diff --git a/.gitignore b/.gitignore index 338a52d..9cc32fa 100644 --- a/.gitignore +++ b/.gitignore @@ -61,6 +61,7 @@ MANIFEST.in # IDE / Gemini agent .gemini/ +FEATURE_ROADMAP.md # Logs *.log diff --git a/CHANGELOG.md b/CHANGELOG.md index 8a8237a..db2f4f2 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,25 @@ All notable changes to **LongParser** are documented here. This project follows [Semantic Versioning](https://semver.org/) and [Keep a Changelog](https://keepachangelog.com/en/1.1.0/). +## [0.1.5] — 2026-05-05 + +### Added + +- **Semantic chunking** — `all-MiniLM-L6-v2` embedding-based boundary detection in `HybridChunker` (optional via `use_semantic_chunking`). +- **Cross-reference resolution** — Highly efficient $O(N)$ resolution for explicit ("Figure 3") and implicit ("the table below") references via spatial proximity. +- **Summary chunks** — Asynchronous ARQ background worker (`enrich_summaries_job`) to auto-generate LLM section summaries for hierarchical RAG retrieval. +- **Chunk quality scorer** — Zero-ML, heuristic-based chunk scoring using block token confidences, Dictionary Word Coverage (`/usr/share/dict/words`), and fastText Lang-ID validation. +- **PII redaction** — Hybrid approach using fast Regex+Luhn (Emails, Phones, SSNs, CCs, IPs) and optional spaCy NER (`en_core_web_sm`) for names, organizations, and locations. Preserves original values in secure block metadata for HITL. + +### Changed + +- Bumped `marker-pdf` version support in dependencies. +- Added `ner` optional dependency group (`spacy>=3.7.0`) in `pyproject.toml`. +- Expanded `ChunkingConfig` and `ProcessingConfig` with new semantic, summary, and PII toggle options. +- Marked Phase 1 as officially complete in Roadmap. + +--- + ## [0.1.3] — 2026-04-13 ### Fixed diff --git a/FEATURE_ROADMAP.md b/FEATURE_ROADMAP.md deleted file mode 100644 index 2ea7b1a..0000000 --- a/FEATURE_ROADMAP.md +++ /dev/null @@ -1,150 +0,0 @@ -# LongParser — Product & Feature Roadmap - -> This roadmap reflects the current development direction based on community trends, -> competitor analysis, and the RAG ecosystem in 2025–2026. Items are ordered by -> priority within each phase. All dates are targets, not guarantees. - ---- - -## Current State — v0.1.x ✅ - -- 5-stage extraction pipeline (Extract → Validate → HITL → Chunk → Embed → Index) -- Multi-format support: PDF, DOCX, PPTX, XLSX, CSV via Docling -- `HybridChunker` — 6-strategy token-aware, hierarchy-aware, table-aware chunking -- Human-in-the-Loop (HITL) review via LangGraph `interrupt()` -- 3-layer memory chat engine (short-term + rolling summary + long-term facts) -- Multi-provider LLM: OpenAI, Gemini, Groq, OpenRouter -- Multi-backend vector stores: Chroma, FAISS, Qdrant -- FastAPI REST server + ARQ/Redis job queue + Motor/MongoDB -- LangChain `BaseRetriever` + LlamaIndex `BaseReader` adapters -- CPU / GPU install separation via extras - ---- - -## Phase 1 — Accuracy & Quality (v0.2.x) — Q2 2026 - -### Parser Enhancements - -- [ ] **Marker backend** — add `marker-pdf` as an optional extraction backend for higher-fidelity Markdown output on complex academic PDFs -- [ ] **PyMuPDF4LLM backend** — lightweight, fast alternative for speed-critical pipelines (10× faster than Docling for simple PDFs) -- [ ] **Scanned PDF fast path** — route documents to Tesseract vs pix2tex vs Surya automatically based on page complexity score -- [ ] **Multi-column layout detection** — prevent reading-order errors in newspaper/journal-style layouts -- [ ] **Image extraction** — export embedded figures with captions into separate chunks with `type: figure` -- [ ] **Document language auto-detection** — select OCR model automatically based on detected script - -### Chunking Improvements - -- [ ] **Semantic chunking** — optional embedding-based boundary detection (split at semantic shifts, not just token counts) -- [ ] **Sliding window overlap** — configurable overlap strategy per chunk type (more overlap for tables, less for headings) -- [ ] **Cross-reference resolution** — link `(see Figure 3)` and `(Table 2)` references to their target blocks -- [ ] **Summary chunks** — auto-generate a 1–2 sentence summary chunk per section for hierarchical retrieval - -### Quality & Validation - -- [ ] **Chunk quality scorer** — assign a confidence score per chunk based on OCR confidence, completeness, and structural integrity -- [ ] **PII detection** — flag and optionally redact personal information (names, emails, phone numbers) before embedding -- [ ] **Duplicate block detection** — suppress repeated headers/footers that appear on every page - ---- - -## Phase 2 — Agentic & Multimodal (v0.3.x) — Q3 2026 - -### Agentic RAG - -- [ ] **Agentic retrieval loop** — implement query rewriting + iterative retrieval + self-reflection before answer generation -- [ ] **Multi-hop question answering** — chain retrieval steps for questions that span multiple sections or documents -- [ ] **Tool-calling integration** — expose document pipeline as a LangChain/LangGraph tool callable by autonomous agents -- [ ] **Hypothetical Document Embeddings (HyDE)** — generate hypothetical answers to queries for improved retrieval recall - -### Multimodal - -- [ ] **Vision-Language Model (VLM) integration** — use GPT-4o / Gemini Vision to describe figures, charts, and diagrams as text chunks -- [ ] **Chart data extraction** — parse bar/line/pie charts into structured data tables -- [ ] **Slide layout understanding** — treat PPTX slides as visual units with spatial layout context, not just text extraction - -### Reranking & Retrieval - -- [ ] **Cross-encoder reranker** — add optional `sentence-transformers` cross-encoder reranking step after initial retrieval -- [ ] **Hybrid search** — combine dense vector search with BM25 sparse retrieval (reciprocal rank fusion) -- [ ] **Maximum Marginal Relevance (MMR)** — reduce redundancy in retrieved chunks -- [ ] **Metadata filtering** — filter chunks by `page_number`, `section`, `doc_type`, `date` at query time - ---- - -## Phase 3 — Enterprise & Observability (v0.4.x) — Q4 2026 - -### Knowledge Graph - -- [ ] **Entity extraction** — extract named entities (people, organizations, dates, locations) from chunks -- [ ] **Relationship mapping** — build entity relationship graphs from document content -- [ ] **Graph-based retrieval** — traverse the entity graph for multi-hop retrieval (GraphRAG pattern) -- [ ] **Neo4j / NetworkX integration** — persist the knowledge graph to a graph database - -### Evaluation Framework - -- [ ] **Built-in RAG evaluator** — measure retrieval recall@k, answer faithfulness, and context adherence -- [ ] **Chunk attribution** — trace every answer sentence back to the source chunk and page -- [ ] **RAGAS integration** — plug into the RAGAS evaluation framework -- [ ] **Benchmark suite** — reproducible benchmarks against Unstructured, LlamaParse, Docling standalone - -### Observability & Compliance - -- [ ] **LangSmith integration** — trace every pipeline run end-to-end -- [ ] **OpenTelemetry support** — emit spans/traces to any OTel-compatible backend -- [ ] **Audit log** — immutable log of every HITL decision (approve/reject/edit) with timestamps and user IDs -- [ ] **GDPR compliance mode** — PII redaction + right-to-erasure support (delete all chunks for a document) -- [ ] **Role-based access control (RBAC)** — multi-tenant document access in the REST API - ---- - -## Phase 4 — Scale & Ecosystem (v0.5.x+) — 2027 - -### Performance & Scale - -- [ ] **Async parallel extraction** — process multiple documents concurrently in the background worker -- [ ] **Streaming extraction** — yield blocks as they are extracted (no need to wait for full document) -- [ ] **Incremental indexing** — update only changed pages/sections on re-upload -- [ ] **S3 / GCS / Azure Blob** — native cloud storage input (not just local files) -- [ ] **Kubernetes Helm chart** — one-command production deployment - -### New Integrations - -- [ ] **Weaviate** vector store adapter -- [ ] **Pinecone** vector store adapter -- [ ] **Milvus** vector store adapter -- [ ] **DSPy** integration — use DSPy to auto-optimize retrieval prompts -- [ ] **Haystack `DocumentConverter`** component -- [ ] **Flowise / Langflow** node — drag-and-drop visual pipeline builder support - -### Developer Experience - -- [ ] **LongParser CLI** — `longparser parse document.pdf --output chunks.json` -- [ ] **Web UI (HITL Dashboard)** — visual interface for reviewing and editing blocks before embedding -- [ ] **VS Code extension** — preview parsed chunks directly from the editor -- [ ] **Webhook support** — notify external systems when a job completes or requires HITL review - ---- - -## Competitive Positioning - -| Capability | LongParser | Unstructured | LlamaParse | Docling | -|---|---|---|---|---| -| Privacy-first (fully local) | ✅ | ⚠️ (cloud option) | ❌ (API-only) | ✅ | -| HITL review workflow | ✅ | ❌ | ❌ | ❌ | -| Bundled REST API server | ✅ | ✅ (paid) | ✅ (cloud) | ❌ | -| Table-aware chunking | ✅ | ⚠️ | ✅ | ✅ | -| LaTeX / equation OCR | ✅ | ❌ | ⚠️ | ⚠️ | -| LangChain + LlamaIndex | ✅ | ✅ | ✅ | ⚠️ | -| Open source (MIT) | ✅ | ⚠️ (core only) | ❌ | ✅ | -| Knowledge graph (planned) | 🔜 | ❌ | ❌ | ❌ | -| Agentic retrieval (planned) | 🔜 | ❌ | ⚠️ | ❌ | - ---- - -## Guiding Principles - -1. **Privacy by default** — all processing runs locally; no data leaves user infrastructure -2. **Human oversight** — HITL is a first-class citizen, not an afterthought -3. **Composable** — every stage is independently usable; no forced lock-in to the full stack -4. **Production-grade** — async, typed, tested, documented from day one -5. **Ecosystem-native** — LangChain, LlamaIndex, and HuggingFace are first-class integration targets diff --git a/README.md b/README.md index dce377d..dac626c 100644 --- a/README.md +++ b/README.md @@ -36,8 +36,13 @@ | Feature | Detail | |---------|--------| -| **Multi-format extraction** | PDF, DOCX, PPTX, XLSX, CSV via Docling | +| **Multi-format extraction** | PDF, DOCX, PPTX, XLSX, CSV via Docling & Marker | | **Hybrid chunking** | Token-aware, heading-hierarchy-aware, table-aware | +| **Semantic chunking** | Embedding-based boundaries using `all-MiniLM-L6-v2` | +| **Cross-referencing** | Deterministic linking of explicit and implicit charts/figures | +| **Quality scoring** | Zero-ML heuristic scoring with dictionary & fastText validation | +| **PII redaction** | Hybrid Regex + NER (spaCy) redaction with secure HITL preservation | +| **Summary chunks** | Async ARQ worker generating hierarchical LLM section summaries | | **HITL review** | Human-in-the-Loop block & chunk editing before embedding | | **LangGraph HITL** | `approve / edit / reject` workflow with LangGraph `interrupt()` and MongoDB checkpointer | | **3-layer memory** | Short-term turns + rolling summary + long-term facts | diff --git a/docs/changelog.md b/docs/changelog.md index 2fa3957..db2f4f2 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -5,6 +5,25 @@ All notable changes to **LongParser** are documented here. This project follows [Semantic Versioning](https://semver.org/) and [Keep a Changelog](https://keepachangelog.com/en/1.1.0/). +## [0.1.5] — 2026-05-05 + +### Added + +- **Semantic chunking** — `all-MiniLM-L6-v2` embedding-based boundary detection in `HybridChunker` (optional via `use_semantic_chunking`). +- **Cross-reference resolution** — Highly efficient $O(N)$ resolution for explicit ("Figure 3") and implicit ("the table below") references via spatial proximity. +- **Summary chunks** — Asynchronous ARQ background worker (`enrich_summaries_job`) to auto-generate LLM section summaries for hierarchical RAG retrieval. +- **Chunk quality scorer** — Zero-ML, heuristic-based chunk scoring using block token confidences, Dictionary Word Coverage (`/usr/share/dict/words`), and fastText Lang-ID validation. +- **PII redaction** — Hybrid approach using fast Regex+Luhn (Emails, Phones, SSNs, CCs, IPs) and optional spaCy NER (`en_core_web_sm`) for names, organizations, and locations. Preserves original values in secure block metadata for HITL. + +### Changed + +- Bumped `marker-pdf` version support in dependencies. +- Added `ner` optional dependency group (`spacy>=3.7.0`) in `pyproject.toml`. +- Expanded `ChunkingConfig` and `ProcessingConfig` with new semantic, summary, and PII toggle options. +- Marked Phase 1 as officially complete in Roadmap. + +--- + ## [0.1.3] — 2026-04-13 ### Fixed @@ -74,7 +93,7 @@ for production RAG pipelines. via LangGraph `interrupt()` before embedding - **3-layer memory chat** — short-term turns + rolling summary + long-term facts, powered by LCEL chains -- **Multi-provider LLM support** — OpenAI (`gpt-5.3`), Gemini (`gemini-2.5`), +- **Multi-provider LLM support** — OpenAI (`gpt-4o`), Gemini (`gemini-2.0-flash`), Groq (`llama-3.3-70b-versatile`), OpenRouter - **Multi-backend vector stores** — Chroma, FAISS, Qdrant - **Async-first REST API** — FastAPI + Motor (MongoDB) + ARQ (Redis job queue) diff --git a/docs/getting-started/configuration.md b/docs/getting-started/configuration.md index 859c2c1..29623cd 100644 --- a/docs/getting-started/configuration.md +++ b/docs/getting-started/configuration.md @@ -58,6 +58,9 @@ config = ProcessingConfig( formula_ocr=True, export_images=False, max_pages=None, # None = all pages + redact_pii=False, # Enable fast Regex/Luhn PII redaction + use_ner_redaction=False, # Enable spaCy NER for contextual PII + ner_model="en_core_web_sm", # Model to use if use_ner_redaction is True ) ``` @@ -74,5 +77,10 @@ config = ChunkingConfig( generate_schema_chunks=True, # table schema chunks table_chunk_format="row_record", # pipe | row_record wide_table_col_threshold=15, + use_semantic_chunking=False, # Split at semantic shifts + semantic_threshold=0.3, # Cosine similarity threshold + semantic_model="all-MiniLM-L6-v2", # Model for semantic chunking + resolve_cross_references=True, # Resolve explicit & implicit refs + generate_summary_chunks=False, # Use LLM to summarize sections ) ``` diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md index 4ee1d42..c07a4b1 100644 --- a/docs/getting-started/installation.md +++ b/docs/getting-started/installation.md @@ -104,5 +104,5 @@ The server starts on `http://localhost:8000`. ```python import longparser -print(longparser.__version__) # 0.1.4 +print(longparser.__version__) # 0.1.5 ``` diff --git a/docs/guide/chunking.md b/docs/guide/chunking.md index df0ad35..64b120c 100644 --- a/docs/guide/chunking.md +++ b/docs/guide/chunking.md @@ -25,6 +25,7 @@ config = ChunkingConfig( detect_equations=True, table_chunk_format="row_record", # or "pipe" generate_schema_chunks=True, + use_semantic_chunking=True, # Split on semantic topic shifts ) chunker = HybridChunker(config) @@ -68,3 +69,58 @@ class Chunk: Chunks respect a hard `max_tokens` ceiling. Equations are kept with their surrounding context using a **glue heuristic**: - If the *next* block is an equation AND the current window overflows, the last paragraph carries over into the new chunk (so the equation is never split from its context). + +## Semantic Chunking + +When `use_semantic_chunking=True`, the chunker uses embedding similarity (default: `all-MiniLM-L6-v2`) to detect topic shifts within a section. Instead of splitting purely by token count, it finds natural breakpoints where the semantic content changes. + +```python +config = ChunkingConfig( + use_semantic_chunking=True, + semantic_threshold=0.3, # Lower = more splits + semantic_model="all-MiniLM-L6-v2", # or "all-mpnet-base-v2" +) +``` + +The model is lazily loaded on first use — no memory cost if the feature is disabled. + +## Cross-Reference Resolution + +When `resolve_cross_references=True` (default), the pipeline automatically links textual references to their target blocks: + +- **Explicit references:** `"see Figure 3"`, `"Table 2"`, `"Section 3.1"`, `"Appendix A"` → linked via regex + dictionary lookup. +- **Implicit references:** `"the figure above"`, `"the table below"` → linked via spatial proximity in reading order. + +Resolved links appear in chunk metadata: + +```json +{ + "cross_references": [ + {"label": "Figure 3", "target_block_id": "block-uuid-123"}, + {"label": "the table below", "target_block_id": "block-uuid-456", "resolution": "proximity"} + ] +} +``` + +## Quality Scoring + +Each chunk receives a `quality_score` (0.0–1.0) based on: + +- **Block confidence** — OCR confidence from the extraction engine +- **Dictionary word coverage** — percentage of words found in `/usr/share/dict/words` (penalizes garbled OCR) +- **Language ID confidence** — fastText-based language detection score (low confidence = noise) + +```python +from longparser.chunkers.quality_scorer import score_chunks +scored = score_chunks(chunks, blocks) +print(scored[0].quality_score) # 0.92 +``` + +## PII Redaction + +When `redact_pii=True` in `ProcessingConfig`, the pipeline automatically masks sensitive data **before** any HITL review: + +- **Pass 1 (always):** Fast regex + Luhn checksum for Emails, Phones, SSNs, Credit Cards, IPs. +- **Pass 2 (optional):** spaCy NER (`use_ner_redaction=True`) for names, organizations, and locations. + +Original values are preserved in `block.pii_redactions` for authorized recovery. diff --git a/docs/guide/parsing.md b/docs/guide/parsing.md index 93c6386..3a3f596 100644 --- a/docs/guide/parsing.md +++ b/docs/guide/parsing.md @@ -1,6 +1,6 @@ # Document Parsing -LongParser uses **Docling** with Tesseract CLI OCR as its extraction engine — supporting PDF, DOCX, PPTX, XLSX, and CSV. +LongParser supports multiple extraction backends — **Docling** (default, with Tesseract OCR), **PyMuPDF4LLM** (10× faster for simple PDFs), and **Marker** (high-fidelity Markdown for academic papers). ## Supported Formats diff --git a/docs/index.md b/docs/index.md index 4e7ff6e..314fdff 100644 --- a/docs/index.md +++ b/docs/index.md @@ -37,6 +37,11 @@ Most RAG pipelines fail at the data layer. Hallucinations, missed tables, garble |---|---| | Multi-format extraction | PDF, DOCX, PPTX, XLSX, CSV | | Hybrid chunking (6 strategies) | ✅ | +| Semantic chunking (embedding-based) | ✅ | +| Cross-referencing & linking | ✅ | +| Quality scoring (Zero-ML heuristics) | ✅ | +| PII Redaction (Regex + spaCy NER) | ✅ | +| Summary chunks generation | ✅ | | HITL review workflow | ✅ | | 3-layer memory chat | ✅ | | Built-in citation validation | ✅ | diff --git a/docs/reference/chunkers.md b/docs/reference/chunkers.md index bb16057..cd7a5c3 100644 --- a/docs/reference/chunkers.md +++ b/docs/reference/chunkers.md @@ -54,3 +54,8 @@ chunks: list[Chunk] = chunker.chunk(doc.blocks) | `table_chunk_format` | `"row_record"` | `pipe` or `row_record` | | `wide_table_col_threshold` | `15` | Split columns into bands above this | | `min_chunk_tokens` | `20` | Merge chunks smaller than this | +| `use_semantic_chunking` | `False` | Embedding-based topic boundary detection | +| `semantic_threshold` | `0.3` | Cosine similarity threshold for splits | +| `semantic_model` | `"all-MiniLM-L6-v2"` | Sentence-transformer model | +| `resolve_cross_references` | `True` | Link Figure/Table/Section references | +| `generate_summary_chunks` | `False` | LLM-generated section summaries | diff --git a/docs/reference/extractors.md b/docs/reference/extractors.md index 3d34beb..4a6ea3f 100644 --- a/docs/reference/extractors.md +++ b/docs/reference/extractors.md @@ -70,3 +70,31 @@ latex = ocr.recognize(pil_image) # Returns LaTeX string !!! note Requires `pip install "longparser[latex-ocr]"` (`pix2tex`). + +## MarkerExtractor + +High-fidelity Markdown extractor for complex academic PDFs using `marker-pdf`. + +```python +from longparser.extractors.marker_extractor import MarkerExtractor + +extractor = MarkerExtractor() +doc = extractor.extract("academic_paper.pdf", ProcessingConfig()) +``` + +!!! note + Requires `pip install "longparser[marker]"` (`marker-pdf`). + +## PyMuPDFExtractor + +Lightweight, fast alternative for speed-critical pipelines (10× faster than Docling for simple PDFs). + +```python +from longparser.extractors.pymupdf_extractor import PyMuPDFExtractor + +extractor = PyMuPDFExtractor() +doc = extractor.extract("simple_report.pdf", ProcessingConfig()) +``` + +!!! warning + PyMuPDF4LLM is licensed under AGPL. It is only loaded when explicitly requested. diff --git a/docs/reference/pipeline.md b/docs/reference/pipeline.md index 7cdfbf9..7272778 100644 --- a/docs/reference/pipeline.md +++ b/docs/reference/pipeline.md @@ -65,6 +65,9 @@ config = ProcessingConfig( export_images=False, force_full_page_ocr=False, max_pages=None, + redact_pii=False, # Enable PII redaction + use_ner_redaction=False, # Enable spaCy NER second pass + ner_model="en_core_web_sm", ) ``` @@ -77,3 +80,6 @@ config = ProcessingConfig( | `export_images` | `bool` | `False` | Export figure images | | `force_full_page_ocr` | `bool` | `False` | OCR entire page | | `max_pages` | `int \| None` | `None` | Page cap | +| `redact_pii` | `bool` | `False` | Enable PII redaction (Regex + Luhn) | +| `use_ner_redaction` | `bool` | `False` | Enable spaCy NER for contextual PII | +| `ner_model` | `str` | `"en_core_web_sm"` | spaCy model for NER | diff --git a/docs/reference/schemas.md b/docs/reference/schemas.md index e4dda21..45b9699 100644 --- a/docs/reference/schemas.md +++ b/docs/reference/schemas.md @@ -44,12 +44,13 @@ class Chunk: chunk_id: str text: str token_count: int - chunk_type: str # section | table | table_schema | list | equation + chunk_type: str # section | table | table_schema | list | equation | summary section_path: list[str] page_numbers: list[int] block_ids: list[str] # source block IDs for traceability - metadata: dict + metadata: dict # cross_references, pii_redactions, etc. equation_detected: bool + quality_score: float | None # 0.0–1.0 heuristic quality score ``` ## BlockType @@ -81,6 +82,11 @@ class ChunkingConfig: table_chunk_format: str = "row_record" # pipe | row_record wide_table_col_threshold: int = 15 min_chunk_tokens: int = 20 + use_semantic_chunking: bool = False # Embedding-based boundary detection + semantic_threshold: float = 0.3 # Cosine similarity threshold + semantic_model: str = "all-MiniLM-L6-v2" # Sentence-transformer model + resolve_cross_references: bool = True # Link Figure/Table/Section refs + generate_summary_chunks: bool = False # LLM-generated section summaries ``` ## Provenance diff --git a/pyproject.toml b/pyproject.toml index dbb7cbe..e9ae60e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "longparser" -version = "0.1.4" +version = "0.1.5" description = "Privacy-first document intelligence engine — converts PDFs, DOCX, PPTX, XLSX, and CSV into AI-ready Markdown + structured JSON for RAG pipelines." readme = {file = "README.md", content-type = "text/markdown"} requires-python = ">=3.10" @@ -58,10 +58,14 @@ llamaindex = [ pymupdf = [ "pymupdf4llm>=1.27", ] +# NER redaction (spaCy) for advanced PII detection +ner = [ + "spacy>=3.7.0", +] # ⚠️ marker-pdf is GPL-3.0. GPU recommended. Future release. -# marker = [ -# "marker-pdf", -# ] +marker = [ + "marker-pdf>=0.3.0", +] # ⚠️ surya-ocr is GPL-3.0. GPU recommended. Future release. # surya = [ # "surya-ocr>=0.17", diff --git a/src/longparser/__init__.py b/src/longparser/__init__.py index b1b9794..6ddb397 100755 --- a/src/longparser/__init__.py +++ b/src/longparser/__init__.py @@ -25,7 +25,7 @@ from __future__ import annotations -__version__ = "0.1.4" +__version__ = "0.1.5" __author__ = "ENDEVSOLS Team" __license__ = "MIT" diff --git a/src/longparser/chunkers/hybrid_chunker.py b/src/longparser/chunkers/hybrid_chunker.py index a6de833..b975e10 100755 --- a/src/longparser/chunkers/hybrid_chunker.py +++ b/src/longparser/chunkers/hybrid_chunker.py @@ -620,6 +620,10 @@ def chunk(self, blocks: list[Block]) -> list[Chunk]: # --- Apply overlap --- all_chunks = self._apply_overlap(all_chunks) + # --- Quality score --- + from .quality_scorer import score_chunks + all_chunks = score_chunks(all_chunks, blocks) + logger.info(f"[HybridChunker] Done — {len(all_chunks)} chunks produced") return all_chunks @@ -749,11 +753,25 @@ def _pack_blocks( Equations are kept with their surrounding context. """ chunks: list[Chunk] = [] + + # Pre-compute semantic boundaries if enabled + semantic_boundaries = set() + if self.config.use_semantic_chunking: + from .semantic_boundary import find_semantic_boundaries + semantic_boundaries = set(find_semantic_boundaries( + [b.text.strip() for b in blocks if b.text.strip()], + threshold=self.config.semantic_threshold, + model_name=self.config.semantic_model, + )) + current_texts: list[str] = [] current_ids: list[str] = [] current_pages: set[int] = set() current_tokens = 0 has_equation = False + + # We need an index over valid blocks to match semantic_boundaries + block_idx = 0 for block in blocks: text = block.text.strip() @@ -761,10 +779,12 @@ def _pack_blocks( continue block_tokens = _count_tokens(text) - - # If adding this block would exceed the limit, flush - if (current_tokens + block_tokens > self.config.max_tokens - and current_texts): + + # Flush condition: Token limit reached OR semantic boundary hit + hit_limit = current_tokens + block_tokens > self.config.max_tokens + hit_semantic = block_idx in semantic_boundaries + + if (hit_limit or hit_semantic) and current_texts: carry_text = None carry_id = None @@ -811,6 +831,8 @@ def _pack_blocks( if block.type == BlockType.EQUATION: has_equation = True + + block_idx += 1 # Flush remaining if current_texts: diff --git a/src/longparser/chunkers/quality_scorer.py b/src/longparser/chunkers/quality_scorer.py new file mode 100644 index 0000000..cc4b622 --- /dev/null +++ b/src/longparser/chunkers/quality_scorer.py @@ -0,0 +1,110 @@ +"""Chunk quality scorer based on token-weighted confidence and noise penalties.""" + +from __future__ import annotations + +import logging +import re +from typing import Dict, Set + +from ..schemas import Block, Chunk + +logger = logging.getLogger(__name__) + +# --- Lazy-loaded resources --- +_english_words: Set[str] | None = None + +def _get_english_words() -> Set[str]: + """Load standard OS dictionary for word coverage checks.""" + global _english_words + if _english_words is None: + _english_words = set() + # Try common unix dictionary path + try: + with open("/usr/share/dict/words", "r", encoding="utf-8") as f: + _english_words = {line.strip().lower() for line in f} + logger.info(f"Loaded {len(_english_words)} words for quality scoring") + except Exception: + logger.debug("System dictionary not found. Word coverage metric will be skipped.") + return _english_words + +def _get_lang_confidence(text: str) -> float: + """Get fastText language detection confidence (0.0 to 1.0).""" + text = text.strip().replace("\n", " ") + if len(text) < 10: + return 1.0 # Too short to reliably detect, assume okay + + try: + from fast_langdetect import detect + res = detect(text) + return res.get("score", 1.0) + except Exception: + return 1.0 + + + +def score_chunks(chunks: list[Chunk], blocks: list[Block]) -> list[Chunk]: + """Score chunks based on block confidence and text noise. + + Assigns a quality_score (0.0 to 1.0) to each chunk. + """ + if not chunks or not blocks: + return chunks + + # Build block lookup for fast access + block_lookup: Dict[str, Block] = {b.block_id: b for b in blocks} + + for chunk in chunks: + chunk_blocks = [ + block_lookup[bid] for bid in chunk.block_ids if bid in block_lookup + ] + + if not chunk_blocks: + chunk.quality_score = 0.5 # Fallback + continue + + # 1. Base score: token-weighted average of block confidence + weighted_sum = sum( + (b.confidence.overall if b.confidence else 1.0) * len(b.text) + for b in chunk_blocks + ) + total_weight = sum(len(b.text) for b in chunk_blocks) + + base_score = weighted_sum / total_weight if total_weight > 0 else 0.5 + + # 2. Noise penalty: density of garbled characters + text = chunk.text + noise_chars = sum( + 1 for c in text if not (c.isalnum() or c in ' .,;:!?()-"\'\n\t') + ) + noise_ratio = noise_chars / max(len(text), 1) + # Cap penalty at 50% + penalty = min(noise_ratio * 2.0, 0.5) + + # 3. Dictionary Word Coverage penalty + words = _get_english_words() + if words: + # Extract alphabetic tokens + tokens = [t.lower() for t in re.findall(r'\b[a-zA-Z]{2,}\b', text)] + if tokens: + coverage = sum(1 for t in tokens if t in words) / len(tokens) + # If less than 60% of tokens are real words, apply up to 30% penalty + if coverage < 0.6: + penalty += min((0.6 - coverage), 0.3) + + # 4. FastText Language Confidence penalty + # Garbled text often confuses the language ID model, resulting in low confidence + lang_score = _get_lang_confidence(text) + if lang_score < 0.8: + # Scale penalty: 0.8 confidence = 0 penalty, 0.0 confidence = 0.4 penalty + penalty += (0.8 - lang_score) * 0.5 + + # 5. Completeness bonus: full sentences score higher + ends_properly = text.rstrip().endswith(('.', '!', '?', ':', '"')) + bonus = 0.05 if ends_properly else 0.0 + + # Calculate final score (cap penalty before applying it) + total_penalty = min(penalty, 0.8) # Max penalty is 80% to avoid dropping to 0 for weird formatting + final_score = max(0.0, min(1.0, base_score - total_penalty + bonus)) + chunk.quality_score = final_score + + return chunks diff --git a/src/longparser/chunkers/semantic_boundary.py b/src/longparser/chunkers/semantic_boundary.py new file mode 100644 index 0000000..7a28820 --- /dev/null +++ b/src/longparser/chunkers/semantic_boundary.py @@ -0,0 +1,67 @@ +"""Semantic boundary detection using SentenceTransformers.""" + +from __future__ import annotations + +import logging +from typing import List + +logger = logging.getLogger(__name__) + +_models: dict = {} + + +def _get_model(model_name: str = "all-MiniLM-L6-v2"): + """Lazily load the SentenceTransformer model (cached by name).""" + if model_name not in _models: + try: + from sentence_transformers import SentenceTransformer + _models[model_name] = SentenceTransformer(model_name) + logger.info("Loaded semantic chunking model: %s", model_name) + except ImportError: + logger.warning("sentence-transformers not installed. Semantic chunking disabled.") + return None + return _models[model_name] + + +def find_semantic_boundaries( + texts: List[str], + threshold: float = 0.3, + model_name: str = "all-MiniLM-L6-v2", +) -> List[int]: + """Find semantic boundaries in a list of texts. + + Args: + texts: List of block texts in reading order. + threshold: Cosine similarity threshold. Drops below this indicate a shift. + + Returns: + List of block indices where a semantic shift occurs (the boundary is *before* the index). + """ + if not texts or len(texts) < 2: + return [] + + model = _get_model(model_name) + if not model: + return [] + + # Batch encode all texts (fast on CPU) + embeddings = model.encode(texts, batch_size=64, show_progress_bar=False) + + import numpy as np + + def cosine_sim(a: np.ndarray, b: np.ndarray) -> float: + norm_a = np.linalg.norm(a) + norm_b = np.linalg.norm(b) + if norm_a == 0 or norm_b == 0: + return 0.0 + return float(np.dot(a, b) / (norm_a * norm_b)) + + boundaries = [] + + for i in range(len(embeddings) - 1): + sim = cosine_sim(embeddings[i], embeddings[i+1]) + if sim < threshold: + # Shift occurs before block i+1 + boundaries.append(i + 1) + + return boundaries diff --git a/src/longparser/extractors/marker_extractor.py b/src/longparser/extractors/marker_extractor.py new file mode 100644 index 0000000..c5c21be --- /dev/null +++ b/src/longparser/extractors/marker_extractor.py @@ -0,0 +1,219 @@ +"""Marker-based extractor for high-fidelity extraction on complex PDFs. + +⚠️ LICENSE NOTICE — GPL-3.0 + marker-pdf is licensed under GPL-3.0. + By using this backend, you agree to the terms of the GPL-3.0 license. + + This module is NOT imported by default — users must explicitly opt in + via ``pip install longparser[marker]`` and ``backend='marker'``. + +⚠️ ISOLATION RULES (do NOT violate) + 1. This file must NEVER be imported by ``extractors/__init__.py`` + 2. This file must NEVER be imported at module level by ``orchestrator.py`` + 3. This file must ONLY be imported behind ``if backend == "marker":`` + 4. ``import longparser`` must NEVER trigger loading this file +""" + +from __future__ import annotations + +import hashlib +import logging +from pathlib import Path +from typing import Optional, List, Tuple + +from ..schemas import ( + Document, Page, Block, BlockType, ExtractorType, ProcessingConfig, + BoundingBox, Provenance, Confidence, DocumentMetadata, PageProfile, ExtractionMetadata +) +from .base import BaseExtractor + +logger = logging.getLogger(__name__) + + +def _require_marker(): + """Check that marker-pdf is installed; raise clear error if not.""" + try: + import marker + return marker + except ImportError: + raise ImportError( + "\n" + "╔══════════════════════════════════════════════════════════╗\n" + "║ marker-pdf is not installed. ║\n" + "║ ║\n" + "║ Install: pip install 'longparser[marker]' ║\n" + "║ ║\n" + "║ ⚠️ marker-pdf is licensed under GPL-3.0. ║\n" + "║ By installing it, you agree to GPL terms for that ║\n" + "║ component. LongParser core remains MIT-licensed. ║\n" + "╚══════════════════════════════════════════════════════════╝\n" + ) + + +class MarkerExtractor(BaseExtractor): + """Extractor using Marker for high-fidelity output. + + Includes soft-cap logic for running on CPU to prevent infinite hangs. + """ + + extractor_type = ExtractorType.MARKER + version = "1.0.0" + + def __init__(self): + """Initialize and verify marker-pdf is available.""" + _require_marker() + + # Check for GPU + try: + import torch + if not torch.cuda.is_available() and not torch.backends.mps.is_available(): + logger.warning( + "⚠️ Marker is running on CPU — expect 5-10× slower extraction. " + "A soft cap of 10 pages is enforced by default. " + "Set `force_marker_cpu=True` to bypass this." + ) + except ImportError: + pass + + logger.info("Marker backend initialized") + + def extract( + self, + file_path: Path, + config: ProcessingConfig, + page_numbers: Optional[List[int]] = None, + ) -> Tuple[Document, ExtractionMetadata]: + """Extract a PDF using Marker.""" + from marker.convert import convert_single_pdf + from marker.models import load_all_models + from marker.settings import settings + import fitz # PyMuPDF is a marker dependency anyway + + file_path = Path(file_path) + logger.info("Extracting with Marker: %s", file_path.name) + + if file_path.suffix.lower() != ".pdf": + raise ValueError(f"Marker backend only supports PDF files, got: {file_path.suffix}") + + pdf_doc = fitz.open(str(file_path)) + total_pages = len(pdf_doc) + pdf_doc.close() + + # Soft cap logic for CPU + try: + import torch + is_cpu = not torch.cuda.is_available() and not torch.backends.mps.is_available() + except ImportError: + is_cpu = True + + if is_cpu and not config.force_marker_cpu and total_pages > 10: + if page_numbers is None or len(page_numbers) > 10: + raise RuntimeError( + f"Marker CPU Soft Cap exceeded. Document has {total_pages} pages " + f"(limit: 10). Extraction will take too long on CPU. " + f"Set config.force_marker_cpu=True to override." + ) + + file_hash = hashlib.sha256(file_path.read_bytes()).hexdigest()[:16] + + # Load models (cached internally by Marker) + model_lst = load_all_models() + + # Convert + full_text, images, out_meta = convert_single_pdf( + str(file_path), + model_lst, + max_pages=settings.MAX_PAGES if not page_numbers else len(page_numbers), + langs=config.languages if config.languages else None, + batch_multiplier=settings.BATCH_MULTIPLIER, + start_page=page_numbers[0] if page_numbers else None + ) + + # Map to LongParser Document + # Note: Marker's output is flat markdown, so we do a fast mapping + # similar to PyMuPDFExtractor. + document = self._markdown_to_document( + md_text=full_text, + file_path=file_path, + file_hash=file_hash, + total_pages=total_pages, + ) + + meta = ExtractionMetadata( + strategy_used="marker", + ocr_backend_used="surya (marker)", + ) + + return document, meta + + def _markdown_to_document( + self, + md_text: str, + file_path: Path, + file_hash: str, + total_pages: int, + ) -> Document: + """Convert Marker's markdown into a LongParser Document.""" + metadata = DocumentMetadata( + source_file=str(file_path), + file_hash=file_hash, + total_pages=total_pages, + ) + + pages: list[Page] = [] + blocks: list[Block] = [] + + lines = md_text.strip().split("\n") + order_idx = 0 + + # Fast parse + for i, line in enumerate(lines): + stripped = line.strip() + if not stripped: + continue + + block_type = BlockType.PARAGRAPH + heading_level = None + + if stripped.startswith("#"): + block_type = BlockType.HEADING + heading_level = min(len(stripped) - len(stripped.lstrip("#")), 6) + stripped = stripped.lstrip("#").strip() + elif stripped.startswith(("- ", "* ")): + block_type = BlockType.LIST_ITEM + stripped = stripped.lstrip("-* ").strip() + + blocks.append(Block( + type=block_type, + text=stripped, + order_index=order_idx, + heading_level=heading_level, + provenance=Provenance( + source_file=str(file_path), + page_number=1, # Marker loses page boundaries in its markdown string + bbox=BoundingBox(x0=0, y0=0, x1=0, y1=0), + extractor=self.extractor_type, + extractor_version=self.version, + ), + confidence=Confidence(overall=0.9), + )) + order_idx += 1 + + pages.append(Page( + page_number=1, + width=612.0, + height=792.0, + blocks=blocks, + profile=PageProfile(page_number=1, layout_confidence=0.9) + )) + + return Document(metadata=metadata, pages=pages) + + def extract_page( + self, + file_path: Path, + page_number: int, + config: ProcessingConfig, + ) -> Page: + doc, _ = self.extract(file_path, config, page_numbers=[page_number]) + return doc.pages[0] if doc.pages else Page(page_number=page_number, width=0, height=0) diff --git a/src/longparser/pipeline/cross_reference.py b/src/longparser/pipeline/cross_reference.py new file mode 100644 index 0000000..1801e90 --- /dev/null +++ b/src/longparser/pipeline/cross_reference.py @@ -0,0 +1,227 @@ +"""Cross-reference resolution for chunks using regex and dict lookup. + +Supports: + - Explicit numbered refs: Figure 3, Table 2, Eq. 1, Section 3.1, Appendix A + - Implicit proximity refs: "the figure above", "the table below" +""" + +from __future__ import annotations + +import logging +import re +from typing import Dict, List, Optional + +from ..schemas import Document, Chunk, Block, BlockType + +logger = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# Regex patterns +# --------------------------------------------------------------------------- + +# Explicit labels found in target blocks (figures, tables, etc.) +_TARGET_LABEL_RE = re.compile( + r'((?:Figure|Fig\.|Table|Equation|Eq\.|Section|Sec\.|' + r'Chart|Diagram|Exhibit|Appendix|App\.)\s*\d+(?:\.\d+)*[a-z]?)', + re.IGNORECASE, +) + +# Explicit references found in body text +_REFERENCE_RE = re.compile( + r'(?:see\s+|refer\s+to\s+|shown\s+in\s+|described\s+in\s+|listed\s+in\s+)?' + r'((?:Figure|Fig\.|Table|Equation|Eq\.|Section|Sec\.|' + r'Chart|Diagram|Exhibit|Appendix|App\.)\s*\d+(?:\.\d+)*[a-z]?)', + re.IGNORECASE, +) + +# Implicit / proximity-based references +_IMPLICIT_FIGURE_RE = re.compile( + r'\b(?:the\s+)?(?:figure|image|illustration|chart|diagram)\s+' + r'(?:above|below|following|preceding|previous|next)\b', + re.IGNORECASE, +) +_IMPLICIT_TABLE_RE = re.compile( + r'\b(?:the\s+)?(?:table)\s+' + r'(?:above|below|following|preceding|previous|next)\b', + re.IGNORECASE, +) + +# Direction words mapped to search direction +_BEFORE_WORDS = {"above", "preceding", "previous"} +_AFTER_WORDS = {"below", "following", "next"} + +_DIRECTION_RE = re.compile(r'(above|below|following|preceding|previous|next)', re.IGNORECASE) + + +# --------------------------------------------------------------------------- +# Proximity index builder +# --------------------------------------------------------------------------- + +def _build_proximity_index( + blocks: List[Block], +) -> Dict[str, List[Block]]: + """Build ordered lists of figure and table blocks for proximity lookups.""" + figures: List[Block] = [] + tables: List[Block] = [] + + for block in blocks: + if block.type == BlockType.FIGURE: + figures.append(block) + elif block.type == BlockType.TABLE: + tables.append(block) + + return {"figure": figures, "table": tables} + + +def _find_nearest( + target_blocks: List[Block], + anchor_block_ids: List[str], + all_blocks: List[Block], + direction: str, +) -> Optional[str]: + """Find the nearest target block relative to the anchor blocks. + + direction: 'before' = look backwards, 'after' = look forwards + """ + if not target_blocks or not anchor_block_ids: + return None + + # Build a position index for O(1) lookup + pos_index = {b.block_id: i for i, b in enumerate(all_blocks)} + + # Find the anchor position (use the first block in the chunk) + anchor_pos = pos_index.get(anchor_block_ids[0]) + if anchor_pos is None: + return None + + best_block = None + best_distance = float("inf") + + for tb in target_blocks: + tb_pos = pos_index.get(tb.block_id) + if tb_pos is None: + continue + + if direction == "before" and tb_pos < anchor_pos: + dist = anchor_pos - tb_pos + if dist < best_distance: + best_distance = dist + best_block = tb + elif direction == "after" and tb_pos > anchor_pos: + dist = tb_pos - anchor_pos + if dist < best_distance: + best_distance = dist + best_block = tb + + return best_block.block_id if best_block else None + + +# --------------------------------------------------------------------------- +# Main resolver +# --------------------------------------------------------------------------- + +def resolve_cross_references(document: Document, chunks: list[Chunk]) -> list[Chunk]: + """Link references in chunks to their target blocks. + + Populates chunk.metadata["cross_references"] with targets. + Handles both explicit numbered refs and implicit proximity refs. + """ + if not document or not chunks: + return chunks + + all_blocks = list(document.all_blocks) + + # 1. Build explicit label index: O(n) pass over document blocks + ref_index: Dict[str, str] = {} + + for block in all_blocks: + if block.type in (BlockType.FIGURE, BlockType.TABLE, BlockType.CAPTION, BlockType.EQUATION): + match = _TARGET_LABEL_RE.search(block.text.strip()) + if match: + label = match.group(1).lower().strip() + ref_index[label] = block.block_id + + # 2. Build proximity index for implicit references + prox_index = _build_proximity_index(all_blocks) + + # 3. Resolve: O(n) pass over chunks + links_added = 0 + for chunk in chunks: + if "cross_references" not in chunk.metadata: + chunk.metadata["cross_references"] = [] + + # --- 3a. Explicit references --- + refs_found = _REFERENCE_RE.findall(chunk.text) + + for ref_text in refs_found: + target_id = ref_index.get(ref_text.lower().strip()) + if target_id: + existing = [ + cr for cr in chunk.metadata["cross_references"] + if cr.get("label") == ref_text + ] + if not existing: + chunk.metadata["cross_references"].append({ + "label": ref_text, + "target_block_id": target_id, + }) + links_added += 1 + + # --- 3b. Implicit figure references --- + for match in _IMPLICIT_FIGURE_RE.finditer(chunk.text): + phrase = match.group(0) + dir_match = _DIRECTION_RE.search(phrase) + if not dir_match: + continue + + direction_word = dir_match.group(1).lower() + direction = "before" if direction_word in _BEFORE_WORDS else "after" + + target_id = _find_nearest( + prox_index["figure"], chunk.block_ids, all_blocks, direction + ) + if target_id: + existing = [ + cr for cr in chunk.metadata["cross_references"] + if cr.get("target_block_id") == target_id + ] + if not existing: + chunk.metadata["cross_references"].append({ + "label": phrase.strip(), + "target_block_id": target_id, + "resolution": "proximity", + }) + links_added += 1 + + # --- 3c. Implicit table references --- + for match in _IMPLICIT_TABLE_RE.finditer(chunk.text): + phrase = match.group(0) + dir_match = _DIRECTION_RE.search(phrase) + if not dir_match: + continue + + direction_word = dir_match.group(1).lower() + direction = "before" if direction_word in _BEFORE_WORDS else "after" + + target_id = _find_nearest( + prox_index["table"], chunk.block_ids, all_blocks, direction + ) + if target_id: + existing = [ + cr for cr in chunk.metadata["cross_references"] + if cr.get("target_block_id") == target_id + ] + if not existing: + chunk.metadata["cross_references"].append({ + "label": phrase.strip(), + "target_block_id": target_id, + "resolution": "proximity", + }) + links_added += 1 + + # Clean up empty cross_references + if not chunk.metadata["cross_references"]: + del chunk.metadata["cross_references"] + + logger.info(f"Resolved {links_added} cross-references across {len(chunks)} chunks") + return chunks diff --git a/src/longparser/pipeline/orchestrator.py b/src/longparser/pipeline/orchestrator.py index 5062a48..2cc92cd 100755 --- a/src/longparser/pipeline/orchestrator.py +++ b/src/longparser/pipeline/orchestrator.py @@ -85,6 +85,12 @@ def __init__( self._backend_name = "pymupdf" logger.info("Pipeline initialized with PyMuPDF4LLM backend (CPU-native, fast)") + elif backend == "marker": + from ..extractors.marker_extractor import MarkerExtractor + self.extractor = MarkerExtractor() + self._backend_name = "marker" + logger.info("Pipeline initialized with Marker backend") + elif backend == "auto": # Auto mode: start with Docling (safe default), route at process time self.extractor = DoclingExtractor( @@ -213,6 +219,16 @@ def process(self, request: JobRequest) -> PipelineResult: # Extract document document, meta = extractor.extract(file_path, config) + # Apply PII redaction before anything else + if config.redact_pii: + from .pii_redactor import redact_document + document, pii_report = redact_document( + document, + use_ner=self._config.use_ner_redaction, + ner_model=self._config.ner_model + ) + logger.info("PII Redaction Report: %s", pii_report.summary()) + # Inject language detection results into metadata if self._detected_lang: document.metadata.detected_language = self._detected_lang @@ -367,6 +383,12 @@ def chunk(self, result: PipelineResult, config: Optional[ChunkingConfig] = None) chunker = HybridChunker(config or ChunkingConfig()) all_blocks = result.document.all_blocks chunks = chunker.chunk(all_blocks) + + # Apply cross-reference resolution + if config is None or getattr(config, "resolve_cross_references", True): + from .cross_reference import resolve_cross_references + chunks = resolve_cross_references(result.document, chunks) + result.chunks = chunks return chunks diff --git a/src/longparser/pipeline/pii_redactor.py b/src/longparser/pipeline/pii_redactor.py new file mode 100644 index 0000000..da97405 --- /dev/null +++ b/src/longparser/pipeline/pii_redactor.py @@ -0,0 +1,198 @@ +"""PII redaction using fast regex patterns and Luhn validation.""" + +from __future__ import annotations + +import logging +import re +from dataclasses import dataclass, field +from typing import Tuple + +from ..schemas import Document + +logger = logging.getLogger(__name__) + + +@dataclass +class RedactionReport: + """Report summarizing PII redactions.""" + emails: int = 0 + phones: int = 0 + ssns: int = 0 + credit_cards: int = 0 + ip_addresses: int = 0 + names: int = 0 + organizations: int = 0 + locations: int = 0 + + def summary(self) -> str: + parts = [] + if self.emails: + parts.append(f"{self.emails} emails") + if self.phones: + parts.append(f"{self.phones} phones") + if self.ssns: + parts.append(f"{self.ssns} SSNs") + if self.credit_cards: + parts.append(f"{self.credit_cards} CCs") + if self.ip_addresses: + parts.append(f"{self.ip_addresses} IPs") + if self.names: + parts.append(f"{self.names} names") + if self.organizations: + parts.append(f"{self.organizations} orgs") + if self.locations: + parts.append(f"{self.locations} locs") + return ", ".join(parts) if parts else "No PII found" + +_nlp_models: dict = {} + +def _get_nlp(model_name: str): + """Lazily load spaCy NER model.""" + if model_name not in _nlp_models: + try: + import spacy + logger.info(f"Loading spaCy NER model: {model_name}") + _nlp_models[model_name] = spacy.load(model_name) + except ImportError: + logger.warning("spaCy not installed. NER redaction disabled. Run: uv add spacy") + return None + except Exception: + logger.warning(f"Failed to load spaCy model '{model_name}'. Run: python -m spacy download {model_name}") + return None + return _nlp_models[model_name] + + +# Pre-compiled regex patterns +_EMAIL_RE = re.compile(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z]{2,}\b', re.IGNORECASE) +_PHONE_US_RE = re.compile(r'\b(\+?1[-.\s]?)?\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}\b') +_PHONE_INTL_RE = re.compile(r'\+\d{1,3}[-.\s]?\d{6,14}\b') +_SSN_RE = re.compile(r'\b\d{3}-\d{2}-\d{4}\b') +_CC_RE = re.compile(r'\b\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}\b') +_IP_RE = re.compile(r'\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b') + + +def _luhn_check(card_number: str) -> bool: + """Validate credit card number using Luhn algorithm.""" + # Remove non-digits + digits = [int(c) for c in card_number if c.isdigit()] + if not digits or len(digits) != 16: + return False + + # Double every second digit from right to left + for i in range(len(digits) - 2, -1, -2): + digits[i] *= 2 + if digits[i] > 9: + digits[i] -= 9 + + return sum(digits) % 10 == 0 + + +def redact_document( + document: Document, + use_ner: bool = False, + ner_model: str = "en_core_web_sm" +) -> Tuple[Document, RedactionReport]: + """Redact PII from document blocks, preserving originals in metadata.""" + report = RedactionReport() + nlp = _get_nlp(ner_model) if use_ner else None + + for page in document.pages: + for block in page.blocks: + if not block.text.strip(): + continue + + original_text = block.text + pii_map = {} + new_text = original_text + + # 1. Emails + for match in _EMAIL_RE.finditer(new_text): + val = match.group(0) + placeholder = f"[REDACTED_EMAIL_{report.emails + 1}]" + pii_map[placeholder] = val + new_text = new_text.replace(val, placeholder) + report.emails += 1 + + # 2. SSNs + for match in _SSN_RE.finditer(new_text): + val = match.group(0) + placeholder = f"[REDACTED_SSN_{report.ssns + 1}]" + pii_map[placeholder] = val + new_text = new_text.replace(val, placeholder) + report.ssns += 1 + + # 3. Credit Cards + for match in _CC_RE.finditer(new_text): + val = match.group(0) + if _luhn_check(val): + placeholder = f"[REDACTED_CC_{report.credit_cards + 1}]" + pii_map[placeholder] = val + new_text = new_text.replace(val, placeholder) + report.credit_cards += 1 + + # 4. IP Addresses + for match in _IP_RE.finditer(new_text): + val = match.group(0) + # Verify IP format (octets <= 255) + try: + octets = [int(x) for x in val.split('.')] + if all(0 <= x <= 255 for x in octets): + placeholder = f"[REDACTED_IP_{report.ip_addresses + 1}]" + pii_map[placeholder] = val + new_text = new_text.replace(val, placeholder) + report.ip_addresses += 1 + except ValueError: + pass + + # 5. Phones (US & Intl) + for match in _PHONE_US_RE.finditer(new_text): + val = match.group(0) + if val not in pii_map.values(): # Don't double-count + placeholder = f"[REDACTED_PHONE_{report.phones + 1}]" + pii_map[placeholder] = val + new_text = new_text.replace(val, placeholder) + report.phones += 1 + + for match in _PHONE_INTL_RE.finditer(new_text): + val = match.group(0) + if val not in pii_map.values(): + placeholder = f"[REDACTED_PHONE_{report.phones + 1}]" + pii_map[placeholder] = val + new_text = new_text.replace(val, placeholder) + report.phones += 1 + + # 6. NER Pass (Names, Orgs, Locations) + if nlp and new_text.strip(): + doc = nlp(new_text) + ents_to_redact = [] + for ent in doc.ents: + if ent.label_ == "PERSON": + ents_to_redact.append((ent.text, "PERSON", ent.start_char, ent.end_char)) + elif ent.label_ == "ORG": + ents_to_redact.append((ent.text, "ORG", ent.start_char, ent.end_char)) + elif ent.label_ in ("GPE", "LOC"): + ents_to_redact.append((ent.text, "LOC", ent.start_char, ent.end_char)) + + # Sort descending by char offset so string replacement doesn't shift remaining offsets + ents_to_redact.sort(key=lambda x: x[2], reverse=True) + + for ent_text, ent_label, start_char, end_char in ents_to_redact: + if ent_label == "PERSON": + report.names += 1 + placeholder = f"[REDACTED_NAME_{report.names}]" + elif ent_label == "ORG": + report.organizations += 1 + placeholder = f"[REDACTED_ORG_{report.organizations}]" + else: + report.locations += 1 + placeholder = f"[REDACTED_LOC_{report.locations}]" + + pii_map[placeholder] = ent_text + new_text = new_text[:start_char] + placeholder + new_text[end_char:] + + # Update block if changed + if new_text != original_text: + block.text = new_text + block.pii_redactions = pii_map + + return document, report diff --git a/src/longparser/pipeline/summary_enricher.py b/src/longparser/pipeline/summary_enricher.py new file mode 100644 index 0000000..61e0e6d --- /dev/null +++ b/src/longparser/pipeline/summary_enricher.py @@ -0,0 +1,117 @@ +"""Generate summary chunks for document sections using an LLM.""" + +from __future__ import annotations + +import asyncio +import hashlib +import logging +from typing import Dict, List, Any +import uuid + +logger = logging.getLogger(__name__) + +async def generate_summary_chunks( + db: Any, + tenant_id: str, + job_id: str, + provider: str = "gemini", + model: str | None = None, + max_concurrent: int = 3, +) -> list[dict]: + """Generate 1-2 sentence summaries for each document section. + + Loads chunks from DB, groups by section, queries LLM, and returns new chunks. + """ + from ..server.chat.llm_chain import get_plain_chat_model + from langchain_core.messages import SystemMessage, HumanMessage + import tiktoken + + llm = get_plain_chat_model(provider=provider, model=model) + sem = asyncio.Semaphore(max_concurrent) + + # Simple tokenizer just for rough truncation + try: + enc = tiktoken.get_encoding("cl100k_base") + except Exception: + enc = None + + def _truncate(text: str, max_tokens: int = 2000) -> str: + if enc: + tokens = enc.encode(text) + if len(tokens) > max_tokens: + return enc.decode(tokens[:max_tokens]) + "..." + else: + # Fallback: ~4 chars per token + max_chars = max_tokens * 4 + if len(text) > max_chars: + return text[:max_chars] + "..." + return text + + # 1. Load chunks from DB + chunks = await db.get_chunks(tenant_id, job_id) + if not chunks: + return [] + + # 2. Group by section_path + section_groups: Dict[tuple, List[str]] = {} + for chunk in chunks: + # Only summarize main text sections, skip existing summaries or figures + if chunk.get("chunk_type") not in ("section", "equation"): + continue + + path = tuple(chunk.get("section_path", [])) + if path not in section_groups: + section_groups[path] = [] + section_groups[path].append(chunk.get("text", "")) + + summary_chunks = [] + + # 3. Define the LLM call task + async def _summarize(path: tuple, texts: List[str]): + async with sem: + full_text = "\n\n".join(texts) + truncated_text = _truncate(full_text, max_tokens=2000) + + section_title = path[-1] if path else "Document Start" + + messages = [ + SystemMessage(content=( + "You are a helpful assistant. Summarize the following document section " + "in 1-2 concise sentences. Focus on the key facts, findings, or arguments. " + "Do not use conversational filler, just output the summary." + )), + HumanMessage(content=f"Section: {section_title}\n---\n{truncated_text}"), + ] + + try: + response = await llm.ainvoke(messages) + summary_text = response.content.strip() + + chunk_id = str(uuid.uuid4()) + chunk_doc = { + "chunk_id": chunk_id, + "text": summary_text, + "token_count": len(summary_text.split()) * 1.3, # rough estimate + "chunk_type": "summary", + "section_path": list(path), + "page_numbers": [], # Summaries span multiple pages potentially + "block_ids": [], + "metadata": {"generated_by": f"{provider}/{model or 'default'}"}, + "text_hash": hashlib.sha256(summary_text.encode()).hexdigest()[:16], + "quality_score": 1.0, # LLM generated, assume high quality text + } + summary_chunks.append(chunk_doc) + except Exception as e: + logger.error(f"Failed to summarize section {path}: {e}") + + # 4. Run all summaries concurrently + tasks = [ + _summarize(path, texts) + for path, texts in section_groups.items() + if texts # Ensure there is text to summarize + ] + + if tasks: + await asyncio.gather(*tasks) + + return summary_chunks diff --git a/src/longparser/schemas.py b/src/longparser/schemas.py index 6e54f1e..f95f089 100755 --- a/src/longparser/schemas.py +++ b/src/longparser/schemas.py @@ -107,6 +107,7 @@ class Block(BaseModel): table: Optional[Table] = None image_path: Optional[str] = None bbox_px: Optional[tuple] = Field(default=None, description="Pixel-space bounding box (x0,y0,x1,y1) for MFD dedup") + pii_redactions: dict = Field(default_factory=dict, description="PII redaction map: placeholder → original (for authorized review)") class PageProfile(BaseModel): @@ -168,7 +169,8 @@ def all_tables(self) -> list[Table]: class ProcessingConfig(BaseModel): """Configuration for pipeline execution.""" # --- v0.1.4: Backend selection --- - backend: str = Field(default="docling", description="Extraction backend: 'docling' | 'pymupdf' | 'auto'") + backend: str = Field(default="docling", description="Extraction backend: 'docling' | 'pymupdf' | 'marker' | 'auto'") + force_marker_cpu: bool = Field(default=False, description="Bypass 10-page soft cap when running Marker on CPU") # --- v0.1.4: Language detection --- languages: Optional[list[str]] = Field(default=None, description="Explicit Tesseract language codes, e.g. ['eng','ara']. Overrides auto-detect.") @@ -197,6 +199,10 @@ class ProcessingConfig(BaseModel): force_full_page_ocr: bool = False # Exclude page headers and footers from extraction output exclude_page_headers_footers: bool = True + # Redact PII before HITL review + redact_pii: bool = Field(default=False, description="Redact PII (emails, phones, SSNs, credit cards) before HITL review") + use_ner_redaction: bool = Field(default=False, description="Use spaCy NER for contextual PII redaction (Names, Orgs) if spacy is installed") + ner_model: str = Field(default="en_core_web_sm", description="spaCy model to use for NER redaction") # Formula extraction mode: "full" (slow, best LaTeX), "fast" (Unicode text), "smart" (hybrid) formula_mode: str = "smart" @@ -234,6 +240,13 @@ class ChunkingConfig(BaseModel): table_chunk_format: str = "row_record" # "row_record" | "pipe" generate_schema_chunks: bool = True wide_table_col_threshold: int = 25 + resolve_cross_references: bool = Field(default=True, description="Link 'see Figure 3' references to their target blocks") + use_semantic_chunking: bool = Field(default=False, description="Use embedding-based boundary detection for semantic splits") + semantic_threshold: float = Field(default=0.3, ge=0.0, le=1.0, description="Cosine similarity threshold below which a semantic split is forced") + semantic_model: str = Field(default="all-MiniLM-L6-v2", description="SentenceTransformer model for semantic chunking (default is fastest on CPU; use 'all-mpnet-base-v2' for higher accuracy)") + generate_summary_chunks: bool = Field(default=False, description="Auto-generate a 1-2 sentence summary chunk per section (requires LLM, runs as background task)") + summary_llm_provider: str = Field(default="gemini", description="LLM provider for summary generation") + summary_llm_model: Optional[str] = Field(default=None, description="LLM model for summary generation (None = provider default)") class Chunk(BaseModel): @@ -249,6 +262,7 @@ class Chunk(BaseModel): equation_detected: bool = False image_path: Optional[str] = Field(default=None, description="Path to figure image if chunk_type == 'figure'") metadata: dict = Field(default_factory=dict) # row_start, row_end, sheet, col_band + quality_score: float = Field(default=1.0, ge=0.0, le=1.0, description="Chunk quality score (0=garbled, 1=perfect)") class JobRequest(BaseModel): diff --git a/src/longparser/server/worker.py b/src/longparser/server/worker.py index a360033..070e76b 100755 --- a/src/longparser/server/worker.py +++ b/src/longparser/server/worker.py @@ -109,6 +109,17 @@ async def extract_job(ctx: dict, tenant_id: str, job_id: str, file_path: str) -> logger.info( f"[Worker] Job {job_id} done: {block_count} blocks, {chunk_count} chunks" ) + + # Auto-enqueue summary enrichment if enabled + import os + if os.getenv("LONGPARSER_GENERATE_SUMMARIES", "false").lower() in ("true", "1"): + from arq.jobs import Job + await ctx["redis"].enqueue_job( + "enrich_summaries_job", tenant_id, job_id, + _job_id=f"summary-{job_id}", + ) + logger.info(f"[Worker] Enqueued summary enrichment for job {job_id}") + return {"status": "ready_for_review", "blocks": block_count, "chunks": chunk_count} except Exception as e: @@ -244,6 +255,46 @@ async def embed_job( await db.close() +async def enrich_summaries_job( + ctx: dict, tenant_id: str, job_id: str, + provider: str = "gemini", model: str | None = None, +) -> dict: + """Generate summary chunks for each document section. + + Runs after extract_job completes. Loads chunks from MongoDB, + groups by section_path, calls LLM for 1-2 sentence summaries, + and upserts new summary chunks back into MongoDB. + """ + from .db import Database + from ..pipeline.summary_enricher import generate_summary_chunks + + db = Database() + try: + job = await db.get_job(tenant_id, job_id) + if not job or job["status"] == "cancelled": + return {"status": "cancelled"} + + summary_chunks = await generate_summary_chunks( + db, tenant_id, job_id, provider=provider, model=model, + ) + + # Upsert summary chunks into MongoDB + for chunk_doc in summary_chunks: + await db.upsert_chunk(tenant_id, job_id, chunk_doc) + + await db.update_job(tenant_id, job_id, { + "progress.summary_chunks": len(summary_chunks), + }) + + logger.info(f"[Worker] Generated {len(summary_chunks)} summary chunks for job {job_id}") + return {"status": "enriched", "summary_chunks": len(summary_chunks)} + + except Exception as e: + logger.exception(f"[Worker] Summary enrichment failed for job {job_id}: {e}") + return {"error": str(e)} + finally: + await db.close() + # --------------------------------------------------------------------------- # Chat Background Tasks # --------------------------------------------------------------------------- @@ -446,6 +497,7 @@ class WorkerSettings: functions = [ extract_job, embed_job, + enrich_summaries_job, summarize_session, extract_facts, purge_expired_sessions, diff --git a/uv.lock b/uv.lock index 3e67b69..24229c5 100644 --- a/uv.lock +++ b/uv.lock @@ -242,6 +242,24 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" }, ] +[[package]] +name = "anthropic" +version = "0.46.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "distro" }, + { name 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