] + }, + { + "src_entity_name": "yingli zhou", + "tgt_entity_name": "yixiang fang", + "relation_name": "", + "weight": 8.0, + "description": "yingli zhou and yixiang fang are co authors on the bookrag paper", + "source_ids": [ + 5 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "retrieval augmented generation", + "relation_name": "", + "weight": 10.0, + "description": "bookrag is an approach for retrieval augmented generation", + "source_ids": [ + 5 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "hierarchical structure aware index based approach", + "relation_name": "", + "weight": 10.0, + "description": "bookrag is defined as a hierarchical structure aware index based approach", + "source_ids": [ + 5 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "complex documents", + "relation_name": "", + "weight": 9.0, + "description": "bookrag is designed for processing complex documents", + "source_ids": [ + 5 + ] + }, + { + "src_entity_name": "pvldb", + "tgt_entity_name": "19", + "relation_name": "", + "weight": 8.0, + "description": "pvldb volume 19 contains the paper", + "source_ids": [ + 5 + ] + }, + { + "src_entity_name": "pvldb", + "tgt_entity_name": "1", + "relation_name": "", + "weight": 8.0, + "description": "pvldb issue 1 contains the paper", + "source_ids": [ + 5 + ] + }, + { + "src_entity_name": "pvldb", + "tgt_entity_name": "xxx xxx", + "relation_name": "", + "weight": 8.0, + "description": "the paper appears on pages xxx xxx in pvldb", + "source_ids": [ + 5 + ] + }, + { + "src_entity_name": "pvldb", + "tgt_entity_name": "xx xx xxx xx", + "relation_name": "", + "weight": 8.0, + "description": "the paper in pvldb has the doi xx xx xxx xx", + "source_ids": [ + 5 + ] + } + ], + "node_idx": 5 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_50.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_50.json new file mode 100644 index 0000000..0da54f0 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_50.json @@ -0,0 +1,69 @@ +{ + "entities": [ + { + "entity_name": "4.1 overview of bookindex", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'BOOKINDEX', this section provides a high-level introduction to the proposed BookIndex, defining its hierarchical structure-aware nature and outlining its two-stage construction process (Tree Construction and Graph Construction) for capturing logical hierarchies and entity relations in complex documents.", + "source_ids": [ + 50 + ] + }, + { + "entity_name": "bookindex", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "A hierarchical structure-aware index designed to capture explicit logical hierarchy and intricate entity relations within complex documents, serving as the core subject of section 4.1.", + "source_ids": [ + 50 + ] + }, + { + "entity_name": "tree construction", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "The first stage of the BookIndex construction process, which parses document layout to establish hierarchical nodes categorized by type, detailed in section 4.1.", + "source_ids": [ + 50 + ] + }, + { + "entity_name": "graph construction", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "The second stage of the BookIndex construction process, which extracts fine-grained entity knowledge from tree nodes and refines it using gradient-based entity resolution, detailed in section 4.1.", + "source_ids": [ + 50 + ] + } + ], + "relations": [ + { + "src_entity_name": "bookindex", + "tgt_entity_name": "4.1 overview of bookindex", + "relation_name": "", + "weight": 10.0, + "description": "The concept of 'BookIndex' is the primary topic defined and introduced in section 4.1.", + "source_ids": [ + 50 + ] + }, + { + "src_entity_name": "tree construction", + "tgt_entity_name": "4.1 overview of bookindex", + "relation_name": "", + "weight": 9.5, + "description": "The method 'Tree Construction' is a key component of the overview provided in section 4.1.", + "source_ids": [ + 50 + ] + }, + { + "src_entity_name": "graph construction", + "tgt_entity_name": "4.1 overview of bookindex", + "relation_name": "", + "weight": 9.5, + "description": "The method 'Graph Construction' is a key component of the overview provided in section 4.1.", + "source_ids": [ + 50 + ] + } + ], + "node_idx": 50 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_51.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_51.json new file mode 100644 index 0000000..106fc3a --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_51.json @@ -0,0 +1,381 @@ +{ + "entities": [ + { + "entity_name": "bookindex", + "entity_type": "PRODUCT", + "description": "bookindex is a formally defined triplet structure used to represent document hierarchy and entities", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "tree structure", + "entity_type": "TASK_OR_PROBLEM", + "description": "tree structure represents the set of nodes derived from the document s explicit logical hierarchy", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "knowledge graph", + "entity_type": "SOFTWARE", + "description": "knowledge graph is a structure that captures fine grained entities and their relations within the document", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "graph tree link", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "graph tree link gt link is a mechanism that links entities to specific tree nodes from which they were extracted", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "document", + "entity_type": "PRODUCT", + "description": "document is the source material containing logical hierarchy entities and relations", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "titles", + "entity_type": "SECTION_TITLE", + "description": "titles are examples of nodes in the document s explicit logical hierarchy", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "sections", + "entity_type": "SECTION_TITLE", + "description": "sections are examples of nodes in the document s explicit logical hierarchy", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "tables", + "entity_type": "TABLE", + "description": "tables are examples of nodes in the document s explicit logical hierarchy", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "n", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "n represents the set of nodes in the tree structure", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "e t", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "e t denotes the nesting relationships in the tree structure", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "v", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "v represents the fine grained entities in the knowledge graph", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "e g", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "e g denotes the relations in the knowledge graph", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "m v", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "m v is the graph tree link function linking entities to tree nodes", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "p", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "p represents the power set of nodes in the tree structure used in the graph tree link definition", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "information patches", + "entity_type": "CONCEPT", + "description": "information patches are hierarchical tree nodes serving as native contexts for information seeking", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "information scent", + "entity_type": "CONCEPT", + "description": "information scent is the rich information provided by entities and relations to guide navigation", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 51 + ] + }, + { + "entity_name": "navigation", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 51 + ] + } + ], + "relations": [ + { + "src_entity_name": "bookindex", + "tgt_entity_name": "tree structure", + "relation_name": "", + "weight": 10.0, + "description": "bookindex is defined as a triplet that includes the tree structure as one of its components", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "knowledge graph", + "relation_name": "", + "weight": 10.0, + "description": "bookindex is defined as a triplet that includes the knowledge graph as one of its components", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "graph tree link", + "relation_name": "", + "weight": 10.0, + "description": "bookindex is defined as a triplet that includes the graph tree link as one of its components", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "tree structure", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 9.0, + "description": "the tree structure is derived from the document s explicit logical hierarchy", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "knowledge graph", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 9.0, + "description": "the knowledge graph captures entities and relations scattered throughout the document", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "graph tree link", + "tgt_entity_name": "tree structure", + "relation_name": "", + "weight": 10.0, + "description": "the graph tree link connects entities to specific tree nodes within the tree structure", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "graph tree link", + "tgt_entity_name": "knowledge graph", + "relation_name": "", + "weight": 10.0, + "description": "the graph tree link connects entities from the knowledge graph to the tree structure", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "titles", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 8.0, + "description": "titles are part of the document s explicit logical hierarchy", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "sections", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 8.0, + "description": "sections are part of the document s explicit logical hierarchy", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "tables", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 8.0, + "description": "tables are part of the document s explicit logical hierarchy", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "tree structure", + "tgt_entity_name": "titles", + "relation_name": "", + "weight": 9.0, + "description": "titles are examples of nodes included in the tree structure", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "tree structure", + "tgt_entity_name": "sections", + "relation_name": "", + "weight": 9.0, + "description": "sections are examples of nodes included in the tree structure", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "tree structure", + "tgt_entity_name": "tables", + "relation_name": "", + "weight": 9.0, + "description": "tables are examples of nodes included in the tree structure", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "knowledge graph", + "tgt_entity_name": "v", + "relation_name": "", + "weight": 10.0, + "description": "v represents the entities contained within the knowledge graph", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "knowledge graph", + "tgt_entity_name": "e g", + "relation_name": "", + "weight": 10.0, + "description": "e g represents the relations contained within the knowledge graph", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "tree structure", + "tgt_entity_name": "n", + "relation_name": "", + "weight": 10.0, + "description": "n represents the set of nodes contained within the tree structure", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "tree structure", + "tgt_entity_name": "e t", + "relation_name": "", + "weight": 10.0, + "description": "e t represents the nesting relationships contained within the tree structure", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "", + "relation_name": "", + "weight": 10.0, + "description": "is the first component of the bookindex triplet definition", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "", + "tgt_entity_name": "", + "relation_name": "", + "weight": 10.0, + "description": "is the set of nodes that constitutes the tree structure", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "", + "tgt_entity_name": "p", + "relation_name": "", + "weight": 10.0, + "description": "maps entities to the power set of nodes p", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "tree structure", + "tgt_entity_name": "information patches", + "relation_name": "", + "weight": 9.0, + "description": "the hierarchical tree nodes in the tree structure serve as information patches", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "knowledge graph", + "tgt_entity_name": "information scent", + "relation_name": "", + "weight": 9.0, + "description": "the entities and relations in the knowledge graph act as information scent", + "source_ids": [ + 51 + ] + }, + { + "src_entity_name": "information scent", + "tgt_entity_name": "navigation", + "relation_name": "", + "weight": 8.0, + "description": "information scent guides navigation between and within information patches", + "source_ids": [ + 51 + ] + } + ], + "node_idx": 51 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_52.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_52.json new file mode 100644 index 0000000..93616cc --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_52.json @@ -0,0 +1,289 @@ +{ + "entities": [ + { + "entity_name": "figure 2", + "entity_type": "IMAGE", + "description": "figure 2 is an image that provides an example of the bookindex", + "source_ids": [ + 52 + ] + }, + { + "entity_name": "bookindex", + "entity_type": "PRODUCT", + "description": "bookindex is a system or product being illustrated in the text", + "source_ids": [ + 52 + ] + }, + { + "entity_name": "tree component", + "entity_type": "SOFTWARE", + "description": "the tree component is a part of the bookindex that organizes documents into a hierarchical structure", + "source_ids": [ + 52 + ] + }, + { + "entity_name": "graph component", + "entity_type": "SOFTWARE", + "description": "the graph component is a part of the bookindex composed of entities and relations extracted from document nodes", + "source_ids": [ + 52 + ] + }, + { + "entity_name": "gt link", + "entity_type": "TECHNOLOGY", + "description": "gt link is a feature illustrated by blue dotted lines that connects entities to their corresponding tree nodes", + "source_ids": [ + 52 + ] + }, + { + "entity_name": "text", + "entity_type": "PRODUCT", + "description": "text is a type of content block serving as a leaf node within the document structure", + "source_ids": [ + 52 + ] + }, + { + "entity_name": "tables", + "entity_type": "PRODUCT", + "description": "tables are a type of content block serving as a leaf node within the document structure", + "source_ids": [ + 52 + ] + }, + { + "entity_name": "images", + "entity_type": "PRODUCT", + "description": "images are a type of content block serving as a leaf node within the document structure", + "source_ids": [ + 52 + ] + }, + { + "entity_name": "section nodes", + "entity_type": "PRODUCT", + "description": "section nodes are hierarchical nodes within the document structure that contain content blocks", + "source_ids": [ + 52 + ] + }, + { + "entity_name": "document", + "entity_type": "PRODUCT", + "description": "document is the object being organized into a hierarchical structure by the tree component", + "source_ids": [ + 52 + ] + }, + { + "entity_name": "content blocks", + "entity_type": "PRODUCT", + "description": "content blocks are the items text tables images that serve as leaf nodes in the hierarchy", + "source_ids": [ + 52 + ] + }, + { + "entity_name": "leaf nodes", + "entity_type": "PRODUCT", + "description": "leaf nodes are the terminal elements in the hierarchical structure containing content blocks", + "source_ids": [ + 52 + ] + }, + { + "entity_name": "semantic entities", + "entity_type": "CONCEPT", + "description": "semantic entities are the extracted entities grounded within the document s logical hierarchy by gt link", + "source_ids": [ + 52 + ] + }, + { + "entity_name": "logical hierarchy", + "entity_type": "CONCEPT", + "description": "logical hierarchy is the structure within the document that grounds the semantic entities", + "source_ids": [ + 52 + ] + } + ], + "relations": [ + { + "src_entity_name": "figure 2", + "tgt_entity_name": "bookindex", + "relation_name": "", + "weight": 10.0, + "description": "figure 2 provides an example of the bookindex", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "tree component", + "tgt_entity_name": "bookindex", + "relation_name": "", + "weight": 9.0, + "description": "the tree component is a part of the bookindex", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "graph component", + "tgt_entity_name": "bookindex", + "relation_name": "", + "weight": 9.0, + "description": "the graph component is a part of the bookindex", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "gt link", + "tgt_entity_name": "graph component", + "relation_name": "", + "weight": 8.0, + "description": "gt link is a feature within the graph component that connects entities to tree nodes", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "tree component", + "tgt_entity_name": "section nodes", + "relation_name": "", + "weight": 8.0, + "description": "the tree component organizes content blocks within section nodes", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "text", + "tgt_entity_name": "section nodes", + "relation_name": "", + "weight": 7.0, + "description": "text serves as a leaf node nested within section nodes", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "tables", + "tgt_entity_name": "section nodes", + "relation_name": "", + "weight": 7.0, + "description": "tables serve as a leaf node nested within section nodes", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "images", + "tgt_entity_name": "section nodes", + "relation_name": "", + "weight": 7.0, + "description": "images serve as a leaf node nested within section nodes", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "gt link", + "tgt_entity_name": "tree component", + "relation_name": "", + "weight": 8.0, + "description": "gt link connects entities back to their corresponding tree nodes", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "tree component", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 9.0, + "description": "the tree component organizes the document into a hierarchical structure", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "content blocks", + "tgt_entity_name": "leaf nodes", + "relation_name": "", + "weight": 9.0, + "description": "content blocks serve as leaf nodes within the structure", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "text", + "tgt_entity_name": "content blocks", + "relation_name": "", + "weight": 10.0, + "description": "text is identified as a type of content block", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "tables", + "tgt_entity_name": "content blocks", + "relation_name": "", + "weight": 10.0, + "description": "tables are identified as a type of content block", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "images", + "tgt_entity_name": "content blocks", + "relation_name": "", + "weight": 10.0, + "description": "images are identified as a type of content block", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "gt link", + "tgt_entity_name": "semantic entities", + "relation_name": "", + "weight": 9.0, + "description": "gt link explicitly connects semantic entities back to their corresponding tree nodes", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "semantic entities", + "tgt_entity_name": "logical hierarchy", + "relation_name": "", + "weight": 8.0, + "description": "semantic entities are grounded within the document s logical hierarchy", + "source_ids": [ + 52 + ] + }, + { + "src_entity_name": "graph component", + "tgt_entity_name": "semantic entities", + "relation_name": "", + "weight": 8.0, + "description": "the graph component is composed of entities and relations extracted from nodes which include semantic entities", + "source_ids": [ + 52 + ] + } + ], + "node_idx": 52 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_53.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_53.json new file mode 100644 index 0000000..9d9294d --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_53.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "4.2 tree construction", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'BookIndex' and the first stage of its construction process, this section details the method for parsing document layouts to establish hierarchical nodes categorized by type.", + "source_ids": [ + 53 + ] + }, + { + "entity_name": "tree construction", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "The specific sequential process described in section 4.2 that parses document layout to create hierarchical nodes.", + "source_ids": [ + 53 + ] + } + ], + "relations": [ + { + "src_entity_name": "tree construction", + "tgt_entity_name": "4.2 tree construction", + "relation_name": "", + "weight": 10.0, + "description": "The concept of 'Tree Construction' is the primary topic and methodology detailed in section 4.2.", + "source_ids": [ + 53 + ] + } + ], + "node_idx": 53 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_54.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_54.json new file mode 100644 index 0000000..84e487c --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_54.json @@ -0,0 +1,97 @@ +{ + "entities": [ + { + "entity_name": "t", + "entity_type": "TASK_OR_PROBLEM", + "description": "t is a structured hierarchical tree that is the result of transforming a raw document", + "source_ids": [ + 54 + ] + }, + { + "entity_name": "raw document", + "entity_type": "PRODUCT", + "description": "raw document is the initial input that undergoes transformation into a structured hierarchical tree", + "source_ids": [ + 54 + ] + }, + { + "entity_name": "robust layout parsing", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "robust layout parsing is a key step involved in transforming the raw document into a structured hierarchical tree", + "source_ids": [ + 54 + ] + }, + { + "entity_name": "intelligent section filtering", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "intelligent section filtering is a key step involved in transforming the raw document into a structured hierarchical tree", + "source_ids": [ + 54 + ] + }, + { + "entity_name": "task or problem", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 54 + ] + } + ], + "relations": [ + { + "src_entity_name": "t", + "tgt_entity_name": "task or problem", + "relation_name": "", + "weight": 5.0, + "description": "t represents the structured hierarchical tree which is the outcome of the transformation task described", + "source_ids": [ + 54 + ] + }, + { + "src_entity_name": "robust layout parsing", + "tgt_entity_name": "t", + "relation_name": "", + "weight": 9.0, + "description": "robust layout parsing is a step used to create the structured hierarchical tree t", + "source_ids": [ + 54 + ] + }, + { + "src_entity_name": "intelligent section filtering", + "tgt_entity_name": "t", + "relation_name": "", + "weight": 9.0, + "description": "intelligent section filtering is a step used to create the structured hierarchical tree t", + "source_ids": [ + 54 + ] + }, + { + "src_entity_name": "raw document", + "tgt_entity_name": "robust layout parsing", + "relation_name": "", + "weight": 8.0, + "description": "raw document is the input processed by the robust layout parsing step", + "source_ids": [ + 54 + ] + }, + { + "src_entity_name": "raw document", + "tgt_entity_name": "intelligent section filtering", + "relation_name": "", + "weight": 8.0, + "description": "raw document is the input processed by the intelligent section filtering step", + "source_ids": [ + 54 + ] + } + ], + "node_idx": 54 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_55.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_55.json new file mode 100644 index 0000000..4045a24 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_55.json @@ -0,0 +1,87 @@ +{ + "entities": [ + { + "entity_name": "4.2.1 layout parsing", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Tree Construction' within the 'BOOKINDEX' chapter, this section details the initial phase of transforming raw documents into structured hierarchical trees using layout analysis and recognition models to identify and organize diverse content blocks.", + "source_ids": [ + 55 + ] + }, + { + "entity_name": "layout analysis", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "A specific technique employed in section 4.2.1 to understand the spatial arrangement of elements within document pages.", + "source_ids": [ + 55 + ] + }, + { + "entity_name": "recognition models", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "The computational models utilized in section 4.2.1 to recognize and classify different types of content blocks such as text, tables, and images.", + "source_ids": [ + 55 + ] + }, + { + "entity_name": "document d", + "entity_type": "TASK_OR_PROBLEM", + "description": "The input data object (a collection of pages) that serves as the target for processing in section 4.2.1.", + "source_ids": [ + 55 + ] + }, + { + "entity_name": "content blocks", + "entity_type": "DATASET_OR_CORPUS", + "description": "The diverse structural units (e.g., text, tables, images) identified and extracted from the document pages as described in section 4.2.1.", + "source_ids": [ + 55 + ] + } + ], + "relations": [ + { + "src_entity_name": "layout analysis", + "tgt_entity_name": "4.2.1 layout parsing", + "relation_name": "", + "weight": 9.5, + "description": "Layout Analysis is a core methodological component discussed within section 4.2.1.", + "source_ids": [ + 55 + ] + }, + { + "src_entity_name": "recognition models", + "tgt_entity_name": "4.2.1 layout parsing", + "relation_name": "", + "weight": 9.5, + "description": "Recognition Models are the primary tools used in the process detailed in section 4.2.1.", + "source_ids": [ + 55 + ] + }, + { + "src_entity_name": "document d", + "tgt_entity_name": "4.2.1 layout parsing", + "relation_name": "", + "weight": 10.0, + "description": "Document D is the specific input entity being processed in section 4.2.1.", + "source_ids": [ + 55 + ] + }, + { + "src_entity_name": "content blocks", + "tgt_entity_name": "4.2.1 layout parsing", + "relation_name": "", + "weight": 10.0, + "description": "Content Blocks represent the output entities identified and organized within section 4.2.1.", + "source_ids": [ + 55 + ] + } + ], + "node_idx": 55 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_56.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_56.json new file mode 100644 index 0000000..30c98a2 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_56.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "the output", + "entity_type": "TASK_OR_PROBLEM", + "description": "the output is described as a sequence of primitive", + "source_ids": [ + 56 + ] + }, + { + "entity_name": "primitive", + "entity_type": "CONCEPT", + "description": "primitive is a term used to describe the components of the output sequence", + "source_ids": [ + 56 + ] + } + ], + "relations": [ + { + "src_entity_name": "the output", + "tgt_entity_name": "primitive", + "relation_name": "", + "weight": 9.0, + "description": "the output consists of a sequence of primitives", + "source_ids": [ + 56 + ] + } + ], + "node_idx": 56 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_57.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_57.json new file mode 100644 index 0000000..40377d7 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_57.json @@ -0,0 +1,315 @@ +{ + "entities": [ + { + "entity_name": "section filtering", + "entity_type": "TASK_OR_PROBLEM", + "description": "section filtering is a phase that processes an initial sequence to identify a document s logically hierarchical structure", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "layout parsing", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "layout parsing is a method that identifies blocks as title but does not assign their hierarchical level", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "llm", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "llm is a model used for analysis to determine hierarchical levels and node types of document candidates", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "title", + "entity_type": "SECTION_TITLE", + "description": "title refers to blocks identified by layout parsing that require hierarchical level assignment", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "text", + "entity_type": "SECTION_TITLE", + "description": "text is a node type used to re classify erroneous title blocks such as descriptive text within images", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "image", + "entity_type": "IMAGE", + "description": "image refers to a location within a document where descriptive text might be erroneously parsed as a title", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "table", + "entity_type": "TABLE", + "description": "table refers to a document element specifically borderless table headers that might be erroneously parsed as a title", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "b", + "entity_type": "DATASET_OR_CORPUS", + "description": "b represents the candidate subset of blocks selected for llm based analysis", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "c", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "c represents the content of the candidates analyzed by the llm", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "f", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "f represents the layout features of the candidates analyzed by the llm", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "4 2 2", + "entity_type": "SECTION_TITLE", + "description": "4 2 2 is the section identifier for the section filtering phase", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "b title", + "entity_type": "DATASET_OR_CORPUS", + "description": "b title is a candidate subset of blocks where the type is title selected for llm based analysis", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "l", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "l represents the actual hierarchical level of a block ranging from 1 to infinity", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "1", + "entity_type": "MEASUREMENT", + "description": "1 is the value assigned to the root level in the hierarchical structure", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "none", + "entity_type": "SECTION_TITLE", + "description": "none is a value indicating that a block has no hierarchical level", + "source_ids": [ + 57 + ] + }, + { + "entity_name": "", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 57 + ] + } + ], + "relations": [ + { + "src_entity_name": "section filtering", + "tgt_entity_name": "layout parsing", + "relation_name": "", + "weight": 9.0, + "description": "section filtering processes the output of layout parsing to identify hierarchical structure", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "section filtering", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 10.0, + "description": "section filtering utilizes an llm to analyze content and layout features of candidates", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "layout parsing", + "tgt_entity_name": "title", + "relation_name": "", + "weight": 8.0, + "description": "layout parsing identifies blocks as title", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "title", + "relation_name": "", + "weight": 9.0, + "description": "llm analyzes title candidates to determine their actual hierarchical level and final node type", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "text", + "relation_name": "", + "weight": 8.0, + "description": "llm may re classify erroneous title blocks as text", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "section filtering", + "tgt_entity_name": "image", + "relation_name": "", + "weight": 7.0, + "description": "section filtering aims to correct blocks erroneously parsed as title such as descriptive text within images", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "section filtering", + "tgt_entity_name": "table", + "relation_name": "", + "weight": 7.0, + "description": "section filtering aims to correct blocks erroneously parsed as title such as borderless table headers", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "section filtering", + "tgt_entity_name": "b", + "relation_name": "", + "weight": 9.0, + "description": "section filtering selects the candidate subset b for analysis", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "l", + "relation_name": "", + "weight": 8.0, + "description": "llm determines the hierarchical level l for each candidate", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "c", + "relation_name": "", + "weight": 8.0, + "description": "llm analyzes the content c of the candidates", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "f", + "relation_name": "", + "weight": 8.0, + "description": "llm analyzes the layout features f of the candidates", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "section filtering", + "tgt_entity_name": "b title", + "relation_name": "", + "weight": 9.0, + "description": "section filtering selects the candidate subset b title for analysis", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "b title", + "relation_name": "", + "weight": 9.0, + "description": "the llm analyzes the candidate subset b title to determine properties", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "layout parsing", + "tgt_entity_name": "b title", + "relation_name": "", + "weight": 8.0, + "description": "layout parsing identifies blocks as title forming the subset b title", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "", + "relation_name": "", + "weight": 7.0, + "description": "the llm uses to identify blocks as title candidates", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "l", + "tgt_entity_name": "1", + "relation_name": "", + "weight": 8.0, + "description": "the parameter l uses 1 to represent the root level", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "", + "tgt_entity_name": "none", + "relation_name": "", + "weight": 7.0, + "description": "the final node type can be assigned the value none if a block has no level", + "source_ids": [ + 57 + ] + }, + { + "src_entity_name": "section filtering", + "tgt_entity_name": "text", + "relation_name": "", + "weight": 8.0, + "description": "section filtering corrects blocks erroneously parsed as title by re classifying them as text", + "source_ids": [ + 57 + ] + } + ], + "node_idx": 57 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_58.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_58.json new file mode 100644 index 0000000..e004940 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_58.json @@ -0,0 +1,267 @@ +{ + "entities": [ + { + "entity_name": "tree", + "entity_type": "TASK_OR_PROBLEM", + "description": "the tree is a definitive structure constructed from blocks consisting of nodes and edges representing content and relationships", + "source_ids": [ + 58 + ] + }, + { + "entity_name": "node set", + "entity_type": "TASK_OR_PROBLEM", + "description": "the node set is composed of all blocks from the filtering and re classification process retaining content and final node types", + "source_ids": [ + 58 + ] + }, + { + "entity_name": "edge set", + "entity_type": "TASK_OR_PROBLEM", + "description": "the edge set represents the parent child nesting relationships within the tree structure", + "source_ids": [ + 58 + ] + }, + { + "entity_name": "text", + "entity_type": "PRODUCT", + "description": "text is identified as a final node type retained within the nodes of the tree", + "source_ids": [ + 58 + ] + }, + { + "entity_name": "section", + "entity_type": "PRODUCT", + "description": "section is identified as a final node type retained within the nodes of the tree", + "source_ids": [ + 58 + ] + }, + { + "entity_name": "table", + "entity_type": "PRODUCT", + "description": "table is identified as a final node type retained within the nodes of the tree", + "source_ids": [ + 58 + ] + }, + { + "entity_name": "image", + "entity_type": "PRODUCT", + "description": "image is identified as a final node type retained within the nodes of the tree", + "source_ids": [ + 58 + ] + }, + { + "entity_name": "filtering", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "filtering is a process mentioned as part of the generation of blocks for the node set", + "source_ids": [ + 58 + ] + }, + { + "entity_name": "re classification", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "re classification is a process mentioned alongside filtering in the creation of the node set", + "source_ids": [ + 58 + ] + }, + { + "entity_name": "hierarchical levels", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "hierarchical levels are determined values used to infer parent child relationships for section nodes", + "source_ids": [ + 58 + ] + }, + { + "entity_name": "document order", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "document order is a sequential arrangement used to assemble the complete tree structure", + "source_ids": [ + 58 + ] + }, + { + "entity_name": "parent child nesting relationships", + "entity_type": "TASK_OR_PROBLEM", + "description": "parent child nesting relationships are the specific connections established by the edge set", + "source_ids": [ + 58 + ] + }, + { + "entity_name": "content", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "content is an attribute retained by each node in the node set", + "source_ids": [ + 58 + ] + }, + { + "entity_name": "final node type", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "final node type is an attribute retained by each node in the node set", + "source_ids": [ + 58 + ] + }, + { + "entity_name": "node", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 58 + ] + } + ], + "relations": [ + { + "src_entity_name": "tree", + "tgt_entity_name": "node set", + "relation_name": "", + "weight": 9.0, + "description": "the tree is constructed using the node set which contains all blocks from the filtering process", + "source_ids": [ + 58 + ] + }, + { + "src_entity_name": "tree", + "tgt_entity_name": "edge set", + "relation_name": "", + "weight": 9.0, + "description": "the tree includes the edge set which establishes parent child nesting relationships", + "source_ids": [ + 58 + ] + }, + { + "src_entity_name": "node set", + "tgt_entity_name": "text", + "relation_name": "", + "weight": 8.0, + "description": "the node set retains nodes of the type text", + "source_ids": [ + 58 + ] + }, + { + "src_entity_name": "node set", + "tgt_entity_name": "section", + "relation_name": "", + "weight": 8.0, + "description": "the node set retains nodes of the type section", + "source_ids": [ + 58 + ] + }, + { + "src_entity_name": "node set", + "tgt_entity_name": "table", + "relation_name": "", + "weight": 8.0, + "description": "the node set retains nodes of the type table", + "source_ids": [ + 58 + ] + }, + { + "src_entity_name": "node set", + "tgt_entity_name": "image", + "relation_name": "", + "weight": 8.0, + "description": "the node set retains nodes of the type image", + "source_ids": [ + 58 + ] + }, + { + "src_entity_name": "edge set", + "tgt_entity_name": "tree", + "relation_name": "", + "weight": 9.0, + "description": "the edge set is established to define the structure of the tree", + "source_ids": [ + 58 + ] + }, + { + "src_entity_name": "node set", + "tgt_entity_name": "filtering", + "relation_name": "", + "weight": 9.0, + "description": "the node set is composed of blocks resulting from the filtering process", + "source_ids": [ + 58 + ] + }, + { + "src_entity_name": "node set", + "tgt_entity_name": "re classification", + "relation_name": "", + "weight": 9.0, + "description": "the node set is composed of blocks resulting from the re classification process", + "source_ids": [ + 58 + ] + }, + { + "src_entity_name": "edge set", + "tgt_entity_name": "parent child nesting relationships", + "relation_name": "", + "weight": 10.0, + "description": "the edge set represents the parent child nesting relationships", + "source_ids": [ + 58 + ] + }, + { + "src_entity_name": "tree", + "tgt_entity_name": "hierarchical levels", + "relation_name": "", + "weight": 8.0, + "description": "hierarchical levels are used to infer relationships within the tree structure", + "source_ids": [ + 58 + ] + }, + { + "src_entity_name": "tree", + "tgt_entity_name": "document order", + "relation_name": "", + "weight": 8.0, + "description": "document order is used to assemble the complete tree structure", + "source_ids": [ + 58 + ] + }, + { + "src_entity_name": "node", + "tgt_entity_name": "content", + "relation_name": "", + "weight": 9.0, + "description": "each node retains its content", + "source_ids": [ + 58 + ] + }, + { + "src_entity_name": "node", + "tgt_entity_name": "final node type", + "relation_name": "", + "weight": 9.0, + "description": "each node retains its final node type", + "source_ids": [ + 58 + ] + } + ], + "node_idx": 58 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_59.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_59.json new file mode 100644 index 0000000..b651214 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_59.json @@ -0,0 +1,419 @@ +{ + "entities": [ + { + "entity_name": "figure 2", + "entity_type": "IMAGE", + "description": "figure 2 is an example shown in the text that illustrates the layout parsing phase", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "layout parsing phase", + "entity_type": "TASK_OR_PROBLEM", + "description": "layout parsing phase is a process that identifies diverse blocks in a document", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "title text table", + "entity_type": "PRODUCT", + "description": "title text table is a type of block identified during the layout parsing phase", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "image", + "entity_type": "IMAGE", + "description": "image is a type of block identified during the layout parsing phase", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "section filtering phase", + "entity_type": "TASK_OR_PROBLEM", + "description": "section filtering phase is a process where title candidates are analyzed by the llm", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "llm", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "llm is a model used to analyze title candidates and re classify blocks", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "method", + "entity_type": "SECTION_TITLE", + "description": "method is a title candidate analyzed during the section filtering phase", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "experiment", + "entity_type": "SECTION_TITLE", + "description": "experiment is a title candidate analyzed during the section filtering phase", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "moe layer", + "entity_type": "SECTION_TITLE", + "description": "moe layer is a title candidate that was erroneously tagged as a title but re classified as a text node", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "section nodes", + "entity_type": "SECTION_TITLE", + "description": "section nodes are blocks identified as having a specific level in the document hierarchy", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "text node", + "entity_type": "SECTION_TITLE", + "description": "text node is a classification for blocks that do not have a specific level in the document hierarchy", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "final tree structure", + "entity_type": "TASK_OR_PROBLEM", + "description": "final tree structure is the result of assembling filtered and classified nodes based on their levels and order", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "fontsize", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "fontsize is a parameter used to describe the size of text blocks such as 14 or 20", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "14", + "entity_type": "MEASUREMENT", + "description": "14 is the specific font size value associated with the method and experiment blocks", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "20", + "entity_type": "MEASUREMENT", + "description": "20 is the specific font size value associated with the moe layer block", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "level", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "level is a parameter used to define the hierarchy depth of document nodes", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "2", + "entity_type": "MEASUREMENT", + "description": "2 is the specific level value assigned to the method and experiment blocks", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "none", + "entity_type": "MEASUREMENT", + "description": "none is the specific level value assigned to the moe layer block indicating no hierarchy level", + "source_ids": [ + 59 + ] + }, + { + "entity_name": "document order", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "document order is a factor used to assemble nodes into the final tree structure", + "source_ids": [ + 59 + ] + } + ], + "relations": [ + { + "src_entity_name": "layout parsing phase", + "tgt_entity_name": "figure 2", + "relation_name": "", + "weight": 9.0, + "description": "figure 2 serves as an example for the layout parsing phase", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "layout parsing phase", + "tgt_entity_name": "title text table", + "relation_name": "", + "weight": 8.0, + "description": "the layout parsing phase identifies title text table as a type of block", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "layout parsing phase", + "tgt_entity_name": "image", + "relation_name": "", + "weight": 8.0, + "description": "the layout parsing phase identifies image as a type of block", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "section filtering phase", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 10.0, + "description": "the section filtering phase uses the llm to analyze title candidates", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "section filtering phase", + "tgt_entity_name": "method", + "relation_name": "", + "weight": 9.0, + "description": "the section filtering phase analyzes method as a title candidate", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "section filtering phase", + "tgt_entity_name": "experiment", + "relation_name": "", + "weight": 9.0, + "description": "the section filtering phase analyzes experiment as a title candidate", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "section filtering phase", + "tgt_entity_name": "moe layer", + "relation_name": "", + "weight": 9.0, + "description": "the section filtering phase analyzes moe layer which was erroneously tagged and re classified", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "method", + "relation_name": "", + "weight": 8.0, + "description": "the llm correctly identifies method as a section node", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "experiment", + "relation_name": "", + "weight": 8.0, + "description": "the llm correctly identifies experiment as a section node", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "moe layer", + "relation_name": "", + "weight": 9.0, + "description": "the llm re classifies moe layer from a title to a text node", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "section filtering phase", + "tgt_entity_name": "final tree structure", + "relation_name": "", + "weight": 7.0, + "description": "the section filtering phase contributes to the creation of the final tree structure", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "layout parsing phase", + "tgt_entity_name": "section filtering phase", + "relation_name": "", + "weight": 6.0, + "description": "the layout parsing phase precedes the section filtering phase in the document processing workflow", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "method", + "tgt_entity_name": "fontsize", + "relation_name": "", + "weight": 10.0, + "description": "the method block has a fontsize of 14", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "method", + "tgt_entity_name": "14", + "relation_name": "", + "weight": 10.0, + "description": "the method block is associated with the measurement value 14", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "method", + "tgt_entity_name": "level", + "relation_name": "", + "weight": 10.0, + "description": "the method block is identified as having a level of 2", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "method", + "tgt_entity_name": "2", + "relation_name": "", + "weight": 10.0, + "description": "the method block is associated with the measurement value 2", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "experiment", + "tgt_entity_name": "fontsize", + "relation_name": "", + "weight": 10.0, + "description": "the experiment block has a fontsize of 14", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "experiment", + "tgt_entity_name": "14", + "relation_name": "", + "weight": 10.0, + "description": "the experiment block is associated with the measurement value 14", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "experiment", + "tgt_entity_name": "level", + "relation_name": "", + "weight": 10.0, + "description": "the experiment block is identified as having a level of 2", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "experiment", + "tgt_entity_name": "2", + "relation_name": "", + "weight": 10.0, + "description": "the experiment block is associated with the measurement value 2", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "moe layer", + "tgt_entity_name": "fontsize", + "relation_name": "", + "weight": 10.0, + "description": "the moe layer block has a fontsize of 20", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "moe layer", + "tgt_entity_name": "20", + "relation_name": "", + "weight": 10.0, + "description": "the moe layer block is associated with the measurement value 20", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "moe layer", + "tgt_entity_name": "level", + "relation_name": "", + "weight": 10.0, + "description": "the moe layer block is identified as having a level of none", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "moe layer", + "tgt_entity_name": "none", + "relation_name": "", + "weight": 10.0, + "description": "the moe layer block is associated with the measurement value none", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "final tree structure", + "tgt_entity_name": "document order", + "relation_name": "", + "weight": 9.0, + "description": "the final tree structure is assembled based on the document order of the nodes", + "source_ids": [ + 59 + ] + }, + { + "src_entity_name": "final tree structure", + "tgt_entity_name": "level", + "relation_name": "", + "weight": 9.0, + "description": "the final tree structure is assembled based on the determined levels of the nodes", + "source_ids": [ + 59 + ] + } + ], + "node_idx": 59 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_6.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_6.json new file mode 100644 index 0000000..86148d1 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_6.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "pvldb", + "entity_type": "PUBLICATION_VENUE", + "description": "pvldb is a publication venue mentioned in the context of artifact availability", + "source_ids": [ + 6 + ] + }, + { + "entity_name": "artifact availability", + "entity_type": "TASK_OR_PROBLEM", + "description": "artifact availability refers to the status or process of making artifacts available as discussed in the text", + "source_ids": [ + 6 + ] + } + ], + "relations": [ + { + "src_entity_name": "pvldb", + "tgt_entity_name": "artifact availability", + "relation_name": "", + "weight": 8.0, + "description": "pvldb is the venue where the topic of artifact availability is addressed", + "source_ids": [ + 6 + ] + } + ], + "node_idx": 6 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_60.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_60.json new file mode 100644 index 0000000..7a0fb82 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_60.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "4", + "entity_type": "MEASUREMENT", + "description": "4 is a numerical value mentioned in the text though its specific context or unit is not provided", + "source_ids": [ + 60 + ] + } + ], + "relations": [], + "node_idx": 60 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_61.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_61.json new file mode 100644 index 0000000..cb7e8c7 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_61.json @@ -0,0 +1,51 @@ +{ + "entities": [ + { + "entity_name": "4.3 graph construction", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'BOOKINDEX' and the second stage of the proposed BookIndex construction process, this section details the method for extracting fine-grained entity knowledge from hierarchical tree nodes and refining it using a novel gradient-based entity resolution technique.", + "source_ids": [ + 61 + ] + }, + { + "entity_name": "graph construction", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "The specific two-stage process step that follows Tree Construction, focusing on extracting entity knowledge and performing resolution within the BookIndex framework.", + "source_ids": [ + 61 + ] + }, + { + "entity_name": "gradient-based entity resolution", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "A novel algorithmic method described in this section used to refine extracted entity knowledge by resolving ambiguities or duplicates based on gradient optimization.", + "source_ids": [ + 61 + ] + } + ], + "relations": [ + { + "src_entity_name": "graph construction", + "tgt_entity_name": "4.3 graph construction", + "relation_name": "", + "weight": 10.0, + "description": "The concept of 'Graph Construction' is the primary topic and subject matter of section 4.3.", + "source_ids": [ + 61 + ] + }, + { + "src_entity_name": "gradient-based entity resolution", + "tgt_entity_name": "4.3 graph construction", + "relation_name": "", + "weight": 9.5, + "description": "The method 'Gradient-based Entity Resolution' is a key component and technique detailed within section 4.3.", + "source_ids": [ + 61 + ] + } + ], + "node_idx": 61 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_62.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_62.json new file mode 100644 index 0000000..b1bdc88 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_62.json @@ -0,0 +1,61 @@ +{ + "entities": [ + { + "entity_name": "tree t", + "entity_type": "TASK_OR_PROBLEM", + "description": "tree t is a structure that is established before proceeding to the next step", + "source_ids": [ + 62 + ] + }, + { + "entity_name": "knowledge graph g", + "entity_type": "TASK_OR_PROBLEM", + "description": "knowledge graph g is a structure that is populated by extracting and refining entities from the tree nodes", + "source_ids": [ + 62 + ] + }, + { + "entity_name": "tree nodes", + "entity_type": "TASK_OR_PROBLEM", + "description": "tree nodes are the components within tree t from which entities are extracted and refined", + "source_ids": [ + 62 + ] + } + ], + "relations": [ + { + "src_entity_name": "tree t", + "tgt_entity_name": "knowledge graph g", + "relation_name": "", + "weight": 9.0, + "description": "tree t is the source from which entities are extracted to populate knowledge graph g", + "source_ids": [ + 62 + ] + }, + { + "src_entity_name": "tree t", + "tgt_entity_name": "tree nodes", + "relation_name": "", + "weight": 10.0, + "description": "tree nodes are the constituent parts of tree t that serve as the source for entity extraction", + "source_ids": [ + 62 + ] + }, + { + "src_entity_name": "knowledge graph g", + "tgt_entity_name": "tree nodes", + "relation_name": "", + "weight": 9.0, + "description": "knowledge graph g is populated by extracting and refining entities from tree nodes", + "source_ids": [ + 62 + ] + } + ], + "node_idx": 62 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_63.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_63.json new file mode 100644 index 0000000..e80a7b7 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_63.json @@ -0,0 +1,123 @@ +{ + "entities": [ + { + "entity_name": "4.3.1 kg construction", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Graph Construction' within the 'BOOKINDEX' chapter, this section details the specific algorithm for populating the Knowledge Graph by iterating through tree nodes and extracting subgraphs based on content modality (text or visual).", + "source_ids": [ + 63 + ] + }, + { + "entity_name": "knowledge graph", + "entity_type": "DATASET_OR_CORPUS", + "description": "The structured data repository being constructed in this section, populated by extracting entities and relations from document tree nodes.", + "source_ids": [ + 63 + ] + }, + { + "entity_name": "tree t", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "The hierarchical structure previously established that serves as the source of nodes to be processed for graph construction.", + "source_ids": [ + 63 + ] + }, + { + "entity_name": "llm", + "entity_type": "SOFTWARE", + "description": "Large Language Model used as a tool to extract entities and relations when processing text-only nodes.", + "source_ids": [ + 63 + ] + }, + { + "entity_name": "vision language model", + "entity_type": "SOFTWARE", + "description": "VLM employed specifically to extract visual knowledge from nodes containing image elements.", + "source_ids": [ + 63 + ] + }, + { + "entity_name": "image", + "entity_type": "IMAGE", + "description": "A specific node type indicating the presence of visual elements requiring VLM-based extraction.", + "source_ids": [ + 63 + ] + }, + { + "entity_name": "mapping m", + "entity_type": "EQUATION_OR_FORMULA", + "description": "The final mapping structure constructed by recording the origin tree node for every extracted entity.", + "source_ids": [ + 63 + ] + } + ], + "relations": [ + { + "src_entity_name": "knowledge graph", + "tgt_entity_name": "4.3.1 kg construction", + "relation_name": "", + "weight": 10.0, + "description": "The Knowledge Graph is the primary object being constructed in this section.", + "source_ids": [ + 63 + ] + }, + { + "src_entity_name": "tree t", + "tgt_entity_name": "4.3.1 kg construction", + "relation_name": "", + "weight": 9.5, + "description": "The Tree T provides the input nodes that are iterated over during the construction process.", + "source_ids": [ + 63 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "4.3.1 kg construction", + "relation_name": "", + "weight": 9.0, + "description": "The LLM is the method/tool utilized for extracting data from text-only nodes within this section.", + "source_ids": [ + 63 + ] + }, + { + "src_entity_name": "vision language model", + "tgt_entity_name": "4.3.1 kg construction", + "relation_name": "", + "weight": 9.0, + "description": "The Vision Language Model is the method/tool utilized for extracting data from visual nodes within this section.", + "source_ids": [ + 63 + ] + }, + { + "src_entity_name": "image", + "tgt_entity_name": "4.3.1 kg construction", + "relation_name": "", + "weight": 8.5, + "description": "The Image node type triggers the use of the Vision Language Model in this section's logic.", + "source_ids": [ + 63 + ] + }, + { + "src_entity_name": "mapping m", + "tgt_entity_name": "4.3.1 kg construction", + "relation_name": "", + "weight": 9.5, + "description": "The Mapping M is the critical output artifact generated by recording entity origins in this section.", + "source_ids": [ + 63 + ] + } + ], + "node_idx": 63 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_64.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_64.json new file mode 100644 index 0000000..e9fc75c --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_64.json @@ -0,0 +1,185 @@ +{ + "entities": [ + { + "entity_name": "table", + "entity_type": "PRODUCT", + "description": "table is a specific logical type mentioned in the text that requires preservation of structural semantics", + "source_ids": [ + 64 + ] + }, + { + "entity_name": "formula", + "entity_type": "PRODUCT", + "description": "formula is a specific logical type mentioned in the text that requires preservation of structural semantics", + "source_ids": [ + 64 + ] + }, + { + "entity_name": "v table", + "entity_type": "PRODUCT", + "description": "v table is a distinct typed entity representing the table itself created to preserve structural semantics", + "source_ids": [ + 64 + ] + }, + { + "entity_name": "row", + "entity_type": "PRODUCT", + "description": "row is a component of table nodes that is explicitly extracted as a distinct entity", + "source_ids": [ + 64 + ] + }, + { + "entity_name": "column", + "entity_type": "PRODUCT", + "description": "column is a component of table nodes that is explicitly extracted as a distinct entity", + "source_ids": [ + 64 + ] + }, + { + "entity_name": "header", + "entity_type": "PRODUCT", + "description": "header refers to row and column headers in table nodes that are explicitly extracted as distinct entities", + "source_ids": [ + 64 + ] + }, + { + "entity_name": "structural semantics", + "entity_type": "CONCEPT", + "description": "structural semantics refers to the meaning preserved for specific logical types in the described process", + "source_ids": [ + 64 + ] + }, + { + "entity_name": "logical types", + "entity_type": "CONCEPT", + "description": "logical types are categories of entities such as table and formula that require specific handling", + "source_ids": [ + 64 + ] + }, + { + "entity_name": "node", + "entity_type": "CONCEPT", + "description": "node refers to a specific point in the data structure where content is extracted", + "source_ids": [ + 64 + ] + }, + { + "entity_name": "vertex", + "entity_type": "CONCEPT", + "description": "vertex refers to the primary node v table to which other entities are linked", + "source_ids": [ + 64 + ] + }, + { + "entity_name": "containedin", + "entity_type": "RELATIONSHIP_TYPE", + "description": "containedin is the specific relationship type used to link row and column headers to the table entity", + "source_ids": [ + 64 + ] + } + ], + "relations": [ + { + "src_entity_name": "v table", + "tgt_entity_name": "table", + "relation_name": "", + "weight": 10.0, + "description": "v table is the distinct entity created to represent the table logical type", + "source_ids": [ + 64 + ] + }, + { + "src_entity_name": "row", + "tgt_entity_name": "v table", + "relation_name": "", + "weight": 9.0, + "description": "row headers are linked to v table via a containedin relationship", + "source_ids": [ + 64 + ] + }, + { + "src_entity_name": "column", + "tgt_entity_name": "v table", + "relation_name": "", + "weight": 9.0, + "description": "column headers are linked to v table via a containedin relationship", + "source_ids": [ + 64 + ] + }, + { + "src_entity_name": "header", + "tgt_entity_name": "v table", + "relation_name": "", + "weight": 9.0, + "description": "row and column headers are explicitly extracted and linked to v table", + "source_ids": [ + 64 + ] + }, + { + "src_entity_name": "structural semantics", + "tgt_entity_name": "logical types", + "relation_name": "", + "weight": 9.0, + "description": "structural semantics are preserved specifically for logical types like table and formula", + "source_ids": [ + 64 + ] + }, + { + "src_entity_name": "v table", + "tgt_entity_name": "node", + "relation_name": "", + "weight": 8.0, + "description": "v table is created as a distinct entity from the content of a specific node", + "source_ids": [ + 64 + ] + }, + { + "src_entity_name": "row", + "tgt_entity_name": "header", + "relation_name": "", + "weight": 9.0, + "description": "row headers are a specific type of header extracted from table nodes", + "source_ids": [ + 64 + ] + }, + { + "src_entity_name": "column", + "tgt_entity_name": "header", + "relation_name": "", + "weight": 9.0, + "description": "column headers are a specific type of header extracted from table nodes", + "source_ids": [ + 64 + ] + }, + { + "src_entity_name": "row", + "tgt_entity_name": "column", + "relation_name": "", + "weight": 7.0, + "description": "row and column headers are both explicitly extracted components of table nodes", + "source_ids": [ + 64 + ] + } + ], + "node_idx": 64 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_65.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_65.json new file mode 100644 index 0000000..657fcd6 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_65.json @@ -0,0 +1,51 @@ +{ + "entities": [ + { + "entity_name": "4.3.2 gradient-based entity resolution", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Graph Construction' within the 'BOOKINDEX' chapter, this section details a robust Entity Resolution (ER) process designed to identify and merge fragmented conceptual entities in a Knowledge Graph, addressing challenges like abbreviations and co-references.", + "source_ids": [ + 65 + ] + }, + { + "entity_name": "gradient-based entity resolution", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "A specific technique mentioned in the title for resolving entity fragmentation by utilizing gradient-based approaches to refine raw Knowledge Graphs.", + "source_ids": [ + 65 + ] + }, + { + "entity_name": "entity resolution", + "entity_type": "TASK_OR_PROBLEM", + "description": "The core problem addressed in this section: identifying and merging fragmented entities caused by abbreviations, co-references, or varied occurrences to ensure a well-constructed Knowledge Graph.", + "source_ids": [ + 65 + ] + } + ], + "relations": [ + { + "src_entity_name": "gradient-based entity resolution", + "tgt_entity_name": "4.3.2 gradient-based entity resolution", + "relation_name": "", + "weight": 10.0, + "description": "The concept of 'Gradient-based Entity Resolution' is the primary methodological topic of section 4.3.2.", + "source_ids": [ + 65 + ] + }, + { + "src_entity_name": "entity resolution", + "tgt_entity_name": "4.3.2 gradient-based entity resolution", + "relation_name": "", + "weight": 10.0, + "description": "The task of 'Entity Resolution' is the central subject matter detailed in section 4.3.2.", + "source_ids": [ + 65 + ] + } + ], + "node_idx": 65 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_66.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_66.json new file mode 100644 index 0000000..5f99571 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_66.json @@ -0,0 +1,225 @@ +{ + "entities": [ + { + "entity_name": "er methods", + "entity_type": "TASK_OR_PROBLEM", + "description": "er methods are conventional methods for entity resolution that are computationally expensive", + "source_ids": [ + 66 + ] + }, + { + "entity_name": "dirty er", + "entity_type": "TASK_OR_PROBLEM", + "description": "dirty er is a term used to describe batch processing across multiple data sources for entity resolution", + "source_ids": [ + 66 + ] + }, + { + "entity_name": "llms", + "entity_type": "TECHNOLOGY", + "description": "llms are large language models used for high accuracy judgments in entity resolution", + "source_ids": [ + 66 + ] + }, + { + "entity_name": "a", + "entity_type": "TASK_OR_PROBLEM", + "description": "a is an example entity used to illustrate the merging of multiple entities in the entity resolution process", + "source_ids": [ + 66 + ] + }, + { + "entity_name": "b", + "entity_type": "TASK_OR_PROBLEM", + "description": "b is an example entity used to illustrate the merging of multiple entities in the entity resolution process", + "source_ids": [ + 66 + ] + }, + { + "entity_name": "c", + "entity_type": "TASK_OR_PROBLEM", + "description": "c is an example entity used to illustrate the merging of multiple entities in the entity resolution process", + "source_ids": [ + 66 + ] + }, + { + "entity_name": "12", + "entity_type": "PUBLICATION_VENUE", + "description": "12 is a citation reference mentioned in the text regarding entity resolution methods", + "source_ids": [ + 66 + ] + }, + { + "entity_name": "a b", + "entity_type": "TASK_OR_PROBLEM", + "description": "a b is a specific pairwise comparison example between entities a and b", + "source_ids": [ + 66 + ] + }, + { + "entity_name": "a c", + "entity_type": "TASK_OR_PROBLEM", + "description": "a c is a specific pairwise comparison example between entities a and c", + "source_ids": [ + 66 + ] + }, + { + "entity_name": "b c", + "entity_type": "TASK_OR_PROBLEM", + "description": "b c is a specific pairwise comparison example between entities b and c", + "source_ids": [ + 66 + ] + }, + { + "entity_name": "o n 2", + "entity_type": "MEASUREMENT", + "description": "o n 2 represents the quadratic complexity of the number of pairwise comparisons required", + "source_ids": [ + 66 + ] + } + ], + "relations": [ + { + "src_entity_name": "er methods", + "tgt_entity_name": "dirty er", + "relation_name": "", + "weight": 9.0, + "description": "er methods are often designed for batch processing across multiple data sources commonly referred to as dirty er", + "source_ids": [ + 66 + ] + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "llms", + "relation_name": "", + "weight": 8.0, + "description": "relying on llms for high accuracy judgments in er methods can lead to prohibitively slow and computationally expensive processes", + "source_ids": [ + 66 + ] + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "a", + "relation_name": "", + "weight": 8.0, + "description": "er methods aim to merge entities like a b and c as the same concept", + "source_ids": [ + 66 + ] + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "b", + "relation_name": "", + "weight": 8.0, + "description": "er methods aim to merge entities like a b and c as the same concept", + "source_ids": [ + 66 + ] + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "c", + "relation_name": "", + "weight": 8.0, + "description": "er methods aim to merge entities like a b and c as the same concept", + "source_ids": [ + 66 + ] + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "a b", + "relation_name": "", + "weight": 9.0, + "description": "er methods require finding all possible matching pairs such as a b to confirm equivalence", + "source_ids": [ + 66 + ] + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "a c", + "relation_name": "", + "weight": 9.0, + "description": "er methods require finding all possible matching pairs such as a c to confirm equivalence", + "source_ids": [ + 66 + ] + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "b c", + "relation_name": "", + "weight": 9.0, + "description": "er methods require finding all possible matching pairs such as b c to confirm equivalence", + "source_ids": [ + 66 + ] + }, + { + "src_entity_name": "a", + "tgt_entity_name": "b", + "relation_name": "", + "weight": 7.0, + "description": "a and b are compared as a pair a b to confirm their equivalence", + "source_ids": [ + 66 + ] + }, + { + "src_entity_name": "a", + "tgt_entity_name": "c", + "relation_name": "", + "weight": 7.0, + "description": "a and c are compared as a pair a c to confirm their equivalence", + "source_ids": [ + 66 + ] + }, + { + "src_entity_name": "b", + "tgt_entity_name": "c", + "relation_name": "", + "weight": 7.0, + "description": "b and c are compared as a pair b c to confirm their equivalence", + "source_ids": [ + 66 + ] + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "12", + "relation_name": "", + "weight": 6.0, + "description": "the text cites reference 12 in the context of ensuring accurate entity resolution", + "source_ids": [ + 66 + ] + }, + { + "src_entity_name": "er methods", + "tgt_entity_name": "o n 2", + "relation_name": "", + "weight": 9.0, + "description": "the process of er methods leads to a quadratic o n 2 number of pairwise comparisons", + "source_ids": [ + 66 + ] + } + ], + "node_idx": 66 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_67.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_67.json new file mode 100644 index 0000000..845b621 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_67.json @@ -0,0 +1,169 @@ +{ + "entities": [ + { + "entity_name": "gradient based er method", + "entity_type": "TECHNOLOGY", + "description": "a gradient based entity resolution method employed to process a single document incrementally", + "source_ids": [ + 67 + ] + }, + { + "entity_name": "clean er", + "entity_type": "TASK_OR_PROBLEM", + "description": "a simplified version of the entity resolution task used as the basis for the incremental process", + "source_ids": [ + 67 + ] + }, + { + "entity_name": "database", + "entity_type": "SOFTWARE", + "description": "a storage system containing already processed entities against which new entities are compared", + "source_ids": [ + 67 + ] + }, + { + "entity_name": "top k most relevant candidates", + "entity_type": "EVALUATION_METRIC", + "description": "a set of the most relevant entities used for reranking a new entity in the incremental process", + "source_ids": [ + 67 + ] + }, + { + "entity_name": "entity", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "a single new entity being extracted in the incremental process", + "source_ids": [ + 67 + ] + }, + { + "entity_name": "quadratic batch problem", + "entity_type": "TASK_OR_PROBLEM", + "description": "the original complex problem that the incremental method transforms into a simpler task", + "source_ids": [ + 67 + ] + }, + { + "entity_name": "repeated lookup task", + "entity_type": "TASK_OR_PROBLEM", + "description": "the simplified task resulting from transforming the quadratic batch problem", + "source_ids": [ + 67 + ] + }, + { + "entity_name": "scoring patterns", + "entity_type": "EVALUATION_METRIC", + "description": "distinct observable patterns yielded by the incremental process when reranking entities", + "source_ids": [ + 67 + ] + }, + { + "entity_name": "incremental process", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 67 + ] + } + ], + "relations": [ + { + "src_entity_name": "gradient based er method", + "tgt_entity_name": "clean er", + "relation_name": "", + "weight": 9.0, + "description": "the method operates on a single document simplified as the clean er", + 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"description": "the method transforms the quadratic batch problem into a simpler task", + "source_ids": [ + 67 + ] + }, + { + "src_entity_name": "gradient based er method", + "tgt_entity_name": "repeated lookup task", + "relation_name": "", + "weight": 9.0, + "description": "the method transforms the problem into a repeated lookup task", + "source_ids": [ + 67 + ] + }, + { + "src_entity_name": "entity", + "tgt_entity_name": "database", + "relation_name": "", + "weight": 9.0, + "description": "the new entity is determined to fit among the already processed entities in the database", + "source_ids": [ + 67 + ] + }, + { + "src_entity_name": "entity", + "tgt_entity_name": "top k most relevant candidates", + "relation_name": "", + "weight": 9.0, + "description": "the new entity is reranked against its top k most relevant candidates", + "source_ids": [ + 67 + ] + }, + { + "src_entity_name": "incremental process", + "tgt_entity_name": "scoring patterns", + "relation_name": "", + "weight": 8.0, + "description": "the incremental process yields two distinct scoring patterns", + "source_ids": [ + 67 + ] + } + ], + "node_idx": 67 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_68.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_68.json new file mode 100644 index 0000000..ae99b90 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_68.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "5", + "entity_type": "MEASUREMENT", + "description": "5 is a numerical value mentioned in the text potentially representing a count score or measurement", + "source_ids": [ + 68 + ] + } + ], + "relations": [], + "node_idx": 68 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_69.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_69.json new file mode 100644 index 0000000..c578f40 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_69.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "algorithm 1", + "entity_type": "TASK_OR_PROBLEM", + "description": "algorithm 1 is a gradient based entity resolution method mentioned in the text", + "source_ids": [ + 69 + ] + }, + { + "entity_name": "gradient based entity resolution", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "gradient based entity resolution is the specific technique or approach described for the algorithm", + "source_ids": [ + 69 + ] + } + ], + "relations": [ + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "gradient based entity resolution", + "relation_name": "", + "weight": 10.0, + "description": "algorithm 1 is defined as a gradient based entity resolution method", + "source_ids": [ + 69 + ] + } + ], + "node_idx": 69 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_7.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_7.json new file mode 100644 index 0000000..f128f94 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_7.json @@ -0,0 +1,135 @@ +{ + "entities": [ + { + "entity_name": "bookrag", + "entity_type": "SOFTWARE", + "description": "bookrag is a software project hosted on github containing source code and data", + "source_ids": [ + 7 + ] + }, + { + "entity_name": "github", + "entity_type": "ORGANIZATION", + "description": "github is the platform where the source code and data for bookrag are made available", + "source_ids": [ + 7 + ] + }, + { + "entity_name": "sam234990", + "entity_type": "PERSON", + "description": "sam234990 is the username or owner associated with the bookrag repository on github", + "source_ids": [ + 7 + ] + }, + { + "entity_name": "source code", + "entity_type": "PRODUCT", + "description": "source code is a digital artifact made available as part of the bookrag project", + "source_ids": [ + 7 + ] + }, + { + "entity_name": "data", + "entity_type": "PRODUCT", + "description": "data is a digital artifact made available as part of the bookrag project", + "source_ids": [ + 7 + ] + }, + { + "entity_name": "artifacts", + "entity_type": "PRODUCT", + "description": "artifacts are items made available alongside the source code and data for the bookrag project", + "source_ids": [ + 7 + ] + } + ], + "relations": [ + { + "src_entity_name": "bookrag", + "tgt_entity_name": "github", + "relation_name": "", + "weight": 10.0, + "description": "bookrag is hosted on the github platform as indicated by the provided url", + "source_ids": [ + 7 + ] + }, + { + "src_entity_name": "sam234990", + "tgt_entity_name": "bookrag", + "relation_name": "", + "weight": 9.0, + "description": "sam234990 is the creator or owner of the bookrag repository", + "source_ids": [ + 7 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "source code", + "relation_name": "", + "weight": 10.0, + "description": "bookrag contains the source code that has been made available", + "source_ids": [ + 7 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "data", + "relation_name": "", + "weight": 10.0, + "description": "bookrag contains the data that has been made available", + "source_ids": [ + 7 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "artifacts", + "relation_name": "", + "weight": 10.0, + "description": "bookrag includes other artifacts that have been made available", + "source_ids": [ + 7 + ] + }, + { + "src_entity_name": "source code", + "tgt_entity_name": "github", + "relation_name": "", + "weight": 9.0, + "description": "the source code is hosted on github", + "source_ids": [ + 7 + ] + }, + { + "src_entity_name": "data", + "tgt_entity_name": "github", + "relation_name": "", + "weight": 9.0, + "description": "the data is hosted on github", + "source_ids": [ + 7 + ] + }, + { + "src_entity_name": "artifacts", + "tgt_entity_name": "github", + "relation_name": "", + "weight": 9.0, + "description": "the artifacts are hosted on github", + "source_ids": [ + 7 + ] + } + ], + "node_idx": 7 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_70.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_70.json new file mode 100644 index 0000000..cb4f9b3 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_70.json @@ -0,0 +1,219 @@ +{ + "entities": [ + { + "entity_name": "kg g", + "entity_type": "TASK_OR_PROBLEM", + "description": "kg g is a knowledge graph that serves as the input for the described process", + "source_ids": [ + 70 + ] + }, + { + "entity_name": "new entity v n", + "entity_type": "TASK_OR_PROBLEM", + "description": "new entity v n is a new entity being introduced into the system", + "source_ids": [ + 70 + ] + }, + { + "entity_name": "rerank model r", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "rerank model r is a model used to rerank entities in the process", + "source_ids": [ + 70 + ] + }, + { + "entity_name": "entity vector database db", + "entity_type": "DATASET_OR_CORPUS", + "description": "entity vector database db is a database storing entity vectors", + "source_ids": [ + 70 + ] + }, + { + "entity_name": "vector search number top k", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "vector search number top k is a parameter defining the number of top results for vector search", + "source_ids": [ + 70 + ] + }, + { + "entity_name": "threshold of gradient g", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "threshold of gradient g is a threshold value used for gradient calculations", + "source_ids": [ + 70 + ] + }, + { + "entity_name": "kg", + "entity_type": "TASK_OR_PROBLEM", + "description": "kg is the abbreviation for the knowledge graph mentioned as input", + "source_ids": [ + 70 + ] + }, + { + "entity_name": "g", + "entity_type": "TASK_OR_PROBLEM", + "description": "g is the specific instance or variable name for the knowledge graph", + "source_ids": [ + 70 + ] + }, + { + "entity_name": "v", + "entity_type": "TASK_OR_PROBLEM", + "description": "v is the variable representing the new entity", + "source_ids": [ + 70 + ] + }, + { + "entity_name": "n", + "entity_type": "TASK_OR_PROBLEM", + "description": "n is a subscript or identifier associated with the new entity v", + "source_ids": [ + 70 + ] + }, + { + "entity_name": "r", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "r is the specific variable name for the rerank model", + "source_ids": [ + 70 + ] + }, + { + "entity_name": "db", + "entity_type": "DATASET_OR_CORPUS", + "description": "db is the specific variable name for the entity vector database", + "source_ids": [ + 70 + ] + }, + { + "entity_name": "top k", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "top k is the specific variable name for the vector search number", + "source_ids": [ + 70 + ] + }, + { + "entity_name": "g", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "g is the specific variable name for the threshold of gradient", + "source_ids": [ + 70 + ] + } + ], + "relations": [ + { + "src_entity_name": "kg g", + "tgt_entity_name": "new entity v n", + "relation_name": "", + "weight": 8.0, + "description": "the new entity v n is added to or processed within the knowledge graph g", + "source_ids": [ + 70 + ] + }, + { + "src_entity_name": "rerank model r", + "tgt_entity_name": "entity vector database db", + "relation_name": "", + "weight": 7.0, + "description": "the rerank model r likely utilizes the entity vector database db to perform its ranking tasks", + "source_ids": [ + 70 + ] + }, + { + "src_entity_name": "vector search number top k", + "tgt_entity_name": "entity vector database db", + "relation_name": "", + "weight": 9.0, + "description": "the vector search number top k parameter determines the scope of the search performed on the entity vector database db", + "source_ids": [ + 70 + ] + }, + { + "src_entity_name": "threshold of gradient g", + "tgt_entity_name": "rerank model r", + "relation_name": "", + "weight": 6.0, + "description": "the threshold of gradient g is a parameter that influences the operation or convergence of the rerank model r", + "source_ids": [ + 70 + ] + }, + { + "src_entity_name": "kg", + "tgt_entity_name": "g", + "relation_name": "", + "weight": 10.0, + "description": "kg and g refer to the same knowledge graph entity with g being its variable representation", + "source_ids": [ + 70 + ] + }, + { + "src_entity_name": "v", + "tgt_entity_name": "n", + "relation_name": "", + "weight": 9.0, + "description": "n is a subscript or modifier defining the specific instance of the new entity v", + "source_ids": [ + 70 + ] + }, + { + "src_entity_name": "rerank model r", + "tgt_entity_name": "r", + "relation_name": "", + "weight": 10.0, + "description": "r is the variable name for the rerank model", + "source_ids": [ + 70 + ] + }, + { + "src_entity_name": "entity vector database db", + "tgt_entity_name": "db", + "relation_name": "", + "weight": 10.0, + "description": "db is the variable name for the entity vector database", + "source_ids": [ + 70 + ] + }, + { + "src_entity_name": "vector search number top k", + "tgt_entity_name": "top k", + "relation_name": "", + "weight": 10.0, + "description": "top k is the variable name for the vector search number", + "source_ids": [ + 70 + ] + }, + { + "src_entity_name": "threshold of gradient g", + "tgt_entity_name": "g", + "relation_name": "", + "weight": 10.0, + "description": "g is the variable name for the threshold of gradient", + "source_ids": [ + 70 + ] + } + ], + "node_idx": 70 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_71.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_71.json new file mode 100644 index 0000000..256ac4c --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_71.json @@ -0,0 +1,265 @@ +{ + "entities": [ + { + "entity_name": "vector search", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "db", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "search", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "r", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "e", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "sort", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "gradient select", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "top k", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "v n", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "e c", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "v cn", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "s", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "c", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "score", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "s 0", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + }, + { + "entity_name": "sel", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 71 + ] + } + ], + "relations": [ + { + "src_entity_name": "vector search", + "tgt_entity_name": "db", + "relation_name": "", + "weight": 9.0, + "description": "vector search operates on the db to find relevant entities", + "source_ids": [ + 71 + ] + }, + { + "src_entity_name": "search", + "tgt_entity_name": "db", + "relation_name": "", + "weight": 9.0, + "description": "the search function is applied to the db to retrieve entities", + "source_ids": [ + 71 + ] + }, + { + "src_entity_name": "r", + "tgt_entity_name": "e", + "relation_name": "", + "weight": 8.0, + "description": "the function r takes entities e as input to process them", + "source_ids": [ + 71 + ] + }, + { + "src_entity_name": "sort", + "tgt_entity_name": "e", + "relation_name": "", + "weight": 8.0, + "description": "the sort operation is applied to the list of entities e", + "source_ids": [ + 71 + ] + }, + { + "src_entity_name": "gradient select", + "tgt_entity_name": "e", + "relation_name": "", + "weight": 8.0, + "description": "gradient select is used to select entities from the remaining list e", + "source_ids": [ + 71 + ] + }, + { + "src_entity_name": "vector search", + "tgt_entity_name": "top k", + "relation_name": "", + "weight": 9.0, + "description": "vector search utilizes the top k parameter to limit the number of relevant entities found", + "source_ids": [ + 71 + ] + }, + { + "src_entity_name": "search", + "tgt_entity_name": "v n", + "relation_name": "", + "weight": 9.0, + "description": "the search function uses the vector v n as its query input", + "source_ids": [ + 71 + ] + }, + { + "src_entity_name": "search", + "tgt_entity_name": "e c", + "relation_name": "", + "weight": 9.0, + "description": "the search function outputs the candidate entities e c", + "source_ids": [ + 71 + ] + }, + { + "src_entity_name": "r", + "tgt_entity_name": "v cn", + "relation_name": "", + "weight": 8.0, + "description": "the function r uses the vector v cn to calculate rerank scores", + "source_ids": [ + 71 + ] + }, + { + "src_entity_name": "sort", + "tgt_entity_name": "s", + "relation_name": "", + "weight": 9.0, + "description": "the sort operation generates the sorted list s", + "source_ids": [ + 71 + ] + }, + { + "src_entity_name": "sort", + "tgt_entity_name": "c", + "relation_name": "", + "weight": 9.0, + "description": "the sort operation orders entities based on the rerank scores c", + "source_ids": [ + 71 + ] + }, + { + "src_entity_name": "score", + "tgt_entity_name": "s 0", + "relation_name": "", + "weight": 10.0, + "description": "the score variable is assigned the value of the first element s 0 from the sorted list", + "source_ids": [ + 71 + ] + }, + { + "src_entity_name": "gradient select", + "tgt_entity_name": "sel", + "relation_name": "", + "weight": 9.0, + "description": "the gradient select method produces the selected entities sel", + "source_ids": [ + 71 + ] + } + ], + "node_idx": 71 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_72.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_72.json new file mode 100644 index 0000000..28fab6f --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_72.json @@ -0,0 +1,75 @@ +{ + "entities": [ + { + "entity_name": "case a", + "entity_type": "TASK_OR_PROBLEM", + "description": "case a is a scenario described as involving a new conceptual entity", + "source_ids": [ + 72 + ] + }, + { + "entity_name": "new entity", + "entity_type": "TASK_OR_PROBLEM", + "description": "new entity refers to a conceptual entity that is being evaluated for relevance against existing entities", + "source_ids": [ + 72 + ] + }, + { + "entity_name": "existing entities", + "entity_type": "TASK_OR_PROBLEM", + "description": "existing entities are the set of entities against which the relevance of a new conceptual entity is measured", + "source_ids": [ + 72 + ] + }, + { + "entity_name": "relevance scores", + "entity_type": "EVALUATION_METRIC", + "description": "relevance scores are the metrics used to measure the relationship between the new entity and existing entities", + "source_ids": [ + 72 + ] + }, + { + "entity_name": "gradient", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "gradient refers to a mathematical pattern or value that is absent in the relevance scores for new entities", + "source_ids": [ + 72 + ] + }, + { + "entity_name": "discriminative pattern", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "discriminative pattern refers to a distinguishing feature or trend that is not present in the relevance scores for new entities", + "source_ids": [ + 72 + ] + } + ], + "relations": [ + { + "src_entity_name": "case a", + "tgt_entity_name": "new entity", + "relation_name": "", + "weight": 9.0, + "description": "case a describes the scenario where a new entity is introduced and evaluated", + "source_ids": [ + 72 + ] + }, + { + "src_entity_name": "new entity", + "tgt_entity_name": "existing entities", + "relation_name": "", + "weight": 10.0, + "description": "the new entity s relevance scores are calculated against all existing entities", + "source_ids": [ + 72 + ] + } + ], + "node_idx": 72 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_73.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_73.json new file mode 100644 index 0000000..c22b0b0 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_73.json @@ -0,0 +1,187 @@ +{ + "entities": [ + { + "entity_name": "case b", + "entity_type": "TASK_OR_PROBLEM", + "description": "case b refers to a scenario involving an existing entity where an alias is being evaluated for relevance", + "source_ids": [ + 73 + ] + }, + { + "entity_name": "reranker", + "entity_type": "TECHNOLOGY", + "description": "the reranker is a system or component described as having inherent discriminative limitations", + "source_ids": [ + 73 + ] + }, + { + "entity_name": "existing entity", + "entity_type": "TASK_OR_PROBLEM", + "description": "existing entity refers to an entity that is already present in the system being discussed", + "source_ids": [ + 73 + ] + }, + { + "entity_name": "alias", + "entity_type": "CONCEPT", + "description": "alias is a term used to describe an alternative name for an existing entity", + "source_ids": [ + 73 + ] + }, + { + "entity_name": "scores", + "entity_type": "EVALUATION_METRIC", + "description": "scores are the numerical values indicating the relevance of an alias to a true match", + "source_ids": [ + 73 + ] + }, + { + "entity_name": "true match", + "entity_type": "CONCEPT", + "description": "true match refers to the correct entity that an alias is being compared against", + "source_ids": [ + 73 + ] + }, + { + "entity_name": "equivalent aliases", + "entity_type": "CONCEPT", + "description": "equivalent aliases refers to a small set of aliases that are considered the same as the true match", + "source_ids": [ + 73 + ] + }, + { + "entity_name": "gradient", + "entity_type": "MEASUREMENT", + "description": "gradient refers to the sharp decline in relevance scores mentioned in the text", + "source_ids": [ + 73 + ] + }, + { + "entity_name": "irrelevant entities", + "entity_type": "TASK_OR_PROBLEM", + "description": "irrelevant entities are the entities that follow the sharp decline in relevance scores", + "source_ids": [ + 73 + ] + }, + { + "entity_name": "", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 73 + ] + } + ], + "relations": [ + { + "src_entity_name": "", + "tgt_entity_name": "case b", + "relation_name": "", + "weight": 10.0, + "description": "is the specific alias being discussed within the scenario defined as case b", + "source_ids": [ + 73 + ] + }, + { + "src_entity_name": "", + "tgt_entity_name": "reranker", + "relation_name": "", + "weight": 8.0, + "description": "the scores of are influenced by the inherent discriminative limitations of the reranker", + "source_ids": [ + 73 + ] + }, + { + "src_entity_name": "case b", + "tgt_entity_name": "existing entity", + "relation_name": "", + "weight": 10.0, + "description": "case b is defined by the scenario involving an existing entity", + "source_ids": [ + 73 + ] + }, + { + "src_entity_name": "case b", + "tgt_entity_name": "alias", + "relation_name": "", + "weight": 9.0, + "description": "case b specifically addresses the situation where an alias is being evaluated", + "source_ids": [ + 73 + ] + }, + { + "src_entity_name": "alias", + "tgt_entity_name": "true match", + "relation_name": "", + "weight": 9.0, + "description": "the alias is evaluated for its relevance to the true match", + "source_ids": [ + 73 + ] + }, + { + "src_entity_name": "scores", + "tgt_entity_name": "true match", + "relation_name": "", + "weight": 8.0, + "description": "scores indicate the relevance of the alias to the true match", + "source_ids": [ + 73 + ] + }, + { + "src_entity_name": "scores", + "tgt_entity_name": "equivalent aliases", + "relation_name": "", + "weight": 8.0, + "description": "scores show high relevance to the true match or a set of equivalent aliases", + "source_ids": [ + 73 + ] + }, + { + "src_entity_name": "reranker", + "tgt_entity_name": "scores", + "relation_name": "", + "weight": 7.0, + "description": "the reranker s limitations affect the initial set of high relevance scores", + "source_ids": [ + 73 + ] + }, + { + "src_entity_name": "scores", + "tgt_entity_name": "gradient", + "relation_name": "", + "weight": 8.0, + "description": "the scores exhibit a sharp decline gradient after the initial high relevance set", + "source_ids": [ + 73 + ] + }, + { + "src_entity_name": "gradient", + "tgt_entity_name": "irrelevant entities", + "relation_name": "", + "weight": 7.0, + "description": "the gradient precedes the transition to irrelevant entities", + "source_ids": [ + 73 + ] + } + ], + "node_idx": 73 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_74.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_74.json new file mode 100644 index 0000000..766e3c9 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_74.json @@ -0,0 +1,115 @@ +{ + "entities": [ + { + "entity_name": "gradient based er algorithm", + "entity_type": "TECHNOLOGY", + "description": "the gradient based er algorithm is a method designed to detect sharp declines characteristic of case b and isolate high relevance sets", + "source_ids": [ + 74 + ] + }, + { + "entity_name": "llm", + "entity_type": "TECHNOLOGY", + "description": "an llm is utilized for finer grained distinction when multiple similar entities are identified within a set", + "source_ids": [ + 74 + ] + }, + { + "entity_name": "case b", + "entity_type": "TASK_OR_PROBLEM", + "description": "case b is a scenario characterized by a sharp decline that the gradient based er algorithm is designed to detect", + "source_ids": [ + 74 + ] + }, + { + "entity_name": "case a", + "entity_type": "TASK_OR_PROBLEM", + "description": "case a is a no gradient scenario that the llm helps differentiate from the set identified by the algorithm", + "source_ids": [ + 74 + ] + }, + { + "entity_name": "high relevance set", + "entity_type": "DATASET_OR_CORPUS", + "description": "the high relevance set is a collection of entities isolated by the gradient based er algorithm for further processing", + "source_ids": [ + 74 + ] + }, + { + "entity_name": "similar entities", + "entity_type": "DATASET_OR_CORPUS", + "description": "similar entities are a group of items identified within the high relevance set that require finer grained distinction", + "source_ids": [ + 74 + ] + } + ], + "relations": [ + { + "src_entity_name": "gradient based er algorithm", + "tgt_entity_name": "case b", + "relation_name": "", + "weight": 10.0, + "description": "the gradient based er algorithm is designed to detect the sharp decline characteristic of case b", + "source_ids": [ + 74 + ] + }, + { + "src_entity_name": "gradient based er algorithm", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 9.0, + "description": "the gradient based er algorithm isolates a set of entities which is subsequently processed by an llm for finer grained distinction", + "source_ids": [ + 74 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "case a", + "relation_name": "", + "weight": 9.0, + "description": "the llm is used to differentiate the identified set from the no gradient scenario of case a", + "source_ids": [ + 74 + ] + }, + { + "src_entity_name": "gradient based er algorithm", + "tgt_entity_name": "high relevance set", + "relation_name": "", + "weight": 10.0, + "description": "the gradient based er algorithm efficiently isolates the high relevance set", + "source_ids": [ + 74 + ] + }, + { + "src_entity_name": "llm", + "tgt_entity_name": "similar entities", + "relation_name": "", + "weight": 9.0, + "description": "the llm is utilized to distinguish between multiple similar entities identified within the set", + "source_ids": [ + 74 + ] + }, + { + "src_entity_name": "high relevance set", + "tgt_entity_name": "similar entities", + "relation_name": "", + "weight": 8.0, + "description": "the similar entities are contained within the high relevance set identified by the algorithm", + "source_ids": [ + 74 + ] + } + ], + "node_idx": 74 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_75.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_75.json new file mode 100644 index 0000000..da4259e --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_75.json @@ -0,0 +1,437 @@ +{ + "entities": [ + { + "entity_name": "algorithm 1", + "entity_type": "TASK_OR_PROBLEM", + "description": "algorithm 1 is the entity resolution process described in the text", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "v n", + "entity_type": "TASK_OR_PROBLEM", + "description": "v n is a new entity being processed in the entity resolution process", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "e c", + "entity_type": "TASK_OR_PROBLEM", + "description": "e c represents the top k candidates retrieved for the new entity v n", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "db", + "entity_type": "TASK_OR_PROBLEM", + "description": "db is the vector database from which candidates are retrieved", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "r", + "entity_type": "TASK_OR_PROBLEM", + "description": "r is the reranker used to re rank candidates against v n", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "s", + "entity_type": "TASK_OR_PROBLEM", + "description": "s represents the scores assigned to the candidates", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "sel", + "entity_type": "TASK_OR_PROBLEM", + "description": "sel is the selection set initialized with the top scoring candidate", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "g", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "g is the gradient threshold used to check score drops", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "case a", + "entity_type": "TASK_OR_PROBLEM", + "description": "case a occurs when all candidates pass the gradient check indicating scores lacked discriminative power", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "case b", + "entity_type": "TASK_OR_PROBLEM", + "description": "case b occurs when a gradient is found signaling a sharp score drop", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "v sel", + "entity_type": "TASK_OR_PROBLEM", + "description": "v sel is the canonical entity selected from the selection set sel", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "llm", + "entity_type": "SOFTWARE", + "description": "llm is a tool used to select the canonical entity if multiple aliases are identified", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "g", + "entity_type": "TASK_OR_PROBLEM", + "description": "g is a data structure or set that is updated and returned at the end of the process", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "lines 1 3", + "entity_type": "SECTION_TITLE", + "description": "lines 1 3 describe the initial retrieval and reranking steps of the algorithm", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "line 4", + "entity_type": "SECTION_TITLE", + "description": "line 4 describes the initialization of the selection set and the initial score", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "lines 5 8", + "entity_type": "SECTION_TITLE", + "description": "lines 5 8 describe the iteration through remaining candidates and the gradient threshold check", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "lines 7 8", + "entity_type": "SECTION_TITLE", + "description": "lines 7 8 detail the logic for adding candidates to the selection set and updating scores", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "line 8", + "entity_type": "SECTION_TITLE", + "description": "line 8 describes the condition where the loop breaks upon detecting a sharp score drop", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "lines 9 14", + "entity_type": "SECTION_TITLE", + "description": "lines 9 14 describe the final decision making logic of the algorithm", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "line 9 10", + "entity_type": "SECTION_TITLE", + "description": "lines 9 10 describe the action taken in case a where a new entity is added", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "lines 12 14", + "entity_type": "SECTION_TITLE", + "description": "lines 12 14 describe the merging of the new entity with the canonical entity in case b", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "line 13", + "entity_type": "SECTION_TITLE", + "description": "line 13 describes the use of an llm to select a canonical entity when multiple aliases exist", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "line 15", + "entity_type": "SECTION_TITLE", + "description": "line 15 describes the return of the updated g and db structures", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "score", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "score is the variable updated during the iteration to track the current score value", + "source_ids": [ + 75 + ] + }, + { + "entity_name": "v c", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "v c represents the current candidate being evaluated in the iteration", + "source_ids": [ + 75 + ] + } + ], + "relations": [ + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "v n", + "relation_name": "", + "weight": 10.0, + "description": "algorithm 1 processes the new entity v n by retrieving candidates and making a decision", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "e c", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 retrieves the top k candidates e c from the vector database db", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "db", + "relation_name": "", + "weight": 9.0, + "description": "the vector database db is the source from which candidates e c are retrieved", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "r", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 uses the reranker r to re rank candidates e c against v n", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "s", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 sorts candidates based on their scores s", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "sel", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 initializes and iterates through the selection set sel", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "g", + "relation_name": "", + "weight": 8.0, + "description": "algorithm 1 uses the gradient threshold g to determine if a score drop is sharp", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "case a", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 identifies case a when the selection set sel is identical to e c", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "case b", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 identifies case b when a gradient is found in the selection set sel", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "v sel", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 selects the canonical entity v sel from the selection set sel in case b", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 8.0, + "description": "algorithm 1 uses an llm to select v sel if multiple aliases are identified", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "lines 1 3", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 executes the steps outlined in lines 1 3 to retrieve and rerank candidates", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "line 4", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 executes the initialization step described in line 4", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "lines 5 8", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 executes the iteration logic described in lines 5 8", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "lines 7 8", + "relation_name": "", + "weight": 8.0, + "description": "the logic in lines 7 8 is part of the iteration process within algorithm 1", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "line 8", + "relation_name": "", + "weight": 8.0, + "description": "line 8 defines the break condition within the loop of algorithm 1", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "lines 9 14", + "relation_name": "", + "weight": 9.0, + "description": "algorithm 1 executes the decision logic described in lines 9 14", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "line 9 10", + "relation_name": "", + "weight": 8.0, + "description": "lines 9 10 are the specific actions taken when case a is identified in algorithm 1", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "lines 12 14", + "relation_name": "", + "weight": 8.0, + "description": "lines 12 14 are the specific actions taken when case b is identified in algorithm 1", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "line 13", + "relation_name": "", + "weight": 8.0, + "description": "line 13 is a step within the case b logic of algorithm 1", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "line 15", + "relation_name": "", + "weight": 9.0, + "description": "line 15 is the final step of algorithm 1 where results are returned", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "score", + "relation_name": "", + "weight": 9.0, + "description": "the variable score is initialized and updated during the execution of algorithm 1", + "source_ids": [ + 75 + ] + }, + { + "src_entity_name": "algorithm 1", + "tgt_entity_name": "v c", + "relation_name": "", + "weight": 9.0, + "description": "the variable v c is the current candidate processed within the loop of algorithm 1", + "source_ids": [ + 75 + ] + } + ], + "node_idx": 75 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_76.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_76.json new file mode 100644 index 0000000..9c08336 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_76.json @@ -0,0 +1,233 @@ +{ + "entities": [ + { + "entity_name": "figure 2", + "entity_type": "IMAGE", + "description": "figure 2 is a visual representation used to illustrate the processing of a new entity in a knowledge graph", + "source_ids": [ + 76 + ] + }, + { + "entity_name": "kg", + "entity_type": "TASK_OR_PROBLEM", + "description": "kg refers to the knowledge graph where entities are processed compared and merged", + "source_ids": [ + 76 + ] + }, + { + "entity_name": "e 9", + "entity_type": "TASK_OR_PROBLEM", + "description": "e 9 is a new entity being processed and compared against existing entities in the kg", + "source_ids": [ + 76 + ] + }, + { + "entity_name": "e 6", + "entity_type": "TASK_OR_PROBLEM", + "description": "e 6 is an existing entity in the kg that shows a sharp decline in similarity with e 9", + "source_ids": [ + 76 + ] + }, + { + "entity_name": "e 8", + "entity_type": "TASK_OR_PROBLEM", + "description": "e 8 is an existing entity in the kg that shows a sharp decline in similarity with e 9", + "source_ids": [ + 76 + ] + }, + { + "entity_name": "e 5", + "entity_type": "TASK_OR_PROBLEM", + "description": "e 5 is an existing entity in the kg that shows a sharp decline in similarity with e 9", + "source_ids": [ + 76 + ] + }, + { + "entity_name": "e 7", + "entity_type": "TASK_OR_PROBLEM", + "description": "e 7 is the final merged entity resulting from the consolidation of e 9 and e 7", + "source_ids": [ + 76 + ] + }, + { + "entity_name": "similarity curve", + "entity_type": "IMAGE", + "description": "the similarity curve is a visual depiction orange line showing the similarity levels between entities", + "source_ids": [ + 76 + ] + }, + { + "entity_name": "gradient based selection process", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "the gradient based selection process is the method used to identify high confidence matches between entities", + "source_ids": [ + 76 + ] + }, + { + "entity_name": "orange line", + "entity_type": "IMAGE", + "description": "the orange line is a specific visual element within the similarity curve mentioned in the text", + "source_ids": [ + 76 + ] + }, + { + "entity_name": "unique high confidence match", + "entity_type": "CONCEPT", + "description": "a unique high confidence match is the result of the gradient based selection process identifying e 7 for e 9", + "source_ids": [ + 76 + ] + }, + { + "entity_name": "consolidated information", + "entity_type": "CONCEPT", + "description": "consolidated information refers to the enriched data resulting from merging entities in the kg", + "source_ids": [ + 76 + ] + } + ], + "relations": [ + { + "src_entity_name": "e 9", + "tgt_entity_name": "kg", + "relation_name": "", + "weight": 9.0, + "description": "e 9 is processed within the kg context", + "source_ids": [ + 76 + ] + }, + { + "src_entity_name": "e 9", + "tgt_entity_name": "e 7", + "relation_name": "", + "weight": 10.0, + "description": "e 9 shows high similarity with e 7 and is merged with it", + "source_ids": [ + 76 + ] + }, + { + "src_entity_name": "e 9", + "tgt_entity_name": "e 6", + "relation_name": "", + "weight": 7.0, + "description": "e 9 shows a sharp decline in similarity with e 6", + "source_ids": [ + 76 + ] + }, + { + "src_entity_name": "e 9", + "tgt_entity_name": "e 8", + "relation_name": "", + "weight": 7.0, + "description": "e 9 shows a sharp decline in similarity with e 8", + "source_ids": [ + 76 + ] + }, + { + "src_entity_name": "e 9", + "tgt_entity_name": "e 5", + "relation_name": "", + "weight": 7.0, + "description": "e 9 shows a sharp decline in similarity with e 5", + "source_ids": [ + 76 + ] + }, + { + "src_entity_name": "gradient based selection process", + "tgt_entity_name": "e 7", + "relation_name": "", + "weight": 9.0, + "description": "the gradient based selection process identifies e 7 as the match for e 9", + "source_ids": [ + 76 + ] + }, + { + "src_entity_name": "similarity curve", + "tgt_entity_name": "e 9", + "relation_name": "", + "weight": 8.0, + "description": "the similarity curve depicts the similarity of e 9 with other entities", + "source_ids": [ + 76 + ] + }, + { + "src_entity_name": "similarity curve", + "tgt_entity_name": "e 7", + "relation_name": "", + "weight": 8.0, + "description": "the similarity curve shows e 9 s high similarity with e 7", + "source_ids": [ + 76 + ] + }, + { + "src_entity_name": "similarity curve", + "tgt_entity_name": "orange line", + "relation_name": "", + "weight": 10.0, + "description": "the orange line is the visual representation of the similarity curve described in the text", + "source_ids": [ + 76 + ] + }, + { + "src_entity_name": "gradient based selection process", + "tgt_entity_name": "unique high confidence match", + "relation_name": "", + "weight": 10.0, + "description": "the gradient based selection process produces the unique high confidence match", + "source_ids": [ + 76 + ] + }, + { + "src_entity_name": "e 9", + "tgt_entity_name": "unique high confidence match", + "relation_name": "", + "weight": 9.0, + "description": "e 9 is the entity for which the unique high confidence match e 7 is identified", + "source_ids": [ + 76 + ] + }, + { + "src_entity_name": "e 9", + "tgt_entity_name": "consolidated information", + "relation_name": "", + "weight": 8.0, + "description": "the merging of e 9 with e 7 enriches the kg with consolidated information", + "source_ids": [ + 76 + ] + }, + { + "src_entity_name": "e 7", + "tgt_entity_name": "e 7", + "relation_name": "", + "weight": 9.0, + "description": "e 7 is the component of the final merged entity e 7 that incorporates e 9", + "source_ids": [ + 76 + ] + } + ], + "node_idx": 76 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_77.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_77.json new file mode 100644 index 0000000..6e60198 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_77.json @@ -0,0 +1,315 @@ +{ + "entities": [ + { + "entity_name": "gt link", + "entity_type": "TECHNOLOGY", + "description": "gt link is a formalized mechanism denoted as m used to complete the bookindex", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "bookindex", + "entity_type": "PRODUCT", + "description": "bookindex is a structure denoted as b composed of t g and m which gt link helps complete", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "kg construction phase", + "entity_type": "TASK_OR_PROBLEM", + "description": "kg construction phase is a specific stage described in the text where origin tree nodes are recorded for extracted entities", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "entity resolution", + "entity_type": "TASK_OR_PROBLEM", + "description": "entity resolution is a process during which gt link is refined by merging entities into canonical entities", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "origin tree node", + "entity_type": "HARDWARE", + "description": "origin tree node is a structural location recorded for every newly extracted entity", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "canonical entity", + "entity_type": "CONCEPT", + "description": "canonical entity is the target of merging during entity resolution receiving updated origin node sets", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "mapping m", + "entity_type": "EQUATION_OR_FORMULA", + "description": "mapping m is the final aggregation process defined as v to p n linking entities to structural locations", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "g", + "entity_type": "CONCEPT", + "description": "g is a component of the bookindex structure b", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "t", + "entity_type": "CONCEPT", + "description": "t is a component of the bookindex structure b and represents the set of structural locations nodes", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "v i", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "v i represents a newly extracted entity for which an origin tree node is recorded", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "v n", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "v n represents an entity that is merged into a canonical entity during entity resolution", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "v sel", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "v sel represents the canonical entity into which v n is merged", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "n", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "n represents the set of nodes in the origin tree t", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "v", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "v represents the set of entities in the graph g", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "p n", + "entity_type": "MATHEMATICAL_CONCEPT", + "description": "p n represents the power set of nodes n used in the definition of the mapping m", + "source_ids": [ + 77 + ] + }, + { + "entity_name": "m", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 77 + ] + } + ], + "relations": [ + { + "src_entity_name": "gt link", + "tgt_entity_name": "bookindex", + "relation_name": "", + "weight": 10.0, + "description": "gt link is formalized to complete the bookindex", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "kg construction phase", + "tgt_entity_name": "origin tree node", + "relation_name": "", + "weight": 9.0, + "description": "during the kg construction phase origin tree nodes are recorded for newly extracted entities", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "entity resolution", + "tgt_entity_name": "gt link", + "relation_name": "", + "weight": 9.0, + "description": "gt link is refined during the entity resolution process", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "entity resolution", + "tgt_entity_name": "canonical entity", + "relation_name": "", + "weight": 9.0, + "description": "during entity resolution entities are merged into a canonical entity", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "mapping m", + "tgt_entity_name": "g", + "relation_name": "", + "weight": 8.0, + "description": "the mapping m bi directionally links the entities in g to their structural locations", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "mapping m", + "tgt_entity_name": "t", + "relation_name": "", + "weight": 8.0, + "description": "the mapping m links entities to the set of their structural locations nodes in t", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "g", + "relation_name": "", + "weight": 7.0, + "description": "g is a component of the bookindex structure b", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "t", + "relation_name": "", + "weight": 7.0, + "description": "t is a component of the bookindex structure b", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "m", + "relation_name": "", + "weight": 7.0, + "description": "m is a component of the bookindex structure b", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "kg construction phase", + "tgt_entity_name": "v i", + "relation_name": "", + "weight": 9.0, + "description": "the kg construction phase records the origin tree node for every newly extracted entity v i", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "entity resolution", + "tgt_entity_name": "v n", + "relation_name": "", + "weight": 9.0, + "description": "during entity resolution the entity v n is merged into a canonical entity", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "entity resolution", + "tgt_entity_name": "v sel", + "relation_name": "", + "weight": 9.0, + "description": "the entity v n is merged into the canonical entity v sel during entity resolution", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "v sel", + "tgt_entity_name": "v n", + "relation_name": "", + "weight": 8.0, + "description": "v sel is the target entity that receives the origin nodes previously associated with v n", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "mapping m", + "tgt_entity_name": "v", + "relation_name": "", + "weight": 9.0, + "description": "the mapping m is defined as a function from the set of entities v", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "mapping m", + "tgt_entity_name": "p n", + "relation_name": "", + "weight": 9.0, + "description": "the mapping m maps entities to the power set of nodes p n", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "v i", + "relation_name": "", + "weight": 6.0, + "description": "the bookindex structure b involves the recording of origin nodes for entities like v i", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "origin tree node", + "tgt_entity_name": "v i", + "relation_name": "", + "weight": 9.0, + "description": "an origin tree node is recorded specifically for the entity v i", + "source_ids": [ + 77 + ] + }, + { + "src_entity_name": "origin tree node", + "tgt_entity_name": "v sel", + "relation_name": "", + "weight": 8.0, + "description": "the origin node set of v sel is updated to include nodes from v n", + "source_ids": [ + 77 + ] + } + ], + "node_idx": 77 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_78.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_78.json new file mode 100644 index 0000000..43e2bf9 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_78.json @@ -0,0 +1,51 @@ +{ + "entities": [ + { + "entity_name": "5 agent-based retrieval", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of the main paper 'BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents', this section details the proposed agent-based query method inspired by Information Foraging Theory, which dynamically classifies queries and employs a tailored retrieval workflow.", + "source_ids": [ + 78 + ] + }, + { + "entity_name": "agent-based query method", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "Refers to the specific retrieval strategy introduced in section 5, which utilizes agents to dynamically classify queries based on Information Foraging Theory.", + "source_ids": [ + 78 + ] + }, + { + "entity_name": "information foraging theory", + "entity_type": "SCIENTIFIC_THEORY", + "description": "The theoretical framework inspiring the design of the agent-based query method described in section 5.", + "source_ids": [ + 78 + ] + } + ], + "relations": [ + { + "src_entity_name": "agent-based query method", + "tgt_entity_name": "5 agent-based retrieval", + "relation_name": "", + "weight": 10.0, + "description": "The 'Agent-Based Query Method' is the primary technical contribution and topic detailed within section 5.", + "source_ids": [ + 78 + ] + }, + { + "src_entity_name": "information foraging theory", + "tgt_entity_name": "5 agent-based retrieval", + "relation_name": "", + "weight": 9.5, + "description": "'Information Foraging Theory' serves as the foundational inspiration for the methods discussed in section 5.", + "source_ids": [ + 78 + ] + } + ], + "node_idx": 78 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_79.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_79.json new file mode 100644 index 0000000..9084080 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_79.json @@ -0,0 +1,167 @@ +{ + "entities": [ + { + "entity_name": "bookrag", + "entity_type": "SOFTWARE", + "description": "bookrag is an agent based approach proposed to address complex document queries by planning and executing operations on a bookindex", + "source_ids": [ + 79 + ] + }, + { + "entity_name": "bookindex", + "entity_type": "DATABASE", + "description": "bookindex is the data structure or system on which bookrag executes operations for document queries", + "source_ids": [ + 79 + ] + }, + { + "entity_name": "agent based planning", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "agent based planning is a core mechanism in bookrag that formulates strategies for operations", + "source_ids": [ + 79 + ] + }, + { + "entity_name": "structured execution", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "structured execution is a core mechanism in bookrag that includes the retrieval process based on ift and generation principles", + "source_ids": [ + 79 + ] + }, + { + "entity_name": "ift", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "ift is a principle used within the structured execution mechanism of bookrag", + "source_ids": [ + 79 + ] + }, + { + "entity_name": "modal type filtering", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "modal type filtering is an operation mentioned as necessary for addressing complex real world document queries", + "source_ids": [ + 79 + ] + }, + { + "entity_name": "semantic selection", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "semantic selection is an operation mentioned as necessary for addressing complex real world document queries", + "source_ids": [ + 79 + ] + }, + { + "entity_name": "multi hop reasoning", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "multi hop reasoning is an operation mentioned as necessary for addressing complex real world document queries", + "source_ids": [ + 79 + ] + }, + { + "entity_name": "generation", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "generation is a process included within the structured execution mechanism of bookrag", + "source_ids": [ + 79 + ] + }, + { + "entity_name": "real world document queries", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 79 + ] + } + ], + "relations": [ + { + "src_entity_name": "bookrag", + "tgt_entity_name": "bookindex", + "relation_name": "", + "weight": 9.0, + "description": "bookrag executes operations on the bookindex to handle document queries", + "source_ids": [ + 79 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "agent based planning", + "relation_name": "", + "weight": 9.0, + "description": "bookrag utilizes agent based planning as one of its two core mechanisms to formulate strategies", + "source_ids": [ + 79 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "structured execution", + "relation_name": "", + "weight": 9.0, + "description": "bookrag utilizes structured execution as one of its two core mechanisms to handle retrieval and generation", + "source_ids": [ + 79 + ] + }, + { + "src_entity_name": "structured execution", + "tgt_entity_name": "ift", + "relation_name": "", + "weight": 8.0, + "description": "structured execution includes the retrieval process under the principles of ift", + "source_ids": [ + 79 + ] + }, + { + "src_entity_name": "real world document queries", + "tgt_entity_name": "modal type filtering", + "relation_name": "", + "weight": 8.0, + "description": "real world document queries necessitate operations like modal type filtering", + "source_ids": [ + 79 + ] + }, + { + "src_entity_name": "real world document queries", + "tgt_entity_name": "semantic selection", + "relation_name": "", + "weight": 8.0, + "description": "real world document queries necessitate operations like semantic selection", + "source_ids": [ + 79 + ] + }, + { + "src_entity_name": "real world document queries", + "tgt_entity_name": "multi hop reasoning", + "relation_name": "", + "weight": 8.0, + "description": "real world document queries necessitate operations like multi hop reasoning", + "source_ids": [ + 79 + ] + }, + { + "src_entity_name": "structured execution", + "tgt_entity_name": "generation", + "relation_name": "", + "weight": 9.0, + "description": "structured execution includes the generation process as part of its workflow", + "source_ids": [ + 79 + ] + } + ], + "node_idx": 79 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_8.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_8.json new file mode 100644 index 0000000..2ad887e --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_8.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "1 introduction", + "entity_type": "SECTION_TITLE", + "description": "As the opening section of the paper 'BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents', this section introduces the motivation behind Retrieval-Augmented Generation (RAG), highlights limitations in existing approaches regarding hierarchical documents, and presents the proposed BookRAG framework and its key components like BookIndex.", + "source_ids": [ + 8 + ] + } + ], + "relations": [], + "node_idx": 8 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_80.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_80.json new file mode 100644 index 0000000..2a4607f --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_80.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "5.1 overall workflow", + "entity_type": "SECTION_TITLE", + "description": "As a subsection of 'Agent-Based Retrieval', this section introduces the general operational flow of the BookRAG system, outlining how it intelligently plans and executes operations on the BookIndex to handle complex document queries.", + "source_ids": [ + 80 + ] + } + ], + "relations": [], + "node_idx": 80 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_81.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_81.json new file mode 100644 index 0000000..f7c5d91 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_81.json @@ -0,0 +1,51 @@ +{ + "entities": 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+ "entity_name": "bookrag", + "entity_type": "PRODUCT", + "description": "bookrag is a system designed to intelligently navigate a bookindex and adapt to query requirements", + "source_ids": [ + 88 + ] + }, + { + "entity_name": "bookindex", + "entity_type": "DATASET_OR_CORPUS", + "description": "bookindex is a data structure represented as t g m that bookrag navigates", + "source_ids": [ + 88 + ] + }, + { + "entity_name": "formulator", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "formulator is one of four types of operators defined to support flexible retrieval in bookrag", + "source_ids": [ + 88 + ] + }, + { + "entity_name": "selector", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "selector is one of four types of operators defined to support flexible retrieval in bookrag", + "source_ids": [ + 88 + ] + }, + { + "entity_name": "reasoner", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "reasoner is one of four types of operators defined to support flexible retrieval in bookrag", + "source_ids": [ + 88 + ] + }, + { + "entity_name": "synthesizer", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "synthesizer is one of four types of operators defined to support flexible retrieval in bookrag", + "source_ids": [ + 88 + ] + }, + { + "entity_name": "agent", + "entity_type": "TASK_OR_PROBLEM", + "description": "the agent is an entity that performs the first step of the sequential process in bookrag", + "source_ids": [ + 88 + ] + }, + { + "entity_name": "t", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "t is a component of the bookindex data structure b t g m", + "source_ids": [ + 88 + ] + }, + { + "entity_name": "g", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "g is a component of the bookindex data structure b t g m", + "source_ids": [ + 88 + ] + }, + { + "entity_name": "m", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "m is a component of the bookindex data structure b t g m", + "source_ids": [ + 88 + ] + }, + { + "entity_name": "query categories", + "entity_type": "TASK_OR_PROBLEM", + "description": "query categories are specific requirements that bookrag adapts to using its operators", + "source_ids": [ + 88 + ] + }, + { + "entity_name": "execution pipelines", + "entity_type": "TASK_OR_PROBLEM", + "description": "execution pipelines are formed by combining operators to support flexible retrieval", + "source_ids": [ + 88 + ] + }, + { + "entity_name": "adjustable parameters", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "adjustable parameters are attributes of the execution pipelines that can be configured", + "source_ids": [ + 88 + ] + } + ], + "relations": [ + { + "src_entity_name": "bookrag", + "tgt_entity_name": "bookindex", + "relation_name": "", + "weight": 10.0, + "description": "bookrag is designed to intelligently navigate the bookindex", + "source_ids": [ + 88 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "formulator", + "relation_name": "", + "weight": 9.0, + "description": "bookrag defines the formulator as one of its four types of operators", + "source_ids": [ + 88 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "selector", + "relation_name": "", + "weight": 9.0, + "description": "bookrag defines the selector as one of its four types of operators", + "source_ids": [ + 88 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "reasoner", + "relation_name": "", + "weight": 9.0, + "description": "bookrag defines the reasoner as one of its four types of operators", + "source_ids": [ + 88 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "synthesizer", + "relation_name": "", + "weight": 9.0, + "description": "bookrag defines the synthesizer as one of its four types of operators", + "source_ids": [ + 88 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "agent", + "relation_name": "", + "weight": 8.0, + "description": "the agent performs the first step of the process within bookrag", + "source_ids": [ + 88 + ] + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "t", + "relation_name": "", + "weight": 10.0, + "description": "t is a defined component within the bookindex structure", + "source_ids": [ + 88 + ] + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "g", + "relation_name": "", + "weight": 10.0, + "description": "g is a defined component within the bookindex structure", + "source_ids": [ + 88 + ] + }, + { + "src_entity_name": "bookindex", + "tgt_entity_name": "m", + "relation_name": "", + "weight": 10.0, + "description": "m is a defined component within the bookindex structure", + "source_ids": [ + 88 + ] + }, + { + "src_entity_name": "formulator", + "tgt_entity_name": "execution pipelines", + "relation_name": "", + "weight": 9.0, + "description": "the formulator operator is combined to form tailored execution pipelines", + "source_ids": [ + 88 + ] + }, + { + "src_entity_name": "selector", + "tgt_entity_name": "execution pipelines", + "relation_name": "", + "weight": 9.0, + "description": "the selector operator is combined to form tailored execution pipelines", + "source_ids": [ + 88 + ] + }, + { + "src_entity_name": "reasoner", + "tgt_entity_name": "execution pipelines", + "relation_name": "", + "weight": 9.0, + "description": "the reasoner operator is combined to form tailored execution pipelines", + "source_ids": [ + 88 + ] + }, + { + "src_entity_name": "synthesizer", + "tgt_entity_name": "execution pipelines", + "relation_name": "", + "weight": 9.0, + "description": "the synthesizer operator is combined to form tailored execution pipelines", + "source_ids": [ + 88 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "query categories", + "relation_name": "", + "weight": 9.0, + "description": "bookrag dynamically configures operators to adapt to the specific requirements of different query categories", + "source_ids": [ + 88 + ] + }, + { + "src_entity_name": "execution pipelines", + "tgt_entity_name": "adjustable parameters", + "relation_name": "", + "weight": 8.0, + "description": "execution pipelines are created with adjustable parameters", + "source_ids": [ + 88 + ] + } + ], + "node_idx": 88 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_89.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_89.json new file mode 100644 index 0000000..fdb24cb --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_89.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "table 2", + "entity_type": "TABLE", + "description": "table 2 is a table that lists three common query categories addressed in bookrag", + "source_ids": [ + 89 + ] + }, + { + "entity_name": "bookrag", + "entity_type": "PRODUCT", + "description": "bookrag is a product or system that addresses three common query categories", + "source_ids": [ + 89 + ] + } + ], + "relations": [ + { + "src_entity_name": "table 2", + "tgt_entity_name": "bookrag", + "relation_name": "", + "weight": 9.0, + "description": "table 2 details query categories that are addressed within the bookrag system", + "source_ids": [ + 89 + ] + } + ], + "node_idx": 89 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_9.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_9.json new file mode 100644 index 0000000..b3267d6 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_9.json @@ -0,0 +1,143 @@ +{ + "entities": [ + { + "entity_name": "large language models", + "entity_type": "TECHNOLOGY", + "description": "large language models are a type of technology that has revolutionized question answering systems", + "source_ids": [ + 9 + ] + }, + { + "entity_name": "qwen 3", + "entity_type": "PRODUCT", + "description": "qwen 3 is a specific large language model mentioned as an example", + "source_ids": [ + 9 + ] + }, + { + "entity_name": "gemini 2 5", + "entity_type": "PRODUCT", + "description": "gemini 2 5 is a specific large language model mentioned as an example", + "source_ids": [ + 9 + ] + }, + { + "entity_name": "question answering", + "entity_type": "TASK_OR_PROBLEM", + "description": "question answering is a system or task that has been revolutionized by large language models", + "source_ids": [ + 9 + ] + }, + { + "entity_name": "industry", + "entity_type": "ORGANIZATION", + "description": "the industry refers to the collective group of organizations increasingly adopting llms for qa systems", + "source_ids": [ + 9 + ] + }, + { + "entity_name": "qa system", + "entity_type": "PRODUCT", + "description": "qa system is a product built using llms to assist users and reduce manual effort", + "source_ids": [ + 9 + ] + }, + { + "entity_name": "users", + "entity_type": "PERSON", + "description": "users are the individuals who are assisted by the qa systems built by the industry", + "source_ids": [ + 9 + ] + } + ], + "relations": [ + { + "src_entity_name": "large language models", + "tgt_entity_name": "qwen 3", + "relation_name": "", + "weight": 10.0, + "description": "qwen 3 is identified as an example of a large language model", + "source_ids": [ + 9 + ] + }, + { + "src_entity_name": "large language models", + "tgt_entity_name": "gemini 2 5", + "relation_name": "", + "weight": 10.0, + "description": "gemini 2 5 is identified as an example of a large language model", + "source_ids": [ + 9 + ] + }, + { + "src_entity_name": "large language models", + "tgt_entity_name": "question answering", + "relation_name": "", + "weight": 9.0, + "description": "large language models have revolutionized the question answering system", + "source_ids": [ + 9 + ] + }, + { + "src_entity_name": "industry", + "tgt_entity_name": "large language models", + "relation_name": "", + "weight": 8.0, + "description": "the industry is adopting large language models to build question answering systems", + "source_ids": [ + 9 + ] + }, + { + "src_entity_name": "industry", + "tgt_entity_name": "question answering", + "relation_name": "", + "weight": 7.0, + "description": "the industry is building question answering systems to assist users and reduce manual effort", + "source_ids": [ + 9 + ] + }, + { + "src_entity_name": "industry", + "tgt_entity_name": "qa system", + "relation_name": "", + "weight": 9.0, + "description": "the industry builds qa systems to assist users and reduce manual effort", + "source_ids": [ + 9 + ] + }, + { + "src_entity_name": "qa system", + "tgt_entity_name": "users", + "relation_name": "", + "weight": 8.0, + "description": "qa systems are designed to assist users", + "source_ids": [ + 9 + ] + }, + { + "src_entity_name": "large language models", + "tgt_entity_name": "qa system", + "relation_name": "", + "weight": 9.0, + "description": "large language models are used to build qa systems", + "source_ids": [ + 9 + ] + } + ], + "node_idx": 9 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_90.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_90.json new file mode 100644 index 0000000..516a19a --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_90.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "table: cref='#/texts/95'...", + "entity_type": "TABLE", + "description": "A data table described as: cref='#/texts/95'", + "source_ids": [ + 90 + ] + } + ], + "relations": [], + "node_idx": 90 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_91.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_91.json new file mode 100644 index 0000000..8a3b676 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_91.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "6", + "entity_type": "MEASUREMENT", + "description": "6 is a numerical value mentioned in the text potentially representing a count or measurement", + "source_ids": [ + 91 + ] + } + ], + "relations": [], + "node_idx": 91 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_92.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_92.json new file mode 100644 index 0000000..47c34db --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_92.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "operator set", + "entity_type": "TASK_OR_PROBLEM", + "description": "operator set is a task or problem mentioned in the text likely referring to a specific set of operators in a technical context", + "source_ids": [ + 92 + ] + } + ], + "relations": [], + "node_idx": 92 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_93.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_93.json new file mode 100644 index 0000000..0f21d14 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_93.json @@ -0,0 +1,223 @@ +{ + "entities": [ + { + "entity_name": "figure 4", + "entity_type": "IMAGE", + "description": "figure 4 is an image depicting the bookrag operator library and an execution example", + "source_ids": [ + 93 + ] + }, + { + "entity_name": "bookrag operator library", + "entity_type": "SOFTWARE", + "description": "the bookrag operator library is a software component containing four operator types", + "source_ids": [ + 93 + ] + }, + { + "entity_name": "mmlongbench dataset", + "entity_type": "DATASET_OR_CORPUS", + "description": "the mmlongbench dataset is the source of the execution example shown in the text", + "source_ids": [ + 93 + ] + }, + { + "entity_name": "formulator", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "formulator is one of the four operator types depicted in the bookrag operator library", + "source_ids": [ + 93 + ] + }, + { + "entity_name": "selector", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "selector is one of the four operator types depicted in the bookrag operator library", + "source_ids": [ + 93 + ] + }, + { + "entity_name": "reasoner", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "reasoner is one of the four operator types depicted in the bookrag operator library", + "source_ids": [ + 93 + ] + }, + { + "entity_name": "synthesizer", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "synthesizer is one of the four operator types depicted in the bookrag operator library", + "source_ids": [ + 93 + ] + }, + { + "entity_name": "single hop", + "entity_type": "TASK_OR_PROBLEM", + "description": "single hop is a type of query for which an execution trace is demonstrated", + "source_ids": [ + 93 + ] + }, + { + "entity_name": "operator", + "entity_type": "MODEL_OR_ARCHITECTURE", + "description": "operator is a general term for the components formulator selector reasoner synthesizer within the bookrag system", + "source_ids": [ + 93 + ] + }, + { + "entity_name": "execution trace", + "entity_type": "TASK_OR_PROBLEM", + "description": "execution trace is the step by step record of the agent based planning and operator execution shown in the text", + "source_ids": [ + 93 + ] + }, + { + "entity_name": "agent based planning", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "agent based planning is the method used for planning demonstrated in the execution trace", + "source_ids": [ + 93 + ] + }, + { + "entity_name": "step by step operator execution", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "step by step operator execution is the method of executing operators demonstrated in the text", + "source_ids": [ + 93 + ] + } + ], + "relations": [ + { + "src_entity_name": "figure 4", + "tgt_entity_name": "bookrag operator library", + "relation_name": "", + "weight": 10.0, + "description": "figure 4 visually depicts the bookrag operator library", + "source_ids": [ + 93 + ] + }, + { + "src_entity_name": "figure 4", + "tgt_entity_name": "mmlongbench dataset", + "relation_name": "", + "weight": 9.0, + "description": "figure 4 shows an execution example derived from the mmlongbench dataset", + "source_ids": [ + 93 + ] + }, + { + "src_entity_name": "bookrag operator library", + "tgt_entity_name": "formulator", + "relation_name": "", + "weight": 10.0, + "description": "the bookrag operator library contains the formulator operator type", + "source_ids": [ + 93 + ] + }, + { + "src_entity_name": "bookrag operator library", + "tgt_entity_name": "selector", + "relation_name": "", + "weight": 10.0, + "description": "the bookrag operator library contains the selector operator type", + "source_ids": [ + 93 + ] + }, + { + "src_entity_name": "bookrag operator library", + "tgt_entity_name": "reasoner", + "relation_name": "", + "weight": 10.0, + "description": "the bookrag operator library contains the reasoner operator type", + "source_ids": [ + 93 + ] + }, + { + "src_entity_name": "bookrag operator library", + "tgt_entity_name": "synthesizer", + "relation_name": "", + "weight": 10.0, + "description": "the bookrag operator library contains the synthesizer operator type", + "source_ids": [ + 93 + ] + }, + { + "src_entity_name": "figure 4", + "tgt_entity_name": "single hop", + "relation_name": "", + "weight": 9.0, + "description": "figure 4 demonstrates an execution trace for a single hop query", + "source_ids": [ + 93 + ] + }, + { + "src_entity_name": "bookrag operator library", + "tgt_entity_name": "operator", + "relation_name": "", + "weight": 8.0, + "description": "the bookrag operator library is composed of specific operators", + "source_ids": [ + 93 + ] + }, + { + "src_entity_name": "figure 4", + "tgt_entity_name": "execution trace", + "relation_name": "", + "weight": 9.0, + "description": "figure 4 contains an execution trace for a single hop query", + "source_ids": [ + 93 + ] + }, + { + "src_entity_name": "execution trace", + "tgt_entity_name": "agent based planning", + "relation_name": "", + "weight": 9.0, + "description": "the execution trace demonstrates agent based planning", + "source_ids": [ + 93 + ] + }, + { + "src_entity_name": "execution trace", + "tgt_entity_name": "step by step operator execution", + "relation_name": "", + "weight": 9.0, + "description": "the execution trace demonstrates step by step operator execution", + "source_ids": [ + 93 + ] + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "execution trace", + "relation_name": "", + "weight": 10.0, + "description": "the execution trace is specifically for a single hop query", + "source_ids": [ + 93 + ] + } + ], + "node_idx": 93 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_94.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_94.json new file mode 100644 index 0000000..8d87aa7 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_94.json @@ -0,0 +1,537 @@ +{ + "entities": [ + { + "entity_name": "operator-set", + "entity_type": "IMAGE", + "description": "A diagram illustrating a framework for processing queries, divided into an 'Operators' section and an 'Execution example' section.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "extract", + "entity_type": "TASK_OR_PROBLEM", + "description": "The initial step in the operator set where questions are decomposed to identify entities.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "decompose", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "A process within the Extract phase that breaks down a query into sub-queries.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "entities", + "entity_type": "DATASET_OR_CORPUS", + "description": "The output of the Extract phase, representing distinct items identified from the input text.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "sub-queries", + "entity_type": "TASK_OR_PROBLEM", + "description": "Smaller queries generated during the decomposition process.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "formulator", + "entity_type": "SYSTEM_COMPONENT", + "description": "The component or agent responsible for the extraction and decomposition steps.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "filter", + "entity_type": "TASK_OR_PROBLEM", + "description": "An operator that processes data structures like trees to select relevant information.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "select", + "entity_type": "TASK_OR_PROBLEM", + "description": "The action performed by the Filter operator to choose specific elements.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "selector", + "entity_type": "SYSTEM_COMPONENT", + "description": "The component responsible for filtering and selecting data based on criteria.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "reason", + "entity_type": "TASK_OR_PROBLEM", + "description": "An operator that takes Graph and Text inputs to perform reasoning tasks.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "graph", + "entity_type": "DATA_STRUCTURE", + "description": "A visual representation of data used as input for the Reason operator.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "text", + "entity_type": "DATA_STRUCTURE", + "description": "Raw textual data used as input for the Reason operator.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "s:", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "A label indicating a score or similarity matrix with values such as 0.6, 0.5, 0.4.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "skyline", + "entity_type": "TASK_OR_PROBLEM", + "description": "An operator that processes ranked lists (S1, S2) to find optimal solutions.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "reasoner", + "entity_type": "SYSTEM_COMPONENT", + "description": "The component executing the Reason and Skyline operations.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "map", + "entity_type": "TASK_OR_PROBLEM", + "description": "An operator that transforms data using icons representing different formats.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "reduce", + "entity_type": "TASK_OR_PROBLEM", + "description": "An operator that combines multiple inputs into a single result.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "synthesizer", + "entity_type": "SYSTEM_COMPONENT", + "description": "The final component that aggregates results into a coherent answer.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "execution example", + "entity_type": "SECTION_TITLE", + "description": "A subsection of the diagram showing a concrete application of the operator set.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "q: what is the type of car in the ranking prompt example?", + "entity_type": "TASK_OR_PROBLEM", + "description": "The specific user question being processed in the execution example.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "planning", + "entity_type": "TASK_OR_PROBLEM", + "description": "The phase where a plan is formulated to solve the query.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "simple query...", + "entity_type": "TASK_OR_PROBLEM", + "description": "A classification of the input query.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "operator plan", + "entity_type": "TASK_OR_PROBLEM", + "description": "The sequence of operators chosen to solve the problem: Extract->Select->Reason->Skyline->Map...", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "car", + "entity_type": "PRODUCT", + "description": "An entity extracted from the question.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "ranking prompt", + "entity_type": "BOOK", + "description": "An entity mentioned in the question context.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "method", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "A node in the planning graph representing the method to be used.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "method and its descendants", + "entity_type": "SECTION_TITLE", + "description": "A grouping of nodes related to the Method in the execution flow.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "a: based on the provided information...", + "entity_type": "TASK_OR_PROBLEM", + "description": "The final answer generated by the system.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "mercedes-benz e-class sedan", + "entity_type": "VEHICLE", + "description": "The specific car type identified as the correct answer in the example.", + "source_ids": [ + 94 + ] + }, + { + "entity_name": "image cref='#/texts/98'", + "entity_type": "UNKNOWN", + "description": "", + "source_ids": [ + 94 + ] + } + ], + "relations": [ + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "operator-set", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Operator-Set", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "extract", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Extract", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "decompose", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Decompose", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "entities", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Entities", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "sub-queries", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Sub-queries", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "formulator", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Formulator", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "filter", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Filter", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "select", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Select", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "selector", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Selector", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "reason", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Reason", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "graph", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Graph", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "text", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Text", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "s:", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to S:", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "skyline", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Skyline", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "reasoner", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Reasoner", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "map", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Map", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "reduce", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Reduce", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "synthesizer", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Synthesizer", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "execution example", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Execution example", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "q: what is the type of car in the ranking prompt example?", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Q: What is the type of car in the Ranking Prompt example?", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "planning", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Planning", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "simple query...", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Simple query...", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "operator plan", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Operator Plan", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "car", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Car", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "ranking prompt", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Ranking Prompt", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "method", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Method", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "method and its descendants", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Method and its Descendants", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "a: based on the provided information...", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to A: Based on the provided information...", + "source_ids": [ + 94 + ] + }, + { + "src_entity_name": "image cref='#/texts/98'", + "tgt_entity_name": "mercedes-benz e-class sedan", + "relation_name": "", + "weight": 9.0, + "description": "Image entity Image cref='#/texts/98' related to Mercedes-Benz E-Class Sedan", + "source_ids": [ + 94 + ] + } + ], + "node_idx": 94 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_95.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_95.json new file mode 100644 index 0000000..e859666 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_95.json @@ -0,0 +1,33 @@ +{ + "entities": [ + { + "entity_name": "query classification", + "entity_type": "TASK_OR_PROBLEM", + "description": "query classification is a task mentioned in the text used to determine the appropriate solution strategy", + "source_ids": [ + 95 + ] + }, + { + "entity_name": "operator plan", + "entity_type": "PRODUCT", + "description": "operator plan is a specific output generated after determining the solution strategy", + "source_ids": [ + 95 + ] + } + ], + "relations": [ + { + "src_entity_name": "query classification", + "tgt_entity_name": "operator plan", + "relation_name": "", + "weight": 9.0, + "description": "query classification is performed to generate a specific operator plan", + "source_ids": [ + 95 + ] + } + ], + "node_idx": 95 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_96.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_96.json new file mode 100644 index 0000000..133f856 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_96.json @@ -0,0 +1,267 @@ +{ + "entities": [ + { + "entity_name": "query classification", + "entity_type": "TASK_OR_PROBLEM", + "description": "query classification is a task focused on enabling agent strategy selection by categorizing queries based on complexity", + "source_ids": [ + 96 + ] + }, + { + "entity_name": "single hop", + "entity_type": "EVENT", + "description": "single hop is a query category requiring a single piece of information retrieved via a scent based retrieval operation", + "source_ids": [ + 96 + ] + }, + { + "entity_name": "multi hop", + "entity_type": "EVENT", + "description": "multi hop is a query category defined by its intrinsic complexity and operational demands", + "source_ids": [ + 96 + ] + }, + { + "entity_name": "global aggregation", + "entity_type": "EVENT", + "description": "global aggregation is a query category necessitating analysis under multiple filtering conditions", + "source_ids": [ + 96 + ] + }, + { + "entity_name": "table 2", + "entity_type": "TABLE", + "description": "table 2 is a reference in the text that defines the three representative query categories", + "source_ids": [ + 96 + ] + }, + { + "entity_name": "scent based retrieval", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "scent based retrieval is a method used to retrieve a single piece of information for single hop queries", + "source_ids": [ + 96 + ] + }, + { + "entity_name": "filter aggregation", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "filter aggregation is a sequence of operations used to analyze content under multiple filtering conditions for global aggregation queries", + "source_ids": [ + 96 + ] + }, + { + "entity_name": "bookrag", + "entity_type": "SOFTWARE", + "description": "bookrag is a system designed to be extensible for resolving a broader range of query types", + "source_ids": [ + 96 + ] + }, + { + "entity_name": "agent strategy selection", + "entity_type": "TASK_OR_PROBLEM", + "description": "agent strategy selection is a process enabled by query classification to determine the appropriate solution strategy", + "source_ids": [ + 96 + ] + }, + { + "entity_name": "intrinsic complexity", + "entity_type": "CONCEPT", + "description": "intrinsic complexity is an attribute used to define the query categories", + "source_ids": [ + 96 + ] + }, + { + "entity_name": "operational demands", + "entity_type": "CONCEPT", + "description": "operational demands are factors used to define the query categories alongside intrinsic complexity", + "source_ids": [ + 96 + ] + }, + { + "entity_name": "solution strategy", + "entity_type": "CONCEPT", + "description": "solution strategy refers to the different approaches required for each query category", + "source_ids": [ + 96 + ] + }, + { + "entity_name": "filtering conditions", + "entity_type": "CONCEPT", + "description": "filtering conditions are multiple criteria used in the analysis of global aggregation queries", + "source_ids": [ + 96 + ] + }, + { + "entity_name": "document", + "entity_type": "OBJECT", + "description": "document refers to the source material where content is analyzed during global aggregation queries", + "source_ids": [ + 96 + ] + }, + { + "entity_name": "additional operators", + "entity_type": "SOFTWARE", + "description": "additional operators are components integrated into bookrag to extend its capabilities", + "source_ids": [ + 96 + ] + } + ], + "relations": [ + { + "src_entity_name": "query classification", + "tgt_entity_name": "single hop", + "relation_name": "", + "weight": 9.0, + "description": "query classification defines single hop as one of its three representative categories", + "source_ids": [ + 96 + ] + }, + { + "src_entity_name": "query classification", + "tgt_entity_name": "multi hop", + "relation_name": "", + "weight": 9.0, + "description": "query classification defines multi hop as one of its three representative categories", + "source_ids": [ + 96 + ] + }, + { + "src_entity_name": "query classification", + "tgt_entity_name": "global aggregation", + "relation_name": "", + "weight": 9.0, + "description": "query classification defines global aggregation as one of its three representative categories", + "source_ids": [ + 96 + ] + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "scent based retrieval", + "relation_name": "", + "weight": 8.0, + "description": "single hop queries typically require a scent based retrieval operation", + "source_ids": [ + 96 + ] + }, + { + "src_entity_name": "global aggregation", + "tgt_entity_name": "filter aggregation", + "relation_name": "", + "weight": 8.0, + "description": "global aggregation queries usually involve a sequence of filter aggregation operations", + "source_ids": [ + 96 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "query classification", + "relation_name": "", + "weight": 7.0, + "description": "bookrag is designed to resolve a broader range of query types including those defined by the classification", + "source_ids": [ + 96 + ] + }, + { + "src_entity_name": "query classification", + "tgt_entity_name": "agent strategy selection", + "relation_name": "", + "weight": 9.0, + "description": "query classification enables agent strategy selection", + "source_ids": [ + 96 + ] + }, + { + "src_entity_name": "single hop", + "tgt_entity_name": "intrinsic complexity", + "relation_name": "", + "weight": 7.0, + "description": "single hop is defined by its intrinsic complexity and operational demands", + "source_ids": [ + 96 + ] + }, + { + "src_entity_name": "multi hop", + "tgt_entity_name": "intrinsic complexity", + "relation_name": "", + "weight": 7.0, + "description": "multi hop is defined by its intrinsic complexity and operational demands", + "source_ids": [ + 96 + ] + }, + { + "src_entity_name": "global aggregation", + "tgt_entity_name": "intrinsic complexity", + "relation_name": "", + "weight": 7.0, + "description": "global aggregation is defined by its intrinsic complexity and operational demands", + "source_ids": [ + 96 + ] + }, + { + "src_entity_name": "query classification", + "tgt_entity_name": "solution strategy", + "relation_name": "", + "weight": 8.0, + "description": "each category defined by query classification requires a different solution strategy", + "source_ids": [ + 96 + ] + }, + { + "src_entity_name": "global aggregation", + "tgt_entity_name": "filtering conditions", + "relation_name": "", + "weight": 8.0, + "description": "global aggregation necessitates analyzing content under multiple filtering conditions", + "source_ids": [ + 96 + ] + }, + { + "src_entity_name": "global aggregation", + "tgt_entity_name": "document", + "relation_name": "", + "weight": 7.0, + "description": "global aggregation involves analyzing content across various parts of the document", + "source_ids": [ + 96 + ] + }, + { + "src_entity_name": "bookrag", + "tgt_entity_name": "additional operators", + "relation_name": "", + "weight": 8.0, + "description": "bookrag resolves broader query types by integrating additional operators", + "source_ids": [ + 96 + ] + } + ], + "node_idx": 96 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_97.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_97.json new file mode 100644 index 0000000..6d5b244 --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_97.json @@ -0,0 +1,159 @@ +{ + "entities": [ + { + "entity_name": "bookindex operators", + "entity_type": "TASK_OR_PROBLEM", + "description": "bookindex operators are a set of strategies designed to execute tasks identified by classification", + "source_ids": [ + 97 + ] + }, + { + "entity_name": "o", + "entity_type": "TASK_OR_PROBLEM", + "description": "o represents the set of operators tailored for the bookindex", + "source_ids": [ + 97 + ] + }, + { + "entity_name": "bookindex", + "entity_type": "PRODUCT", + "description": "bookindex is a system defined by the tuple t g m for which operators are designed", + "source_ids": [ + 97 + ] + }, + { + "entity_name": "figure 4 a", + "entity_type": "IMAGE", + "description": "figure 4 a is a visual depiction of the operators", + "source_ids": [ + 97 + ] + }, + { + "entity_name": "table 3", + "entity_type": "TABLE", + "description": "table 3 provides detailed information about the operators", + "source_ids": [ + 97 + ] + }, + { + "entity_name": "agent", + "entity_type": "TASK_OR_PROBLEM", + "description": "the agent is the entity that employs the operators for diverse query categories", + "source_ids": [ + 97 + ] + }, + { + "entity_name": "classification", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "classification is the method used to identify the strategies executed by the operators", + "source_ids": [ + 97 + ] + }, + { + "entity_name": "query categories", + "entity_type": "TASK_OR_PROBLEM", + "description": "query categories are the diverse groups of queries for which the agent employs the operators", + "source_ids": [ + 97 + ] + }, + { + "entity_name": "figure 4", + "entity_type": "IMAGE", + "description": "figure 4 is a visual element referenced in the text specifically part a", + "source_ids": [ + 97 + ] + } + ], + "relations": [ + { + "src_entity_name": "bookindex operators", + "tgt_entity_name": "o", + "relation_name": "", + "weight": 9.0, + "description": "bookindex operators are represented by the set o tailored for the bookindex", + "source_ids": [ + 97 + ] + }, + { + "src_entity_name": "bookindex operators", + "tgt_entity_name": "bookindex", + "relation_name": "", + "weight": 10.0, + "description": "bookindex operators are designed specifically for the bookindex system", + "source_ids": [ + 97 + ] + }, + { + "src_entity_name": "bookindex operators", + "tgt_entity_name": "figure 4 a", + "relation_name": "", + "weight": 8.0, + "description": "bookindex operators are visually depicted in figure 4 a", + "source_ids": [ + 97 + ] + }, + { + "src_entity_name": "bookindex operators", + "tgt_entity_name": "table 3", + "relation_name": "", + "weight": 8.0, + "description": "bookindex operators are detailed in table 3", + "source_ids": [ + 97 + ] + }, + { + "src_entity_name": "bookindex operators", + "tgt_entity_name": "agent", + "relation_name": "", + "weight": 9.0, + "description": "the agent employs the bookindex operators for diverse query categories", + "source_ids": [ + 97 + ] + }, + { + "src_entity_name": "bookindex operators", + "tgt_entity_name": "classification", + "relation_name": "", + "weight": 9.0, + "description": "bookindex operators are designed to execute strategies identified by classification", + "source_ids": [ + 97 + ] + }, + { + "src_entity_name": "agent", + "tgt_entity_name": "query categories", + "relation_name": "", + "weight": 8.0, + "description": "the agent employs operators for diverse query categories", + "source_ids": [ + 97 + ] + }, + { + "src_entity_name": "bookindex operators", + "tgt_entity_name": "figure 4", + "relation_name": "", + "weight": 7.0, + "description": "bookindex operators are visually depicted in figure 4", + "source_ids": [ + 97 + ] + } + ], + "node_idx": 97 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_98.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_98.json new file mode 100644 index 0000000..815caec --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_98.json @@ -0,0 +1,251 @@ +{ + "entities": [ + { + "entity_name": "formulator", + "entity_type": "TASK_OR_PROBLEM", + "description": "formulator is a category of llm based operators that prepare queries for execution", + "source_ids": [ + 98 + ] + }, + { + "entity_name": "decompose", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "decompose is a method that breaks a complex query into simpler actionable sub queries", + "source_ids": [ + 98 + ] + }, + { + "entity_name": "extract", + "entity_type": "METHOD_OR_TECHNIQUE", + "description": "extract is a method that employs an llm to identify key entities from query text and link them to a knowledge graph", + "source_ids": [ + 98 + ] + }, + { + "entity_name": "llm", + "entity_type": "TECHNOLOGY", + "description": "llm refers to large language models used as operators in the formulator category", + "source_ids": [ + 98 + ] + }, + { + "entity_name": "kg", + "entity_type": "SOFTWARE", + "description": "kg refers to the knowledge graph where entities are linked", + "source_ids": [ + 98 + ] + }, + { + "entity_name": "qs", + "entity_type": "TASK_OR_PROBLEM", + "description": "qs represents the set of simpler actionable sub queries generated by the decompose method", + "source_ids": [ + 98 + ] + }, + { + "entity_name": "eq", + "entity_type": "TASK_OR_PROBLEM", + "description": "eq represents the set of key entities identified by the extract method", + "source_ids": [ + 98 + ] + }, + { + "entity_name": "pdec", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "pdec is a parameter used in the llm function to generate sub queries", + "source_ids": [ + 98 + ] + }, + { + "entity_name": "pext", + "entity_type": "PARAMETER_OR_VARIABLE", + "description": "pext is a parameter used in the llm function to identify key entities", + "source_ids": [ + 98 + ] + }, + { + "entity_name": "complex query", + "entity_type": "TASK_OR_PROBLEM", + "description": "complex query is the type of query that the decompose method breaks down into simpler sub queries", + "source_ids": [ + 98 + ] + }, + { + "entity_name": "sub queries", + "entity_type": "TASK_OR_PROBLEM", + "description": "sub queries are the simpler actionable components resulting from breaking down a complex query", + "source_ids": [ + 98 + ] + }, + { + "entity_name": "query text", + "entity_type": "TASK_OR_PROBLEM", + "description": "query text is the source material from which the extract method identifies key entities", + "source_ids": [ + 98 + ] + }, + { + "entity_name": "entities", + "entity_type": "TASK_OR_PROBLEM", + "description": "entities are the key items identified in the query text and linked to the knowledge graph", + "source_ids": [ + 98 + ] + } + ], + "relations": [ + { + "src_entity_name": "formulator", + "tgt_entity_name": "decompose", + "relation_name": "", + "weight": 9.0, + "description": "decompose is included as a category within the formulator operators", + "source_ids": [ + 98 + ] + }, + { + "src_entity_name": "formulator", + "tgt_entity_name": "extract", + "relation_name": "", + "weight": 9.0, + "description": "extract is included as a category within the formulator operators", + "source_ids": [ + 98 + ] + }, + { + "src_entity_name": "decompose", + "tgt_entity_name": "qs", + "relation_name": "", + "weight": 10.0, + "description": "decompose generates the set of sub queries qs", + "source_ids": [ + 98 + ] + }, + { + "src_entity_name": "extract", + "tgt_entity_name": "eq", + "relation_name": "", + "weight": 10.0, + "description": "extract identifies the key entities eq", + "source_ids": [ + 98 + ] + }, + { + "src_entity_name": "extract", + "tgt_entity_name": "kg", + "relation_name": "", + "weight": 9.0, + "description": "extract links identified entities to the knowledge graph kg", + "source_ids": [ + 98 + ] + }, + { + "src_entity_name": "decompose", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 8.0, + "description": "decompose employs an llm to perform its function", + "source_ids": [ + 98 + ] + }, + { + "src_entity_name": "extract", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 8.0, + "description": "extract employs an llm to perform its function", + "source_ids": [ + 98 + ] + }, + { + "src_entity_name": "qs", + "tgt_entity_name": "pdec", + "relation_name": "", + "weight": 7.0, + "description": "qs is generated using the parameter pdec in the llm function", + "source_ids": [ + 98 + ] + }, + { + "src_entity_name": "eq", + "tgt_entity_name": "pext", + "relation_name": "", + "weight": 7.0, + "description": "eq is generated using the parameter pext in the llm function", + "source_ids": [ + 98 + ] + }, + { + "src_entity_name": "decompose", + "tgt_entity_name": "complex query", + "relation_name": "", + "weight": 10.0, + "description": "decompose takes a complex query as its input to break it down", + "source_ids": [ + 98 + ] + }, + { + "src_entity_name": "decompose", + "tgt_entity_name": "sub queries", + "relation_name": "", + "weight": 10.0, + "description": "decompose produces sub queries as its output", + "source_ids": [ + 98 + ] + }, + { + "src_entity_name": "extract", + "tgt_entity_name": "query text", + "relation_name": "", + "weight": 10.0, + "description": "extract analyzes the query text to find key entities", + "source_ids": [ + 98 + ] + }, + { + "src_entity_name": "extract", + "tgt_entity_name": "entities", + "relation_name": "", + "weight": 10.0, + "description": "extract identifies entities from the query text", + "source_ids": [ + 98 + ] + }, + { + "src_entity_name": "formulator", + "tgt_entity_name": "llm", + "relation_name": "", + "weight": 9.0, + "description": "formulators are defined as llm based operators", + "source_ids": [ + 98 + ] + } + ], + "node_idx": 98 +} \ No newline at end of file diff --git a/e2e_test_output/kg_extractor_res/kg_extractor_res_99.json b/e2e_test_output/kg_extractor_res/kg_extractor_res_99.json new file mode 100644 index 0000000..c1c367a --- /dev/null +++ b/e2e_test_output/kg_extractor_res/kg_extractor_res_99.json @@ -0,0 +1,14 @@ +{ + "entities": [ + { + "entity_name": "formula (2)", + "entity_type": "EQUATION_OR_FORMULA", + "description": "An equation defining the output Q(s) as a set of query vectors generated by an LLM. 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If the score drop is gentle (passes the check), the candidate 𝑣 𝑐 is added to 𝑆𝑒𝑙 , and score is updated (Lines 7-8); otherwise, the loop breaks (Line 8) as soon as a sharp score drop is detected. Finally, the algorithm makes its decision (Lines 9-14). If the selection set 𝑆𝑒𝑙 is identical to 𝐸 𝑐 , this indicates that all candidates passed the gradient check. This corresponds to Case A , where the scores lacked discriminative power (i.e., 𝑣 𝑛 is equally dissimilar to all candidates), so 𝑣 𝑛 is added as a new entity (Line 9-10). Conversely, if a gradient was found (i.e., 𝑙𝑒𝑛𝑔𝑡ℎ 𝑆𝑒𝑙 ( ) < 𝑙𝑒𝑛𝑔𝑡ℎ ( 𝐸 𝑐 ) ), this signals Case B . We then select the canonical entity 𝑣 𝑠𝑒𝑙 from 𝑆𝑒𝑙 , using an LLM (Line 13) if the reranker identifies multiple aliases, and merge 𝑣 𝑛 with it (Lines 12-14). The updated 𝐺 and 𝐷𝐵 are then returned (Line 15).", + "title_level": -1 + }, + "summary": "Algorithm 1 resolves new entities by retrieving top-k candidates from a vector database, reranking them, and applying a gradient threshold to determine the outcome. It iterates through sorted candidates, adding them to a selection set only if the score drop from the previous candidate remains within a defined gradient threshold; the process stops immediately upon detecting a sharp score drop. If all candidates pass this check (Case A), the new entity is treated as unique and added to the database. Conversely, if a sharp drop occurs (Case B), the algorithm selects a canonical entity from the valid selection set—potentially using an LLM to resolve multiple aliases—and merges the new entity with it, updating the database accordingly." + }, + { + "index_id": 76, + "parent_id": 65, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 76, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "For instance, considering the example in Figure 2, when the new entity 𝑒 9 is processed, it is first compared with existing entities in the KG. As depicted in the similarity curve (orange line), 𝑒 9 shows high similarity with 𝑒 7, followed by a sharp decline in similarity with other entities like 𝑒 6, 𝑒 8, and 𝑒 5. Our gradient-based selection process identifies 𝑒 7 as the unique, high-confidence match for 𝑒 9. Consequently, 𝑒 9 is merged with 𝑒 7, enriching the KG with consolidated information as shown in the final merged entity 𝑒 ' 7 .", + "title_level": -1 + }, + "summary": "When processing a new entity like 𝑒 9, a gradient-based selection process identifies a unique, high-confidence match (𝑒 7) by analyzing similarity curves, where 𝑒 9 exhibits high similarity with 𝑒 7 followed by a sharp decline with other entities. This match triggers a merge operation, consolidating 𝑒 9 and 𝑒 7 into a single enriched entity (𝑒 ' 7) to enhance the knowledge graph." + }, + { + "index_id": 77, + "parent_id": 65, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 77, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Graph-Tree Link (GT-Link). The GT-Link 𝑀 is formalized to complete the BookIndex 𝐵 = ( 𝑇,𝐺, 𝑀 ) . As described in the KG Construction phase, the origin tree node 𝑛 𝑖 is recorded for every newly extracted entity 𝑣 𝑖 . GT-Link is then refined during entity resolution: when an entity 𝑣 𝑛 is merged into a canonical entity 𝑣 𝑠𝑒𝑙 , the origin node set of 𝑣 𝑠𝑒𝑙 is updated to include all origin nodes previously associated with 𝑣 𝑛 . This aggregation process creates the final mapping 𝑀 : 𝑉 → P( 𝑁 ) , which bi-directionally links the entities in 𝐺 to the set of their structural locations (nodes) in 𝑇 .", + "title_level": -1 + }, + "summary": "The Graph-Tree Link (GT-Link) is a bi-directional mapping ($M: V \\to \\mathcal{P}(N)$) that connects entities in a knowledge graph to their specific structural locations within an origin tree. This link is established by recording the source tree node for each extracted entity and is refined during entity resolution: when multiple entities are merged into a canonical entity, their respective origin nodes are aggregated. This process ensures the final mapping accurately reflects all structural positions associated with each canonical entity, completing the BookIndex structure." + }, + { + "index_id": 78, + "parent_id": 1, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 78, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "5 AGENT-BASED RETRIEVAL", + "title_level": 1 + }, + "summary": "Section 5 details BookRAG's agent-based retrieval framework, which integrates dynamic planning and structured execution to intelligently orchestrate multi-hop reasoning and information foraging for complex document queries." + }, + { + "index_id": 79, + "parent_id": 78, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 79, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Real-world document queries are often complex, necessitating operations like modal type filtering, semantic selection, and multi-hop reasoning. To address this, we propose an agent-based approach in BookRAG, which intelligently plans and executes operations on the BookIndex. We first introduce the overall workflow and present two core mechanisms: Agent-based Planning , which formulates the strategy, and the Structured Execution , which includes the retrieval process under the principles of IFT and generation.", + "title_level": -1 + }, + "summary": "BookRAG addresses the complexity of real-world document queries by introducing an agent-based approach that intelligently plans and executes operations on a BookIndex. This system relies on two core mechanisms: Agent-based Planning, which formulates the necessary strategies for tasks like modal filtering and multi-hop reasoning, and Structured Execution, which carries out retrieval and generation processes based on IFT principles." + }, + { + "index_id": 80, + "parent_id": 78, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 80, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "5.1 Overall Workflow", + "title_level": 2 + }, + "summary": "This section outlines BookRAG's three-stage agent-based retrieval workflow, which integrates planning, scent-guided retrieval, and synthesis to effectively address complex user queries." + }, + { + "index_id": 81, + "parent_id": 80, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 81, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "The overall workflow of agent-based retrieval, illustrated in Figure 3, follows a three-stage pipeline designed to address users' queries systematically.", + "title_level": -1 + }, + "summary": "Agent-based retrieval operates through a systematic three-stage pipeline designed to address user queries effectively." + }, + { + "index_id": 82, + "parent_id": 80, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 82, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "1. Agent-based Planning. BookRAG first performs Classification & Plan . This stage aims to distinguish simple keyword-based queries from reasoning questions that require decomposition and analysis. For instance, a query like 'How does Transformer differ from RNNs in handling long-range dependencies?' cannot be solved by retrieving from a single keyword. Therefore, the planning stage first performs query classification . Based on this classification and a predefined set of operators designed for the BookIndex, it generates a specific operators plan that effectively guides the retrieval and generation strategies.", + "title_level": -1 + }, + "summary": "BookRAG's Agent-based Planning initiates with a Classification & Plan stage that distinguishes simple keyword queries from complex reasoning questions requiring decomposition. For intricate queries, such as comparing Transformer and RNN architectures, the system classifies the input and generates a tailored operator plan based on predefined BookIndex operators, thereby guiding effective retrieval and generation strategies rather than relying on basic keyword matching." + }, + { + "index_id": 83, + "parent_id": 80, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 83, + "pdf_para_block": { + "docling_label": "caption" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Figure 3: The general workflow of agent-based retrieval in BookRAG, which contains agent-based planning, retrieval, and generation processes.", + "title_level": -1 + }, + "summary": "BookRAG's agent-based retrieval operates through a comprehensive workflow integrating three core processes: planning, retrieval, and generation, as illustrated in Figure 3." + }, + { + "index_id": 84, + "parent_id": 80, + "type": "NodeType.IMAGE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 84, + "pdf_para_block": { + "docling_label": "picture" + }, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-3.png", + "image_width": 0, + "image_height": 0, + "caption": "cref='#/texts/89'", + "footnote": "", + "table_body": null, + "content": "cref='#/texts/89'", + "title_level": -1 + }, + "summary": "The provided content does not contain any substantive information to summarize, as it consists solely of a placeholder indicating an image with a reference caption code and lacks any actual text, data, or descriptive details." + }, + { + "index_id": 85, + "parent_id": 80, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 85, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "2. Retrieval Process. Guided by the operator plan, the retrieval process executes Scent/Filter-based Retrieval . This stage navigates the BookIndex 𝐵 = ( 𝑇,𝐺, 𝑀 ) , either utilizing a scent-based retrieval principle (e.g., following relevant entities in 𝐺 ) to find information, or employing various filters (e.g., modal type) to refine the selection. After reasoning, BookRAG gets the retrieval set of highly relevant information blocks from the BookIndex.", + "title_level": -1 + }, + "summary": "The retrieval process executes Scent/Filter-based Retrieval guided by an operator plan to navigate the BookIndex, utilizing scent-based principles to follow relevant entities or applying filters like modal type to refine selections, ultimately yielding a set of highly relevant information blocks for reasoning." + }, + { + "index_id": 86, + "parent_id": 80, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 86, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "3. Generation Process. Finally, all retrieved information enters the generation stage for Analysis & Merging . This stage synthesizes these (often fragmented) pieces of evidence, performs final analysis, and formulates a coherent response.", + "title_level": -1 + }, + "summary": "The generation process synthesizes retrieved, often fragmented information to perform final analysis and formulate a coherent response." + }, + { + "index_id": 87, + "parent_id": 78, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 87, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "5.2 Agent-based Planning", + "title_level": 2 + }, + "summary": "Section 5.2 details BookRAG's agent-based planning mechanism, which dynamically constructs tailored retrieval pipelines by classifying queries into single-hop, multi-hop, or global aggregation categories and orchestrating a flexible sequence of Formulator, Selector, Reasoner, and Synthesizer operators to execute the optimal strategy." + }, + { + "index_id": 88, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 88, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "The planning stage is the core of BookRAG, designed to intelligently navigate our BookIndex 𝐵 = ( 𝑇,𝐺, 𝑀 ) . To support flexible retrieval, we define four types of operators: Formulator, Selector, Reasoner, and Synthesizer. These operators can be arbitrarily combined to form tailored execution pipelines, each with adjustable parameters. BookRAG dynamically configures and assembles these operators to adapt to the specific requirements of different query categories. This process involves two sequential steps: first, the agent performs", + "title_level": -1 + }, + "summary": "BookRAG's planning stage serves as its core mechanism for intelligently navigating the BookIndex by dynamically assembling flexible retrieval pipelines. It achieves this through four adaptable operators—Formulator, Selector, Reasoner, and Synthesizer—which can be arbitrarily combined and parameterized to tailor execution strategies for specific query categories." + }, + { + "index_id": 89, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 89, + "pdf_para_block": { + "docling_label": "caption" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Table 2: Three common query categories addressed in BookRAG.", + "title_level": -1 + }, + "summary": "BookRAG addresses three common query categories, as detailed in Table 2." + }, + { + "index_id": 90, + "parent_id": 87, + "type": "NodeType.TABLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 90, + "pdf_para_block": { + "docling_label": "table" + }, + "img_path": "", + "image_width": 0, + "image_height": 0, + "caption": "cref='#/texts/95'", + "footnote": "", + "table_body": "| Query Category | Description | Core Task | Example Query |\n|--------------------|-------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------|\n| Single-hop | Queries with a single, distinct information target. | Scent-based Retrieval : Retrieve content related to a specific entity or section. | What is the definition of Information Scent? |\n| Multi-hop | Queries that require synthesizing information from multiple blocks, often by decomposing into sub-problems. | Decomposing & Merging : Decompose into sub-problems, retrieve for each, and synthesize the final answer. | How does Transformer differ from RNNs in handling long-range dependencies? |\n| Global Aggregation | Queries that require filtering across the entire document and performing calculations. | Filter & Aggregation : Apply filters across the document & perform aggregation operations (e.g., Count, Sum). | How many figures related to IFT are in Section 4? |", + "content": "cref='#/texts/95'", + "title_level": -1 + }, + "summary": "The document categorizes information retrieval queries into three distinct types based on their complexity and required processing: **Single-hop** queries target a specific entity using scent-based retrieval; **Multi-hop** queries demand decomposing complex questions into sub-problems, retrieving data for each, and synthesizing the final answer; and **Global Aggregation** queries involve filtering the entire document to perform calculations like counting or summing." + }, + { + "index_id": 91, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 5, + "page_path": null, + "pdf_id": 91, + "pdf_para_block": { + "docling_label": "page_footer" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "6", + "title_level": -1 + }, + "summary": "The provided input consists solely of the number \"6\" and contains no substantive information, context, or data to summarize." + }, + { + "index_id": 92, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 92, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "(a) Operator Set", + "title_level": -1 + }, + "summary": "The provided content is limited to the heading \"Operator Set\" and contains no substantive information, data, or context to summarize; therefore, no core conclusion or key points can be extracted." + }, + { + "index_id": 93, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 93, + "pdf_para_block": { + "docling_label": "caption" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Figure 4: The BookRAG Operator Library and an Execution Example from MMLongBench dataset: (a) a visual depiction of the four operator types (Formulator, Selector, Reasoner, and Synthesizer) and (b) an execution trace for a 'Single-hop' query, demonstrating the agent-based planning and step-by-step operator execution.", + "title_level": -1 + }, + "summary": "Figure 4 illustrates the BookRAG Operator Library, which utilizes four distinct agent types—Formulator, Selector, Reasoner, and Synthesizer—to execute complex queries. Through an execution trace of a 'Single-hop' query from the MMLongBench dataset, the figure demonstrates how these operators collaborate in a step-by-step, agent-based planning process to retrieve and synthesize information." + }, + { + "index_id": 94, + "parent_id": 87, + "type": "NodeType.IMAGE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 94, + "pdf_para_block": { + "docling_label": "picture" + }, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-4.png", + "image_width": 0, + "image_height": 0, + "caption": "cref='#/texts/98'", + "footnote": "", + "table_body": null, + "content": "cref='#/texts/98'", + "title_level": -1 + }, + "summary": "The provided content consists solely of an image with a reference caption and lacks any descriptive text, data, or context necessary to extract a meaningful summary or core conclusion." + }, + { + "index_id": 95, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 95, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Query Classification to determine the appropriate solution strategy, then generates a specific Operator Plan .", + "title_level": -1 + }, + "summary": "The process begins by classifying the query to determine the optimal solution strategy, which then guides the generation of a specific operator plan." + }, + { + "index_id": 96, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 96, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· Query Classification . To enable agent strategy selection, we focus on three representative query categories defined by their intrinsic complexity and operational demands (Table 2): Single-hop , Multi-hop , and Global Aggregation . This classification is crucial because each category requires a different solution strategy. For instance, a Single-hop query typically requires a single piece of information retrieved via a Scent-based Retrieval operation. In contrast, a Global Aggregation query often necessitates analyzing content under multiple filtering conditions, usually involving a sequence of Filter & Aggregation operations across various parts of the document. Furthermore, BookRAG is designed to be extensible, allowing for the resolution of a broader range of query types by integrating additional operators.", + "title_level": -1 + }, + "summary": "BookRAG employs a query classification system—categorizing requests as Single-hop, Multi-hop, or Global Aggregation based on their complexity—to dynamically select the most effective retrieval and processing strategies, such as Scent-based Retrieval or Filter & Aggregation sequences, while maintaining extensibility to support additional query types through new operators." + }, + { + "index_id": 97, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 97, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· BookIndex Operators . To execute the strategies identified by classification, we designed a set of operators ( O ) tailored for the BookIndex 𝐵 = ( 𝑇,𝐺, 𝑀 ) . These operators, visually depicted in Figure 4(a) and detailed in Table 3, define the set of operations the agent can employ for diverse query categories. We group them into four types, which we describe in sequence:", + "title_level": -1 + }, + "summary": "To execute strategies identified by classification, a set of tailored operators (O) was designed for the BookIndex (B = T, G, M). These operators, categorized into four distinct types and detailed in Table 3, define the specific operations an agent can perform across diverse query categories." + }, + { + "index_id": 98, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 98, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "❶ Formulator. These are LLM-based operators that prepare the query for execution. This category includes Decompose , which breaks a Complex query into a set of simpler, actionable sub-queries 𝑄 𝑠 . It also includes Extract , which employs an LLM to identify key entities 𝐸 𝑞 from the query text and link them to corresponding entities in the KG, 𝐺 : 𝑄 𝑠 = LLM ( 𝑃 𝐷𝑒𝑐 , 𝑞 ) = { 𝑞 , 𝑞 1 2 , . . . , 𝑞 𝑘 } (2) 𝐸 𝑞 = LLM ( 𝑃 𝐸𝑥𝑡 , 𝑞 ) = { 𝑒 1 , 𝑒 2 , . . . , 𝑒 𝑚 } (3)", + "title_level": -1 + }, + "summary": "Formulators are LLM-based operators designed to prepare complex queries for execution by transforming them into actionable components. This process primarily involves two functions: Decompose, which breaks a complex query into a set of simpler sub-queries, and Extract, which identifies key entities within the query text and links them to corresponding entities in the knowledge graph." + }, + { + "index_id": 99, + "parent_id": 87, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 99, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝑄 𝑠 = LLM ( 𝑃 𝐷𝑒𝑐 , 𝑞 ) = { 𝑞 , 𝑞 1 2 , . . . , 𝑞 𝑘 } (2)", + "title_level": -1 + }, + "summary": "The equation defines a process where a Large Language Model (LLM) takes a prompt ($P_{Dec}$) and an initial query ($q$) as inputs to generate a set of $k$ related queries ($Q_s$), starting with the original query and followed by $k-1$ derived variations." + }, + { + "index_id": 100, + "parent_id": 87, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 100, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝐸 𝑞 = LLM ( 𝑃 𝐸𝑥𝑡 , 𝑞 ) = { 𝑒 1 , 𝑒 2 , . . . , 𝑒 𝑚 } (3)", + "title_level": -1 + }, + "summary": "The equation defines a process where a Large Language Model (LLM) takes an external prompt ($P_{Ext}$) and a query ($q$) as inputs to generate a set of $m$ distinct embeddings ($e_1$ through $e_m$), representing the model's encoded response to that specific query." + }, + { + "index_id": 101, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 101, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Here, 𝑞 is the original user query, while 𝑃 𝐷𝑒𝑐 and 𝑃 𝐸𝑥𝑡 represent the prompts used to guide the LLM for the decomposition and extraction tasks, respectively.", + "title_level": -1 + }, + "summary": "In this framework, the original user query ($q$) is processed using two distinct prompts: $P_{Dec}$ to guide the Large Language Model (LLM) in decomposing the task, and $P_{Ext}$ to direct it in extracting the necessary information." + }, + { + "index_id": 102, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 102, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "❷ Selector. These operators filter or select specific content ranges from the BookIndex. Filter_Modal and Filter_Range directly apply the explicit constraints 𝐶 (e.g., modal types, page ranges) generated during the plan. Operating on the Tree 𝑇 = ( 𝑁, 𝐸 𝑇 ) , these operators produce a filtered subset 𝑁 𝑓 where the predicate 𝐶 𝑛 ( ) holds true for each node: 𝑁 𝑓 = { 𝑛 ∈ 𝑁 | 𝐶 𝑛 ( )} (4)", + "title_level": -1 + }, + "summary": "The Selector operators (Filter_Modal and Filter_Range) filter the BookIndex by applying explicit constraints to a tree structure, generating a subset of nodes that satisfy specific criteria such as modal types or page ranges." + }, + { + "index_id": 103, + "parent_id": 87, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 103, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝑁 𝑓 = { 𝑛 ∈ 𝑁 | 𝐶 𝑛 ( )} (4)", + "title_level": -1 + }, + "summary": "Equation (4) defines the set $N_f$ as the collection of all natural numbers $n$ that satisfy the condition $C_n$." + }, + { + "index_id": 104, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 104, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "In contrast, Select_by_Entity and Select_by_Section target contiguous document segments by retrieving subtrees rooted at specific section nodes. This process first identifies a set of target section nodes 𝑆 target ⊂ 𝑁 at a specified depth, where 𝑆 target consists of sections either linked to entities 𝐸 𝑞 via the GT-Link 𝑀 or selected by the LLM. It then retrieves all descendants of these targets to form the selected node set 𝑁 𝑠 : 𝑁 𝑠 = GLYPH<216> 𝑠 ∈ 𝑆 target Subtree ( 𝑠 ) (5)", + "title_level": -1 + }, + "summary": "Select_by_Entity and Select_by_Section condense documents by retrieving contiguous segments rooted at specific section nodes; this process first identifies target sections (either linked to entities via GT-Link or selected by an LLM) at a defined depth and then aggregates all their descendants to form the final selected content set." + }, + { + "index_id": 105, + "parent_id": 87, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 105, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝑁 𝑠 = GLYPH<216> 𝑠 ∈ 𝑆 target Subtree ( 𝑠 ) (5)", + "title_level": -1 + }, + "summary": "The equation $N_s = \\sum_{s \\in S} \\text{target Subtree}(s)$ defines the total count $N_s$ as the sum of target subtrees across all elements $s$ within the set $S$." + }, + { + "index_id": 106, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 106, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "❸ Reasoner. These operators analyze and refine selected tree nodes. Graph_Reasoning performs multi-hop inference on a subgraph 𝐺 ' ( 𝑉 , 𝐸 ' ' ) (extracted from selected nodes 𝑁 𝑠 ) starting from entity 𝑒 . Starting from the retrieved entities, it computes an entity importance vector 𝐼 𝐺 ∈ R | 𝑉 ' | over the subgraph 𝐺 ' using the PageRank algorithm [20]. These entity scores are then mapped to the tree nodes via the GT-Link matrix 𝑀 to derive the final tree node importance scores vector 𝑆 𝐺 ∈ R | 𝑁 𝑠 | : 𝐼 𝐺 = PageRank ( 𝐺 , 𝑒 ' ) (6) 𝑆 𝐺 = 𝐼 𝐺 × 𝑀 (7)", + "title_level": -1 + }, + "summary": "The Reasoner operator refines selected tree nodes by performing multi-hop inference on a subgraph extracted from those nodes. It begins at a specific entity, calculates an entity importance vector using the PageRank algorithm, and then maps these scores to the tree nodes via the GT-Link matrix to generate a final vector of tree node importance scores." + }, + { + "index_id": 107, + "parent_id": 87, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 107, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝐼 𝐺 = PageRank ( 𝐺 , 𝑒 ' ) (6)", + "title_level": -1 + }, + "summary": "Equation (6) defines the PageRank of a graph $G$ as a function dependent on the graph structure and a specific parameter $e'$, denoted as $I_G = \\text{PageRank}(G, e')$." + }, + { + "index_id": 108, + "parent_id": 87, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 108, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝑆 𝐺 = 𝐼 𝐺 × 𝑀 (7)", + "title_level": -1 + }, + "summary": "The total system gain ($S_G$) is calculated by multiplying the individual gain ($I_G$) by the multiplier ($M$), as defined by the equation $S_G = I_G \\times M$." + }, + { + "index_id": 109, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 109, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Text_Ranker evaluates the semantic relevance of the tree node's content to the query 𝑞 , assigning a relevance score 𝑆 𝑇 to each node. Skyline_Ranker employs the Skyline operator to filter nodes based on these multiple criteria (e.g., 𝑆 𝐺 and 𝑆 𝑇 ), retaining only those nodes that are not dominated by any others in terms of the specified scoring dimensions.", + "title_level": -1 + }, + "summary": "The system combines Text_Ranker, which assigns semantic relevance scores to tree nodes based on a query, with Skyline_Ranker, which filters these nodes by retaining only those that are not dominated by others across multiple scoring dimensions." + }, + { + "index_id": 110, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 6, + "page_path": null, + "pdf_id": 110, + "pdf_para_block": { + "docling_label": "page_footer" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "7", + "title_level": -1 + }, + "summary": "The provided input contains only the number \"7\" and lacks sufficient context, text, or data to generate a meaningful summary." + }, + { + "index_id": 111, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 111, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "❹ Synthesizer. These operators are responsible for content generation. Map performs analysis on specific retrieved information segments to generate partial responses. Reduce synthesizes a final coherent answer by aggregating information from multiple sources, such as partial answers or a collection of retrieved evidence.", + "title_level": -1 + }, + "summary": "Synthesizer operators drive content generation by analyzing retrieved information to produce partial responses (Map) and aggregating these partial answers or evidence from multiple sources to construct a final, coherent response (Reduce)." + }, + { + "index_id": 112, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 112, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· Operator Plan . After classifying the query ( 𝑞 ) into its category ( 𝑐 ), the agent's final task is to generate an executable plan 𝑃 . This plan is a specific sequence of operators ⟨ 𝑜 , . . . , 𝑜 1 𝑛 ⟩ selected from our library O with parameters dynamically instantiated based on 𝑞 . This process is formulated as: 𝑃 = Agent Plan ( 𝑞, 𝑐, O) (8)", + "title_level": -1 + }, + "summary": "The agent's final task is to generate an executable plan by selecting a specific sequence of operators from a library and dynamically instantiating their parameters based on the classified query and its category." + }, + { + "index_id": 113, + "parent_id": 87, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 113, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝑃 = Agent Plan ( 𝑞, 𝑐, O) (8)", + "title_level": -1 + }, + "summary": "The agent's plan ($P$) is a function determined by the query ($q$), context ($c$), and observations ($O$)." + }, + { + "index_id": 114, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 114, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "The plan follows a structured workflow tailored to each category:", + "title_level": -1 + }, + "summary": "The plan utilizes a structured workflow that is specifically tailored to each category." + }, + { + "index_id": 115, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 115, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· Single-hop : The agent first attempts to Extract an entity. If successful, it executes a 'scent-based' selection; otherwise, it falls back to a section-based strategy. Both paths then proceed to standard reasoning and generation, denoted as 𝑃 std .", + "title_level": -1 + }, + "summary": "The single-hop strategy initiates with entity extraction, employing a scent-based selection upon success or a section-based fallback if extraction fails, before both paths converge on standard reasoning and generation." + }, + { + "index_id": 116, + "parent_id": 87, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 116, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝑃 s = ( Extract success - - - - -→ Select_by_Entity → 𝑃 std Extract fail - -→ Select_by_Section → 𝑃 std (9)", + "title_level": -1 + }, + "summary": "The success rate ($P_s$) is determined by a conditional selection process: if extraction succeeds, the probability is calculated by selecting based on the entity ($P_{std}$); if extraction fails, the probability is calculated by selecting based on the section ($P_{std}$)." + }, + { + "index_id": 117, + "parent_id": 87, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 117, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝑃 std = ( Graph ∥ Text ) → Skyline → Reduce (10)", + "title_level": -1 + }, + "summary": "The process condenses a combined graph and text input into a final result of 10 items by first generating a skyline and then applying a reduction step." + }, + { + "index_id": 118, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 118, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· Complex : The agent first decomposes the problem, applies the Single-hop workflow 𝑃 s to each sub-problem, and finally synthesizes the results.", + "title_level": -1 + }, + "summary": "The Complex workflow operates by decomposing a problem into sub-problems, applying the Single-hop workflow to each individually, and then synthesizing the results to form the final solution." + }, + { + "index_id": 119, + "parent_id": 87, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 119, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝑃 complex = Decompose → 𝑃 s → Map → Reduce (11)", + "title_level": -1 + }, + "summary": "The complex probability $P_{complex}$ is derived through a four-step process: decomposition into $P_s$, followed by mapping and reduction operations." + }, + { + "index_id": 120, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 120, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· Global Aggregation : The workflow involves applying a sequence of filters followed by synthesis.", + "title_level": -1 + }, + "summary": "The workflow operates through global aggregation, which applies a sequence of filters followed by synthesis." + }, + { + "index_id": 121, + "parent_id": 87, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 121, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝑃 global = GLYPH<214> ( Filter_Modal | Filter_Range ) → Map → Reduce (12)", + "title_level": -1 + }, + "summary": "The global process `P` executes a two-stage pipeline where a combined filter (either modal or range-based) is applied to data, followed by a Map operation and a Reduce operation with a parallelism factor of 12." + }, + { + "index_id": 122, + "parent_id": 87, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 122, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Here, the symbol ˛ denotes the nested composition of filters, applying either a modal or range filter at each step.", + "title_level": -1 + }, + "summary": "The symbol ˛ represents the nested composition of filters, where a modal or range filter is applied sequentially at each step." + }, + { + "index_id": 123, + "parent_id": 78, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 123, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "5.3 Structured Execution", + "title_level": 2 + }, + "summary": "This section details BookRAG's structured execution methodology, which applies Information Foraging Theory through a three-stage pipeline of Selector-based retrieval, Reasoner-driven sensemaking, and Synthesizer-based answer generation to efficiently minimize computational costs." + }, + { + "index_id": 124, + "parent_id": 123, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 124, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Following the planning stage, BookRAG executes the generated workflow 𝑃 . This execution phase embodies the cognitive principles of Information Foraging Theory (IFT), effectively translating abstract textual queries into concrete operations. Specifically, the Selector operators mirror the act of 'navigating to information patches,' narrowing the vast document space down to relevant scopes. Subsequently, the Reasoner operators perform 'sensemaking within patches,' where they analyze and refine the information within these focused scopes. Finally, the Synthesizer generates the answer based on the processed evidence. This design minimizes the cost of attention by ensuring computational resources are focused solely on high-value data patches.", + "title_level": -1 + }, + "summary": "BookRAG's execution phase applies Information Foraging Theory to efficiently answer queries by minimizing computational costs through a three-step process: the Selector navigates to relevant document patches, the Reasoner performs sensemaking within those focused scopes, and the Synthesizer generates the final answer based on the processed evidence." + }, + { + "index_id": 125, + "parent_id": 123, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 125, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Scent/Filter-based Retrieval. The execution begins by narrowing the scope. Aligning with IFT, Selector operators identify relevant 'patches' by following 'information scents' (e.g., key entities in question) or applying explicit filter constraints. This process reduces the full node set 𝑁 to a focused node subset 𝑁 𝑠 : 𝑁 𝑠 = Selector ( 𝑁, params sel ) (13)", + "title_level": -1 + }, + "summary": "Scent and filter-based retrieval initiates the process by narrowing the scope of a full node set to a focused subset through the use of Selector operators. These operators identify relevant data \"patches\" by following information scents, such as key entities within a question, or by applying explicit filter constraints, thereby reducing the search space before further processing." + }, + { + "index_id": 126, + "parent_id": 123, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 126, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝑁 𝑠 = Selector ( 𝑁, params sel ) (13)", + "title_level": -1 + }, + "summary": "The notation $N_s = \\text{Selector}(N, \\text{params}_{\\text{sel}})$ defines a selector operation that generates a specific subset $N_s$ from a dataset $N$ by applying a selection function configured with a set of parameters." + }, + { + "index_id": 127, + "parent_id": 123, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 127, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "This pre-selection minimizes noise and ensures that subsequent reasoning is applied only to highly relevant contexts, optimizing the foraging cost. Subsequently, within this focused scope, Reasoner operators evaluate nodes using multiple dimensions, such as graph topology and semantic relevance. We then employ the Skyline_Ranker to get the final retrieval set. Unlike fixed top𝑘 retrieval, the Skyline operator retains the Pareto frontier of nodes, retaining nodes that are valuable in at least one dimension while discarding dominated ones: 𝑁 𝑅 = Skyline_Ranker ({ 𝑆 𝐺 ( 𝑛 , 𝑆 ) 𝑇 ( 𝑛 ) | 𝑛 ∈ 𝑁 𝑠 }) (14)", + "title_level": -1 + }, + "summary": "The proposed retrieval method optimizes efficiency by first pre-selecting highly relevant contexts to minimize noise, then applying multi-dimensional evaluation (graph topology and semantic relevance) via Reasoner operators. Finally, it utilizes a Skyline_Ranker to generate the final retrieval set by preserving the Pareto frontier of nodes—keeping those valuable in at least one dimension while discarding dominated ones—thereby outperforming fixed top-k retrieval." + }, + { + "index_id": 128, + "parent_id": 123, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 128, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝑁 𝑅 = Skyline_Ranker ({ 𝑆 𝐺 ( 𝑛 , 𝑆 ) 𝑇 ( 𝑛 ) | 𝑛 ∈ 𝑁 𝑠 }) (14)", + "title_level": -1 + }, + "summary": "The Skyline Ranker ($N_R$) is defined as a ranking function that processes a set of candidate nodes ($n \\in N_s$) by evaluating their skyline score $SG(n, S)$ and time cost $T(n)$ to determine the optimal selection order." + }, + { + "index_id": 129, + "parent_id": 123, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 129, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Analysis & Merging Generation. In the final stage, the Synthesizer operator generates the coherent answer by aggregating the refined evidence: 𝐴 = Synthesizer ( 𝑞, 𝑁 𝑅 ) (15)", + "title_level": -1 + }, + "summary": "In the final stage of the process, the Synthesizer operator generates a coherent answer by aggregating refined evidence, mathematically represented as $A = \\text{Synthesizer}(q, N_R)$." + }, + { + "index_id": 130, + "parent_id": 123, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 130, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝐴 = Synthesizer ( 𝑞, 𝑁 𝑅 ) (15)", + "title_level": -1 + }, + "summary": "The equation $A = \\text{Synthesizer}(q, N_R)$ defines $A$ as the output generated by a Synthesizer function that takes a query $q$ and a set of $N_R$ elements as inputs." + }, + { + "index_id": 131, + "parent_id": 123, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 131, + "pdf_para_block": { + "docling_label": "caption" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Table 3: Operators utilized in our BookRAG, categorized by their function.", + "title_level": -1 + }, + "summary": "BookRAG employs a diverse set of operators categorized by their specific functional roles to facilitate its operations." + }, + { + "index_id": 132, + "parent_id": 123, + "type": "NodeType.TABLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 132, + "pdf_para_block": { + "docling_label": "table" + }, + "img_path": "", + "image_width": 0, + "image_height": 0, + "caption": "cref='#/texts/136'", + "footnote": "", + "table_body": "| Operator | Type | Description | Parameters |\n|-------------------------------|-----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------|\n| Decompose | Formulator Formulator | Decompose a complex query into simpler, actionable sub-queries. Identify and extract key entities from the query (links to 𝐺 ). | (Self-contained) (Self-contained) |\n| Extract Filter_Modal | Selector | Filter retrieved nodes by their modal type (e.g., Table, Figure). | modal_type: str |\n| Filter_Range | Selector | Filter nodes based on a specified range (e.g., pages, section). | range: (start, end) |\n| Select_by_Entity | Selector | Selects all tree nodes ( 𝑁 ) in sections linked to a given entity ( 𝑉 ). | entity_name: str |\n| Select_by_Section | Selector | Uses an LLM to select relevant sections and selects all tree nodes ( 𝑁 ) within them. | query: str, sections: List[str] |\n| Graph_Reasoning | Reasoner | Performs multi-hop reasoning on subgraph ( 𝐺 ' ) and score tree nodes ( 𝑁 ) using graph importance and GT-links. Rerank retrieved tree nodes ( 𝑁 ) based on the relevance. | start_entity: str, subgraph: 𝐺 ' query: str |\n| Text_Reasoning Skyline_Ranker | Reasoner Reasoner | | criteria: List[str] |\n| | | Rerank nodes based on multiple criteria. | (Input: List[str]) |\n| Map | Synthesizer | Uses partially retrieved information to generate a partial answer. | |\n| Reduce | Synthesizer | Synthesizes the final answer from partial information or all sub-problem answers. | (Input: List[str]) |", + "content": "cref='#/texts/136'", + "title_level": -1 + }, + "summary": "The provided table defines a structured query processing framework comprising six functional categories: **Formulators** decompose complex queries into actionable sub-queries; **Selectors** filter or retrieve specific nodes based on modal types, page ranges, entities, or LLM-assisted section selection; **Reasoners** perform multi-hop graph analysis and multi-criteria ranking to score and reorder results; and **Synthesizers** generate partial answers and combine them into a final response. This pipeline systematically transforms raw queries into refined, synthesized answers through entity extraction, targeted filtering, logical reasoning, and information aggregation." + }, + { + "index_id": 133, + "parent_id": 123, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 7, + "page_path": null, + "pdf_id": 133, + "pdf_para_block": { + "docling_label": "page_footer" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "8", + "title_level": -1 + }, + "summary": "The provided content consists solely of the number \"8\" and lacks sufficient context, narrative, or data to form a meaningful summary or draw any conclusions." + }, + { + "index_id": 134, + "parent_id": 123, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 134, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "The Map operator performs fine-grained analysis on individual evidence blocks or sub-problems (from Decompose ) to generate intermediate insights. The Reduce operator then aggregates these partial results, such as answers to decomposed sub-queries or statistical counts from a global filter, to construct the final response. This separation ensures that the system can handle both detailed content extraction and high-level reasoning synthesis effectively.", + "title_level": -1 + }, + "summary": "The Map and Reduce operators function as a complementary pair to ensure effective system performance: the Map operator conducts fine-grained analysis on individual evidence blocks to generate intermediate insights, while the Reduce operator aggregates these partial results to synthesize the final response, thereby enabling the system to seamlessly handle both detailed content extraction and high-level reasoning." + }, + { + "index_id": 135, + "parent_id": 123, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 135, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "To illustrate this end-to-end process, Figure 4(b) presents an execution trace for a 'Single-hop' query: 'What is the type of car in the Ranking Prompt example?'. In the planning phase, the agent classifies the query and generates a specific workflow. Subsequently, it identifies key entities (e.g., 'car') via Extract , retrieves relevant nodes via Select_by_Entity , refines them through reasoning and Skyline filtering, and finally synthesizes the answer using Reduce .", + "title_level": -1 + }, + "summary": "Figure 4(b) illustrates an end-to-end execution trace for a single-hop query regarding car types, where an agent classifies the request to generate a workflow that extracts key entities, retrieves and refines relevant nodes through reasoning and Skyline filtering, and finally synthesizes the answer." + }, + { + "index_id": 136, + "parent_id": 1, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 136, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "6 EXPERIMENTS", + "title_level": 1 + }, + "summary": "Section 6 presents a comprehensive experimental evaluation of BookRAG, detailing the setup, benchmark results, and ablation studies that demonstrate its state-of-the-art performance in document question-answering tasks." + }, + { + "index_id": 137, + "parent_id": 136, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 137, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "In our experiments, we evaluate BookRAG against several strong baseline methods, with an in-depth comparison of their efficiency and accuracy on document QA tasks.", + "title_level": -1 + }, + "summary": "BookRAG was evaluated against strong baseline methods, demonstrating a detailed comparison of its efficiency and accuracy in document question-answering tasks." + }, + { + "index_id": 138, + "parent_id": 136, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 138, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "6.1 Setup", + "title_level": 2 + }, + "summary": "Section 6.1 outlines the experimental setup by defining the benchmark datasets and evaluation metrics, describing the baseline RAG architectures, and specifying the unified model configurations and implementation details used to ensure fair performance comparison." + }, + { + "index_id": 139, + "parent_id": 138, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 139, + "pdf_para_block": { + "docling_label": "caption" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Table 4: Datasets used in our experiments. EM and F1 denote Exact Match and F1-score, respectively.", + "title_level": -1 + }, + "summary": "Table 4 lists the datasets employed in the experiments, utilizing Exact Match (EM) and F1-score as the primary evaluation metrics." + }, + { + "index_id": 140, + "parent_id": 138, + "type": "NodeType.TABLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 140, + "pdf_para_block": { + "docling_label": "table" + }, + "img_path": "", + "image_width": 0, + "image_height": 0, + "caption": "cref='#/texts/143'", + "footnote": "", + "table_body": "| Dataset | MMLongBench | M3DocVQA | Qasper |\n|-------------|---------------|------------|--------------|\n| Questions | 669 | 633 | 640 |\n| Documents | 85 | 500 | 192 |\n| Avg. Pages | 42.16 | 8.52 | 10.95 |\n| Avg. Images | 25.92 | 3.51 | 3.43 |\n| Tokens | 2,816,155 | 3,553,774 | 2,265,349 |\n| Metrics | EM, F1 | EM, F1 | Accuracy, F1 |", + "content": "cref='#/texts/143'", + "title_level": -1 + }, + "summary": "The table summarizes three long-document benchmark datasets—MMLongBench, M3DocVQA, and Qasper—highlighting their distinct characteristics in scale and evaluation metrics. M3DocVQA contains the highest number of documents (500) and tokens (3.55 million) but the fewest pages per document (8.52), while MMLongBench features the longest average document length (42.16 pages) and the highest image density (25.92 images per document). All three datasets contain over 600 questions each, with MMLongBench and M3DocVQA evaluated using Exact Match (EM) and F1 scores, whereas Qasper utilizes Accuracy and F1." + }, + { + "index_id": 141, + "parent_id": 138, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 141, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Datasets & Question Synthesis. We use three widely adopted benchmarking datasets for complex document QA tasks: MMLongBench [33], M3DocVQA [11], and Qasper [14]. MMLongBench is a comprehensive benchmark designed to evaluate QA capabilities on long-form documents, covering diverse categories such as guidebooks, financial reports, and industry files. M3DocVQA is an open-domain benchmark designed to test RAG systems on a diverse collection of HTML-type documents sourced from Wikipedia pages 1 . Qasper is a QA dataset focused on scientific papers, where questions require retrieving evidence from the entire document. We filtered the datasets to remove documents with low clarity or incoherent structures. To address the scarcity of global-level questions in the original benchmarks, we synthesize additional QA pairs by having an LLM generate global questions from selected document elements (e.g., tables or figures). These questions are then answered and meticulously refined by human annotators via an outsourcing process, with this additional QA pairs constituting less than 20% of our final QA pairs. The statistics of these datasets are presented in Table 4.", + "title_level": -1 + }, + "summary": "To evaluate complex document question answering, the study utilizes three benchmark datasets—MMLongBench for long-form documents, M3DocVQA for open-domain HTML content, and Qasper for scientific papers—after filtering for clarity and synthesizing additional global-level questions via LLMs and human refinement to address data scarcity." + }, + { + "index_id": 142, + "parent_id": 138, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 142, + "pdf_para_block": { + "docling_label": "footnote" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "1 https://www.wikipedia.org/", + "title_level": -1 + }, + "summary": "Wikipedia is a free, open-source online encyclopedia that allows anyone to edit its content, serving as a vast, collaboratively maintained repository of knowledge across countless topics." + }, + { + "index_id": 143, + "parent_id": 138, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 143, + "pdf_para_block": { + "docling_label": "page_footer" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "9", + "title_level": -1 + }, + "summary": "The provided input contains only the number \"9\" and lacks sufficient context, data, or descriptive text to generate a meaningful summary or identify a core conclusion." + }, + { + "index_id": 144, + "parent_id": 138, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 144, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Metrics. Weadheretotheofficial metrics specified by each dataset for QA. Our primary evaluation relies on Exact Match (EM), accuracy, and token-based F1-score. To assess efficiency, we also measure time cost and token usage during the response phase. Additionally, for methods including PDF parsing, we also evaluate retrieval recall. To establish the ground truth for this, we manually label the specific PDF blocks (e.g., texts, titles, tables, images, and formulas) required to answer each question. This labeling process is guided by the metadata of ground-truth evidence provided in each dataset; we filter candidate blocks using the given modality (all datasets), page numbers (MMLongBench), and evidence statements (Qasper). Any blocks that remained non-unique after this filtering process are manually annotated. In cases where a PDF parsing error made the ground-truth item unavailable, the retrieval recall for that query is recorded as 0.", + "title_level": -1 + }, + "summary": "Evaluation relies on standard QA metrics (Exact Match, accuracy, and token-based F1-score) alongside efficiency measures (time cost and token usage), with retrieval recall specifically assessed for PDF parsing methods. Ground truth for recall is established by manually labeling essential PDF blocks (text, tables, images, etc.) using dataset metadata and filtering criteria, with manual annotation applied to non-unique cases; any retrieval failure due to parsing errors is recorded as a recall score of zero." + }, + { + "index_id": 145, + "parent_id": 138, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 145, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Baselines. Our experiments consider three model configurations:", + "title_level": -1 + }, + "summary": "The experiments evaluate three distinct model configurations to establish performance baselines." + }, + { + "index_id": 146, + "parent_id": 138, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 146, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· Conventional RAG: These methods are the most common pipeline for document analysis, where the raw text is first extracted and then chunked into segments of a specified size. We select strong and widely used retrieval models: BM25 [44] and Vanilla RAG. We also implement Layout+Vanilla, a variant that uses document layout analysis for semantic chunking.", + "title_level": -1 + }, + "summary": "Conventional Retrieval-Augmented Generation (RAG) pipelines typically extract raw text, segment it into fixed-size chunks, and employ standard retrieval models like BM25 or Vanilla RAG, with a specialized variant known as Layout+Vanilla enhancing this process by utilizing document layout analysis for more effective semantic chunking." + }, + { + "index_id": 147, + "parent_id": 138, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 147, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· Graph-based RAG: These methods first extract textual content from documents and then leverage graph data during retrieval. We select RAPTOR [45] and GraphRAG [16]. Specifically, GraphRAG has two versions: GraphRAG-Global and GraphRAG-Local, which employ global and local search methods, respectively.", + "title_level": -1 + }, + "summary": "Graph-based Retrieval-Augmented Generation (RAG) methods enhance document retrieval by first extracting text and then utilizing graph structures, with prominent examples including RAPTOR and two variants of GraphRAG: GraphRAG-Global, which employs global search, and GraphRAG-Local, which utilizes local search." + }, + { + "index_id": 148, + "parent_id": 138, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 148, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· LayoutsegmentedRAG: This category encompasses methods that utilize layout analysis to segment document content into discrete structural units. We include: MM-Vanilla, which utilizes multi-modal embeddings for visual and textual content; a tree-based method inspired by PageIndex [39], denoted as TreeTraverse, where an LLM navigates the document's tree structure; DocETL [47], a declarative system for complex document processing; and GraphRanker, a graphbased method extended from HippoRAG [19] that applies Personalized PageRank [20] to rank the relevant nodes.", + "title_level": -1 + }, + "summary": "LayoutsegmentedRAG is a category of retrieval-augmented generation methods that leverage layout analysis to divide documents into discrete structural units for processing. Key approaches within this category include MM-Vanilla, which uses multi-modal embeddings for visual and textual data; TreeTraverse, an LLM-driven method navigating document tree structures; DocETL, a declarative system for complex processing; and GraphRanker, which extends HippoRAG by applying Personalized PageRank to rank relevant nodes." + }, + { + "index_id": 149, + "parent_id": 138, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 149, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Implementation details. For a fair comparison, both BookRAG and all baseline methods are powered by a unified set of state-of-theart (SOTA) and widely adopted backbone models from the Qwen family [4, 60, 63, 64]. We employ MinerU [52] for robust document layout parsing. We set the threshold of gradient 𝑔 as 0 6, and more . details are provided in the appendix of our technical report [57]. Our source code, prompts, and detailed configurations are available at github.com/sam234990/BookRAG.", + "title_level": -1 + }, + "summary": "To ensure a fair comparison, BookRAG and all baseline methods utilize unified Qwen family backbone models and MinerU for document layout parsing, with a gradient threshold set at 0.6; full implementation details, source code, and prompts are publicly available on GitHub." + }, + { + "index_id": 150, + "parent_id": 136, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 150, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "6.2 Overall results", + "title_level": 2 + }, + "summary": "Section 6.2 presents the overall results demonstrating that BookRAG achieves state-of-the-art performance in complex question answering, retrieval effectiveness, and query efficiency across multiple benchmarks by synergizing its unified Tree-Graph BookIndex with agent-based planning." + }, + { + "index_id": 151, + "parent_id": 150, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 151, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "In this section, we present a comprehensive evaluation of BookRAG, analyzing its complex QA performance, retrieval effectiveness, and query efficiency compared to state-of-the-art baselines.", + "title_level": -1 + }, + "summary": "BookRAG demonstrates superior performance over state-of-the-art baselines across complex question answering, retrieval effectiveness, and query efficiency." + }, + { + "index_id": 152, + "parent_id": 150, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 8, + "page_path": null, + "pdf_id": 152, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· QA Performance of BookRAG . We compare the QA performance of BookRAG against three categories of baselines, as shown in Table 5. The results indicate that BookRAG achieves state-of-the-art performance across all datasets, substantially outperforming the top-performing baseline by 18.0% in Exact Match on M3DocVQA. Layout + Vanilla consistently outperforms Vanilla RAG, confirming that layout parsing preserves essential structural information for better retrieval. Besides, the suboptimal results of Tree-Traverse and GraphRanker highlight the limitations of relying solely on hierarchical navigation or graph-based reasoning, which often miss cross-sectional context or drift into irrelevant scopes. In contrast, BookRAG's superiority stems from the synergy of its unified Tree-Graph BookIndex and Agent-based Planning. By effectively classifying queries and configuring optimal workflows, our BookRAG overcomes limitations of context fragmentation and static query workflow within existing baselines, ensuring precise evidence retrieval and accurate generation.", + "title_level": -1 + }, + "summary": "BookRAG achieves state-of-the-art QA performance across all tested datasets, outperforming the top baseline by 18.0% in Exact Match on M3DocVQA. This superiority is driven by its unified Tree-Graph BookIndex and Agent-based Planning, which synergize to classify queries and configure optimal workflows, thereby overcoming the context fragmentation and static limitations of existing methods. While layout parsing proves essential for preserving structural information compared to vanilla RAG, approaches relying solely on hierarchical navigation or graph-based reasoning fall short due to missed cross-sectional context. Ultimately, BookRAG ensures precise evidence retrieval and accurate generation by effectively integrating these advanced indexing and planning capabilities." + }, + { + "index_id": 153, + "parent_id": 150, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 9, + "page_path": null, + "pdf_id": 153, + "pdf_para_block": { + "docling_label": "caption" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Table 5: Performance comparison of different methods across various datasets for solving complex document QA tasks. The best and second-best results are marked in bold and underlined, respectively.", + "title_level": -1 + }, + "summary": "Table 5 presents a performance comparison of various methods on complex document QA tasks across multiple datasets, highlighting the top two performers in each category with bold and underlined formatting to indicate the best and second-best results, respectively." + }, + { + "index_id": 154, + "parent_id": 150, + "type": "NodeType.TABLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 9, + "page_path": null, + "pdf_id": 154, + "pdf_para_block": { + "docling_label": "table" + }, + "img_path": "", + "image_width": 0, + "image_height": 0, + "caption": "cref='#/texts/156'", + "footnote": "", + "table_body": "| Baseline Type | Method | MMLongBench | MMLongBench | M3DocVQA | M3DocVQA | Qasper | Qasper |\n|----------------------|------------------|---------------|---------------|---------------|------------|------------|------------|\n| | Method | (Exact Match) | (F1-score) | (Exact Match) | (F1-score) | (Accuracy) | (F1-score) |\n| Conventional RAG | BM25 | 18.3 | 20.2 | 34.6 | 37.8 | 38.1 | 42.5 |\n| Conventional RAG | Vanilla RAG | 16.5 | 18.0 | 36.5 | 40.2 | 40.6 | 44.4 |\n| | Layout + Vanilla | 18.1 | 19.8 | 36.9 | 40.2 | 40.7 | 44.6 |\n| Graph-based RAG | RAPTOR | 21.3 | 21.8 | 34.3 | 37.3 | 39.4 | 44.1 |\n| Graph-based RAG | GraphRAG-Local | 7.7 | 8.5 | 23.7 | 25.6 | 35.9 | 39.2 |\n| Graph-based RAG | GraphRAG-Global | 5.3 | 5.6 | 20.2 | 22.0 | 24.0 | 24.1 |\n| Layout segmented RAG | MM-Vanilla | 6.8 | 8.4 | 25.1 | 27.7 | 27.9 | 29.3 |\n| Layout segmented RAG | Tree-Traverse | 12.7 | 14.4 | 33.3 | 36.2 | 27.3 | 32.1 |\n| Layout segmented RAG | GraphRanker | 21.2 | 22.7 | 43.0 | 47.8 | 32.9 | 37.6 |\n| Layout segmented RAG | DocETL | 27.5 | 28.6 | 40.9 | 43.3 | 42.3 | 50.4 |\n| Our proposed | BookRAG | 43.8 | 44.9 | 61.0 | 66.2 | 55.2 | 61.1 |", + "content": "cref='#/texts/156'", + "title_level": -1 + }, + "summary": "BookRAG, the proposed method, significantly outperforms all conventional, graph-based, and layout-segmented RAG baselines across three long-document benchmarks (MMLongBench, M3DocVQA, and Qasper). While traditional approaches like BM25 and Vanilla RAG achieve modest scores (e.g., 18.3–40.6 accuracy/F1), and graph-based methods often underperform, BookRAG demonstrates superior capability in handling complex document retrieval and reasoning. Specifically, BookRAG achieves a 43.8 Exact Match and 44.9 F1-score on MMLongBench, 61.0 Exact Match and 66.2 F1-score on M3DocVQA, and 55.2 Accuracy and 61.1 F1-score on Qasper, marking a substantial improvement over the next best performer, DocETL, which trails by roughly 16–20 percentage points in most metrics." + }, + { + "index_id": 155, + "parent_id": 150, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 9, + "page_path": null, + "pdf_id": 155, + "pdf_para_block": { + "docling_label": "caption" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Table 6: Retrieval recall comparison among layout-based methods. The best and second-best results are marked in bold and underlined, respectively.", + "title_level": -1 + }, + "summary": "Layout-based retrieval methods exhibit varying performance levels, with specific approaches achieving the highest recall rates (marked in bold) and the next best results (marked in underlined) as detailed in the comparative analysis." + }, + { + "index_id": 156, + "parent_id": 150, + "type": "NodeType.TABLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 9, + "page_path": null, + "pdf_id": 156, + "pdf_para_block": { + "docling_label": "table" + }, + "img_path": "", + "image_width": 0, + "image_height": 0, + "caption": "cref='#/texts/158'", + "footnote": "", + "table_body": "| Method | MMLongBench | M3DocVQA | Qasper |\n|------------------|---------------|------------|----------|\n| Layout + Vanilla | 26.3 | 33.8 | 33.5 |\n| MM-Vanilla | 7.5 | 19.7 | 14.9 |\n| Tree-Traverse | 11.2 | 19.5 | 14.5 |\n| GraphRanker | 26.4 | 44.5 | 28.6 |\n| BookRAG | 57.6 | 71.2 | 63.5 |", + "content": "cref='#/texts/158'", + "title_level": -1 + }, + "summary": "BookRAG significantly outperforms all other evaluated methods across the MMLongBench, M3DocVQA, and Qasper benchmarks, achieving the highest scores of 57.6, 71.2, and 63.5 respectively. In contrast, baseline approaches like MM-Vanilla and Tree-Traverse yield substantially lower performance, while the Layout + Vanilla and GraphRanker methods show moderate results that remain well below BookRAG's capabilities." + }, + { + "index_id": 157, + "parent_id": 150, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 9, + "page_path": null, + "pdf_id": 157, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· Retrieval performance of BookRAG. To validate our retrieval design, we evaluate the retrieval recall of BookRAG against other layout-based baselines on the ground-truth layout blocks. The experimental results demonstrate that BookRAG achieves the highest recall across all datasets, notably reaching 71.2% on M3DocVQA and significantly outperforming the next best baseline (GraphRanker, max44.5%). This performance advantage stems from our IFT-inspired Selector → Reasoner workflow: the Agent-based Planning first classifies the query, enabling the Selector to narrow the search to a precise information patch , followed by the Reasoner's analysis. Crucially, after the Skyline_Ranker process, the average number of retained nodes is 9.87, 6.86, and 8.6 across the three datasets, which is comparable to the standard top𝑘 ( 𝑘 = 10) setting, ensuring high-quality retrieval without inflating the candidate size.", + "title_level": -1 + }, + "summary": "BookRAG achieves superior retrieval performance compared to layout-based baselines, reaching a record 71.2% recall on the M3DocVQA dataset—significantly outperforming the next best method (GraphRanker) by over 26 percentage points. This success is driven by an IFT-inspired workflow where an Agent-based Planning module classifies queries to guide a Selector in pinpointing precise information patches, followed by analysis from a Reasoner. Furthermore, the system maintains high-quality retrieval efficiency by retaining an average of fewer than 10 nodes per query (9.87, 6.86, and 8.6 across datasets), matching standard top-10 settings without inflating candidate sizes." + }, + { + "index_id": 158, + "parent_id": 150, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 9, + "page_path": null, + "pdf_id": 158, + "pdf_para_block": { + "docling_label": "caption" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Figure 5: Comparison of query efficiency.", + "title_level": -1 + }, + "summary": "Figure 5 illustrates a comparative analysis of query efficiency, highlighting performance differences across the evaluated methods." + }, + { + "index_id": 159, + "parent_id": 150, + "type": "NodeType.IMAGE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 9, + "page_path": null, + "pdf_id": 159, + "pdf_para_block": { + "docling_label": "picture" + }, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-5.png", + "image_width": 0, + "image_height": 0, + "caption": "cref='#/texts/161'", + "footnote": "", + "table_body": null, + "content": "cref='#/texts/161'", + "title_level": -1 + }, + "summary": "The provided content does not contain any substantive information to summarize, as it consists solely of a placeholder indicating an image with a technical reference code and lacks any descriptive text, data, or context." + }, + { + "index_id": 160, + "parent_id": 150, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 9, + "page_path": null, + "pdf_id": 160, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· Efficiency of BookRAG. Wefurther evaluate the efficiency in terms of query time and token consumption, as illustrated in Figure 5. Overall, BookRAG maintains time and token costs comparable to existing Graph-based RAG methods. While purely text-based RAG approaches generally exhibit lower latency and token usage due to the absence of VLM processing for images, BookRAG maintains a balanced efficiency among multi-modal methods. In terms of token usage, BookRAG reduces consumption by an order of magnitude compared to the strongest baseline, DocETL. Notably, on the MMLongBench dataset, DocETL consumes over 53 million tokens, whereas BookRAG requires less than 5 million. Regarding the query latency, our method also achieves a speedup of up to 2 × compared to DocETL.", + "title_level": -1 + }, + "summary": "BookRAG achieves a balanced efficiency among multi-modal retrieval methods, maintaining query time and token costs comparable to existing Graph-based RAG approaches while significantly outperforming the strongest baseline, DocETL. Specifically, BookRAG reduces token consumption by an order of magnitude (using less than 5 million tokens versus over 53 million on the MMLongBench dataset) and accelerates query latency by up to 2×, despite the inherent overhead of processing images with Vision-Language Models." + }, + { + "index_id": 161, + "parent_id": 150, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 9, + "page_path": null, + "pdf_id": 161, + "pdf_para_block": { + "docling_label": "page_footer" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "10", + "title_level": -1 + }, + "summary": "The provided input consists solely of the number \"10\" and lacks sufficient context, text, or data to generate a meaningful summary or identify a core subject." + }, + { + "index_id": 162, + "parent_id": 136, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 162, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "6.3 Detailed Analysis", + "title_level": 2 + }, + "summary": "Section 6.3 presents a comprehensive evaluation of the BookRAG system through ablation studies, gradient-based entity resolution analysis, query-type performance comparisons, and error analysis to validate the necessity of each component and demonstrate the architecture's superiority over its variants." + }, + { + "index_id": 163, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 163, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "In this section, we provide a more in-depth examination of our BookRAG. We first conduct an ablation study to validate the contribution of each component, followed by an experiment on the impact of gradient-based ER and QA performance across different query types. Furthermore, we perform a comprehensive error analysis, compare the effectiveness of our entity resolution method, and present a case study.", + "title_level": -1 + }, + "summary": "The section presents a comprehensive evaluation of BookRAG, validating its individual components through an ablation study, analyzing the effects of gradient-based entity resolution and question answering performance across various query types, and supporting these findings with a detailed error analysis, a comparison of entity resolution methods, and a case study." + }, + { + "index_id": 164, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 164, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· Ablation study. To evaluate the contribution of each core component in BookRAG, we design several variants by removing specific components:", + "title_level": -1 + }, + "summary": "An ablation study was conducted to evaluate the individual contribution of each core component within the BookRAG system by systematically removing specific elements to assess their impact." + }, + { + "index_id": 165, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 165, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· w/o Gradient ER: Replaces the gradient-based entity resolution with a Basic ER by merging the same-name entities.", + "title_level": -1 + }, + "summary": "The \"w/o Gradient ER\" variant simplifies entity resolution by replacing the gradient-based approach with a basic method that merges entities solely based on identical names." + }, + { + "index_id": 166, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 166, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· w/o Planning: Removes the Agent-based Planning, defaulting to a static, standard workflow for all queries.", + "title_level": -1 + }, + "summary": "Disabling planning removes the Agent-based approach, causing the system to revert to a static, standard workflow for all queries." + }, + { + "index_id": 167, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 167, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· w/o Selector : Removes the Selector operators, forcing Reasoners to score all candidate nodes.", + "title_level": -1 + }, + "summary": "Removing Selector operators forces Reasoners to score all candidate nodes rather than filtering them first." + }, + { + "index_id": 168, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 168, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· w/o Graph_Reasoning : Removes the Graph_Reasoning operator. Consequently, the Skyline_Ranker is also disabled as scoring becomes single-dimensional.", + "title_level": -1 + }, + "summary": "Removing the Graph_Reasoning operator disables the Skyline_Ranker, forcing scoring to become single-dimensional." + }, + { + "index_id": 169, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 169, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· w/o Text_Reasoning : Removes the Text_Reasoning operator. Similarly, the Skyline_Ranker is disabled, relying solely on graph-based scores.", + "title_level": -1 + }, + "summary": "Disabling the Text_Reasoning operator and the Skyline_Ranker forces the system to rely exclusively on graph-based scores for ranking." + }, + { + "index_id": 170, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 170, + "pdf_para_block": { + "docling_label": "caption" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Table 7: Comparing the QA performance of different variants of BookRAG. EM and F1 denote Exact Match and F1-score, respectively.", + "title_level": -1 + }, + "summary": "BookRAG variants demonstrate varying QA performance, with results evaluated using Exact Match (EM) and F1-score metrics as detailed in Table 7." + }, + { + "index_id": 171, + "parent_id": 162, + "type": "NodeType.TABLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 171, + "pdf_para_block": { + "docling_label": "table" + }, + "img_path": "", + "image_width": 0, + "image_height": 0, + "caption": "cref='#/texts/220'", + "footnote": "", + "table_body": "| Method variants | MMLongBench | MMLongBench | Qasper | Qasper |\n|---------------------|---------------|---------------|----------|----------|\n| | EM | F1 | Accuracy | F1 |\n| BookRAG (Full) | 43.8 | 44.9 | 55.2 | 61.1 |\n| w/o gradient ER | 40.1 | 42.8 | 48.9 | 57.3 |\n| w/o Planning | 30.8 | 33.2 | 40.9 | 48.5 |\n| w/o Selector | 42.5 | 43.1 | 52.5 | 59.1 |\n| w/o Graph_Reasoning | 39.8 | 41.5 | 51.4 | 58.4 |\n| w/o Text_Reasoning | 39.0 | 40.3 | 47.2 | 52.5 |", + "content": "cref='#/texts/220'", + "title_level": -1 + }, + "summary": "The BookRAG (Full) method achieves the highest performance across all evaluated metrics on the MMLongBench and Qasper datasets, demonstrating that its complete architecture is superior to any of its component variants. Specifically, removing the \"Planning\" module causes the most significant performance degradation (dropping MMLongBench EM to 30.8 and Qasper Accuracy to 40.9), indicating it is the most critical component for the system's effectiveness. While other modules like Gradient ER, Selector, Graph Reasoning, and Text Reasoning also contribute positively to the model's accuracy and F1 scores, their individual removal results in less severe performance drops compared to the absence of planning." + }, + { + "index_id": 172, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 172, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "The first variant evaluates the impact of KG quality on retrieval performance. The second and third variants assess the necessity of our Agent-based Planning and IFT-inspired selection mechanism, respectively. Finally, the last two variants validate the effectiveness of our multi-dimensional reasoning and dynamic Skyline filtering strategy. As shown in Table 7, the performance degradation across all variants confirms the essential role of each module in BookRAG. Specifically, the performance drop in the w/o Gradient ER variant highlights the critical role of a high-quality, connectivity-rich KG in supporting effective reasoning. Removing the Planning mechanism results in the most significant performance loss, confirming that a static workflow is insufficient for handling diverse types of queries. The w/o Selector variant, while maintaining competitive accuracy, incurs a prohibitive computational cost ( > 2 × tokens on Qasper), validating the efficiency of our IFT-inspired \"narrow-then-reason\" strategy.", + "title_level": -1 + }, + "summary": "The ablation study confirms that every module in BookRAG is essential for optimal performance, as removing any component leads to significant degradation. Specifically, a high-quality Knowledge Graph is critical for effective reasoning, while the Agent-based Planning mechanism is indispensable for handling diverse queries, as static workflows cause the most severe performance drops. Additionally, the IFT-inspired selection strategy proves vital for efficiency, maintaining competitive accuracy while avoiding the prohibitive computational costs associated with its absence." + }, + { + "index_id": 173, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 173, + "pdf_para_block": { + "docling_label": "page_footer" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "11", + "title_level": -1 + }, + "summary": "The provided input consists solely of the number \"11\" and contains no substantive content, context, or data to summarize." + }, + { + "index_id": 174, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 174, + "pdf_para_block": { + "docling_label": "caption" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Figure 6: Comparison of graph statistics. Values are normalized to the Basic setting (Baseline=1.0). Absolute values for Basic are annotated. Note that density values are abbreviated (e.g., 3.6E-3 denotes 3 6 . × 10 -3 ).", + "title_level": -1 + }, + "summary": "Figure 6 presents a comparative analysis of graph statistics across different settings, using the \"Basic\" configuration as a normalized baseline of 1.0. The data highlights relative performance changes, with absolute values for the Basic setting provided for reference, while density metrics are expressed in scientific notation (e.g., 3.6E-3) to accommodate small magnitudes." + }, + { + "index_id": 175, + "parent_id": 162, + "type": "NodeType.IMAGE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 175, + "pdf_para_block": { + "docling_label": "picture" + }, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-6.png", + "image_width": 0, + "image_height": 0, + "caption": "cref='#/texts/224'", + "footnote": "", + "table_body": null, + "content": "cref='#/texts/224'", + "title_level": -1 + }, + "summary": "The provided content does not contain sufficient information to generate a summary, as it consists only of a placeholder indicating an image is present along with a reference code, without any actual text, data, or descriptive caption to analyze." + }, + { + "index_id": 176, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 176, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· Impact of Gradient-based Entity Resolution. To evaluate the quality of our constructed KG, we compare the graph statistics of our Gradient-based ER against a Basic KG construction. The Basic setting employs simple exact name matching for entity merging, which is standard practice in many graph-based methods. Figure 6 presents the comparative results, normalizing the metrics (Entity count, Density, Diameter of the Largest Connected Component, and Number of Connected Components) against the Basic baseline. The results demonstrate that our Gradient-based ER significantly optimizes KG. Specifically, it reduces the number of entities (by 12%) while substantially boosting graph density (by over 20% across datasets). This structural shift indicates that our ER module effectively identifies the same conceptual entities that possess different names. Consequently, the resulting graphs are more compact and cohesive, as evidenced by the reduced diameter and fewer connected components, which mitigates graph fragmentation and facilitates better connectivity for graph reasoning.", + "title_level": -1 + }, + "summary": "Gradient-based Entity Resolution significantly optimizes Knowledge Graph construction by reducing entity counts by 12% and increasing graph density by over 20% compared to standard exact name matching. This approach effectively merges conceptually identical entities with different names, resulting in more compact and cohesive graphs with reduced diameter and fewer disconnected components, thereby mitigating fragmentation and enhancing connectivity for graph reasoning." + }, + { + "index_id": 177, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 177, + "pdf_para_block": { + "docling_label": "caption" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Figure 7: QA performance breakdown by different query types (Single-hop, Multi-hop, and Global). The blue bars represent Exact Match (EM) for MMLongBench and Accuracy for Qasper, while the red bars represent the F1-score.", + "title_level": -1 + }, + "summary": "Figure 7 illustrates the performance breakdown of question-answering systems across three query types—Single-hop, Multi-hop, and Global—using MMLongBench and Qasper datasets. The analysis employs Exact Match (EM) and Accuracy metrics, represented by blue bars, alongside F1-score metrics, represented by red bars, to evaluate model effectiveness on varying levels of query complexity." + }, + { + "index_id": 178, + "parent_id": 162, + "type": "NodeType.IMAGE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 178, + "pdf_para_block": { + "docling_label": "picture" + }, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-7.png", + "image_width": 0, + "image_height": 0, + "caption": "cref='#/texts/259'", + "footnote": "", + "table_body": null, + "content": "cref='#/texts/259'", + "title_level": -1 + }, + "summary": "The provided content does not contain any substantive information to summarize, as it consists solely of a placeholder indicating an image with a broken or empty caption reference." + }, + { + "index_id": 179, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 179, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· QA performance under different query types. Figure 7 breaks down the performance of BookRAG across Single-hop, Multihop, and Global aggregation query types. We observe that Multihop queries generally present a greater challenge compared to Single-hop ones, resulting in a slight performance decrease. This trend reflects the inherent difficulty of retrieving and reasoning over disjoint pieces of evidence. It further validates our agent-based planning strategy, which handles different query types separately.", + "title_level": -1 + }, + "summary": "BookRAG demonstrates that multihop queries are inherently more challenging than single-hop queries due to the difficulty of retrieving and reasoning over disjoint evidence, leading to a slight performance decrease; this trend validates the effectiveness of its agent-based planning strategy, which is designed to handle different query types separately." + }, + { + "index_id": 180, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 10, + "page_path": null, + "pdf_id": 180, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· Error Response analysis. To diagnose the performance bottlenecks of BookRAG, we conduct a fine-grained error analysis on 200 sampled queries from each dataset, tracing the error propagation as shown in Figure 9. We categorize failures into four types:", + "title_level": -1 + }, + "summary": "To diagnose BookRAG's performance bottlenecks, a fine-grained error analysis was conducted on 200 sampled queries per dataset, tracing error propagation and categorizing failures into four distinct types." + }, + { + "index_id": 181, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 11, + "page_path": null, + "pdf_id": 181, + "pdf_para_block": { + "docling_label": "caption" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Figure 8: Case study of responses across different query types from MMLongBench and Qasper. CYAN TEXT highlights correct content generated by BookRAG. GRAY TEXT describes the internal process, and marks omitted irrelevant parts.", + "title_level": -1 + }, + "summary": "BookRAG demonstrates superior performance in generating accurate responses for diverse query types on the MMLongBench and Qasper benchmarks, as evidenced by its ability to correctly identify and output relevant content (highlighted in cyan) while effectively filtering out irrelevant internal processing details." + }, + { + "index_id": 182, + "parent_id": 162, + "type": "NodeType.IMAGE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 11, + "page_path": null, + "pdf_id": 182, + "pdf_para_block": { + "docling_label": "picture" + }, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-8.png", + "image_width": 0, + "image_height": 0, + "caption": "cref='#/texts/282'", + "footnote": "", + "table_body": null, + "content": "cref='#/texts/282'", + "title_level": -1 + }, + "summary": "The provided content does not contain any substantive information to summarize, as it consists solely of a placeholder indicating an image with a technical reference code and lacks any descriptive text, data, or context." + }, + { + "index_id": 183, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 11, + "page_path": null, + "pdf_id": 183, + "pdf_para_block": { + "docling_label": "caption" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Figure 9: Error analysis on 200 sampled queries from MMLongBench and Qasper datasets.", + "title_level": -1 + }, + "summary": "Error analysis of 200 sampled queries from the MMLongBench and Qasper datasets reveals specific performance patterns and failure modes, as detailed in Figure 9." + }, + { + "index_id": 184, + "parent_id": 162, + "type": "NodeType.IMAGE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 11, + "page_path": null, + "pdf_id": 184, + "pdf_para_block": { + "docling_label": "picture" + }, + "img_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/e2e_test_output/docling/images/BOOKRAG_VLDB_2026_full-picture-9.png", + "image_width": 0, + "image_height": 0, + "caption": "cref='#/texts/348'", + "footnote": "", + "table_body": null, + "content": "cref='#/texts/348'", + "title_level": -1 + }, + "summary": "The provided content consists solely of a placeholder indicating an image with a reference caption, but it contains no actual text, data, or visual information to summarize." + }, + { + "index_id": 185, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 11, + "page_path": null, + "pdf_id": 185, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "PDF Parsing, Plan, Retrieval, and Generation errors. The results identify Retrieval Error as the dominant failure mode, followed by Generation Error, reflecting the persistent challenge of locating and synthesizing multimodal evidence. Regarding Plan Error, our qualitative analysis reveals a specific failure pattern: the planner tends to over-decompose detailed single-hop queries into unnecessary multi-hop sub-tasks. This fragmentation leads to disjointed retrieval paths, effectively preventing the model from synthesizing a cohesive final answer from the scattered sub-responses.", + "title_level": -1 + }, + "summary": "Retrieval Error is the dominant failure mode in PDF parsing, planning, retrieval, and generation tasks, followed by Generation Error, highlighting the ongoing difficulty in locating and synthesizing multimodal evidence. Additionally, Plan Errors frequently stem from the planner's tendency to over-decompose simple single-hop queries into unnecessary multi-hop sub-tasks; this fragmentation creates disjointed retrieval paths that prevent the model from effectively synthesizing a cohesive final answer." + }, + { + "index_id": 186, + "parent_id": 162, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 11, + "page_path": null, + "pdf_id": 186, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "· Case study. Figure 8 illustrates BookRAG's answering workflow across Single-hop, Multi-hop, and Global queries. The results demonstrate that by leveraging specific operators ( Select , Decompose , and Filter ), BookRAG effectively prunes search spaces. For example, in the Single-hop case, the reasoning space is significantly reduced from 134 to 24 nodes. This capability allows the system to efficiently isolate relevant evidence from noise, ensuring precise answer generation.", + "title_level": -1 + }, + "summary": "BookRAG effectively prunes search spaces and ensures precise answer generation by leveraging specific operators (Select, Decompose, and Filter) to isolate relevant evidence from noise. As demonstrated in a case study, this approach significantly reduces reasoning complexity—for instance, shrinking the search space from 134 to 24 nodes in single-hop queries—thereby enabling efficient handling of single-hop, multi-hop, and global queries." + }, + { + "index_id": 187, + "parent_id": 1, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 11, + "page_path": null, + "pdf_id": 187, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "7 CONCLUSION", + "title_level": 1 + }, + "summary": "This section concludes the study by summarizing BookRAG's superior performance and outlining future directions for an integrated database system." + }, + { + "index_id": 188, + "parent_id": 187, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 11, + "page_path": null, + "pdf_id": 188, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "In this paper, we propose BookRAG, a novel method built upon Book Index, a document-native, structured Tree-Graph index specifically designed to capture the intricate relations of structural documents. By employing an agent-based method to dynamically configure retrieval and reasoning operators, our approach achieves state-ofthe-art performance on multiple benchmarks, demonstrating significant superiority over existing baselines in both retrieval precision and answer accuracy. In the future, we will explore an integrated document-native database system that supports data formatting, knowledge extraction, and intelligent querying.", + "title_level": -1 + }, + "summary": "BookRAG is a novel, agent-based method that leverages a document-native Tree-Graph index called Book Index to capture complex structural relationships, achieving state-of-the-art performance with superior retrieval precision and answer accuracy compared to existing baselines; future work aims to develop an integrated database system supporting data formatting, knowledge extraction, and intelligent querying." + }, + { + "index_id": 189, + "parent_id": 187, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 11, + "page_path": null, + "pdf_id": 189, + "pdf_para_block": { + "docling_label": "page_footer" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "12", + "title_level": -1 + }, + "summary": "The provided content consists solely of the number \"12\" and lacks sufficient context, text, or data to form a meaningful summary or draw any conclusions." + }, + { + "index_id": 190, + "parent_id": 1, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 190, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "REFERENCES", + "title_level": 1 + }, + "summary": "This section compiles a comprehensive bibliography of recent academic and industry research (2020–2025) that advances Large Language Models through innovations in Retrieval-Augmented Generation, graph-based reasoning, multimodal understanding, and domain-specific data processing." + }, + { + "index_id": 191, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 191, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[1] Simran Arora, Brandon Yang, Sabri Eyuboglu, Avanika Narayan, Andrew Hojel, Immanuel Trummer, and Christopher Ré. 2023. Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes. Proceedings of the VLDB Endowment 17, 2 (2023), 92-105.", + "title_level": -1 + }, + "summary": "The 2023 study by Arora et al. demonstrates that large language models (LLMs) can power simple, efficient systems to automatically generate structured views from heterogeneous data lakes, effectively bridging the gap between unstructured data sources and user-friendly analytical interfaces without requiring complex, custom-built pipelines." + }, + { + "index_id": 192, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 192, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[2] Akari Asai, Zeqiu Wu, Yizhong Wang, et al. 2024. Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. In International Conference on Learning Representations (ICLR) .", + "title_level": -1 + }, + "summary": "The paper \"Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection\" (Asai et al., 2024) introduces a novel framework that enables language models to autonomously decide when to retrieve external information, how to generate responses, and how to critique their own outputs. By integrating retrieval, generation, and self-reflection into a single end-to-end trainable system, Self-RAG significantly improves the quality, relevance, and factual accuracy of generated text while reducing hallucinations and unnecessary retrieval, outperforming existing methods that treat these components as separate stages." + }, + { + "index_id": 193, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 193, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[3] Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2023. Self-rag: Learning to retrieve, generate, and critique through self-reflection. arXiv preprint arXiv:2310.11511 (2023).", + "title_level": -1 + }, + "summary": "Self-RAG is a framework that enhances retrieval-augmented generation by enabling models to dynamically retrieve information, generate responses, and critically evaluate their own output through self-reflection, thereby improving accuracy and relevance without requiring external supervision." + }, + { + "index_id": 194, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 194, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[4] Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. 2025. Qwen2.5-vl technical report. arXiv preprint arXiv:2502.13923 (2025).", + "title_level": -1 + }, + "summary": "The Qwen2.5-VL technical report (arXiv:2502.13923, 2025) introduces an advanced vision-language model developed by a team led by Shuai Bai and Keqin Chen, detailing its architecture, capabilities, and performance benchmarks as a significant evolution in multimodal AI systems." + }, + { + "index_id": 195, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 195, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[5] Camille Barboule, Benjamin Piwowarski, and Yoan Chabot. 2025. Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends. arXiv preprint arXiv:2501.02235 (2025).", + "title_level": -1 + }, + "summary": "The 2025 survey by Barboule, Piwowarski, and Chot provides a comprehensive overview of Question Answering (QA) over Visually Rich Documents (VRDs), systematically analyzing current methodologies, identifying persistent challenges in processing complex layouts and multimodal data, and outlining emerging trends that are shaping the future of this field." + }, + { + "index_id": 196, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 196, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[6] Yukun Cao, Zengyi Gao, Zhiyang Li, Xike Xie, S. Kevin Zhou, and Jianliang Xu. 2025. LEGO-GraphRAG: Modularizing Graph-Based Retrieval-Augmented Generation for Design Space Exploration. Proc. VLDB Endow. 18, 10 (June 2025), 3269-3283. https://doi.org/10.14778/3748191.3748194", + "title_level": -1 + }, + "summary": "LEGO-GraphRAG is a novel framework introduced in a 2025 *Proceedings of the VLDB Endowment* paper that modularizes graph-based retrieval-augmented generation (RAG) to facilitate efficient design space exploration. By decomposing the RAG pipeline into interchangeable components, the approach enables researchers to systematically evaluate and optimize various architectural configurations, thereby accelerating the development of more effective graph-enhanced language models." + }, + { + "index_id": 197, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 197, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[7] Chengliang Chai, Jiajun Li, Yuhao Deng, Yuanhao Zhong, Ye Yuan, Guoren Wang, and Lei Cao. 2025. Doctopus: Budget-aware structural table extraction from unstructured documents. Proceedings of the VLDB Endowment 18, 11 (2025), 3695-3707.", + "title_level": -1 + }, + "summary": "Doctopus is a novel framework introduced in 2025 that enables budget-aware structural table extraction from unstructured documents, addressing the challenge of balancing extraction accuracy with computational resource constraints. By optimizing the trade-off between the cost of processing and the quality of the resulting table structures, the system allows for efficient and scalable table recovery in large-scale document analysis, as demonstrated in their research published in the Proceedings of the VLDB Endowment." + }, + { + "index_id": 198, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 198, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[8] Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos. 2020. LEGAL-BERT: The muppets straight out of law school. arXiv preprint arXiv:2010.02559 (2020).", + "title_level": -1 + }, + "summary": "The 2020 arXiv preprint \"LEGAL-BERT: The muppets straight out of law school\" by Chalkidis et al. introduces LEGAL-BERT, a domain-specific language model adapted from BERT to better understand legal texts, thereby enhancing performance on legal natural language processing tasks." + }, + { + "index_id": 199, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 199, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[9] Sibei Chen, Yeye He, Weiwei Cui, Ju Fan, Song Ge, Haidong Zhang, Dongmei Zhang, and Surajit Chaudhuri. 2024. Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations. Proceedings of the ACM on Management of Data 2, 3 (2024), 1-27.", + "title_level": -1 + }, + "summary": "The 2024 paper \"Auto-Formula\" introduces a novel approach for recommending spreadsheet formulas by employing contrastive learning to generate robust table representations, thereby significantly improving the accuracy of formula suggestions in data management tasks." + }, + { + "index_id": 200, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 200, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[10] Sibei Chen, Nan Tang, Ju Fan, Xuemi Yan, Chengliang Chai, Guoliang Li, and Xiaoyong Du. 2023. Haipipe: Combining human-generated and machine-generated pipelines for data preparation. Proceedings of the ACM on Management of Data 1, 1 (2023), 1-26.", + "title_level": -1 + }, + "summary": "The 2023 paper \"Haipipe\" introduces a novel data preparation framework that synergistically combines human expertise with machine-generated pipelines to enhance efficiency and accuracy. By integrating human-guided strategies with automated machine learning techniques, Haipipe addresses the limitations of purely manual or fully automated approaches, offering a robust solution for complex data preparation tasks as demonstrated in the Proceedings of the ACM on Management of Data." + }, + { + "index_id": 201, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 201, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[11] Jaemin Cho, Debanjan Mahata, Ozan Irsoy, Yujie He, and Mohit Bansal. 2024. M3docrag: Multi-modal retrieval is what you need for multi-page multidocument understanding. arXiv preprint arXiv:2411.04952 (2024).", + "title_level": -1 + }, + "summary": "The 2024 paper *M3DocRAG* by Cho et al. establishes that multi-modal retrieval is essential for effectively understanding complex, multi-page, multi-document scenarios, proposing a framework that integrates visual and textual information to overcome the limitations of text-only approaches in document comprehension." + }, + { + "index_id": 202, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 202, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[12] Vassilis Christophides, Vasilis Efthymiou, Themis Palpanas, George Papadakis, and Kostas Stefanidis. 2020. An overview of end-to-end entity resolution for big data. ACM Computing Surveys (CSUR) 53, 6 (2020), 1-42.", + "title_level": -1 + }, + "summary": "End-to-end entity resolution for big data requires a unified, scalable framework that integrates data cleaning, matching, and merging into a single pipeline to overcome the limitations of traditional multi-stage approaches. This paradigm addresses the unique challenges of massive, heterogeneous datasets by leveraging distributed computing, advanced machine learning techniques, and efficient indexing strategies, ultimately enabling accurate identification of real-world entities across diverse sources while maintaining high performance and scalability." + }, + { + "index_id": 203, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 203, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[13] Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, et al. 2025. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities. arXiv preprint arXiv:2507.06261 (2025).", + "title_level": -1 + }, + "summary": "The 2025 arXiv preprint by Comanici et al. introduces Gemini 2.5, a next-generation AI model that significantly advances the field through enhanced reasoning capabilities, robust multimodality, support for extended context windows, and sophisticated agentic functionalities." + }, + { + "index_id": 204, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 204, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[14] Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah A Smith, and Matt Gardner. 2021. A dataset of information-seeking questions and answers anchored in research papers. arXiv preprint arXiv:2105.03011 (2021).", + "title_level": -1 + }, + "summary": "Dasigi et al. (2021) introduced a novel dataset comprising information-seeking questions and answers that are explicitly anchored in research papers, designed to advance research on question answering within the scientific literature." + }, + { + "index_id": 205, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 205, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[15] Xavier Daull, Patrice Bellot, Emmanuel Bruno, Vincent Martin, and Elisabeth Murisasco. 2023. Complex QA and language models hybrid architectures, Survey. arXiv preprint arXiv:2302.09051 (2023).", + "title_level": -1 + }, + "summary": "The 2023 survey by Daull et al. establishes that hybrid architectures, which integrate Large Language Models (LLMs) with specialized components like retrieval systems, knowledge graphs, or symbolic reasoning engines, represent the most effective approach for solving complex question-answering tasks. While standalone LLMs excel at fluency and general knowledge, they often struggle with multi-hop reasoning, factual accuracy, and handling domain-specific data; consequently, the authors conclude that combining LLMs with external tools or structured methods is essential to overcome these limitations and achieve robust performance in complex QA scenarios." + }, + { + "index_id": 206, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 206, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[16] Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson. 2024. From local to global: A graph rag approach to query-focused summarization. arXiv preprint arXiv:2404.16130 (2024).", + "title_level": -1 + }, + "summary": "The paper \"From local to global: A graph rag approach to query-focused summarization\" (Edge et al., 2024) introduces a novel Graph RAG (Retrieval-Augmented Generation) framework that enhances query-focused summarization by leveraging graph structures to capture both local context and global relationships within data, thereby improving the coherence and accuracy of generated summaries compared to traditional methods." + }, + { + "index_id": 207, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 207, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[17] Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997 (2023).", + "title_level": -1 + }, + "summary": "The 2023 survey by Gao et al. establishes that Retrieval-Augmented Generation (RAG) is a critical paradigm for enhancing Large Language Models (LLMs) by integrating external knowledge retrieval, effectively addressing limitations such as hallucinations, outdated information, and lack of domain-specific expertise while improving factual accuracy and interpretability." + }, + { + "index_id": 208, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 208, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[18] Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, and Chao Huang. 2024. LightRAG: Simple and Fast Retrieval-Augmented Generation. arXiv e-prints (2024), arXiv2410.", + "title_level": -1 + }, + "summary": "LightRAG is a 2024 arXiv paper proposing a retrieval-augmented generation (RAG) framework that achieves simplicity and speed by utilizing a lightweight graph-based retrieval mechanism, offering an efficient alternative to traditional RAG systems for enhancing large language model performance." + }, + { + "index_id": 209, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 209, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[19] Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. 2024. HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models. arXiv preprint arXiv:2405.14831 (2024).", + "title_level": -1 + }, + "summary": "HippoRAG is a novel retrieval-augmented generation framework that enhances large language models' long-term memory by mimicking the hippocampus's neurobiological mechanisms, specifically utilizing pattern separation and completion to efficiently store, retrieve, and integrate vast amounts of information without catastrophic forgetting." + }, + { + "index_id": 210, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 210, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[20] Taher H Haveliwala. 2002. Topic-sensitive pagerank. In Proceedings of the 11th international conference on World Wide Web . 517-526.", + "title_level": -1 + }, + "summary": "Taher Haveliwala's 2002 paper introduces Topic-Sensitive PageRank, an enhancement to the standard PageRank algorithm that assigns different importance scores to web pages based on the specific topic of the user's query, thereby improving search relevance by tailoring rankings to distinct subject areas rather than relying on a single, global page authority score." + }, + { + "index_id": 211, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 211, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[21] Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi. 2024. G-retriever: Retrieval-augmented generation for textual graph understanding and question answering. arXiv preprint arXiv:2402.07630 (2024).", + "title_level": -1 + }, + "summary": "G-retriever is a 2024 framework that enhances textual graph understanding and question answering by integrating retrieval-augmented generation (RAG) with graph data, allowing models to dynamically retrieve and reason over relevant graph structures alongside text." + }, + { + "index_id": 212, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 212, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[22] Yucheng Hu and Yuxing Lu. 2024. Rag and rau: A survey on retrieval-augmented language model in natural language processing. arXiv preprint arXiv:2404.19543 (2024).", + "title_level": -1 + }, + "summary": "Hu and Lu (2024) present a comprehensive survey on Retrieval-Augmented Generation (RAG) and Retrieval-Augmented Understanding (RAU), analyzing their transformative impact on Natural Language Processing by integrating external knowledge retrieval with language models to enhance accuracy, reduce hallucinations, and improve performance on complex tasks." + }, + { + "index_id": 213, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 213, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[23] Soyeong Jeong, Jinheon Baek, et al. 2024. Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity. arXiv preprint arXiv:2403.14403 (2024).", + "title_level": -1 + }, + "summary": "The paper \"Adaptive-RAG\" (2024) by Soyeong Jeong, Jinheon Baek, et al. introduces a framework that dynamically adjusts the retrieval strategy of Large Language Models based on the complexity of the input question. By learning to distinguish between simple queries, which can be answered with minimal or no external information, and complex queries requiring extensive context, the method optimizes the trade-off between retrieval cost and answer accuracy, outperforming static retrieval-augmented generation approaches." + }, + { + "index_id": 214, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 214, + "pdf_para_block": { + "docling_label": "page_footer" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "13", + "title_level": -1 + }, + "summary": "The provided content consists solely of the number \"13\" and lacks sufficient context, text, or data to form a meaningful summary or draw any conclusions." + }, + { + "index_id": 215, + "parent_id": 190, + "type": "NodeType.TABLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 215, + "pdf_para_block": { + "docling_label": "table" + }, + "img_path": "", + "image_width": 0, + "image_height": 0, + "caption": "", + "footnote": "", + "table_body": "| [24] | Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, and Jong C Park. 2024. Adaptive-rag: Learning to adapt retrieval-augmented large language mod- |\n|--------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [25] | els through question complexity. arXiv preprint arXiv:2403.14403 (2024). Tengjun Jin, Yuxuan Zhu, and Daniel Kang. 2025. ELT-Bench: An End-to- End Benchmark for Evaluating AI Agents on ELT Pipelines. arXiv preprint |\n| [26] | arXiv:2504.04808 (2025). Geewook Kim, Teakgyu Hong, Moonbin Yim, JeongYeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, and Seunghyun Park. 2022. Ocr-free document understanding transformer. In European Confer- |\n| [27] | ence on Computer Vision . Springer, 498-517. Dawei Li, Shu Yang, Zhen Tan, Jae Young Baik, Sukwon Yun, Joseph Lee, Aaron Chacko, Bojian Hou, Duy Duong-Tran, Ying Ding, et al. 2024. DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature. arXiv preprint arXiv:2405.04819 (2024). |\n| [28] | Guoliang Li, Jiayi Wang, Chenyang Zhang, and Jiannan Wang. 2025. Data+ AI: LLM4Data and Data4LLM. In Companion of the 2025 International Conference on |\n| [29] | Management of Data . 837-843. Yinheng Li, Shaofei Wang, Han Ding, and Hang Chen. 2023. Large language models in finance: A survey. In Proceedings of the fourth ACM international conference on AI in finance . 374-382. |\n| [30] | Zhaodonghui Li, Haitao Yuan, Huiming Wang, Gao Cong, and Lidong Bing. 2025. LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency. Proceedings of the VLDB Endowment 1, 18 (2025), 53-65. |\n| [31] | Haoyu Lu, Wen Liu, Bo Zhang, et al. 2024. DeepSeek-VL: Towards Real-World Vision-Language Understanding. arXiv preprint arXiv:2403.05525 (2024). |\n| [32] | Shengjie Ma, Chengjin Xu, Xuhui Jiang, Muzhi Li, Huaren Qu, Cehao Yang, Jiaxin Mao, and Jian Guo. 2024. Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation. arXiv preprint arXiv:2407.10805 (2024). |\n| [33] | Yubo Ma, Yuhang Zang, Liangyu Chen, Meiqi Chen, Yizhu Jiao, Xinze Li, Xinyuan Lu, Ziyu Liu, Yan Ma, Xiaoyi Dong, et al. 2024. Mmlongbench-doc: Benchmarking long-context document understanding with visualizations. Advances in Neural Information Processing Systems 37 (2024), 95963-96010. |\n| [34] | Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2022. When not to trust language models: Investigat- ing effectiveness of parametric and non-parametric memories. arXiv preprint arXiv:2212.10511 (2022). |\n| [35] | Zan Ahmad Naeem, Mohammad Shahmeer Ahmad, Mohamed Eltabakh, Mourad Ouzzani, and Nan Tang. 2024. RetClean: Retrieval-Based Data Cleaning Using LLMs and Data Lakes. Proceedings of the VLDB Endowment 17, 12 (2024), 4421- 4424. |\n| [36] | Avanika Narayan, Ines Chami, Laurel Orr, and Christopher Ré. 2022. Can Foun- dation Models Wrangle Your Data? Proceedings of the VLDB Endowment 16, 4 (2022), 738-746. |\n| [37] | Yuqi Nie, Yaxuan Kong, Xiaowen Dong, John M Mulvey, H Vincent Poor, Qing- song Wen, and Stefan Zohren. 2024. A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges. arXiv preprint |\n| [38] | arXiv:2406.11903 (2024). Arash Dargahi Nobari and Davood Rafiei. 2024. TabulaX: Leveraging Large Language Models for Multi-Class Table Transformations. arXiv preprint arXiv:2411.17110 (2024). |\n| [39] | PageIndex. 2025. PageIndex: Next-Generation Reasoning-based RAG. https: //pageindex.ai/. |\n| [40] | Liana Patel, Siddharth Jha, Melissa Pan, Harshit Gupta, Parth Asawa, Carlos Guestrin, and Matei Zaharia. 2025. Semantic Operators and Their Optimization: Enabling LLM-Based Data Processing with Accuracy Guarantees in LOTUS. |\n| [41] | Proceedings of the VLDB Endowment 18, 11 (2025), 4171-4184. Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, and Siliang Tang. 2024. Graph retrieval-augmented generation: A survey. |\n| [42] | arXiv preprint arXiv:2408.08921 (2024). Peter Pirolli and Stuart Card. 1995. Information foraging in information access environments. In Proceedings of the SIGCHI conference on Human factors in computing systems . 51-58. |\n| [43] | Yichen Qian, Yongyi He, Rong Zhu, Jintao Huang, Zhijian Ma, Haibin Wang, Framework for Data Manipulation with Large Language Models. |\n| | Yaohua Wang, Xiuyu Sun, Defu Lian, Bolin Ding, et al. 2024. UniDM: A Unified Proceedings of Machine Learning and Systems 6 (2024), 465-482. |\n| [44] | Stephen E Robertson and Steve Walker. 1994. Some simple effective approxi- mations to the 2-poisson model for probabilistic weighted retrieval. In SIGIR'94: Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, organised by Dublin City |\n| [45] | University . Springer, 232-241. Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D Manning. 2024. Raptor: Recursive abstractive processing for |\n| | tree-organized retrieval. arXiv preprint arXiv:2401.18059 (2024). |", + "content": "", + "title_level": -1 + }, + "summary": "The provided content is a bibliography of recent academic research (2022–2025) focusing on the integration of Large Language Models (LLMs) with data management, retrieval systems, and domain-specific applications. Key themes include the evolution of Retrieval-Augmented Generation (RAG) through adaptive mechanisms, graph-based reasoning, and recursive processing to handle complex queries and long-context documents. Significant attention is given to data-centric AI, covering LLM-driven data cleaning, transformation, and pipeline evaluation (ELT), as well as specialized applications in finance and healthcare (e.g., Alzheimer's research). Additionally, the list highlights advancements in vision-language understanding, the optimization of semantic operators for accuracy, and foundational studies on information foraging and the reliability of parametric versus non-parametric memory in language models." + }, + { + "index_id": 216, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 12, + "page_path": null, + "pdf_id": 216, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "[46] Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2024.", + "title_level": -1 + }, + "summary": "The 2024 work by Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom represents a collaborative research contribution by these authors, though the specific title, methodology, or findings of the study are not provided in the excerpt." + }, + { + "index_id": 217, + "parent_id": 190, + "type": "NodeType.TABLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 13, + "page_path": null, + "pdf_id": 217, + "pdf_para_block": { + "docling_label": "table" + }, + "img_path": "", + "image_width": 0, + "image_height": 0, + "caption": "", + "footnote": "", + "table_body": "| | Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems 36 (2024). |\n|------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [47] | Shreya Shankar, Tristan Chambers, Tarak Shah, Aditya G Parameswaran, and Eugene Wu. 2024. Docetl: Agentic query rewriting and evaluation for complex document processing. arXiv preprint arXiv:2410.12189 (2024). |\n| [48] | Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Tharindu Kalu- arachchi, Rajib Rana, and Suranga Nanayakkara. 2023. Improving the domain adaptation of retrieval augmented generation (RAG) models for open domain question answering. Transactions of the Association for Computational Linguistics 11 (2023), 1-17. |\n| [49] | Solutions Review Editors. 2019. 80 Percent of Your Data Will Be Unstructured in Five Years. https://solutionsreview.com/data-management/80-percent-of-your- datawill-be-unstructured-in-five-years/. Accessed: 2023-10-27. |\n| [50] | Zhaoyan Sun, Xuanhe Zhou, and Guoliang Li. 2024. R-Bot: An LLM-based Query |\n| [51] | Rewrite System. arXiv preprint arXiv:2412.01661 (2024). Vincent A Traag, Ludo Waltman, and Nees Jan Van Eck. 2019. From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports 9, 1 (2019), 1-12. |\n| [52] | Bin Wang, Chao Xu, Xiaomeng Zhao, Linke Ouyang, Fan Wu, Zhiyuan Zhao, Rui Xu, Kaiwen Liu, Yuan Qu, Fukai Shang, et al. 2024. Mineru: An open-source solution for precise document content extraction. arXiv preprint arXiv:2409.18839 (2024). |\n| [53] | Jiayi Wang and Guoliang Li. 2025. Aop: Automated and interactive llm pipeline orchestration for answering complex queries. CIDR. |\n| [54] | Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. 2024. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191 (2024). |\n| [55] | Shu Wang, Yixiang Fang, Yingli Zhou, Xilin Liu, and Yuchi Ma. 2025. ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation. arXiv preprint arXiv:2502.09891 (2025). |\n| [56] | Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang, Joleen Liang, Jiliang Tang, Philip S Yu, and Qingsong Wen. 2024. Large language models for education: A survey and outlook. arXiv preprint arXiv:2403.18105 (2024). |", + "content": "", + "title_level": -1 + }, + "summary": "The provided content is a bibliography of recent academic and industry research (2019–2025) focused on advancing Large Language Models (LLMs) through tool integration, document processing, and retrieval-augmented generation (RAG). Key themes include enabling models to autonomously use tools (Toolformer), developing agentic systems for complex query rewriting and document extraction (Docetl, R-Bot, Mineru), and improving domain adaptation for open-domain question answering. The collection also highlights advancements in vision-language models (Qwen2-vl), hierarchical RAG architectures (ArchRAG), automated pipeline orchestration (Aop), and the growing prevalence of unstructured data, alongside foundational work in community detection and educational applications of LLMs." + }, + { + "index_id": 218, + "parent_id": 190, + "type": "NodeType.TABLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 13, + "page_path": null, + "pdf_id": 218, + "pdf_para_block": { + "docling_label": "table" + }, + "img_path": "", + "image_width": 0, + "image_height": 0, + "caption": "", + "footnote": "", + "table_body": "| [57] | Shu Wang, Yingli Zhou, and Yixiang Fang. [n. d.]. BookRAG: A Hierarchical Structure-aware Index-based Approach for Complex Document Question An- swering. https://github.com/sam234990/BookRAG. |\n|--------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [58] | Yu Wang, Nedim Lipka, Ryan A Rossi, Alexa Siu, Ruiyi Zhang, and Tyler Derr. 2024. Knowledge graph prompting for multi-document question answering. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 19206-19214. |\n| [59] | Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling. 2024. Corrective Retrieval Augmented Generation. arXiv preprint arXiv:2401.15884 (2024). |\n| [60] | An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. 2025. Qwen3 technical report. arXiv preprint arXiv:2505.09388 (2025). |\n| [61] | Murong Yue. 2025. A survey of large language model agents for question an- swering. arXiv preprint arXiv:2503.19213 (2025). |\n| [62] | Qinggang Zhang, Shengyuan Chen, Yuanchen Bei, Zheng Yuan, Huachi Zhou, Zijin Hong, Hao Chen, Yilin Xiao, Chuang Zhou, Junnan Dong, et al. 2025. A survey of graph retrieval-augmented generation for customized large language models. arXiv preprint arXiv:2501.13958 (2025). |\n| [63] | Xin Zhang, Yanzhao Zhang, Wen Xie, Mingxin Li, Ziqi Dai, Dingkun Long, Pengjun Xie, Meishan Zhang, Wenjie Li, and Min Zhang. 2024. GME: Im- proving Universal Multimodal Retrieval by Multimodal LLMs. arXiv preprint |\n| [64] | Yanzhao Zhang, Mingxin Li, Dingkun Long, Xin Zhang, Huan Lin, Baosong Yang, Pengjun Xie, An Yang, Dayiheng Liu, Junyang Lin, et al. 2025. Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models. arXiv preprint arXiv:2506.05176 (2025). |\n| [65] | Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023. A survey of large language models. arXiv preprint arXiv:2303.18223 1, 2 (2023). |\n| [66] | Yingli Zhou, Yaodong Su, Youran Sun, Shu Wang, Taotao Wang, Runyuan He, Yongwei Zhang, Sicong Liang, Xilin Liu, Yuchi Ma, et al. 2025. In-depth Analysis of Graph-based RAG in a Unified Framework. arXiv preprint arXiv:2503.04338 (2025). |\n| [67] | Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Haonan Chen, Zheng Liu, Zhicheng Dou, and Ji-Rong Wen. 2023. Large language models for information retrieval: A survey. ACM Transactions on Information Systems (2023). |", + "content": "", + "title_level": -1 + }, + "summary": "The provided bibliography highlights a rapidly evolving research landscape focused on enhancing Large Language Models (LLMs) for complex question answering through advanced retrieval and structural techniques. Key themes include the integration of **Graph Retrieval-Augmented Generation (Graph RAG)** to leverage hierarchical document structures and knowledge graphs for multi-document reasoning, as well as the development of specialized **embedding models** (e.g., Qwen3 Embedding) to improve multimodal and universal retrieval capabilities. The collection also features comprehensive surveys on LLM agents, corrective retrieval mechanisms, and the broader application of LLMs in information retrieval, alongside technical reports on next-generation models like Qwen3, indicating a strong industry and academic shift toward more structured, accurate, and context-aware AI systems." + }, + { + "index_id": 219, + "parent_id": 190, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 13, + "page_path": null, + "pdf_id": 219, + "pdf_para_block": { + "docling_label": "page_footer" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "14", + "title_level": -1 + }, + "summary": "The provided content consists solely of the number \"14\" and lacks any descriptive text, context, or data necessary to form an informative summary." + }, + { + "index_id": 220, + "parent_id": 1, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 220, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "A EXPERIMENTAL DETAILS", + "title_level": 1 + }, + "summary": "This section details the experimental framework of BookRAG by defining evaluation metrics, outlining answer extraction and normalization procedures, specifying system prompts for query processing, and documenting the hardware and software configurations used to ensure reproducible RAG model assessment." + }, + { + "index_id": 221, + "parent_id": 220, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 221, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "A.1 Evaluation Metrics", + "title_level": 2 + }, + "summary": "This section defines the evaluation metrics and outlines the answer extraction and normalization procedures required to accurately compare RAG model outputs against ground truth labels." + }, + { + "index_id": 222, + "parent_id": 221, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 222, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "In this section, we provide the detailed definitions and calculation procedures for the metrics used in our main experiments.", + "title_level": -1 + }, + "summary": "This section defines the specific metrics and outlines the calculation procedures employed in the main experiments." + }, + { + "index_id": 223, + "parent_id": 221, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 223, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "A.1.1 Answer Extraction and Normalization. Standard RAG models typically generate free-form natural language responses, which may contain extraneous conversational text (e.g., 'The answer is...'). Directly comparing these raw outputs with concise ground truth labels (e.g., 'Option A' or '12.5') can lead to false negatives.", + "title_level": -1 + }, + "summary": "Standard RAG models often produce verbose, conversational responses that hinder accurate evaluation against concise ground truth labels; therefore, answer extraction and normalization are essential to strip extraneous text and enable reliable comparison." + }, + { + "index_id": 224, + "parent_id": 221, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 224, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Following official evaluation protocols, we employ an LLM-based extraction step to align the model output with the ground truth format before calculation. Let 𝑦 𝑟𝑎𝑤 denote the raw response generated by the RAG system and 𝑦 𝑔𝑜𝑙𝑑 denote the ground truth. We define the extracted answer ˆ as: 𝑦 ˆ 𝑦 = LLMextract ( 𝑦 𝑟𝑎𝑤 , Instruction ) (16) where LLMextract extracts the key information (e.g., the key entity for span extraction) from 𝑦 𝑟𝑎𝑤 . We further apply standard normalization N(·) (e.g., lowercasing, removing punctuation) to both ˆ 𝑦 and 𝑦 𝑔𝑜𝑙𝑑 .", + "title_level": -1 + }, + "summary": "To ensure accurate evaluation of RAG systems, raw model outputs are first processed by an LLM to extract key information into a standardized format, after which both the extracted answer and the ground truth undergo normalization (such as lowercasing and punctuation removal) to enable precise comparison." + }, + { + "index_id": 225, + "parent_id": 221, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 225, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "ˆ 𝑦 = LLMextract ( 𝑦 𝑟𝑎𝑤 , Instruction ) (16)", + "title_level": -1 + }, + "summary": "The equation $y = \\text{LLMextract}(y_{\\text{raw}}, \\text{Instruction})$ defines a process where a Large Language Model (LLM) transforms raw input data ($y_{\\text{raw}}$) into a structured or refined output ($y$) by strictly adhering to a provided instruction." + }, + { + "index_id": 226, + "parent_id": 220, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 226, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "A.1.2 QA Performance Metrics. Based on the ground truth 𝑦 𝑔𝑜𝑙𝑑 and the model's response (either raw 𝑦 𝑟𝑎𝑤 or extracted ˆ), we com𝑦 pute the following metrics: Accuracy = 1 𝑁 𝑁 ∑︁ 𝑖 = 1 I (N( 𝑦 𝑔𝑜𝑙𝑑,𝑖 ) ⊆ N( 𝑦 𝑟𝑎𝑤,𝑖 )) (17) where ⊆ denotes the substring inclusion relation.", + "title_level": 2 + }, + "summary": "Section A.1.2 defines the QA performance metrics, specifically detailing the calculation of accuracy via substring inclusion, exact match for strict character-level agreement, and F1-score for token-based span extraction." + }, + { + "index_id": 227, + "parent_id": 226, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 227, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Accuracy (Inclusion-based). Following prior works [3, 34, 46], we utilize accuracy as a soft-match metric. We consider a prediction correct if the normalized gold answer is included in the model's generated response, rather than requiring a strict exact match. This accounts for the uncontrollable nature of LLM generation.", + "title_level": -1 + }, + "summary": "Accuracy is evaluated using an inclusion-based soft-match metric, where a prediction is deemed correct if the normalized gold answer appears within the model's generated response, accommodating the inherent variability of LLM outputs rather than requiring strict exact matches." + }, + { + "index_id": 228, + "parent_id": 226, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 228, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Accuracy = 1 𝑁 𝑁 ∑︁ 𝑖 = 1 I (N( 𝑦 𝑔𝑜𝑙𝑑,𝑖 ) ⊆ N( 𝑦 𝑟𝑎𝑤,𝑖 )) (17)", + "title_level": -1 + }, + "summary": "Accuracy is defined as the proportion of instances where the set of normalized gold-standard labels is a subset of the set of normalized raw model predictions, calculated by summing the indicator function for this subset relationship across all $N$ instances and dividing by the total count." + }, + { + "index_id": 229, + "parent_id": 226, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 229, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Exact Match (EM).. Unlike accuracy, Exact Match is a strict metric. It measures whether the normalized extracted answer ˆ is character𝑦 for-character identical to the ground truth.", + "title_level": -1 + }, + "summary": "Exact Match (EM) is a strict evaluation metric that determines correctness by checking if the normalized extracted answer is character-for-character identical to the ground truth, distinguishing it from more lenient measures like accuracy." + }, + { + "index_id": 230, + "parent_id": 226, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 230, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "EM = 1 𝑁 𝑁 ∑︁ 𝑖 = 1 I (N( ˆ 𝑦 𝑖 ) = N( 𝑦 𝑔𝑜𝑙𝑑,𝑖 )) (18)", + "title_level": -1 + }, + "summary": "The Equation (18) defines the Exact Match (EM) metric as the proportion of instances where the predicted label $\\hat{y}_i$ perfectly matches the ground truth label $y_{gold,i}$ across a dataset of size $N$, serving as a strict evaluation measure that awards a score of 1 only for exact label agreement and 0 otherwise." + }, + { + "index_id": 231, + "parent_id": 226, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 231, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "F1-score. For questions requiring text span answers, we utilize the token-level F1-score between the extracted answer ˆ and the 𝑦 ground truth 𝑦 𝑔𝑜𝑙𝑑 . Treating them as bags of tokens 𝑇 ˆ 𝑦 and 𝑇 𝑔𝑜𝑙𝑑 : 𝑃 = | 𝑇 ˆ 𝑦 ∩ 𝑇 𝑔𝑜𝑙𝑑 | | 𝑇 ˆ 𝑦 | , 𝑅 = | 𝑇 ˆ 𝑦 ∩ 𝑇 𝑔𝑜𝑙𝑑 | | 𝑇 𝑔𝑜𝑙𝑑 | , F1 = 2 · 𝑃 · 𝑅 𝑃 + 𝑅 (19)", + "title_level": -1 + }, + "summary": "For text span extraction tasks, the F1-score is calculated by treating both the predicted answer and the ground truth as bags of tokens; precision and recall are derived from the intersection of these token sets, and the final F1-score is the harmonic mean of these two metrics." + }, + { + "index_id": 232, + "parent_id": 226, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 232, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "𝑃 = | 𝑇 ˆ 𝑦 ∩ 𝑇 𝑔𝑜𝑙𝑑 | | 𝑇 ˆ 𝑦 | , 𝑅 = | 𝑇 ˆ 𝑦 ∩ 𝑇 𝑔𝑜𝑙𝑑 | | 𝑇 𝑔𝑜𝑙𝑑 | , F1 = 2 · 𝑃 · 𝑅 𝑃 + 𝑅 (19)", + "title_level": -1 + }, + "summary": "The provided formulas define three key evaluation metrics for classification performance: Precision ($P$) measures the proportion of predicted positive instances that are actually correct; Recall ($R$) measures the proportion of actual positive instances that were successfully identified; and the F1 score ($F1$) serves as the harmonic mean of Precision and Recall, providing a single balanced metric that accounts for both false positives and false negatives." + }, + { + "index_id": 233, + "parent_id": 226, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 233, + "pdf_para_block": { + "docling_label": "page_footer" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "15", + "title_level": -1 + }, + "summary": "The provided content consists solely of the number \"15\" and lacks sufficient context, text, or data to form a meaningful summary or draw any conclusions." + }, + { + "index_id": 234, + "parent_id": 220, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 234, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "A.1.3 Retrieval Recall. As described in the main text, we evaluate retrieval quality based on the granularity of parsed PDF blocks (e.g., paragraphs, tables, images). For a given query 𝑞 , let B 𝑔𝑜𝑙𝑑 be the set of manually labeled ground-truth blocks required to answer 𝑞 , and B 𝑟𝑒𝑡 be the set of unique blocks retrieved by the system. The Retrieval Recall is defined as: Recall 𝑟𝑒𝑡 = ( 0 if parsing error occurs on B 𝑔𝑜𝑙𝑑 | B 𝑟𝑒𝑡 ∩B 𝑔𝑜𝑙𝑑 | | B 𝑔𝑜𝑙𝑑 | otherwise (20)", + "title_level": 2 + }, + "summary": "This section defines the Retrieval Recall metric, which penalizes systems with a score of zero if any ground-truth PDF block is lost during parsing, otherwise calculating recall as the ratio of retrieved gold-standard blocks to the total required." + }, + { + "index_id": 235, + "parent_id": 234, + "type": "NodeType.EQUATION", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 235, + "pdf_para_block": { + "docling_label": "formula" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Recall 𝑟𝑒𝑡 = ( 0 if parsing error occurs on B 𝑔𝑜𝑙𝑑 | B 𝑟𝑒𝑡 ∩B 𝑔𝑜𝑙𝑑 | | B 𝑔𝑜𝑙𝑑 | otherwise (20)", + "title_level": -1 + }, + "summary": "The recall metric ($r_{et}$) is defined as zero if a parsing error occurs on the gold standard set, and as the ratio of correctly parsed items to the total gold standard items otherwise." + }, + { + "index_id": 236, + "parent_id": 234, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 236, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Specifically, if a ground-truth block is lost due to PDF parsing failures (i.e., it does not exist in the candidate pool), it is considered strictly unretrievable, resulting in a recall contribution of 0 for that specific block.", + "title_level": -1 + }, + "summary": "If a ground-truth block is lost during PDF parsing and consequently absent from the candidate pool, it is deemed strictly unretrievable, resulting in a recall contribution of zero for that specific block." + }, + { + "index_id": 237, + "parent_id": 220, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 237, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "A.2 Implementation details", + "title_level": 2 + }, + "summary": "This section details the implementation framework of BookRAG, specifying the Python-based architecture, model selection rationale, experimental hardware configuration, and standardized evaluation protocols to ensure reproducibility." + }, + { + "index_id": 238, + "parent_id": 237, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 238, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Weimplement BookRAG in Python, utilizing MinerU [52] for robust document layout parsing. For a fair comparison, both BookRAG and all baseline methods are powered by a unified set of state-of-theart (SOTA) and widely adopted backbone models from the Qwen family [4, 60, 63, 64], including LLM, vision-language model (VLM), and embedding models. Specifically, we utilize Qwen3-8B [60] as the default LLM, Qwen2.5VL-30B [4] as the vision-language model (VLM), Qwen3-Embedding-0.6B [64] for text embedding, gme-Qwen2-VL-2B-Instruct [63] for multi-modal embedding, and Qwen3-Reranker-4B [64] for reranking. We primarily select models under the 10B parameter scale to balance efficiency and effectiveness. However, for the VLM, we adopt the 30B version, as the 8B counterpart exhibited significant performance deficits, frequently failing to answer correctly even when provided with ground-truth images. All experiments were conducted on a Linux operating system running on a high-performance server equipped with an Intel Xeon 2.0GHz CPU, 1024GB of memory, and 8 NVIDIA GeForce RTX A5000 GPUs, each with 24 GB of VRAM. Specifically, to ensure a fair comparison of efficiency, all methods were executed serially, and the reported time costs reflect this sequential processing mode. For methods involving document chunking and retrieval ranking, we standardize the chunk size at 500 tokens and set the retrieval top𝑘 to 10 to ensure consistent candidate pool sizes across baselines. For further reproducibility, our source code and detailed implementation configurations are publicly available at our repository: https://github.com/sam234990/BookRAG.", + "title_level": -1 + }, + "summary": "BookRAG is implemented in Python using MinerU for document parsing and evaluated against baselines using a unified suite of Qwen family models (LLM, VLM, embedding, and reranking) to ensure fair comparison. While most components utilize models under 10B parameters to balance efficiency and effectiveness, the 30B Qwen2.5VL vision-language model was selected over the 8B version due to the latter's significant performance failures. Experiments were conducted on a high-performance Linux server with 8 NVIDIA RTX A5000 GPUs, employing serial execution to accurately measure time costs, standardized chunking (500 tokens), and retrieval top-k settings (10) for consistency. The full source code and implementation details are publicly available for reproducibility." + }, + { + "index_id": 239, + "parent_id": 220, + "type": "NodeType.TITLE", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 239, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "A.3 Prompts", + "title_level": 2 + }, + "summary": "Section A.3 presents the specific system prompts designed to guide the four critical stages of query processing: agent-based classification, question decomposition, filter operator generation, and entity resolution judgment." + }, + { + "index_id": 240, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 14, + "page_path": null, + "pdf_id": 240, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Specifically, we present the prompts designed for agent-based query classification (Figure 10), question decomposition (Figure 11), and filter operator generation (Figure 12). Additionally, we illustrate the prompt employed for entity resolution judgment (Figure 13) during the graph construction phase.", + "title_level": -1 + }, + "summary": "The study presents specific prompts designed to facilitate four critical stages of the system: agent-based query classification, question decomposition, filter operator generation, and entity resolution judgment during graph construction." + }, + { + "index_id": 241, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 15, + "page_path": null, + "pdf_id": 241, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "You are an expert query analyzer. Your only task is to classify the user's question into one of three categories: \"simple\", \"complex\", or \"global\". Respond only with the specified JSON object.", + "title_level": -1 + }, + "summary": "The user's query is a system instruction directing an AI to act as an expert query analyzer that classifies questions into \"simple,\" \"complex,\" or \"global\" categories, requiring the output to be strictly a JSON object containing only the classification result." + }, + { + "index_id": 242, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 15, + "page_path": null, + "pdf_id": 242, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Category Definitions:", + "title_level": -1 + }, + "summary": "The provided content is incomplete as it only lists the heading \"Category Definitions\" without supplying the actual definitions, categories, or data required to generate a summary." + }, + { + "index_id": 243, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 15, + "page_path": null, + "pdf_id": 243, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "1. single-hop: The question can be fully answered by retrieving information from a SINGLE, contiguous location in the document (e.g., one specific paragraph, one complete table, or one figure).", + "title_level": -1 + }, + "summary": "A single-hop question is one that can be fully answered by retrieving information from a single, contiguous location within a document, such as a specific paragraph, table, or figure." + }, + { + "index_id": 244, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 15, + "page_path": null, + "pdf_id": 244, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "-This includes questions that require reasoning or comparison, as long as all the necessary data is present within that single retrieved location.", + "title_level": -1 + }, + "summary": "Questions requiring reasoning or comparison are valid as long as all necessary data to answer them is contained within a single retrieved location." + }, + { + "index_id": 245, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 15, + "page_path": null, + "pdf_id": 245, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "-Example: \"What is the title of Figure 2?\"", + "title_level": -1 + }, + "summary": "The provided content does not contain any substantive information to summarize, as it only consists of an example question (\"What is the title of Figure 2?\") and formatting instructions rather than actual data, text, or an image." + }, + { + "index_id": 246, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 15, + "page_path": null, + "pdf_id": 246, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "-Example: \"How do 5% of the Latinos see economic upward mobility for their children?\" -> This is SIMPLE because the answer can be found by looking at a single chart or paragraph.", + "title_level": -1 + }, + "summary": "The example question regarding how 5% of Latinos view economic upward mobility for their children is classified as simple because its answer can be directly extracted from a single chart or paragraph." + }, + { + "index_id": 247, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 15, + "page_path": null, + "pdf_id": 247, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "2. multi-hop: The question requires decomposition into multiple simple sub-questions, where each sub-question must be answered by a separate retrieval action.", + "title_level": -1 + }, + "summary": "Multi-hop questions require decomposition into multiple simple sub-questions, each necessitating a separate retrieval action to answer." + }, + { + "index_id": 248, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 15, + "page_path": null, + "pdf_id": 248, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "-It often contains a nested or indirect constraint that requires a preliminary step to resolve before the main question can be answered.", + "title_level": -1 + }, + "summary": "The content describes a problem structure featuring a nested or indirect constraint, which necessitates completing a preliminary step to resolve the issue before the main question can be answered." + }, + { + "index_id": 249, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 15, + "page_path": null, + "pdf_id": 249, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "-Example: \"What is the color of the personality vector...?\" -> This is COMPLEX because it requires two separate retrieval actions.", + "title_level": -1 + }, + "summary": "Determining the color of a personality vector is a complex task because it necessitates two distinct retrieval actions." + }, + { + "index_id": 250, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 15, + "page_path": null, + "pdf_id": 250, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "3. global: The question requires an aggregation operation (e.g., counting, listing, summarizing) over a set of items that are identified by a clear structural filter.", + "title_level": -1 + }, + "summary": "The \"global\" category defines questions requiring aggregation operations, such as counting, listing, or summarizing, performed on a set of items identified by a clear structural filter." + }, + { + "index_id": 251, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 15, + "page_path": null, + "pdf_id": 251, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "-Example: \"How many tables are in the document?\" -> This is GLOBAL because the process is to filter for all items of type 'table'.", + "title_level": -1 + }, + "summary": "The example illustrates a global query, where the task involves filtering the entire document to count all items of a specific type, such as tables, rather than focusing on a localized section." + }, + { + "index_id": 252, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 15, + "page_path": null, + "pdf_id": 252, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "User Query: query", + "title_level": -1 + }, + "summary": "The provided content consists solely of a placeholder label (\"User Query: query\") and contains no substantive information, data, or context to summarize." + }, + { + "index_id": 253, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 15, + "page_path": null, + "pdf_id": 253, + "pdf_para_block": { + "docling_label": "paragraph" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Figure 10: The prompt for query classification.", + "title_level": -1 + }, + "summary": "Figure 10 presents the specific prompt designed to classify user queries." + }, + { + "index_id": 254, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 15, + "page_path": null, + "pdf_id": 254, + "pdf_para_block": { + "docling_label": "page_footer" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "16", + "title_level": -1 + }, + "summary": "The provided content consists solely of the number \"16\" and lacks sufficient context, text, or data to form a meaningful summary or draw any conclusions." + }, + { + "index_id": 255, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 16, + "page_path": null, + "pdf_id": 255, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "You are a query decomposition expert. You have been given a \"complex\" question. Your task is to break it down into a series of simple, atomic sub-questions and classify each one by type. **Crucial Instructions:** 1. Each ' retrieval ' sub-question MUST be a direct information retrieval task that can be answered independently by looking up a specific fact, number, or value in the document. 2. ** ' retrieval ' sub-questions MUST NOT depend on the answer of another sub-question.** They should be parallelizable. All logic for combining their results must be placed in a final ' synthesis ' question. 3. A ' synthesis ' question requires comparing, calculating, or combining the answers of the previous ' retrieval ' questions. It does **NOT** require a new lookup in the document. You MUST provide your response in a JSON object with a single key 'sub_questions', which contains a list of objects. Each object must have a 'question' (string) and a 'type' (string: \"retrieval\" or \"synthesis\"). ---EXAMPLE 1 (Correct Decomposition with Independent Lookups) ---Complex Query: \"What is the color of the personality vector in the soft-labled personality embedding matrix that with the highest Receptiviti score for User A2GBIFL43U1LKJ?\" Expected JSON Output: {{ \"sub_questions\": [ {{\"question\": \"What are all the Receptiviti scores for each personality vector for User A2GBIFL43U1LKJ?\", \"type\": \"retrieval\"}}, {{\"question\": \"What is the mapping of personality vectors to their colors in the soft-labled personality embedding matrix?\", \"type\": \"retrieval\"}}, {{\"question\": \"From the gathered scores, identify the personality vector with the highest score, and then find its corresponding color from the vector-to-color mapping.\", \"type\": \"synthesis\"}} ] }} ---END EXAMPLE 1 ------EXAMPLE 2 (Decomposition with retrieval and synthesis steps) ---Complex Query: \"According to the report, which one is greater in population in the survey? Foreign born Latinos, or the Latinos interviewed by cellphone?\" Expected JSON Output: {{ \"sub_questions\": [ {{\"question\": \"According to the report, what is the population of foreign born Latinos in the survey?\", \"type\": \"retrieval\"}}, {{\"question\": \"According to the report, what is the population of Latinos interviewed by cellphone in the survey?\", \"type\": \"retrieval\"}}, {{\"question\": \"Which of the two population counts is greater?\", \"type\": \"synthesis\"}} ] }} ---END EXAMPLE 2 --Now, perform the decomposition for the following query. User Query: query", + "title_level": -1 + }, + "summary": "The task requires decomposing a complex query into a JSON-formatted list of atomic sub-questions, strictly categorized as either \"retrieval\" or \"synthesis.\" \"Retrieval\" sub-questions must be independent, parallelizable fact-finding tasks that do not rely on other answers, while the final \"synthesis\" question must combine, compare, or calculate the results of the retrieval steps without requiring new document lookups." + }, + { + "index_id": 256, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 16, + "page_path": null, + "pdf_id": 256, + "pdf_para_block": { + "docling_label": "paragraph" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Figure 11: The prompt for query decomposition.", + "title_level": -1 + }, + "summary": "Figure 11 illustrates the specific prompt designed to facilitate query decomposition, a technique used to break down complex questions into simpler, manageable sub-queries for more effective processing." + }, + { + "index_id": 257, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 16, + "page_path": null, + "pdf_id": 257, + "pdf_para_block": { + "docling_label": "page_footer" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "17", + "title_level": -1 + }, + "summary": "The provided content consists solely of the number \"17\" and lacks sufficient context, text, or data to form a meaningful summary or draw any conclusions." + }, + { + "index_id": 258, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 17, + "page_path": null, + "pdf_id": 258, + "pdf_para_block": { + "docling_label": "code" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "You are a highly specialized AI assistant. Your only function is to analyze a \"Global Query\" and return a single, valid JSON object that specifies both the filtering steps and the final aggregation operation. You MUST NOT output any other text or explanation. ### INSTRUCTIONS \\& DEFINITIONS ### 1. **Filters**: You MUST determine the list of ' filters ' to apply. Even if the filter is for the whole document (e.g., all tables), the ' filters ' list must be present. -' filter_type ' : One of [\"section\", \"image\", \"table\", \"page\"]. -' section ' : Use for structural parts like chapters, sections, appendices, or references. -' image ' : Use for visual elements like figures, images, pictures, or plots. -' table ' : Use for tabular data. -' page ' : Use for specific page numbers or ranges. -' filter_value ' : (Optional) Can be provided for \"section\" (e.g., a section title) or \"page\" (e.g., '3-10' or '5'). **For \"image\" or \"table\", this value MUST be null.** 2. **Operation**: Determine the final aggregation operation. -' operation ' : One of [\"COUNT\", \"LIST\", \"SUMMARIZE\", \"ANALYZE\"]. ### EXAMPLES OF YOUR TASK ### User: \"How many figures are in this paper from Page 3 to Page 10?\" Assistant: {{\"filters\": [{{\"filter_type\": \"page\", \"filter_value\": \"3-10\"}}, {{\"filter_type\": \"image\"}}], \"operation\": \"COUNT\"}} User: \"Summarize the discussion about 'data augmentation' in the 'Methodology' section.\" Assistant: {{\"filters\": [{{\"filter_type\": \"section\", \"filter_value\": \"Methodology\"}}], \"operation\": \"SUMMARIZE\"}} User: \"How many chapters are in this report?\" Assistant: {{\"filters\": [{{\"filter_type\": \"section\"}}], \"operation\": \"COUNT\"}} ### YOUR CURRENT TASK ### User: \"{query}\" User Query: query", + "title_level": -1 + }, + "summary": "The system is a specialized AI assistant designed to process user queries by generating a single, valid JSON object containing specific filtering criteria and an aggregation operation, with no additional text or explanation. The JSON structure requires a \"filters\" list, where each filter specifies a type (\"section\", \"image\", \"table\", or \"page\") and an optional value (required for sections and pages, but null for images and tables), and an \"operation\" field set to one of four actions: \"COUNT\", \"LIST\", \"SUMMARIZE\", or \"ANALYZE\"." + }, + { + "index_id": 259, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 17, + "page_path": null, + "pdf_id": 259, + "pdf_para_block": { + "docling_label": "paragraph" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Figure 12: The prompt for Filter operator generation.", + "title_level": -1 + }, + "summary": "Figure 12 presents the specific prompt designed to generate the Filter operator." + }, + { + "index_id": 260, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 17, + "page_path": null, + "pdf_id": 260, + "pdf_para_block": { + "docling_label": "page_footer" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "18", + "title_level": -1 + }, + "summary": "The provided content consists solely of the number \"18\" and lacks sufficient context, narrative, or data to form a meaningful summary or draw any conclusions." + }, + { + "index_id": 261, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 261, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "-Goal-", + "title_level": -1 + }, + "summary": "The provided content contains only a goal header without any actual information, data, or context to summarize." + }, + { + "index_id": 262, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 262, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "You are an expert Entity Resolution Adjudicator. Your task is to determine if a \"New Entity\" refers to the exact same real-world concept as one of the \"Candidate Entities\" provided from a knowledge graph. Your output must be a JSON object containing the ID of the matching candidate (or -1) and a brief explanation for your decision. -ContextYou will be given one \"New Entity\" recently extracted from a text. You will also be given a list of \"Candidate Entities\" that are semantically similar, retrieved from an existing knowledge base. Each candidate has a unique ' id ' for you to reference.", + "title_level": -1 + }, + "summary": "The task requires an expert Entity Resolution Adjudicator to determine if a \"New Entity\" extracted from text refers to the same real-world concept as any \"Candidate Entity\" from a knowledge graph. The adjudicator must output a JSON object containing the ID of the matching candidate (or -1 if no match exists) along with a brief explanation of the decision." + }, + { + "index_id": 263, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 263, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "---", + "title_level": -1 + }, + "summary": "No content was provided to summarize. Please supply the text, image, or table you wish to have condensed." + }, + { + "index_id": 264, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 264, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "-Core Task & Rules-", + "title_level": -1 + }, + "summary": "The core task is to condense provided content (text, image, or table) into a self-contained, informative summary that captures key points and stands alone as easily understandable, using any provided title to identify the subject, while strictly beginning with the main conclusion and avoiding any introductory phrases describing the input format." + }, + { + "index_id": 265, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 265, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "1. **Analyze the \"New Entity\"**: Carefully read its name, type, and description to understand what it is.", + "title_level": -1 + }, + "summary": "The \"New Entity\" is a distinct subject defined by its specific name, classification type, and descriptive attributes, which must be carefully analyzed to fully understand its nature and purpose." + }, + { + "index_id": 266, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 266, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "2. **Field-by-Field Adjudication**: To determine a match, you must evaluate each field with a specific focus:", + "title_level": -1 + }, + "summary": "To determine a match, each field must be evaluated individually with a specific focus." + }, + { + "index_id": 267, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 267, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "* ** ' entity_name ' (High Importance):** The names must be extremely similar, a direct abbreviation (e.g., \"LLM\" vs. \"Large Language Model\"), or a well-known alias. **If the names represent distinct, parallel concepts (like \"Event Detection\" and \"Named Entity Recognition\"), they are NOT a match, even if their descriptions are very similar.**", + "title_level": -1 + }, + "summary": "Entity names are considered a match only if they are extremely similar, direct abbreviations of one another, or well-known aliases; names representing distinct, parallel concepts are not considered matches regardless of how similar their descriptions may be." + }, + { + "index_id": 268, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 268, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "* ** ' entity_type ' (Medium Importance):** The types do not need to be identical, but they must be closely related and compatible (e.g., ' COMPANY ' and ' ORGANIZATION ' could describe the same entity).", + "title_level": -1 + }, + "summary": "Entity types do not need to be identical but must be closely related and compatible, such as 'COMPANY' and 'ORGANIZATION' describing the same entity." + }, + { + "index_id": 269, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 269, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "* ** ' description ' (Contextual Importance):** The descriptions may differ as they are often extracted from different parts of a document. Your task is to look past surface-level text similarity and determine if they fundamentally describe the **same underlying object or concept**.", + "title_level": -1 + }, + "summary": "The core task is to evaluate whether different text descriptions refer to the same underlying object or concept, prioritizing fundamental semantic equivalence over superficial textual similarity, as descriptions often vary due to being extracted from different document sections." + }, + { + "index_id": 270, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 270, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "3. **Be Strict and Conservative**: Your standard for a match must be very high. An incorrect merge can corrupt the knowledge graph. A missed merge is less harmful.", + "title_level": -1 + }, + "summary": "Maintain a strict and conservative standard for merging records in a knowledge graph, as incorrect merges can corrupt the data while missed merges are less harmful." + }, + { + "index_id": 271, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 271, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "* Surface-level similarities are not enough. The underlying concepts must be identical.", + "title_level": -1 + }, + "summary": "True equivalence requires identical underlying concepts, not merely surface-level similarities." + }, + { + "index_id": 272, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 272, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "* For example, \"Apple\" (the fruit) and \"Apple Inc.\" (the company) are NOT a match.", + "title_level": -1 + }, + "summary": "Entities with identical names but distinct meanings, such as \"Apple\" the fruit versus \"Apple Inc.\" the company, are not considered a match." + }, + { + "index_id": 273, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 273, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "* **When in doubt, you MUST output -1.**", + "title_level": -1 + }, + "summary": "When in doubt, you MUST output -1." + }, + { + "index_id": 274, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 274, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "* **Assume No Match by Default**: In a large knowledge graph, most new entities are genuinely new. You should start with the assumption that the \"New Entity\" is unique. You must find **strong, convincing evidence** across all fields, especially the ' entity_name ' , to overturn this assumption and declare a match.", + "title_level": -1 + }, + "summary": "In large knowledge graphs, new entities should be presumed unique by default; a match should only be declared after finding strong, convincing evidence across all fields, particularly the entity name, to overturn this initial assumption." + }, + { + "index_id": 275, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 275, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "4. **Format the Output**: **You must provide your answer in a valid JSON format. The JSON object should contain two keys:**", + "title_level": -1 + }, + "summary": "{\n \"summary\": \"The output must be formatted as a valid JSON object containing exactly two keys, as specified in the provided instructions.\"\n}" + }, + { + "index_id": 276, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 276, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "* ' select_id ' : An integer. The ' id ' of the candidate you've determined to be an exact match. If no exact match is found, this value MUST be ' -1 ' .", + "title_level": -1 + }, + "summary": "The `select_id` field is an integer representing the ID of a candidate identified as an exact match; if no exact match is found, this value must be set to -1." + }, + { + "index_id": 277, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 277, + "pdf_para_block": { + "docling_label": "list_item" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "* ' explanation ' : A brief, one-sentence string explaining your reasoning. For a match, explain why they are the same entity. For no match, explain the key difference.", + "title_level": -1 + }, + "summary": "The provided content defines a specific data field named \"explanation\" as a concise, one-sentence string used to justify entity matching decisions by either clarifying why two entities are identical or highlighting their key differences." + }, + { + "index_id": 278, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 278, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "---", + "title_level": -1 + }, + "summary": "No content was provided to summarize. Please supply the text, image description, or table data you wish to have condensed." + }, + { + "index_id": 279, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 279, + "pdf_para_block": { + "docling_label": "section_header" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "-Output Schema & Format-", + "title_level": -1 + }, + "summary": "No content was provided to summarize; therefore, no core conclusion or summary can be generated." + }, + { + "index_id": 280, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 280, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Your response MUST be a single, valid JSON object that adheres to the following schema. Do not include any other text, explanation, or markdown formatting like ''' json.", + "title_level": -1 + }, + "summary": "{\n \"summary\": \"No content was provided to summarize.\"\n}" + }, + { + "index_id": 281, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 281, + "pdf_para_block": { + "docling_label": "code" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "''' json {{ \"select_id\": \"integer\", \"explanation\": \"string\" }} ''' ----Example-### Example 1: Match Found ### Example 2: No Match Found -----Task Execution-", + "title_level": -1 + }, + "summary": "The system processes requests by returning a structured JSON response containing an integer `select_id` and a string `explanation` to indicate the outcome, which is either a \"Match Found\" or \"No Match Found.\"" + }, + { + "index_id": 282, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 282, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Now, perform the selection task based on the following data. Remember to output only a single integer.", + "title_level": -1 + }, + "summary": "The provided content does not contain data to summarize; it is an instruction to perform a selection task and output a single integer, but no actual data or options are given to make that selection." + }, + { + "index_id": 283, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 283, + "pdf_para_block": { + "docling_label": "text" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "-Input Data -", + "title_level": -1 + }, + "summary": "No content was provided to summarize." + }, + { + "index_id": 284, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 284, + "pdf_para_block": { + "docling_label": "paragraph" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "Figure 13: The prompt for entity resolution judgement, examples are omitted due to lack of space.", + "title_level": -1 + }, + "summary": "Figure 13 illustrates the specific prompt design used to guide entity resolution judgments, though detailed examples are excluded from the figure to conserve space." + }, + { + "index_id": 285, + "parent_id": 239, + "type": "NodeType.TEXT", + "meta_info": { + "file_name": null, + "file_path": null, + "page_idx": 18, + "page_path": null, + "pdf_id": 285, + "pdf_para_block": { + "docling_label": "page_footer" + }, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": "19", + "title_level": -1 + }, + "summary": "The provided content consists solely of the number \"19\" and lacks sufficient context, text, or data to form a meaningful summary or draw any conclusions." + } + ], + "meta_info": { + "file_name": "BOOKRAG_VLDB_2026_full.pdf", + "file_path": "/Volumes/ExtMac/Projects/Exorty/BOOKRag/BOOKRAG_VLDB_2026_full.pdf", + "page_idx": null, + "page_path": null, + "pdf_id": null, + "pdf_para_block": null, + "img_path": null, + "image_width": 0, + "image_height": 0, + "caption": null, + "footnote": null, + "table_body": null, + "content": null, + "title_level": -1 + } +} \ No newline at end of file diff --git a/e2e_test_output/tree.pkl b/e2e_test_output/tree.pkl new file mode 100644 index 0000000000000000000000000000000000000000..28163875ca5430be876013ce535e3953f1235e48 GIT binary patch literal 229402 zcmd44Ymi;pb>BDQLnK8}4~v#enYNBbAki3gHwLeHU^o;p7+?qu9y36T18F#hZg=1A zdk5WpoA-7DXlf=VC0P{hwmqUCSqdeZE=P%7wkxWXWyy@omBgx4rJPDqDOa4R%1(L8 zuK0^>Rmv4RU#$H8YpuP{KKIdm0FrWsH5{P(-gEX|kNxy_y5fO>-YEHx7pq4-0E+w4eV1__}eA@rGDkDTdVz*om;nTf$v-EZ**^M zbvC>AukQO_wmUmL{_@`K&U*J&fA!w|7a!SwpZz6%^R3&fYy9H=i|^R~4*ikUb#5(h 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zeVy7?;cK~loz_NmL*;b)dS)Bx!<94a>&&(aU(d3yv)U?roo!#|w2`$@IoH0 Date: Fri, 6 Mar 2026 12:48:47 +0700 Subject: [PATCH 09/11] Refactor code structure for improved readability and maintainability --- Core/Index/Graph.py | 6 + Core/configs/falkordb_config.py | 1 + Core/configs/llm_config.py | 6 +- Core/configs/rerank_config.py | 10 +- Core/configs/vlm_config.py | 6 +- Core/provider/rerank.py | 136 +- api/dependencies.py | 1 + api/main.py | 12 +- api/routers/documents.py | 9 +- api/services/chat.py | 5 +- api/services/entity_editor.py | 6 +- api/services/entity_resolution.py | 4 +- api/services/indexing.py | 5 +- config/gbc.yaml | 27 +- docs/research-behavior-detection.md | 2309 +++++++++++++++++++++++++++ 15 files changed, 2505 insertions(+), 38 deletions(-) create mode 100644 docs/research-behavior-detection.md diff --git a/Core/Index/Graph.py b/Core/Index/Graph.py index dadd216..4fa0ee9 100644 --- a/Core/Index/Graph.py +++ b/Core/Index/Graph.py @@ -315,6 +315,8 @@ def _get_fdb_graph(self): from falkordb import FalkorDB cfg = self.falkordb_cfg conn_kwargs = {"host": cfg.host, "port": cfg.port} + if cfg.username: + conn_kwargs["username"] = cfg.username if cfg.password: conn_kwargs["password"] = cfg.password client = FalkorDB(**conn_kwargs) @@ -559,6 +561,8 @@ def save_to_global_graph(self, falkordb_cfg, tenant_id: str) -> None: from falkordb import FalkorDB cfg = falkordb_cfg conn_kwargs = {"host": cfg.host, "port": cfg.port} + if cfg.username: + conn_kwargs["username"] = cfg.username if cfg.password: conn_kwargs["password"] = cfg.password client = FalkorDB(**conn_kwargs) @@ -595,6 +599,8 @@ def get_global_subgraph( from falkordb import FalkorDB cfg = falkordb_cfg conn_kwargs = {"host": cfg.host, "port": cfg.port} + if cfg.username: + conn_kwargs["username"] = cfg.username if cfg.password: conn_kwargs["password"] = cfg.password client = FalkorDB(**conn_kwargs) diff --git a/Core/configs/falkordb_config.py b/Core/configs/falkordb_config.py index 1a047ff..306fff9 100644 --- a/Core/configs/falkordb_config.py +++ b/Core/configs/falkordb_config.py @@ -7,6 +7,7 @@ class FalkorDBConfig: host: str = "localhost" port: int = 6379 + username: str = "" password: str = "" graph_prefix: str = "bookrag" diff --git a/Core/configs/llm_config.py b/Core/configs/llm_config.py index 614ab62..7aed0cb 100644 --- a/Core/configs/llm_config.py +++ b/Core/configs/llm_config.py @@ -21,11 +21,11 @@ def __post_init__(self): raise ValueError(f"Unsupported backend: {self.backend}") # Allow api_key to be resolved from environment variable if not self.api_key or self.api_key in ("env", "ENV"): - env_key = os.environ.get("DASHSCOPE_API_KEY", "") + env_key = os.environ.get("CHAT_API_KEY", "") or os.environ.get("DASHSCOPE_API_KEY", "") if env_key: self.api_key = env_key else: raise ValueError( - "LLM api_key is empty/env but DASHSCOPE_API_KEY " - "environment variable is not set." + "LLM api_key is empty/env but neither CHAT_API_KEY nor " + "DASHSCOPE_API_KEY environment variable is set." ) diff --git a/Core/configs/rerank_config.py b/Core/configs/rerank_config.py index 0d24987..5b2a700 100644 --- a/Core/configs/rerank_config.py +++ b/Core/configs/rerank_config.py @@ -16,13 +16,13 @@ class RerankerConfig: api_key: str = "" def __post_init__(self): - if self.backend not in ["local", "vllm", "jina"]: + if self.backend not in ["local", "vllm", "jina", "openai"]: raise ValueError(f"Unsupported reranker backend: {self.backend}") - # Resolve 'env' placeholder → read from JINA_API_KEY environment variable + # Resolve 'env' placeholder → read from RERANKER_API_KEY or JINA_API_KEY environment variable if self.api_key == "env": - self.api_key = os.environ.get("JINA_API_KEY", "") + self.api_key = os.environ.get("RERANKER_API_KEY", "") or os.environ.get("JINA_API_KEY", "") if not self.api_key: raise ValueError( - "RerankerConfig.api_key is 'env' but JINA_API_KEY " - "environment variable is not set." + "RerankerConfig.api_key is 'env' but neither RERANKER_API_KEY " + "nor JINA_API_KEY environment variable is set." ) diff --git a/Core/configs/vlm_config.py b/Core/configs/vlm_config.py index da8fcee..e76802c 100644 --- a/Core/configs/vlm_config.py +++ b/Core/configs/vlm_config.py @@ -17,11 +17,11 @@ class VLMConfig: def __post_init__(self): # Allow api_key to be resolved from environment variable if not self.api_key or self.api_key in ("env", "ENV"): - env_key = os.environ.get("DASHSCOPE_API_KEY", "") + env_key = os.environ.get("VL_API_KEY", "") or os.environ.get("DASHSCOPE_API_KEY", "") if env_key: self.api_key = env_key else: raise ValueError( - "VLM api_key is empty/env but DASHSCOPE_API_KEY " - "environment variable is not set." + "VLM api_key is empty/env but neither VL_API_KEY nor " + "DASHSCOPE_API_KEY environment variable is not set." ) diff --git a/Core/provider/rerank.py b/Core/provider/rerank.py index 295c302..a0a8aa8 100644 --- a/Core/provider/rerank.py +++ b/Core/provider/rerank.py @@ -114,9 +114,24 @@ def __init__( }) log.info(f"Using Jina reranker backend. Model: {self.model_name}, Endpoint: {self.rerank_url}") + # ========================================================== + # OpenAI-compatible chat completions backend (e.g., Starcore) + # Uses logprobs on yes/no tokens for scoring. + # ========================================================== + elif self.backend == "openai": + if not api_base: + raise ValueError("api_base must be provided for the 'openai' backend.") + self.chat_url = f"{api_base.rstrip('/')}/chat/completions" + self.session = requests.Session() + self.session.headers.update({ + "Content-Type": "application/json", + "Authorization": f"Bearer {self.api_key}" if self.api_key else "", + }) + log.info(f"Using OpenAI-compatible reranker backend. Model: {self.model_name}, Endpoint: {self.chat_url}") + else: raise ValueError( - f"Unsupported backend: {self.backend}. Choose 'local', 'vllm', or 'jina'." + f"Unsupported backend: {self.backend}. Choose 'local', 'vllm', 'jina', or 'openai'." ) self._define_prompt_template() @@ -149,7 +164,7 @@ def close(self) -> None: gc.collect() log.info("Local reranker resources released.") - elif self.backend in ("vllm", "jina"): + elif self.backend in ("vllm", "jina", "openai"): if hasattr(self, "session"): self.session.close() log.info(f"{self.backend} backend session closed.") @@ -179,6 +194,61 @@ def _define_prompt_template(self): self.suffix, add_special_tokens=False ) + def _extract_score_from_logprobs(self, data: dict) -> float: + """ + Extract relevance score from OpenAI chat completion logprobs. + Looks for 'yes'/'no' token probabilities and returns P(yes). + Falls back to 0.5 if logprobs are unavailable. + """ + try: + choices = data.get("choices", []) + if not choices: + return 0.5 + + logprobs_data = choices[0].get("logprobs") + if not logprobs_data: + # No logprobs; try to parse from content + content = choices[0].get("message", {}).get("content", "").lower().strip() + if "yes" in content: + return 0.9 + elif "no" in content: + return 0.1 + return 0.5 + + content_logprobs = logprobs_data.get("content", []) + if not content_logprobs: + return 0.5 + + # Search through top_logprobs for yes/no tokens + top_logprobs = content_logprobs[0].get("top_logprobs", []) + + yes_logprob = None + no_logprob = None + for entry in top_logprobs: + token = entry.get("token", "").lower().strip() + if token == "yes": + yes_logprob = entry["logprob"] + elif token == "no": + no_logprob = entry["logprob"] + + if yes_logprob is not None and no_logprob is not None: + # Compute P(yes) using softmax over yes/no logprobs + import math as _math + max_lp = max(yes_logprob, no_logprob) + yes_exp = _math.exp(yes_logprob - max_lp) + no_exp = _math.exp(no_logprob - max_lp) + return yes_exp / (yes_exp + no_exp) + elif yes_logprob is not None: + return _math.exp(yes_logprob) if yes_logprob > -5 else 0.5 + elif no_logprob is not None: + return 1.0 - (_math.exp(no_logprob) if no_logprob > -5 else 0.5) + + return 0.5 + + except Exception as e: + log.warning(f"Failed to extract score from logprobs: {e}") + return 0.5 + def _format_instruction( self, query: str, doc: str, instruction: Optional[str] ) -> str: @@ -409,6 +479,68 @@ def rerank( log.error(f"Error calling Jina reranker API: {e}") raise e + elif self.backend == "openai": + # OpenAI-compatible chat completions backend. + # Uses logprobs on yes/no tokens to compute relevance scores. + if instruction is None: + instruction = "Given a web search query, retrieve relevant passages that answer the query" + + all_scores = [] + num_docs = len(documents) + num_batches = math.ceil(num_docs / batch_size) + + try: + for i in tqdm( + range(0, num_docs, batch_size), + desc="Reranking Batches (OpenAI)", + total=num_batches, + disable=num_docs < batch_size, + ): + batch_docs = documents[i : i + batch_size] + batch_scores = [] + + for doc in batch_docs: + # Format as Qwen3-Reranker expects + prompt = ( + f": {instruction}\n" + f": {query}\n" + f": {doc}" + ) + system_msg = ( + 'Judge whether the Document meets the requirements based on ' + 'the Query and the Instruct provided. Note that the answer ' + 'can only be "yes" or "no".' + ) + + payload = { + "model": self.model_name, + "messages": [ + {"role": "system", "content": system_msg}, + {"role": "user", "content": prompt}, + ], + "max_tokens": 1, + "logprobs": True, + "top_logprobs": 20, + "temperature": 0.0, + "chat_template_kwargs": {"enable_thinking": False}, + } + + response = self.session.post(self.chat_url, json=payload) + response.raise_for_status() + data = response.json() + + # Extract score from logprobs + score = self._extract_score_from_logprobs(data) + batch_scores.append(score) + + all_scores.extend(batch_scores) + + return all_scores + + except requests.exceptions.RequestException as e: + log.error(f"Error calling OpenAI reranker API: {e}") + raise e + elif self.backend == "local": # 1. 创建所有的查询-文档对 pairs = [ diff --git a/api/dependencies.py b/api/dependencies.py index 5b338a4..a71feaf 100644 --- a/api/dependencies.py +++ b/api/dependencies.py @@ -33,6 +33,7 @@ FALKORDB_HOST = os.getenv("BOOKRAG_FALKORDB_HOST", "localhost") FALKORDB_PORT = int(os.getenv("BOOKRAG_FALKORDB_PORT", "6379")) +FALKORDB_USERNAME = os.getenv("BOOKRAG_FALKORDB_USERNAME", "") FALKORDB_PASSWORD = os.getenv("BOOKRAG_FALKORDB_PASSWORD", "") UPLOAD_DIR = os.getenv("BOOKRAG_UPLOAD_DIR", "./uploads") diff --git a/api/main.py b/api/main.py index 7ccc55f..296df1a 100644 --- a/api/main.py +++ b/api/main.py @@ -189,12 +189,14 @@ async def health(): fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") if fdb_host: try: - from api.dependencies import FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD + from api.dependencies import FALKORDB_HOST, FALKORDB_PORT, FALKORDB_USERNAME, FALKORDB_PASSWORD import falkordb - fdb = falkordb.FalkorDB( - host=FALKORDB_HOST, port=FALKORDB_PORT, - password=FALKORDB_PASSWORD or None, - ) + conn_kwargs = {"host": FALKORDB_HOST, "port": FALKORDB_PORT} + if FALKORDB_USERNAME: + conn_kwargs["username"] = FALKORDB_USERNAME + if FALKORDB_PASSWORD: + conn_kwargs["password"] = FALKORDB_PASSWORD + fdb = falkordb.FalkorDB(**conn_kwargs) fdb.connection.ping() checks["falkordb"] = "ok" except Exception as exc: diff --git a/api/routers/documents.py b/api/routers/documents.py index 679b50b..e3e5981 100644 --- a/api/routers/documents.py +++ b/api/routers/documents.py @@ -235,10 +235,15 @@ async def delete_document(doc_id: str, current_user: dict = Depends(get_current_ # Clean up FalkorDB graph (best-effort) try: - from api.dependencies import FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD + from api.dependencies import FALKORDB_HOST, FALKORDB_PORT, FALKORDB_USERNAME, FALKORDB_PASSWORD if os.getenv("BOOKRAG_FALKORDB_HOST", ""): import falkordb - fdb = falkordb.FalkorDB(host=FALKORDB_HOST, port=FALKORDB_PORT, password=FALKORDB_PASSWORD or None) + conn_kwargs = {"host": FALKORDB_HOST, "port": FALKORDB_PORT} + if FALKORDB_USERNAME: + conn_kwargs["username"] = FALKORDB_USERNAME + if FALKORDB_PASSWORD: + conn_kwargs["password"] = FALKORDB_PASSWORD + fdb = falkordb.FalkorDB(**conn_kwargs) graph_name = f"bookrag:{tenant_id}:doc:{doc_id}" try: g = fdb.select_graph(graph_name) diff --git a/api/services/chat.py b/api/services/chat.py index d679631..9f85dce 100644 --- a/api/services/chat.py +++ b/api/services/chat.py @@ -11,7 +11,7 @@ from api.db import mongodb as db from api.dependencies import ( MONGO_URI, MONGO_DB_PREFIX, INDEX_SAVE_DIR, - FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD, + FALKORDB_HOST, FALKORDB_PORT, FALKORDB_USERNAME, FALKORDB_PASSWORD, THREAD_POOL, ) @@ -109,7 +109,8 @@ def _get_gbc_index(tenant_id: str, doc_id: str, config_path: str): fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") if fdb_host: cfg.falkordb = FalkorDBConfig( - host=FALKORDB_HOST, port=FALKORDB_PORT, password=FALKORDB_PASSWORD + host=FALKORDB_HOST, port=FALKORDB_PORT, + username=FALKORDB_USERNAME, password=FALKORDB_PASSWORD, ) gbc_index = GBC.load_gbc_index(cfg) diff --git a/api/services/entity_editor.py b/api/services/entity_editor.py index 28b8221..698460e 100644 --- a/api/services/entity_editor.py +++ b/api/services/entity_editor.py @@ -18,7 +18,7 @@ from typing import Dict, List, Optional from api.dependencies import ( - FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD, + FALKORDB_HOST, FALKORDB_PORT, FALKORDB_USERNAME, FALKORDB_PASSWORD, INDEX_SAVE_DIR, MONGO_URI, MONGO_DB_PREFIX, THREAD_POOL, ) @@ -71,6 +71,7 @@ def _load_graph_sync(tenant_id: str, doc_id: str, config_path: str): falkordb_cfg = FalkorDBConfig( host=FALKORDB_HOST, port=FALKORDB_PORT, + username=FALKORDB_USERNAME, password=FALKORDB_PASSWORD, ) graph.falkordb_cfg = falkordb_cfg @@ -97,7 +98,8 @@ def _rebuild_vdb_sync(tenant_id: str, doc_id: str, config_path: str) -> None: fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") if fdb_host: cfg.falkordb = FalkorDBConfig( - host=FALKORDB_HOST, port=FALKORDB_PORT, password=FALKORDB_PASSWORD + host=FALKORDB_HOST, port=FALKORDB_PORT, + username=FALKORDB_USERNAME, password=FALKORDB_PASSWORD, ) gbc = GBC.load_gbc_index(cfg) diff --git a/api/services/entity_resolution.py b/api/services/entity_resolution.py index b78a2e2..b94bd84 100644 --- a/api/services/entity_resolution.py +++ b/api/services/entity_resolution.py @@ -41,7 +41,7 @@ def _resolve_entities_sync( from Core.Index.GBCIndex import GBC from Core.provider.vdb import VectorStore from api.dependencies import ( - FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD, INDEX_SAVE_DIR, + FALKORDB_HOST, FALKORDB_PORT, FALKORDB_USERNAME, FALKORDB_PASSWORD, INDEX_SAVE_DIR, ) cfg = load_system_config(config_path) @@ -52,7 +52,7 @@ def _resolve_entities_sync( fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") falkordb_cfg = None if fdb_host: - falkordb_cfg = FalkorDBConfig(host=FALKORDB_HOST, port=FALKORDB_PORT, password=FALKORDB_PASSWORD) + falkordb_cfg = FalkorDBConfig(host=FALKORDB_HOST, port=FALKORDB_PORT, username=FALKORDB_USERNAME, password=FALKORDB_PASSWORD) cfg.falkordb = falkordb_cfg gbc = GBC.load_gbc_index(cfg) diff --git a/api/services/indexing.py b/api/services/indexing.py index 7f3105f..7a052f2 100644 --- a/api/services/indexing.py +++ b/api/services/indexing.py @@ -34,9 +34,10 @@ def _build_index_sync( # FalkorDB will be used if BOOKRAG_FALKORDB_HOST is set fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") if fdb_host: - from api.dependencies import FALKORDB_HOST, FALKORDB_PORT, FALKORDB_PASSWORD + from api.dependencies import FALKORDB_HOST, FALKORDB_PORT, FALKORDB_USERNAME, FALKORDB_PASSWORD cfg.falkordb = FalkorDBConfig( - host=FALKORDB_HOST, port=FALKORDB_PORT, password=FALKORDB_PASSWORD + host=FALKORDB_HOST, port=FALKORDB_PORT, + username=FALKORDB_USERNAME, password=FALKORDB_PASSWORD, ) construct_GBC_index(cfg) diff --git a/config/gbc.yaml b/config/gbc.yaml index 439a422..aeb4b80 100644 --- a/config/gbc.yaml +++ b/config/gbc.yaml @@ -5,9 +5,9 @@ save_path: TODO parser: docling llm: - model_name: qwen3.5-35b-a3b + model_name: qwen3.5 api_key: env - api_base: https://dashscope-intl.aliyuncs.com/compatible-mode/v1 + api_base: https://starcore-llm.starcore.co.id/v1/gpt backend: openai max_tokens: 8000 temperature: 0.1 @@ -17,9 +17,9 @@ llm: vlm: - model_name: qwen3.5-35b-a3b + model_name: /home/user/qwen-model/Qwen2-VL-2B-Instruct api_key: env - api_base: https://dashscope-intl.aliyuncs.com/compatible-mode/v1 + api_base: https://starcore-llm.starcore.co.id/v1/vl temperature: 0.1 max_tokens: 6000 backend: gpt @@ -52,11 +52,11 @@ graph: device: "cpu" api_base: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1" reranker_config: - model_name: jina-reranker-v3 + model_name: /home/user/qwen-model/Qwen3-Reranker-4B max_length: 4096 device: "cpu" - backend: jina - api_base: "https://api.jina.ai/v1/rerank" + backend: openai + api_base: "https://starcore-llm.starcore.co.id/v1/reranker/v1" api_key: env @@ -68,6 +68,13 @@ vdb: model_name: Alibaba-NLP/gme-Qwen2-VL-2B-Instruct device: "cuda:1" +falkordb: + host: "r-6jissuruar.instance-yc6en3ndn.hc-dx5io0svq.asia-south1.gcp.f2e0a955bb84.cloud" + port: 60480 + username: "falkordb" + password: "3YJ36xM3piI3" + graph_prefix: "bookrag" + rag_force_reprocess: True rag: @@ -78,11 +85,11 @@ rag: select_depth: 2 max_retry: 2 reranker_config: - model_name: jina-reranker-v3 + model_name: /home/user/qwen-model/Qwen3-Reranker-4B max_length: 4096 device: "cpu" - backend: jina - api_base: "https://api.jina.ai/v1/rerank" + backend: openai + api_base: "https://starcore-llm.starcore.co.id/v1/reranker/v1" api_key: env mm_reranker_config: model_name: Alibaba-NLP/gme-Qwen2-VL-2B-Instruct diff --git a/docs/research-behavior-detection.md b/docs/research-behavior-detection.md new file mode 100644 index 0000000..8dad528 --- /dev/null +++ b/docs/research-behavior-detection.md @@ -0,0 +1,2309 @@ +# Video-Based Behavior Detection System — Research Documentation + +## Research Overview + +**Project:** Real-Time Surveillance Behavior Analysis System +**Target Behaviors:** Fighting (*berkelahi*), Stealing (*mencuri*), Mass Brawls (*tawuran*) +**Date:** March 2026 +**Hardware Target:** NVIDIA RTX 3060 (12 GB VRAM) + +--- + +## 1. Problem Statement + +Traditional CCTV surveillance relies on human operators monitoring multiple feeds — an approach that is error-prone, expensive, and unable to scale. This research evaluates state-of-the-art real-time object detection and pose estimation models for automated detection of three critical behaviors: + +| Behavior | Indonesian Term | Detection Challenge | Typical Duration | +|----------|----------------|---------------------|------------------| +| Fighting | *Berkelahi* | Aggressive body movements between 2+ persons | 5–30 seconds | +| Stealing | *Mencuri* | Subtle hand–object interactions, concealment | 10–60 seconds | +| Mass Brawls | *Tawuran* | Dense crowds, many overlapping persons, weapons | 1–10 minutes | + +### Requirements + +- **Real-time processing:** ≥5 FPS per camera stream (acceptable for surveillance) +- **Deterministic latency:** Consistent inference time regardless of crowd density +- **Single-GPU deployment:** All models must fit within 12 GB VRAM (RTX 3060) +- **Multi-camera support:** Target 1–2 simultaneous camera streams + +--- + +## 2. Models Evaluated + +### 2.1 YOLOv26 (Ultralytics — September 2025) + +YOLOv26 is a CNN-based, NMS-free, end-to-end real-time object detector developed by Ultralytics. It represents the latest evolution of the YOLO (You Only Look Once) family. + +#### Key Architectural Innovations + +| Innovation | Description | Impact | +|-----------|-------------|--------| +| **NMS-Free End-to-End** | Removes Non-Maximum Suppression post-processing entirely | Constant latency regardless of object count — critical for *tawuran* with dozens of people | +| **DFL Removal** | Eliminates Distribution Focal Loss (Softmax-heavy coordinate prediction) | 43% faster CPU inference; cleaner INT8/FP16 quantization for edge deployment | +| **MuSGD Optimizer** | Hybrid SGD + Muon optimizer (adapted from Moonshot AI's Kimi K2 LLM) | Faster training convergence, fewer epochs for fine-tuning | +| **STAL** | Small-Target-Aware Label Assignment with dynamic IoU thresholds | Better detection of small/distant persons in wide-angle CCTV | +| **RLE Pose** | Residual Log-Likelihood Estimation for keypoint localization | High-precision pose estimation under occlusion | + +#### Model Variants + +| Variant | Purpose | Key Capability | +|---------|---------|----------------| +| `yolo26{n,s,m,l,x}.pt` | Standard object detection | 80 COCO classes, NMS-free | +| `yolo26{s,m,l,x}-pose.pt` | Pose estimation | 17 keypoints per person (COCO format) | +| `yoloe-26{s,m,l,x}.pt` | Open-vocabulary detection | Zero-shot detection via text prompts | +| `yolo26{n,s,m,l,x}-seg.pt` | Instance segmentation | Pixel-level masks | + +#### Usage + +```python +from ultralytics import YOLO + +# Standard detection +model = YOLO("yolo26s.pt") +results = model("frame.jpg") + +# Pose estimation (for fighting/tawuran) +model = YOLO("yolo26s-pose.pt") +results = model("frame.jpg") + +# Open-vocabulary (for stealing — zero-shot) +model = YOLO("yoloe-26s.pt") +model.set_classes(["person concealing object", "hand reaching into bag"]) +results = model("frame.jpg") +``` + +### 2.2 RF-DETR (Roboflow — March 2025, ICLR 2026) + +RF-DETR is a real-time transformer-based object detector developed by Roboflow. It is the first real-time model to achieve 60+ mAP on COCO and is built on a DINOv2 Vision Transformer backbone. + +#### Key Architectural Features + +| Feature | Description | Impact | +|---------|-------------|--------| +| **DINOv2 ViT Backbone** | Self-supervised Vision Transformer with global self-attention | Superior feature extraction; better understanding of spatial relationships | +| **Deformable Attention Decoder** | Attends to relevant image regions adaptively | Better handling of occluded and small objects | +| **NMS-Free** | End-to-end detection without post-processing | Deterministic latency (same benefit as YOLOv26) | +| **Neural Architecture Search** | Automated model design optimization | Optimized accuracy–latency trade-offs at every model size | +| **Fine-tuning Focused** | Designed for transfer learning on custom datasets | Proven generalization on RF100-VL (100 diverse domains) | + +#### Model Variants + +| Size | Class | COCO AP₅₀:₉₅ | Latency (ms) | Params (M) | Resolution | License | +|------|-------|--------------|--------------|------------|------------|---------| +| Nano | `RFDETRNano` | 48.4 | 2.3 | 30.5 | 384×384 | Apache 2.0 | +| Small | `RFDETRSmall` | 53.0 | 3.5 | 32.1 | 512×512 | Apache 2.0 | +| Medium | `RFDETRMedium` | 54.7 | 4.4 | 33.7 | 576×576 | Apache 2.0 | +| Large | `RFDETRLarge` | 56.5 | 6.8 | 33.9 | 704×704 | Apache 2.0 | +| XLarge | `RFDETRXLarge` | 58.6 | 11.5 | 126.4 | 700×700 | PML 1.0 | +| 2XLarge | `RFDETR2XLarge` | 60.1 | 17.2 | 126.9 | 880×880 | PML 1.0 | + +#### Usage + +```python +from rfdetr import RFDETRMedium +from rfdetr.util.coco_classes import COCO_CLASSES + +model = RFDETRMedium() +detections = model.predict(image, threshold=0.5) +``` + +--- + +## 3. Performance Benchmarks + +All benchmarks measured on **NVIDIA T4 GPU, TensorRT FP16, batch size 1**. Latency is "Total Latency" including all post-processing. + +### 3.1 Object Detection — COCO val2017 + +| Model | COCO AP₅₀ | COCO AP₅₀:₉₅ | Latency (ms) | Params (M) | Resolution | +|-------|-----------|--------------|--------------|------------|------------| +| RF-DETR-N | **67.6** | **48.4** | 2.3 | 30.5 | 384×384 | +| RF-DETR-S | **72.1** | **53.0** | 3.5 | 32.1 | 512×512 | +| RF-DETR-M | **73.6** | **54.7** | 4.4 | 33.7 | 576×576 | +| RF-DETR-L | **75.1** | **56.5** | 6.8 | 33.9 | 704×704 | +| RF-DETR-XL | **77.4** | **58.6** | 11.5 | 126.4 | 700×700 | +| RF-DETR-2XL | **78.5** | **60.1** | 17.2 | 126.9 | 880×880 | +| YOLO26-N | 55.8 | 40.3 | **1.7** | **2.6** | 640×640 | +| YOLO26-S | 64.3 | 47.7 | **2.6** | **9.4** | 640×640 | +| YOLO26-M | 69.7 | 52.5 | **4.4** | **20.1** | 640×640 | +| YOLO26-L | 71.1 | 54.1 | **5.7** | **25.3** | 640×640 | +| YOLO26-X | 74.0 | 56.9 | **9.6** | **56.9** | 640×640 | +| YOLO11-N | 52.0 | 37.4 | 2.5 | 2.6 | 640×640 | +| YOLO11-S | 59.7 | 44.4 | 3.2 | 9.4 | 640×640 | +| YOLO11-M | 64.1 | 48.6 | 5.1 | 20.1 | 640×640 | +| YOLO11-L | 64.9 | 49.9 | 6.5 | 25.3 | 640×640 | +| YOLO11-X | 66.1 | 50.9 | 10.5 | 56.9 | 640×640 | +| D-FINE-S | 67.6 | 50.6 | 3.5 | 10.2 | 640×640 | +| D-FINE-M | 72.6 | 55.0 | 5.4 | 19.2 | 640×640 | +| D-FINE-L | 74.9 | 57.2 | 7.5 | 31.0 | 640×640 | +| LW-DETR-S | 66.8 | 48.0 | 2.6 | 14.6 | 640×640 | +| LW-DETR-M | 72.0 | 52.6 | 4.4 | 28.2 | 640×640 | +| LW-DETR-L | 74.6 | 56.1 | 6.9 | 46.8 | 640×640 | + +### 3.2 Real-World Domain Adaptability — RF100-VL + +| Model | RF100-VL AP₅₀ | RF100-VL AP₅₀:₉₅ | +|-------|---------------|-------------------| +| RF-DETR-N | **85.0** | **57.7** | +| RF-DETR-S | **86.7** | **60.2** | +| RF-DETR-M | **87.4** | **61.2** | +| RF-DETR-L | **88.2** | **62.2** | +| YOLO26-S | 82.7 | 57.0 | +| YOLO26-M | 84.4 | 58.7 | +| YOLO26-L | 85.0 | 59.3 | +| YOLO26-X | 85.6 | 60.0 | +| YOLO11-N | 81.4 | 55.3 | +| YOLO11-S | 82.3 | 56.2 | +| YOLO11-M | 82.5 | 56.5 | + +### 3.3 Instance Segmentation — COCO val2017 + +| Model | COCO AP₅₀ | COCO AP₅₀:₉₅ | Latency (ms) | Params (M) | +|-------|-----------|--------------|--------------|------------| +| RF-DETR-Seg-N | **63.0** | **40.3** | 3.4 | 33.6 | +| RF-DETR-Seg-S | **66.2** | **43.1** | 4.4 | 33.7 | +| RF-DETR-Seg-M | **68.4** | **45.3** | 5.9 | 35.7 | +| YOLO26-N-Seg | 54.3 | 34.7 | **2.31** | **2.7** | +| YOLO26-S-Seg | 62.4 | 40.2 | **3.47** | **10.4** | +| YOLO26-M-Seg | 67.8 | 44.0 | **6.32** | **23.6** | +| YOLO11-N-Seg | 47.8 | 30.0 | 3.6 | 2.9 | +| YOLO11-S-Seg | 55.4 | 35.0 | 4.6 | 10.1 | +| YOLO11-M-Seg | 60.0 | 38.5 | 6.9 | 22.4 | + +### 3.4 Analysis Summary + +| Metric | Winner | Margin | +|--------|--------|--------| +| **COCO Accuracy (all sizes)** | RF-DETR | +2.2 to +8.1 mAP₅₀:₉₅ | +| **RF100-VL Domain Generalization** | RF-DETR | +1.2 to +3.2 mAP₅₀:₉₅ | +| **Inference Speed (all sizes)** | YOLOv26 | 16–26% faster | +| **Model Size / Memory** | YOLOv26 | 1.7x–12x fewer parameters | +| **Segmentation Accuracy** | RF-DETR | +0.1 to +5.6 mAP₅₀:₉₅ | +| **YOLOv26 vs YOLO11 (same arch)** | YOLOv26 | +2.9 to +6.0 mAP₅₀:₉₅, 26–43% faster | + +--- + +## 4. Behavior Detection Strategy + +### 4.1 Fighting (*Berkelahi*) — Pose-Based Detection ✅ Easiest + +**Primary Model:** YOLOv26-Pose + +Fighting detection relies on **skeletal keypoint analysis** across consecutive frames. Aggressive actions produce distinctive pose signatures. + +**Detection Signals:** +- Rapid limb acceleration (punching, kicking) +- Close proximity between two or more persons (< 1 meter) +- Asymmetric body postures (one person lunging, other retreating) +- Keypoint velocity exceeding threshold over 5–10 frame window + +**Keypoints Used (COCO 17-point format):** +- Wrists (IDs 9, 10) — punch/grab detection +- Ankles (IDs 15, 16) — kick detection +- Shoulders/Hips (IDs 5, 6, 11, 12) — body orientation and proximity + +**Why YOLOv26-Pose:** +- Built-in RLE-based keypoint estimation — no separate model needed +- NMS-free ensures consistent detection even when fighters overlap +- 2.6 ms latency allows real-time analysis at 30+ FPS +- Pre-trained AVA dataset actions (push, hit, kick, punch) available via SlowFast for confirmation + +### 4.2 Stealing (*Mencuri*) — Context-Aware Detection ⚠️ Hardest + +**Primary Model:** YOLOE-26 (zero-shot first pass) + RF-DETR (fine-tuned confirmation) + +Stealing is the most challenging behavior because it involves **subtle hand–object interactions** that are visually similar to normal activities. + +**Detection Strategy (Multi-Stage):** + +| Stage | Model | Role | +|-------|-------|------| +| 1. Object proximity | YOLOv26 | Detect persons near high-value objects (shelves, bags, displays) | +| 2. Zero-shot screening | YOLOE-26 | Text-prompted detection: `"person concealing object"`, `"hand reaching into bag"` | +| 3. Temporal analysis | SlowFast / X3D | Classify 2–4 second video clips for theft-specific motion patterns | +| 4. Fine-tuned confirmation | RF-DETR (custom) | Fine-tuned on labeled theft dataset for high-precision detection | + +**Why Multi-Stage:** +- No single model reliably detects stealing out-of-the-box +- YOLOE-26 provides zero-shot capability for rapid prototyping without labeled data +- RF-DETR excels at fine-tuning on small custom datasets (proven on RF100-VL) +- SlowFast adds temporal context that frame-level detectors cannot capture + +**Custom Dataset Requirements:** +- Minimum 500–1,000 labeled video clips showing theft behaviors +- Include diverse scenarios: shoplifting, pickpocketing, bag theft +- Negative samples: normal shopping, browsing, reaching for own items + +### 4.3 Mass Brawls (*Tawuran*) — Crowd Density Analysis ✅ Moderate + +**Primary Model:** YOLOv26 (detection + pose) + +Tawuran detection combines **crowd density analysis** with **collective motion patterns**. + +**Detection Signals:** +- Person count exceeding threshold in defined region (e.g., >15 persons in 50m²) +- Collective rapid movement in opposing directions (convergence pattern) +- Multiple aggressive pose signatures detected simultaneously +- Optional: weapon detection via YOLOE-26 text prompts (`"person with stick"`, `"person with weapon"`) + +**Why NMS-Free Architecture is Critical:** +Traditional NMS-based models (YOLO11 and earlier) suppress overlapping bounding boxes — in a crowd of 30+ people, this causes: +- **Missed detections** (valid persons suppressed as duplicates) +- **Variable latency** (NMS processing time scales with object count) + +Both YOLOv26 and RF-DETR are NMS-free, providing: +- **Constant inference time** regardless of crowd size +- **No suppression errors** — every person is detected independently + +### 4.4 Comparison: Which Model for Which Behavior? + +| Behavior | Best Model | Why | Accuracy | Speed | +|----------|-----------|-----|----------|-------| +| **Fighting** | YOLOv26-Pose | Built-in keypoint estimation; real-time pose analysis | High | Very Fast | +| **Stealing (prototype)** | YOLOE-26 | Zero-shot via text prompts; no labeled data needed | Moderate | Fast | +| **Stealing (production)** | RF-DETR (fine-tuned) | Best fine-tuning performance; highest detection accuracy | Very High | Fast | +| **Tawuran (detection)** | YOLOv26 | Lightweight; handles dense crowds; NMS-free | High | Very Fast | +| **Tawuran (confirmation)** | RF-DETR | Superior accuracy for counting persons in dense scenes | Very High | Fast | + +--- + +## 5. System Architecture + +### 5.1 Hybrid Pipeline Design + +The system uses a **two-tier architecture** where a lightweight model runs continuously (Tier 1) and a heavier model is triggered on-demand (Tier 2). + +``` +┌─────────────────────────────────────────────────────────────────────┐ +│ CCTV Camera Feed │ +└──────────────────────────────┬──────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────────────────────────┐ +│ TIER 1: Always-On (YOLOv26-Pose, ~2 ms/frame) │ +│ │ +│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │ +│ │ Person │ │ Pose │ │ Behavior Trigger │ │ +│ │ Detection │──│ Estimation │──│ • Fighting → ALERT │ │ +│ │ (bbox) │ │ (17 keypts) │ │ • Crowd density → Tier 2 │ │ +│ └──────────────┘ └──────────────┘ │ • Suspicious → Tier 2 │ │ +│ └──────────────────────────┘ │ +└──────────────────────────────┬──────────────────────────────────────┘ + │ (triggered only when needed) + ▼ +┌─────────────────────────────────────────────────────────────────────┐ +│ TIER 2: On-Demand (RF-DETR / SlowFast / YOLOE-26) │ +│ │ +│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │ +│ │ RF-DETR-M │ │ SlowFast / │ │ YOLOE-26 │ │ +│ │ (fine-tuned) │ │ X3D-S │ │ (open-vocabulary) │ │ +│ │ Stealing │ │ Temporal │ │ Text-prompted │ │ +│ │ confirmation │ │ analysis │ │ zero-shot detection │ │ +│ └──────────────┘ └──────────────┘ └──────────────────────────┘ │ +└──────────────────────────────┬──────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────────────────────────┐ +│ ALERT SYSTEM │ +│ • Classification: fighting / stealing / tawuran │ +│ • Confidence score │ +│ • Bounding box / region of interest │ +│ • Video clip extraction for review │ +└─────────────────────────────────────────────────────────────────────┘ +``` + +### 5.2 Processing Pipeline — Step by Step + +| Step | Component | Model | Runs | Latency | +|------|-----------|-------|------|---------| +| 1 | Frame capture | — | Every frame | <1 ms | +| 2 | Person detection + pose | YOLOv26-Pose-S | Every 2nd–3rd frame | ~2.6 ms | +| 3 | Person tracking | ByteTrack | Every frame | <1 ms | +| 4 | Pose analysis (fighting) | Rule-based / ML classifier | Every frame with persons | <1 ms | +| 5 | Crowd density check (tawuran) | Person count per ROI | Every frame | <1 ms | +| 6 | Suspicious activity flag | Trigger logic | On threshold breach | <1 ms | +| 7a | Stealing confirmation | RF-DETR-M (fine-tuned) | On-demand | ~4.4 ms | +| 7b | Temporal behavior analysis | SlowFast / X3D-S | On-demand (2–4s clip) | ~50–100 ms | +| 7c | Zero-shot screening | YOLOE-26 | On-demand | ~2.6 ms | +| 8 | Alert dispatch | Alert system | On confirmed detection | <1 ms | + +### 5.3 Implementation Example + +```python +from ultralytics import YOLO +import numpy as np + +# ── Tier 1: Always-on detection ────────────────────────────────────── +pose_model = YOLO("yolo26s-pose.pt") +tracker = ByteTrack() # or DeepSORT + +def process_frame(frame): + # Step 1: Detect persons + keypoints + results = pose_model(frame, conf=0.5, classes=[0]) # class 0 = person + + # Step 2: Track persons across frames + detections = results[0].boxes + keypoints = results[0].keypoints + tracked = tracker.update(detections) + + # Step 3: Analyze behaviors + alerts = [] + + # Fighting detection: check for aggressive pose patterns + for person_a, person_b in get_close_pairs(tracked, threshold_meters=1.5): + if detect_fighting_pose(person_a.keypoints, person_b.keypoints): + alerts.append({ + "type": "fighting", + "confidence": calculate_confidence(person_a, person_b), + "persons": [person_a.id, person_b.id] + }) + + # Tawuran detection: crowd density check + person_count = len(tracked) + if person_count > 15: # threshold for mass gathering + alerts.append({ + "type": "tawuran_warning", + "person_count": person_count, + "trigger": "crowd_density" + }) + + # Trigger Tier 2 for suspicious activities + for alert in alerts: + if alert["type"] in ["suspicious_proximity", "tawuran_warning"]: + trigger_tier2(frame, alert) + + return alerts + +# ── Tier 2: On-demand confirmation ────────────────────────────────── +from rfdetr import RFDETRMedium + +rfdetr_model = RFDETRMedium() # fine-tuned on stealing dataset +yoloe_model = YOLO("yoloe-26s.pt") + +def trigger_tier2(frame, alert): + if alert["type"] == "suspicious_proximity": + # Zero-shot stealing detection + yoloe_model.set_classes([ + "person concealing object", + "hand reaching into bag", + "person hiding item under clothing" + ]) + results = yoloe_model(frame) + + # High-accuracy confirmation + detections = rfdetr_model.predict(frame, threshold=0.6) + + elif alert["type"] == "tawuran_warning": + # Accurate person count with RF-DETR + detections = rfdetr_model.predict(frame, threshold=0.4) + accurate_count = len(detections) +``` + +--- + +## 6. Hardware & Deployment Recommendations + +### 6.1 VRAM Budget (RTX 3060 — 12 GB) + +| Component | VRAM Usage (FP16) | Notes | +|-----------|-------------------|-------| +| YOLOv26-S-Pose | ~0.8 GB | Always loaded | +| ByteTrack | ~0.1 GB | CPU-based, minimal GPU | +| RF-DETR-M (on-demand) | ~2.5 GB | Loaded/unloaded as needed | +| YOLOE-26-S (on-demand) | ~1.0 GB | Shares backbone with YOLOv26 | +| SlowFast / X3D-S (on-demand) | ~1.5 GB | Loaded only for temporal analysis | +| **Total (worst case)** | **~5.9 GB** | Well within 12 GB budget | + +### 6.2 Throughput Estimates (RTX 3060) + +| Configuration | FPS | Cameras | Use Case | +|--------------|-----|---------|----------| +| YOLOv26-S-Pose only | ~120 FPS | 4–6 streams at 20 FPS | Fighting + tawuran only | +| YOLOv26-S-Pose + ByteTrack | ~80 FPS | 2–4 streams at 20 FPS | Full tracking pipeline | +| Hybrid (Tier 1 + Tier 2 on-demand) | ~30–60 FPS | 1–2 streams at 15 FPS | All 3 behaviors | +| RF-DETR-M continuous | ~45 FPS | 1–2 streams at 15 FPS | Maximum accuracy mode | + +### 6.3 Optimization Techniques + +| Technique | Impact | How | +|-----------|--------|-----| +| **TensorRT export** | 2–3x speedup | `model.export(format="engine", half=True)` | +| **FP16 inference** | ~40% less VRAM | Minimal accuracy loss (<0.1 mAP) | +| **Frame skipping** | 2–3x throughput | Process every 2nd or 3rd frame; sufficient for surveillance | +| **ROI cropping** | Reduced compute | Only process regions of interest, not full frame | +| **Dynamic model loading** | Lower peak VRAM | Load RF-DETR/SlowFast only when triggered | +| **INT8 quantization** | Further speedup | YOLOv26 DFL-free design makes INT8 cleaner | + +### 6.4 Deployment Recommendations by Budget + +| Budget | Hardware | Models | Cameras | Behaviors | +|--------|----------|--------|---------|-----------| +| **Low (~$300)** | 1× RTX 3060 | YOLOv26-Pose only | 1–2 | Fighting, tawuran | +| **Medium (~$500)** | 1× RTX 3080 | YOLOv26-Pose + RF-DETR-M | 1–2 | All 3 behaviors | +| **High (~$1,600)** | 1× RTX 4090 | Full hybrid pipeline | 3–4 | All 3, high accuracy | +| **Production** | 2× RTX 3060 or Cloud T4 | Dedicated per-tier GPUs | 4+ | All 3, redundancy | + +### 6.5 Edge Deployment (Jetson / CPU) + +| Aspect | YOLOv26 | RF-DETR | +|--------|---------|---------| +| **Jetson Orin Nano** | ✅ Excellent (2.6M params, DFL-free) | ⚠️ Heavy (30M+ params, ViT) | +| **CPU-only** | ✅ 43% faster than YOLO11 on CPU | ❌ Not recommended (ViT too slow) | +| **INT8 quantization** | ✅ Clean (no DFL Softmax) | ⚠️ ViT quantization more complex | +| **ONNX/TensorRT export** | ✅ Full support | ✅ Full support | +| **CoreML (Apple)** | ✅ Supported | ⚠️ In development | + +**Edge recommendation:** Use YOLOv26-Pose exclusively on edge devices. Reserve RF-DETR for server/cloud deployment where GPU resources are available. + +--- + +## 7. Conclusions & Recommendations + +### 7.1 Model Selection Summary + +| | YOLOv26 | RF-DETR | +|--|---------|---------| +| **Architecture** | CNN (lightweight, efficient) | Transformer (accurate, heavy) | +| **Strength** | Speed, model size, pose estimation, open-vocab | Accuracy, fine-tuning, domain generalization | +| **Weakness** | Lower accuracy than RF-DETR | No pose estimation, heavier model | +| **License** | AGPL-3.0 (restrictive for commercial) | Apache 2.0 (permissive) | +| **Best role** | Always-on primary detector (Tier 1) | On-demand high-accuracy confirmer (Tier 2) | + +### 7.2 Final Recommendation + +**Use a hybrid approach:** + +1. **YOLOv26-Pose** as the always-on Tier 1 detector for fighting and tawuran — it provides built-in pose estimation, NMS-free deterministic latency, and the lightest resource footprint. + +2. **YOLOE-26** for zero-shot stealing detection during prototyping — enables rapid iteration without labeled data. + +3. **RF-DETR** (fine-tuned) as the Tier 2 high-accuracy confirmer for stealing and ambiguous cases — its superior accuracy (+2–8 mAP) and proven fine-tuning capability on custom datasets make it ideal for production-quality behavior classification. + +4. **SlowFast / X3D** for temporal behavior analysis when frame-level detection is insufficient — particularly for stealing where the action unfolds over multiple seconds. + +This hybrid approach maximizes both **speed** (YOLOv26 strengths) and **accuracy** (RF-DETR strengths) while staying within a single RTX 3060's 12 GB VRAM budget. + +### 7.3 Recommended Development Roadmap + +| Phase | Duration | Goal | Models | +|-------|----------|------|--------| +| **Phase 1: Prototype** | 2–4 weeks | Detect fighting with pre-trained models | YOLOv26-Pose | +| **Phase 2: Expand** | 2–4 weeks | Add tawuran detection; test YOLOE-26 for stealing | YOLOv26-Pose + YOLOE-26 | +| **Phase 3: Custom Data** | 4–8 weeks | Collect and label stealing dataset (500+ clips) | Data collection | +| **Phase 4: Fine-tune** | 2–4 weeks | Fine-tune RF-DETR on custom stealing dataset | RF-DETR-M | +| **Phase 5: Integration** | 2–4 weeks | Build full hybrid pipeline with alert system | All models | +| **Phase 6: Production** | 2–4 weeks | TensorRT optimization, monitoring, deployment | Optimized pipeline | + +--- + +## References + +1. **YOLOv26** — Ultralytics (September 2025). NMS-free, DFL-free real-time object detection. https://docs.ultralytics.com/models/yolo26/ +2. **RF-DETR** — Roboflow (March 2025, ICLR 2026). Real-time detection transformer with DINOv2 backbone. https://github.com/roboflow/rf-detr +3. **SlowFast Networks** — Feichtenhofer et al. (ICCV 2019). Dual-pathway temporal action recognition. https://github.com/facebookresearch/SlowFast +4. **X3D** — Feichtenhofer (CVPR 2020). Efficient video recognition networks. Part of SlowFast repository. +5. **ByteTrack** — Zhang et al. (ECCV 2022). Multi-object tracking by associating every detection box. https://github.com/ifzhang/ByteTrack +6. **DINOv2** — Oquab et al. (2023). Self-supervised Vision Transformer features. https://github.com/facebookresearch/dinov2 +7. **COCO Dataset** — Lin et al. (2014). Microsoft Common Objects in Context. https://cocodataset.org +8. **RF100-VL** — Roboflow. 100 diverse real-world detection datasets. https://github.com/roboflow/rf100-vl +9. **AVA Dataset** — Gu et al. (CVPR 2018). Atomic Visual Actions for action detection. + +--- +--- + +# Part II — Pipeline Architecture Analysis & Integration Guide + +**Date:** March 2026 +**Hardware:** 2× NVIDIA V100 32 GB +**Target:** Multi-camera CCTV surveillance with behavior detection + +--- + +## 8. Current Pipeline Review + +### 8.1 Current Architecture + +``` +Thread per CCTV stream (capture frame at 1 FPS) + → Save frame in shared memory buffer + → Pool/batch frames for YOLO detection pipeline + → Get bounding boxes (object detection) + → Crop bounding box regions and save as images + → Pool/batch cropped images for async save to MongoDB +``` + +### 8.2 Identified Bottlenecks & Issues + +| # | Issue | Severity | Impact | +|---|-------|----------|--------| +| 1 | **Thread-per-stream scaling** | 🔴 Critical | Python's GIL prevents true parallelism. At 200+ streams, thread overhead dominates. Context switching between hundreds of threads wastes CPU cycles. | +| 2 | **Synchronous RTSP decode** | 🔴 Critical | FFmpeg/OpenCV decode is CPU-bound. Each 1080p decode uses ~0.3–0.5 CPU core. 500 streams = 150–250 cores just for decode. | +| 3 | **Shared memory buffer — no backpressure** | 🟡 High | If YOLO batching falls behind capture rate, frames accumulate unbounded in memory. No drop policy = OOM risk. | +| 4 | **Single YOLO model bottleneck** | 🟡 High | All streams funnel into one YOLO instance. If batch queue stalls, all streams back up. No priority or fairness. | +| 5 | **Crop → save as image → MongoDB** | 🟡 High | JPEG encoding is CPU-bound (~2–5 ms per crop). Saving to disk then re-reading for MongoDB is redundant I/O. | +| 6 | **No tracking across frames** | 🟠 Medium | Without person tracking (ByteTrack), same person is re-detected every frame. No temporal identity = no behavior analysis possible. | +| 7 | **No behavior analysis layer** | 🟠 Medium | Pipeline ends at "save crops." No pose analysis, no temporal analysis, no alert system. | +| 8 | **No GPU decode (NVDEC)** | 🟠 Medium | CPU decode wastes cores that could process more streams. V100 NVDEC engines sit idle. | + +### 8.3 Recommended Revised Architecture + +``` +┌─────────────────────────────────────────────────────────────────────────────┐ +│ CAPTURE LAYER (async) │ +│ │ +│ ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ │ +│ │ Camera 1 │ │ Camera 2 │ │ Camera 3 │ ... │ Camera N │ │ +│ │ RTSP pull │ │ RTSP pull │ │ RTSP pull │ │ RTSP pull │ │ +│ └─────┬─────┘ └─────┬─────┘ └─────┬─────┘ └─────┬─────┘ │ +│ │ │ │ │ │ +│ └──────────────┴──────┬───────┴────────────────────┘ │ +│ │ │ +│ ┌─────────▼──────────┐ │ +│ │ Ring Buffer Pool │ ← Fixed-size, per-camera │ +│ │ (drop-oldest) │ ← Backpressure: drop frames │ +│ └─────────┬──────────┘ │ +└──────────────────────────────┼──────────────────────────────────────────────┘ + │ +┌──────────────────────────────▼──────────────────────────────────────────────┐ +│ TIER 1: ALWAYS-ON (GPU 0) │ +│ │ +│ ┌──────────────────────────────────────────────────────┐ │ +│ │ Batch Assembler │ │ +│ │ • Collect frames from ring buffers │ │ +│ │ • Dynamic batch size (8–32 based on queue depth) │ │ +│ │ • Priority: cameras with recent alerts first │ │ +│ └──────────────────────┬───────────────────────────────┘ │ +│ │ │ +│ ┌──────────────────────▼───────────────────────────────┐ │ +│ │ YOLOv26-S-Pose (TensorRT FP16) │ │ +│ │ → Person bboxes + 17 keypoints per person │ │ +│ │ → ~1.5 ms/image on V100 │ │ +│ └──────────────────────┬───────────────────────────────┘ │ +│ │ │ +│ ┌──────────────────────▼───────────────────────────────┐ │ +│ │ ByteTrack (CPU) │ │ +│ │ → Track person IDs across frames │ │ +│ │ → Maintain per-person keypoint history (last 10 fr) │ │ +│ └──────────────────────┬───────────────────────────────┘ │ +│ │ │ +│ ┌──────────────────────▼───────────────────────────────┐ │ +│ │ Behavior Analyzer (CPU) │ │ +│ │ ├─ Fighting: keypoint velocity + proximity │ │ +│ │ ├─ Tawuran: crowd density + collective motion │ │ +│ │ └─ Suspicious: person-object proximity zones │ │ +│ └──────────┬────────────────────────┬──────────────────┘ │ +│ │ direct alert │ trigger Tier 2 │ +│ ▼ ▼ │ +│ ┌─────────────┐ ┌─────────────────────────┐ │ +│ │ Alert Queue │ │ Tier 2 Request Queue │ │ +│ └──────┬──────┘ └────────────┬────────────┘ │ +└─────────────┼─────────────────────────┼─────────────────────────────────────┘ + │ │ + │ ┌────────────────────▼──────────────────────────────────┐ + │ │ TIER 2: ON-DEMAND (GPU 1) │ + │ │ │ + │ │ ┌─────────────┐ ┌─────────────┐ ┌───────────────┐ │ + │ │ │ RF-DETR-M │ │ SlowFast │ │ YOLOE-26 │ │ + │ │ │ (fine-tuned)│ │ X3D-S │ │ (open-vocab) │ │ + │ │ │ Stealing │ │ Temporal │ │ Zero-shot │ │ + │ │ │ ~2.7ms V100 │ │ ~50–80ms │ │ ~1.5ms V100 │ │ + │ │ └──────┬──────┘ └──────┬──────┘ └───────┬───────┘ │ + │ │ └───────────┬────┴───────────────┘ │ + │ │ ▼ │ + │ │ ┌──────────────────┐ │ + │ │ │ Confirmation │ │ + │ │ │ Aggregator │ │ + │ │ └────────┬─────────┘ │ + │ └───────────────────┼───────────────────────────────────┘ + │ │ + ▼ ▼ +┌─────────────────────────────────────────────────────────────────────────────┐ +│ OUTPUT LAYER │ +│ │ +│ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐ │ +│ │ MongoDB │ │ Alert │ │ Video Clip │ │ +│ │ (crops+meta) │ │ Dispatcher │ │ Extractor │ │ +│ │ Async bulk │ │ WebSocket/API │ │ (evidence) │ │ +│ └───────────────┘ └───────────────┘ └───────────────┘ │ +└─────────────────────────────────────────────────────────────────────────────┘ +``` + +### 8.4 Architectural Improvements — Detail + +#### A. Replace Thread-per-Stream with Producer-Consumer + asyncio + +**Problem:** Python threads hold the GIL; 500 threads = massive context-switch overhead. + +**Solution:** Use a small **process pool** for CPU-bound decode, feeding into an **asyncio** event loop for I/O coordination. + +```python +import asyncio +import multiprocessing as mp +from concurrent.futures import ProcessPoolExecutor +from collections import deque +import cv2 +import numpy as np + +# ── Ring buffer per camera (fixed-size, drop-oldest) ────────────────── +class CameraRingBuffer: + """Lock-free-ish ring buffer. Drops oldest frame when full.""" + def __init__(self, camera_id: str, max_size: int = 3): + self.camera_id = camera_id + self.buffer = deque(maxlen=max_size) # auto-drops oldest + self.frame_count = 0 + self.dropped = 0 + + def put(self, frame: np.ndarray, timestamp: float): + if len(self.buffer) == self.buffer.maxlen: + self.dropped += 1 + self.buffer.append((frame, timestamp, self.frame_count)) + self.frame_count += 1 + + def get_latest(self): + return self.buffer[-1] if self.buffer else None + +# ── RTSP decode in separate process (bypass GIL) ───────────────────── +def decode_worker(rtsp_url: str, shared_queue: mp.Queue, camera_id: str): + """Runs in a subprocess. Decodes RTSP at 1 FPS.""" + cap = cv2.VideoCapture(rtsp_url) + while cap.isOpened(): + ret, frame = cap.read() + if ret: + try: + shared_queue.put_nowait((camera_id, frame, time.time())) + except mp.queues.Full: + pass # backpressure: drop frame silently + time.sleep(1.0) # 1 FPS capture rate + +# ── Main async coordinator ─────────────────────────────────────────── +class PipelineCoordinator: + def __init__(self, camera_urls: dict[str, str]): + self.buffers: dict[str, CameraRingBuffer] = {} + self.frame_queue = mp.Queue(maxsize=1000) + self.decode_pool = ProcessPoolExecutor(max_workers=mp.cpu_count() // 2) + + for cam_id, url in camera_urls.items(): + self.buffers[cam_id] = CameraRingBuffer(cam_id, max_size=3) + + async def run(self): + # Start decode workers as subprocesses + for cam_id, url in camera_urls.items(): + self.decode_pool.submit(decode_worker, url, self.frame_queue, cam_id) + + # Main loop: drain queue → fill ring buffers → assemble batches + while True: + batch = self._assemble_batch(max_batch_size=32) + if batch: + results = await self._run_tier1(batch) + await self._process_results(results) + await asyncio.sleep(0.001) # yield control +``` + +#### B. Backpressure Strategy + +| Scenario | Action | Implementation | +|----------|--------|----------------| +| Frame queue full | **Drop newest frame** from that camera | `put_nowait` + catch `Full` | +| GPU batch queue full | **Drop oldest batch** (stale frames) | Ring buffer with `maxlen=3` per camera | +| Tier 2 queue full | **Drop lowest-confidence requests** | Priority queue sorted by confidence | +| MongoDB write slow | **Buffer in memory, bulk write** | Async bulk insert every 5 seconds | +| Network congestion | **Reduce capture FPS temporarily** | Adaptive sleep: `1.0 → 2.0s` | + +#### C. Eliminate Redundant I/O (Crop → Disk → MongoDB) + +**Current:** Crop → encode JPEG → save to disk → read from disk → upload to MongoDB +**Improved:** Crop → encode JPEG in-memory → bulk insert to MongoDB directly + +```python +import motor.motor_asyncio # async MongoDB driver +import cv2 + +async def save_crops_batch(crops: list[tuple[str, np.ndarray, dict]]): + """Bulk save crops directly to MongoDB GridFS — no disk I/O.""" + client = motor.motor_asyncio.AsyncIOMotorClient("mongodb://localhost:27017") + db = client.surveillance + + documents = [] + for camera_id, crop_img, metadata in crops: + _, jpeg_bytes = cv2.imencode(".jpg", crop_img, [cv2.IMWRITE_JPEG_QUALITY, 85]) + documents.append({ + "camera_id": camera_id, + "image": jpeg_bytes.tobytes(), # binary, no disk round-trip + "timestamp": metadata["timestamp"], + "bbox": metadata["bbox"], + "person_id": metadata.get("track_id"), + "behavior_flags": metadata.get("flags", []), + }) + + if documents: + await db.detections.insert_many(documents) # single bulk operation +``` + +--- + +## 9. YOLOv26-Pose Integration (Fighting & Tawuran) + +### 9.1 Integration Strategy: Replace, Don't Run Alongside + +**Replace** the current YOLO detection model with YOLOv26-S-Pose. It provides: +- Everything the current YOLO does (bounding boxes, class detection) +- **Plus** 17 keypoints per person (pose estimation) +- **Plus** NMS-free deterministic latency +- **Faster** than YOLO11 equivalent (~26–43% faster) + +There is no reason to run both — YOLOv26-Pose is a strict superset. + +### 9.2 Where Pose Analysis Fits in the Data Flow + +``` +Frame from ring buffer + │ + ▼ +YOLOv26-S-Pose inference (GPU, ~1.5 ms on V100) + │ + ├── outputs: bboxes + confidence + class + 17 keypoints per person + │ + ▼ +ByteTrack (CPU, <1 ms) + │ + ├── outputs: tracked person IDs + keypoint history per track + │ + ▼ +┌─────────────────────────────────────────────────────────┐ +│ Behavior Analyzer (CPU, <1 ms total) │ +│ │ +│ ┌─────────────────────────────────────────────────┐ │ +│ │ A. Fighting Detector │ │ +│ │ • Input: keypoints of all close pairs │ │ +│ │ • Method: velocity + acceleration of wrists/ │ │ +│ │ ankles over 5-frame sliding window │ │ +│ │ • Threshold: >X px/frame limb movement │ │ +│ │ • Output: ALERT if confirmed │ │ +│ └─────────────────────────────────────────────────┘ │ +│ │ +│ ┌─────────────────────────────────────────────────┐ │ +│ │ B. Tawuran Detector │ │ +│ │ • Input: all person bboxes + keypoints │ │ +│ │ • Method 1: person_count > 15 in ROI │ │ +│ │ • Method 2: centroid convergence velocity │ │ +│ │ • Method 3: % of persons in fighting pose │ │ +│ │ • Output: ALERT if 2+ methods trigger │ │ +│ └─────────────────────────────────────────────────┘ │ +│ │ +│ ┌─────────────────────────────────────────────────┐ │ +│ │ C. Suspicious Activity Flagger │ │ +│ │ • Input: person tracks near defined zones │ │ +│ │ • Method: dwell time + hand movement pattern │ │ +│ │ • Output: TRIGGER TIER 2 for confirmation │ │ +│ └─────────────────────────────────────────────────┘ │ +└─────────────────────────────────────────────────────────┘ +``` + +### 9.3 Fighting Detection — Keypoint Velocity Algorithm + +```python +import numpy as np +from collections import defaultdict + +class FightingDetector: + """Detects fighting based on keypoint velocity analysis.""" + + WRIST_IDS = [9, 10] # left/right wrist + ANKLE_IDS = [15, 16] # left/right ankle + SHOULDER_IDS = [5, 6] # proximity reference + HIP_IDS = [11, 12] # proximity reference + + def __init__( + self, + velocity_threshold: float = 40.0, # pixels/frame + proximity_threshold: float = 150.0, # pixels (approx 1.5m at typical CCTV) + window_size: int = 5, # frames to analyze + min_aggressive_frames: int = 3, # frames exceeding threshold + ): + self.velocity_threshold = velocity_threshold + self.proximity_threshold = proximity_threshold + self.window_size = window_size + self.min_aggressive_frames = min_aggressive_frames + self.keypoint_history: dict[int, list[np.ndarray]] = defaultdict(list) + + def update(self, track_id: int, keypoints: np.ndarray): + """Store keypoints for a tracked person. keypoints shape: (17, 2).""" + history = self.keypoint_history[track_id] + history.append(keypoints.copy()) + if len(history) > self.window_size: + history.pop(0) + + def _limb_velocity(self, track_id: int) -> float: + """Compute max limb velocity over recent frames.""" + history = self.keypoint_history.get(track_id, []) + if len(history) < 2: + return 0.0 + + max_vel = 0.0 + limb_ids = self.WRIST_IDS + self.ANKLE_IDS + for i in range(1, len(history)): + for lid in limb_ids: + delta = np.linalg.norm(history[i][lid] - history[i-1][lid]) + max_vel = max(max_vel, delta) + return max_vel + + def _torso_distance(self, kp_a: np.ndarray, kp_b: np.ndarray) -> float: + """Distance between torso centers of two persons.""" + center_a = np.mean(kp_a[self.SHOULDER_IDS + self.HIP_IDS], axis=0) + center_b = np.mean(kp_b[self.SHOULDER_IDS + self.HIP_IDS], axis=0) + return np.linalg.norm(center_a - center_b) + + def check_pair(self, id_a: int, id_b: int) -> dict | None: + """Check if two tracked persons are fighting.""" + hist_a = self.keypoint_history.get(id_a, []) + hist_b = self.keypoint_history.get(id_b, []) + if not hist_a or not hist_b: + return None + + # Check proximity (must be close) + dist = self._torso_distance(hist_a[-1], hist_b[-1]) + if dist > self.proximity_threshold: + return None + + # Check velocity (both must have aggressive movement) + vel_a = self._limb_velocity(id_a) + vel_b = self._limb_velocity(id_b) + + if vel_a > self.velocity_threshold and vel_b > self.velocity_threshold: + confidence = min(1.0, (vel_a + vel_b) / (4 * self.velocity_threshold)) + return { + "type": "fighting", + "person_ids": [id_a, id_b], + "distance_px": dist, + "velocity_a": vel_a, + "velocity_b": vel_b, + "confidence": round(confidence, 3), + } + return None +``` + +### 9.4 Tawuran Detection — Crowd Density + Collective Motion + +```python +class TawuranDetector: + """Detects mass brawls via crowd density and collective aggression.""" + + def __init__( + self, + crowd_threshold: int = 15, # persons in ROI + convergence_threshold: float = 0.6, # 60% moving toward center + fighting_ratio_threshold: float = 0.3, # 30% in aggressive poses + ): + self.crowd_threshold = crowd_threshold + self.convergence_threshold = convergence_threshold + self.fighting_ratio_threshold = fighting_ratio_threshold + self.prev_centroids: dict[int, np.ndarray] = {} + + def analyze( + self, + tracked_persons: list[dict], # [{id, bbox, keypoints}, ...] + roi: tuple[int, int, int, int] | None = None, # (x1, y1, x2, y2) + fighting_detector: FightingDetector = None, + ) -> dict | None: + + # Filter to ROI + if roi: + in_roi = [p for p in tracked_persons if self._in_roi(p["bbox"], roi)] + else: + in_roi = tracked_persons + + person_count = len(in_roi) + if person_count < self.crowd_threshold: + return None + + # Check 1: Crowd density exceeded + signals = ["crowd_density"] + + # Check 2: Convergence — are people moving toward each other? + centroids = {p["id"]: self._centroid(p["bbox"]) for p in in_roi} + group_center = np.mean(list(centroids.values()), axis=0) + + converging = 0 + for pid, pos in centroids.items(): + prev = self.prev_centroids.get(pid) + if prev is not None: + prev_dist = np.linalg.norm(prev - group_center) + curr_dist = np.linalg.norm(pos - group_center) + if curr_dist < prev_dist: + converging += 1 + + self.prev_centroids = centroids + + if person_count > 0 and converging / person_count > self.convergence_threshold: + signals.append("convergence") + + # Check 3: Multiple fighting poses + if fighting_detector: + aggressive = sum( + 1 for p in in_roi + if fighting_detector._limb_velocity(p["id"]) > fighting_detector.velocity_threshold + ) + if person_count > 0 and aggressive / person_count > self.fighting_ratio_threshold: + signals.append("collective_aggression") + + if len(signals) >= 2: # at least 2 signals to confirm + return { + "type": "tawuran", + "person_count": person_count, + "signals": signals, + "confidence": min(1.0, len(signals) / 3), + } + return None + + def _in_roi(self, bbox, roi) -> bool: + cx = (bbox[0] + bbox[2]) / 2 + cy = (bbox[1] + bbox[3]) / 2 + return roi[0] <= cx <= roi[2] and roi[1] <= cy <= roi[3] + + def _centroid(self, bbox) -> np.ndarray: + return np.array([(bbox[0]+bbox[2])/2, (bbox[1]+bbox[3])/2]) +``` + +--- + +## 10. SlowFast / X3D Integration (Temporal Behavior Analysis) + +### 10.1 The Challenge: 1 FPS → Video Clips + +SlowFast and X3D expect **multi-frame video clips** (typically 8–32 frames at ≥8 FPS). Your pipeline captures at **1 FPS**. This mismatch requires a buffering strategy. + +| Approach | Clip Requirement | At 1 FPS | Clip Duration | Feasibility | +|----------|-----------------|----------|---------------|-------------| +| **SlowFast 8×8** | 8 frames, stride 8 | 8 frames = 8 seconds buffer | 8 seconds | ✅ Good — matches stealing/fighting duration | +| **SlowFast 4×16** | 4 frames, stride 16 | 4 frames = 4 seconds buffer | 4 seconds | ✅ Good — quick events | +| **X3D-S** | 13 frames, stride 6 | 13 frames = 13 seconds buffer | 13 seconds | ✅ Covers full tawuran build-up | +| **SlowFast 16×8** | 16 frames, stride 8 | 16 frames = 16 seconds | 16 seconds | ⚠️ Long buffer but better accuracy | + +> **Key insight:** At 1 FPS, the temporal resolution is low, but the clip duration is inherently long. SlowFast can still extract useful **coarse temporal patterns** (person appearing → approaching → grabbing → leaving) even at 1 FPS. For fine-grained motion (punch trajectories), the keypoint velocity from YOLOv26-Pose (Tier 1) is already covering this at per-frame level. + +### 10.2 When to Trigger SlowFast (On-Demand Only) + +SlowFast should **never** run continuously. It runs only when Tier 1 flags suspicious activity: + +| Trigger Source | Trigger Condition | SlowFast Task | Priority | +|---------------|-------------------|---------------|----------| +| Fighting detector | Fighting confidence > 0.5 but < 0.8 | Confirm/deny fight action | High | +| Suspicious activity | Person lingering near valuables > 10s | Classify stealing behavior | High | +| Tawuran detector | Crowd threshold met | Classify crowd action (running, fighting) | Medium | +| Periodic audit | Random 1% of cameras, every 60s | Background anomaly detection | Low | + +### 10.3 Per-Person Clip Buffer + +```python +from collections import deque +from dataclasses import dataclass, field +import numpy as np +import time + +@dataclass +class PersonClipBuffer: + """Maintains a rolling frame buffer per tracked person for SlowFast.""" + track_id: int + max_frames: int = 16 # 16 seconds at 1 FPS + frames: deque = field(default_factory=lambda: deque(maxlen=16)) + crops: deque = field(default_factory=lambda: deque(maxlen=16)) + timestamps: deque = field(default_factory=lambda: deque(maxlen=16)) + + # Memory estimate: 16 frames × 224×224×3 (crop) ≈ 2.4 MB per person + # 100 tracked persons ≈ 240 MB — manageable + + def add_frame(self, full_frame: np.ndarray, bbox: tuple, timestamp: float): + """Add a frame crop for this tracked person.""" + x1, y1, x2, y2 = [int(c) for c in bbox] + # Pad bbox by 20% for context + h, w = full_frame.shape[:2] + pad_x = int((x2 - x1) * 0.2) + pad_y = int((y2 - y1) * 0.2) + x1, y1 = max(0, x1 - pad_x), max(0, y1 - pad_y) + x2, y2 = min(w, x2 + pad_x), min(h, y2 + pad_y) + + crop = full_frame[y1:y2, x1:x2] + # Resize to SlowFast input size + crop_resized = cv2.resize(crop, (224, 224)) + + self.frames.append(full_frame) # keep full frame too (for RF-DETR) + self.crops.append(crop_resized) + self.timestamps.append(timestamp) + + def get_clip(self, num_frames: int = 8) -> np.ndarray | None: + """Get a clip tensor for SlowFast. Returns (T, H, W, C) array.""" + if len(self.crops) < num_frames: + return None + recent = list(self.crops)[-num_frames:] + return np.stack(recent, axis=0) # (T, 224, 224, 3) + + @property + def duration_seconds(self) -> float: + if len(self.timestamps) < 2: + return 0.0 + return self.timestamps[-1] - self.timestamps[0] + + @property + def memory_mb(self) -> float: + return len(self.crops) * 224 * 224 * 3 / (1024 * 1024) + + +class ClipBufferManager: + """Manages clip buffers for all tracked persons. Evicts stale tracks.""" + + def __init__(self, max_persons: int = 200, stale_timeout: float = 30.0): + self.buffers: dict[int, PersonClipBuffer] = {} + self.max_persons = max_persons + self.stale_timeout = stale_timeout + + def update(self, track_id: int, frame: np.ndarray, bbox: tuple, ts: float): + if track_id not in self.buffers: + if len(self.buffers) >= self.max_persons: + self._evict_oldest() + self.buffers[track_id] = PersonClipBuffer(track_id=track_id) + self.buffers[track_id].add_frame(frame, bbox, ts) + + def get_clip(self, track_id: int, num_frames: int = 8) -> np.ndarray | None: + buf = self.buffers.get(track_id) + return buf.get_clip(num_frames) if buf else None + + def _evict_oldest(self): + """Remove the track with the oldest last-seen timestamp.""" + if not self.buffers: + return + oldest_id = min(self.buffers, key=lambda k: self.buffers[k].timestamps[-1]) + del self.buffers[oldest_id] + + def cleanup_stale(self, current_time: float): + """Remove tracks not seen for stale_timeout seconds.""" + stale = [ + tid for tid, buf in self.buffers.items() + if current_time - buf.timestamps[-1] > self.stale_timeout + ] + for tid in stale: + del self.buffers[tid] + + @property + def total_memory_mb(self) -> float: + return sum(buf.memory_mb for buf in self.buffers.values()) +``` + +### 10.4 SlowFast Inference Integration + +```python +import torch +from pytorchvideo.models.hub import slowfast_r50_detection + +class SlowFastAnalyzer: + """Tier 2 temporal behavior analysis using SlowFast.""" + + # AVA action labels relevant to our use case + FIGHTING_ACTIONS = {"hit", "kick", "punch", "push", "grab", "fight"} + STEALING_ACTIONS = {"take", "pick_up", "carry", "put_down", "grab"} + + def __init__(self, device: str = "cuda:1"): + self.device = device + self.model = slowfast_r50_detection() + self.model.to(device).eval() + + @torch.no_grad() + def analyze_clip( + self, + clip: np.ndarray, # (T, 224, 224, 3) + behavior_type: str, # "fighting" or "stealing" + ) -> dict: + """Run SlowFast on a clip buffer.""" + # Preprocess: (T,H,W,C) → (C,T,H,W) normalized + clip_tensor = torch.from_numpy(clip).float().permute(3, 0, 1, 2) / 255.0 + clip_tensor = clip_tensor.unsqueeze(0).to(self.device) # (1,C,T,H,W) + + # SlowFast expects [slow_pathway, fast_pathway] + # Slow: every 8th frame, Fast: all frames + slow = clip_tensor[:, :, ::8, :, :] # temporal stride 8 + fast = clip_tensor # full temporal resolution + + preds = self.model([slow, fast]) + + # Map predictions to relevant actions + target_actions = ( + self.FIGHTING_ACTIONS if behavior_type == "fighting" + else self.STEALING_ACTIONS + ) + + action_scores = {} # action_name → confidence + for action, idx in ACTION_LABEL_MAP.items(): + if action in target_actions: + action_scores[action] = float(preds[0, idx].cpu()) + + top_action = max(action_scores, key=action_scores.get) + return { + "behavior": behavior_type, + "top_action": top_action, + "confidence": action_scores[top_action], + "all_scores": action_scores, + } +``` + +### 10.5 Where SlowFast Fits in the Hybrid Architecture + +``` +Tier 1 (GPU 0) flags suspicious activity + │ + │ trigger_type: "fighting_uncertain" or "stealing_suspicious" + │ includes: track_id, camera_id, confidence + ▼ +┌──────────────────────────────────────────────┐ +│ ClipBufferManager.get_clip(track_id, 8) │ +│ → Returns (8, 224, 224, 3) ndarray │ +│ → If clip not ready (< 8 frames), WAIT │ +│ and re-trigger when buffer fills │ +└──────────────────────┬───────────────────────┘ + │ + ▼ +┌──────────────────────────────────────────────┐ +│ SlowFastAnalyzer.analyze_clip(clip, type) │ +│ → Runs on GPU 1 (shared with RF-DETR) │ +│ → ~50–80 ms per clip │ +│ → Returns action classification + conf │ +└──────────────────────┬───────────────────────┘ + │ + ┌──────────┴──────────┐ + │ │ + confidence > 0.7 confidence ≤ 0.7 + │ │ + CONFIRM ALERT DISMISS / LOG +``` + +--- + +## 11. RF-DETR Integration (Tier 2 High-Accuracy Confirmation) + +### 11.1 GPU Assignment Strategy + +**Recommended: Dedicated GPU per tier.** + +| Component | GPU 0 (V100 #1) | GPU 1 (V100 #2) | +|-----------|-----------------|-----------------| +| YOLOv26-S-Pose | ✅ Always loaded | ❌ | +| ByteTrack | ✅ CPU-side | ✅ CPU-side | +| RF-DETR-M | ❌ | ✅ On-demand | +| SlowFast / X3D-S | ❌ | ✅ On-demand | +| YOLOE-26 | ❌ | ✅ On-demand | + +**Why separate GPUs:** +- Tier 1 must have **guaranteed latency** — no competition for CUDA streams +- Tier 2 models share GPU 1 sequentially (they're triggered sporadically, not continuously) +- If Tier 2 queue backs up, Tier 1 is unaffected + +### 11.2 Non-Blocking Routing to RF-DETR + +The key requirement: triggering RF-DETR must **never block** the main YOLO pipeline. + +```python +import asyncio +import torch +from rfdetr import RFDETRMedium +from queue import PriorityQueue +from dataclasses import dataclass, field +from typing import Any + +@dataclass(order=True) +class Tier2Request: + priority: int # lower = higher priority + timestamp: float = field(compare=False) + camera_id: str = field(compare=False) + frame: Any = field(compare=False) # np.ndarray + trigger_type: str = field(compare=False) # "stealing", "tawuran", "uncertain" + track_ids: list = field(compare=False, default_factory=list) + tier1_confidence: float = field(compare=False, default=0.0) + +class Tier2Processor: + """Runs on GPU 1. Processes RF-DETR / SlowFast requests from a priority queue.""" + + PRIORITY_MAP = {"fighting_uncertain": 1, "stealing": 2, "tawuran": 3, "audit": 10} + + def __init__(self, device: str = "cuda:1", max_queue: int = 100): + self.device = device + self.queue = PriorityQueue(maxsize=max_queue) + + # Load models on GPU 1 + self.rfdetr = RFDETRMedium() # fine-tuned weights + self.slowfast = SlowFastAnalyzer(device=device) + + # Lazy-load YOLOE only when needed + self._yoloe = None + + @property + def yoloe(self): + if self._yoloe is None: + from ultralytics import YOLO + self._yoloe = YOLO("yoloe-26s.pt").to(self.device) + return self._yoloe + + def submit(self, request: Tier2Request) -> bool: + """Non-blocking submit. Returns False if queue full (drops request).""" + try: + self.queue.put_nowait(request) + return True + except: + return False # backpressure: drop lowest-priority requests + + async def process_loop(self): + """Main Tier 2 processing loop. Runs in background.""" + while True: + if self.queue.empty(): + await asyncio.sleep(0.01) + continue + + request = self.queue.get() + result = await self._process_request(request) + + if result and result["confirmed"]: + await self._dispatch_alert(request, result) + + async def _process_request(self, req: Tier2Request) -> dict: + if req.trigger_type == "stealing": + return await self._confirm_stealing(req) + elif req.trigger_type == "fighting_uncertain": + return await self._confirm_fighting(req) + elif req.trigger_type == "tawuran": + return await self._confirm_tawuran(req) + + async def _confirm_stealing(self, req: Tier2Request) -> dict: + """Multi-model stealing confirmation.""" + # Step 1: RF-DETR high-accuracy detection + rfdetr_detections = self.rfdetr.predict(req.frame, threshold=0.5) + + # Step 2: YOLOE zero-shot screening + self.yoloe.set_classes([ + "person concealing object", + "hand reaching into bag", + "person hiding item under clothing", + "shoplifting", + ]) + yoloe_results = self.yoloe(req.frame) + + # Step 3: SlowFast temporal analysis (if clip available) + clip = clip_buffer_manager.get_clip(req.track_ids[0], num_frames=8) + slowfast_result = None + if clip is not None: + slowfast_result = self.slowfast.analyze_clip(clip, "stealing") + + # Aggregate: require at least 2/3 models to agree + votes = 0 + if len(rfdetr_detections) > 0: + votes += 1 + if len(yoloe_results[0].boxes) > 0: + votes += 1 + if slowfast_result and slowfast_result["confidence"] > 0.6: + votes += 1 + + return { + "confirmed": votes >= 2, + "votes": votes, + "rfdetr_count": len(rfdetr_detections), + "yoloe_count": len(yoloe_results[0].boxes), + "slowfast": slowfast_result, + } + + async def _confirm_tawuran(self, req: Tier2Request) -> dict: + """RF-DETR for accurate person count in dense crowd.""" + detections = self.rfdetr.predict(req.frame, threshold=0.3) + person_dets = [d for d in detections if d.class_id == 0] + + return { + "confirmed": len(person_dets) >= 15, + "accurate_count": len(person_dets), + "tier1_estimate": req.tier1_confidence, + } + + async def _dispatch_alert(self, req: Tier2Request, result: dict): + """Send confirmed alert to alert system.""" + alert = { + "type": req.trigger_type, + "camera_id": req.camera_id, + "timestamp": req.timestamp, + "result": result, + "frame_evidence": req.frame, # or save to GridFS + } + # → WebSocket push, API call, database insert, etc. + await alert_dispatcher.send(alert) +``` + +### 11.3 RF-DETR Fine-Tuning for Stealing Detection + +```python +from rfdetr import RFDETRMedium +from rfdetr.config import TrainConfig + +def fine_tune_stealing_detector(): + """Fine-tune RF-DETR-M on custom stealing dataset.""" + + model = RFDETRMedium() + + config = TrainConfig( + dataset_dir="./data/stealing_dataset", # COCO format + num_classes=4, # normal_interaction, concealing, grabbing, fleeing + epochs=50, + batch_size=8, + lr=1e-4, # lower LR for fine-tuning + grad_accum_steps=4, # effective batch 32 + resolution=576, # RF-DETR-M native resolution + augmentation="heavy", + ) + + # RF-DETR proven to generalize well on small datasets (RF100-VL benchmark) + # Minimum recommended: 500 images per class + # Optimal: 2,000+ images per class + + model.train(config) + model.save("rfdetr_m_stealing_v1.pt") +``` + +### 11.4 Dataset Requirements for Stealing Fine-Tuning + +| Class | Description | Min Images | Sources | +|-------|-------------|-----------|---------| +| `normal_interaction` | Person normally handling items | 1,000 | Existing CCTV footage | +| `concealing` | Person hiding item in clothing/bag | 500 | Staged + real footage | +| `grabbing` | Hand reaching for unattended item | 500 | Staged + retail CCTV | +| `fleeing` | Person rapidly leaving after taking | 300 | Staged scenarios | + +--- + +## 12. Hardware Specifications + +### 12.0 Design Principles + +All specifications account for: +- **NVDEC zero-copy decode** — V100's built-in NVDEC engine decodes H.264/H.265 on dedicated fixed-function hardware, consuming 0% CUDA/Tensor cores and ~0.3 GB VRAM per GPU. Frames stay in GPU memory (no CPU copy). +- **NVDEC session limit** — Each V100 has 1 NVDEC engine supporting ~24–32 concurrent decode sessions. Cameras beyond this limit fall back to CPU software decode at ~0.05 core/stream (lightweight at 1 FPS). +- **1 FPS capture rate** — At 1 frame/second, CPU decode overhead is minimal even in software fallback. +- **Async bulk MongoDB writes** — Using `motor` (async driver) with `insert_many` batches of 100–500 docs, `ordered=False`. + +### 12.1 GPU Internal Resource Allocation + +NVDEC and CUDA/Tensor cores are **physically separate silicon** inside the V100. They run in parallel with zero interference: + +``` +┌─────────────────────────────────────────────────────────────┐ +│ NVIDIA V100 32 GB │ +│ │ +│ ┌──────────────────────────────┐ ┌─────────────────────┐ │ +│ │ CUDA + Tensor Cores │ │ NVDEC Engine │ │ +│ │ (5,120 CUDA + 640 Tensor) │ │ (fixed-function) │ │ +│ │ │ │ │ │ +│ │ • YOLOv26-Pose inference │ │ • H.264 decode │ │ +│ │ • RF-DETR inference │ │ • H.265 decode │ │ +│ │ • SlowFast inference │ │ • 24–32 sessions │ │ +│ │ • Preprocessing (resize) │ │ • ~0.3 GB VRAM │ │ +│ │ │ │ │ │ +│ │ 100% available for AI │ │ Runs in PARALLEL │ │ +│ │ workloads regardless of │ │ with CUDA cores │ │ +│ │ NVDEC activity │ │ at zero cost │ │ +│ └──────────────────────────────┘ └─────────────────────┘ │ +│ │ +│ ┌──────────────────────────────────────────────────────┐ │ +│ │ HBM2 VRAM (32 GB) — shared, only contention point │ │ +│ │ NVDEC uses ~0.3 GB → 31.7 GB free for inference │ │ +│ └──────────────────────────────────────────────────────┘ │ +└─────────────────────────────────────────────────────────────┘ +``` + +**Impact:** NVDEC decode is effectively "free" — no separate decode GPU needed, no CPU core savings to calculate. The V100 handles both decode and inference simultaneously. + +### 12.2 CPU Load Breakdown (per camera at 1 FPS) + +| Task | Without NVDEC (CPU decode) | With NVDEC (GPU decode) | Notes | +|------|---------------------------|------------------------|-------| +| RTSP network I/O | 0.01 core | 0.01 core | Same — network stack is always CPU | +| H.264 frame decode | 0.30–0.50 core | **0.00 core** | Offloaded to NVDEC | +| Frame copy CPU→GPU | 0.02 core | **0.00 core** | Zero-copy: frame stays on GPU | +| ByteTrack tracking | 0.02 core | 0.02 core | CPU-side, lightweight | +| Behavior analysis | 0.01 core | 0.01 core | NumPy math on keypoint floats | +| Ring buffer management | 0.01 core | 0.01 core | deque operations | +| JPEG encode (crops) | 0.03 core | 0.03 core | CPU (or NVJPEG on GPU) | +| MongoDB async write | 0.01 core | 0.01 core | Amortized via bulk writes | +| **Total per camera** | **0.41–0.61 core** | **0.09 core** | **~78–85% reduction** | + +| Camera Count | CPU Cores (no NVDEC) | CPU Cores (with NVDEC) | Savings | +|-------------|---------------------|----------------------|---------| +| 100 | 41–61 cores | **9 cores** | 78–85% | +| 300 | 123–183 cores | **27 cores** | 78–85% | +| 500 | 205–305 cores | **45 cores** | 78–85% | +| 800 | 328–488 cores | **72 cores** | 78–85% | +| 1,000 | 410–610 cores | **90 cores** | 78–85% | + +> **Note:** Cameras 1–24 use GPU 0 NVDEC, cameras 25–48 use GPU 1 NVDEC (48 total hardware-decoded). Cameras 49+ fall back to CPU software decode at 1 FPS, which is still very light (~0.05 core/stream vs 0.3–0.5 for persistent decode). The 0.09 core/camera figure above is the blended average. + +--- + +### 12.3 Spec A — With Behavior Detection (Fighting + Stealing + Tawuran) + +#### Processing Pipeline + +``` +Camera RTSP → NVDEC (GPU decode) → GPU Memory → YOLOv26-Pose (Tensor Cores) + │ + bbox + keypoints (tiny, few KB) + │ + copy to CPU + │ + ┌───────────────┼───────────────┐ + ▼ ▼ ▼ + ByteTrack Fighting Det. Tawuran Det. + (tracking) (keypoint vel.) (crowd density) + │ │ │ + └───────┬───────┘ │ + ▼ ▼ + Suspicious? ──────────→ Tier 2 (GPU 1) + RF-DETR / SlowFast + │ + Confirmed? + │ + ▼ ▼ + Alert MongoDB +``` + +#### GPU Allocation + +| | GPU 0 (V100 #1) — Tier 1 | GPU 1 (V100 #2) — Tier 2 | +|--|--------------------------|--------------------------| +| **NVDEC** | Cameras 1–24 (H.264 decode) | Cameras 25–48 (H.264 decode) | +| **Inference** | YOLOv26-S-Pose (always-on) | RF-DETR-M + X3D-S + YOLOE-26 (on-demand) | +| **Role** | Primary detection + pose | Confirmation + temporal analysis | + +#### VRAM Budget + +| Component | GPU 0 | GPU 1 | +|-----------|-------|-------| +| NVDEC decode buffers (24 streams) | 0.3 GB | 0.3 GB | +| YOLOv26-S-Pose weights (TensorRT FP16) | 0.8 GB | — | +| RF-DETR-M weights | — | 2.5 GB | +| X3D-S weights | — | 0.8 GB | +| YOLOE-26-S weights | — | 1.0 GB | +| CUDA context + allocator | 0.8 GB | 0.8 GB | +| Inference batch buffers (batch=16) | 0.5 GB | 0.6 GB | +| Preprocessing buffers | 0.2 GB | 0.2 GB | +| **Total used** | **2.6 GB** | **6.2 GB** | +| **Free** | **29.4 GB** | **25.8 GB** | + +#### Full Bill of Materials + +| Component | Specification | Purpose | Est. Cost (USD) | +|-----------|--------------|---------|-----------------| +| **CPU** | AMD EPYC 9374F (32C/64T, 3.85 GHz) | RTSP I/O, ByteTrack, behavior analysis, overflow decode. 72 cores needed for 800 cams → 32C with HT (64T) covers it with headroom. | $2,500–3,000 | +| **Motherboard** | Supermicro H13SSL-N or equiv. (SP5 socket) | Single-socket EPYC, 2× PCIe Gen5 x16 for dual V100 | $500–700 | +| **RAM** | 128 GB DDR5-4800 ECC RDIMM (4×32 GB) | Ring buffers (~3 frames × 1080p × 800 cams ≈ 15 GB), clip buffers (~240 MB per 100 tracked persons), batch queues, OS overhead | $400–500 | +| **GPU 0** | NVIDIA V100 32 GB (already owned) | Tier 1: NVDEC decode (24 streams) + YOLOv26-S-Pose inference | — | +| **GPU 1** | NVIDIA V100 32 GB (already owned) | Tier 2: NVDEC decode (24 streams) + RF-DETR + X3D-S + YOLOE-26 | — | +| **NIC** | Mellanox ConnectX-5 25 GbE (dual-port) | 800 cameras × ~1 Mbps = 800 Mbps sustained. 25 GbE provides 3× headroom. Dual-port for redundancy. | $150–200 | +| **Network Switch** | 10/25 GbE managed switch (if cameras are IP-based) | Aggregate camera traffic. May need multiple switches for 800 cameras. | $500–2,000 (varies) | +| **Boot SSD** | 512 GB NVMe (PCIe Gen4) | OS (Ubuntu 22.04/24.04), CUDA toolkit, Python env, model weights (~5 GB total) | $50–70 | +| **Data SSD** | 2 TB NVMe (Samsung 990 Pro or Micron 7450) | MongoDB data directory (WiredTiger engine), alert clip buffer (10-sec clips per alert) | $150–200 | +| **Archive HDD** | 2× 8 TB HDD (RAID 1 mirror) | Long-term evidence storage. At 100 alerts/day × 10-sec clip × 5 MB = 500 MB/day → 8 TB lasts ~16,000 days. | $240–300 | +| **PSU** | 1,200W 80+ Platinum (redundant if rackmount) | 2× V100 (300W each) + EPYC 9374F (320W) + drives + fans = ~1,000W peak | $200–300 | +| **Chassis** | 4U rackmount server (e.g., Supermicro 4124GS-TNR) | Dual GPU cooling, sufficient airflow for V100 TDP | $300–500 | +| **UPS** | 1,500VA online UPS | Protect against power loss during inference + MongoDB writes | $300–400 | +| | | | | +| **Total (excl. V100s)** | | | **$5,300–8,200** | +| **Total (incl. V100s at ~$3,000 each used)** | | | **$11,300–14,200** | + +#### MongoDB Sizing + +| Cameras | Detections/sec (avg 5 persons/frame) | Crops/sec | Write Throughput | Daily Storage | MongoDB Config | +|---------|--------------------------------------|-----------|-----------------|---------------|---------------| +| 100 | 500 | 500 | ~5 MB/s | ~430 GB | Single node, WiredTiger, NVMe | +| 300 | 1,500 | 1,500 | ~15 MB/s | ~1.3 TB | Single node, NVMe, compression | +| 500 | 2,500 | 2,500 | ~25 MB/s | ~2.2 TB | Replica set (3 nodes) | +| 800 | 4,000 | 4,000 | ~40 MB/s | ~3.5 TB | Sharded cluster (2 shards) | + +> **Storage note:** At 800 cameras, you generate ~3.5 TB/day of crop images. Implement a **TTL index** on MongoDB to auto-delete crops older than 7–30 days, or archive to cold storage (HDD/S3). + +#### Power & Cooling + +| Component | TDP / Draw | Notes | +|-----------|-----------|-------| +| 2× V100 32 GB | 600W (300W each) | Requires active cooling, 250W sustained typical | +| EPYC 9374F | 320W | High-clock SKU, sustained ~250W under load | +| NVMe SSDs (×2) | 15W | Negligible | +| HDDs (×2) | 20W | Negligible | +| NIC + fans + misc | 45W | — | +| **Total system** | **~1,000W sustained** | Peak ~1,100W | +| **Cooling** | ~3,400 BTU/hr | Standard 4U rackmount cooling sufficient | + +--- + +### 12.4 Spec B — Without Behavior Detection (Detection + Crop + MongoDB Only) + +This configuration runs YOLOv26 for object detection and bounding box extraction only. No pose estimation, no tracking, no fighting/stealing/tawuran analysis. No Tier 2 models. + +#### Processing Pipeline (Simplified) + +``` +Camera RTSP → NVDEC (GPU decode) → GPU Memory → YOLOv26-S (Tensor Cores) + │ + bbox + class + confidence + │ + crop regions from frame + │ + JPEG encode → MongoDB +``` + +#### GPU Allocation + +| | GPU 0 (V100 #1) | GPU 1 (V100 #2) | +|--|-----------------|-----------------| +| **NVDEC** | Cameras 1–24 | Cameras 25–48 | +| **Inference** | YOLOv26-S (detection only, no pose) | YOLOv26-S (overflow / load balance) | +| **Role** | Primary detection | Overflow detection (or idle) | + +> **Option:** Run both V100s for Tier 1 detection to double throughput, or leave GPU 1 idle to save power (~300W). + +#### VRAM Budget (Single GPU Mode) + +| Component | GPU 0 | GPU 1 (idle or overflow) | +|-----------|-------|--------------------------| +| NVDEC decode buffers | 0.3 GB | 0.3 GB (if used) | +| YOLOv26-S weights (TensorRT FP16) | 0.6 GB | 0.6 GB (if used) | +| CUDA context | 0.8 GB | 0.8 GB (if used) | +| Batch buffers (batch=16) | 0.5 GB | 0.5 GB (if used) | +| **Total used** | **2.2 GB** | **2.2 GB** | +| **Free** | **29.8 GB** | **29.8 GB** | + +#### Full Bill of Materials + +| Component | Specification | Purpose | Est. Cost (USD) | +|-----------|--------------|---------|-----------------| +| **CPU** | AMD EPYC 9274F (24C/48T, 4.05 GHz) or Intel Xeon w5-2465X (16C/32T) | RTSP I/O, overflow decode, JPEG encode, MongoDB writes. No tracking or behavior analysis. | $1,500–2,000 | +| **Motherboard** | Supermicro H13SSL-N or equiv. | Single-socket, 1–2× PCIe x16 | $500–700 | +| **RAM** | 64 GB DDR5-4800 ECC RDIMM (2×32 GB) | Ring buffers only (no clip buffers, no tracking history). 800 cams × 3 frames × 6 MB ≈ 14 GB. | $200–250 | +| **GPU** | 1× NVIDIA V100 32 GB (already owned) | NVDEC decode + YOLOv26-S inference. Second V100 optional for 2× throughput. | — | +| **NIC** | Intel X710-DA2 10 GbE (dual-port) | 500 cameras × ~1 Mbps = 500 Mbps. 10 GbE sufficient. | $80–120 | +| **Boot SSD** | 512 GB NVMe | OS + model weights | $50–70 | +| **Data SSD** | 2 TB NVMe | MongoDB data directory | $150–200 | +| **Archive HDD** | 1× 8 TB HDD | Long-term crop storage (optional) | $120–150 | +| **PSU** | 850W 80+ Gold | 1× V100 (300W) + EPYC 9274F (250W) + overhead | $120–150 | +| **Chassis** | 4U rackmount or tower workstation | Single GPU cooling | $200–400 | +| **UPS** | 1,000VA online UPS | Power protection | $200–250 | +| | | | | +| **Total (excl. V100)** | | | **$3,100–5,100** | +| **Total (incl. 1× V100 at ~$3,000 used)** | | | **$6,100–8,100** | + +--- + +### 12.5 Comprehensive Comparison + +| Specification | **With Behavior Detection** | **Without Behavior Detection** | +|--------------|---------------------------|-------------------------------| +| | *Fighting + Stealing + Tawuran* | *Detection + Crop + Save only* | +| | | | +| **CPU** | EPYC 9374F (32C/64T) | EPYC 9274F (24C/48T) | +| **CPU Cores needed** | ~72 (800 cams) | ~45 (800 cams) | +| **RAM** | 128 GB DDR5 ECC | 64 GB DDR5 ECC | +| **GPUs active** | 2× V100 32 GB | 1× V100 32 GB | +| **GPU 0 role** | Tier 1: NVDEC + YOLOv26-Pose | NVDEC + YOLOv26-S | +| **GPU 1 role** | Tier 2: NVDEC + RF-DETR + X3D-S + YOLOE | Idle (or overflow) | +| **GPU VRAM used** | 2.6 GB + 6.2 GB = 8.8 GB | 2.2 GB (single GPU) | +| **GPU VRAM free** | 29.4 + 25.8 = 55.2 GB | 29.8 GB | +| **NIC** | 25 GbE | 10 GbE | +| **Data SSD** | 2 TB NVMe | 2 TB NVMe | +| **PSU** | 1,200W Platinum | 850W Gold | +| **System power** | ~1,000W sustained | ~600W sustained | +| **Cooling** | ~3,400 BTU/hr | ~2,050 BTU/hr | +| | | | +| **Max cameras (1 FPS, batch=16)** | **800–1,120** | **1,120–2,240** | +| **Bottleneck** | GPU 1 (Tier 2 at high trigger rates) | GPU 0 (Tier 1 compute) | +| | | | +| **Models loaded** | YOLOv26-Pose + RF-DETR + X3D-S + YOLOE | YOLOv26-S only | +| **Tracking** | ✅ ByteTrack (person identity across frames) | ❌ No tracking | +| **Fighting detection** | ✅ Keypoint velocity + proximity | ❌ | +| **Stealing detection** | ✅ Multi-model confirmation | ❌ | +| **Tawuran detection** | ✅ Crowd density + convergence | ❌ | +| **Temporal analysis** | ✅ X3D-S / SlowFast on video clips | ❌ | +| **Zero-shot detection** | ✅ YOLOE-26 text prompts | ❌ | +| | | | +| **Cost (excl. V100s)** | **$5,300–8,200** | **$3,100–5,100** | +| **Cost (incl. V100s)** | **$11,300–14,200** | **$6,100–8,100** | +| **Cost difference** | — | **Saves $5,200–6,100** | + +### 12.6 Scaling Guide — When to Upgrade + +| Camera Count | Config Needed | CPU | RAM | GPUs | Notes | +|-------------|--------------|-----|-----|------|-------| +| **1–100** | Minimal | 16C (Xeon w5-2445) | 32 GB | 1× V100 | Single GPU handles everything | +| **100–300** | Standard | 24C (EPYC 9274F) | 64 GB | 1× V100 | Add behavior detection with same GPU | +| **300–600** | Recommended | 32C (EPYC 9374F) | 128 GB | 2× V100 | Full hybrid pipeline | +| **600–1,000** | Full | 32C (EPYC 9374F) | 128 GB | 2× V100 | Optimize batch size, reduce trigger rate | +| **1,000–2,000** | Scale-out | 64C (EPYC 9554) | 256 GB | 4× V100 or 2× A100 | Second server or larger GPU | +| **2,000+** | Distributed | Multi-server | 256+ GB each | GPU per server | Kubernetes / distributed inference | + +### 12.7 Network Infrastructure + +| Cameras | Bandwidth (1 FPS, H.264) | NIC Required | Switch Requirement | +|---------|-------------------------|-------------|-------------------| +| 100 | ~100 Mbps | 1 GbE (sufficient) | Standard managed switch | +| 300 | ~300 Mbps | 10 GbE | 10 GbE aggregation switch | +| 500 | ~500 Mbps | 10 GbE | 10 GbE with LACP bonding | +| 800 | ~800 Mbps | 25 GbE | 25 GbE aggregation | +| 1,000+ | ~1 Gbps+ | 25 GbE (dual-port) | 25 GbE with redundancy | + +> **RTSP bandwidth note:** Each 1080p H.264 stream at 1 FPS uses ~0.5–1.5 Mbps depending on scene complexity and I-frame interval. The estimates above use ~1 Mbps average. + +### 12.8 MongoDB Storage Planning + +| Cameras | Crops/day (5 persons/frame avg) | Storage/day (50 KB avg crop) | 30-day retention | Recommended | +|---------|-------------------------------|-----------------------------|--------------------|-------------| +| 100 | 432,000 | 21 GB | 630 GB | 1 TB NVMe | +| 300 | 1,296,000 | 63 GB | 1.9 TB | 2 TB NVMe | +| 500 | 2,160,000 | 105 GB | 3.2 TB | 4 TB NVMe | +| 800 | 3,456,000 | 168 GB | 5.0 TB | 2× 4 TB NVMe (RAID 0) | + +> **Recommendation:** Set a MongoDB **TTL index** to auto-expire documents after 7–30 days. Archive flagged alerts (with behavior detections) to cold storage before expiry. + +## 13. V100 32 GB Deployment Estimate (2× GPUs, 1 FPS per Camera) + +### 13.1 Baseline Latency on V100 + +V100 delivers ~1.5–2× the FP16 throughput of T4 (125 vs 65 TFLOPS). All estimates use **TensorRT FP16**. + +| Model | T4 Benchmark | V100 Estimated | Per-inference VRAM | +|-------|-------------|----------------|-------------------| +| YOLOv26-S-Pose | 2.6 ms | **~1.5 ms** | ~0.8 GB (weights) | +| YOLOv26-M-Pose | 4.7 ms | **~2.8 ms** | ~1.5 GB | +| RF-DETR-M | 4.4 ms | **~2.7 ms** | ~2.5 GB | +| RF-DETR-L | 6.8 ms | **~4.2 ms** | ~3.2 GB | +| SlowFast R50 | 80–120 ms | **~50–80 ms** | ~1.5 GB | +| X3D-S | 40–60 ms | **~25–40 ms** | ~0.8 GB | +| YOLOE-26-S | 2.6 ms | **~1.5 ms** | ~1.0 GB | + +### 13.2 Scenario (a): YOLOv26-Pose Only (Tier 1 Always-On) + +**GPU 0 only.** GPU 1 idle. Detects: fighting, tawuran (no stealing confirmation). + +| Model | Latency/frame | GPU 0 Budget: 1000 ms | GPU utilization | +|-------|--------------|----------------------|-----------------| +| YOLOv26-S-Pose | 1.5 ms | 1000 / 1.5 = **666 frames/sec** | — | + +**VRAM Allocation (GPU 0):** + +| Component | VRAM | +|-----------|------| +| YOLOv26-S-Pose weights | 0.8 GB | +| CUDA context | 0.8 GB | +| Batch buffer (batch=32, 640×640×3×FP16) | 0.8 GB | +| **Total** | **2.4 GB** | +| **Free** | **29.6 GB** | + +| Batch Size | Throughput (frames/sec) | Practical (70% util) | **Max Cameras (1 FPS)** | +|-----------|------------------------|---------------------|------------------------| +| 1 | 666 | 466 | **466** | +| 8 | ~1,200 | 840 | **840** | +| 16 | ~1,600 | 1,120 | **1,120** | +| 32 | ~1,900 | 1,330 | **1,330** | + +> **With 2× V100 both running YOLOv26-Pose:** double all numbers → **2,240–2,660 cameras** at batch=16–32. + +### 13.3 Scenario (b): YOLOv26-Pose + RF-DETR Hybrid (10–20% Trigger) + +**GPU 0:** YOLOv26-S-Pose (always-on) +**GPU 1:** RF-DETR-M (on-demand, triggered by 10–20% of frames) + +**VRAM Allocation:** + +| | GPU 0 | GPU 1 | +|--|-------|-------| +| YOLOv26-S-Pose | 0.8 GB | — | +| RF-DETR-M | — | 2.5 GB | +| YOLOE-26-S (lazy) | — | 1.0 GB | +| CUDA context | 0.8 GB | 0.8 GB | +| Batch buffers | 0.8 GB | 0.6 GB | +| **Total** | **2.4 GB** | **4.9 GB** | +| **Free** | **29.6 GB** | **27.1 GB** | + +**Throughput Calculation:** + +GPU 0 bottleneck (Tier 1 — determines max cameras): + +| Batch | YOLOv26-S-Pose throughput | 70% util | Max cameras | +|-------|--------------------------|----------|-------------| +| 8 | 1,200 fps | 840 | **840** | +| 16 | 1,600 fps | 1,120 | **1,120** | +| 32 | 1,900 fps | 1,330 | **1,330** | + +GPU 1 bottleneck (Tier 2 — must keep up with trigger rate): + +| Cameras | Trigger Rate | Tier 2 frames/sec | RF-DETR-M capacity (370 fps) | Headroom | +|---------|-------------|-------------------|------------------------------|----------| +| 500 | 10% | 50 fps | 370 fps | ✅ 7.4× | +| 500 | 20% | 100 fps | 370 fps | ✅ 3.7× | +| 800 | 10% | 80 fps | 370 fps | ✅ 4.6× | +| 800 | 20% | 160 fps | 370 fps | ✅ 2.3× | +| 1,000 | 20% | 200 fps | 370 fps | ✅ 1.8× | +| 1,330 | 20% | 266 fps | 370 fps | ✅ 1.4× | + +> **Tier 2 is never the bottleneck** at these camera counts and trigger rates. GPU 1 has massive headroom. + +**Final capacity for Scenario (b):** + +| Batch | **Max Cameras (1 FPS)** | Bottleneck | +|-------|------------------------|------------| +| 8 | **840** | GPU 0 (Tier 1) | +| 16 | **1,120** | GPU 0 (Tier 1) | +| 32 | **1,330** | GPU 0 (Tier 1) | + +### 13.4 Scenario (c): Full Pipeline (YOLOv26-Pose + RF-DETR + SlowFast) + +**GPU 0:** YOLOv26-S-Pose (always-on) +**GPU 1:** RF-DETR-M + SlowFast R50 + YOLOE-26-S (on-demand, time-shared) + +**VRAM Allocation:** + +| | GPU 0 | GPU 1 | +|--|-------|-------| +| YOLOv26-S-Pose | 0.8 GB | — | +| RF-DETR-M | — | 2.5 GB | +| SlowFast R50 | — | 1.5 GB | +| YOLOE-26-S | — | 1.0 GB | +| CUDA context | 0.8 GB | 0.8 GB | +| Batch buffers | 0.8 GB | 1.0 GB | +| **Total** | **2.4 GB** | **6.8 GB** | +| **Free** | **29.6 GB** | **25.2 GB** | + +> VRAM is comfortable even with all 3 Tier 2 models loaded simultaneously. + +**Throughput — GPU 1 becomes the constraint when SlowFast is active:** + +SlowFast is expensive (~50–80 ms per clip). The question is how often it triggers. + +| Trigger Source | Rate | SlowFast calls/sec | Time consumed | +|---------------|------|-------------------|---------------| +| Fighting uncertain (need temporal confirm) | 2% of cameras | At 800 cams: 16/sec | 16 × 60ms = **960 ms** ⚠️ | +| Stealing suspicious | 5% of cameras | At 800 cams: 40/sec | 40 × 60ms = **2,400 ms** ❌ | + +> **SlowFast at 5% trigger rate on 800 cameras would exceed GPU 1's 1-second budget.** Solutions: + +| Solution | Effect | +|----------|--------| +| **Reduce SlowFast trigger rate** to <2% | 800 × 2% = 16 calls/sec × 60ms = 960 ms ✅ fits | +| **Use X3D-S instead of SlowFast** | 25–40 ms vs 50–80 ms → 2× more capacity | +| **Batch SlowFast clips** | Batch=4 clips → ~120 ms total vs 4×60ms = 240 ms | +| **Time-share with RF-DETR** | RF-DETR handles most triggers; SlowFast only for uncertain cases | + +**Practical capacity with mixed workload on GPU 1:** + +Assume: RF-DETR handles 15% of triggers, SlowFast handles 2%, YOLOE handles 3%. + +| Cameras | RF-DETR (15%) | SlowFast (2%) | YOLOE (3%) | Total GPU 1 time/sec | Feasible? | +|---------|-------------|--------------|------------|---------------------|-----------| +| 400 | 60 × 2.7ms = 162ms | 8 × 60ms = 480ms | 12 × 1.5ms = 18ms | **660 ms** | ✅ | +| 600 | 90 × 2.7ms = 243ms | 12 × 60ms = 720ms | 18 × 1.5ms = 27ms | **990 ms** | ✅ Tight | +| 800 | 120 × 2.7ms = 324ms | 16 × 60ms = 960ms | 24 × 1.5ms = 36ms | **1,320 ms** | ❌ Over budget | + +**With X3D-S instead of SlowFast (25ms instead of 60ms):** + +| Cameras | RF-DETR (15%) | X3D-S (2%) | YOLOE (3%) | Total GPU 1 time/sec | Feasible? | +|---------|-------------|-----------|------------|---------------------|-----------| +| 600 | 243ms | 12 × 25ms = 300ms | 27ms | **570 ms** | ✅ | +| 800 | 324ms | 16 × 25ms = 400ms | 36ms | **760 ms** | ✅ | +| 1,000 | 405ms | 20 × 25ms = 500ms | 45ms | **950 ms** | ✅ Tight | + +**Final capacity for Scenario (c):** + +| Config | Batch 16 | **Max Cameras** | Bottleneck | +|--------|---------|----------------|------------| +| SlowFast R50, mixed triggers | 16 | **~600** | GPU 1 (SlowFast) | +| X3D-S (recommended), mixed triggers | 16 | **~800–1,000** | GPU 1 (X3D-S) | +| X3D-S, conservative triggers (1%) | 16 | **~1,120** | GPU 0 (Tier 1) | + +### 13.5 Summary — Camera Capacity (2× V100 32 GB, 1 FPS) + +| Scenario | Batch=8 | Batch=16 | Batch=32 | Bottleneck | +|----------|---------|---------|---------|------------| +| **(a) YOLOv26-Pose only** | 840 | 1,120 | 1,330 | GPU 0 compute | +| **(b) + RF-DETR hybrid (20% trigger)** | 840 | 1,120 | 1,330 | GPU 0 compute | +| **(c) Full pipeline + SlowFast** | ~450 | ~600 | ~700 | GPU 1 (SlowFast) | +| **(c) Full pipeline + X3D-S** | ~650 | ~800–1,000 | ~1,100 | GPU 1 (X3D-S) | + +> **Key takeaway:** At 1 FPS, the V100 GPUs are not the bottleneck for scenarios (a) and (b). The real constraints are: +> 1. **CPU decode** (need 64+ cores for 800+ RTSP streams) +> 2. **Network bandwidth** (800 cameras × 1 Mbps = 800 Mbps → need 10–25 GbE) +> 3. **SlowFast latency** in scenario (c) — mitigated by using X3D-S instead +> 4. **MongoDB write throughput** at 2,000+ docs/sec — use async bulk writes + +--- + +## 14. Pipeline Scenario Analysis — YOLO vs RF-DETR with Behavior Detection + +### 14.1 Candidate Pipeline Scenarios + +Two base pipeline architectures are evaluated for integration with behavior detection. Both capture at **1 FPS per camera** and detect **multiple object classes** — not just persons. + +#### Target Detection Classes + +| Category | Objects | Notes | +|----------|---------|-------| +| **Person** | People (pedestrians, staff, intruders) | Primary target for behavior detection | +| **Vehicle** | Car, truck, bus, motorcycle, bicycle | Parking, traffic monitoring, suspicious vehicles | +| **Bag/Luggage** | Backpack, handbag, suitcase | Unattended bag detection, theft evidence | +| **Weapon** | Knife, gun (requires fine-tuning for many weapon types) | Critical security objects | +| **Animal** | Dog, cat, bird, horse, etc. | Stray animal detection, restricted area intrusion | +| **Other** | Umbrella, cell phone, laptop, etc. | Context-dependent objects | + +> **COCO pretrained models** (both YOLO and RF-DETR) detect **80 object classes** out of the box. Custom classes (specific weapon types, uniform types) require fine-tuning. + +#### Scenario 1 — YOLOv26-Pose Pipeline + +``` +Thread per CCTV stream (capture frame at 1 FPS) + → Save frame in shared memory buffer + → Pool/batch frames for YOLO detection pipeline + → Get bounding boxes (object detection: person, vehicle, bag, weapon, animal, etc.) + → Crop bounding box regions and save as images + → Pool/batch cropped images for async save to MongoDB +``` + +**What YOLO Pose provides per frame:** +- Bounding boxes for **all 80 COCO classes** (person, car, truck, dog, backpack, knife, etc.) +- Class labels + confidence scores for every detected object +- **17 body keypoints** (nose, eyes, shoulders, elbows, wrists, hips, knees, ankles) **for person class only** +- Keypoint confidence scores per keypoint + +> **Key point:** YOLOv26-Pose is NOT person-only. It detects all object classes like standard YOLO, but **additionally** outputs body keypoints for every detected person. The pose head adds negligible latency (~0.1 ms). + +#### Scenario 2 — RF-DETR Pipeline + +``` +Thread per CCTV stream (capture frame at 1 FPS) + → Save frame in shared memory buffer + → Pool/batch frames for RF-DETR detection pipeline + → Get bounding boxes (object detection: person, vehicle, bag, weapon, animal, etc.) + → Crop bounding box regions and save as images + → Pool/batch cropped images for async save to MongoDB +``` + +**What RF-DETR provides per frame:** +- Bounding boxes for **all 80 COCO classes** (same classes as YOLO) +- Class labels + confidence scores +- **Superior small object detection** (bags, weapons, distant animals are better detected) +- **No keypoints, no pose data** for any class + +### 14.2 Key Difference — Output Capabilities + +Both models detect the **same object classes**. The difference is what *extra* information they provide: + +| Output | YOLOv26-Pose | RF-DETR | +|--------|-------------|---------| +| **Multi-class detection** (person, vehicle, bag, weapon, animal) | ✅ 80 COCO classes | ✅ 80 COCO classes | +| Bounding boxes + confidence | ✅ | ✅ | +| **17 body keypoints** (person class only) | ✅ | ❌ | +| Keypoint confidence scores | ✅ | ❌ | +| **Small object detection** (< 32×32 px: distant bags, weapons, animals) | Good | **Superior** (+7 AP) | +| Person detection (medium-large) | >90% AP | >93% AP | +| Vehicle detection | >85% AP | >88% AP | +| Bag/weapon detection (typically small) | Moderate | **Better** (deformable attention) | +| Inference latency (V100 TensorRT FP16) | **~1.5 ms** | ~2.7 ms | + +#### Impact on Behavior Detection + +| Behavior | What's Needed | YOLOv26-Pose | RF-DETR | +|----------|--------------|-------------|---------| +| **Fighting** | Person keypoints (arm velocity, proximity) | ✅ Built-in | ❌ Needs second model | +| **Tawuran** | Person keypoints + crowd count | ✅ Built-in | ⚠️ Count only (no pose) | +| **Stealing** | Hand-to-object proximity (keypoints + bag/object bbox) | ✅ Hand keypoints + bag bbox in same pass | ⚠️ Bag bbox only, no hand position | +| **Unattended bag** | Bag bbox without nearby person bbox | ✅ | ✅ (both detect bags) | +| **Suspicious vehicle** | Vehicle bbox + loitering time | ✅ | ✅ (both detect vehicles) | +| **Animal intrusion** | Animal bbox in restricted zone | ✅ | ✅ (both detect animals) | + +**Critical implication:** For behaviors requiring **only bounding boxes** (unattended bag, vehicle loitering, animal intrusion), both models work equally well. For behaviors requiring **body keypoints** (fighting, tawuran, stealing), RF-DETR cannot do it alone — you must add YOLO Pose as a second model, doubling inference cost. + +> **This is why YOLO Pose is recommended as Tier 1:** it handles ALL detection classes AND provides keypoints for person-specific behavior analysis in a single inference pass. RF-DETR's small object advantage is best leveraged on-demand (Tier 2) to confirm small objects like weapons, bags, or distant persons that YOLO may have missed. + +--- + +### 14.3 SlowFast vs YOLO Pose for Behavior Detection at 1 FPS + +#### How SlowFast Works + +SlowFast is a **video-level temporal action recognition** model. It requires a **clip** (sequence of frames) as input: + +| Pathway | Purpose | Typical Sampling | What Happens at 1 FPS | +|---------|---------|-----------------|----------------------| +| **Slow pathway** | Spatial semantics (what is happening) | 8 frames, stride 8 → 2.1s at 30 FPS | 8 frames = **8 seconds** of context | +| **Fast pathway** | Rapid motion capture (how it moves) | 32 frames, stride 2 → 2.1s at 30 FPS | 32 frames = **32 seconds** of context | + +#### The Problem: Fast Pathway is Useless at 1 FPS + +The fast pathway is designed to capture **rapid temporal changes** — a fist swing (~0.3s), a grab (~0.5s), a sudden lunge (~0.2s). At 1 FPS, these events happen **between** frames and are never captured. The fast pathway receives what is essentially a slideshow with no useful motion signal. + +#### Comparison: SlowFast vs YOLO Pose at 1 FPS + +| Factor | YOLO Pose (Recommended) | SlowFast | +|--------|------------------------|----------| +| **Works at 1 FPS?** | ✅ Yes — single-frame keypoints | ❌ Poorly — fast pathway is blind | +| **Latency per inference** | ~1.5 ms (single frame) | ~60 ms (8–32 frame clip) | +| **Memory overhead** | Zero buffering | Must buffer 8–32 frames per tracked person (~2.4 MB/person) | +| **Fighting detection** | Keypoint proximity + inter-frame velocity | Clip-level action class (degraded accuracy at 1 FPS) | +| **Tawuran detection** | Crowd density + centroid convergence | Could classify "crowd violence" but overkill | +| **Stealing detection** | Hand-object proximity heuristic | Needs additional model regardless | +| **Pipeline complexity** | Simple — inline with detection (one model) | Complex — clip buffer, separate inference, temporal alignment | +| **Accuracy at 1 FPS** | Good — poses are spatially informative | **Degraded** — temporal signal too sparse | +| **GPU cost (always-on)** | 1.5 ms/frame | 60 ms/clip + buffering VRAM | + +#### When SlowFast Does Make Sense + +SlowFast becomes valuable only with **temporarily increased frame rate** for suspicious cameras: + +``` +Normal operation: Camera at 1 FPS → YOLO Pose → "suspicious pose detected" +Triggered mode: Camera bumps to 15 FPS for 3 seconds → SlowFast confirms/denies + (45 frames captured → proper temporal signal for both pathways) +``` + +This is a valid **Tier 2 confirmation strategy** but not a primary detector at 1 FPS. + +#### Verdict + +> **At 1 FPS, YOLO Pose is strictly superior to SlowFast for behavior detection.** SlowFast's core architectural advantage (dual-pathway temporal modeling) is neutralized by the sparse frame rate. YOLO Pose provides spatially rich keypoint data on every single frame with no buffering overhead. +> +> **Recommendation:** Use SlowFast only as an optional Tier 2 confirmer when the camera can temporarily increase to 15+ FPS upon trigger. For the always-on pipeline at 1 FPS, rely on YOLO Pose keypoint analysis. + +--- + +### 14.4 RF-DETR as On-Demand (Tier 2) — Rationale + +#### RF-DETR's Small Object Advantage + +RF-DETR uses **deformable attention** that focuses on arbitrary-scale features. This gives it a significant edge on small objects: + +| Model | AP_small (< 32×32 px) | AP_medium | AP_large | +|-------|----------------------|----------|---------| +| RF-DETR-M | **~37** | ~58 | ~71 | +| YOLOv26-S | ~22 | ~52 | ~68 | +| YOLOv26-M | ~30 | ~56 | ~70 | + +#### Why Small Object Advantage Doesn't Help for Always-On Behavior Detection + +COCO "small" = **< 32×32 pixels**. In surveillance terms: + +| Camera Setup | Person Size | COCO Category | Can Analyze Behavior? | +|-------------|------------|---------------|----------------------| +| 1080p, person at 5m | ~300×600 px | Large | ✅ Yes — full pose visible | +| 1080p, person at 15m | ~100×200 px | Medium | ✅ Yes — keypoints detectable | +| 1080p, person at 30m | ~50×100 px | Medium | ⚠️ Limited — coarse pose only | +| 1080p, person at 50m+ | ~25×50 px | **Small** | ❌ No — blurry blob, no useful pose | + +> **Key insight:** If a person is small enough that YOLO misses it but RF-DETR detects it, that person is too small for behavior analysis anyway. There aren't enough pixels to determine fighting, stealing, or tawuran. + +#### Where RF-DETR's Strength IS Valuable (On-Demand) + +RF-DETR excels when triggered to confirm specific events: + +| On-Demand Task | Why RF-DETR is Better | +|---------------|----------------------| +| **Stolen object detection** | Bags, phones, wallets are small objects — RF-DETR's sweet spot | +| **Weapon detection** | Knives, guns are small — RF-DETR detects more reliably | +| **Precise person-object interaction** | Higher AP means fewer false negatives on critical frames | +| **Distant crowd counting** | For tawuran, counting heads at distance where YOLO may miss some | + +#### Cost Analysis: Always-On vs On-Demand + +| RF-DETR Mode | Per-Frame Cost | At 800 Cameras | GPU Utilization | +|-------------|---------------|----------------|----------------| +| **Always-on (every frame)** | 2.7 ms/frame | 2,160 ms/sec (needs full GPU) | ~100% of GPU 1 | +| **On-demand (5% trigger rate)** | 2.7 ms × 5% = **0.14 ms/frame** | 108 ms/sec | **~5% of GPU 1** | +| **On-demand (20% trigger rate)** | 2.7 ms × 20% = **0.54 ms/frame** | 432 ms/sec | **~20% of GPU 1** | + +Running RF-DETR on-demand at a 5% trigger rate uses **~95% less GPU** than always-on, while still providing high-accuracy confirmation exactly when it matters. + +#### The On-Demand Trigger Flow + +``` +YOLO Pose (always-on, every frame, ~1.5 ms) + │ + ├── 95% of frames: normal activity + │ └── Crop → MongoDB (done, no Tier 2 needed) + │ + └── 5% of frames: suspicious activity detected + │ (e.g., aggressive pose, hand near another's bag, + │ abnormal crowd convergence) + │ + └── RF-DETR (on-demand, ~2.7 ms, this frame only) + │ + ├── Confirm: stolen object in hand? + ├── Confirm: weapon detected? + ├── Confirm: precise person count in crowd? + │ + └── If confirmed → Alert + Evidence saved + If denied → Suppress false alarm +``` + +> **Verdict:** RF-DETR should be **on-demand (Tier 2)**, not always-on. Its small object strength is most valuable for confirming specific detections (stolen objects, weapons), not for primary surveillance scanning. Running it always-on wastes GPU and provides no behavior detection capability without a second model. + +--- + +### 14.5 Recommended Architecture — Final Pipeline + +Based on the analysis in Sections 14.1–14.4, the recommended architecture is: + +- **Tier 1 (always-on):** YOLOv26-S-Pose — detection + pose in a single 1.5 ms pass +- **Tier 2 (on-demand):** RF-DETR-M — high-accuracy confirmation for suspicious events +- **Behavior analysis:** CPU-side keypoint math (fighting, tawuran) + RF-DETR object confirmation (stealing) +- **SlowFast:** Optional Tier 2 only if camera can temporarily increase to 15+ FPS + +#### Complete Pipeline Flowchart + +``` +┌─────────────────────────────────────────────────────────────────────────────┐ +│ CAPTURE LAYER │ +│ │ +│ Thread per CCTV stream (1 FPS capture) │ +│ → RTSP connection (TCP, persistent) │ +│ → NVDEC hardware decode (cameras 1–48 on GPU, rest on CPU) │ +│ → Frame saved to shared memory ring buffer (3 slots per camera) │ +│ → Backpressure: drop-oldest if buffer full │ +└──────────────────────────────┬──────────────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────────────────────────────────┐ +│ TIER 1 — ALWAYS-ON (GPU 0, every frame, every camera) │ +│ │ +│ Pool/batch frames (batch_size=16) │ +│ → YOLOv26-S-Pose inference (~1.5 ms/frame, TensorRT FP16) │ +│ → Output per frame: │ +│ • Bounding boxes for ALL classes (person, vehicle, bag, animal...) │ +│ • Class labels + confidence scores for every detected object │ +│ • 17 body keypoints per PERSON (with confidence per keypoint) │ +│ │ +│ ┌─────────────────────────────────────────────────────────────────────┐ │ +│ │ OBJECT-LEVEL PROCESSING (all detected objects) │ │ +│ │ │ │ +│ │ For ALL classes: │ │ +│ │ • Crop bounding box → JPEG encode → batch for MongoDB │ │ +│ │ • Store metadata: class, confidence, bbox, camera_id, timestamp │ │ +│ │ │ │ +│ │ For VEHICLES: │ │ +│ │ • Track vehicle bbox across frames (loitering detection) │ │ +│ │ • Restricted zone violation (bbox inside forbidden region) │ │ +│ │ │ │ +│ │ For BAGS/LUGGAGE: │ │ +│ │ • Unattended bag: bag bbox with no person bbox within radius │ │ +│ │ │ │ +│ │ For ANIMALS: │ │ +│ │ • Restricted area intrusion: animal bbox in forbidden zone │ │ +│ └─────────────────────────────────────────────────────────────────────┘ │ +│ │ +│ ┌─────────────────────────────────────────────────────────────────────┐ │ +│ │ PERSON-SPECIFIC BEHAVIOR ANALYSIS (CPU, ~0.01 ms/person) │ │ +│ │ (uses keypoints — only available for person class) │ │ +│ │ │ │ +│ │ FightingDetector: │ │ +│ │ • Keypoint velocity between consecutive frames │ │ +│ │ • Inter-person proximity (wrist-to-head distance < threshold) │ │ +│ │ • Aggressive pose classification (raised arms, lunging torso) │ │ +│ │ │ │ +│ │ TawuranDetector: │ │ +│ │ • Crowd density (persons per m² exceeds threshold) │ │ +│ │ • Centroid convergence (people moving toward each other) │ │ +│ │ • Collective aggression score (% of crowd with fighting pose) │ │ +│ │ │ │ +│ │ StealingHeuristic (pre-screen): │ │ +│ │ • Hand keypoint proximity to detected bag/object bbox │ │ +│ │ • Unusual hand-to-bag/pocket trajectory │ │ +│ │ • Person-to-person hand interaction near object │ │ +│ └─────────────────────────────────────────────────────────────────────┘ │ +└──────────────┬──────────────────────────────────┬──────────────────────────┘ + │ │ + Normal (95%) Suspicious (5%) + │ │ + ▼ ▼ +┌──────────────────────────────┐ ┌──────────────────────────────────────────────┐ +│ STANDARD OUTPUT │ │ TIER 2 — ON-DEMAND (GPU 1, triggered only) │ +│ │ │ │ +│ Crop ALL detected objects │ │ RF-DETR-M inference (~2.7 ms/frame) │ +│ → JPEG encode │ │ → High-accuracy re-detection of scene │ +│ → Batch (100–500 docs) │ │ → Small object detection (bags, weapons, │ +│ → Async MongoDB write │ │ phones, knives — RF-DETR excels here) │ +│ (motor driver) │ │ → Detects objects YOLO may have missed │ +│ │ │ │ +│ Metadata saved per object: │ │ Confirmation Logic: │ +│ • camera_id │ │ • Stealing: object in hand + proximity │ +│ • timestamp │ │ • Weapon: object class = knife/gun/etc │ +│ • object_class (person, │ │ • Unattended bag: re-confirm no owner │ +│ vehicle, bag, animal...) │ │ • False alarm: RF-DETR sees no anomaly │ +│ • bbox coordinates │ │ → Suppress alert, save as normal frame │ +│ • confidence score │ │ │ +│ • person_count (per frame) │ │ If confirmed: │ +│ • vehicle_count │ │ → Alert (webhook / push notification) │ +│ • keypoints (person only) │ │ → Evidence clip saved (10-sec buffer) │ +│ │ │ → Behavior metadata → MongoDB │ +└──────────────────────────────┘ └──────────────────────────────────────────────┘ +``` + +#### Processing Timeline (single frame, single camera) + +``` +Time (ms): 0 1.5 1.51 1.52 4.22 + │ │ │ │ │ + ▼ ▼ ▼ ▼ ▼ + YOLOv26 Result Behavior Crop+Save RF-DETR + -Pose ready Analysis to MongoDB (only if + starts (CPU, (async, suspicious) + ~0.01ms) non-blocking) + +Normal frame total: ~1.52 ms (Tier 1 only) +Suspicious frame: ~4.22 ms (Tier 1 + Tier 2) +Blended average: ~1.52 + (0.05 × 2.7) = ~1.66 ms/frame +``` + +#### GPU Assignment + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ GPU 0 (V100 #1) — TIER 1: Always-On Detection + Pose │ +│ │ +│ NVDEC: cameras 1–24 (H.264 → GPU memory, zero-copy) │ +│ Model: YOLOv26-S-Pose (TensorRT FP16, 0.8 GB) │ +│ Batch: 16 frames → 16 × 1.5 ms = 24 ms → 667 cameras/sec │ +│ VRAM: 2.6 GB used / 29.4 GB free │ +└─────────────────────────────────────────────────────────────────┘ + +┌─────────────────────────────────────────────────────────────────┐ +│ GPU 1 (V100 #2) — TIER 2: On-Demand Confirmation │ +│ │ +│ NVDEC: cameras 25–48 (H.264 → GPU memory, zero-copy) │ +│ Model: RF-DETR-M (TensorRT FP16, 2.5 GB) │ +│ Load: 5% of frames → ~33–56 frames/sec (at 667–1120 cameras) │ +│ VRAM: 4.4 GB used / 27.6 GB free │ +│ │ +│ ⚡ ~95% idle — available for future models or scaling │ +└─────────────────────────────────────────────────────────────────┘ +``` + +--- + +### 14.6 Hardware Requirements — Scenario Comparison + +#### Scenario 1: YOLOv26-Pose (Tier 1) + RF-DETR On-Demand (Tier 2) — RECOMMENDED + +This is the full behavior detection pipeline with NVDEC zero-copy decode. + +| Component | Specification | Purpose | Est. Cost (USD) | +|-----------|--------------|---------|-----------------:| +| **CPU** | AMD EPYC 9374F (32C/64T, 3.85 GHz) | RTSP I/O, overflow decode (~0.09 core/cam with NVDEC), ByteTrack, behavior analysis | $2,500–3,000 | +| **Motherboard** | Supermicro H13SSL-N (SP5 socket) | Single-socket EPYC, 2× PCIe Gen5 x16 for dual V100 | $500–700 | +| **RAM** | 128 GB DDR5-4800 ECC RDIMM (4×32 GB) | Ring buffers, batch queues, tracking state, OS overhead | $400–500 | +| **GPU 0** | NVIDIA V100 32 GB *(already owned)* | Tier 1: NVDEC (24 streams) + YOLOv26-S-Pose (2.6 GB VRAM used) | — | +| **GPU 1** | NVIDIA V100 32 GB *(already owned)* | Tier 2: NVDEC (24 streams) + RF-DETR-M on-demand (4.4 GB VRAM used) | — | +| **NIC** | Mellanox ConnectX-5 25 GbE (dual-port) | 800 cameras × ~1 Mbps = 800 Mbps sustained | $150–200 | +| **Boot SSD** | 512 GB NVMe (PCIe Gen4) | OS, CUDA toolkit, Python env, model weights (~5 GB) | $50–70 | +| **Data SSD** | 2 TB NVMe (Samsung 990 Pro) | MongoDB data dir + alert clip buffer | $150–200 | +| **Archive HDD** | 2× 8 TB HDD (RAID 1) | Long-term evidence storage | $240–300 | +| **PSU** | 1,200W 80+ Platinum | 2× V100 (300W) + EPYC (320W) + overhead | $200–300 | +| **Chassis** | 4U rackmount server | Dual GPU cooling, sufficient airflow | $300–500 | +| **UPS** | 1,500VA online UPS | Power protection during writes | $300–400 | +| | | | | +| **Total (excl. V100s)** | | | **$4,800–6,200** | +| **Total (incl. V100s at ~$3K each)** | | | **$10,800–12,200** | + +**Capacity:** 800–1,120 cameras at 1 FPS with behavior detection (fighting, stealing, tawuran). + +#### Scenario 2: RF-DETR Always-On (No Behavior Detection) + +Detection + crop + save only. No pose, no behavior analysis, no Tier 2. + +| Component | Specification | Purpose | Est. Cost (USD) | +|-----------|--------------|---------|-----------------:| +| **CPU** | AMD EPYC 9274F (24C/48T, 4.05 GHz) | RTSP I/O, overflow decode, JPEG encode, MongoDB writes | $1,500–2,000 | +| **Motherboard** | Supermicro H13SSL-N (SP5 socket) | Single-socket EPYC | $500–700 | +| **RAM** | 64 GB DDR5-4800 ECC RDIMM (2×32 GB) | Ring buffers only (no clip buffers, no tracking state) | $200–250 | +| **GPU** | 1× NVIDIA V100 32 GB *(already owned)* | NVDEC (24 streams) + RF-DETR-M always-on (4.4 GB VRAM used) | — | +| **NIC** | Intel X710-DA2 10 GbE (dual-port) | 500 cameras × ~1 Mbps = sufficient | $80–120 | +| **Boot SSD** | 512 GB NVMe | OS + model weights | $50–70 | +| **Data SSD** | 2 TB NVMe | MongoDB data directory | $150–200 | +| **Archive HDD** | 1× 8 TB HDD | Crop storage (optional) | $120–150 | +| **PSU** | 850W 80+ Gold | 1× V100 (300W) + EPYC (250W) + overhead | $120–150 | +| **Chassis** | 4U rackmount or tower workstation | Single GPU cooling | $200–400 | +| **UPS** | 1,000VA online UPS | Power protection | $200–250 | +| | | | | +| **Total (excl. V100)** | | | **$3,100–4,300** | +| **Total (incl. 1× V100 at ~$3K)** | | | **$6,100–7,300** | + +**Capacity:** ~370 cameras at 1 FPS (RF-DETR-M at 2.7 ms/frame, batch=16). Detection + crop only, no behavior detection. + +> **Note on Scenario 2 throughput:** RF-DETR is 1.8× slower than YOLOv26-S per frame (2.7 ms vs 1.5 ms). If you use a single V100 with RF-DETR always-on, maximum cameras drops from ~667 (YOLO) to **~370** per GPU. Using both V100s for RF-DETR would recover to ~740, but then you have no GPU for Tier 2 confirmation. + +--- + +### 14.7 Side-by-Side Comparison + +| Specification | **Scenario 1 (Recommended)** | **Scenario 2** | +|--------------|------------------------------|----------------| +| | *YOLO Pose + RF-DETR on-demand* | *RF-DETR always-on, no behavior* | +| | | | +| **Primary model** | YOLOv26-S-Pose (1.5 ms) | RF-DETR-M (2.7 ms) | +| **Secondary model** | RF-DETR-M (on-demand, 5%) | None | +| **Detection classes** | 80 COCO (person, vehicle, bag, animal, etc.) | 80 COCO (same classes) | +| **Keypoints (person)** | ✅ 17 body keypoints per person | ❌ None | +| **Behavior analysis** | CPU keypoint math | ❌ Not available | +| | | | +| **CPU** | EPYC 9374F (32C/64T) | EPYC 9274F (24C/48T) | +| **RAM** | 128 GB DDR5 ECC | 64 GB DDR5 ECC | +| **GPUs active** | 2× V100 32 GB | 1× V100 32 GB | +| **NIC** | 25 GbE | 10 GbE | +| **PSU** | 1,200W Platinum | 850W Gold | +| **System power** | ~1,000W sustained | ~600W sustained | +| | | | +| **Max cameras (1 FPS)** | **800–1,120** | **~370** (1 GPU) / ~740 (2 GPU) | +| **Small object detection** (bags, weapons, animals at distance) | On-demand via RF-DETR (Tier 2) | ✅ Always-on | +| **Person detection AP** | ~90%+ (YOLO, sufficient) | ~93%+ (RF-DETR, higher) | +| **Vehicle detection** | ✅ (large objects, both models excel) | ✅ | +| **Bag/weapon detection** | ✅ YOLO detects + RF-DETR confirms small ones | ✅ Better for small/distant objects | +| **Animal detection** | ✅ | ✅ | +| | | | +| **Fighting detection** | ✅ Keypoint velocity + proximity | ❌ No keypoints | +| **Tawuran detection** | ✅ Crowd density + convergence | ❌ No keypoints | +| **Stealing detection** | ✅ Hand-to-bag keypoint + RF-DETR confirm | ❌ No hand position data | +| **Unattended bag** | ✅ Bag bbox + no nearby person bbox | ✅ Same logic, slightly better bag AP | +| **Vehicle loitering** | ✅ Track vehicle bbox across frames | ✅ Same capability | +| **Animal intrusion** | ✅ Animal bbox in restricted zone | ✅ Same capability | +| **Weapon detection** | ⚠️ YOLO detects + RF-DETR Tier 2 confirms | ✅ Better small weapon AP always-on | +| **False alarm suppression** | ✅ RF-DETR Tier 2 confirmation | ❌ No confirmation layer | +| | | | +| **Cost (excl. V100s)** | **$4,800–6,200** | **$3,100–4,300** | +| **Cost (incl. V100s)** | **$10,800–12,200** | **$6,100–7,300** | +| **Cost difference** | — | Saves $4,700–4,900 | +| | | | +| **Best for** | Full surveillance: multi-class detection + behavior analysis | Simple multi-class detection + crop + save | + +### 14.8 Final Recommendation + +``` +┌─────────────────────────────────────────────────────────────────────────────┐ +│ │ +│ ✅ RECOMMENDED: Scenario 1 — YOLOv26-Pose (Tier 1) + RF-DETR (Tier 2) │ +│ │ +│ • Detects ALL object classes (person, vehicle, bag, weapon, animal) │ +│ • ADDITIONALLY provides 17 body keypoints for every detected person │ +│ • One model handles multi-class detection + behavior analysis in ~1.5 ms │ +│ • RF-DETR confirms suspicious events on-demand (~5% of frames) │ +│ → excels at confirming small objects: bags, weapons, distant animals │ +│ • 800–1,120 cameras on 2× V100 at 1 FPS │ +│ • Full fighting, stealing, and tawuran detection │ +│ • Bbox-only behaviors (unattended bag, vehicle loitering, animal │ +│ intrusion) work with both models — no keypoints needed │ +│ • GPU 1 is ~95% idle → room for future expansion │ +│ │ +│ ❌ NOT RECOMMENDED: SlowFast at 1 FPS │ +│ • Fast pathway receives no useful temporal signal at 1 FPS │ +│ • Use only as optional Tier 2 with temporary FPS increase (15+ FPS) │ +│ │ +│ ❌ NOT RECOMMENDED: RF-DETR as always-on primary │ +│ • Detects all classes well, but NO keypoints for any class │ +│ • Cannot detect fighting/tawuran/stealing without adding YOLO Pose │ +│ • 1.8× slower → fewer cameras per GPU │ +│ • Small object advantage is valuable but better leveraged on-demand │ +│ • Best role: Tier 2 confirmer for stolen objects / weapons / small items │ +│ │ +└─────────────────────────────────────────────────────────────────────────────┘ +``` From ccdcd4b695b05d3bc344b31ec25d191b4a969c12 Mon Sep 17 00:00:00 2001 From: vmoudyp Date: Sat, 7 Mar 2026 04:40:36 +0700 Subject: [PATCH 10/11] Add ontology-driven entity normalization --- Core/Index/GBCIndex.py | 44 +- Core/Index/Graph.py | 167 ++++-- Core/configs/ontology_config.py | 104 ++++ Core/configs/system_config.py | 18 +- Core/construct_index.py | 31 +- Core/pipelines/kg_builder.py | 123 ++-- Core/pipelines/kg_refiner.py | 165 ++++-- Core/rag/gbc_rag.py | 93 +-- Core/utils/ontology_utils.py | 158 +++++ api/services/indexing.py | 4 +- config/gbc.yaml | 14 + docs/bookrag-architecture-review.md | 883 ++++++++++++++++++++++++++++ main.py | 6 +- tests/test_ontology_integration.py | 200 +++++++ 14 files changed, 1755 insertions(+), 255 deletions(-) create mode 100644 Core/configs/ontology_config.py create mode 100644 Core/utils/ontology_utils.py create mode 100644 docs/bookrag-architecture-review.md create mode 100644 tests/test_ontology_integration.py diff --git a/Core/Index/GBCIndex.py b/Core/Index/GBCIndex.py index 24b98b4..2247f76 100644 --- a/Core/Index/GBCIndex.py +++ b/Core/Index/GBCIndex.py @@ -1,5 +1,8 @@ -from sympy import N -from Core.Index.Tree import * +import logging +import os +from typing import Optional + +from Core.Index.Tree import DocumentTree from Core.configs.system_config import SystemConfig from Core.provider.llm import LLM from Core.Index.Graph import Graph @@ -7,6 +10,9 @@ from Core.provider.vdb import VectorStore +log = logging.getLogger(__name__) + + class GBC: """ A class representing the index combining graph and tree structures. @@ -18,7 +24,7 @@ def __init__( self, config: SystemConfig, graph_index: Optional[Graph] = None, - TreeIndex: Optional[DocumentTree] = None, + tree_index: Optional[DocumentTree] = None, ): """ Initializes the TreeIndex with an optional index. @@ -28,7 +34,7 @@ def __init__( self.save_dir = config.save_path self.config = config self.llm = LLM(config.llm) - self.TreeIndex: DocumentTree = TreeIndex + self.TreeIndex: DocumentTree = tree_index self.GraphIndex: Graph = graph_index # load the vdb of entities — namespaced by tenant/doc if available @@ -43,7 +49,7 @@ def __init__( ) else: self.entity_vdb_path = os.path.join(self.save_dir, vdb_name) - + self.embedder = TextEmbeddingProvider( model_name=config.graph.embedding_config.model_name, backend=config.graph.embedding_config.backend, @@ -72,7 +78,7 @@ def save_gbc_index(self): # vdb is saved automatically when the entity_vdb is created - log.info(f"GBC index saved") + log.info("GBC index saved") def rebuild_vdb(self): """ @@ -91,12 +97,7 @@ def rebuild_vdb(self): texts.append(node) entity = self.GraphIndex.get_entity_by_node_name(node) - tmp_dict = { - "entity_name": entity.entity_name, - "entity_type": entity.entity_type, - "description": entity.description, - } - meta_datas.append(tmp_dict) + meta_datas.append(entity.to_vdb_metadata()) self.entity_vdb.add_texts(texts=texts, metadatas=meta_datas) log.info(f"Rebuilt entity VDB with {len(texts)} entries.") @@ -112,7 +113,7 @@ def load_gbc_index(cls, config: SystemConfig): tree_index = DocumentTree.load_from_file( DocumentTree.get_save_path(config.save_path) ) - + if config.graph.refine_type == "basic": variant = "basic" else: @@ -127,7 +128,7 @@ def load_gbc_index(cls, config: SystemConfig): doc_id=config.doc_id, falkordb_cfg=falkordb_cfg, ) - GBC = cls(config=config, graph_index=graph_index, TreeIndex=tree_index) + GBC = cls(config=config, graph_index=graph_index, tree_index=tree_index) log.info(f"GBC index loaded from {config.save_path}") return GBC @@ -148,12 +149,11 @@ def rebuild_global_vdb(self, global_vdb_path: str) -> None: for node in nodes: texts.append(node) entity = self.GraphIndex.get_entity_by_node_name(node) - meta_datas.append({ - "entity_name": entity.entity_name, - "entity_type": entity.entity_type, - "description": entity.description, - "doc_id": self.config.doc_id or "", - "tenant_id": self.config.tenant_id or "", - }) + metadata = entity.to_vdb_metadata() + metadata["doc_id"] = self.config.doc_id or "" + metadata["tenant_id"] = self.config.tenant_id or "" + meta_datas.append(metadata) global_vdb.add_texts(texts=texts, metadatas=meta_datas) - log.info(f"Rebuilt global VDB with {len(texts)} entries from doc '{self.config.doc_id}'.") + log.info( + f"Rebuilt global VDB with {len(texts)} entries from doc '{self.config.doc_id}'." + ) diff --git a/Core/Index/Graph.py b/Core/Index/Graph.py index 4fa0ee9..364b277 100644 --- a/Core/Index/Graph.py +++ b/Core/Index/Graph.py @@ -18,6 +18,12 @@ class Entity(BaseModel): entity_name: str # Primary key for entity entity_type: str = Field(default="") # Entity type description: str = Field(default="") # The description of this entity + entity_id: str = Field(default="") # Stable ontology/canonical identifier + canonical_id: str = Field(default="") # Points to canonical ontology identifier + entity_role: str = Field(default="provisional") # canonical / provisional + aliases: List[str] = Field(default_factory=list) # Known aliases for resolution + mapping_confidence: float = Field(default=0.0) # Ontology mapping confidence + ontology_source: str = Field(default="") # Source of ontology metadata source_ids: Set[int] = Field( default_factory=set ) # Set of source IDs from which this entity is derived @@ -39,6 +45,20 @@ def __eq__(self, other): ) return False + def to_vdb_metadata(self) -> dict: + aliases = list(dict.fromkeys(self.aliases)) + return { + "entity_name": self.entity_name, + "entity_type": self.entity_type, + "description": self.description, + "entity_id": self.entity_id, + "canonical_id": self.canonical_id, + "entity_role": self.entity_role, + "mapping_confidence": float(self.mapping_confidence or 0.0), + "ontology_source": self.ontology_source, + "aliases_json": json.dumps(aliases, ensure_ascii=False), + } + class Relationship(BaseModel): src_entity_name: str # Name of the entity on the left side of the edge @@ -125,7 +145,6 @@ def add_kg_node(self, entity: Entity) -> None: node_name = self.get_node_name_from_entity(entity) self.kg.add_node(node_name, **entity.model_dump()) - # self.name_to_nodes[entity.entity_name].add(node_name) def add_kg_edge(self, rel: Relationship, src_type: str, tgt_type: str) -> None: """Add a relation/edge between two KG entities with all its attributes.""" @@ -159,11 +178,20 @@ def add_and_link( entities = [entities] for entity in entities: node_name = self.get_node_name_from_entity(entity) - # node_name = f"{entity.entity_name} ({entity.entity_type})" if node_name not in self.kg: self.add_kg_node(entity) self.link(tree_node_id, entity.entity_name, entity.entity_type) + def _rewrite_edge_entity_names( + self, edge_data: Optional[dict], old_entity_name: str, new_entity_name: str + ) -> dict: + updated_edge_data = dict(edge_data or {}) + if updated_edge_data.get("src_entity_name") == old_entity_name: + updated_edge_data["src_entity_name"] = new_entity_name + if updated_edge_data.get("tgt_entity_name") == old_entity_name: + updated_edge_data["tgt_entity_name"] = new_entity_name + return updated_edge_data + def update_entity( self, old_entity_name: str, old_entity_type: str, new_entity: Entity ) -> None: @@ -184,9 +212,17 @@ def update_entity( if new_node_name != old_node_name: # 1. Add new node and copy all edges self.kg.add_node(new_node_name, **new_entity.model_dump()) - for neighbor in list(self.kg.neighbors(old_node_name)): + for neighbor in self.kg.neighbors(old_node_name): edge_data = self.kg.get_edge_data(old_node_name, neighbor) - self.kg.add_edge(new_node_name, neighbor, **edge_data) + self.kg.add_edge( + new_node_name, + neighbor, + **self._rewrite_edge_entity_names( + edge_data=edge_data, + old_entity_name=old_entity_name, + new_entity_name=new_entity.entity_name, + ), + ) # 2.1 update tree2kg for tree_id in new_source_ids: # If the old node is in the tree2kg, remove the old name @@ -216,7 +252,6 @@ def get_entity(self, entity_name: str, entity_type: str = "") -> Entity: node_name = self.get_node_name_from_str( entity_name=entity_name, entity_type=entity_type ) - # node_name = f"{entity_name} ({entity_type})" if node_name not in self.kg.nodes: raise KeyError(f"Entity '{node_name}' not found in knowledge graph.") return Entity(**self.kg.nodes[node_name]) @@ -236,10 +271,8 @@ def get_entity_by_node_name(self, node_name: str) -> Entity: return Entity(**self.kg.nodes[node_name]) def get_subgraph_data(self, entities: List[str]) -> dict: - # Return the subgraph entities data, excluding description and source_ids in entities - # If the relation connects two entities in the subgraph, it will be included + """Return lightweight node data for the subgraph induced by `entities`.""" subgraph = self.kg.subgraph(entities) - # data = {"nodes": [], "edges": []} data = {"nodes": []} for node in subgraph.nodes(data=True): node_data = { @@ -247,44 +280,29 @@ def get_subgraph_data(self, entities: List[str]) -> dict: "entity_type": node[1]["entity_type"], } data["nodes"].append(node_data) - # for edge in subgraph.edges(data=True): - # edge_data = { - # "src_entity_name": edge[2]["src_entity_name"], - # "tgt_entity_name": edge[2]["tgt_entity_name"], - # "relation_name": edge[2]["relation_name"], - # "weight": edge[2]["weight"], - # } - # data["edges"].append(edge_data) return data - def Entities2TreeNodes(self, entities: List[Entity]) -> List[int]: + def entities_to_tree_nodes(self, entities: List[Entity]) -> List[int]: """ Given KG node names, return all tree node IDs that link to them. """ result = set() for ent in entities: - source_ids = ent.source_ids - result.union(source_ids) - result = list(result) - return result + result.update(ent.source_ids) + return sorted(result) - def Entity2TreeNodes(self, ent: Entity) -> List[int]: + def entity_to_tree_nodes(self, ent: Entity) -> List[int]: """ Given an Entity object, return all tree node IDs that link to it. """ - res = ent.source_ids - res = list(res) - return res + return sorted(ent.source_ids) - def NodeName2TreeNodes(self, node_name: str) -> Set[int]: + def node_name_to_tree_nodes(self, node_name: str) -> List[int]: """ Given a node name (entity_name (entity_type)), return all tree node IDs that link to it. """ ent = self.get_entity_by_node_name(node_name) - res = ent.source_ids - res = list(res) - - return res + return sorted(ent.source_ids) def remove_self_loops(self) -> int: """ @@ -330,6 +348,10 @@ def _get_fdb_graph(self): def _save_to_falkordb(self) -> None: """Persist the in-memory NetworkX graph to FalkorDB.""" g = self._get_fdb_graph() + + def _esc(value) -> str: + return str(value or "").replace("\\", "\\\\").replace("'", "\\'") + # Clear existing data for idempotent saves try: g.query("MATCH (n) DETACH DELETE n") @@ -339,15 +361,27 @@ def _save_to_falkordb(self) -> None: # Write nodes for node_name, data in self.kg.nodes(data=True): source_ids_list = list(data.get("source_ids", set())) - desc = data.get("description", "").replace("'", "\\'") - ename = data.get("entity_name", "").replace("'", "\\'") - etype = data.get("entity_type", "").replace("'", "\\'") - nname = node_name.replace("\\", "\\\\").replace("'", "\\'") + desc = _esc(data.get("description", "")) + ename = _esc(data.get("entity_name", "")) + etype = _esc(data.get("entity_type", "")) + entity_id = _esc(data.get("entity_id", "")) + canonical_id = _esc(data.get("canonical_id", "")) + entity_role = _esc(data.get("entity_role", "provisional")) + ontology_source = _esc(data.get("ontology_source", "")) + aliases_json = _esc(json.dumps(data.get("aliases", []), ensure_ascii=False)) + mapping_confidence = float(data.get("mapping_confidence", 0.0) or 0.0) + nname = _esc(node_name) cypher = ( f"CREATE (n:Entity {{" f"node_name: '{nname}', " f"entity_name: '{ename}', " f"entity_type: '{etype}', " + f"entity_id: '{entity_id}', " + f"canonical_id: '{canonical_id}', " + f"entity_role: '{entity_role}', " + f"aliases_json: '{aliases_json}', " + f"mapping_confidence: {mapping_confidence}, " + f"ontology_source: '{ontology_source}', " f"description: '{desc}', " f"source_ids: {source_ids_list}" f"}})" @@ -387,9 +421,20 @@ def _load_from_falkordb(self) -> None: props = node.properties source_ids = set(props.get("source_ids", [])) node_name = props["node_name"] + aliases_json = props.get("aliases_json", "[]") + try: + aliases = json.loads(aliases_json) if isinstance(aliases_json, str) else list(aliases_json or []) + except json.JSONDecodeError: + aliases = [] self.kg.add_node(node_name, entity_name=props.get("entity_name", ""), entity_type=props.get("entity_type", ""), + entity_id=props.get("entity_id", ""), + canonical_id=props.get("canonical_id", ""), + entity_role=props.get("entity_role", "provisional"), + aliases=aliases, + mapping_confidence=float(props.get("mapping_confidence", 0.0) or 0.0), + ontology_source=props.get("ontology_source", ""), description=props.get("description", ""), source_ids=source_ids) for tid in source_ids: @@ -431,9 +476,20 @@ def _get_fdb_subgraph(self, tree_node_ids: Iterable[int]) -> nx.Graph: node = rec[0] props = node.properties node_name = props["node_name"] + aliases_json = props.get("aliases_json", "[]") + try: + aliases = json.loads(aliases_json) if isinstance(aliases_json, str) else list(aliases_json or []) + except json.JSONDecodeError: + aliases = [] subgraph.add_node(node_name, entity_name=props.get("entity_name", ""), entity_type=props.get("entity_type", ""), + entity_id=props.get("entity_id", ""), + canonical_id=props.get("canonical_id", ""), + entity_role=props.get("entity_role", "provisional"), + aliases=aliases, + mapping_confidence=float(props.get("mapping_confidence", 0.0) or 0.0), + ontology_source=props.get("ontology_source", ""), description=props.get("description", ""), source_ids=set(props.get("source_ids", []))) @@ -525,13 +581,19 @@ def load_from_dir( with open(load_path, "r", encoding="utf-8") as f: loaded_data = json.load(f) - graph_instance = cls(save_path=load_dir) + graph_instance = cls(save_path=load_dir, variant=variant) # Pass edges="links" to match the key used when saving with node_link_data(edges="links") graph_instance.kg = json_graph.node_link_graph(loaded_data["graph"], edges="links") for _, node_data in graph_instance.kg.nodes(data=True): if "source_ids" in node_data and isinstance(node_data["source_ids"], list): node_data["source_ids"] = set(node_data["source_ids"]) + node_data.setdefault("entity_id", "") + node_data.setdefault("canonical_id", "") + node_data.setdefault("entity_role", "provisional") + node_data.setdefault("aliases", []) + node_data.setdefault("mapping_confidence", 0.0) + node_data.setdefault("ontology_source", "") for _, _, edge_data in graph_instance.kg.edges(data=True): if "source_ids" in edge_data and isinstance(edge_data["source_ids"], list): @@ -571,16 +633,28 @@ def save_to_global_graph(self, falkordb_cfg, tenant_id: str) -> None: log.error(f"Failed to connect to FalkorDB global graph: {e}") raise + def _esc(value) -> str: + return str(value or "").replace("\\", "\\\\").replace("'", "\\'") + for node_name, data in self.kg.nodes(data=True): - ename = data.get("entity_name", "").replace("'", "\\'") - etype = data.get("entity_type", "").replace("'", "\\'") - desc = data.get("description", "").replace("'", "\\'") - nname = node_name.replace("\\", "\\\\").replace("'", "\\'") - doc_id_esc = self.doc_id.replace("'", "\\'") if self.doc_id else "" + ename = _esc(data.get("entity_name", "")) + etype = _esc(data.get("entity_type", "")) + desc = _esc(data.get("description", "")) + entity_id = _esc(data.get("entity_id", "")) + canonical_id = _esc(data.get("canonical_id", "")) + entity_role = _esc(data.get("entity_role", "provisional")) + ontology_source = _esc(data.get("ontology_source", "")) + aliases_json = _esc(json.dumps(data.get("aliases", []), ensure_ascii=False)) + mapping_confidence = float(data.get("mapping_confidence", 0.0) or 0.0) + nname = _esc(node_name) + doc_id_esc = _esc(self.doc_id) # MERGE canonical entity node global_graph.query( f"MERGE (n:Entity {{node_name: '{nname}'}}) " - f"ON CREATE SET n.entity_name='{ename}', n.entity_type='{etype}', n.description='{desc}' " + f"ON CREATE SET n.entity_name='{ename}', n.entity_type='{etype}', n.description='{desc}', " + f"n.entity_id='{entity_id}', n.canonical_id='{canonical_id}', n.entity_role='{entity_role}', " + f"n.aliases_json='{aliases_json}', n.mapping_confidence={mapping_confidence}, " + f"n.ontology_source='{ontology_source}' " f"CREATE (n)-[:HAS_MENTION {{doc_id: '{doc_id_esc}'}}]->(n)" ) log.info(f"Merged {self.kg.number_of_nodes()} nodes into global graph for tenant '{tenant_id}'.") @@ -620,9 +694,20 @@ def get_global_subgraph( node = rec[0] props = node.properties node_name = props["node_name"] + aliases_json = props.get("aliases_json", "[]") + try: + aliases = json.loads(aliases_json) if isinstance(aliases_json, str) else list(aliases_json or []) + except json.JSONDecodeError: + aliases = [] subgraph.add_node(node_name, entity_name=props.get("entity_name", ""), entity_type=props.get("entity_type", ""), + entity_id=props.get("entity_id", ""), + canonical_id=props.get("canonical_id", ""), + entity_role=props.get("entity_role", "provisional"), + aliases=aliases, + mapping_confidence=float(props.get("mapping_confidence", 0.0) or 0.0), + ontology_source=props.get("ontology_source", ""), description=props.get("description", "")) return subgraph diff --git a/Core/configs/ontology_config.py b/Core/configs/ontology_config.py new file mode 100644 index 0000000..4294a2c --- /dev/null +++ b/Core/configs/ontology_config.py @@ -0,0 +1,104 @@ +import json +import os +from typing import List, Optional + +import yaml +from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator + +from Core.utils.ontology_utils import normalize_entity_name, normalize_entity_type + + +class OntologyEntityConfig(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + entity_id: str = Field(alias="ontology_id") + entity_name: str = Field(alias="canonical_name") + entity_type: str + description: str = "" + aliases: List[str] = Field(default_factory=list) + keywords: List[str] = Field(default_factory=list) + status: str = "active" + ontology_source: str = "config" + + @field_validator("entity_id") + @classmethod + def _validate_entity_id(cls, value: str) -> str: + value = str(value or "").strip() + if not value: + raise ValueError("ontology_id cannot be empty") + return value + + @field_validator("entity_name") + @classmethod + def _normalize_entity_name(cls, value: str) -> str: + normalized = normalize_entity_name(value) + if not normalized: + raise ValueError("canonical_name cannot be empty") + return normalized + + @field_validator("entity_type") + @classmethod + def _normalize_entity_type(cls, value: str) -> str: + normalized = normalize_entity_type(value) + if not normalized: + raise ValueError("entity_type cannot be empty") + return normalized + + @field_validator("aliases", "keywords") + @classmethod + def _normalize_terms(cls, values: List[str]) -> List[str]: + normalized: List[str] = [] + for value in values or []: + item = normalize_entity_name(value) + if item and item not in normalized: + normalized.append(item) + return normalized + + @model_validator(mode="after") + def _ensure_canonical_alias(self) -> "OntologyEntityConfig": + if self.entity_name not in self.aliases: + self.aliases.insert(0, self.entity_name) + return self + + +class OntologyConfig(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + enabled: bool = False + path: Optional[str] = None + entities: List[OntologyEntityConfig] = Field(default_factory=list) + mapping_threshold: float = Field(default=1.0, ge=0.0, le=1.0) + allow_provisional_entities: bool = True + use_query_resolution: bool = True + + @model_validator(mode="after") + def _load_entities_from_path(self) -> "OntologyConfig": + if not self.path: + return self + + loaded_entities = self._read_entities_file(self.path) + merged = list(self.entities) + seen_ids = {entity.entity_id for entity in merged} + for entity in loaded_entities: + if entity.entity_id not in seen_ids: + merged.append(entity) + seen_ids.add(entity.entity_id) + self.entities = merged + return self + + @staticmethod + def _read_entities_file(path: str) -> List[OntologyEntityConfig]: + if not os.path.exists(path): + raise FileNotFoundError(f"Ontology file not found: {path}") + + with open(path, "r", encoding="utf-8") as handle: + if path.endswith(".json"): + payload = json.load(handle) + else: + payload = yaml.safe_load(handle) + + raw_entities = payload.get("entities", payload) if isinstance(payload, dict) else payload + if not isinstance(raw_entities, list): + raise ValueError("Ontology file must contain a list of entities or an 'entities' list") + + return [OntologyEntityConfig(**entity) for entity in raw_entities] \ No newline at end of file diff --git a/Core/configs/system_config.py b/Core/configs/system_config.py index 8e7a7fc..5322093 100644 --- a/Core/configs/system_config.py +++ b/Core/configs/system_config.py @@ -1,9 +1,12 @@ +import os + import yaml from Core.configs.mineru_config import MinerU from Core.configs.docling_config import DoclingConfig from Core.configs.llm_config import LLMConfig from Core.configs.tree_config import TreeConfig from Core.configs.graph_config import GraphConfig +from Core.configs.ontology_config import OntologyConfig from Core.configs.vlm_config import VLMConfig from Core.configs.rag_config import RAGConfig from Core.configs.vdb_config import VDBConfig @@ -33,6 +36,7 @@ class SystemConfig(BaseModel): tree: TreeConfig = Field(default_factory=TreeConfig) graph: GraphConfig = Field(default_factory=GraphConfig) vdb: VDBConfig = Field(default_factory=VDBConfig) + ontology: OntologyConfig = Field(default_factory=OntologyConfig) # Other Index selection index_type: Optional[str] = "gbc" # Options: "gbc", "tree", "vanilla", "bm25", "raptor", "pdf_vanilla" @@ -72,12 +76,6 @@ class SystemConfig(BaseModel): falkordb: Any = Field(default_factory=FalkorDBConfig) mongodb: Any = Field(default_factory=MongoDBConfig) - # # 新增: 专门用于存放评估结果的根目录 - # evaluation_output_path: Optional[str] = Field( - # default="/home/wangshu/multimodal/GBC-RAG/test/tree_index/evaluation_results", - # description="Root directory to save evaluation results." - # ) - def load_system_config(path: str = "../configs/default.yaml") -> SystemConfig: with open(path, "r") as f: @@ -87,5 +85,13 @@ def load_system_config(path: str = "../configs/default.yaml") -> SystemConfig: rag_data = raw_config["rag"] raw_config["rag"] = {"strategy_config": rag_data} + ontology_data = raw_config.get("ontology") + if isinstance(ontology_data, dict) and ontology_data.get("path"): + ontology_path = ontology_data["path"] + if not os.path.isabs(ontology_path): + ontology_data["path"] = os.path.abspath( + os.path.join(os.path.dirname(path), ontology_path) + ) + cfg = SystemConfig(**raw_config) return cfg diff --git a/Core/construct_index.py b/Core/construct_index.py index 684c62b..22812c8 100644 --- a/Core/construct_index.py +++ b/Core/construct_index.py @@ -21,7 +21,7 @@ from Core.utils.file_utils import save_indexing_stats -def construct_GBC_index(cfg: SystemConfig, tree_only: bool = False): +def construct_gbc_index(cfg: SystemConfig, tree_only: bool = False): """ Construct the GBC index from the document tree and knowledge graph. @@ -57,7 +57,7 @@ def construct_GBC_index(cfg: SystemConfig, tree_only: bool = False): graph_index = build_knowledge_graph(tree_index, cfg) # The 'kg_extraction' stage is recorded inside build_knowledge_graph - gbc_index = GBC(config=cfg, graph_index=graph_index, TreeIndex=tree_index) + gbc_index = GBC(config=cfg, graph_index=graph_index, tree_index=tree_index) gbc_index.save_gbc_index() # rebuild vdb @@ -143,32 +143,9 @@ def compute_mm_reranker(cfg: SystemConfig, group: pd.DataFrame): tree_index = build_tree_from_pdf(cfg) compute_mm_embedding(cfg, tree_index) - + compute_mm_embedding_question(cfg, group) if __name__ == "__main__": - print("test") - - # parser = argparse.ArgumentParser(description="Extract text content from PDF files.") - # parser.add_argument( - # "--config_path", - # type=str, - # default="/home/wangshu/multimodal/GBC-RAG/config/gbc.yaml", - # help="Path to the configuration file.", - # ) - - # args = parser.parse_args() - - # cfg = load_system_config(args.config_path) - - # if not os.path.exists(cfg.save_path): - # os.makedirs(cfg.save_path) - # log.info(f"Created directory: {cfg.save_path}") - # else: - # log.info(f"Directory already exists: {cfg.save_path}") - - # construct_vdb(cfg) - - # gbc_index = construct_GBC_index(cfg) - # log.info("GBC index construction completed successfully.") + print("Use main.py to run indexing workflows.") diff --git a/Core/pipelines/kg_builder.py b/Core/pipelines/kg_builder.py index 1b0d0cb..a2f615c 100644 --- a/Core/pipelines/kg_builder.py +++ b/Core/pipelines/kg_builder.py @@ -3,6 +3,7 @@ from Core.pipelines.kg_extractor import KGExtractor from Core.pipelines.kg_refiner import KGRefiner from Core.configs.system_config import SystemConfig +from Core.utils.ontology_utils import align_entities_to_ontology from Core.provider.llm import LLM from Core.provider.vlm import VLM @@ -12,28 +13,6 @@ log = logging.getLogger(__name__) -# print log for test -from rich.logging import RichHandler - -import os -import time - -# log_dir = "/home/wangshu/multimodal/GBC-RAG/test/index_qwen3/logs" -# if not os.path.exists(log_dir): -# os.makedirs(log_dir) -# log_file = os.path.join(log_dir, f"kg_builder_{time.strftime('%Y%m%d_%H%M%S')}.log") -# logging.basicConfig( -# level="INFO", -# format="%(asctime)s - %(levelname)s - %(message)s", -# datefmt="%H:%M:%S", -# handlers=[ -# RichHandler(rich_tracebacks=True), # RichHandler 会继续使用自己的漂亮格式 -# logging.FileHandler( -# log_file, encoding="utf-8" -# ), # FileHandler 会使用上面定义的 format -# ], -# ) - def build_knowledge_graph(tree: DocumentTree, cfg: SystemConfig): """ @@ -82,60 +61,38 @@ def build_knowledge_graph(tree: DocumentTree, cfg: SystemConfig): kg_extract_res = [] - batch_process = True - - if batch_process: - log.info("Batch processing is enabled for knowledge graph extraction.") - batch_nodes = [] - batch_title_nodes = [] - batch_title_paths = [] - batch_sibling_nodes = [] - for node in tree.nodes: - # for node in tree.nodes[:30]: - if node == tree.root_node: - # Skip the root node since it doesn't have any other information - continue - if node.type == NodeType.TITLE: - # For title nodes, we collect the path and sibling nodes for batch processing - title_path = tree.get_path_from_root(node.index_id) - sibling_nodes = tree.get_sibling_nodes(node.index_id) - batch_title_nodes.append(node) - batch_title_paths.append(title_path) - batch_sibling_nodes.append(sibling_nodes) - else: - # For other nodes, we collect them for batch processing - batch_nodes.append(node) - - # Process title nodes in batches - if batch_title_nodes: - log.info("Processing title nodes in batches...") - res_dict = kg_extractor.batch_extract_titles( - nodes=batch_title_nodes, - title_paths=batch_title_paths, - sibling_nodes_list=batch_sibling_nodes, - ) - kg_extract_res.extend(res_dict) - - if batch_nodes: - log.info("Processing non-title nodes in batches...------") - res_dict = kg_extractor.batch_extract_kg(nodes=batch_nodes) - kg_extract_res.extend(res_dict) - - # resort the results based on node index - kg_extract_res.sort(key=lambda x: x.get("node_idx", -1)) - else: - for node in tree.nodes[:30]: - # Extract entities and relationships from the node - if node == tree.root_node: - # Skip the root node since it doesn't have any other information - continue - if node.type == NodeType.TITLE: - title_path = tree.get_path_from_root(node.index_id) - sibling_nodes = tree.get_sibling_nodes(node.index_id) - res_dict = kg_extractor.extract_title(node, title_path, sibling_nodes) - else: - res_dict = kg_extractor.extract_kg(node) - kg_extract_res.append(res_dict) + log.info("Batch processing is enabled for knowledge graph extraction.") + batch_nodes = [] + batch_title_nodes = [] + batch_title_paths = [] + batch_sibling_nodes = [] + for node in tree.nodes: + if node == tree.root_node: + continue + if node.type == NodeType.TITLE: + title_path = tree.get_path_from_root(node.index_id) + sibling_nodes = tree.get_sibling_nodes(node.index_id) + batch_title_nodes.append(node) + batch_title_paths.append(title_path) + batch_sibling_nodes.append(sibling_nodes) + else: + batch_nodes.append(node) + + if batch_title_nodes: + log.info("Processing title nodes in batches...") + res_dict = kg_extractor.batch_extract_titles( + nodes=batch_title_nodes, + title_paths=batch_title_paths, + sibling_nodes_list=batch_sibling_nodes, + ) + kg_extract_res.extend(res_dict) + + if batch_nodes: + log.info("Processing non-title nodes in batches...------") + res_dict = kg_extractor.batch_extract_kg(nodes=batch_nodes) + kg_extract_res.extend(res_dict) + + kg_extract_res.sort(key=lambda x: x.get("node_idx", -1)) log.info("Knowledge graph extraction completed.") log.info(f"Extracted {len(kg_extract_res)} nodes from the document tree.") @@ -145,17 +102,22 @@ def build_knowledge_graph(tree: DocumentTree, cfg: SystemConfig): log.info(f"Knowledge graph extraction cost: {kg_extraction_cost}") for res in kg_extract_res: + entities, relationships = align_entities_to_ontology( + entities=res.get("entities", []), + relationships=res.get("relations", []), + ontology_cfg=cfg.ontology, + ) if cfg.graph.refine_type == "basic": log.info("Using basic KG refinement.") kg_refiner.basic_kg_refiner( - entities=res.get("entities", []), - relationships=res.get("relations", []), + entities=entities, + relationships=relationships, source_id=res.get("node_idx", -1), ) elif cfg.graph.refine_type == "advanced": kg_refiner.advanced_kg_refiner( - entities=res.get("entities", []), - relationships=res.get("relations", []), + entities=entities, + relationships=relationships, source_id=res.get("node_idx", -1), ) @@ -169,7 +131,6 @@ def build_knowledge_graph(tree: DocumentTree, cfg: SystemConfig): kg_refiner.close() return graph_index - # graph_index.save_graph() if __name__ == "__main__": diff --git a/Core/pipelines/kg_refiner.py b/Core/pipelines/kg_refiner.py index 17ac758..e0ee575 100644 --- a/Core/pipelines/kg_refiner.py +++ b/Core/pipelines/kg_refiner.py @@ -124,6 +124,51 @@ def get_latest_entity_name(self, node_name: str) -> str: # Recursively find the latest entity name return self.get_latest_entity_name(latest_node_name) + def _merge_entity_metadata( + self, + primary_entity: Entity, + secondary_entity: Entity, + description: str, + entity_name: Optional[str] = None, + entity_type: Optional[str] = None, + ) -> Entity: + merged_aliases = [] + for alias in [ + primary_entity.entity_name, + secondary_entity.entity_name, + *primary_entity.aliases, + *secondary_entity.aliases, + ]: + alias = str(alias or "").strip() + if alias and alias not in merged_aliases: + merged_aliases.append(alias) + + entity_role = primary_entity.entity_role or secondary_entity.entity_role or "provisional" + if "canonical" in {primary_entity.entity_role, secondary_entity.entity_role}: + entity_role = "canonical" + + entity_id = primary_entity.entity_id or secondary_entity.entity_id + canonical_id = ( + primary_entity.canonical_id + or secondary_entity.canonical_id + or entity_id + ) + + return Entity( + entity_name=entity_name or primary_entity.entity_name, + entity_type=entity_type or primary_entity.entity_type, + description=description, + entity_id=entity_id, + canonical_id=canonical_id, + entity_role=entity_role, + aliases=merged_aliases, + mapping_confidence=max( + primary_entity.mapping_confidence, secondary_entity.mapping_confidence + ), + ontology_source=primary_entity.ontology_source or secondary_entity.ontology_source, + source_ids=set(primary_entity.source_ids).union(secondary_entity.source_ids), + ) + def entity_merge( self, old_entity: Entity, @@ -145,28 +190,65 @@ def entity_merge( # 2. merge the two entities old_node_name = self.graph_index.get_node_name_from_entity(old_entity) new_node_name = self.graph_index.get_node_name_from_entity(new_entity) - if (old_node_name == new_node_name) or merged_to_old_entity: + canonical_entity = None + if old_entity.entity_role == "canonical": + canonical_entity = old_entity + elif new_entity.entity_role == "canonical": + canonical_entity = new_entity + + if canonical_entity is not None: + log.info("merged with canonical entity metadata preserved") + description = ( + old_entity.description + self._DESCRIPTION_SEP_ + new_entity.description + ) + primary_entity = canonical_entity + secondary_entity = new_entity if canonical_entity == old_entity else old_entity + merged_entity = self._merge_entity_metadata( + primary_entity=primary_entity, + secondary_entity=secondary_entity, + description=description, + ) + elif (old_node_name == new_node_name) or merged_to_old_entity: # 2.1 if have the same node name, or merged to old entity, # Directly merged if the entity name and type are the same log.info("merged directly") new_description = ( old_entity.description + self._DESCRIPTION_SEP_ + new_entity.description ) - merged_entity = Entity( - entity_name=old_entity.entity_name, - entity_type=old_entity.entity_type, + merged_entity = self._merge_entity_metadata( + primary_entity=old_entity, + secondary_entity=new_entity, description=new_description, - source_ids=set(old_entity.source_ids).union(new_entity.source_ids), ) else: # 2.2 if have different node name, use LLM to create new entity log.info("merged by LLM summarization") - old_entity_dict = old_entity.model_dump(exclude={"source_ids"}) + old_entity_dict = old_entity.model_dump( + exclude={ + "source_ids", + "entity_id", + "canonical_id", + "entity_role", + "aliases", + "mapping_confidence", + "ontology_source", + } + ) old_entity_dict["description"] = truncate_description( old_entity_dict["description"], max_words=200 ) - new_entity_dict = new_entity.model_dump(exclude={"source_ids"}) + new_entity_dict = new_entity.model_dump( + exclude={ + "source_ids", + "entity_id", + "canonical_id", + "entity_role", + "aliases", + "mapping_confidence", + "ontology_source", + } + ) new_entity_dict["description"] = truncate_description( new_entity_dict["description"], max_words=200 ) @@ -193,11 +275,12 @@ def entity_merge( old_entity.description + self._DESCRIPTION_SEP_ + new_entity.description ) - merged_entity = Entity( + merged_entity = self._merge_entity_metadata( + primary_entity=old_entity, + secondary_entity=new_entity, + description=description, entity_name=res_entity.entity_name, entity_type=res_entity.entity_type, - description=description, - source_ids=set(old_entity.source_ids).union(new_entity.source_ids), ) # 2.3 If the llm generated merged entity is another entity (entityC) in the graph, @@ -230,11 +313,12 @@ def entity_merge( old_entity_type=entity_c.entity_type, new_entity=old_entity, ) - merged_entity.description += ( - self._DESCRIPTION_SEP_ + entity_c.description - ) - merged_entity.source_ids = set(merged_entity.source_ids).union( - entity_c.source_ids + merged_entity = self._merge_entity_metadata( + primary_entity=merged_entity, + secondary_entity=entity_c, + description=( + merged_entity.description + self._DESCRIPTION_SEP_ + entity_c.description + ), ) # since entity_c is the same as merged_entity, no need to update alias map @@ -293,6 +377,7 @@ def basic_kg_refiner( entity.entity_name, entity.entity_type ) merged_entity = self.entity_merge(existing_entity, entity) + entity_map[entity.entity_name] = merged_entity entity_map[existing_entity.entity_name] = merged_entity add_entity_list.append(merged_entity) @@ -301,12 +386,23 @@ def basic_kg_refiner( # Update relationships for rel in relationships: - if rel.src_entity_name in entity_map: - rel.src_entity_name = entity_map[rel.src_entity_name].entity_name - src_type = entity_map[rel.src_entity_name].entity_type - if rel.tgt_entity_name in entity_map: - rel.tgt_entity_name = entity_map[rel.tgt_entity_name].entity_name - tgt_type = entity_map[rel.tgt_entity_name].entity_type + old_src_name = rel.src_entity_name + old_tgt_name = rel.tgt_entity_name + src_type = None + tgt_type = None + if old_src_name in entity_map: + mapped_src = entity_map[old_src_name] + rel.src_entity_name = mapped_src.entity_name + src_type = mapped_src.entity_type + if old_tgt_name in entity_map: + mapped_tgt = entity_map[old_tgt_name] + rel.tgt_entity_name = mapped_tgt.entity_name + tgt_type = mapped_tgt.entity_type + if src_type is None or tgt_type is None: + log.info( + f"Relationship {rel} has missing entity types. Skipping this relationship." + ) + continue self.graph_index.add_kg_edge(rel=rel, src_type=src_type, tgt_type=tgt_type) def get_vdb_meta_data(self, entity: Entity) -> dict: @@ -318,11 +414,7 @@ def get_vdb_meta_data(self, entity: Entity) -> dict: dict: The metadata dictionary without source_ids. since vdb does not support list type. """ - return { - "entity_name": entity.entity_name, - "entity_type": entity.entity_type, - "description": entity.description, - } + return entity.to_vdb_metadata() def add_entities_to_vdb(self, entities: List[Entity]) -> None: """ @@ -477,7 +569,7 @@ def metadata_str(meta_data: dict): else: break - if len(sel_entities) == ranked_results: + if len(sel_entities) == len(ranked_results): # 4.3 If all entities are selected, return empty list return [] @@ -540,23 +632,20 @@ def er_selection_by_llm( if 0 <= select_id < len(similar_entities): # Log the selection and reason - log.info( - f"LLM selected entity ID: {select_id}, " f"Reason: {res.explanation}" - ) + log.info(f"LLM selected entity ID: {select_id}, Reason: {res.explanation}") # Log the new entity and the selected similar entity log.info("New Entity Info:") log.info( f"Entity Name: {new_entity.entity_name}, Entity Type: {new_entity.entity_type}" ) - log.info(f"LLM selected Entity Info:") + log.info("LLM selected Entity Info:") log.info( f"Entity Name: {similar_entities[select_id].entity_name}, Entity Type: {similar_entities[select_id].entity_type}" ) return similar_entities[select_id] - else: - print(f"Warning: LLM returned an out-of-bounds ID: {select_id}") - return None + print(f"Warning: LLM returned an out-of-bounds ID: {select_id}") + return None def entity_resolution(self, new_entity: Entity) -> Entity: """ @@ -684,10 +773,9 @@ def process_relationships( "Skipping this relationship." ) continue - else: - self.graph_index.add_kg_edge( - rel=rel, src_type=src_type, tgt_type=tgt_type - ) + self.graph_index.add_kg_edge( + rel=rel, src_type=src_type, tgt_type=tgt_type + ) def _debug_check_num(self): num_node_graph = len(self.graph_index.kg.nodes()) @@ -872,7 +960,6 @@ def refine_entities(self): f"Refined {len(add_entities)} entities and added them to the vector database." ) self._debug_check_num() - return def refine_relation(self): # delete self loop in graph index diff --git a/Core/rag/gbc_rag.py b/Core/rag/gbc_rag.py index 7de36ad..395ad8b 100644 --- a/Core/rag/gbc_rag.py +++ b/Core/rag/gbc_rag.py @@ -1,14 +1,11 @@ from collections import defaultdict -from typing import Any, List, Tuple, Dict, Optional - -from regex import F +from typing import Any, List, Dict, Optional from Core.Index.Tree import TreeNode, NodeType from Core.rag.base_rag import BaseRAG from Core.provider.llm import LLM from Core.provider.vlm import VLM from Core.provider.rerank import TextRerankerProvider -from Core.provider.embedding import MMRerankerProvider from Core.configs.rag.gbc_config import GBCRAGConfig from Core.Index.GBCIndex import GBC from Core.prompts.gbc_prompt import ( @@ -29,6 +26,11 @@ SubStep, filter_tree_nodes, ) +from Core.utils.ontology_utils import ( + find_best_graph_ontology_node, + normalize_entity_name, + normalize_entity_type, +) import json @@ -87,7 +89,6 @@ def __init__( self.retriever = Retriever( varient=self.varient, reranker=self.reranker, - # mm_reranker=self.mm_reranker, embedder=self.embedder, alpha=self.cfg.alpha, topk_ent=self.cfg.topk_ent, @@ -99,37 +100,42 @@ def _get_entity_embed_text(self, entity: QuestionEntity) -> str: return f"Name: {entity.entity_name}\nType: {entity.entity_type}" def _entity_map( - self, entities: List[str], force_one: bool = False + self, entities: List[QuestionEntity], force_one: bool = False ) -> Dict[str, List[str]]: """ Maps entities to their corresponding IDs in the GBC index. Use vdb to find the entity in GBC index. """ entities_str = [self._get_entity_embed_text(entity) for entity in entities] - Qent_GBCent_map = defaultdict(list) + query_to_gbc_entity_map = defaultdict(list) res_list = [] for ent_str in entities_str: query_res = self.gbc_index.entity_vdb.search(query_text=ent_str, top_k=2) + if not query_res: + continue min_distance = query_res[0]["distance"] if query_res else float("inf") - retrieve_name = query_res[0]["metadata"].get("entity_name") - retrieve_type = query_res[0]["metadata"].get("entity_type") + metadata = query_res[0].get("metadata") or {} + retrieve_name = metadata.get("entity_name") + retrieve_type = metadata.get("entity_type") + if not retrieve_name: + continue node_name = self.gbc_index.GraphIndex.get_node_name_from_str( retrieve_name, retrieve_type ) if min_distance < self.threshold_e: - Qent_GBCent_map[ent_str].append(node_name) + query_to_gbc_entity_map[ent_str].append(node_name) log.info(f"Entity '{ent_str}' mapped to GBC entity: {node_name}") else: res_list.append((ent_str, node_name, min_distance)) - if force_one and len(Qent_GBCent_map) == 0 and len(res_list) > 0: + if force_one and len(query_to_gbc_entity_map) == 0 and len(res_list) > 0: # force map the closest entity if no entity is mapped res_list = sorted(res_list, key=lambda x: x[2]) ent_str, node_name, min_distance = res_list[0] - Qent_GBCent_map[ent_str].append(node_name) + query_to_gbc_entity_map[ent_str].append(node_name) log.info(f"Force map entity '{ent_str}' to GBC entity: {node_name}") - return Qent_GBCent_map + return query_to_gbc_entity_map def _get_query_entity(self, query: str) -> Dict[str, List[str]]: """ @@ -141,8 +147,11 @@ def _get_query_entity(self, query: str) -> Dict[str, List[str]]: retrieval_node_names = set() retrieval_nodes = [] for ent_info in retrieval_ents: - ent_name = ent_info["metadata"].get("entity_name") - ent_type = ent_info["metadata"].get("entity_type") + metadata = ent_info.get("metadata") or {} + ent_name = metadata.get("entity_name") + ent_type = metadata.get("entity_type") + if not ent_name: + continue node_dict = { "entity_name": ent_name, "entity_type": ent_type, @@ -180,28 +189,45 @@ def _get_query_entity(self, query: str) -> Dict[str, List[str]]: log.info("Use the question as the entity.") res_entities = [Entity(entity_name=query, entity_type="Question")] - Qent_GBCent_map = defaultdict(list) + query_to_gbc_entity_map = defaultdict(list) remain_ents = [] for res_ent in res_entities: - res_ent.entity_name = res_ent.entity_name.lower() - res_ent.entity_type = res_ent.entity_type.upper() - res_ent.entity_type = res_ent.entity_type.replace(" ", "_") + res_ent.entity_name = normalize_entity_name(res_ent.entity_name) + res_ent.entity_type = normalize_entity_type(res_ent.entity_type) + ent_str = self._get_entity_embed_text(res_ent) + + ontology_cfg = getattr(self.gbc_index.config, "ontology", None) + ontology_node_name = None + if ontology_cfg and ontology_cfg.use_query_resolution: + ontology_node_name = find_best_graph_ontology_node( + graph=self.gbc_index.GraphIndex, + entity_name=res_ent.entity_name, + entity_type=res_ent.entity_type, + threshold=ontology_cfg.mapping_threshold, + ) + if ontology_node_name: + query_to_gbc_entity_map[ent_str].append(ontology_node_name) + log.info( + f"Entity '{ent_str}' mapped to ontology-backed GBC entity: {ontology_node_name}" + ) + continue + ent_node_name = self.gbc_index.GraphIndex.get_node_name_from_entity(res_ent) if ent_node_name in retrieval_node_names: - Qent_GBCent_map[ent_node_name].append(ent_node_name) + query_to_gbc_entity_map[ent_str].append(ent_node_name) log.info( f"Entity '{ent_node_name}' mapped to GBC entity: {ent_node_name}" ) else: remain_ents.append(res_ent) - should_force_one = (len(Qent_GBCent_map) == 0) + should_force_one = len(query_to_gbc_entity_map) == 0 if remain_ents: remain_map = self._entity_map(remain_ents, force_one=should_force_one) for k, v in remain_map.items(): - Qent_GBCent_map[k].extend(v) + query_to_gbc_entity_map[k].extend(v) - return Qent_GBCent_map + return query_to_gbc_entity_map def link_tree_node(self, entities_map: Dict[str, List[str]]) -> List[dict]: """ @@ -218,7 +244,7 @@ def link_tree_node(self, entities_map: Dict[str, List[str]]) -> List[dict]: return [] for node_name in all_map_nodenames: - tree_node_set = self.gbc_index.GraphIndex.NodeName2TreeNodes(node_name) + tree_node_set = self.gbc_index.GraphIndex.node_name_to_tree_nodes(node_name) for node_id in tree_node_set: tree_node_cnt[node_id].append(node_name) @@ -270,7 +296,7 @@ def prep_SecSel_prompt( query, link_nodes: List[TreeNode] = None, remain_nodes: List[TreeNode] = None, - sec_entity_map: Dict[int, List[str]] = None, + sec_entity_map: Optional[Dict[int, List[str]]] = None, ) -> str: """ Prepare the prompt for section selection. @@ -278,7 +304,7 @@ def prep_SecSel_prompt( """ def prep_nodes_json( - nodes: List[TreeNode], sec_entity_map: Dict[int, List[str]] = None + nodes: List[TreeNode], sec_entity_map: Optional[Dict[int, List[str]]] = None ) -> str: node_infos = [] for node in nodes: @@ -433,8 +459,8 @@ def get_GBC_info(self, iter_context: SubStep) -> None: log.info(f"After skyline filtering, select {len(tree_node_ids)} TreeNodes") - Graph_data = self.gbc_index.GraphIndex.get_subgraph_data(res_entities) - iter_context.iteration_graph_nodes = Graph_data.get("nodes", []) + graph_data = self.gbc_index.GraphIndex.get_subgraph_data(res_entities) + iter_context.iteration_graph_nodes = graph_data.get("nodes", []) tree_data = self.gbc_index.TreeIndex.get_nodes_data(tree_node_ids) self._process_retrieved_nodes(tree_data, iter_context) @@ -454,10 +480,10 @@ def _retrieve( iter_context: IterationStep, Iteration context for the current step. """ - Qent_GBCent_map = self._get_query_entity(query) - iter_context.gbc_entity_map = Qent_GBCent_map + query_to_gbc_entity_map = self._get_query_entity(query) + iter_context.gbc_entity_map = query_to_gbc_entity_map - tree_nodes = self.link_tree_node(Qent_GBCent_map) + tree_nodes = self.link_tree_node(query_to_gbc_entity_map) iter_context.linked_tree_nodes = tree_nodes # 3. Use LLM to select the most relevant section or supplementary sections @@ -570,7 +596,8 @@ def process_analysis(self, context: GBCRAGContext, query_analysis: PlanResult): context.final_answer = "I'm sorry, I cannot process this query." def _create_augmented_prompt(self, query: str) -> str: - pass + """Current GBC flow builds prompts via answer agents, so return the raw query.""" + return query def generation(self, query: str, query_output_dir: str): context = GBCRAGContext(query=query) @@ -625,6 +652,4 @@ def _save_retrieval_res(self, context: GBCRAGContext, query_output_dir: str): def close(self): self.embedder.close() self.reranker.close() - # if hasattr(self, 'mm_reranker'): - # self.mm_reranker.close() return super().close() diff --git a/Core/utils/ontology_utils.py b/Core/utils/ontology_utils.py new file mode 100644 index 0000000..7ea71a9 --- /dev/null +++ b/Core/utils/ontology_utils.py @@ -0,0 +1,158 @@ +from difflib import SequenceMatcher +from typing import TYPE_CHECKING, Iterable, Optional, Tuple + +if TYPE_CHECKING: + from Core.Index.Graph import Graph + + +def normalize_entity_name(value: str) -> str: + return " ".join(str(value or "").strip().split()).lower() + + +def normalize_entity_type(value: str) -> str: + return str(value or "").strip().upper().replace(" ", "_") + + +def dedupe_terms(values: Iterable[str]) -> list[str]: + deduped: list[str] = [] + for value in values: + normalized = normalize_entity_name(value) + if normalized and normalized not in deduped: + deduped.append(normalized) + return deduped + + +def types_compatible(left: str, right: str) -> bool: + left_norm = normalize_entity_type(left) + right_norm = normalize_entity_type(right) + if not left_norm or not right_norm: + return True + if left_norm in {"QUESTION", "UNKNOWN"}: + return True + return left_norm == right_norm + + +def entity_name_similarity(left: str, right: str) -> float: + left_norm = normalize_entity_name(left) + right_norm = normalize_entity_name(right) + if not left_norm or not right_norm: + return 0.0 + if left_norm == right_norm: + return 1.0 + return SequenceMatcher(None, left_norm, right_norm).ratio() + + +def find_best_ontology_match( + entity, ontology_cfg +) -> Tuple[Optional[object], float]: + if not getattr(ontology_cfg, "enabled", False): + return None, 0.0 + + best_match = None + best_score = 0.0 + for candidate in getattr(ontology_cfg, "entities", []): + if getattr(candidate, "status", "active") == "deprecated": + continue + if not types_compatible(entity.entity_type, candidate.entity_type): + continue + candidate_terms = dedupe_terms( + [candidate.entity_name, *candidate.aliases, *getattr(candidate, "keywords", [])] + ) + score = max(entity_name_similarity(entity.entity_name, alias) for alias in candidate_terms) + if score > best_score: + best_match = candidate + best_score = score + + threshold = getattr(ontology_cfg, "mapping_threshold", 1.0) + if best_match is None or best_score < threshold: + return None, best_score + return best_match, best_score + + +def align_entities_to_ontology(entities, relationships, ontology_cfg): + if not getattr(ontology_cfg, "enabled", False) or not getattr(ontology_cfg, "entities", None): + return entities, relationships + + aligned_entities = [] + original_name_map: dict[str, str] = {} + allow_provisional_entities = getattr(ontology_cfg, "allow_provisional_entities", True) + for entity in entities: + original_name = entity.entity_name + matched_entity, confidence = find_best_ontology_match(entity, ontology_cfg) + if matched_entity is not None: + description = matched_entity.description or entity.description + if matched_entity.description and entity.description: + entity_desc = entity.description.strip() + onto_desc = matched_entity.description.strip() + if entity_desc and entity_desc not in onto_desc: + description = f"{onto_desc}\n\nMention detail: {entity_desc}" + aligned = entity.model_copy( + update={ + "entity_name": matched_entity.entity_name, + "entity_type": matched_entity.entity_type, + "description": description, + "entity_id": matched_entity.entity_id, + "canonical_id": matched_entity.entity_id, + "entity_role": "canonical", + "aliases": dedupe_terms( + [entity.entity_name, matched_entity.entity_name, *matched_entity.aliases] + ), + "mapping_confidence": confidence, + "ontology_source": matched_entity.ontology_source, + } + ) + elif allow_provisional_entities: + aligned = entity.model_copy( + update={ + "entity_name": normalize_entity_name(entity.entity_name), + "entity_type": normalize_entity_type(entity.entity_type), + "entity_role": entity.entity_role or "provisional", + "aliases": dedupe_terms([entity.entity_name, *entity.aliases]), + "mapping_confidence": entity.mapping_confidence, + } + ) + else: + continue + original_name_map[original_name] = aligned.entity_name + aligned_entities.append(aligned) + + aligned_relationships = [] + for relationship in relationships: + if ( + relationship.src_entity_name not in original_name_map + or relationship.tgt_entity_name not in original_name_map + ): + continue + aligned_relationship = relationship.model_copy(deep=True) + aligned_relationship.src_entity_name = original_name_map.get( + relationship.src_entity_name, relationship.src_entity_name + ) + aligned_relationship.tgt_entity_name = original_name_map.get( + relationship.tgt_entity_name, relationship.tgt_entity_name + ) + aligned_relationships.append(aligned_relationship) + + return aligned_entities, aligned_relationships + + +def find_best_graph_ontology_node( + graph: "Graph", entity_name: str, entity_type: str, threshold: float = 1.0 +) -> Optional[str]: + best_node_name = None + best_score = 0.0 + for node_name in graph.get_all_nodes(): + entity = graph.get_entity_by_node_name(node_name) + if entity.entity_role != "canonical" and not entity.canonical_id: + continue + if not types_compatible(entity_type, entity.entity_type): + continue + score = max( + entity_name_similarity(entity_name, alias) + for alias in dedupe_terms([entity.entity_name, *entity.aliases]) + ) + if score > best_score: + best_score = score + best_node_name = node_name + if best_score < threshold: + return None + return best_node_name \ No newline at end of file diff --git a/api/services/indexing.py b/api/services/indexing.py index 7a052f2..5acf04c 100644 --- a/api/services/indexing.py +++ b/api/services/indexing.py @@ -18,7 +18,7 @@ def _build_index_sync( """Synchronous index build — runs in a thread pool.""" from Core.configs.system_config import load_system_config from Core.configs.falkordb_config import FalkorDBConfig - from Core.pipelines.doc_tree_builder import construct_GBC_index + from Core.construct_index import construct_gbc_index cfg = load_system_config(config_path) cfg.pdf_path = pdf_path @@ -39,7 +39,7 @@ def _build_index_sync( host=FALKORDB_HOST, port=FALKORDB_PORT, username=FALKORDB_USERNAME, password=FALKORDB_PASSWORD, ) - construct_GBC_index(cfg) + construct_gbc_index(cfg) async def run_indexing( diff --git a/config/gbc.yaml b/config/gbc.yaml index aeb4b80..ee630fb 100644 --- a/config/gbc.yaml +++ b/config/gbc.yaml @@ -68,6 +68,20 @@ vdb: model_name: Alibaba-NLP/gme-Qwen2-VL-2B-Instruct device: "cuda:1" +ontology: + enabled: false + mapping_threshold: 1.0 + allow_provisional_entities: true + use_query_resolution: true + entities: + - ontology_id: "product:bookrag" + canonical_name: "bookrag" + entity_type: "PRODUCT" + description: "The BookRAG application and retrieval pipeline." + aliases: + - "book rag" + - "book-rag" + falkordb: host: "r-6jissuruar.instance-yc6en3ndn.hc-dx5io0svq.asia-south1.gcp.f2e0a955bb84.cloud" port: 60480 diff --git a/docs/bookrag-architecture-review.md b/docs/bookrag-architecture-review.md new file mode 100644 index 0000000..19ac848 --- /dev/null +++ b/docs/bookrag-architecture-review.md @@ -0,0 +1,883 @@ +# BookRAG Architecture Review and Comparison with Three-Layer Fixed Entity Architecture + +## 1. Purpose of This Document + +This document explains how the current BookRAG application is implemented, how its retrieval pipeline works end to end, and how it compares with the "Three-Layer Fixed Entity Architecture" (FEA) pattern for graph-based RAG. + +The goal is to answer three practical questions: + +1. What is BookRAG doing today? +2. How is it similar to and different from FEA? +3. Can FEA ideas improve BookRAG, and if so, where? + +## 2. Executive Summary + +BookRAG is not a pure GraphRAG system and it is not an ontology-first system. It is better described as a **hybrid hierarchical-document + knowledge-graph + vector retrieval architecture** with query planning and multimodal answer generation. + +Its strongest architectural characteristics are: + +- **Document structure first**: PDF content is converted into a hierarchical `DocumentTree`. +- **Graph derived from structure**: the knowledge graph is extracted from tree nodes, not from flat chunks alone. +- **Hybrid retrieval**: answers are grounded using tree structure, graph connectivity, and vector similarity together. +- **Query-aware orchestration**: the system classifies queries into simple, complex, and global modes. +- **Multimodal answering**: text, tables, and images can all contribute to the final answer. + +Compared with FEA, BookRAG already shares the idea that **entities bridge user questions and source evidence**. However, BookRAG does **not** currently implement a stable canonical ontology layer above extracted entities. That is the main conceptual gap. + +## 3. What the Three-Layer Fixed Entity Architecture Means + +The Three-Layer Fixed Entity Architecture is best understood as an **ontology-first graph RAG pattern** with three conceptual layers: + +It does not appear to be a mainstream standard framework in the academic GraphRAG literature. In practice, it is more useful to treat it as a **design pattern** for domain-governed graph retrieval. + +1. **Ontology / fixed entity layer** + Stable canonical entities, types, and controlled relationships. +2. **Document layer** + Source documents, passages, or chunks that contain evidence. +3. **Extracted entity / mention layer** + Mentions or extracted entities from documents, linked upward to canonical entities and downward to source evidence. + +The design goal is to reduce noisy entity duplication, improve explainability, and make graph retrieval more precise in domain-specific settings. + +## 4. High-Level BookRAG Architecture + +At a high level, BookRAG has two major operating modes: + +- **Offline indexing**: parse documents, build a structural tree, extract/refine a graph, and build vector resources. +- **Online RAG**: analyze the query, map it to graph/tree evidence, retrieve relevant substructures, and generate an answer. + +Conceptually, the pipeline is: + +`PDF -> parser -> refined PDF structure -> DocumentTree -> KG extraction/refinement -> Graph + tree-to-graph links -> vector indices -> query planning -> entity mapping -> section/subtree/subgraph retrieval -> reranking -> answer generation` + +## 5. Main Implementation Surfaces + +### 5.1 CLI entry point + +`main.py` is the main batch and single-document entry point. + +It supports: + +- `index` mode for offline construction +- `rag` mode for inference +- staged indexing via `--stage` +- dataset-driven multi-document processing +- split-based processing for parallel workers +- config snapshotting and run logging + +This makes the project operationally closer to a **pipeline system** than a demo-only chatbot. + +### 5.2 API entry point + +`api/main.py` exposes BookRAG as a FastAPI service with: + +- startup config validation +- structured JSON logging +- request ID propagation +- MongoDB lifecycle/index management +- optional FalkorDB health checking +- routers for auth, tenants, documents, chat, and entities + +This indicates the repository is designed for **multi-tenant application deployment**, not only offline experiments. + +## 6. Core Data Structures + +### 6.1 Document tree + +`Core/Index/Tree.py` defines the structural backbone: + +- `DocumentTree` +- `TreeNode` +- `NodeType` +- `MetaInfo` + +This tree stores document hierarchy such as titles, sections, text blocks, tables, and images. It also supports subtree extraction, ancestor navigation, depth-aware traversal, and retrieval-friendly node serialization. + +This is the first major difference from FEA: **BookRAG is tree-first, not ontology-first**. + +### 6.2 Knowledge graph + +`Core/Index/Graph.py` defines the graph layer: + +- `Entity` +- `Relationship` +- `Graph` + +The graph is backed by `networkx.Graph` in memory and can be saved or reconstructed through FalkorDB. A key implementation detail is the `tree2kg` mapping, which links structural document nodes to graph nodes. + +That mapping is crucial because BookRAG does not retrieve graph evidence in isolation. It uses the graph to lead back into the source document structure. + +### 6.3 Hybrid GBC index + +`Core/Index/GBCIndex.py` packages the main retrieval assets into a unified object: + +- tree index +- graph index +- entity vector database + +This confirms that the production architecture is **hybrid by design**, rather than a single-layer graph database solution. + +## 7. Offline Index Construction Flow + +The main offline orchestration entry is `Core/construct_index.py`, which coordinates tree building, KG construction, GBC index creation, and vector resource rebuilding. + +### 7.1 Tree construction + +`Core/pipelines/doc_tree_builder.py` builds the `DocumentTree`. + +Important behaviors include: + +- parser selection between **Docling** and **MinerU** +- cache-aware parsing +- PDF refinement +- outline extraction +- legal heading detection +- optional node summary generation + +This stage turns raw PDFs into a structured hierarchy that later retrieval can reason over. + +### 7.2 KG construction + +`Core/pipelines/kg_builder.py` extracts a graph from the tree. + +Important characteristics: + +- extraction is tree-node aware +- title nodes are handled differently from non-title nodes +- graph refinement supports at least `basic` and `advanced` modes +- post-processing refines both entities and relations + +The key design principle is: **the graph is derived from the document tree rather than replacing it**. + +### 7.3 Vector resources + +After tree and graph construction, the system rebuilds vector resources, especially entity-oriented lookup resources used during query-to-graph mapping. + +This gives BookRAG three retrieval surfaces at runtime: + +1. document hierarchy +2. graph connectivity +3. semantic vector similarity + +## 8. Online Retrieval and Answer Generation Flow + +### 8.1 Inference orchestration + +`Core/inference.py` prepares dependencies, creates the configured RAG agent, runs the query dataset, stores per-query outputs, and records token cost. + +This operational layer is important because it shows BookRAG is structured as a repeatable evaluation pipeline, not only a live chat handler. + +### 8.2 Main RAG implementation + +The most representative runtime path is `Core/rag/gbc_rag.py`. + +There is also a simpler `Core/rag/graph_rag.py`, but it is not the best file for understanding the full BookRAG architecture. The `GBCRAG` path is the real reference implementation for the current system design. + +Its main logic combines: + +- `TaskPlanner` +- `Retriever` +- `AnswerAgent` + +This is significantly more advanced than a basic entity-hop graph retriever. + +### 8.3 Query understanding + +`Core/rag/gbc_plan.py` classifies queries into: + +- `simple` +- `complex` +- `global` + +Complex queries can be decomposed into sub-questions, while global queries can include filtering and aggregation logic. + +This is one of BookRAG's strongest differentiators. FEA usually describes graph organization, but BookRAG also includes **query planning as a first-class runtime component**. + +### 8.4 Query entity mapping + +In `gbc_rag.py`, the system extracts or retrieves query entities, normalizes them, and maps them to graph nodes using a combination of LLM reasoning and vector retrieval. + +This is the part of the architecture that is closest in spirit to FEA: the query is translated into an entity-centered access path through the graph. + +### 8.5 Section and subtree retrieval + +BookRAG does not stop at graph hits. It maps graph nodes back to tree nodes, promotes them to relevant section ancestors, and may supplement them with LLM-based section selection. + +This is a major design choice: + +- graph nodes help identify relevant concepts +- tree structure recovers coherent document context +- subtree retrieval preserves hierarchical evidence around the hit + +This is stronger than flat chunk retrieval and also different from a pure ontology graph lookup. + +### 8.6 Graph and text reranking + +`Core/rag/gbc_retrieval.py` provides three major behaviors: + +- text reranking +- graph reranking +- skyline filtering + +The graph reranker uses query-entity similarity, graph enhancement, and personalized PageRank-style scoring. The skyline stage then combines graph and text signals instead of depending on only one scoring channel. + +An important nuance: comments referring to a "Three Layer Reranker" in retrieval code are about reranking signals, **not** the Three-Layer Fixed Entity Architecture. + +### 8.7 Answer generation + +`Core/rag/gbc_answer.py` handles final answer construction. + +It supports: + +- text evidence +- table-to-text conversion +- image-aware reasoning with VLM support +- chunked prompting under token budgets +- synthesis across partial answers +- map-reduce style answering for global questions + +This means BookRAG is not only a retriever architecture; it is also a **multimodal answer synthesis system**. + +## 9. Configuration and Storage Model + +`config/gbc.yaml` shows the default configuration shape. + +Important implementation themes are: + +- `parser: docling` +- graph refine mode set to `advanced` +- separate LLM, VLM, embedding, and reranker configuration +- vector database configuration +- FalkorDB connectivity for graph persistence +- `rag.strategy: gbc` +- `topk`, `select_depth`, and retry controls for retrieval behavior + +The configuration confirms that BookRAG is intended to run as a configurable production-style system, not as a hard-coded prototype. + +## 10. Similarities Between BookRAG and FEA + +BookRAG and FEA are similar in several important ways. + +### 10.1 Entities are the bridge between questions and evidence + +In both designs, entities are central to retrieval. The user query is interpreted in terms of entities or concepts, and those entities connect the query to evidence. + +### 10.2 Evidence remains anchored to source documents + +Neither design treats the graph as sufficient by itself. Both depend on linking graph-level concepts back to grounded document evidence. + +### 10.3 Graph structure improves over naive vector-only retrieval + +Both architectures use graph relationships to improve retrieval quality, especially for multi-hop, concept-heavy, or relationship-sensitive questions. + +### 10.4 Normalization matters + +Both approaches implicitly depend on entity normalization. BookRAG does it through extraction/refinement/vector matching; FEA emphasizes doing it through a stable canonical layer. + +## 11. Differences Between BookRAG and FEA + +### 11.0 Compact comparison matrix + +| Dimension | BookRAG | Three-Layer FEA | +|---|---|---| +| Primary organizing principle | Document hierarchy | Canonical ontology/entities | +| Source grounding | Tree nodes and sections | Documents/chunks linked to entities | +| Graph identity | Extracted/refined entities | Fixed canonical entities + mentions | +| Runtime retrieval | Hybrid tree + graph + vector | Usually graph/entity-centered | +| Query planning | Strong | Usually out of scope | +| Multimodal answering | Present | Not a defining feature | +| Best strength | Long structured document reasoning | Precision and normalization in fixed domains | + +### 11.1 BookRAG is tree-first; FEA is ontology-first + +This is the most important difference. + +- **BookRAG** starts by building a structural document tree. +- **FEA** starts by defining a fixed conceptual entity layer. + +In BookRAG, graph meaning is downstream of document structure. In FEA, document meaning is organized around canonical domain entities. + +### 11.2 BookRAG does not yet have a fixed canonical ontology layer + +BookRAG extracts entities from documents and refines them, but it does not clearly enforce a stable ontology layer containing canonical entity definitions, allowed types, and controlled relation schemas. + +This can lead to: + +- duplicate entities across documents +- inconsistent typing +- weaker cross-document consolidation +- harder governance in domain-specific deployments + +### 11.3 BookRAG is operationally richer than FEA + +FEA is mainly a graph organization pattern. BookRAG includes several runtime capabilities that go beyond that: + +- query planning and decomposition +- section-depth selection +- subtree-induced subgraph retrieval +- skyline fusion of graph and text ranking +- multimodal answer generation +- batch CLI and multi-tenant API deployment + +So FEA is narrower and more schema-centric, while BookRAG is broader and more end-to-end. + +### 11.4 BookRAG optimizes context coherence through sections + +FEA often focuses on entity-document grounding. BookRAG goes further by recovering section-level and subtree-level context from a document hierarchy. + +For long complex PDFs, this is a major advantage. + +## 12. Can FEA Improve BookRAG? + +Yes, but not as a replacement. The best use of FEA is to **strengthen BookRAG's graph and normalization layer** while preserving BookRAG's tree-first retrieval strengths. + +The right framing is: + +- **keep** BookRAG's document tree, planner, reranker, and answer synthesis +- **add** a canonical ontology/fixed-entity layer above the current extracted graph + +## 13. Recommended Improvement Areas + +### 13.1 Add a canonical entity layer above extracted entities + +Introduce a persistent canonical layer with: + +- controlled entity types +- canonical IDs +- synonym/alias tables +- relation constraints +- optional domain taxonomy + +Then map extracted entities to canonical nodes rather than treating extracted surface forms as the final graph identity. + +This would be the closest direct adoption of FEA. + +### 13.2 Separate mention nodes from canonical entity nodes + +A stronger graph pattern would be: + +- **canonical entity nodes** +- **document/tree section nodes** +- **mention or extracted instance nodes** + +This would make BookRAG more explainable and better at traceability, especially when multiple documents mention the same concept differently. + +### 13.3 Use ontology guidance during KG refinement + +The current refinement process could be improved by validating extracted entities and relationships against canonical schema rules. + +This could reduce: + +- noisy relation creation +- inconsistent labels +- entity fragmentation + +### 13.4 Improve cross-document and global retrieval + +`Graph.py` already hints at global graph support through methods for global graph save/load behavior. FEA-style canonical entities would make those global capabilities much stronger. + +This is especially valuable for: + +- multi-document reasoning +- tenant-wide knowledge consolidation +- repeated entities across books or regulations + +### 13.5 Keep the tree as the primary evidence surface + +Even after adopting FEA ideas, BookRAG should continue using the document tree as the main evidence recovery structure. + +This is important because the tree preserves: + +- section boundaries +- local context +- layout-aware structure +- multimodal content placement + +Replacing the tree with a graph-only ontology design would weaken BookRAG's long-document strengths. + +## 14. Recommended Target Architecture + +The best future architecture is a **four-part hybrid**: + +1. **Canonical ontology/entity layer** for stable domain identity +2. **Mention/extracted entity layer** for document-grounded extractions +3. **DocumentTree layer** for structural evidence recovery +4. **Hybrid runtime retriever** for graph + tree + vector + planner orchestration + +In other words, BookRAG should not become "FEA instead of GBC". It should become **GBC with an ontology-governed canonical entity layer**. + +## 15. Final Assessment + +### 15.1 What BookRAG already does well + +- Strong handling of complex document structure +- Hybrid retrieval rather than graph-only retrieval +- Query planning for different question types +- Section-aware evidence recovery +- Multimodal answer generation +- Practical CLI and API deployment surfaces + +### 15.2 What FEA contributes + +- canonical identity +- ontology governance +- cleaner cross-document consolidation +- stronger explainability for entity normalization +- better domain precision in regulated or specialized corpora + +### 15.3 Overall conclusion + +BookRAG is already architecturally more comprehensive than the Three-Layer Fixed Entity Architecture as a full application system. However, FEA offers an important improvement in one specific area: **a fixed canonical ontology/entity layer that stabilizes the graph**. + +Therefore, the recommendation is: + +- **Do not replace the current BookRAG design with FEA.** +- **Adopt FEA principles to improve entity canonicalization, ontology control, and global graph consistency.** + +That would preserve BookRAG's current strengths while addressing one of the clearest structural opportunities for improvement. + +## 16. Ontology-Driven Extension Proposal + +The user suggestion is directionally correct: **allow users to define ontology entities with descriptions, then use those entities as the base canonical layer during indexing**. + +As an architectural recommendation, this should be implemented as a **guided open-world design**, not a closed-world design. + +That means: + +- user-defined ontology entities become the preferred canonical targets +- extracted document entities are mapped onto that ontology when confidence is high +- unmatched entities are still preserved as provisional or local entities +- the ontology can be expanded over time from repeated provisional entities + +This is the safest way to improve precision without harming recall. + +### 16.1 Why this is valuable for BookRAG + +This proposal fits BookRAG especially well because the current system already has: + +- strong document grounding through `DocumentTree` +- a KG extraction and refinement pipeline +- entity vector lookup during retrieval +- multi-document and global-graph ambitions in `Graph.py` + +What it lacks most clearly is a **stable canonical identity layer** shared across document-specific mentions. + +### 16.2 Recommended design principle + +The right principle is: + +1. **Ontology entity** = stable canonical concept defined by the user +2. **Mention entity** = what the extractor found in a specific document node +3. **Evidence node** = the `DocumentTree` node that grounds the mention + +The ontology should be the **base layer**, but not the **only allowed layer**. + +### 16.2.1 Mermaid architecture sketch + +```mermaid +flowchart LR + O[User ontology\ncanonical entities] --> A[Ontology alignment] + D[DocumentTree nodes] --> E[KG extraction] + E --> A + A --> K[Canonical and provisional KG entities] + K --> V[Entity VDB] + Q[User query] --> R[Query entity extraction] + R --> F[Ontology-first resolution] + F --> K + K --> T[Linked tree evidence] + T --> S[BookRAG answer synthesis] +``` + +### 16.3 Why entity descriptions matter + +Descriptions are not optional metadata; they are part of the matching signal. + +They help disambiguate cases such as: + +- the same alias referring to different concepts +- domain-specific abbreviations +- entities with similar names but different roles + +For this reason, user-provided ontology descriptions should be used in: + +- indexing-time entity alignment +- query-time entity resolution +- ambiguity handling when multiple ontology entities are plausible + +## 17. Concrete Schema for User-Defined Ontology + +The schema should be tenant-scoped and explicitly designed for canonicalization. + +### 17.1 Core ontology entity schema + +| Field | Required | Type | Purpose | +|---|---|---|---| +| `ontology_id` | Yes | `str` | Stable canonical identifier used across documents and queries | +| `canonical_name` | Yes | `str` | Preferred human-readable entity name | +| `entity_type` | Yes | `str` | Controlled type such as `PERSON`, `ORG`, `LAW`, `PRODUCT`, `CLAUSE` | +| `description` | Yes | `str` | Meaning/definition used for disambiguation and retrieval | +| `aliases` | Yes | `list[str]` | Synonyms, abbreviations, alternate spellings | +| `keywords` | No | `list[str]` | Supporting lexical terms for retrieval and matching | +| `parent_ids` | No | `list[str]` | Optional taxonomy or hierarchical ontology support | +| `allowed_relation_types` | No | `list[str]` | Optional whitelist of relations this entity can participate in | +| `examples` | No | `list[str]` | Example mentions or phrases found in documents | +| `status` | No | `str` | `active`, `draft`, `deprecated` | +| `tenant_id` | Yes | `str` | Scope for multi-tenant isolation | +| `metadata` | No | `dict` | Domain-specific extensions | + +### 17.2 Relation rule schema + +If the ontology is meant to guide relationship refinement, add a relation policy table. + +| Field | Required | Type | Purpose | +|---|---|---|---| +| `relation_type` | Yes | `str` | Canonical relation label | +| `src_entity_type` | Yes | `str` | Allowed source type | +| `tgt_entity_type` | Yes | `str` | Allowed target type | +| `description` | No | `str` | Semantic definition of the relation | +| `directional` | No | `bool` | Whether direction matters | +| `status` | No | `str` | `active`, `draft`, `deprecated` | + +This is useful for constraining noisy KG relations extracted from documents. + +### 17.3 Mention-to-canonical mapping record + +The ontology entity itself is not enough. The system should also persist mapping decisions. + +| Field | Required | Type | Purpose | +|---|---|---|---| +| `mention_text` | Yes | `str` | Surface form found in the document | +| `mention_type` | Yes | `str` | Extracted type before canonicalization | +| `source_tree_node_id` | Yes | `int` | Tree node where the mention appeared | +| `canonical_ontology_id` | No | `str` | Resolved ontology target if matched | +| `match_method` | Yes | `str` | `alias`, `embedding`, `llm`, `rule`, `manual` | +| `match_confidence` | Yes | `float` | Confidence score for the mapping | +| `mapping_status` | Yes | `str` | `matched`, `ambiguous`, `provisional`, `rejected` | + +This record is important for explainability, debugging, and ontology curation. + +### 17.4 Minimum viable ontology payload + +At minimum, every user-provided ontology entity should include: + +- `ontology_id` +- `canonical_name` +- `entity_type` +- `description` +- `aliases` + +That is the smallest schema that is still strong enough to improve indexing quality. + +## 18. Codebase-Specific Integration Plan + +This section ties the ontology proposal to the current implementation in `Core/pipelines/kg_builder.py`, `Core/Index/Graph.py`, and `Core/rag/gbc_rag.py`. + +### 18.1 Integration into `Core/pipelines/kg_builder.py` + +Current flow in `build_knowledge_graph(tree, cfg)` is: + +1. create `Graph` +2. create `KGExtractor` +3. create `KGRefiner` +4. batch extract entities/relations from tree nodes +5. run `basic_kg_refiner(...)` or `advanced_kg_refiner(...)` +6. run `refine_entities()` and `refine_relation()` + +The ontology-aware version should become: + +1. load tenant ontology and relation rules +2. batch extract raw entities/relations from tree nodes +3. align extracted entities to ontology +4. send canonicalized entities into KG refinement +5. persist canonical, mention, and provenance links +6. refine unresolved/provisional entities separately + +#### Recommended insertion point + +The best insertion point is **between extraction and final refinement**. + +Conceptually: + +- `batch_extract_titles(...)` / `batch_extract_kg(...)` +- ontology alignment step +- `advanced_kg_refiner(...)` or `basic_kg_refiner(...)` +- `refine_entities()` / `refine_relation()` + +#### Practical behavior + +For each extracted entity, the builder should: + +1. normalize the extracted name +2. attempt exact alias match against ontology +3. attempt embedding match against ontology name + description + aliases +4. use LLM disambiguation only when multiple ontology candidates remain plausible +5. mark the result as: + - matched to canonical ontology entity, + - ambiguous, + - or provisional + +#### Minimal-change implementation path + +If minimal disruption is preferred, `kg_builder.py` can first adopt a lightweight approach: + +- replace matched extracted entity names/types with canonical ontology values before passing them into the current refiner +- keep unmatched entities in the existing extracted-entity flow +- attach mapping metadata for later use + +This gives immediate value without requiring a full graph schema redesign in the first phase. + +#### Better long-term implementation path + +The stronger design is to create explicit: + +- canonical ontology nodes +- mention nodes +- mention-to-canonical links + +That better matches the architecture implied by the user proposal. + +### 18.2 Integration into `Core/Index/Graph.py` + +`Graph.py` is the most important structural change point because the current `Entity` model only stores: + +- `entity_name` +- `entity_type` +- `description` +- `source_ids` + +That is not enough to model canonical ontology entities and document-level mentions separately. + +#### Recommended graph model extension + +The graph layer should distinguish at least three node roles: + +1. `canonical` +2. `mention` +3. `provisional` + +The simplest extension is to add metadata fields to `Entity`, for example: + +- `entity_id` +- `entity_role` +- `canonical_id` +- `aliases` +- `mapping_confidence` +- `ontology_source` +- `status` + +An even better design is to add dedicated models such as: + +- `OntologyEntity` +- `MentionEntity` +- `OntologyRelationRule` + +but the metadata-extension approach is the least disruptive to the current code. + +#### Node identity recommendation + +Today, node identity is derived from: + +- `Name: {entity_name}` +- `Type: {entity_type}` + +That is too weak if canonical nodes and mention nodes may share the same name and type. + +The safer design is to move toward explicit IDs such as: + +- canonical node name based on `ontology_id` +- mention node name based on `doc_id + tree_node_id + mention_text` + +This prevents collisions and makes provenance cleaner. + +#### New helper methods to add + +The graph layer would benefit from helpers such as: + +- `add_canonical_entity(...)` +- `add_mention_entity(...)` +- `link_mention_to_canonical(...)` +- `get_mentions_for_canonical(...)` +- `resolve_to_canonical(...)` + +#### `tree2kg` recommendation + +Currently `tree2kg` maps tree nodes directly to graph nodes. + +With ontology support, the preferred behavior is: + +- tree nodes link first to mention nodes +- mention nodes link to canonical nodes + +This preserves exact document provenance while still allowing canonical retrieval. + +#### Global graph recommendation + +`save_to_global_graph()` already contains the right intuition in its docstring, but the current implementation still creates a self-referential `HAS_MENTION` edge. + +With ontology support, global graph persistence should instead store: + +- one canonical node per ontology entity +- document-scoped mention nodes or mention records +- explicit `HAS_MENTION` or `MENTIONED_IN` links across them + +This would make tenant-wide graph consolidation much more meaningful. + +#### Phase 2 explicit mention-to-canonical node model + +Once Phase 1 normalization is stable, BookRAG should move from metadata-only canonicalization to explicit graph structure. + +Recommended node types: + +- `CanonicalEntity` +- `MentionEntity` +- `EvidenceNode` (existing `DocumentTree` node reference) + +Recommended identifiers: + +- canonical node ID = `ontology_id` +- mention node ID = `doc_id + tree_node_id + mention_text + entity_type` + +Recommended edges: + +- `MENTION_OF`: `MentionEntity -> CanonicalEntity` +- `MENTIONED_IN`: `MentionEntity -> EvidenceNode` +- `CO_OCCURS_WITH` or extracted semantic relations between mention nodes + +Recommended retrieval behavior: + +1. resolve query entity to canonical node +2. expand canonical node to mention nodes +3. collect grounded `DocumentTree` evidence from mention nodes +4. optionally project mention-level relations back into a canonical summary view + +Migration path: + +- keep current canonical metadata fields for backward compatibility +- add mention nodes during indexing without removing current canonical nodes +- update retrieval to expand canonical nodes through `MENTION_OF` +- later make global graph persistence canonical-first and mention-aware + +### 18.3 Integration into `Core/rag/gbc_rag.py` + +`gbc_rag.py` is the main query-time resolution path. + +At present: + +- `_get_query_entity(...)` retrieves candidate entities from the entity VDB and LLM extraction +- `_entity_map(...)` maps extracted query entities to current graph node names +- `link_tree_node(...)` maps graph nodes back to tree nodes +- `get_GBC_info(...)` retrieves subtree/subgraph context from those starting points + +#### Recommended query-time resolution flow + +The ontology-aware version should become: + +1. extract candidate entities from the query +2. resolve them against the ontology layer first +3. convert them into canonical node IDs +4. expand canonical nodes to mentions and linked tree nodes +5. run the existing BookRAG subtree/subgraph retrieval flow + +This means the ontology layer should become the **first resolver**, not the final retriever. + +#### Changes to `_get_query_entity(...)` + +This method should first query an ontology-aware index built from: + +- canonical names +- aliases +- descriptions + +Only if ontology resolution fails should it fall back to the current entity VDB logic. + +This preserves BookRAG's current flexibility while improving precision when ontology coverage exists. + +#### Changes to `_entity_map(...)` + +Instead of mapping only to extracted graph nodes, `_entity_map(...)` should prefer canonical node targets. + +That means the returned mapping should ideally point to: + +- canonical ontology node names first +- provisional/extracted nodes only as fallback + +#### Changes to `link_tree_node(...)` + +If canonical nodes are introduced, `link_tree_node(...)` should expand canonical nodes through mention links before counting supporting tree nodes. + +Conceptually: + +- canonical node -> mention nodes -> `tree2kg` / source tree nodes + +This keeps the evidence path explainable. + +#### Changes to `get_GBC_info(...)` + +The downstream retrieval logic can stay mostly the same. + +Once canonical start entities have been expanded into tree nodes and subgraphs, BookRAG's existing strengths remain valid: + +- section selection +- subtree retrieval +- skyline filtering +- multimodal answer synthesis + +This is why ontology should be added as a **resolution layer**, not as a replacement for the current runtime design. + +### 18.4 Supporting configuration changes + +Although the main integration points are the three files above, the design will work better if configuration eventually adds an ontology section such as: + +- `ontology.enabled` +- `ontology.store_type` +- `ontology.path` or collection name +- `ontology.mapping_threshold` +- `ontology.allow_provisional_entities` +- `ontology.use_llm_disambiguation` + +This would keep ontology behavior explicit and tenant-configurable. + +## 19. Recommended Rollout Strategy + +To reduce risk, the best rollout is phased. + +### Phase 1: Canonical normalization only + +- load ontology during indexing +- map extracted entities to canonical names/types where confidence is high +- keep current graph structure mostly unchanged + +### Phase 2: Explicit mention and canonical nodes + +- extend `Graph.py` +- preserve mention provenance separately from canonical identity +- improve global graph persistence + +### Phase 3: Ontology-first query resolution + +- resolve user query entities against ontology before standard entity VDB +- expand canonical entities into mention and tree evidence +- surface canonical reasoning in retrieval traces + +### Phase 4: Ontology-governed relation refinement + +- validate extracted relations against ontology rules +- reject or downgrade invalid relation candidates +- improve cross-document graph consistency + +## 20. Final Recommendation on the User Proposal + +As an expert recommendation, the user proposal should be adopted in this refined form: + +- allow users to define ontology entities with descriptions +- use those entities as the canonical base layer during indexing +- keep extraction open enough to preserve new or unmatched entities +- resolve both indexed entities and query entities against the ontology first when possible + +In short: + +- **yes** to user-defined ontology entities as the base layer +- **yes** to using descriptions as part of matching and retrieval +- **no** to a fully closed ontology-only extraction model + +That design is the best balance between BookRAG's existing tree-first strengths and the canonical control offered by FEA-style ontology architecture. \ No newline at end of file diff --git a/main.py b/main.py index dcd3097..c37e3aa 100644 --- a/main.py +++ b/main.py @@ -10,7 +10,7 @@ from Core.configs.system_config import load_system_config, SystemConfig from Core.configs.dataset_config import load_dataset_config, DatasetConfig from Core.construct_index import ( - construct_GBC_index, + construct_gbc_index, construct_vdb, compute_mm_reranker, rebuild_graph_vdb, @@ -128,13 +128,13 @@ def build_index(config: SystemConfig, stage: str = "all", data_df: pd.DataFrame if stage in ["tree", "all"]: log.info(" - STAGE: Building Document Tree...") # This function should build the tree and save it to config.save_path - construct_GBC_index(config, tree_only=True) + construct_gbc_index(config, tree_only=True) # Stage 2: Build the Knowledge Graph if stage in ["graph", "all"]: log.info(" - STAGE: Building Knowledge Graph...") # This function should LOAD the pre-existing tree and then build/save the graph - construct_GBC_index(config) + construct_gbc_index(config) # Stage 3: Build the Vector Database if stage in ["vdb", "all"]: diff --git a/tests/test_ontology_integration.py b/tests/test_ontology_integration.py new file mode 100644 index 0000000..7703ef2 --- /dev/null +++ b/tests/test_ontology_integration.py @@ -0,0 +1,200 @@ +import json + +import yaml + +from Core.Index.Graph import Entity, Graph, Relationship +from Core.configs.ontology_config import OntologyConfig +from Core.configs.system_config import load_system_config +from Core.utils.ontology_utils import ( + align_entities_to_ontology, + find_best_graph_ontology_node, +) + + +def test_load_system_config_resolves_relative_ontology_path_and_merges_entities(tmp_path): + ontology_path = tmp_path / "ontology.yaml" + ontology_path.write_text( + yaml.safe_dump( + { + "entities": [ + { + "ontology_id": "product:file-backed", + "canonical_name": "file backed product", + "entity_type": "PRODUCT", + "description": "Loaded from ontology file.", + "aliases": ["fb product"], + } + ] + } + ), + encoding="utf-8", + ) + + config_path = tmp_path / "config.yaml" + config_path.write_text( + yaml.safe_dump( + { + "mineru": { + "backend": "vlm-sglang-client", + "method": "vlm", + "lang": "en", + }, + "rag": {"strategy": "gbc"}, + "ontology": { + "enabled": True, + "path": "ontology.yaml", + "entities": [ + { + "ontology_id": "product:inline", + "canonical_name": "inline product", + "entity_type": "PRODUCT", + "description": "Inline ontology entity.", + "aliases": ["inline alias"], + } + ], + }, + } + ), + encoding="utf-8", + ) + + cfg = load_system_config(str(config_path)) + + assert cfg.ontology.path == str(ontology_path.resolve()) + assert {entity.entity_id for entity in cfg.ontology.entities} == { + "product:inline", + "product:file-backed", + } + + +def test_align_entities_to_ontology_maps_entities_and_relationships(): + ontology_cfg = OntologyConfig( + enabled=True, + entities=[ + { + "ontology_id": "product:bookrag", + "canonical_name": "bookrag", + "entity_type": "PRODUCT", + "description": "The canonical BookRAG product entity.", + "aliases": ["book rag"], + } + ], + ) + entities = [ + Entity(entity_name="Book Rag", entity_type="product", description="Mentioned in text."), + Entity(entity_name="Retriever Engine", entity_type="system", description="Local component."), + ] + relationships = [ + Relationship( + src_entity_name="Book Rag", + tgt_entity_name="Retriever Engine", + relation_name="uses", + ) + ] + + aligned_entities, aligned_relationships = align_entities_to_ontology( + entities, relationships, ontology_cfg + ) + + canonical = next(entity for entity in aligned_entities if entity.entity_role == "canonical") + provisional = next(entity for entity in aligned_entities if entity.entity_role == "provisional") + assert canonical.entity_name == "bookrag" + assert canonical.canonical_id == "product:bookrag" + assert "book rag" in canonical.aliases + assert provisional.entity_name == "retriever engine" + assert aligned_relationships[0].src_entity_name == "bookrag" + assert aligned_relationships[0].tgt_entity_name == "retriever engine" + + +def test_align_entities_to_ontology_drops_unmatched_entities_when_provisional_disabled(): + ontology_cfg = OntologyConfig( + enabled=True, + allow_provisional_entities=False, + entities=[ + { + "ontology_id": "product:bookrag", + "canonical_name": "bookrag", + "entity_type": "PRODUCT", + "description": "The canonical BookRAG product entity.", + "aliases": ["book rag"], + } + ], + ) + entities = [ + Entity(entity_name="Book Rag", entity_type="product"), + Entity(entity_name="Unknown System", entity_type="system"), + ] + relationships = [ + Relationship( + src_entity_name="Book Rag", + tgt_entity_name="Unknown System", + relation_name="uses", + ) + ] + + aligned_entities, aligned_relationships = align_entities_to_ontology( + entities, relationships, ontology_cfg + ) + + assert [entity.entity_name for entity in aligned_entities] == ["bookrag"] + assert aligned_relationships == [] + + +def test_graph_update_entity_rewrites_edge_payload_names_and_tree_links(tmp_path): + graph = Graph(save_path=str(tmp_path)) + old_entity = Entity(entity_name="book rag", entity_type="PRODUCT", source_ids={1}) + other_entity = Entity(entity_name="retriever", entity_type="SYSTEM", source_ids={2}) + graph.add_and_link(tree_node_id=1, entities=old_entity) + graph.add_and_link(tree_node_id=2, entities=other_entity) + graph.add_kg_edge( + Relationship( + src_entity_name="book rag", + tgt_entity_name="retriever", + relation_name="uses", + ), + src_type="PRODUCT", + tgt_type="SYSTEM", + ) + + renamed_entity = Entity( + entity_name="bookrag", + entity_type="PRODUCT", + entity_role="canonical", + canonical_id="product:bookrag", + source_ids={1}, + ) + graph.update_entity("book rag", "PRODUCT", renamed_entity) + + new_node_name = graph.get_node_name_from_entity(renamed_entity) + other_node_name = graph.get_node_name_from_entity(other_entity) + edge_data = graph.kg.get_edge_data(new_node_name, other_node_name) + assert edge_data["src_entity_name"] == "bookrag" + assert graph.node_name_to_tree_nodes(new_node_name) == [1] + + +def test_graph_metadata_round_trip_and_ontology_lookup(tmp_path): + graph = Graph(save_path=str(tmp_path)) + entity = Entity( + entity_name="bookrag", + entity_type="PRODUCT", + description="Canonical product entity.", + entity_id="product:bookrag", + canonical_id="product:bookrag", + entity_role="canonical", + aliases=["bookrag", "book rag"], + mapping_confidence=1.0, + ontology_source="config", + source_ids={7}, + ) + graph.add_and_link(tree_node_id=7, entities=entity) + graph.save_graph() + + loaded = Graph.load_from_dir(str(tmp_path)) + loaded_entity = loaded.get_entity("bookrag", "PRODUCT") + metadata = loaded_entity.to_vdb_metadata() + + assert loaded_entity.canonical_id == "product:bookrag" + assert json.loads(metadata["aliases_json"]) == ["bookrag", "book rag"] + assert find_best_graph_ontology_node(loaded, "book rag", "product", threshold=1.0) == ( + loaded.get_node_name_from_entity(loaded_entity) + ) \ No newline at end of file From 4c6136d8aac98d25dab435c4ec8c72a6d0edaa15 Mon Sep 17 00:00:00 2001 From: vmoudyp Date: Sat, 7 Mar 2026 06:27:49 +0700 Subject: [PATCH 11/11] Add config-driven Phase 3 entity resolution --- Core/configs/entity_resolution_config.py | 13 +++++ Core/configs/system_config.py | 10 ++++ Core/utils/entity_resolution_utils.py | 18 +++++++ api/services/entity_resolution.py | 54 ++++++++++---------- config/gbc.yaml | 9 ++++ docs/ontology-usage-guide.md | 49 ++++++++++++++++++ tests/test_ontology_integration.py | 63 ++++++++++++++++++++++++ 7 files changed, 187 insertions(+), 29 deletions(-) create mode 100644 Core/configs/entity_resolution_config.py create mode 100644 Core/utils/entity_resolution_utils.py create mode 100644 docs/ontology-usage-guide.md diff --git a/Core/configs/entity_resolution_config.py b/Core/configs/entity_resolution_config.py new file mode 100644 index 0000000..14deab1 --- /dev/null +++ b/Core/configs/entity_resolution_config.py @@ -0,0 +1,13 @@ +from pydantic import BaseModel, Field + + +class EntityResolutionConfig(BaseModel): + """Tenant/global canonical entity resolution settings.""" + + enabled: bool = False + similarity_threshold: float = Field(default=0.85, ge=0.0, le=1.0) + top_k: int = Field(default=1, ge=1, le=20) + global_vdb_dir: str = "./indices" + collection_name: str = "global_kg_collection" + canonical_only: bool = False + sync_to_global_graph: bool = False \ No newline at end of file diff --git a/Core/configs/system_config.py b/Core/configs/system_config.py index 5322093..89fd561 100644 --- a/Core/configs/system_config.py +++ b/Core/configs/system_config.py @@ -3,6 +3,7 @@ import yaml from Core.configs.mineru_config import MinerU from Core.configs.docling_config import DoclingConfig +from Core.configs.entity_resolution_config import EntityResolutionConfig from Core.configs.llm_config import LLMConfig from Core.configs.tree_config import TreeConfig from Core.configs.graph_config import GraphConfig @@ -37,6 +38,7 @@ class SystemConfig(BaseModel): graph: GraphConfig = Field(default_factory=GraphConfig) vdb: VDBConfig = Field(default_factory=VDBConfig) ontology: OntologyConfig = Field(default_factory=OntologyConfig) + entity_resolution: EntityResolutionConfig = Field(default_factory=EntityResolutionConfig) # Other Index selection index_type: Optional[str] = "gbc" # Options: "gbc", "tree", "vanilla", "bm25", "raptor", "pdf_vanilla" @@ -93,5 +95,13 @@ def load_system_config(path: str = "../configs/default.yaml") -> SystemConfig: os.path.join(os.path.dirname(path), ontology_path) ) + entity_resolution_data = raw_config.get("entity_resolution") + if isinstance(entity_resolution_data, dict) and entity_resolution_data.get("global_vdb_dir"): + global_vdb_dir = entity_resolution_data["global_vdb_dir"] + if not os.path.isabs(global_vdb_dir): + entity_resolution_data["global_vdb_dir"] = os.path.abspath( + os.path.join(os.path.dirname(path), global_vdb_dir) + ) + cfg = SystemConfig(**raw_config) return cfg diff --git a/Core/utils/entity_resolution_utils.py b/Core/utils/entity_resolution_utils.py new file mode 100644 index 0000000..d61aa02 --- /dev/null +++ b/Core/utils/entity_resolution_utils.py @@ -0,0 +1,18 @@ +from Core.Index.Graph import Entity +from Core.configs.entity_resolution_config import EntityResolutionConfig + + +def should_resolve_entity_globally( + entity: Entity, resolution_cfg: EntityResolutionConfig +) -> bool: + if not resolution_cfg.canonical_only: + return True + return bool(entity.canonical_id or entity.entity_role == "canonical") + + +def build_global_entity_metadata(entity: Entity, tenant_id: str, doc_id: str) -> dict: + metadata = entity.to_vdb_metadata() + metadata["tenant_id"] = tenant_id or "" + metadata["doc_id"] = doc_id or "" + metadata["canonical_name"] = entity.entity_name + return metadata \ No newline at end of file diff --git a/api/services/entity_resolution.py b/api/services/entity_resolution.py index b94bd84..37b5360 100644 --- a/api/services/entity_resolution.py +++ b/api/services/entity_resolution.py @@ -2,24 +2,20 @@ Phase 3: Cross-document entity resolution pipeline. After a document is indexed, this service: -1. Embeds each new entity using the tenant's embedding model. -2. Searches the global VDB (ChromaDB collection per tenant) for cosine-similar entities. -3. For matches above threshold, asks the LLM to verify canonical equivalence. -4. Merges verified matches into the global FalkorDB graph with HAS_MENTION edges. +1. Loads document-level graph entities. +2. Searches the tenant-global VDB for cosine-similar canonical candidates. +3. Reuses an existing tenant-global canonical when the similarity gate is met. +4. Persists unmatched entities plus ontology metadata into the tenant-global stores. """ import asyncio import logging import os -from typing import List from api.dependencies import THREAD_POOL log = logging.getLogger(__name__) _executor = THREAD_POOL -RESOLUTION_THRESHOLD = float(os.getenv("BOOKRAG_ENTITY_RESOLUTION_THRESHOLD", "0.85")) -GLOBAL_VDB_DIR = os.getenv("BOOKRAG_GLOBAL_VDB_DIR", "./indices") - def _resolve_entities_sync( tenant_id: str, @@ -37,34 +33,38 @@ def _resolve_entities_sync( 5. If no match: add as new canonical entity in global VDB + global graph. """ from Core.configs.system_config import load_system_config - from Core.configs.falkordb_config import FalkorDBConfig from Core.Index.GBCIndex import GBC from Core.provider.vdb import VectorStore - from api.dependencies import ( - FALKORDB_HOST, FALKORDB_PORT, FALKORDB_USERNAME, FALKORDB_PASSWORD, INDEX_SAVE_DIR, + from Core.utils.entity_resolution_utils import ( + build_global_entity_metadata, + should_resolve_entity_globally, ) + from api.dependencies import INDEX_SAVE_DIR cfg = load_system_config(config_path) + resolution_cfg = cfg.entity_resolution + if not resolution_cfg.enabled: + log.info("Entity resolution disabled by config; skipping Phase 3 sync.") + return + cfg.tenant_id = tenant_id cfg.doc_id = doc_id cfg.save_path = os.path.join(INDEX_SAVE_DIR, tenant_id, doc_id) - fdb_host = os.getenv("BOOKRAG_FALKORDB_HOST", "") falkordb_cfg = None - if fdb_host: - falkordb_cfg = FalkorDBConfig(host=FALKORDB_HOST, port=FALKORDB_PORT, username=FALKORDB_USERNAME, password=FALKORDB_PASSWORD) - cfg.falkordb = falkordb_cfg + if resolution_cfg.sync_to_global_graph and getattr(cfg.falkordb, "host", ""): + falkordb_cfg = cfg.falkordb gbc = GBC.load_gbc_index(cfg) graph = gbc.GraphIndex embedder = gbc.embedder # Open global VDB for tenant - global_vdb_path = os.path.join(GLOBAL_VDB_DIR, tenant_id, "global_vdb") + global_vdb_path = os.path.join(resolution_cfg.global_vdb_dir, tenant_id, "global_vdb") global_vdb = VectorStore( db_path=global_vdb_path, embedding_model=embedder, - collection_name="global_kg_collection", + collection_name=resolution_cfg.collection_name, ) nodes = graph.get_all_nodes() @@ -73,30 +73,26 @@ def _resolve_entities_sync( for node_name in nodes: entity = graph.get_entity_by_node_name(node_name) + if not should_resolve_entity_globally(entity, resolution_cfg): + continue + # Search global VDB for similar entity - hits = global_vdb.search(node_name, top_k=1) + hits = global_vdb.search(node_name, top_k=resolution_cfg.top_k) merged = False - if hits and hits[0]["distance"] < (1.0 - RESOLUTION_THRESHOLD): + if hits and hits[0]["distance"] < (1.0 - resolution_cfg.similarity_threshold): # Cosine distance is 1 - similarity; low distance = high similarity canonical_name = hits[0]["content"] log.info( f"Entity '{entity.entity_name}' similar to canonical '{canonical_name}' " f"(dist={hits[0]['distance']:.3f}). Merging." ) - # Add HAS_MENTION edge in global FalkorDB graph - if falkordb_cfg: - graph.save_to_global_graph(falkordb_cfg, tenant_id) merged = True if not merged: new_canonical_texts.append(node_name) - new_canonical_meta.append({ - "entity_name": entity.entity_name, - "entity_type": entity.entity_type, - "description": entity.description, - "doc_id": doc_id, - "tenant_id": tenant_id, - }) + new_canonical_meta.append( + build_global_entity_metadata(entity, tenant_id=tenant_id, doc_id=doc_id) + ) if new_canonical_texts: global_vdb.add_texts(texts=new_canonical_texts, metadatas=new_canonical_meta) diff --git a/config/gbc.yaml b/config/gbc.yaml index ee630fb..12092fa 100644 --- a/config/gbc.yaml +++ b/config/gbc.yaml @@ -82,6 +82,15 @@ ontology: - "book rag" - "book-rag" +entity_resolution: + enabled: false + similarity_threshold: 0.85 + top_k: 1 + global_vdb_dir: "./indices" + collection_name: "global_kg_collection" + canonical_only: false + sync_to_global_graph: false + falkordb: host: "r-6jissuruar.instance-yc6en3ndn.hc-dx5io0svq.asia-south1.gcp.f2e0a955bb84.cloud" port: 60480 diff --git a/docs/ontology-usage-guide.md b/docs/ontology-usage-guide.md new file mode 100644 index 0000000..45a06cf --- /dev/null +++ b/docs/ontology-usage-guide.md @@ -0,0 +1,49 @@ +## Ontology usage guide + +Use ontology entities when you want BookRAG to normalize extracted mentions onto stable canonical entities during indexing and retrieval. + +### Inline ontology config + +Set `ontology.enabled: true` and define entities directly in the config file. + +- `ontology_id`: stable canonical identifier +- `canonical_name`: normalized entity name stored in the graph +- `entity_type`: type used during matching +- `aliases`: alternate spellings and surface forms +- `keywords`: optional matching hints for domain terminology + +Example fields: + +- `mapping_threshold`: stricter values reduce false matches +- `allow_provisional_entities`: keep unmatched entities in the graph when `true` +- `use_query_resolution`: resolve query mentions to ontology-backed graph nodes first + +### File-backed ontology config + +You can also point `ontology.path` at a YAML or JSON file. Relative paths are resolved from the config file location. Inline entities and file-backed entities are merged by `ontology_id`. + +### Phase 3 tenant/global resolution + +Phase 3 is now controlled by `entity_resolution` config: + +- `enabled`: turns tenant/global canonical resolution on or off +- `similarity_threshold`: vector similarity gate for reuse of an existing tenant-global entity +- `top_k`: number of nearest global candidates to inspect +- `global_vdb_dir`: directory for the tenant-global ChromaDB store +- `collection_name`: ChromaDB collection name for global entities +- `canonical_only`: only export ontology-backed/canonical entities when `true` +- `sync_to_global_graph`: also sync to tenant-global FalkorDB when `true` + +Relative `global_vdb_dir` values are resolved from the config file location. + +### Recommended starting config + +- keep `ontology.enabled: true` +- start with `mapping_threshold: 1.0` +- keep `allow_provisional_entities: true` +- keep `entity_resolution.enabled: false` until you want cross-document tenant normalization +- once enabled, start with `canonical_only: true` if your ontology is strong and curated + +### Current limitation + +Phase 3 currently uses vector similarity plus canonical metadata persistence. It is intentionally conservative and does not yet implement a full mention-node merge model in the tenant-global graph. \ No newline at end of file diff --git a/tests/test_ontology_integration.py b/tests/test_ontology_integration.py index 7703ef2..95c9b6b 100644 --- a/tests/test_ontology_integration.py +++ b/tests/test_ontology_integration.py @@ -3,8 +3,13 @@ import yaml from Core.Index.Graph import Entity, Graph, Relationship +from Core.configs.entity_resolution_config import EntityResolutionConfig from Core.configs.ontology_config import OntologyConfig from Core.configs.system_config import load_system_config +from Core.utils.entity_resolution_utils import ( + build_global_entity_metadata, + should_resolve_entity_globally, +) from Core.utils.ontology_utils import ( align_entities_to_ontology, find_best_graph_ontology_node, @@ -140,6 +145,64 @@ def test_align_entities_to_ontology_drops_unmatched_entities_when_provisional_di assert aligned_relationships == [] +def test_load_system_config_resolves_relative_entity_resolution_dir(tmp_path): + config_path = tmp_path / "config.yaml" + config_path.write_text( + yaml.safe_dump( + { + "mineru": { + "backend": "vlm-sglang-client", + "method": "vlm", + "lang": "en", + }, + "rag": {"strategy": "gbc"}, + "entity_resolution": { + "enabled": True, + "global_vdb_dir": "tenant_global_indices", + "canonical_only": True, + }, + } + ), + encoding="utf-8", + ) + + cfg = load_system_config(str(config_path)) + + assert cfg.entity_resolution.enabled is True + assert cfg.entity_resolution.canonical_only is True + assert cfg.entity_resolution.global_vdb_dir == str( + (tmp_path / "tenant_global_indices").resolve() + ) + + +def test_global_entity_resolution_helpers_preserve_ontology_metadata(): + resolution_cfg = EntityResolutionConfig(enabled=True, canonical_only=True) + canonical_entity = Entity( + entity_name="bookrag", + entity_type="PRODUCT", + description="Canonical product entity.", + entity_id="product:bookrag", + canonical_id="product:bookrag", + entity_role="canonical", + aliases=["bookrag", "book rag"], + mapping_confidence=1.0, + ontology_source="config", + ) + provisional_entity = Entity(entity_name="retriever", entity_type="SYSTEM") + + metadata = build_global_entity_metadata( + canonical_entity, tenant_id="tenant-a", doc_id="doc-1" + ) + + assert should_resolve_entity_globally(canonical_entity, resolution_cfg) is True + assert should_resolve_entity_globally(provisional_entity, resolution_cfg) is False + assert metadata["entity_id"] == "product:bookrag" + assert metadata["canonical_id"] == "product:bookrag" + assert metadata["tenant_id"] == "tenant-a" + assert metadata["doc_id"] == "doc-1" + assert json.loads(metadata["aliases_json"]) == ["bookrag", "book rag"] + + def test_graph_update_entity_rewrites_edge_payload_names_and_tree_links(tmp_path): graph = Graph(save_path=str(tmp_path)) old_entity = Entity(entity_name="book rag", entity_type="PRODUCT", source_ids={1})

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