diff --git a/.dockerignore b/.dockerignore index c5ebba77..3874860a 100644 --- a/.dockerignore +++ b/.dockerignore @@ -119,4 +119,4 @@ vite.config.js *.tfstate.* .terraform/ .aws/ -.gcloud/ \ No newline at end of file +.gcloud/ diff --git a/.github/workflows/database.yml b/.github/workflows/database.yml index 33361463..90c53f2e 100644 --- a/.github/workflows/database.yml +++ b/.github/workflows/database.yml @@ -12,7 +12,7 @@ jobs: runs-on: ubuntu-latest steps: - name: checkout repo content - uses: actions/checkout@v2 + uses: actions/checkout@v4 - name: install git lfs run: | @@ -20,15 +20,14 @@ jobs: sudo apt-get install git-lfs git lfs install + - name: Install uv + uses: astral-sh/setup-uv@v4 + - name: setup python - uses: actions/setup-python@v2 - with: - python-version: "3.10" + run: uv python install 3.10 - name: install python packages - run: | - python -m pip install --upgrade pip - pip install . + run: uv pip install --system . - name: track large files run: git lfs track "database/pipeline.pkl" diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml new file mode 100644 index 00000000..caf5eb38 --- /dev/null +++ b/.github/workflows/lint.yml @@ -0,0 +1,44 @@ +name: Lint and Test + +on: + push: + branches: [main, master] + pull_request: + branches: [main, master] + +jobs: + lint: + name: Lint + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v4 + + - name: Set up Python + run: uv python install 3.11 + + - name: Run Ruff linter + run: uvx ruff check . + + - name: Run Ruff formatter check + run: uvx ruff format --check . + + - name: Run mypy + run: uvx mypy . --ignore-missing-imports + + pre-commit: + name: Pre-commit hooks + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v4 + + - name: Set up Python + run: uv python install 3.11 + + - name: Run pre-commit + run: uvx pre-commit run --all-files diff --git a/.gitignore b/.gitignore index 834f8692..dc32b2ce 100644 --- a/.gitignore +++ b/.gitignore @@ -132,4 +132,4 @@ dmypy.json # Pyre type checker .pyre/ -*DS_Store \ No newline at end of file +*DS_Store diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..1cff9f64 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,22 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.5.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-added-large-files + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.8.0 + hooks: + - id: ruff + args: [--fix] + - id: ruff-format + + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.13.0 + hooks: + - id: mypy + additional_dependencies: [] + args: [--ignore-missing-imports] diff --git a/Dockerfile b/Dockerfile index 16f3e0c6..b4d8c250 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,18 +1,19 @@ FROM python:3.10-slim +# Install uv +COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ + # Clone the repository WORKDIR /code -# Copy the necessary files (you may skip this if already in the repository) +# Copy the necessary files COPY database/pipeline.pkl /code/database/pipeline.pkl -COPY requirements.txt /code/requirements.txt -COPY setup.py /code/setup.py +COPY pyproject.toml /code/pyproject.toml COPY knowledge_database /code/knowledge_database COPY api /code/api -# Install Python dependencies -RUN pip install pip --upgrade -RUN pip install --no-cache-dir -r requirements.txt +# Install Python dependencies using uv +RUN uv pip install --system . # Set up the secret environment variable for OpenAI API Key RUN --mount=type=secret,id=OPENAI_API_KEY sh -c 'echo "export OPENAI_API_KEY=$(cat /run/secrets/OPENAI_API_KEY)" >> /etc/profile.d/openai.sh' diff --git a/LICENSE b/LICENSE index 77cc3e0c..5bd621a9 100644 --- a/LICENSE +++ b/LICENSE @@ -671,4 +671,4 @@ into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read -. \ No newline at end of file +. diff --git a/Makefile b/Makefile index 7be0676b..879e49da 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,74 @@ -launch: +.PHONY: install install-dev sync dev api run lint lint-fix check pre-commit pre-commit-install docker-build docker-run launch clean + +# Install dependencies +install: + uv sync --no-dev + +# Install with dev dependencies +install-dev: + uv sync --all-extras + +# Sync dependencies (alias for install-dev) +sync: + uv sync --all-extras + +# Start local dev server +dev: + uv run uvicorn api.api:app --reload --port 8000 + +# Start API server (production-like) +api: + uv run uvicorn api.api:app --host 0.0.0.0 --port 8080 + +# Run the data extraction pipeline +run: + uv run python run.py + +# Linting and formatting check +lint: + uv run ruff check . + uv run ruff format --check . + uv run mypy . --ignore-missing-imports + +# Auto-fix linting issues +lint-fix: + uv run ruff check --fix . + uv run ruff format . + +# Run all checks (lint + type check) +check: lint + +# Run pre-commit hooks on all files +pre-commit: + uv run pre-commit run --all-files + +# Install pre-commit hooks +pre-commit-install: + uv run pre-commit install + +# Build Docker image +docker-build: echo ${OPENAI_API_KEY} > mysecret.txt docker build --secret id=OPENAI_API_KEY,src=mysecret.txt -t knowledge . + rm -f mysecret.txt + +# Run Docker container +docker-run: docker run -d --add-host host.docker.internal:host-gateway --name run_knowledge -p 8080:8080 knowledge -local-dev-api: - uvicorn api.api:app --reload +# Build and run Docker (legacy command) +launch: docker-build docker-run + +# Stop and remove Docker container +docker-stop: + docker stop run_knowledge || true + docker rm run_knowledge || true + +# Clean up +clean: + rm -rf .venv + rm -rf __pycache__ + rm -rf .mypy_cache + rm -rf .ruff_cache + find . -type d -name "__pycache__" -exec rm -rf {} + 2>/dev/null || true + find . -type f -name "*.pyc" -delete 2>/dev/null || true diff --git a/api/api.py b/api/api.py index cc08c987..336e7557 100644 --- a/api/api.py +++ b/api/api.py @@ -1,28 +1,36 @@ +""" +FastAPI server for the Knowledge Search Engine. + +This module provides REST API endpoints for searching documents, visualizing +the knowledge graph, and interacting with an LLM for document recommendations. +""" + import datetime -import json import pickle -import typing from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import ORJSONResponse, StreamingResponse from openai import OpenAI +# Initialize FastAPI application app = FastAPI( - description="Personnal knowledge graph.", - title="FactGPT", - version="0.0.1", + title="Knowledge Search Engine", + description="Personal knowledge graph with neural search and visualization.", + version="1.0.0", ) -origins = [ +# Configure CORS for frontend access +ALLOWED_ORIGINS = [ "https://raphaelsty.github.io", "http://127.0.0.1:8000", + "http://127.0.0.1:3000", + "http://localhost:3000", ] - app.add_middleware( CORSMiddleware, - allow_origins=origins, + allow_origins=ALLOWED_ORIGINS, allow_credentials=True, allow_methods=["GET", "POST", "OPTIONS"], allow_headers=["*"], @@ -30,28 +38,66 @@ class Knowledge: - """This class is a wrapper around the pipeline.""" + """ + Wrapper class for the knowledge pipeline. + + Manages the lifecycle of the search pipeline, providing methods for + searching documents and generating graph visualizations. + + Attributes + ---------- + pipeline : Pipeline | None + The loaded knowledge pipeline, or None before initialization. + """ def __init__(self) -> None: self.pipeline = None - def start(self): - """Load the pipeline and the documents.""" + def start(self) -> "Knowledge": + """ + Load the serialized pipeline from disk. + + Returns + ------- + Knowledge + Self reference for method chaining. + """ with open("database/pipeline.pkl", "rb") as f: self.pipeline = pickle.load(f) - return self - def get_latest_documents(self, count: int): - """Returns the most recently added documents.""" + def get_latest_documents(self, count: int) -> list[dict]: + """ + Get the most recently added documents. + + Parameters + ---------- + count : int + Number of documents to return. + + Returns + ------- + list[dict] + List of recent documents sorted by date. + """ return self.pipeline.get_latest_documents(count=count) - def search( - self, - q: str, - tags: str, - ) -> typing.Dict: - """Returns the documents.""" + def search(self, q: str, tags: str) -> list[dict]: + """ + Search for documents matching a query. + + Parameters + ---------- + q : str + Search query string. + tags : str + Whether to filter by tags ("true" or other). + + Returns + ------- + list[dict] + List of matching documents ranked by relevance. + """ return self.pipeline.search(q=q, tags=tags) def plot( @@ -60,8 +106,26 @@ def plot( k_tags: int, k_yens: int = 1, k_walk: int = 3, - ) -> typing.Dict: - """Returns the graph.""" + ) -> dict: + """ + Generate knowledge graph data for visualization. + + Parameters + ---------- + q : str + Search query to build the graph around. + k_tags : int + Maximum number of tags to include. + k_yens : int, default=1 + Number of shortest paths between tags. + k_walk : int, default=3 + Number of neighbors for random walks. + + Returns + ------- + dict + Dictionary with 'nodes' and 'links' for graph visualization. + """ nodes, links = self.pipeline.plot( q=q, k_tags=k_tags, @@ -71,49 +135,139 @@ def plot( return {"nodes": nodes, "links": links} +# Global knowledge instance knowledge = Knowledge() @app.get("/latest/{count}") -def get_latest(count: int): - """Returns the most recently added documents.""" +def get_latest(count: int) -> dict: + """ + Get the most recently added documents. + + Parameters + ---------- + count : int + Number of documents to return. + + Returns + ------- + dict + Dictionary containing 'documents' list. + """ documents = knowledge.get_latest_documents(count=count) return {"documents": documents} @app.get("/search/{sort}/{tags}/{q}") -def search(tags: str, sort: bool, q: str): - """Search for documents.""" +def search(tags: str, sort: bool, q: str) -> dict: + """ + Search for documents with optional sorting and tag filtering. + + Parameters + ---------- + tags : str + Tag filter mode ("null" for no filter, any other value to enable). + sort : bool + Whether to sort results by date (newest first). + q : str + Search query string. + + Returns + ------- + dict + Dictionary containing 'documents' list. + """ tags = tags != "null" documents = knowledge.search(q=q, tags=tags) + + # Sort by date if requested if bool(sort): documents = [ document for _, document in sorted( [(document["date"], document) for document in documents], - key=lambda document: datetime.datetime.strptime( - document[0], "%Y-%m-%d" - ), + key=lambda doc: datetime.datetime.strptime(doc[0], "%Y-%m-%d"), reverse=True, ) ] + return {"documents": documents} @app.get("/plot/{k_tags}/{q}", response_class=ORJSONResponse) -def plot(k_tags: int, q: str): - """Plot tags.""" +def plot(k_tags: int, q: str) -> dict: + """ + Generate knowledge graph visualization data. + + Parameters + ---------- + k_tags : int + Maximum number of tags to include in the graph. + q : str + Search query to build the graph around. + + Returns + ------- + dict + Dictionary with 'nodes' and 'links' for D3/ForceGraph visualization. + """ return knowledge.plot(q=q, k_tags=k_tags) +@app.get("/expand/{node_id}", response_class=ORJSONResponse) +def expand_node(node_id: str) -> dict: + """ + Expand a node to show its neighbors. + + Used for progressive graph exploration where users click nodes + to reveal their connections. + + Parameters + ---------- + node_id : str + The tag name of the node to expand. + + Returns + ------- + dict + Dictionary with 'nodes' and 'links' for the expanded subgraph. + """ + nodes, links = knowledge.pipeline.graph.expand(node_id=node_id) + return {"nodes": nodes, "links": links} + + @app.on_event("startup") -def start(): - """Intialiaze the pipeline.""" +def start() -> Knowledge: + """ + Initialize the knowledge pipeline on server startup. + + Returns + ------- + Knowledge + The initialized knowledge instance. + """ return knowledge.start() async def async_chat(query: str, content: str): - """Re-rank the documents using ChatGPT.""" + """ + Stream LLM responses for document recommendations. + + Uses GPT-4 to analyze retrieved documents and provide natural language + recommendations based on the user's query. + + Parameters + ---------- + query : str + The user's search query. + content : str + Formatted string of retrieved document metadata. + + Yields + ------ + str + Incrementally built response text as tokens arrive. + """ client = OpenAI() response = client.chat.completions.create( @@ -121,13 +275,15 @@ async def async_chat(query: str, content: str): messages=[ { "role": "system", - "content": """ - You are a helpful assistant designed to output JSON. - """, + "content": "You are a helpful assistant designed to output JSON.", }, { "role": "user", - "content": f"Hi, answer in comprehensible english, do not reply with json, among the set of documents retrieved, which documents are related to my query: {query}, set of documents: {content}.", + "content": ( + f"Hi, answer in comprehensible english, do not reply with json, " + f"among the set of documents retrieved, which documents are related " + f"to my query: {query}, set of documents: {content}." + ), }, ], max_tokens=200, @@ -148,18 +304,39 @@ async def async_chat(query: str, content: str): @app.get("/chat/{k_tags}/{q}") -async def chat(k_tags: int, q: str): - """LLM recommendation.""" +async def chat(k_tags: int, q: str) -> StreamingResponse: + """ + Get LLM-powered document recommendations. + + Searches for relevant documents and streams GPT-4's analysis + of which documents best match the query. + + Parameters + ---------- + k_tags : int + Not used (kept for API compatibility). + q : str + Search query for finding relevant documents. + + Returns + ------- + StreamingResponse + Streaming text response from the LLM. + """ documents = knowledge.search(q=q, tags=False) + + # Format documents for LLM context content = "" for document in documents: content += "title: " + document["title"] + "\n" content += "summary: " + document["summary"][:30] + "\n" - content += "targs: " + ( - ", ".join(document["tags"] + document["extra-tags"]) + "\n" - ) + content += "tags: " + (", ".join(document["tags"] + document["extra-tags"]) + "\n") content += "url: " + document["url"] + "\n\n" + + # Truncate to fit context window content = "title: ".join(content[:3000].split("title:")[:-1]) + return StreamingResponse( - async_chat(query=q, content=content), media_type="text/plain" + async_chat(query=q, content=content), + media_type="text/plain", ) diff --git a/database/database.json b/database/database.json index a3b3d42b..ebaca642 100644 --- a/database/database.json +++ b/database/database.json @@ -51600,4 +51600,4 @@ "robotics" ] } -} \ No newline at end of file +} diff --git a/database/triples.json b/database/triples.json index d6a63cf7..27b1647b 100644 --- a/database/triples.json +++ b/database/triples.json @@ -237703,4 +237703,4 @@ "head": "memoryvla", "tail": "pretrained models" } -] \ No newline at end of file +] diff --git a/docs/favicon_io/site.webmanifest b/docs/favicon_io/site.webmanifest index 45dc8a20..1dd91123 100644 --- a/docs/favicon_io/site.webmanifest +++ b/docs/favicon_io/site.webmanifest @@ -1 +1 @@ -{"name":"","short_name":"","icons":[{"src":"/android-chrome-192x192.png","sizes":"192x192","type":"image/png"},{"src":"/android-chrome-512x512.png","sizes":"512x512","type":"image/png"}],"theme_color":"#ffffff","background_color":"#ffffff","display":"standalone"} \ No newline at end of file +{"name":"","short_name":"","icons":[{"src":"/android-chrome-192x192.png","sizes":"192x192","type":"image/png"},{"src":"/android-chrome-512x512.png","sizes":"512x512","type":"image/png"}],"theme_color":"#ffffff","background_color":"#ffffff","display":"standalone"} diff --git a/docs/index.html b/docs/index.html index 0b1a0e93..1b13dc35 100644 --- a/docs/index.html +++ b/docs/index.html @@ -10,9 +10,9 @@ - + - + @@ -26,13 +26,13 @@ - - + + - + - @@ -98,6 +69,20 @@
+
+
+ + From documents +
+
+ + From query +
+
+ Click to zoom | Double-click to expand +
+
+ @@ -132,7 +117,12 @@ const [modelStatus, setModelStatus] = useState("Loading Script..."); const [documents, setDocuments] = useState([]); const [isSortedByDate, setIsSortedByDate] = useState(false); - const [resultsReranked, setResultsReranked] = useState(false); // New: Tracks re-ranking status + const [resultsReranked, setResultsReranked] = useState(false); + + // --- Graph Interaction State --- + const [highlightNodes, setHighlightNodes] = useState(new Set()); + const [highlightLinks, setHighlightLinks] = useState(new Set()); + const [graphData, setGraphData] = useState({ nodes: [], links: [] }); // --- Refs --- const searchTimerRef = useRef(null); @@ -140,7 +130,9 @@ const workerRef = useRef(null); const latestQueryIdRef = useRef(0); const graphRootRef = useRef(null); - const rerankTimerRef = useRef(null); // New: Timer for inactivity-based re-ranking + const rerankTimerRef = useRef(null); + const fgRef = useRef(null); + const lastClickTimeRef = useRef(0); // --- Function Declarations (in dependency order) --- @@ -189,7 +181,34 @@ }, [selectedNode, fetchLatest]); // modelStatus dependency removed /** - * Handles hovering over a node in the 3D graph. + * Preprocesses graph data to build neighbor and link references for highlighting. + */ + const preprocessGraphData = useCallback((data) => { + const nodeById = {}; + data.nodes.forEach(node => { + nodeById[node.id] = node; + node.neighbors = []; + node.links = []; + }); + + data.links.forEach(link => { + const sourceId = typeof link.source === 'object' ? link.source.id : link.source; + const targetId = typeof link.target === 'object' ? link.target.id : link.target; + const a = nodeById[sourceId]; + const b = nodeById[targetId]; + if (a && b) { + a.neighbors.push(b); + b.neighbors.push(a); + a.links.push(link); + b.links.push(link); + } + }); + + return data; + }, []); + + /** + * Handles hovering over a node in the 3D graph - triggers search. */ const handleHoverNode = useCallback((hoveredNode) => { if (hoveredNode) { @@ -200,7 +219,101 @@ }, [query, search, documentsToFetch]); /** - * Fetches data and renders the 3D force-directed graph. + * Handles node hover for path highlighting. + */ + const handleGraphNodeHover = useCallback((node) => { + const newHighlightNodes = new Set(); + const newHighlightLinks = new Set(); + + if (node) { + newHighlightNodes.add(node); + if (node.neighbors) { + node.neighbors.forEach(neighbor => newHighlightNodes.add(neighbor)); + } + if (node.links) { + node.links.forEach(link => newHighlightLinks.add(link)); + } + } + + setHighlightNodes(newHighlightNodes); + setHighlightLinks(newHighlightLinks); + + if (node) { + handleHoverNode(node); + } + }, [handleHoverNode]); + + /** + * Handles link hover for highlighting. + */ + const handleGraphLinkHover = useCallback((link) => { + const newHighlightNodes = new Set(); + const newHighlightLinks = new Set(); + + if (link) { + newHighlightLinks.add(link); + newHighlightNodes.add(link.source); + newHighlightNodes.add(link.target); + } + + setHighlightNodes(newHighlightNodes); + setHighlightLinks(newHighlightLinks); + }, []); + + /** + * Handles click on a node - zooms camera to it. + */ + const handleGraphNodeClick = useCallback((node) => { + const now = Date.now(); + const isDoubleClick = now - lastClickTimeRef.current < 300; + lastClickTimeRef.current = now; + + if (isDoubleClick) { + // Double-click: expand neighbors + fetch(`${API_BASE_URL}/expand/${encodeURIComponent(node.id)}`) + .then(res => res.json()) + .then(newData => { + setGraphData(prev => { + const existingNodeIds = new Set(prev.nodes.map(n => n.id)); + const existingLinkKeys = new Set(prev.links.map(l => { + const sId = typeof l.source === 'object' ? l.source.id : l.source; + const tId = typeof l.target === 'object' ? l.target.id : l.target; + return `${sId}-${tId}`; + })); + + const newNodes = newData.nodes.filter(n => !existingNodeIds.has(n.id)); + const newLinks = newData.links.filter(l => { + const key = `${l.source}-${l.target}`; + const reverseKey = `${l.target}-${l.source}`; + return !existingLinkKeys.has(key) && !existingLinkKeys.has(reverseKey); + }); + + const merged = { + nodes: [...prev.nodes, ...newNodes], + links: [...prev.links, ...newLinks] + }; + + return preprocessGraphData(merged); + }); + }) + .catch(error => console.error("[APP] Failed to expand node:", error)); + } else { + // Single click: zoom to node + if (fgRef.current) { + const distance = 120; + const distRatio = 1 + distance / Math.hypot(node.x, node.y, node.z); + fgRef.current.cameraPosition( + { x: node.x * distRatio, y: node.y * distRatio, z: node.z * distRatio }, + node, + 2000 + ); + } + handleHoverNode(node); + } + }, [handleHoverNode, preprocessGraphData]); + + /** + * Fetches data and renders the 3D force-directed graph with enhanced features. */ const plot = useCallback((plotQuery, count) => { const graphContainer = document.getElementById('graph'); @@ -208,37 +321,78 @@ unmountGraph(); return; } - const endpoint = `${API_BASE_URL}/plot/25/${plotQuery.replace("/", "")}`; + const endpoint = `${API_BASE_URL}/plot/8/${plotQuery.replace("/", "")}`; fetch(endpoint) .then(res => res.json()) .then(data => { if (!graphContainer) return; + + const processedData = preprocessGraphData(data); + setGraphData(processedData); + if (!graphRootRef.current) { graphRootRef.current = createRoot(graphContainer); } graphRootRef.current.render( highlightLinks.has(link) ? 0.08 : 0.005} + linkColor={link => highlightLinks.has(link) ? '#ffffff' : 'rgba(255, 255, 255, 0.2)'} + + // Subtle single particles + linkDirectionalParticles={1} + linkDirectionalParticleSpeed={0.003} + linkDirectionalParticleWidth={link => highlightLinks.has(link) ? 1 : 0.5} + linkDirectionalParticleColor={link => highlightLinks.has(link) ? '#ffffff' : 'rgba(255, 255, 255, 0.4)'} + + // Clean text-only nodes - bigger text nodeThreeObject={node => { const sprite = new SpriteText(node.id); - sprite.color = node.color; - sprite.textHeight = 4; - sprite.fontSize = 50; - sprite.fontFace = "Futura"; + const isHighlighted = highlightNodes.size === 0 || highlightNodes.has(node); + + sprite.color = isHighlighted ? node.color : 'rgba(255, 255, 255, 0.35)'; + sprite.textHeight = 6; + sprite.fontSize = 140; + sprite.fontFace = "Inter"; + sprite.fontWeight = isHighlighted ? "600" : "500"; + return sprite; }} - onNodeHover={handleHoverNode} + + // Minimal tooltip + nodeLabel={node => ` +
+ ${node.id} +
+ `} + + // Interactions + onNodeClick={handleGraphNodeClick} + onNodeHover={handleGraphNodeHover} + onLinkHover={handleGraphLinkHover} + + // Physics + cooldownTicks={100} + d3AlphaDecay={0.02} + d3VelocityDecay={0.3} + + // Camera - closer zoom + cameraPosition={{ x: 0, y: 0, z: 140 }} /> ); }).catch(error => console.error("[APP] Failed to plot graph:", error)); - }, [isMobile, handleHoverNode, unmountGraph]); + }, [isMobile, unmountGraph, preprocessGraphData, handleGraphNodeClick, handleGraphNodeHover, handleGraphLinkHover, highlightNodes, highlightLinks]); /** * A utility to run search and plot immediately, clearing any pending debounced calls. diff --git a/docs/pkg/.gitignore b/docs/pkg/.gitignore index f59ec20a..72e8ffc0 100644 --- a/docs/pkg/.gitignore +++ b/docs/pkg/.gitignore @@ -1 +1 @@ -* \ No newline at end of file +* diff --git a/docs/pkg/package.json b/docs/pkg/package.json index fdc95b6b..b3f00e1a 100644 --- a/docs/pkg/package.json +++ b/docs/pkg/package.json @@ -18,4 +18,4 @@ "sideEffects": [ "./snippets/*" ] -} \ No newline at end of file +} diff --git a/docs/style.css b/docs/style.css index 44f2b00d..57836a68 100644 --- a/docs/style.css +++ b/docs/style.css @@ -1,192 +1,411 @@ -/* General Body Styles */ +/* ============================================ + Modern Knowledge Search Engine Styles + ============================================ */ + +/* Import modern font */ +@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap'); + +/* CSS Variables for easy theming */ +:root { + --bg-primary: #0a0a0f; + --bg-secondary: #12121a; + --bg-card: rgba(8, 8, 14, 0.85); + --bg-card-hover: rgba(16, 16, 26, 0.95); + --bg-glass: rgba(255, 255, 255, 0.03); + --bg-input: rgba(255, 255, 255, 0.05); + + --text-primary: #f0f0f5; + --text-secondary: #a0a0b0; + --text-muted: #6b6b7b; + + --accent-primary: #10b981; + --accent-secondary: #06d6a0; + --accent-glow: rgba(16, 185, 129, 0.15); + --accent-gradient: linear-gradient(135deg, #10b981 0%, #06d6a0 100%); + + --border-subtle: rgba(255, 255, 255, 0.06); + --border-hover: rgba(255, 255, 255, 0.12); + + --shadow-sm: 0 2px 8px rgba(0, 0, 0, 0.3); + --shadow-md: 0 4px 20px rgba(0, 0, 0, 0.4); + --shadow-lg: 0 8px 40px rgba(0, 0, 0, 0.5); + --shadow-glow: 0 0 30px var(--accent-glow); + + --radius-sm: 8px; + --radius-md: 12px; + --radius-lg: 16px; + --radius-xl: 24px; + + --transition-fast: 0.15s ease; + --transition-normal: 0.25s ease; + --transition-slow: 0.4s cubic-bezier(0.4, 0, 0.2, 1); +} + +/* Reset and Base */ +*, *::before, *::after { + box-sizing: border-box; + margin: 0; + padding: 0; +} + +html { + scroll-behavior: smooth; +} + body { - background-color: #131317; + background: #080808; + min-height: 100vh; margin: 0; padding: 0; overflow-x: hidden; - font-family: 'Futura', sans-serif; + font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; + color: var(--text-primary); + line-height: 1.6; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; } -/* Graph container positioning */ +/* Custom Scrollbar */ +::-webkit-scrollbar { + width: 8px; + height: 8px; +} + +::-webkit-scrollbar-track { + background: var(--bg-secondary); +} + +::-webkit-scrollbar-thumb { + background: rgba(255, 255, 255, 0.1); + border-radius: 4px; +} + +::-webkit-scrollbar-thumb:hover { + background: rgba(255, 255, 255, 0.2); +} + +/* Selection */ +::selection { + background: var(--accent-primary); + color: var(--bg-primary); +} + +/* ============================================ + Layout + ============================================ */ + +/* Graph container */ #graph { position: fixed; top: 0; right: 0; width: 50%; height: 100vh; + background: #080808; + border-left: 1px solid rgba(255, 255, 255, 0); } /* Search content container */ #searchbox { width: 50%; float: left; - height: 100vh; + min-height: 100vh; overflow-y: auto; - padding: 1%; + padding: 24px 48px; box-sizing: border-box; } -/* --- CORRECTED STYLES START HERE --- */ +#backsearch { + max-width: 100%; + margin: 0; +} + +/* ============================================ + Search Container + ============================================ */ -/* Container is now the fixed element, matching the old #search rules */ #search-container { position: fixed; - width: 44%; - margin-left: 2%; - top: 1%; - height: 35px; - /* Explicitly set height */ + width: calc(50% - 96px); + left: 48px; + top: 24px; + height: 52px; + z-index: 100; } -/* Search input now fills its fixed container */ #search { width: 100%; height: 100%; - padding-left: 20px; - padding-right: 35px; - /* Make space for indicator */ - background-color: #1E1E1E; - color: #FFFFFF; - font-family: 'Futura', sans-serif; + padding: 0 50px 0 24px; + background: var(--bg-glass); + backdrop-filter: blur(20px); + -webkit-backdrop-filter: blur(20px); + color: var(--text-primary); + font-family: 'Inter', sans-serif; font-size: 15px; - border: none; + font-weight: 400; + letter-spacing: 0.01em; + border: 1px solid var(--border-subtle); + border-radius: var(--radius-xl); outline: none; - box-shadow: none; - border-radius: 8px; - box-sizing: border-box; + box-shadow: var(--shadow-md); + transition: all var(--transition-normal); +} + +#search::placeholder { + color: var(--text-muted); + font-weight: 400; } -/* Status Indicator is now positioned inside the new fixed container */ +#search:hover { + border-color: var(--border-hover); + background: rgba(255, 255, 255, 0.05); +} + +#search:focus { + border-color: var(--accent-primary); + box-shadow: var(--shadow-md), var(--shadow-glow); + background: rgba(255, 255, 255, 0.06); +} + +/* Status Indicator */ .status-indicator { position: absolute; - right: 12px; - /* Position from the right edge of the container */ + right: 18px; top: 50%; - /* Center vertically */ transform: translateY(-50%); width: 10px; height: 10px; border-radius: 50%; - transition: background-color 0.3s ease; + transition: all var(--transition-normal); + box-shadow: 0 0 10px currentColor; +} + +.status-loading { + background-color: #f59e0b; + animation: pulse 1.5s ease-in-out infinite; +} + +.status-ready { + background-color: #22c55e; +} + +.status-error { + background-color: #ef4444; +} + +@keyframes pulse { + 0%, 100% { opacity: 1; transform: translateY(-50%) scale(1); } + 50% { opacity: 0.6; transform: translateY(-50%) scale(0.9); } } *:focus { outline: none; } -/* Documents container */ +/* ============================================ + Documents Container + ============================================ */ + #documents { width: 100%; - margin-top: 60px; - color: #FFFFFF; - background-color: #131317; + max-width: 100%; + margin: 90px 0 40px 0; + padding: 0; } -/* Individual document item */ +/* ============================================ + Document Card + ============================================ */ + .document { - margin-bottom: 30px; + position: relative; + padding: 24px; + margin-bottom: 16px; + background: linear-gradient(145deg, rgba(12, 12, 20, 0.9), rgba(6, 6, 12, 0.95)); + backdrop-filter: blur(16px); + -webkit-backdrop-filter: blur(16px); + border: 1px solid rgba(255, 255, 255, 0.04); + border-radius: var(--radius-lg); + transition: all var(--transition-normal); + box-shadow: + 0 4px 24px rgba(0, 0, 0, 0.4), + inset 0 1px 0 rgba(255, 255, 255, 0.02); } -/* Document title link */ -.document a.title { - font-size: 15px; - color: #FFFFFF; - text-decoration: none; +.document:hover { + background: linear-gradient(145deg, rgba(18, 18, 28, 0.95), rgba(10, 10, 18, 0.98)); + border-color: rgba(16, 185, 129, 0.15); + transform: translateY(-3px); + box-shadow: + 0 8px 32px rgba(0, 0, 0, 0.5), + 0 0 0 1px rgba(16, 185, 129, 0.08), + inset 0 1px 0 rgba(255, 255, 255, 0.04); } -.document a.title:hover { - text-decoration: underline; +/* Title wrapper */ +.title-wrapper { + display: flex; + align-items: center; + gap: 12px; } -.score-badge { - font-family: monospace; - font-size: 12px; - padding: 2px 8px; - border-radius: 5px; - font-weight: bold; +/* Logo container */ +.logo { + font-size: 1em; + display: inline-flex; + align-items: center; + justify-content: center; + width: 16px; + height: 16px; flex-shrink: 0; - /* Prevents badge from shrinking or wrapping */ - align-self: center; - /* Ensures vertical alignment with tags */ + opacity: 0.85; + transition: all var(--transition-fast); } -.retriever-score { - color: #ACADA8; - background-color: #2a2a2e; +.document:hover .logo { + opacity: 1; } -.reranker-score { - color: #131317; - background-color: #19bc8e; +.logo img { + width: 100%; + height: 100%; + object-fit: contain; +} + +/* Document title */ +.document a.title { + font-size: 16px; + font-weight: 600; + color: var(--text-primary); + text-decoration: none; + line-height: 1.4; + transition: color var(--transition-fast); } -/* Date display */ +.document a.title:hover { + color: var(--accent-secondary); + text-decoration: none; +} + +/* Date */ .date { font-size: 12px; - color: #ACADA8; - font-weight: lighter; + font-weight: 500; + color: var(--text-muted); cursor: pointer; - margin-top: 7px; + margin-top: 10px; + padding: 4px 10px; + background: rgba(255, 255, 255, 0.03); + border-radius: var(--radius-sm); + display: inline-block; + transition: all var(--transition-fast); +} + +.date:hover { + background: rgba(255, 255, 255, 0.08); + color: var(--text-secondary); } -/* Tags container */ +/* Summary */ +.summary { + margin-top: 14px; + font-size: 14px; + font-weight: 400; + color: var(--text-secondary); + line-height: 1.7; +} + +/* ============================================ + Tags + ============================================ */ + .tags { - margin-top: 12px; + margin-top: 16px; display: flex; flex-wrap: wrap; - gap: 7px; + gap: 8px; + align-items: center; } -/* Individual tag */ .tag { cursor: pointer; - display: inline-block; - background-color: #2a2a2e; - padding: 2px 8px; - border-radius: 4px; + display: inline-flex; + align-items: center; + background: rgba(255, 255, 255, 0.04); + padding: 5px 12px; + border-radius: 20px; font-size: 12px; - color: #ACADA8; - font-style: italic; + font-weight: 500; + color: var(--text-secondary); + border: 1px solid transparent; + transition: all var(--transition-fast); } .tag:hover { - background-color: #19bc8e; - color: #131317; + background: var(--accent-gradient); + color: var(--bg-primary); + border-color: transparent; + transform: translateY(-1px); + box-shadow: 0 4px 12px var(--accent-glow); } -/* Summary text */ -.summary { - margin-top: 10px; - font-size: 14px; - color: #CCCDC6; - font-weight: lighter; - text-align: justify; +/* Score badges */ +.score-badge { + font-family: 'SF Mono', 'Fira Code', monospace; + font-size: 11px; + padding: 4px 10px; + border-radius: 20px; + font-weight: 600; + flex-shrink: 0; + align-self: center; + letter-spacing: 0.02em; + transition: all var(--transition-fast); +} + +.retriever-score { + color: var(--text-muted); + background: rgba(255, 255, 255, 0.04); + border: 1px solid var(--border-subtle); +} + +.reranker-score { + color: var(--bg-primary); + background: var(--accent-gradient); + border: none; + box-shadow: 0 2px 8px var(--accent-glow); } -/* Highlighting styles */ +/* ============================================ + Highlighting + ============================================ */ + .highlight { - color: #19bc8e; + color: var(--accent-secondary); display: inline; - background-color: transparent; + background: transparent; + font-weight: 500; } .inline { display: inline; } -.status-loading { - background-color: #f59e0b; -} +/* ============================================ + Responsive Styles + ============================================ */ -.status-ready { - background-color: #22c55e; -} +@media (max-width: 1200px) { + #search-container { + width: calc(50% - 60px); + left: 30px; + } -.status-error { - background-color: #ef4444; + #searchbox { + padding: 24px; + } } -/* --- Responsive Styles for Mobile --- */ @media (max-width: 768px) { #graph { display: none; @@ -194,18 +413,190 @@ body { #searchbox { width: 100%; - padding: 2%; + padding: 16px; + } + + #backsearch { + max-width: 100%; } - /* Make container full width in mobile */ #search-container { - width: calc(100% - 4%); - margin-left: 0; - left: 2%; + width: calc(100% - 32px); + max-width: none; + left: 16px; + top: 16px; + height: 48px; + } + + #search { + font-size: 14px; + padding: 0 45px 0 20px; } #documents { width: 100%; - margin-left: 0; + margin-top: 80px; + max-width: 100%; + } + + .document { + padding: 20px; + margin-bottom: 12px; + border-radius: var(--radius-md); + background: linear-gradient(145deg, rgba(10, 10, 18, 0.92), rgba(5, 5, 10, 0.96)); + } + + .document a.title { + font-size: 15px; + } + + .summary { + font-size: 13px; + } + + .tag { + padding: 4px 10px; + font-size: 11px; + } +} + +@media (max-width: 480px) { + #searchbox { + padding: 12px; + } + + #search-container { + width: calc(100% - 24px); + left: 12px; + top: 12px; + } + + .document { + padding: 16px; + } + + .title-wrapper { + gap: 10px; + } + + .logo { + width: 14px; + height: 14px; + } +} + +/* ============================================ + Animations + ============================================ */ + +@keyframes fadeInUp { + from { + opacity: 0; + transform: translateY(20px); } -} \ No newline at end of file + to { + opacity: 1; + transform: translateY(0); + } +} + +.document { + animation: fadeInUp 0.4s ease forwards; +} + +.document:nth-child(1) { animation-delay: 0.02s; } +.document:nth-child(2) { animation-delay: 0.04s; } +.document:nth-child(3) { animation-delay: 0.06s; } +.document:nth-child(4) { animation-delay: 0.08s; } +.document:nth-child(5) { animation-delay: 0.10s; } +.document:nth-child(6) { animation-delay: 0.12s; } +.document:nth-child(7) { animation-delay: 0.14s; } +.document:nth-child(8) { animation-delay: 0.16s; } +.document:nth-child(9) { animation-delay: 0.18s; } +.document:nth-child(10) { animation-delay: 0.20s; } + +/* ============================================ + Graph Tooltips & Interactions + ============================================ */ + +/* Override ForceGraph3D tooltip styles */ +.scene-tooltip { + background: rgba(10, 10, 15, 0.95) !important; + backdrop-filter: blur(10px); + -webkit-backdrop-filter: blur(10px); + border: 1px solid var(--border-subtle) !important; + border-radius: var(--radius-md) !important; + padding: 0 !important; + font-family: 'Inter', sans-serif !important; + color: var(--text-primary) !important; + box-shadow: var(--shadow-lg) !important; + max-width: 250px; + pointer-events: none; +} + +/* Graph container enhancements */ +#graph canvas { + cursor: grab; +} + +#graph canvas:active { + cursor: grabbing; +} + +/* Graph Legend */ +.graph-legend { + position: fixed; + bottom: 16px; + right: 16px; + background: rgba(0, 0, 0, 0.6); + backdrop-filter: blur(8px); + -webkit-backdrop-filter: blur(8px); + border: 1px solid rgba(255, 255, 255, 0.06); + border-radius: 6px; + padding: 10px 12px; + z-index: 50; + font-size: 10px; +} + +.graph-legend-item { + display: flex; + align-items: center; + gap: 8px; + margin-bottom: 6px; + color: rgba(255, 255, 255, 0.5); + font-weight: 400; +} + +.graph-legend-item:last-of-type { + margin-bottom: 0; +} + +.graph-legend-dot { + width: 6px; + height: 6px; + border-radius: 50%; + flex-shrink: 0; +} + +.graph-legend-dot.query { + background-color: #86E5FF; +} + +.graph-legend-dot.retrieved { + background-color: #10b981; +} + +.graph-legend-hint { + margin-top: 8px; + padding-top: 8px; + border-top: 1px solid rgba(255, 255, 255, 0.06); + color: rgba(255, 255, 255, 0.3); + font-size: 9px; +} + +/* Hide legend on mobile */ +@media (max-width: 768px) { + .graph-legend { + display: none; + } +} diff --git a/knowledge_database/__init__.py b/knowledge_database/__init__.py index a367a2a3..29783e31 100644 --- a/knowledge_database/__init__.py +++ b/knowledge_database/__init__.py @@ -1,12 +1,55 @@ +""" +Knowledge Database - A personal knowledge management system. + +This package provides tools for collecting, organizing, and searching through +personal knowledge from various sources including GitHub, HackerNews, Zotero, +Semanlink, and HuggingFace. + +Modules +------- +github + Extract starred repositories from GitHub. +hackernews + Extract upvoted posts from HackerNews. +huggingface + Extract liked models, datasets, and spaces from HuggingFace. +zotero + Extract bookmarks from Zotero libraries. +semanlink + Parse knowledge from Semanlink RDF/Turtle files. +twitter + Extract liked tweets from Twitter. +tags + Generate tag relationships and extra tags for documents. +retriever + BM25-based document and tag retrieval. +graph + Knowledge graph construction and traversal. +pipeline + Unified pipeline combining retrieval and graph visualization. + +Example +------- +>>> from knowledge_database import github, pipeline +>>> +>>> # Collect GitHub stars +>>> gh = github.Github(user="raphaelsty") +>>> documents = gh() +>>> +>>> # Search through documents +>>> pipe = pipeline.Pipeline(documents=documents, triples=triples) +>>> results = pipe.search("neural networks") +""" + __all__ = [ "github", - "tags", - "papers", - "pipeline", - "semanlik", "hackernews", + "huggingface", + "zotero", + "semanlink", "twitter", + "tags", "retriever", "graph", - "Zotero", + "pipeline", ] diff --git a/knowledge_database/github/github.py b/knowledge_database/github/github.py index 7dd74b5f..1cc587de 100644 --- a/knowledge_database/github/github.py +++ b/knowledge_database/github/github.py @@ -1,7 +1,12 @@ +""" +GitHub module for extracting starred repositories. + +This module fetches starred repositories from a GitHub user's profile and +extracts relevant metadata including topics, descriptions, and README content. +""" + import collections import datetime -import json -import os import re import time @@ -11,63 +16,93 @@ class Github: - """Github Knowledge. + """ + Extract knowledge from GitHub starred repositories. + + Fetches starred repositories and extracts metadata including repository + topics, descriptions, and a clean text summary from README files. Parameters ---------- - username - Github username. + user : str + GitHub username whose starred repositories to fetch. - Examples - -------- + Attributes + ---------- + user : str + The GitHub username. + Example + ------- >>> from knowledge_database import github - - >>> github_raph = github.Github(user="raphaelsty") - - >>> github_raph(per_page=3, limit=3) - + >>> + >>> gh = github.Github(user="raphaelsty") + >>> documents = gh(per_page=100, limit=10) + >>> + >>> # Each document contains: title, summary, date, tags + >>> for url, doc in documents.items(): + ... print(f"{doc['title']}: {len(doc['tags'])} tags") """ def __init__(self, user: str): self.user = user - def __call__(self, per_page: int = 100, limit: int = 100): - + def __call__( + self, + per_page: int = 100, + limit: int = 100, + ) -> dict[str, dict]: + """ + Fetch starred repositories and extract document metadata. + + Parameters + ---------- + per_page : int, default=100 + Number of results per API page (max 100). + limit : int, default=100 + Maximum number of pages to fetch. + + Returns + ------- + dict[str, dict] + Dictionary mapping repository URLs to document metadata containing: + - title: Repository name + - summary: Description + README excerpt + - date: Current date (when starred info was fetched) + - tags: Repository topics + programming language + """ stars = [] + # Paginate through starred repositories for page in range(limit): + response = requests.get(f"https://api.github.com/users/{self.user}/starred?{per_page}=10&page={page}") - r = requests.get( - f"https://api.github.com/users/{self.user}/starred?{per_page}=10&page={page}" - ) - - if r.status_code != 200: + if response.status_code != 200: print("Github request failed.") break - r = r.json() - if len(r) == 0: + page_data = response.json() + if len(page_data) == 0: break - stars += r - - time.sleep(0.1) + stars += page_data + time.sleep(0.1) # Rate limiting - data = collections.defaultdict(dict) + data: dict[str, dict] = collections.defaultdict(dict) for repository in stars: - if "url" not in repository: continue url = repository["html_url"] + # Collect tags from topics and language tags = [tag.lower() for tag in repository["topics"]] - if repository.get("language", None) is not None: + if repository.get("language") is not None: tags += [repository["language"].lower()] tags = list(set(tags)) + # Extract clean text from README readme_text = self.get_readme_text_by_token_count( repository["html_url"], min_tokens=50, @@ -78,9 +113,7 @@ def __call__(self, per_page: int = 100, limit: int = 100): data[url] = { "date": datetime.datetime.today().strftime("%Y-%m-%d"), "title": f"{repository['name']}", - "summary": ( - f"{description} \n {readme_text}" if readme_text else description - ), + "summary": f"{description} \n {readme_text}" if readme_text else description, "tags": tags, } @@ -92,15 +125,22 @@ def get_readme_text_by_token_count( min_tokens: int = 50, ) -> str | None: """ - Fetches a README, removes HTML, markdown artifacts, and special tokens, - and extracts clean plain text until a minimum token count is reached. - - Args: - github_url: The URL of the GitHub repository. - min_tokens: The minimum number of tokens (words) to collect. - - Returns: - A clean string of plain text from the README, or None if it cannot be found. + Extract clean plain text from a repository's README. + + Fetches the README.md file and extracts readable paragraph text, + filtering out markdown artifacts, headings, and code blocks. + + Parameters + ---------- + github_url : str + URL of the GitHub repository. + min_tokens : int, default=50 + Minimum number of words to collect before stopping. + + Returns + ------- + str | None + Clean text excerpt from the README, or None if not found. """ match = re.search(r"github\.com/([^/]+)/([^/]+)", github_url) if not match: @@ -108,13 +148,12 @@ def get_readme_text_by_token_count( user, repo = match.groups() + # Try common default branch names branches_to_try = ["main", "master"] readme_content = None for branch in branches_to_try: - raw_url = ( - f"https://raw.githubusercontent.com/{user}/{repo}/{branch}/README.md" - ) + raw_url = f"https://raw.githubusercontent.com/{user}/{repo}/{branch}/README.md" try: response = requests.get(raw_url) response.raise_for_status() @@ -126,20 +165,20 @@ def get_readme_text_by_token_count( if not readme_content: return None + # Strip HTML tags text_only_content = re.sub(r"<[^>]+>", "", readme_content) collected_text = "" lines = text_only_content.splitlines() + # Extract only paragraph text, skipping markdown artifacts for line in lines: stripped_line = line.strip() is_heading = stripped_line.startswith("#") is_list_item = stripped_line.startswith(("* ", "- ", "+ ")) is_blockquote = stripped_line.startswith(">") - is_just_an_image_or_link = stripped_line.startswith( - "[" - ) and stripped_line.endswith(")") + is_just_an_image_or_link = stripped_line.startswith("[") and stripped_line.endswith(")") is_horizontal_rule = re.match(r"^[-*_]{3,}$", stripped_line) is not None if ( @@ -161,6 +200,7 @@ def get_readme_text_by_token_count( if not collected_text: return None + # Clean remaining special characters clean_text = re.sub(r"[^a-zA-Z0-9\s.,?!'-]", "", collected_text) normalized_text = re.sub(r"\s+", " ", clean_text) diff --git a/knowledge_database/graph/graph.py b/knowledge_database/graph/graph.py index 3fee3bb4..10714ba9 100644 --- a/knowledge_database/graph/graph.py +++ b/knowledge_database/graph/graph.py @@ -1,5 +1,12 @@ +""" +Knowledge Graph module for visualizing tag relationships. + +This module provides a graph-based representation of tag relationships, +enabling path finding and neighborhood exploration for interactive +knowledge visualization. +""" + import itertools -import typing import networkx as nx @@ -7,103 +14,189 @@ class Graph: - """Knowledge Graph. - - Example: - -------- - + """ + A knowledge graph built from tag co-occurrence relationships. + + The graph connects tags that appear together in documents, enabling + visualization of knowledge domains and discovery of related topics + through path finding algorithms. + + Parameters + ---------- + triples : list[dict] + List of edge dictionaries with 'head' and 'tail' keys representing + connected tags. + + Attributes + ---------- + graph : nx.Graph + The underlying NetworkX graph structure. + idx_to_node : dict[int, str] + Mapping from node indices to tag names. + node_to_idx : dict[str, int] + Mapping from tag names to node indices. + + Example + ------- >>> import json >>> from knowledge_database import graph - - >>> with open("database/triples.json", "r") as f: + >>> + >>> with open("database/triples.json") as f: ... triples = json.load(f) - - >>> knowledge_graph = graph.Graph(triples=triples) - - >>> knowledge_graph(tags=["nlp", "llms", "deep-learning"]) - + >>> + >>> kg = graph.Graph(triples=triples) + >>> nodes, links = kg(tags=["nlp", "transformers"], retrieved_tags=["bert"]) """ - def __init__(self, triples): + def __init__(self, triples: list[dict]): self.graph = nx.Graph() + self._document_counts: dict[str, int] = {} + # Extract unique nodes from all triples nodes = { **{node["head"]: True for node in triples}, **{node["tail"]: True for node in triples}, } - self.idx_to_node = {idx: node for idx, node in enumerate(nodes)} + # Create bidirectional mappings between indices and node names + self.idx_to_node = dict(enumerate(nodes)) self.node_to_idx = {node: idx for idx, node in self.idx_to_node.items()} + # Build the graph edges for triple in triples: head = triple["head"] tail = triple["tail"] self.graph.add_edge(self.node_to_idx[head], self.node_to_idx[tail]) self.graph.add_edge(self.node_to_idx[tail], self.node_to_idx[head]) + @property + def document_counts(self) -> dict[str, int]: + """ + Get the number of documents associated with each tag. + + Returns + ------- + dict[str, int] + Mapping from tag names to document counts. + """ + return getattr(self, "_document_counts", {}) + + def set_document_counts(self, document_counts: dict[str, int]) -> None: + """ + Set document counts for each tag. + + Parameters + ---------- + document_counts : dict[str, int] + Mapping from tag names to the number of documents containing that tag. + """ + self._document_counts = document_counts + def __call__( self, - tags: typing.List, - retrieved_tags: typing.List, + tags: list[str], + retrieved_tags: list[str], k_yens: int = 3, k_walk: int = 3, - ): - + ) -> tuple[list[dict], list[dict]]: + """ + Generate a subgraph connecting the given tags. + + Finds paths between tags from documents and retrieved tags, creating + a visualization-ready subgraph with nodes and links. + + Parameters + ---------- + tags : list[str] + Tags extracted from matching documents (displayed in blue). + retrieved_tags : list[str] + Tags matched directly from the query (displayed in green). + k_yens : int, default=3 + Maximum number of shortest paths to find between tag pairs. + k_walk : int, default=3 + Number of neighbors to explore in random walks. + + Returns + ------- + nodes : list[dict] + List of node dictionaries with 'id', 'color', 'degree', and 'documentCount'. + links : list[dict] + List of edge dictionaries with 'source', 'target', and 'weight'. + """ nodes, lonely = [], [] output_nodes = {} + # Color coding: blue for document tags, green for query-matched tags for list_tag, color in [(tags, "#86E5FF"), (retrieved_tags, "#19bc8e")]: for tag in list_tag: idx = self.node_to_idx.get(tag, None) if idx is None: lonely.append(tag) + degree = 0 else: nodes.append(idx) - output_nodes[tag] = {"id": tag, "color": color} + degree = self.graph.degree(idx) + output_nodes[tag] = { + "id": tag, + "color": color, + "degree": degree, + "documentCount": self.document_counts.get(tag, 0), + } paths = [] + # Find shortest paths between all pairs of known nodes if len(nodes) >= 2: - for start, end in itertools.combinations(nodes, 2): - if start != end: try: paths += self.yens(start=start, end=end, k=k_yens) - except: - # No path between start and end + except Exception: + # No path exists between start and end continue + # Fall back to neighborhood exploration for single nodes or disconnected graphs if len(nodes) == 1 or len(paths) == 0: - for start in nodes: - paths.append(self.walk(start=start, k=k_walk)) + # Add intermediate nodes discovered through path finding for path in paths: - for node in path: - node = self.idx_to_node[node] - if node not in output_nodes: - output_nodes[node] = { - "id": node, + for node_idx in path: + node_name = self.idx_to_node[node_idx] + if node_name not in output_nodes: + output_nodes[node_name] = { + "id": node_name, "color": "#FFFFFF", + "degree": self.graph.degree(node_idx), + "documentCount": self.document_counts.get(node_name, 0), } return list(output_nodes.values()), self.format_triples(paths=paths) - def yens(self, start: int, end: int, k: int): - """K-shortest path between start and end node.""" + def yens(self, start: int, end: int, k: int) -> list[list[int]]: + """ + Find k-shortest paths between two nodes using Yen's algorithm. + + Parameters + ---------- + start : int + Index of the starting node. + end : int + Index of the ending node. + k : int + Maximum number of paths to return. + + Returns + ------- + list[list[int]] + List of paths, where each path is a list of node indices. + Only paths with 3 or fewer nodes are included. + """ paths = [] - for idx, path in enumerate( - nx.shortest_simple_paths( - self.graph, - start, - end, - ) - ): - - # Avoid too long paths. + for idx, path in enumerate(nx.shortest_simple_paths(self.graph, start, end)): + # Filter out overly long paths for cleaner visualization if len(path) <= 3: paths.append(path) @@ -112,7 +205,22 @@ def yens(self, start: int, end: int, k: int): return paths - def walk(self, start: int, k): + def walk(self, start: int, k: int) -> list[int]: + """ + Perform a breadth-first walk from a starting node. + + Parameters + ---------- + start : int + Index of the starting node. + k : int + Maximum number of neighbors to collect. + + Returns + ------- + list[int] + List of node indices including the start node and its neighbors. + """ neighbours = [start] for n, node in enumerate(nx.all_neighbors(self.graph, start)): neighbours.append(node) @@ -121,26 +229,106 @@ def walk(self, start: int, k): return neighbours - def format_triples(self, paths: typing.List[typing.List[str]]): - """Convert nodes as triples.""" - triples = {} + def format_triples(self, paths: list[list[int]]) -> list[dict]: + """ + Convert paths to weighted edge dictionaries for visualization. + + Aggregates edge occurrences across all paths to compute edge weights, + where more frequently traversed edges have higher weights. + + Parameters + ---------- + paths : list[list[int]] + List of paths, where each path is a list of node indices. + + Returns + ------- + list[dict] + List of edge dictionaries with 'source', 'target', 'value', + 'weight', and 'relation' keys. + """ + triples: dict[tuple, int] = {} + + # Count edge occurrences across all paths for path in paths: - for start, end in zip(path[:-1], path[1:]): - if start != end and f"{end}_{start}" not in triples: - triples[f"{start}_{end}"] = True + for start, end in zip(path[:-1], path[1:], strict=False): + key = tuple(sorted([start, end])) + if key not in triples: + triples[key] = 1 + else: + triples[key] += 1 + + max_weight = max(triples.values()) if triples else 1 + # Convert to visualization format with normalized weights links = [] - for triple in triples: - head, tail = tuple(triple.split("_")) - head = self.idx_to_node[int(head)] - tail = self.idx_to_node[int(tail)] + for (start, end), count in triples.items(): + head = self.idx_to_node[start] + tail = self.idx_to_node[end] links.append( { "source": head, "relation": "link", "target": tail, - "value": 1, + "value": count, + "weight": count / max_weight, } ) return links + + def expand(self, node_id: str, k: int = 10) -> tuple[list[dict], list[dict]]: + """ + Get neighbors of a node for progressive graph expansion. + + Used for interactive exploration where users can click on nodes + to reveal their connections. + + Parameters + ---------- + node_id : str + The tag name of the node to expand. + k : int, default=10 + Maximum number of neighbors to return. + + Returns + ------- + nodes : list[dict] + List of node dictionaries including the expanded node and its neighbors. + links : list[dict] + List of edge dictionaries connecting the node to its neighbors. + """ + idx = self.node_to_idx.get(node_id) + if idx is None: + return [], [] + + output_nodes = { + node_id: { + "id": node_id, + "color": "#86E5FF", + "degree": self.graph.degree(idx), + "documentCount": self.document_counts.get(node_id, 0), + } + } + + links = [] + for i, neighbor_idx in enumerate(nx.all_neighbors(self.graph, idx)): + if i >= k: + break + neighbor_name = self.idx_to_node[neighbor_idx] + output_nodes[neighbor_name] = { + "id": neighbor_name, + "color": "#FFFFFF", + "degree": self.graph.degree(neighbor_idx), + "documentCount": self.document_counts.get(neighbor_name, 0), + } + links.append( + { + "source": node_id, + "target": neighbor_name, + "value": 1, + "weight": 0.5, + } + ) + + return list(output_nodes.values()), links diff --git a/knowledge_database/hackernews/hackernews.py b/knowledge_database/hackernews/hackernews.py index f5046408..6c8700c1 100644 --- a/knowledge_database/hackernews/hackernews.py +++ b/knowledge_database/hackernews/hackernews.py @@ -1,3 +1,10 @@ +""" +HackerNews module for extracting upvoted posts. + +This module fetches upvoted posts from a HackerNews user's profile and +extracts article content using web scraping. +""" + import datetime import re @@ -9,52 +16,68 @@ class HackerNews: - """HackerNews upvoted posts. + """ + Extract knowledge from HackerNews upvoted posts. + + Authenticates with HackerNews and scrapes the user's upvoted posts, + extracting article titles and summarized content from linked URLs. Parameters ---------- - username + username : str HackerNews username. - password - HackerNews password. - - Examples - -------- + password : str + HackerNews password for authentication. + Example + ------- >>> from knowledge_database import hackernews - - >>> news = hackernews.HackerNews( - ... username="username", - ... password="password", + >>> + >>> hn = hackernews.HackerNews( + ... username="your_username", + ... password="your_password", ... ) - - >>> documents = news() - + >>> documents = hn() + >>> + >>> for url, doc in documents.items(): + ... print(f"{doc['title']}") """ def __init__(self, username: str, password: str): self.username = username self.password = password - def __call__(self): - """Get upvoted content on HackerNews.""" - data = {} + def __call__(self) -> dict[str, dict]: + """ + Fetch upvoted posts and extract document metadata. + + Returns + ------- + dict[str, dict] + Dictionary mapping article URLs to document metadata containing: + - title: "Hackernews" prefix + article title + - summary: First ~50 tokens of article content + - date: Current date + - tags: ["hackernews"] + """ + data: dict[str, dict] = {} with requests.Session() as session: - p = session.post( + # Authenticate with HackerNews + login_response = session.post( "https://news.ycombinator.com/login?goto=news", data={"acct": self.username, "pw": self.password}, ) - if ("user?id=" + self.username) in p.text: + if ("user?id=" + self.username) in login_response.text: print("Hackernews - login successful") - html = session.get( - f"https://news.ycombinator.com/upvoted?id={self.username}" - ).text + # Fetch upvoted posts page + html = session.get(f"https://news.ycombinator.com/upvoted?id={self.username}").text soup = BeautifulSoup(html, "html.parser") + # Extract links from upvoted posts for entry in soup.find_all("td", class_="title"): record = entry.find("a") if record is None: @@ -66,6 +89,7 @@ def __call__(self): if attributes is None: continue + # Skip self-referential links if self.username in attributes["href"]: continue @@ -79,18 +103,24 @@ def __call__(self): return data @staticmethod - def get_summary(url, num_tokens=50): + def get_summary(url: str, num_tokens: int = 50) -> str: """ - Fetches the core article content from a URL, cleans it, - and returns the first N tokens. - - Args: - url (str): The URL of the webpage to process. - num_tokens (int): The number of tokens to return from the core body. - - Returns: - str: The first N words of the core article text, - or None if the content cannot be fetched or extracted. + Extract article summary from a URL. + + Uses trafilatura to extract main content from web pages, + returning the first N tokens as a summary. + + Parameters + ---------- + url : str + URL of the article to summarize. + num_tokens : int, default=50 + Number of words to include in the summary. + + Returns + ------- + str + First N words of the article content, or empty string on failure. """ try: headers = { @@ -100,13 +130,14 @@ def get_summary(url, num_tokens=50): response.raise_for_status() html_content = response.text + # Extract main article content core_text = trafilatura.extract(html_content) if not core_text: return "" + # Normalize whitespace and truncate cleaned_text = re.sub(r"\s+", " ", core_text).strip() - tokens = cleaned_text.split() first_n_tokens = tokens[:num_tokens] diff --git a/knowledge_database/huggingface/huggingface.py b/knowledge_database/huggingface/huggingface.py index 1f50fbdf..3a51aced 100644 --- a/knowledge_database/huggingface/huggingface.py +++ b/knowledge_database/huggingface/huggingface.py @@ -1,3 +1,10 @@ +""" +HuggingFace module for extracting liked models, datasets, and spaces. + +This module fetches liked items from a HuggingFace user's profile and +extracts metadata from README files. +""" + import datetime import re @@ -9,22 +16,47 @@ class HuggingFace: - """HuggingFace liked models, datasets, and spaces. + """ + Extract knowledge from HuggingFace liked repositories. + + Fetches liked models, datasets, and spaces from a user's HuggingFace + profile, extracting titles and summaries from repository README files. Parameters ---------- token : str, optional - HuggingFace User Access Token. If not provided, it relies on - local authentication (huggingface-cli login). + HuggingFace User Access Token. If not provided, relies on + local authentication via `huggingface-cli login`. + + Example + ------- + >>> from knowledge_database import huggingface + >>> + >>> hf = huggingface.HuggingFace(token="hf_xxxxx") + >>> documents = hf() + >>> + >>> for url, doc in documents.items(): + ... print(f"{doc['title']}: {doc['tags']}") """ def __init__(self, token: str = None): self.token = token self.api = HfApi(token=self.token) - def __call__(self): - """Get liked content on HuggingFace.""" - data = {} + def __call__(self) -> dict[str, dict]: + """ + Fetch liked repositories and extract document metadata. + + Returns + ------- + dict[str, dict] + Dictionary mapping HuggingFace URLs to document metadata containing: + - title: Repository name with HuggingFace emoji prefix + - summary: First ~50 tokens of README content + - date: Current date + - tags: ["huggingface", ] + """ + data: dict[str, dict] = {} try: likes = self.api.list_liked_repos() @@ -32,70 +64,110 @@ def __call__(self): print(f"Error fetching likes: {e}") return data - # Process Models + # Process liked models if hasattr(likes, "models"): for model in likes.models: repo_id = model.repo_id if hasattr(model, "repo_id") else str(model) url = f"https://huggingface.co/{repo_id}" - - # Fetch raw README for summary raw_url = f"https://huggingface.co/{repo_id}/resolve/main/README.md" self._process_entry(data, url, raw_url, repo_id, "model") - # Process Datasets + # Process liked datasets if hasattr(likes, "datasets"): for dataset in likes.datasets: - repo_id = ( - dataset.repo_id if hasattr(dataset, "repo_id") else str(dataset) - ) + repo_id = dataset.repo_id if hasattr(dataset, "repo_id") else str(dataset) dataset_url = f"https://huggingface.co/datasets/{repo_id}" - - # specific logic to find branch for datasets branch = self._get_default_branch(repo_id, "dataset") raw_url = f"https://huggingface.co/datasets/{repo_id}/resolve/{branch}/README.md" - self._process_entry(data, dataset_url, raw_url, repo_id, "dataset") - # Process Spaces + # Process liked spaces if hasattr(likes, "spaces"): for space in likes.spaces: repo_id = space.repo_id if hasattr(space, "repo_id") else str(space) url = f"https://huggingface.co/spaces/{repo_id}" - - # specific logic to find branch for spaces branch = self._get_default_branch(repo_id, "space") raw_url = f"https://huggingface.co/spaces/{repo_id}/resolve/{branch}/README.md" - self._process_entry(data, url, raw_url, repo_id, "space") return data - def _get_default_branch(self, repo_id, repo_type): - """Helper to safely get default branch.""" + def _get_default_branch(self, repo_id: str, repo_type: str) -> str: + """ + Get the default branch name for a repository. + + Parameters + ---------- + repo_id : str + Repository identifier (e.g., "username/repo-name"). + repo_type : str + Type of repository: "model", "dataset", or "space". + + Returns + ------- + str + Default branch name, or "main" if unable to determine. + """ try: repo_info = self.api.repo_info(repo_id=repo_id, repo_type=repo_type) - return repo_info.default_branch if repo_info.default_branch else "main" + branch = getattr(repo_info, "default_branch", None) + return branch if branch else "main" except Exception: return "main" - def _process_entry(self, data, data_url, summarization_url, title_suffix, tag_type): - """Helper to fetch summary and populate data dict.""" + def _process_entry( + self, + data: dict, + data_url: str, + summarization_url: str, + title_suffix: str, + tag_type: str, + ) -> None: + """ + Process a single repository and add it to the data dictionary. + + Parameters + ---------- + data : dict + Dictionary to store the processed entry. + data_url : str + URL to use as the document key. + summarization_url : str + URL to fetch README content from. + title_suffix : str + Repository name to include in the title. + tag_type : str + Type tag to add: "model", "dataset", or "space". + """ print(f"Processing {tag_type}: {title_suffix}") summary = self.get_summary(summarization_url) data[data_url] = { - "title": f"šŸ¤— HuggingFace {title_suffix}", + "title": f"HuggingFace {title_suffix}", "tags": ["huggingface", tag_type], "summary": summary, "date": datetime.datetime.today().strftime("%Y-%m-%d"), } @staticmethod - def get_summary(url, num_tokens=50): + def get_summary(url: str, num_tokens: int = 50) -> str: """ - Fetches the content from a URL. - If it's a raw Markdown file with YAML frontmatter, it strips the metadata. - Otherwise, it attempts to extract text using trafilatura. + Extract summary from a HuggingFace README URL. + + Handles YAML frontmatter commonly found in HuggingFace READMEs + and extracts clean text content. + + Parameters + ---------- + url : str + URL to the raw README.md file. + num_tokens : int, default=50 + Number of words to include in the summary. + + Returns + ------- + str + First N words of the README content, or empty string on failure. """ try: headers = { @@ -105,24 +177,17 @@ def get_summary(url, num_tokens=50): response.raise_for_status() content = response.text - # Check for YAML frontmatter (starts with ---) + # Handle YAML frontmatter (common in HuggingFace READMEs) if content.strip().startswith("---"): - # Regex to remove the first block enclosed in --- - # DOTALL allows . to match newlines - cleaned_text = re.sub( - r"^---\n.*?\n---", "", content, count=1, flags=re.DOTALL - ) - - # Since it is markdown, we might want to strip common markdown syntax - # for a cleaner summary (optional, but makes it readable) + # Remove YAML block between --- markers + cleaned_text = re.sub(r"^---\n.*?\n---", "", content, count=1, flags=re.DOTALL) + # Strip remaining markdown syntax cleaned_text = re.sub(r"[#*`]", "", cleaned_text) cleaned_text = re.sub(r"\s+", " ", cleaned_text).strip() - else: - # Fallback to trafilatura for non-markdown/HTML content + # Fall back to trafilatura for HTML content core_text = trafilatura.extract(content) if not core_text: - # If trafilatura fails (e.g. on raw text), use raw content cleaned_text = re.sub(r"\s+", " ", content).strip() else: cleaned_text = re.sub(r"\s+", " ", core_text).strip() diff --git a/knowledge_database/pipeline/pipeline.py b/knowledge_database/pipeline/pipeline.py index 3f24198c..50c6570d 100644 --- a/knowledge_database/pipeline/pipeline.py +++ b/knowledge_database/pipeline/pipeline.py @@ -1,3 +1,10 @@ +""" +Pipeline module for unified knowledge search and visualization. + +This module combines document retrieval with graph-based tag visualization, +providing a single interface for searching and exploring the knowledge base. +""" + import datetime from ..graph import Graph @@ -7,56 +14,113 @@ class Pipeline: - """Knowledge Pipeline. - - Example: - -------- - + """ + Unified pipeline for knowledge search and graph visualization. + + Combines BM25-based document retrieval with a knowledge graph for + interactive exploration of tag relationships. Supports both text + search and chronological browsing. + + Parameters + ---------- + documents : dict[str, dict] + Dictionary mapping URLs to document metadata. Each document should + contain 'title', 'summary', 'date', 'tags', and optionally 'extra-tags'. + triples : list[dict] + List of tag relationship edges for building the knowledge graph. + excluded_tags : dict[str, bool], optional + Tags to exclude from graph visualization (e.g., generic tags). + k_latest_documents : int, default=200 + Number of recent documents to cache for the "latest" view. + + Attributes + ---------- + retriever : Retriever + BM25-based document and tag search engine. + graph : Graph + Knowledge graph for tag relationship visualization. + latest_documents : list[dict] + Cached list of most recently added documents. + + Example + ------- >>> import json >>> from knowledge_database import pipeline - - >>> with open("database/database.json", "r") as f: + >>> + >>> with open("database/database.json") as f: ... documents = json.load(f) - - >>> with open("database/triples.json", "r") as f: + >>> with open("database/triples.json") as f: ... triples = json.load(f) - - >>> knowledge_pipeline = pipeline.Pipeline(documents=documents, triples=triples) - - >>> documents, nodes, links = knowledge_pipeline("knowledge graph embeddings") - >>> nodes, links = knowledge_pipeline.plot("knowledge graph embeddings") - >>> documents = knowledge_pipeline.search("knowledge graph embeddings") - >>> documents = knowledge_pipeline.search_documents_tags("knowledge graph embeddings") - + >>> + >>> pipe = pipeline.Pipeline(documents=documents, triples=triples) + >>> + >>> # Search for documents + >>> results = pipe.search("neural networks") + >>> + >>> # Get documents with graph visualization + >>> docs, nodes, links = pipe("machine learning") + >>> + >>> # Get only graph data for visualization + >>> nodes, links = pipe.plot("deep learning") """ def __init__( - self, documents, triples, excluded_tags=None, k_latest_documents: int = 200 + self, + documents: dict, + triples: list, + excluded_tags: dict | None = None, + k_latest_documents: int = 200, ): self.retriever = Retriever(documents=documents) self.graph = Graph(triples=triples) self.excluded_tags = {} if excluded_tags is None else excluded_tags + # Pre-compute document counts per tag for graph node sizing + document_counts: dict[str, int] = {} + for _url, document in documents.items(): + for tag in document.get("tags", []) + document.get("extra-tags", []): + document_counts[tag] = document_counts.get(tag, 0) + 1 + self.graph.set_document_counts(document_counts) + + # Cache recent documents sorted by date (newest first) self.latest_documents = sorted( [{"url": url, **document} for url, document in documents.items()], key=lambda doc: datetime.datetime.strptime(doc["date"], "%Y-%m-%d"), reverse=True, )[:k_latest_documents] - def get_latest_documents(self, count: int): - """Returns the most recently added documents.""" + def get_latest_documents(self, count: int) -> list[dict]: + """ + Get the most recently added documents. + + Parameters + ---------- + count : int + Number of documents to return. + + Returns + ------- + list[dict] + List of document dictionaries sorted by date (newest first). + """ return self.latest_documents[:count] - def search(self, q: str, tags: bool = False): - """Search for documents. + def search(self, q: str, tags: bool = False) -> list[dict]: + """ + Search for documents matching a query. Parameters ---------- - q - Query. - tags - If tags is set to True, documents returned will have tags and extra-tags that match the - query. + q : str + Search query string. + tags : bool, default=False + If True, filter results to documents whose tags match the query. + If False, search across all document fields. + + Returns + ------- + list[dict] + List of matching documents ranked by relevance. """ if tags: return self.retriever.documents_tags(q) @@ -68,17 +132,44 @@ def __call__( k_tags: int = 20, k_yens: int = 3, k_walk: int = 3, - ): - """Search for documents and tags.""" + ) -> tuple[list[dict], list[dict], list[dict]]: + """ + Search for documents and generate a knowledge graph visualization. + + Performs document retrieval and extracts tags from matching documents + to build an interactive graph showing tag relationships. + + Parameters + ---------- + q : str + Search query string. + k_tags : int, default=20 + Maximum number of document tags to include in the graph. + k_yens : int, default=3 + Maximum number of shortest paths between tag pairs. + k_walk : int, default=3 + Number of neighbors for random walk exploration. + + Returns + ------- + documents : list[dict] + Matching documents ranked by relevance. + nodes : list[dict] + Graph nodes representing tags with metadata. + links : list[dict] + Graph edges connecting related tags. + """ documents = self.retriever.documents(q) retrieved_tags = self.retriever.tags(q) - tags = {} + # Collect unique tags from matching documents + tags: dict[str, bool] = {} for document in documents: for tag in document["tags"] + document["extra-tags"]: if tag not in self.excluded_tags: tags[tag] = True + # Build graph from document tags and query-matched tags nodes, links = self.graph( tags=list(tags)[:k_tags], retrieved_tags=retrieved_tags, @@ -87,7 +178,36 @@ def __call__( ) return documents, nodes, links - def plot(self, q: str, k_tags: int = 20, k_yens: int = 3, k_walk: int = 3): - """Search for tags.""" + def plot( + self, + q: str, + k_tags: int = 20, + k_yens: int = 3, + k_walk: int = 3, + ) -> tuple[list[dict], list[dict]]: + """ + Generate a knowledge graph visualization for a query. + + Similar to __call__ but returns only the graph data without documents. + Useful for graph-only visualizations. + + Parameters + ---------- + q : str + Search query string. + k_tags : int, default=20 + Maximum number of document tags to include in the graph. + k_yens : int, default=3 + Maximum number of shortest paths between tag pairs. + k_walk : int, default=3 + Number of neighbors for random walk exploration. + + Returns + ------- + nodes : list[dict] + Graph nodes representing tags with metadata. + links : list[dict] + Graph edges connecting related tags. + """ _, nodes, links = self(q=q, k_tags=k_tags, k_yens=k_yens, k_walk=k_walk) return nodes, links diff --git a/knowledge_database/retriever/retriever.py b/knowledge_database/retriever/retriever.py index 2614f55d..63534787 100644 --- a/knowledge_database/retriever/retriever.py +++ b/knowledge_database/retriever/retriever.py @@ -1,5 +1,11 @@ +""" +Retriever module for BM25-based document and tag search. + +This module provides sparse retrieval capabilities using character-level +BM25 vectorization for typo-tolerant search across documents and tags. +""" + import copy -import typing from cherche import retrieve from lenlp import sparse @@ -8,35 +14,51 @@ class Retriever: - """Knowledge retriever. + """ + BM25-based retriever for documents and tags. + + Uses character n-gram BM25 vectorization for fuzzy matching that handles + typos and partial matches. Maintains separate indices for document search + and tag search. Parameters ---------- - documents - List of documents. + documents : dict[str, dict] + Dictionary mapping URLs to document metadata. Each document should + contain 'title', 'summary', 'date', 'tags', and optionally 'extra-tags'. - Examples: - --------- + Attributes + ---------- + retriever : cherche.retrieve.TfIdf + Document retriever with BM25 scoring over title, summary, tags, and date. + retriever_tags : cherche.retrieve.TfIdf + Tag retriever for matching query terms to known tags. + Example + ------- >>> import json >>> from knowledge_database import retriever - - >>> with open("database/database.json", "r") as f: + >>> + >>> with open("database/database.json") as f: ... documents = json.load(f) - - >>> knowledge_retriever = retriever.Retriever(documents=documents) - - >>> candidates = knowledge_retriever.documents("neural search") - >>> candidates = knowledge_retriever.tags("neural search") - >>> candidates = knowledge_retriever.documents_tags("neural search") - + >>> + >>> r = retriever.Retriever(documents=documents) + >>> + >>> # Search documents + >>> results = r.documents("neural search") + >>> + >>> # Find matching tags + >>> tags = r.tags("deep learning") """ - def __init__(self, documents: typing.Dict): + def __init__(self, documents: dict): updated_documents = copy.deepcopy(documents) - documents = [{"url": url, **document} for url, document in documents.items()] - updated_documents = [ + # Convert dict to list format with URL included + documents_list = [{"url": url, **document} for url, document in documents.items()] + + # Flatten tags into searchable text field + updated_documents_list = [ { **{ "url": url, @@ -47,6 +69,8 @@ def __init__(self, documents: typing.Dict): for url, document in updated_documents.items() ] + # Build document retriever with character n-gram BM25 + # Using char_wb analyzer for word-boundary-aware character n-grams self.retriever = ( retrieve.TfIdf( key="url", @@ -56,18 +80,20 @@ def __init__(self, documents: typing.Dict): normalize=True, ngram_range=(3, 5), analyzer="char_wb", - b=0, + b=0, # Disable length normalization for consistent scoring ), - documents=updated_documents, + documents=updated_documents_list, ) - ) + documents + ) + documents_list - tags = {} - for document in documents: + # Build unique tag index + tags_dict: dict[str, bool] = {} + for document in documents_list: for tag in document["tags"] + document["extra-tags"]: - tags[tag] = True - tags = [{"tag": tag} for tag in tags] + tags_dict[tag] = True + tags_list = [{"tag": tag} for tag in tags_dict] + # Build tag retriever with character n-gram BM25 self.retriever_tags = ( retrieve.TfIdf( key="tag", @@ -78,19 +104,59 @@ def __init__(self, documents: typing.Dict): ngram_range=(2, 5), analyzer="char", ), - documents=tags, + documents=tags_list, ) - + tags + + tags_list ) - def documents(self, q: str): - """Match documents.""" + def documents(self, q: str) -> list[dict]: + """ + Search for documents matching the query. + + Parameters + ---------- + q : str + Search query string. + + Returns + ------- + list[dict] + List of matching documents ranked by BM25 score, each containing + 'url', 'title', 'summary', 'date', 'tags', 'extra-tags', and 'similarity'. + """ return self.retriever(q) - def tags(self, q: str): - """Match tags.""" + def tags(self, q: str) -> list[str]: + """ + Find tags matching the query. + + Parameters + ---------- + q : str + Search query string. + + Returns + ------- + list[str] + List of matching tag names ranked by BM25 score. + """ return [tag["tag"] for tag in self.retriever_tags(q)] - def documents_tags(self, q: str): - """Match documents and tags.""" + def documents_tags(self, q: str) -> list[dict]: + """ + Search for documents with tag-based filtering. + + Currently identical to documents() but can be extended for + tag-specific filtering logic. + + Parameters + ---------- + q : str + Search query string. + + Returns + ------- + list[dict] + List of matching documents ranked by BM25 score. + """ return self.retriever(q) diff --git a/knowledge_database/semanlink/semanlink.py b/knowledge_database/semanlink/semanlink.py index aa1b2df3..7ef11210 100644 --- a/knowledge_database/semanlink/semanlink.py +++ b/knowledge_database/semanlink/semanlink.py @@ -1,6 +1,12 @@ +""" +Semanlink module for parsing RDF/Turtle knowledge bases. + +This module parses Semanlink's semantic web data format to extract +document metadata from arXiv papers and other linked resources. +""" + import collections import datetime -import typing import rdflib @@ -8,85 +14,93 @@ class Semanlink: - """Semanlink Knowledge base. + """ + Extract knowledge from Semanlink RDF/Turtle files. + + Parses Semanlink's semantic knowledge base format, which uses RDF + triples to represent documents, tags, and their relationships. + Focuses on extracting arXiv paper metadata. Parameters ---------- - urls - List of urls to the Semanlink knowledge base. + urls : list[str] + List of URLs pointing to Turtle (.ttl) files to parse. Example ------- - >>> from knowledge_database import semanlink - - >>> semanlink_knowledge = semanlink.Semanlink( - ... urls = [ + >>> + >>> sl = semanlink.Semanlink( + ... urls=[ ... "https://raw.githubusercontent.com/fpservant/semanlink-kdmkb/master/files/sldocs-2023-01-26.ttl", ... "https://raw.githubusercontent.com/fpservant/semanlink-kdmkb/master/files/sltags-2020-11-18.ttl", ... ] ... ) - - >>> semanlink_knowledge() - + >>> documents = sl() + >>> + >>> for url, doc in documents.items(): + ... print(f"{doc['title']}: {len(doc['tags'])} tags") """ - def __init__(self, urls: typing.List): + def __init__(self, urls: list[str]): self.urls = urls - def __call__(self): + def __call__(self) -> dict[str, dict]: + """ + Parse Turtle files and extract document metadata. + + Returns + ------- + dict[str, dict] + Dictionary mapping URLs to document metadata containing: + - title: Paper title + - summary: arXiv abstract + - date: Publication date + - tags: Semantic tags (cleaned and normalized) + """ graph = rdflib.Graph() triples = [] + + # Parse all Turtle files and collect triples for url in self.urls: turtle = graph.parse(url, format="turtle") + triples += [(head.toPython(), relation.toPython(), tail.toPython()) for head, relation, tail in turtle] - triples += [ - (head.toPython(), relation.toPython(), tail.toPython()) - for head, relation, tail in turtle - ] - - data = collections.defaultdict(lambda: collections.defaultdict(list)) + # Group triples by subject into a nested dictionary + data: dict = collections.defaultdict(lambda: collections.defaultdict(list)) for head, relation, tail in triples: + # Extract relation name from full URI relation = relation.split("/")[-1].split("#")[-1] data[head][relation].append(tail) - clean = collections.defaultdict(dict) + # Extract documents with required arXiv metadata + clean: dict[str, dict] = collections.defaultdict(dict) + + required_relations = [ + "bookmarkOf", + "arxiv_summary", + "title", + "arxiv_published", + "arxiv_author", + ] for _, metadata in data.items(): - valid = True - - for relation in [ - "bookmarkOf", - "arxiv_summary", - "title", - "arxiv_published", - "arxiv_author", - ]: - if relation not in metadata: - valid = False - break + # Check all required fields are present + valid = all(relation in metadata for relation in required_relations) if not valid: continue url = metadata["bookmarkOf"][0] - summary = metadata["arxiv_summary"][0] - date = datetime.datetime.strptime( - metadata["arxiv_published"][0], "%Y-%m-%dT%H:%M:%SZ" - ).strftime("%Y-%m-%d") + # Parse arXiv timestamp + date = datetime.datetime.strptime(metadata["arxiv_published"][0], "%Y-%m-%dT%H:%M:%SZ").strftime("%Y-%m-%d") title = metadata["title"][0] - tags = list( - set( - [ - " ".join(tag.split("/")[-1].lower().split("_")) - for tag in metadata["tag"] - ] - ) - ) + # Clean and normalize tags (convert underscores to spaces, lowercase) + tags = list({" ".join(tag.split("/")[-1].lower().split("_")) for tag in metadata["tag"]}) clean[url] = { "tags": tags, diff --git a/knowledge_database/tags/tags.py b/knowledge_database/tags/tags.py index d2c3d175..74439600 100644 --- a/knowledge_database/tags/tags.py +++ b/knowledge_database/tags/tags.py @@ -1,6 +1,13 @@ +""" +Tags module for generating tag relationships and automatic tagging. + +This module provides utilities for building a tag co-occurrence graph and +automatically discovering additional relevant tags for documents based on +their content. +""" + import collections import itertools -import typing from cherche import retrieve from sklearn.feature_extraction.text import TfidfVectorizer @@ -8,20 +15,55 @@ __all__ = ["get_extra_tags", "get_tags_triples"] -def get_tags_triples(data: typing.List, excluded_tags=None): - """Create a graph of interconnected tags.""" +def get_tags_triples( + data: dict[str, dict], + excluded_tags: dict[str, bool] | None = None, +) -> list[dict]: + """ + Build a graph of tag co-occurrence relationships. + + Creates edges between tags that appear together in the same document, + enabling visualization of knowledge domain relationships. + + Parameters + ---------- + data : dict[str, dict] + Dictionary mapping URLs to document metadata. Each document should + contain 'tags' and 'extra-tags' lists. + excluded_tags : dict[str, bool], optional + Tags to exclude from the graph (e.g., generic source tags like 'github'). + + Returns + ------- + list[dict] + List of edge dictionaries with 'head' and 'tail' keys representing + connected tags. Each edge appears only once (undirected graph). + + Example + ------- + >>> documents = { + ... "url1": {"tags": ["python", "ml"], "extra-tags": ["pytorch"]}, + ... "url2": {"tags": ["python", "web"], "extra-tags": []}, + ... } + >>> triples = get_tags_triples(documents) + >>> # Creates edges: python-ml, python-pytorch, ml-pytorch, python-web + """ excluded_tags = {} if excluded_tags is None else excluded_tags - triples = [] - seen = collections.defaultdict(dict) + # Track seen edges to avoid duplicates (undirected graph) + seen: dict[str, dict[str, bool]] = collections.defaultdict(dict) for _, document in data.items(): - tags = document["tags"] + document["extra-tags"] - for head, tail in itertools.combinations(tags, 2): + all_tags = document["tags"] + document["extra-tags"] + + # Create edges for all tag pairs within the document + for head, tail in itertools.combinations(all_tags, 2): + # Skip excluded tags if head in excluded_tags or tail in excluded_tags: continue + # Skip if edge already exists (either direction) if head in seen[tail] or tail in seen[head]: continue @@ -32,18 +74,46 @@ def get_tags_triples(data: typing.List, excluded_tags=None): return triples -def get_extra_tags(data: typing.List): - """Create extra-tags for each document.""" - - documents = {} - tagged = collections.defaultdict(dict) +def get_extra_tags(data: dict[str, dict]) -> dict[str, dict]: + """ + Automatically discover additional relevant tags for documents. + + Uses TF-IDF similarity between document content (title + summary) and + existing tags to suggest new tags that weren't manually assigned. + + Parameters + ---------- + data : dict[str, dict] + Dictionary mapping URLs to document metadata. Each document should + contain 'title', 'summary', and 'tags'. + + Returns + ------- + dict[str, dict] + Updated document dictionary with 'extra-tags' added to each document. + Extra tags are existing tags from other documents that match the + document's content with similarity > 0.2. + + Example + ------- + >>> documents = { + ... "url1": {"title": "PyTorch Tutorial", "summary": "Deep learning...", "tags": ["pytorch"]}, + ... "url2": {"title": "TensorFlow Guide", "summary": "Neural networks...", "tags": ["tensorflow"]}, + ... } + >>> enriched = get_extra_tags(documents) + >>> # Documents may now have cross-referenced tags based on content similarity + """ + # Build index of existing document-tag assignments + documents_dict: dict[str, bool] = {} + tagged: dict[str, dict[str, bool]] = collections.defaultdict(dict) for url, document in data.items(): for tag in document["tags"]: - documents[tag] = True + documents_dict[tag] = True tagged[url][tag] = True - documents = [{"tag": tag} for tag in documents] + # Create tag retriever using character n-gram TF-IDF + documents_list = [{"tag": tag} for tag in documents_dict] retriever = ( retrieve.Flash( key="tag", @@ -53,27 +123,25 @@ def get_extra_tags(data: typing.List): | retrieve.TfIdf( key="tag", on="tag", - documents=documents, + documents=documents_list, k=3, tfidf=TfidfVectorizer( - lowercase=True, ngram_range=(4, 7), analyzer="char_wb" + lowercase=True, + ngram_range=(4, 7), + analyzer="char_wb", ), ) - ).add(documents) + ).add(documents_list) - extra_tags = {} + # Find extra tags for each document based on content similarity + extra_tags: dict[str, list[str]] = {} for url, document in data.items(): + content = document.get("title", "") + " " + document.get("summary", "") extra_tags[url] = [ - tag["tag"] - for tag in retriever( - document.get("title", "") + " " + document.get("summary", "") - ) - if tag["similarity"] > 0.2 and tag["tag"] not in tagged[url] + tag["tag"] for tag in retriever(content) if tag["similarity"] > 0.2 and tag["tag"] not in tagged[url] ] - data = { - url: {**{"extra-tags": extra_tags[url]}, **document} - for url, document in data.items() - } + # Merge extra tags into document data + enriched_data = {url: {**{"extra-tags": extra_tags[url]}, **document} for url, document in data.items()} - return data + return enriched_data diff --git a/knowledge_database/twitter/twitter.py b/knowledge_database/twitter/twitter.py index 4a20b873..2f2961d0 100644 --- a/knowledge_database/twitter/twitter.py +++ b/knowledge_database/twitter/twitter.py @@ -1,3 +1,10 @@ +""" +Twitter module for extracting liked tweets. + +This module uses the Twitter API v2 to fetch a user's liked tweets +and extract relevant metadata including annotations and entities. +""" + import datetime import time @@ -7,88 +14,122 @@ class Twitter: - """Twitter knowledge. + """ + Extract knowledge from Twitter liked tweets. + + Uses Twitter API v2 to fetch liked tweets and extract text content, + publication dates, and named entity annotations. Parameters ---------- - username - Twitter username. - user_id - Twitter user id. - token - Twitter API token. - - Example: - -------- + username : str + Twitter username (for constructing tweet URLs). + user_id : int + Numeric Twitter user ID (required for API calls). + token : str + Twitter API Bearer token with read permissions. + + Attributes + ---------- + url : str + Constructed API endpoint URL for fetching liked tweets. + Example + ------- >>> from knowledge_database import twitter - - >>> news = twitter.Twitter( - ... username = "username", - ... user_id = "user_id", - ... token = "token" + >>> + >>> tw = twitter.Twitter( + ... username="your_username", + ... user_id=123456789, + ... token="your_bearer_token", ... ) - - >>> news(limit=1) - - References - ---------- - 1. [Get Tweeter ID](https://tweeterid.com/) - + >>> documents = tw(limit=10) + >>> + >>> for url, doc in documents.items(): + ... print(f"{doc['title']}: {doc['summary'][:50]}...") + + Notes + ----- + To find your Twitter user ID, use https://tweeterid.com/ """ def __init__(self, username: str, user_id: int, token: str): self.username = username self.user_id = user_id self.token = token - self.url = f"https://api.twitter.com/2/users/{self.user_id}/liked_tweets?max_results=100&expansions=author_id&user.fields=name&tweet.fields=attachments,author_id,context_annotations,created_at,entities,id,referenced_tweets,source,text,withheld" - def __call__(self, limit: int = 100): - data = {} + # Construct API URL with all required fields + self.url = ( + f"https://api.twitter.com/2/users/{self.user_id}/liked_tweets" + f"?max_results=100" + f"&expansions=author_id" + f"&user.fields=name" + f"&tweet.fields=attachments,author_id,context_annotations," + f"created_at,entities,id,referenced_tweets,source,text,withheld" + ) + + def __call__(self, limit: int = 100) -> dict[str, dict]: + """ + Fetch liked tweets and extract document metadata. + + Paginates through the user's liked tweets, extracting tweet content + and any named entity annotations. + + Parameters + ---------- + limit : int, default=100 + Maximum number of API pages to fetch (100 tweets per page). + + Returns + ------- + dict[str, dict] + Dictionary mapping tweet URLs to document metadata containing: + - title: "Twitter @username" format + - summary: Full tweet text + - date: Tweet creation date + - tags: ["twitter"] + extracted entity annotations + """ + data: dict[str, dict] = {} next_token = "" for _ in range(limit): - tweets = requests.get( - self.url + next_token, headers={"Authorization": f"Bearer {self.token}"} - ).json() + # Fetch page of liked tweets + response = requests.get( + self.url + next_token, + headers={"Authorization": f"Bearer {self.token}"}, + ) + tweets = response.json() if "data" not in tweets: break - # Get user names - users = { - user["id"]: user["username"] for user in tweets["includes"]["users"] - } - - data = { - **data, - **{ - f"https://twitter.com/{users[tweet['author_id']]}/status/{tweet['id']}": { - "date": datetime.datetime.strptime( - tweet["created_at"], "%Y-%m-%dT%H:%M:%S.%fZ" - ).strftime("%Y-%m-%d"), - "title": f"Twitter @{users[tweet['author_id']]}", - "summary": tweet["text"], - "tags": list( - set( - ["twitter"] - + [ - annotation["normalized_text"].lower() - for annotation in tweet.get("entities", {}).get( - "annotations", [] - ) - ] - ) - ), - } - for tweet in tweets["data"] - }, - } + # Build user ID to username mapping + users = {user["id"]: user["username"] for user in tweets["includes"]["users"]} + + # Process each tweet in the response + for tweet in tweets["data"]: + author_username = users[tweet["author_id"]] + tweet_url = f"https://twitter.com/{author_username}/status/{tweet['id']}" + + # Parse tweet timestamp + date = datetime.datetime.strptime(tweet["created_at"], "%Y-%m-%dT%H:%M:%S.%fZ").strftime("%Y-%m-%d") + + # Extract entity annotations as tags + annotations = tweet.get("entities", {}).get("annotations", []) + entity_tags = [annotation["normalized_text"].lower() for annotation in annotations] + + data[tweet_url] = { + "date": date, + "title": f"Twitter @{author_username}", + "summary": tweet["text"], + "tags": list(set(["twitter"] + entity_tags)), + } + # Check for more pages if "next_token" not in tweets["meta"]: break next_token = "&pagination_token=" + tweets["meta"]["next_token"] - time.sleep(1) + time.sleep(1) # Rate limiting return data diff --git a/knowledge_database/zotero/zotero.py b/knowledge_database/zotero/zotero.py index b9a66ba8..88a7861e 100644 --- a/knowledge_database/zotero/zotero.py +++ b/knowledge_database/zotero/zotero.py @@ -1,3 +1,10 @@ +""" +Zotero module for extracting bookmarks from Zotero libraries. + +This module interfaces with the Zotero API to fetch saved items +and extract document metadata for the knowledge base. +""" + import datetime from pyzotero import zotero @@ -6,52 +13,73 @@ class Zotero: - """Class for interacting with Zotero API + """ + Extract knowledge from Zotero reference libraries. + + Connects to a Zotero library (user or group) and fetches saved items, + extracting titles, abstracts, dates, and tags. Parameters ---------- - library_id - The ID of the library to access. - library_type - The type of library to access. Must be one of "user" or "group". - api_key - The API key for the library. - - Example: - -------- + library_id : str + The numeric ID of the Zotero library. + library_type : str + Type of library: "user" for personal libraries or "group" for shared. + api_key : str + Zotero API key with read permissions for the library. + + Attributes + ---------- + client : pyzotero.zotero.Zotero + Authenticated Zotero API client. + Example + ------- >>> from knowledge_database import zotero - - >>> knowledge = zotero.Zotero( - ... library_id="library_id", + >>> + >>> z = zotero.Zotero( + ... library_id="12345", ... library_type="group", - ... api_key="api_key", + ... api_key="your_api_key", ... ) - - >>> knowledge(limit=10) - + >>> documents = z(limit=100) + >>> + >>> for url, doc in documents.items(): + ... print(f"{doc['title']}: {doc['date']}") """ def __init__(self, library_id: str, library_type: str, api_key: str): - self.client = zotero.Zotero( - library_id, library_type, api_key, preserve_json_order=True - ) - - def __call__(self, limit: int = 10000): - """Get bookmarks from Zotero.""" - data = {} - - for idx, document in enumerate(self.client.top(limit=limit)): - date = datetime.datetime.strptime( - document["data"]["dateAdded"], "%Y-%m-%dT%H:%M:%SZ" - ).strftime("%Y-%m-%d") + self.client = zotero.Zotero(library_id, library_type, api_key, preserve_json_order=True) + + def __call__(self, limit: int = 10000) -> dict[str, dict]: + """ + Fetch items from Zotero and extract document metadata. + + Parameters + ---------- + limit : int, default=10000 + Maximum number of items to fetch from the library. + + Returns + ------- + dict[str, dict] + Dictionary mapping URLs to document metadata containing: + - title: Item title + - summary: Abstract/note content + - date: Date the item was added to Zotero + - tags: Lowercase tags assigned to the item + """ + data: dict[str, dict] = {} + + for document in self.client.top(limit=limit): + # Parse the date added timestamp + date = datetime.datetime.strptime(document["data"]["dateAdded"], "%Y-%m-%dT%H:%M:%SZ").strftime("%Y-%m-%d") url = document["data"]["url"] - title = document["data"]["title"] - summary = document["data"]["abstractNote"] + # Extract and normalize tags tags = [tag["tag"].lower() for tag in document["data"]["tags"]] data[url] = { diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..7968e97e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,92 @@ +[project] +name = "knowledge" +version = "0.0.1" +description = "Database" +authors = [{ name = "Raphael Sourty", email = "raphael.sourty@gmail.com" }] +readme = "readme.md" +requires-python = ">=3.10,<3.13" +license = { text = "MIT" } +classifiers = [ + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Operating System :: OS Independent", +] + +dependencies = [ + "beautifulsoup4==4.9.3", + "pandas==1.5.3", + "requests==2.32.2", + "rdflib==6.1.1", + "cherche==2.2.1", + "networkx==2.8.5", + "uvicorn==0.17.5", + "fastapi==0.116.1", + "pydantic==2.11.7", + "python-multipart==0.0.18", + "pyyaml_env_tag==0.1", + "pyzotero==1.5.5", + "scikit-learn==1.5.0", + "openai==1.35.2", + "orjson==3.9.15", + "lenlp==1.1.0", + "sentence_transformers==5.1.2", + "numpy==2.0.0", + "trafilatura==2.0.0", + "transformers==4.56.2", + "huggingface-hub==0.36.0", + "tokenizers==0.22.1", +] + +[project.optional-dependencies] +dev = ["ruff>=0.8.0", "mypy>=1.13.0", "pre-commit>=4.0.0"] + +[project.urls] +Homepage = "https://github.com/raphaelsty/knowledge" +Repository = "https://github.com/raphaelsty/knowledge" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["knowledge_database", "api"] + +[tool.ruff] +target-version = "py310" +line-length = 120 + +[tool.ruff.lint] +select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # Pyflakes + "I", # isort + "B", # flake8-bugbear + "C4", # flake8-comprehensions + "UP", # pyupgrade +] +ignore = [ + "E501", # line too long (handled by formatter) + "B008", # do not perform function calls in argument defaults +] + +[tool.ruff.lint.isort] +known-first-party = ["knowledge_database"] + +[tool.mypy] +python_version = "3.10" +warn_return_any = false +warn_unused_configs = true +disallow_untyped_defs = false +ignore_missing_imports = true +check_untyped_defs = false +disable_error_code = [ + "assignment", + "attr-defined", + "arg-type", + "var-annotated", + "dict-item", + "no-any-return", + "import-untyped", +] diff --git a/pytest.ini b/pytest.ini index b9d76fb1..f15fa5b1 100644 --- a/pytest.ini +++ b/pytest.ini @@ -12,4 +12,4 @@ norecursedirs = node_modules markers = web: tests that require using the Internet - slow: tests that take a long time to run \ No newline at end of file + slow: tests that take a long time to run diff --git a/readme.md b/readme.md index 53de7fe4..eaa91189 100644 --- a/readme.md +++ b/readme.md @@ -30,7 +30,7 @@ My Personal Knowledge Base is available at [raphaelsty.github.io/knowledge](http ## šŸ› ļø How It Works -A GitHub Actions workflow runs twice a day to perform the following tasks: +A GitHub Actions workflow runs once a day to perform the following tasks: 1. **Extracts Content** from specified accounts: - GitHub Stars @@ -40,7 +40,7 @@ A GitHub Actions workflow runs twice a day to perform the following tasks: 2. **Processes and Stores Data** in the `database/` directory: - `database.json`: Contains all the raw records. - `triples.json`: Stores the knowledge graph data (topics and relationships). - - `retriever.pkl`: The serialized search engine model. + - `pipeline.pkl`: The serialized search engine and knowledge graph pipeline. 3. **Deploys Updates**: - The backend API is automatically updated and pushed to the Fly.io instance. - The frontend on GitHub Pages is refreshed with the latest data. @@ -103,20 +103,34 @@ The application requires API keys and credentials to function. These must be set Optional HackerNews password. + +HUGGINGFACE_TOKEN +HuggingFace +Optional +Token to fetch your HuggingFace liked models and datasets. + #### B. Specify Sources -Next, edit the `sources.yml` file at the root of the repository to specify which GitHub users' starred repositories you want to track. +Next, edit the `sources.yml` file at the root of the repository to configure your data sources. ```yml github: - "raphaelsty" - "gbolmier" - "MaxHalford" + +semanlink: False + +huggingface: True ``` +- **github**: List of GitHub usernames whose starred repositories you want to track. +- **semanlink**: Set to `True` to enable Semanlink RDF data extraction. +- **huggingface**: Set to `True` to fetch your HuggingFace liked models and datasets (requires `HUGGINGFACE_TOKEN` secret). + ### 3\. Deployment #### A. Deploy the API to Fly.io @@ -205,4 +219,4 @@ My personal Knowledge Base is inspired by and extracts resources from the Knowle This project is licensed under the **GNU General Public License v3.0**. -Knowledge Copyright (C) 2023 RaphaĆ«l Sourty +Knowledge Copyright (C) 2023-2025 RaphaĆ«l Sourty diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 859668d5..00000000 --- a/requirements.txt +++ /dev/null @@ -1,22 +0,0 @@ -beautifulsoup4 == 4.9.3 -pandas == 1.5.3 -requests == 2.32.2 -rdflib == 6.1.1 -cherche == 2.2.1 -networkx == 2.8.5 -uvicorn == 0.17.5 -fastapi == 0.116.1 -pydantic == 2.11.7 -python-multipart == 0.0.18 -pyyaml_env_tag == 0.1 -pyzotero == 1.5.5 -scikit-learn == 1.5.0 -openai == 1.35.2 -orjson == 3.9.15 -lenlp == 1.1.0 -sentence_transformers == 5.1.2 -numpy == 2.0.0 -trafilatura == 2.0.0 -transformers == 4.56.2 -huggingface-hub==0.36.0 -tokenizers==0.22.1 \ No newline at end of file diff --git a/run.py b/run.py index 9c53160d..212825ca 100644 --- a/run.py +++ b/run.py @@ -1,3 +1,39 @@ +#!/usr/bin/env python3 +""" +Knowledge Database Builder + +This script aggregates knowledge from multiple sources (GitHub, HackerNews, +Zotero, Semanlink, HuggingFace) into a unified database and builds the +search pipeline. + +Environment Variables +--------------------- +HACKERNEWS_USERNAME : str + HackerNews username for fetching upvoted posts. +HACKERNEWS_PASSWORD : str + HackerNews password for authentication. +ZOTERO_LIBRARY_ID : str + Zotero library ID for fetching bookmarks. +ZOTERO_API_KEY : str + Zotero API key with read permissions. +HUGGINGFACE_TOKEN : str + HuggingFace token for fetching liked items. + +Configuration +------------- +sources.yml : file + YAML configuration specifying which sources to enable. + +Output Files +------------ +database/database.json : JSON + Aggregated document database. +database/triples.json : JSON + Tag co-occurrence graph edges. +database/pipeline.pkl : Pickle + Serialized search pipeline for the API. +""" + import json import os import pickle @@ -14,125 +50,132 @@ zotero, ) -with open("sources.yml", "r") as f: +# ============================================================================= +# Configuration +# ============================================================================= + +# Load source configuration +with open("sources.yml") as f: sources = yaml.load(f, Loader=yaml.FullLoader) -twitter_token = os.environ.get("TWITTER_TOKEN") +# Load credentials from environment hackernews_username = os.environ.get("HACKERNEWS_USERNAME") hackernews_password = os.environ.get("HACKERNEWS_PASSWORD") zotero_library_id = os.environ.get("ZOTERO_LIBRARY_ID") zotero_api_key = os.environ.get("ZOTERO_API_KEY") huggingface_token = os.environ.get("HUGGINGFACE_TOKEN") -data = {} +# ============================================================================= +# Load Existing Database +# ============================================================================= + +data: dict = {} if os.path.exists("database/database.json"): - with open("database/database.json", "r", encoding="utf-8", errors="replace") as f: + with open("database/database.json", encoding="utf-8", errors="replace") as f: data = json.load(f) +# ============================================================================= +# Fetch Data from Sources +# ============================================================================= + -# Github +def merge_new_documents(existing: dict, new: dict) -> dict: + """Merge new documents, skipping URLs already in the database.""" + new_only = {url: doc for url, doc in new.items() if url not in existing} + print(f"Found {len(new_only)} new documents.") + return {**existing, **new_only} + + +# GitHub starred repositories if sources.get("github") is not None: - print("Github knowledge.") + print("Fetching GitHub stars...") for user in sources["github"]: - knowledge = github.Github(user=user) - knowledge = { - url: document for url, document in knowledge().items() if url not in data - } - print(f"Found {len(knowledge)} new Github documents.") - data = {**data, **knowledge} + fetcher = github.Github(user=user) + data = merge_new_documents(data, fetcher()) - -# Hackernews +# HackerNews upvoted posts if hackernews_username is not None and hackernews_password is not None: - print("Hackernews knowledge.") - knowledge = hackernews.HackerNews( + print("Fetching HackerNews upvotes...") + fetcher = hackernews.HackerNews( username=hackernews_username, password=hackernews_password, ) - knowledge = { - url: document for url, document in knowledge().items() if url not in data - } - print(f"Found {len(knowledge)} new Hackernews documents.") - data = {**data, **knowledge} + data = merge_new_documents(data, fetcher()) else: - print("No Hackernews credentials.") + print("Skipping HackerNews (no credentials).") -# Zotero +# Zotero library if zotero_library_id is not None and zotero_api_key is not None: - print("Zotero knowledge.") - knowledge = zotero.Zotero( + print("Fetching Zotero library...") + fetcher = zotero.Zotero( library_id=zotero_library_id, library_type="group", api_key=zotero_api_key, ) - knowledge = { - url: document for url, document in knowledge().items() if url not in data - } - print(f"Found {len(knowledge)} new Zotero documents.") - data = {**data, **knowledge} + data = merge_new_documents(data, fetcher()) else: - print("No Zotero credentials.") + print("Skipping Zotero (no credentials).") -# Semanlink -if sources["semanlink"]: - print("Semanlink knowledge.") - knowledge = semanlink.Semanlink( +# Semanlink knowledge base +if sources.get("semanlink"): + print("Fetching Semanlink data...") + fetcher = semanlink.Semanlink( urls=[ "https://raw.githubusercontent.com/fpservant/semanlink-kdmkb/master/files/sldocs-2023-01-26.ttl", "https://raw.githubusercontent.com/fpservant/semanlink-kdmkb/master/files/sltags-2020-11-18.ttl", ] ) - knowledge = { - url: document for url, document in knowledge().items() if url not in data - } - print(f"Found {len(knowledge)} new semanlink documents.") - data = {**data, **knowledge} + data = merge_new_documents(data, fetcher()) else: - print("Semanlink disabled.") - + print("Skipping Semanlink (disabled).") -# HuggingFace +# HuggingFace liked items if huggingface_token is not None and sources.get("huggingface") is not None: - print("HuggingFace knowledge.") - knowledge = huggingface.HuggingFace(token=huggingface_token) - knowledge = { - url: document for url, document in knowledge().items() if url not in data - } - print(f"Found {len(knowledge)} new HuggingFace documents.") - data = {**data, **knowledge} + print("Fetching HuggingFace likes...") + fetcher = huggingface.HuggingFace(token=huggingface_token) + data = merge_new_documents(data, fetcher()) else: - print("No HuggingFace token.") + print("Skipping HuggingFace (no token).") +# ============================================================================= +# Data Cleaning +# ============================================================================= -# Sanity check. -for url, document in data.items(): - for field in ["title", "tags", "summary", "date"]: - if document.get(field, None) is None: - document[field] = "" +print("Cleaning document data...") -# Sanity check and data cleaning. -for url, document in data.items(): - # Ensure all required fields exist, defaulting to an empty string. +for _url, document in data.items(): + # Ensure all required fields exist for field in ["title", "tags", "summary", "date"]: - if document.get(field, None) is None: - document[field] = "" + if document.get(field) is None: + document[field] = "" if field != "tags" else [] - # Clean text fields of invalid Unicode characters before they are processed by the pipeline. - # This handles malformed data like the lone surrogate '\udd4a'. + # Clean invalid Unicode characters (e.g., lone surrogates) for field in ["title", "summary"]: if isinstance(document.get(field), str): - cleaned_text = document[field].encode("utf-8", "replace").decode("utf-8") - document[field] = cleaned_text + document[field] = document[field].encode("utf-8", "replace").decode("utf-8") + +# ============================================================================= +# Generate Extra Tags +# ============================================================================= -print("Adding extra tags.") +print("Generating extra tags from document content...") data = tags.get_extra_tags(data=data) -print("Saving database.") +# ============================================================================= +# Save Database +# ============================================================================= + +print("Saving database...") with open("database/database.json", "w") as f: json.dump(data, f, indent=4) -excluded_tags = { +# ============================================================================= +# Build Knowledge Graph +# ============================================================================= + +# Tags to exclude from graph visualization (too generic) +EXCLUDED_TAGS = { "twitter": True, "github": True, "semanlink": True, @@ -140,16 +183,24 @@ "arxiv doc": True, } -print("Exporting tree of tags.") -triples = tags.get_tags_triples(data=data, excluded_tags=excluded_tags) +print("Building tag co-occurrence graph...") +triples = tags.get_tags_triples(data=data, excluded_tags=EXCLUDED_TAGS) with open("database/triples.json", "w") as f: json.dump(triples, f, indent=4) -print("Serializing pipeline.") +# ============================================================================= +# Build Search Pipeline +# ============================================================================= + +print("Building search pipeline...") knowledge_pipeline = pipeline.Pipeline( documents=data, triples=triples, - excluded_tags=excluded_tags, + excluded_tags=EXCLUDED_TAGS, ) + +print("Serializing pipeline...") with open("database/pipeline.pkl", "wb") as f: pickle.dump(knowledge_pipeline, f) + +print("Done!") diff --git a/setup.py b/setup.py deleted file mode 100644 index 9940cd7e..00000000 --- a/setup.py +++ /dev/null @@ -1,20 +0,0 @@ -import setuptools - -with open("requirements.txt") as f: - required = f.read().splitlines() - -setuptools.setup( - name="knowledge", - version="0.0.1", - author="Raphael Sourty", - author_email="raphael.sourty@gmail.com", - description="Database", - url="https://github.com/raphaelsty/knowledge", - packages=setuptools.find_packages(), - install_requires=required, - classifiers=[ - "Programming Language :: Python :: 3", - "Operating System :: OS Independent", - ], - python_requires=">=3.8", -) diff --git a/uv.lock b/uv.lock new file mode 100644 index 00000000..3fb366e8 --- /dev/null +++ b/uv.lock @@ -0,0 +1,1959 @@ +version = 1 +revision = 2 +requires-python = ">=3.10, <3.13" +resolution-markers = [ + "python_full_version >= '3.12'", + "python_full_version == 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