Compare commits
@@ -0,0 +1,23 @@
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# Service URLs for local dev (pointing to your server)
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TEST_HOST=192.168.86.149
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WIKIJS_URL=http://192.168.86.149:8088
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NEO4J_URI=bolt://192.168.86.149:7687
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QDRANT_HOST=192.168.86.149
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QDRANT_PORT=6333
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OLLAMA_URL=http://192.168.86.149:11434
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SEARXNG_URL=http://192.168.86.149:8080
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REDIS_HOST=192.168.86.149
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OLLAMA_MODEL=mistral-nemo-large:latest
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OLLAMA_EMBEDDING_MODEL=nomic-embed-text
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# Wiki.js auth
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WIKIJS_USERNAME=librarian@schweitz.net
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WIKIJS_PASSWORD=key_here
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# Wiki.js GraphQL API token (generate from Admin → API Access)
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WIKI_GRAPHQL_API=your_jwt_token_here
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LIBRARY_API_KEY=key_here
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NEO4J_PASSWORD=key_here
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WIKIJS_DB_PASSWORD=key_here
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SCHEDULER_API_KEY=key_here
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@@ -23,6 +23,47 @@
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* **Update `CHANGELOG.md`** with every user-facing change.
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* Format: `## [Unreleased] - YYYY-MM-DD` followed by `### Added`, `### Changed`, or `### Fixed`.
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### 🚀 Release Flow
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When changes are ready for deployment:
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1. **Ask user if deploy cycle is desired **
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2. **Update version** in `pyproject.toml`:
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- Bug fixes: bump patch version (1.8.3 → 1.8.4)
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- New features: bump minor version (1.8.4 → 1.9.0)
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3. **Update CHANGELOG.md**:
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- Move items from `[Unreleased]` to new version section
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- Add release date: `## [1.8.4] - 2025-12-16`
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4. **Commit and tag**:
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```bash
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git add -A
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git commit -m "fix: description of changes"
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git tag v1.8.4
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git push origin main --tags
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```
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5. **CI/CD triggers automatically**:
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- Gitea CI builds Docker image on new tag
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- Watchtower pulls and deploys to production
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- Verify deployment: `curl http://192.168.86.149:8000/health`
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---
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### 🧪 Local Development Setup
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* **Always test locally first** before committing and deploying. The build-deploy loop is slow.
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* **Start the local server** with `./wakeup.sh` - logs are written to `logs/server.log` for easy tailing
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* **Auto-reload**: The wakeup script runs uvicorn in reload mode - code changes are picked up automatically without restart (except for requirements.txt changes)
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* **Test REST endpoints** against `http://localhost:8778` using curl or similar tools
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* **Only deploy** when a phase or feature is complete and tested locally
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* **Environment**: Copy `.env.example` to `.env` and configure for your local setup (Ollama, Redis, Neo4j, Qdrant, Wiki.js hosts)
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* **Running tests**: Always use the venv explicitly to avoid environment mismatches:
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```bash
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.venv/bin/python -m pytest tests/ # All tests
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.venv/bin/python -m pytest tests/ -v # Verbose output
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```
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---
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## 2. FastAPI Architecture & Best Practices
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+140
@@ -5,6 +5,146 @@ All notable changes to Library Desk will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [1.4.0] - 2025-12-24
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### Added
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- **Maintenance Router** - New `/maintenance` endpoints for system health and cleanup
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- `GET /maintenance/health` - Lightweight health check (detailed mode available)
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- `POST /maintenance/cleanup/all` - Full orphan cleanup (vectors + graph)
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- `POST /maintenance/cleanup/vectors` - Purge orphan vector chunks
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- `POST /maintenance/cleanup/graph` - Purge orphan graph nodes
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- `POST /maintenance/reconcile-index` - Combined cleanup + reindex missing pages
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- **Bidirectional Orphan Detection** - Cross-validate vectors and graph nodes
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- `find_documents_without_vectors()` - Graph nodes missing vector chunks
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- `find_chunks_without_graph_nodes()` - Vector chunks missing graph nodes
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- **Qdrant Client Methods** - Bulk operations for maintenance
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- `scroll_all_points()` - Iterate all points with pagination
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- `delete_by_ids()` - Batch delete by point IDs
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- **Graph Service Cleanup** - Node deletion methods
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- `delete_document_node()` - Remove document and relationships
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- `delete_collection_node()` - Remove collection and contained documents
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- `get_all_document_references()` - Get all document references for validation
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- **Redis Timestamp Tracking** - `last_cleanup` timestamp for scheduler integration
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- **Memory System Plan** - Documented three-tier architecture (volatile/documents/knowledge)
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### Changed
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- **Wiki.js Authentication** - Switched from username/password to API token
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- New `WIKI_GRAPHQL_API` environment variable for JWT token
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- Deprecated `WIKIJS_USERNAME` and `WIKIJS_PASSWORD` (kept for backwards compatibility)
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- **Service Dependencies** - Added `VectorServiceDep` and `GraphServiceDep` type aliases
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### Fixed
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- Wiki.js client now properly handles API token auth without login flow
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## [1.3.3] - 2025-12-23
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### Added
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- Temperature parameter to `OllamaClient.generate_text()` for controlling output determinism
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- `TODO.md` tracking remaining stub endpoints to implement
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- Wired `/query/semantic` endpoint to VectorService
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- Wired `/query/graph` endpoint to GraphService
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### Changed
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- **Improved LLM prompts** based on llm-findings.md recommendations:
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- Keyword extraction: temperature 0.0, negative constraints
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- LLM re-ranking: temperature 0.0, explicit rules
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- Conflict detection: temperature 0.0, analysis steps (CoT)
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- Wiki page creation: temperature 0.3, anti-hallucination constraints
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- Page reconstruction: temperature 0.2, preservation constraints
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- Web results analysis: temperature 0.0, conservative approach
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- Test fixtures now use configurable host (TEST_HOST) instead of Docker hostnames
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### Removed
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- Dead code: unused `get_default_user()` function
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- Unused imports from routers (wiki.py, graph.py, hybrid_rag.py)
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- Stub endpoints shadowed by real implementations (/stats, /ingest/document, /ingest/batch)
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## [1.3.2] - 2025-12-22
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### Changed
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- **Consolidated Ollama model configuration** - All LLM operations now use single `OLLAMA_MODEL` environment variable
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- Removed separate `reranker_model` setting
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- HybridRAG re-ranking, consolidation analysis, and wiki page writing all use the same model
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- Improves VRAM efficiency by keeping one model hot
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- Added `OLLAMA_EMBEDDING_MODEL` environment variable for embedding model (previously overloaded `OLLAMA_MODEL`)
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- Updated WikiPageWriter to accept settings instead of hardcoded model name
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## [1.3.1] - 2025-12-16
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### Fixed
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- Smart create endpoint missing `content_extractor` dependency causing 500 errors on `POST /wiki/pages/smart-create`
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## [1.3.0] - 2025-12-15
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### Changed
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- **Two-Stage RRF Architecture** - Major refactor to level the playing field between wiki and web results
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- Stage 1: Vector and graph results merged into single "wiki" ranking using mini-RRF
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- Stage 2: Final RRF between wiki (single source) and web (single source)
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- Wiki pages no longer get 2x advantage from appearing in both vector and graph searches
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- Multi-source confirmation still determines wiki internal ranking
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- **Skip synonyms in graph search** - LLM-generated synonyms (e.g., "author") no longer match unrelated graph entities (e.g., "author2000")
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- Vector search still uses synonyms for semantic similarity
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- Graph search uses only core keywords for exact entity matching
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### Added
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- `VECTOR_SIMILARITY_THRESHOLD` config setting (default: 0.7) to filter weak vector matches
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- Deduplication in graph search to prevent same document appearing multiple times
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### Fixed
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- Graph search duplicate entity bug where same document could appear twice if entity linked multiple times
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## [1.2.1] - 2025-12-15
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### Fixed
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- HybridRAG router missing `content_extractor` dependency causing 500 errors on `/query/hybrid` endpoint
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## [1.2.0] - 2025-12-15
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### Added
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- **RAG Search Endpoint** (`POST /rag/search`)
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- Web, news, and image search via SearXNG
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- Full content extraction using Trafilatura (F1 score 0.958)
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- Redis caching with configurable TTL
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- Markdown sources summary for LLM consumption
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- Returns both extracted content and original snippets
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- **Content Extraction Endpoints** (`/content/*`)
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- `POST /content/extract` - Extract content from a single URL
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- `POST /content/extract/batch` - Batch extraction (up to 20 URLs)
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- Reusable ContentExtractor client for use across the codebase
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- **HybridRAG Content Extraction Enhancement**
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- Web search results now include full extracted content via Trafilatura
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- Falls back to original snippets if extraction fails
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- Improves context quality for LLM re-ranking and consumption
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### Changed
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- Added new configuration options:
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- `SEARCH_CACHE_TTL` - Search cache TTL in seconds (default: 300)
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- `SEARCH_TIMEOUT` - SearXNG timeout (default: 10s)
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- `CONTENT_EXTRACTION_TIMEOUT` - Per-URL extraction timeout (default: 5s)
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- `CONTENT_MAX_LENGTH` - Max extracted content length (default: 2000)
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- `SEARCH_DEFAULT_LIMIT` - Default search results (default: 10)
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### Dependencies
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- Added `trafilatura~=1.12.0` for content extraction
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## [1.1.3] - 2025-12-14
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### Added
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@@ -0,0 +1,17 @@
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# Claude Code Instructions
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**MANDATORY: Read AGENTS.md instead of this file.**
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This project uses a unified configuration file for all LLM coding agents.
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## Instructions
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1. **Read and follow AGENTS.md** - All project guidelines are located there
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2. **Do not modify this file** - Only update AGENTS.md
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3. **Do not create or modify other agent-specific files** - Use AGENTS.md as the single source of truth
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This approach ensures consistent behavior across all LLM coding agents without managing separate configuration files.
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---
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If you need to update project guidelines, edit AGENTS.md, not this file.
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@@ -455,6 +455,154 @@ LIBRARY_BATCH_SIZE=50
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LIBRARY_SYNC_ENABLED=true
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```
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## Maintenance Tasks
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### Index Reconciliation (Daily)
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The `reconcile-index` endpoint performs full index maintenance:
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1. **Cleanup Phase**: Remove orphaned data
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- Vector chunks without wiki source
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- Graph nodes without vectors (bidirectional)
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- Vectors without graph nodes (bidirectional)
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- Orphan entities (no MENTIONS relationships)
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- Broken relationships
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2. **Reindex Phase**: Index missing pages
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- Wiki pages without vector embeddings
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- Wiki pages without graph Document nodes
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**Scheduler Task: `library_reconcile_index`**
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```yaml
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Task Name: library_reconcile_index
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Description: Daily index reconciliation - cleanup orphans + reindex missing pages
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Schedule: Daily at 04:00 (after library_sync at 03:30)
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Priority: 10 (system maintenance)
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Service: library
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Executor: POST /maintenance/reconcile-index
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Configuration:
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- LIBRARY_DESK_URL: http://library-desk:8089
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- LIBRARY_API_KEY: ${LIBRARY_API_KEY}
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Parameters:
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- user: jpmschweitzer
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- dry_run: false
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Outputs:
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- Vector orphans purged
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- Entity orphans purged
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- Missing pages reindexed
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```
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### Maintenance Endpoints
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| Endpoint | Method | Purpose |
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|----------|--------|---------|
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| `/maintenance/reconcile-index` | POST | **Recommended**: Full cleanup + reindex missing |
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| `/maintenance/cleanup/all` | POST | Cleanup only (orphan removal) |
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| `/maintenance/cleanup/vectors` | POST | Clean orphan vector chunks only |
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| `/maintenance/cleanup/graph` | POST | Clean orphan entities & stale docs only |
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| `/maintenance/health` | GET | Lightweight health check (for uptime monitoring) |
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| `/maintenance/health?detailed=true` | GET | Full analysis with orphan counts |
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| `/maintenance/reindex/{page_id}` | POST | Force re-index a specific page |
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### Health Check Modes
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|
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**Lightweight (default)** - Use for frequent uptime checks (every 30s):
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```bash
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curl "http://library-desk:8089/maintenance/health?user=jpmschweitzer" \
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-H "Authorization: Bearer ${LIBRARY_API_KEY}"
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```
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Returns only last cleanup timestamp and basic status (no database queries).
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**Detailed** - Use for dashboards or before reconciliation:
|
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```bash
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curl "http://library-desk:8089/maintenance/health?user=jpmschweitzer&detailed=true" \
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-H "Authorization: Bearer ${LIBRARY_API_KEY}"
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```
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Returns full orphan analysis (runs database queries).
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### Example Reconcile Request
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|
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```bash
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curl -X POST "http://library-desk:8089/maintenance/reconcile-index?user=jpmschweitzer" \
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-H "Authorization: Bearer ${LIBRARY_API_KEY}"
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```
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### Example Response
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|
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```json
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{
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"success": true,
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"cleanup": {
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"success": true,
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"vector_cleanup": {
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"wiki_chunks": {"orphans_found": 5, "orphans_purged": 5},
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"document_chunks": {"orphans_found": 0, "orphans_purged": 0},
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"chunks_without_graph": {"orphans_found": 2, "orphans_purged": 2},
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"total_chunks_scanned": 1250,
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"total_orphans_purged": 7
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},
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"graph_cleanup": {
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"orphan_entities": {"orphans_found": 3, "orphans_purged": 3},
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"stale_wiki_documents": {"orphans_found": 1, "orphans_purged": 1},
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"stale_store_documents": {"orphans_found": 0, "orphans_purged": 0},
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"docs_without_vectors": {"orphans_found": 0, "orphans_purged": 0},
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"broken_relationships_cleaned": 0
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},
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"total_duration_ms": 1523.5
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},
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"reindex_missing": {
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"pages_without_vectors": 2,
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"pages_without_graph": 1,
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"pages_reindexed": 2,
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"pages_failed": 0,
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"failed_page_ids": [],
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"duration_ms": 3421.2
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},
|
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"total_duration_ms": 4944.7
|
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}
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```
|
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|
||||
### Scheduler Integration Code
|
||||
|
||||
```python
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# scheduler/src/tasks/library_maintenance.py
|
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|
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async def library_reconcile_index_task(user: str = "jpmschweitzer"):
|
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"""Run daily Library Desk index reconciliation."""
|
||||
|
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async with httpx.AsyncClient() as client:
|
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# Run reconcile-index (cleanup + reindex missing)
|
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result = await client.post(
|
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f"{LIBRARY_DESK_URL}/maintenance/reconcile-index",
|
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params={"user": user, "dry_run": False},
|
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headers={"Authorization": f"Bearer {LIBRARY_API_KEY}"},
|
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timeout=600.0 # 10 minutes for large indexes
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||||
)
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||||
|
||||
data = result.json()
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||||
|
||||
# Log summary
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||||
cleanup = data["cleanup"]
|
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reindex = data["reindex_missing"]
|
||||
|
||||
logger.info(
|
||||
f"Reconcile complete: "
|
||||
f"{cleanup['vector_cleanup']['total_orphans_purged']} vector orphans, "
|
||||
f"{cleanup['graph_cleanup']['orphan_entities']['orphans_purged']} entity orphans, "
|
||||
f"{reindex['pages_reindexed']} pages reindexed"
|
||||
)
|
||||
|
||||
if reindex["pages_failed"] > 0:
|
||||
logger.warning(f"Failed to reindex pages: {reindex['failed_page_ids']}")
|
||||
|
||||
return data
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. Implement ingestion endpoints in Library Desk
|
||||
|
||||
@@ -49,7 +49,8 @@ QDRANT_PORT=6333
|
||||
WIKIJS_URL=http://wiki:3000
|
||||
SEARXNG_URL=http://searxng:8080
|
||||
OLLAMA_URL=http://ollama:11434
|
||||
OLLAMA_MODEL=nomic-embed-text
|
||||
OLLAMA_MODEL=mistral-nemo-large:latest
|
||||
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
|
||||
REDIS_HOST=redis-shared
|
||||
REDIS_PORT=6379
|
||||
REDIS_DB=2
|
||||
|
||||
@@ -0,0 +1,52 @@
|
||||
# TODO
|
||||
|
||||
Outstanding work items for Library Desk.
|
||||
|
||||
## Stub Endpoints to Implement
|
||||
|
||||
The following endpoints in `src/main.py` return stub responses and need real implementations:
|
||||
|
||||
### Ingestion Status Endpoints
|
||||
|
||||
#### `POST /ingest/check-updates`
|
||||
Check which documents need updating based on content hashes. Used by Scheduler to determine what changed since last sync.
|
||||
|
||||
**Implementation needed:**
|
||||
1. Query existing documents by path
|
||||
2. Compare content hashes
|
||||
3. Return list of updates needed
|
||||
|
||||
#### `GET /ingest/status/{document_id}`
|
||||
Get processing status for a document.
|
||||
|
||||
**Implementation needed:**
|
||||
- Status tracking system (Redis or database)
|
||||
- Track ingestion progress per document
|
||||
|
||||
#### `GET /ingest/repo-status/{repository}`
|
||||
Get indexing status for an entire repository.
|
||||
|
||||
**Implementation needed:**
|
||||
- Repository-level statistics
|
||||
- Track which documents from a repo are indexed
|
||||
|
||||
### Deduplication
|
||||
|
||||
#### `POST /deduplicate/check`
|
||||
Check for duplicate or highly similar documents using vector similarity and graph analysis.
|
||||
|
||||
**Implementation needed:**
|
||||
1. Get document embedding from Qdrant
|
||||
2. Find similar vectors above threshold
|
||||
3. Check graph relationships
|
||||
4. Return candidates with similarity scores
|
||||
|
||||
## System Statistics
|
||||
|
||||
#### `GET /stats`
|
||||
Get system statistics (wiki pages, neo4j nodes, qdrant vectors).
|
||||
|
||||
**Implementation needed:**
|
||||
- Query Neo4j for node count
|
||||
- Query Qdrant for vector count
|
||||
- Query Wiki.js for page count
|
||||
@@ -0,0 +1,58 @@
|
||||
# HybridRAG Architecture
|
||||
|
||||
## Overview
|
||||
|
||||
HybridRAG combines three search sources to provide comprehensive results:
|
||||
- **Vector search** (Qdrant) - Semantic similarity via embeddings
|
||||
- **Graph search** (Neo4j) - Entity relationships in knowledge graph
|
||||
- **Web search** (SearXNG) - External web results via Trafilatura extraction
|
||||
|
||||
## Two-Stage RRF Fusion (v1.3.0+)
|
||||
|
||||
To ensure fair ranking between wiki and web results, we use a two-stage Reciprocal Rank Fusion:
|
||||
|
||||
```
|
||||
Stage 1: Wiki Merge
|
||||
vector results ─┬─→ Mini-RRF ─→ Unified wiki ranking
|
||||
graph results ─┘
|
||||
|
||||
Stage 2: Final RRF
|
||||
wiki (merged) ─┬─→ Final RRF ─→ Combined results
|
||||
web results ─┘
|
||||
```
|
||||
|
||||
**Why two stages?**
|
||||
|
||||
Previously, wiki pages found by BOTH vector and graph received double RRF contribution, giving them an unfair 2x advantage over web results. The two-stage approach:
|
||||
1. Merges vector+graph into a single "wiki" source
|
||||
2. Wiki's internal ranking still benefits from multi-source confirmation
|
||||
3. Wiki and web compete as equals in final ranking
|
||||
|
||||
## Configuration
|
||||
|
||||
| Setting | Default | Description |
|
||||
|---------|---------|-------------|
|
||||
| `VECTOR_SIMILARITY_THRESHOLD` | 0.7 | Minimum similarity score for vector results |
|
||||
| `HYBRID_RAG_VECTOR_LIMIT` | 10 | Max vector results |
|
||||
| `HYBRID_RAG_GRAPH_LIMIT` | 10 | Max graph results |
|
||||
| `HYBRID_RAG_WEB_LIMIT` | 5 | Max web results |
|
||||
|
||||
## Known Limitations & Future Improvements
|
||||
|
||||
### Vector Search Noise
|
||||
|
||||
**Status:** Open for improvement if needed after observation period.
|
||||
|
||||
Vector search may return generic category/index pages (e.g., "Reference", "Projects", "Places") with high similarity scores (~0.86). These pages often have similar boilerplate content leading to uniform scores.
|
||||
|
||||
**Potential solutions if this becomes problematic:**
|
||||
1. **Raise threshold** - Increase `VECTOR_SIMILARITY_THRESHOLD` to 0.85+
|
||||
2. **Page-type filtering** - Exclude pages tagged as category/index/stub
|
||||
3. **Content length signal** - Penalize pages with minimal content
|
||||
4. **Duplicate score detection** - Flag results with suspiciously identical scores
|
||||
|
||||
The LLM re-ranking phase typically demotes these low-quality results, so this may not require immediate action.
|
||||
|
||||
### Graph Search
|
||||
|
||||
Graph search uses only core keywords (no LLM-generated synonyms) to avoid false matches like "author" → "author2000". This is intentional - vector search handles semantic similarity via embeddings.
|
||||
@@ -0,0 +1,283 @@
|
||||
# Memory Management System - Implementation Plan
|
||||
|
||||
## Overview
|
||||
|
||||
A three-tier memory architecture for Library Desk with intelligent orchestration:
|
||||
|
||||
| Tier | Storage | Purpose | TTL |
|
||||
|------|---------|---------|-----|
|
||||
| **Volatile** | Redis | Weather, news, financial, ephemeral context | 5min - 2hr |
|
||||
| **Documents** | TBD (research) | Git mirrors, PDFs, video, images | Permanent |
|
||||
| **Knowledge** | Wiki + Neo4j | Personal dossiers, research, summaries | Permanent |
|
||||
|
||||
**Implementation Priority**: Cleanup → Volatile → Documents
|
||||
|
||||
---
|
||||
|
||||
## Phase 1: Cleanup System Completion
|
||||
|
||||
### Current State
|
||||
- **COMPLETE** - All Phase 1 tasks implemented
|
||||
- Redis timestamp tracking for last cleanup
|
||||
- Bidirectional orphan detection between vectors and graph
|
||||
- Scheduler integration endpoints ready
|
||||
|
||||
### Tasks
|
||||
|
||||
#### 1.1 Add Scheduler Integration Points ✅
|
||||
**Files**: `src/routers/maintenance.py`
|
||||
|
||||
- [x] Add `last_cleanup` timestamp tracking in Redis
|
||||
- [x] Return cleanup stats in format scheduler can log
|
||||
- [x] Added `RedisDep` to cleanup endpoints
|
||||
|
||||
#### 1.2 Bidirectional Orphan Detection ✅
|
||||
**Files**: `src/services/graph_service.py`, `src/services/vector_service.py`
|
||||
|
||||
- [x] `find_documents_without_vectors()` - graph nodes with no vectors
|
||||
- [x] `find_chunks_without_graph_nodes()` - vectors with no graph node
|
||||
- [x] Updated maintenance endpoints to use bidirectional checks
|
||||
- [x] Added `chunks_without_graph` and `docs_without_vectors` to response models
|
||||
|
||||
#### 1.3 Scheduler Configuration ✅
|
||||
**Scheduler-side task definition:**
|
||||
```json
|
||||
{
|
||||
"task_name": "library_reconcile_index",
|
||||
"schedule": "0 4 * * *",
|
||||
"endpoint": "POST /maintenance/reconcile-index?user=jpmschweitzer",
|
||||
"description": "Daily index reconciliation - cleanup + reindex missing"
|
||||
}
|
||||
```
|
||||
|
||||
- [x] Documented in `LIBRARIAN_INTEGRATION.md`
|
||||
- [x] Added `reconcile-index` endpoint (cleanup + reindex missing)
|
||||
- [x] Lightweight health check mode for uptime monitoring
|
||||
- [x] Detailed health check mode for dashboards
|
||||
|
||||
---
|
||||
|
||||
## Phase 2: Volatile Memory System
|
||||
|
||||
### Architecture
|
||||
|
||||
```
|
||||
┌─────────────────┐ ┌──────────────┐ ┌─────────────────┐
|
||||
│ Library-Desk │◄───│ Scheduler │───►│ External APIs │
|
||||
│ │ │ │ │ (weather, news) │
|
||||
│ VolatileCache │ │ Refresh │ └─────────────────┘
|
||||
│ Service │ │ Jobs │
|
||||
└────────┬────────┘ └──────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────┐
|
||||
│ Redis │
|
||||
│ (DB 4, TTL) │
|
||||
└─────────────────┘
|
||||
```
|
||||
|
||||
### Data Model
|
||||
|
||||
```python
|
||||
class VolatileRecord(BaseModel):
|
||||
key: str # e.g., "weather:rotterdam"
|
||||
namespace: str # e.g., "weather", "news", "financial"
|
||||
data: dict # Actual content
|
||||
source: Optional[str] # Origin API/service
|
||||
created_at: datetime
|
||||
updated_at: datetime
|
||||
ttl: int # Seconds until expiration
|
||||
refresh_schedule: Optional[str] # Cron expression, if repeating
|
||||
user: str # Multi-tenant isolation
|
||||
```
|
||||
|
||||
**Key pattern**: `{user}:volatile:{namespace}:{key_hash}`
|
||||
|
||||
### Implementation Order: Integration-First
|
||||
|
||||
1. **Start with Consolidation Hook** - Understand data flow through existing system
|
||||
2. **Build Service Layer** - VolatileCacheService with Redis operations
|
||||
3. **Add API Endpoints** - REST interface for volatile data
|
||||
4. **Biographer Integration** - Query user preferences for relevance
|
||||
|
||||
### Tasks
|
||||
|
||||
#### 2.1 Integrate with Consolidation (FIRST)
|
||||
**New file**: `src/services/volatile_service.py`
|
||||
|
||||
```python
|
||||
class VolatileCacheService:
|
||||
async def get(user, namespace, key) -> Optional[VolatileRecord]
|
||||
async def set(user, namespace, key, data, ttl, refresh_schedule=None)
|
||||
async def delete(user, namespace, key)
|
||||
async def list_namespace(user, namespace) -> List[str]
|
||||
async def get_scheduled(user) -> List[VolatileRecord] # For scheduler
|
||||
```
|
||||
|
||||
#### 2.2 Create Volatile API Router
|
||||
**New file**: `src/routers/volatile.py`
|
||||
|
||||
| Endpoint | Method | Purpose |
|
||||
|----------|--------|---------|
|
||||
| `/volatile/{namespace}/{key}` | GET | Retrieve record |
|
||||
| `/volatile/{namespace}/{key}` | POST | Store/update record |
|
||||
| `/volatile/{namespace}/{key}` | DELETE | Remove record |
|
||||
| `/volatile/{namespace}` | GET | List keys in namespace |
|
||||
| `/volatile/scheduled` | GET | List records needing refresh |
|
||||
| `/volatile/stats` | GET | Cache statistics |
|
||||
|
||||
#### 2.3 Integrate with Consolidation
|
||||
**File**: `src/services/consolidation_service.py`
|
||||
|
||||
Add relevance trigger detection:
|
||||
1. During consolidation, analyze search results for location/interest patterns
|
||||
2. Query tatlock's Biographer collection for user preferences
|
||||
3. If match found, create/update volatile refresh schedule
|
||||
|
||||
#### 2.4 Biographer Integration
|
||||
**File**: `src/core/dependencies.py`
|
||||
|
||||
```python
|
||||
def get_biographer_qdrant() -> QdrantClientWrapper:
|
||||
"""Direct access to tatlock's Biographer collection."""
|
||||
# Configure to connect to tatlock's Qdrant
|
||||
```
|
||||
|
||||
#### 2.5 Scheduler-Side Configuration
|
||||
Document required scheduler tasks:
|
||||
```json
|
||||
{
|
||||
"task_name": "volatile_refresh",
|
||||
"schedule": "*/15 * * * *",
|
||||
"endpoint": "GET /volatile/scheduled",
|
||||
"follow_up": "For each record, call refresh endpoint with record.refresh_schedule"
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 3: Document Storage (Research + Implementation)
|
||||
|
||||
### Research Scope
|
||||
|
||||
Evaluate FOSS self-hosted options for:
|
||||
- Git repository mirroring
|
||||
- PDF/document storage with metadata
|
||||
- Image/video blob storage
|
||||
- Full-text search capability
|
||||
|
||||
**Constraints**:
|
||||
- Must be self-hosted, Docker-deployable
|
||||
- Performance is priority (can wrap complexity in API)
|
||||
- No cloud dependencies
|
||||
|
||||
**Candidates to evaluate**:
|
||||
1. MinIO (S3-compatible object storage) + metadata in Neo4j
|
||||
2. Paperless-ngx (document management with OCR)
|
||||
3. SeaweedFS (distributed file system)
|
||||
4. Custom: filesystem + Neo4j metadata
|
||||
|
||||
### Category Descriptors
|
||||
|
||||
**Wiki page structure for document collections**:
|
||||
```markdown
|
||||
# FastAPI Documentation
|
||||
|
||||
## Overview
|
||||
[LLM-generated summary from web search about FastAPI]
|
||||
|
||||
## Collection Statistics
|
||||
- **Documents**: 342 files
|
||||
- **Last Sync**: 2025-12-24 03:30 UTC
|
||||
- **Source**: github.com/tiangolo/fastapi
|
||||
- **Coverage**: API reference, tutorials, deployment guides
|
||||
|
||||
## What's Included
|
||||
[LLM summary of collection contents based on document analysis]
|
||||
|
||||
## Related Topics
|
||||
- [[Python Web Frameworks]]
|
||||
- [[REST API Design]]
|
||||
```
|
||||
|
||||
### Tasks
|
||||
|
||||
#### 3.1 Storage Research
|
||||
**Deliverable**: Evaluation document comparing options
|
||||
|
||||
#### 3.2 Storage Service Implementation
|
||||
**New file**: `src/services/document_store_service.py`
|
||||
(Details pending research results)
|
||||
|
||||
#### 3.3 Category Descriptor Generation
|
||||
**File**: `src/services/consolidation_service.py`
|
||||
|
||||
Add LLM-powered category descriptor generation:
|
||||
1. Web search for topic overview
|
||||
2. Analyze collection contents
|
||||
3. Generate/update wiki page with template
|
||||
|
||||
---
|
||||
|
||||
## Files to Modify/Create
|
||||
|
||||
### Phase 1 (Cleanup)
|
||||
- `src/routers/maintenance.py` - Add timestamp tracking
|
||||
- `src/services/graph_service.py` - Bidirectional validation
|
||||
- `src/services/vector_service.py` - Cross-reference checks
|
||||
- `LIBRARIAN_INTEGRATION.md` - Scheduler config docs
|
||||
|
||||
### Phase 2 (Volatile)
|
||||
- `src/services/volatile_service.py` - **NEW**
|
||||
- `src/routers/volatile.py` - **NEW**
|
||||
- `src/models/volatile.py` - **NEW**
|
||||
- `src/core/dependencies.py` - Add Biographer client
|
||||
- `src/services/consolidation_service.py` - Relevance triggers
|
||||
- `tests/test_volatile.py` - **NEW**
|
||||
|
||||
### Phase 3 (Documents)
|
||||
- `docs/DOCUMENT_STORAGE_RESEARCH.md` - **NEW**
|
||||
- `src/services/document_store_service.py` - **NEW** (post-research)
|
||||
- `src/routers/documents.py` - **NEW** (post-research)
|
||||
|
||||
---
|
||||
|
||||
## Resolved Design Decisions
|
||||
|
||||
1. **Biographer Qdrant**: Same Qdrant instance, different collection. Library-Desk queries directly.
|
||||
2. **Scheduler API**: Has REST API for task registration. Library-Desk can programmatically create refresh schedules.
|
||||
3. **External API calls**: Library-Desk routes through SearXNG for web search. Consider dedicated API integrations for high-value volatiles (weather, financial) for consistent quality.
|
||||
|
||||
---
|
||||
|
||||
## Future Consideration: Dedicated API Integrations
|
||||
|
||||
For volatile data where quality/consistency matters (weather, financial), consider:
|
||||
- OpenWeatherMap API for weather (daily refresh cycle)
|
||||
- Financial data API (Alpha Vantage, Yahoo Finance)
|
||||
- News APIs (NewsAPI, GDELT)
|
||||
- **NOS.nl** - Explicit source for Dutch news
|
||||
|
||||
This would live in a new `src/clients/` module with:
|
||||
- `weather_client.py` - Daily refresh cycle
|
||||
- `financial_client.py`
|
||||
- `news_client.py` - Include NOS.nl scraper/API for Dutch coverage
|
||||
|
||||
These provide structured, reliable data vs. SearXNG web scraping. Implementation deferred to later phase.
|
||||
|
||||
---
|
||||
|
||||
## Refresh Schedules
|
||||
|
||||
**Note:** TTL should be longer than refresh interval to prevent data gaps.
|
||||
|
||||
| Volatile Type | TTL | Refresh Cycle | Refresh Interval | Sources |
|
||||
|---------------|-----|---------------|------------------|---------|
|
||||
| Weather | 86400s (24hr) | Daily | Every 24hr | OpenWeatherMap |
|
||||
| Dutch News | 28800s (8hr) | 4x daily | Every 6hr | NOS.nl |
|
||||
| Global News | 28800s (8hr) | 4x daily | Every 6hr | NewsAPI, GDELT |
|
||||
| Financial | 600s (10min) | On-demand | N/A | Alpha Vantage |
|
||||
|
||||
**TTL Logic:**
|
||||
- TTL = Refresh Interval × 1.5 (buffer for failed refreshes)
|
||||
- On-demand data gets shorter TTL since it's fetched when needed
|
||||
+1
-1
@@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "library-desk"
|
||||
version = "1.1.3"
|
||||
version = "1.4.0"
|
||||
description = "Coordination service for The Library system - HybridRAG queries, document ingestion, entity extraction, and knowledge consolidation"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.12"
|
||||
|
||||
@@ -25,6 +25,9 @@ python-multipart~=0.0.20
|
||||
# Utilities
|
||||
python-dateutil~=2.9.0
|
||||
|
||||
# Content Extraction
|
||||
trafilatura~=1.12.0
|
||||
|
||||
# Testing
|
||||
pytest~=8.3.0
|
||||
pytest-asyncio~=0.24.0
|
||||
|
||||
@@ -0,0 +1,310 @@
|
||||
"""
|
||||
Content extraction client for Library Desk.
|
||||
|
||||
A reusable Trafilatura wrapper that can be used throughout library-desk:
|
||||
- RAG search service (extract content from search results)
|
||||
- Ingestion service (extract content from URLs)
|
||||
- Standalone endpoint (ad-hoc content extraction)
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from typing import List, Optional
|
||||
|
||||
import trafilatura
|
||||
|
||||
from src.models.content import ContentExtractionResult
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ContentExtractor:
|
||||
"""
|
||||
Generic content extraction client using Trafilatura.
|
||||
|
||||
Provides async wrappers around Trafilatura's synchronous extraction,
|
||||
with support for parallel batch processing and configurable timeouts.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
timeout: int = 5,
|
||||
max_length: int = 2000,
|
||||
max_workers: int = 10
|
||||
):
|
||||
"""
|
||||
Initialize ContentExtractor.
|
||||
|
||||
Args:
|
||||
timeout: Per-URL timeout in seconds
|
||||
max_length: Maximum content length to return (truncated if longer)
|
||||
max_workers: Max concurrent extractions for batch operations
|
||||
"""
|
||||
self.timeout = timeout
|
||||
self.max_length = max_length
|
||||
self._executor = ThreadPoolExecutor(max_workers=max_workers)
|
||||
logger.info(
|
||||
f"Initialized ContentExtractor: timeout={timeout}s, "
|
||||
f"max_length={max_length}, max_workers={max_workers}"
|
||||
)
|
||||
|
||||
def _extract_sync(
|
||||
self,
|
||||
url: str,
|
||||
include_metadata: bool = True,
|
||||
max_length: Optional[int] = None
|
||||
) -> ContentExtractionResult:
|
||||
"""
|
||||
Synchronous extraction (runs in thread pool).
|
||||
|
||||
Args:
|
||||
url: URL to extract content from
|
||||
include_metadata: Whether to extract title, author, date
|
||||
max_length: Override default max length
|
||||
|
||||
Returns:
|
||||
ContentExtractionResult with extracted content or error
|
||||
"""
|
||||
effective_max_length = max_length or self.max_length
|
||||
|
||||
try:
|
||||
# Fetch the URL
|
||||
downloaded = trafilatura.fetch_url(url)
|
||||
if not downloaded:
|
||||
return ContentExtractionResult(
|
||||
url=url,
|
||||
content="",
|
||||
success=False,
|
||||
error="Failed to fetch URL"
|
||||
)
|
||||
|
||||
# Extract content
|
||||
content = trafilatura.extract(
|
||||
downloaded,
|
||||
include_comments=False,
|
||||
include_tables=True,
|
||||
output_format='txt'
|
||||
)
|
||||
|
||||
if not content:
|
||||
return ContentExtractionResult(
|
||||
url=url,
|
||||
content="",
|
||||
success=False,
|
||||
error="No content extracted"
|
||||
)
|
||||
|
||||
# Truncate if needed
|
||||
if len(content) > effective_max_length:
|
||||
content = content[:effective_max_length] + "..."
|
||||
|
||||
# Extract metadata if requested
|
||||
title = None
|
||||
author = None
|
||||
date = None
|
||||
language = None
|
||||
|
||||
if include_metadata:
|
||||
metadata = trafilatura.extract(
|
||||
downloaded,
|
||||
output_format='xml',
|
||||
include_comments=False
|
||||
)
|
||||
# Parse metadata from XML if available
|
||||
# trafilatura.extract with output_format='xml' returns XML with metadata
|
||||
# For simplicity, we'll use bare_extraction which returns a dict
|
||||
try:
|
||||
meta_dict = trafilatura.bare_extraction(
|
||||
downloaded,
|
||||
include_comments=False
|
||||
)
|
||||
if meta_dict:
|
||||
title = meta_dict.get('title')
|
||||
author = meta_dict.get('author')
|
||||
date = meta_dict.get('date')
|
||||
language = meta_dict.get('language')
|
||||
except Exception as e:
|
||||
logger.debug(f"Metadata extraction failed for {url}: {e}")
|
||||
|
||||
return ContentExtractionResult(
|
||||
url=url,
|
||||
title=title,
|
||||
content=content,
|
||||
author=author,
|
||||
date=date,
|
||||
language=language,
|
||||
success=True,
|
||||
error=None
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Content extraction failed for {url}: {e}")
|
||||
return ContentExtractionResult(
|
||||
url=url,
|
||||
content="",
|
||||
success=False,
|
||||
error=str(e)
|
||||
)
|
||||
|
||||
async def extract(
|
||||
self,
|
||||
url: str,
|
||||
include_metadata: bool = True,
|
||||
max_length: Optional[int] = None
|
||||
) -> ContentExtractionResult:
|
||||
"""
|
||||
Extract content from a single URL asynchronously.
|
||||
|
||||
Args:
|
||||
url: URL to extract content from
|
||||
include_metadata: Whether to extract title, author, date
|
||||
max_length: Override default max length
|
||||
|
||||
Returns:
|
||||
ContentExtractionResult with extracted content or error
|
||||
"""
|
||||
loop = asyncio.get_event_loop()
|
||||
|
||||
try:
|
||||
result = await asyncio.wait_for(
|
||||
loop.run_in_executor(
|
||||
self._executor,
|
||||
self._extract_sync,
|
||||
url,
|
||||
include_metadata,
|
||||
max_length
|
||||
),
|
||||
timeout=self.timeout
|
||||
)
|
||||
return result
|
||||
except asyncio.TimeoutError:
|
||||
logger.warning(f"Content extraction timed out for {url}")
|
||||
return ContentExtractionResult(
|
||||
url=url,
|
||||
content="",
|
||||
success=False,
|
||||
error=f"Extraction timed out after {self.timeout}s"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Unexpected error extracting {url}: {e}")
|
||||
return ContentExtractionResult(
|
||||
url=url,
|
||||
content="",
|
||||
success=False,
|
||||
error=str(e)
|
||||
)
|
||||
|
||||
async def extract_batch(
|
||||
self,
|
||||
urls: List[str],
|
||||
include_metadata: bool = True,
|
||||
max_length: Optional[int] = None
|
||||
) -> List[ContentExtractionResult]:
|
||||
"""
|
||||
Extract content from multiple URLs in parallel.
|
||||
|
||||
Args:
|
||||
urls: List of URLs to extract content from
|
||||
include_metadata: Whether to extract title, author, date
|
||||
max_length: Override default max length
|
||||
|
||||
Returns:
|
||||
List of ContentExtractionResult in same order as input URLs
|
||||
"""
|
||||
tasks = [
|
||||
self.extract(url, include_metadata, max_length)
|
||||
for url in urls
|
||||
]
|
||||
results = await asyncio.gather(*tasks)
|
||||
return list(results)
|
||||
|
||||
async def extract_from_html(
|
||||
self,
|
||||
html: str,
|
||||
url: str = "",
|
||||
include_metadata: bool = True,
|
||||
max_length: Optional[int] = None
|
||||
) -> ContentExtractionResult:
|
||||
"""
|
||||
Extract content from raw HTML string.
|
||||
|
||||
Args:
|
||||
html: Raw HTML content
|
||||
url: Optional URL for reference (not fetched)
|
||||
include_metadata: Whether to extract title, author, date
|
||||
max_length: Override default max length
|
||||
|
||||
Returns:
|
||||
ContentExtractionResult with extracted content or error
|
||||
"""
|
||||
effective_max_length = max_length or self.max_length
|
||||
|
||||
def _extract():
|
||||
try:
|
||||
content = trafilatura.extract(
|
||||
html,
|
||||
include_comments=False,
|
||||
include_tables=True,
|
||||
output_format='txt'
|
||||
)
|
||||
|
||||
if not content:
|
||||
return ContentExtractionResult(
|
||||
url=url,
|
||||
content="",
|
||||
success=False,
|
||||
error="No content extracted from HTML"
|
||||
)
|
||||
|
||||
# Truncate if needed
|
||||
if len(content) > effective_max_length:
|
||||
content = content[:effective_max_length] + "..."
|
||||
|
||||
# Extract metadata
|
||||
title = None
|
||||
author = None
|
||||
date = None
|
||||
language = None
|
||||
|
||||
if include_metadata:
|
||||
try:
|
||||
meta_dict = trafilatura.bare_extraction(
|
||||
html,
|
||||
include_comments=False
|
||||
)
|
||||
if meta_dict:
|
||||
title = meta_dict.get('title')
|
||||
author = meta_dict.get('author')
|
||||
date = meta_dict.get('date')
|
||||
language = meta_dict.get('language')
|
||||
except Exception as e:
|
||||
logger.debug(f"Metadata extraction failed: {e}")
|
||||
|
||||
return ContentExtractionResult(
|
||||
url=url,
|
||||
title=title,
|
||||
content=content,
|
||||
author=author,
|
||||
date=date,
|
||||
language=language,
|
||||
success=True,
|
||||
error=None
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"HTML content extraction failed: {e}")
|
||||
return ContentExtractionResult(
|
||||
url=url,
|
||||
content="",
|
||||
success=False,
|
||||
error=str(e)
|
||||
)
|
||||
|
||||
loop = asyncio.get_event_loop()
|
||||
return await loop.run_in_executor(self._executor, _extract)
|
||||
|
||||
async def close(self):
|
||||
"""Shutdown the thread pool executor."""
|
||||
self._executor.shutdown(wait=False)
|
||||
logger.info("ContentExtractor closed")
|
||||
@@ -249,7 +249,8 @@ class OllamaClient:
|
||||
self,
|
||||
prompt: str,
|
||||
model: Optional[str] = None,
|
||||
stream: bool = False
|
||||
stream: bool = False,
|
||||
temperature: Optional[float] = None
|
||||
) -> Optional[str]:
|
||||
"""
|
||||
Generate text completion (for non-embedding use cases).
|
||||
@@ -258,12 +259,15 @@ class OllamaClient:
|
||||
prompt: Input prompt
|
||||
model: Model name (defaults to self.model)
|
||||
stream: Enable streaming response
|
||||
temperature: Sampling temperature (0.0 = deterministic, higher = more creative)
|
||||
None uses model default (~0.7 for mistral-nemo)
|
||||
|
||||
Returns:
|
||||
Generated text or None on failure
|
||||
|
||||
Note: This is primarily for debugging/testing. Use specialized
|
||||
LLM services for production text generation.
|
||||
Note: Use temperature=0.0 for deterministic outputs like JSON parsing,
|
||||
ranking, and factual extraction. Use higher values (0.3-0.7) for
|
||||
creative content generation.
|
||||
"""
|
||||
try:
|
||||
payload = {
|
||||
@@ -272,6 +276,10 @@ class OllamaClient:
|
||||
"stream": stream
|
||||
}
|
||||
|
||||
# Add temperature to options if specified
|
||||
if temperature is not None:
|
||||
payload["options"] = {"temperature": temperature}
|
||||
|
||||
response = await self.client.post(
|
||||
self.generate_url,
|
||||
json=payload
|
||||
|
||||
@@ -551,6 +551,97 @@ class QdrantClientWrapper:
|
||||
logger.error(f"Search failed: {e}", exc_info=True)
|
||||
return []
|
||||
|
||||
async def scroll_all_points(
|
||||
self,
|
||||
collection_name: str,
|
||||
batch_size: int = 100,
|
||||
with_payload: bool = True,
|
||||
with_vectors: bool = False,
|
||||
filter_conditions: Optional[Dict[str, Any]] = None
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Scroll through all points in a collection.
|
||||
|
||||
Args:
|
||||
collection_name: Collection name
|
||||
batch_size: Number of points per batch
|
||||
with_payload: Include payload in results
|
||||
with_vectors: Include vectors in results
|
||||
filter_conditions: Optional filter conditions
|
||||
|
||||
Returns:
|
||||
List of all points with id and payload
|
||||
"""
|
||||
all_points = []
|
||||
offset = None
|
||||
|
||||
# Build filter if provided
|
||||
scroll_filter = None
|
||||
if filter_conditions:
|
||||
conditions = []
|
||||
for key, value in filter_conditions.items():
|
||||
conditions.append(
|
||||
FieldCondition(key=key, match=MatchValue(value=value))
|
||||
)
|
||||
scroll_filter = Filter(must=conditions)
|
||||
|
||||
try:
|
||||
while True:
|
||||
points, next_offset = self.client.scroll(
|
||||
collection_name=collection_name,
|
||||
scroll_filter=scroll_filter,
|
||||
limit=batch_size,
|
||||
offset=offset,
|
||||
with_payload=with_payload,
|
||||
with_vectors=with_vectors
|
||||
)
|
||||
|
||||
for point in points:
|
||||
all_points.append({
|
||||
"id": str(point.id),
|
||||
"payload": dict(point.payload) if point.payload else {}
|
||||
})
|
||||
|
||||
if next_offset is None:
|
||||
break
|
||||
offset = next_offset
|
||||
|
||||
return all_points
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to scroll collection {collection_name}: {e}", exc_info=True)
|
||||
return []
|
||||
|
||||
async def delete_by_ids(
|
||||
self,
|
||||
collection_name: str,
|
||||
point_ids: List[str]
|
||||
) -> int:
|
||||
"""
|
||||
Delete points by their IDs.
|
||||
|
||||
Args:
|
||||
collection_name: Collection name
|
||||
point_ids: List of point IDs to delete
|
||||
|
||||
Returns:
|
||||
Number of points deleted
|
||||
"""
|
||||
if not point_ids:
|
||||
return 0
|
||||
|
||||
try:
|
||||
self.client.delete(
|
||||
collection_name=collection_name,
|
||||
points_selector=point_ids
|
||||
)
|
||||
logger.info(f"Deleted {len(point_ids)} points from {collection_name}")
|
||||
return len(point_ids)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to delete points by IDs: {e}", exc_info=True)
|
||||
return 0
|
||||
|
||||
async def list_collections(self) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
List all collections with stats.
|
||||
|
||||
@@ -20,96 +20,31 @@ class WikiJSClient:
|
||||
Wiki.js GraphQL API client.
|
||||
|
||||
Documentation: https://docs.requarks.io/dev/api
|
||||
Authentication: Username/password login to get user-specific JWT token
|
||||
Authentication: API token (JWT) generated from Wiki.js admin panel
|
||||
"""
|
||||
|
||||
def __init__(self, base_url: str, username: str, password: str):
|
||||
def __init__(self, base_url: str, api_token: str):
|
||||
"""
|
||||
Initialize Wiki.js client.
|
||||
|
||||
Args:
|
||||
base_url: Wiki.js base URL (e.g., "http://wiki:3000")
|
||||
username: Wiki.js username (e.g., "librarian@schweitz.net")
|
||||
password: Wiki.js password
|
||||
api_token: Wiki.js API token (JWT from admin panel)
|
||||
"""
|
||||
self.base_url = base_url.rstrip("/")
|
||||
self.graphql_url = f"{self.base_url}/graphql"
|
||||
self.username = username
|
||||
self.password = password
|
||||
self.jwt_token: Optional[str] = None
|
||||
self.api_token = api_token
|
||||
self.client = httpx.AsyncClient(timeout=30.0)
|
||||
logger.info(f"Initialized Wiki.js client: {base_url} (user: {username})")
|
||||
logger.info(f"Initialized Wiki.js client: {base_url} (using API token)")
|
||||
|
||||
async def close(self):
|
||||
"""Close HTTP client"""
|
||||
await self.client.aclose()
|
||||
|
||||
async def login(self) -> bool:
|
||||
"""
|
||||
Authenticate with Wiki.js using username/password.
|
||||
|
||||
Returns:
|
||||
True if login successful, False otherwise
|
||||
"""
|
||||
login_mutation = """
|
||||
mutation Login($username: String!, $password: String!, $strategy: String!) {
|
||||
authentication {
|
||||
login(username: $username, password: $password, strategy: $strategy) {
|
||||
responseResult {
|
||||
succeeded
|
||||
errorCode
|
||||
message
|
||||
}
|
||||
jwt
|
||||
}
|
||||
}
|
||||
}
|
||||
"""
|
||||
|
||||
variables = {
|
||||
"username": self.username,
|
||||
"password": self.password,
|
||||
"strategy": "local"
|
||||
}
|
||||
|
||||
try:
|
||||
response = await self.client.post(
|
||||
self.graphql_url,
|
||||
headers={"Content-Type": "application/json"},
|
||||
json={"query": login_mutation, "variables": variables}
|
||||
)
|
||||
response.raise_for_status()
|
||||
result = response.json()
|
||||
|
||||
if "errors" in result:
|
||||
logger.error(f"Login failed: {result['errors']}")
|
||||
return False
|
||||
|
||||
login_result = result.get("data", {}).get("authentication", {}).get("login", {})
|
||||
response_result = login_result.get("responseResult", {})
|
||||
|
||||
if not response_result.get("succeeded"):
|
||||
logger.error(f"Login failed: {response_result.get('message')}")
|
||||
return False
|
||||
|
||||
self.jwt_token = login_result.get("jwt")
|
||||
if not self.jwt_token:
|
||||
logger.error("Login succeeded but no JWT token received")
|
||||
return False
|
||||
|
||||
logger.info(f"Successfully authenticated as {self.username}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Login failed: {e}", exc_info=True)
|
||||
return False
|
||||
|
||||
async def _ensure_authenticated(self):
|
||||
"""Ensure we have a valid JWT token, login if needed."""
|
||||
if not self.jwt_token:
|
||||
success = await self.login()
|
||||
if not success:
|
||||
raise Exception("Failed to authenticate with Wiki.js")
|
||||
def _ensure_authenticated(self):
|
||||
"""Verify API token is configured."""
|
||||
if not self.api_token:
|
||||
raise Exception("Wiki.js API token not configured")
|
||||
|
||||
async def _execute_query(
|
||||
self,
|
||||
@@ -129,8 +64,8 @@ class WikiJSClient:
|
||||
Raises:
|
||||
Exception: If query fails or returns errors
|
||||
"""
|
||||
# Ensure we're authenticated before making requests
|
||||
await self._ensure_authenticated()
|
||||
# Ensure API token is configured
|
||||
self._ensure_authenticated()
|
||||
|
||||
payload = {
|
||||
"query": query,
|
||||
@@ -138,7 +73,7 @@ class WikiJSClient:
|
||||
}
|
||||
|
||||
headers = {
|
||||
"Authorization": f"Bearer {self.jwt_token}",
|
||||
"Authorization": f"Bearer {self.api_token}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
|
||||
|
||||
+32
-5
@@ -43,8 +43,10 @@ class Settings(BaseSettings):
|
||||
|
||||
# Wiki.js Configuration
|
||||
wikijs_url: str = Field(default="http://wiki:3000", description="Wiki.js URL")
|
||||
wikijs_username: str = Field(..., description="Wiki.js username")
|
||||
wikijs_password: str = Field(..., description="Wiki.js password")
|
||||
wiki_graphql_api: str = Field(..., description="Wiki.js GraphQL API token (JWT)")
|
||||
# Legacy auth fields - kept for backwards compatibility but deprecated
|
||||
wikijs_username: str = Field(default="", description="Wiki.js username (deprecated, use wiki_graphql_api)")
|
||||
wikijs_password: str = Field(default="", description="Wiki.js password (deprecated, use wiki_graphql_api)")
|
||||
|
||||
# Wiki.js Database Configuration (for change listener)
|
||||
wikijs_db_host: str = Field(default="postgres-shared", description="Wiki.js PostgreSQL host")
|
||||
@@ -62,16 +64,17 @@ class Settings(BaseSettings):
|
||||
# SearXNG Configuration
|
||||
searxng_url: str = Field(default="http://searxng:8080", description="SearXNG URL")
|
||||
|
||||
# Ollama Configuration (for embeddings)
|
||||
# Ollama Configuration
|
||||
ollama_url: str = Field(default="http://ollama:11434", description="Ollama URL")
|
||||
ollama_model: str = Field(default="nomic-embed-text", description="Ollama embedding model")
|
||||
ollama_model: str = Field(default="mistral-nemo-large:latest", description="Ollama LLM model")
|
||||
ollama_embedding_model: str = Field(default="nomic-embed-text", description="Ollama embedding model")
|
||||
|
||||
# HybridRAG Configuration
|
||||
reranker_model: str = Field(default="mistral-nemo", description="Model for LLM re-ranking")
|
||||
reranker_enabled: bool = Field(default=True, description="Enable LLM re-ranking")
|
||||
hybrid_rag_vector_limit: int = Field(default=10, ge=1, le=50, description="Vector search limit")
|
||||
hybrid_rag_graph_limit: int = Field(default=10, ge=1, le=50, description="Graph search limit")
|
||||
hybrid_rag_web_limit: int = Field(default=5, ge=1, le=20, description="Web search limit")
|
||||
vector_similarity_threshold: float = Field(default=0.7, ge=0.0, le=1.0, description="Minimum similarity score for vector results")
|
||||
|
||||
# Entity Linking Fuzzy Matching Configuration
|
||||
entity_linking_min_confidence: float = Field(default=0.70, ge=0.0, le=1.0, description="Minimum confidence for entity-document matching")
|
||||
@@ -89,6 +92,30 @@ class Settings(BaseSettings):
|
||||
app_version: str = Field(default=__version__, description="Application version")
|
||||
debug: bool = Field(default=False, description="Debug mode")
|
||||
|
||||
# RAG Search Configuration
|
||||
search_cache_ttl: int = Field(default=300, ge=0, le=3600, description="Search cache TTL in seconds")
|
||||
search_timeout: int = Field(default=10, ge=1, le=60, description="SearXNG timeout in seconds")
|
||||
search_default_limit: int = Field(default=10, ge=1, le=20, description="Default number of search results")
|
||||
|
||||
# Content Extraction Configuration
|
||||
content_extraction_timeout: int = Field(default=5, ge=1, le=30, description="Trafilatura per-URL timeout in seconds")
|
||||
content_max_length: int = Field(default=2000, ge=500, le=10000, description="Max extracted content length per result")
|
||||
|
||||
# Document Store Configuration
|
||||
document_store_enabled: bool = Field(default=True, description="Enable document store feature")
|
||||
document_catalog_path_prefix: str = Field(default="docs", description="Wiki path prefix for catalog pages")
|
||||
|
||||
# Volatile Cache Configuration
|
||||
volatile_cache_enabled: bool = Field(default=True, description="Enable volatile cache feature")
|
||||
volatile_default_ttl: int = Field(default=3600, ge=60, le=86400, description="Default TTL in seconds")
|
||||
volatile_weather_ttl: int = Field(default=1800, ge=60, le=7200, description="Weather data TTL in seconds")
|
||||
volatile_news_ttl: int = Field(default=7200, ge=300, le=86400, description="News data TTL in seconds")
|
||||
volatile_financial_ttl: int = Field(default=300, ge=60, le=3600, description="Financial data TTL in seconds")
|
||||
|
||||
# Maintenance Configuration
|
||||
maintenance_orphan_cleanup_enabled: bool = Field(default=True, description="Enable automatic orphan cleanup")
|
||||
maintenance_cleanup_batch_size: int = Field(default=100, ge=10, le=1000, description="Cleanup batch size")
|
||||
|
||||
@property
|
||||
def qdrant_url(self) -> str:
|
||||
"""Computed Qdrant URL."""
|
||||
|
||||
+65
-14
@@ -13,12 +13,15 @@ from typing import Annotated
|
||||
from fastapi import Depends
|
||||
import logging
|
||||
|
||||
import redis.asyncio as aioredis
|
||||
|
||||
from src.config import Settings, get_settings
|
||||
from src.clients.neo4j_client import Neo4jClient
|
||||
from src.clients.qdrant_client import QdrantClientWrapper
|
||||
from src.clients.wikijs_client import WikiJSClient
|
||||
from src.clients.searxng_client import SearXNGClient
|
||||
from src.clients.ollama_client import OllamaClient
|
||||
from src.clients.content_extractor import ContentExtractor
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -73,13 +76,12 @@ def get_wikijs_client() -> WikiJSClient:
|
||||
Get Wiki.js client singleton.
|
||||
|
||||
Returns:
|
||||
Initialized Wiki.js GraphQL client with username/password auth
|
||||
Initialized Wiki.js GraphQL client with API token auth
|
||||
"""
|
||||
settings = get_settings()
|
||||
client = WikiJSClient(
|
||||
base_url=settings.wikijs_url,
|
||||
username=settings.wikijs_username,
|
||||
password=settings.wikijs_password
|
||||
api_token=settings.wiki_graphql_api
|
||||
)
|
||||
logger.debug("Created Wiki.js client instance")
|
||||
return client
|
||||
@@ -110,12 +112,49 @@ def get_ollama_client() -> OllamaClient:
|
||||
settings = get_settings()
|
||||
client = OllamaClient(
|
||||
base_url=settings.ollama_url,
|
||||
model=settings.ollama_model
|
||||
model=settings.ollama_embedding_model
|
||||
)
|
||||
logger.debug("Created Ollama client instance")
|
||||
return client
|
||||
|
||||
|
||||
@lru_cache
|
||||
def get_redis_client() -> aioredis.Redis:
|
||||
"""
|
||||
Get Redis client singleton for caching.
|
||||
|
||||
Returns:
|
||||
Async Redis client connected to the configured database
|
||||
|
||||
Note: Uses Redis DB 4 (configured for library-desk)
|
||||
"""
|
||||
settings = get_settings()
|
||||
client = aioredis.from_url(
|
||||
settings.redis_url,
|
||||
encoding="utf-8",
|
||||
decode_responses=True
|
||||
)
|
||||
logger.debug(f"Created Redis client: {settings.redis_url}")
|
||||
return client
|
||||
|
||||
|
||||
@lru_cache
|
||||
def get_content_extractor() -> ContentExtractor:
|
||||
"""
|
||||
Get ContentExtractor singleton.
|
||||
|
||||
Returns:
|
||||
Initialized content extraction client using Trafilatura
|
||||
"""
|
||||
settings = get_settings()
|
||||
extractor = ContentExtractor(
|
||||
timeout=settings.content_extraction_timeout,
|
||||
max_length=settings.content_max_length
|
||||
)
|
||||
logger.debug("Created ContentExtractor instance")
|
||||
return extractor
|
||||
|
||||
|
||||
# Type aliases for FastAPI endpoint dependencies
|
||||
# Usage: def my_endpoint(neo4j: Neo4jDep):
|
||||
Neo4jDep = Annotated[Neo4jClient, Depends(get_neo4j_client)]
|
||||
@@ -123,6 +162,8 @@ QdrantDep = Annotated[QdrantClientWrapper, Depends(get_qdrant_client)]
|
||||
WikiJSDep = Annotated[WikiJSClient, Depends(get_wikijs_client)]
|
||||
SearXNGDep = Annotated[SearXNGClient, Depends(get_searxng_client)]
|
||||
OllamaDep = Annotated[OllamaClient, Depends(get_ollama_client)]
|
||||
RedisDep = Annotated[aioredis.Redis, Depends(get_redis_client)]
|
||||
ContentExtractorDep = Annotated[ContentExtractor, Depends(get_content_extractor)]
|
||||
|
||||
|
||||
# Lifecycle management functions
|
||||
@@ -336,20 +377,21 @@ def get_hybrid_rag_service() -> "HybridRAGService":
|
||||
graph_service=get_graph_service(),
|
||||
searxng_client=get_searxng_client(),
|
||||
ollama_client=get_ollama_client(),
|
||||
content_extractor=get_content_extractor(),
|
||||
settings=get_settings()
|
||||
)
|
||||
|
||||
|
||||
# Utility: Get default user from settings or multi_tenancy
|
||||
def get_default_user() -> str:
|
||||
"""
|
||||
Get default user for operations.
|
||||
|
||||
Returns:
|
||||
Default user identifier
|
||||
"""
|
||||
from src.core.multi_tenancy import DEFAULT_USER
|
||||
return DEFAULT_USER
|
||||
@lru_cache
|
||||
def get_rag_search_service() -> "RAGSearchService":
|
||||
"""Get RAGSearchService singleton."""
|
||||
from src.services.rag_search_service import RAGSearchService
|
||||
return RAGSearchService(
|
||||
searxng_client=get_searxng_client(),
|
||||
content_extractor=get_content_extractor(),
|
||||
redis_client=get_redis_client(),
|
||||
settings=get_settings()
|
||||
)
|
||||
|
||||
|
||||
# Authentication
|
||||
@@ -382,3 +424,12 @@ async def verify_api_key(
|
||||
detail="Invalid API key"
|
||||
)
|
||||
return credentials.credentials
|
||||
|
||||
|
||||
# Service type aliases for FastAPI endpoint dependencies
|
||||
# These are defined after the factory functions
|
||||
from src.services.vector_service import VectorService
|
||||
from src.services.graph_service import GraphService
|
||||
|
||||
VectorServiceDep = Annotated[VectorService, Depends(get_vector_service)]
|
||||
GraphServiceDep = Annotated[GraphService, Depends(get_graph_service)]
|
||||
|
||||
+77
-101
@@ -8,7 +8,7 @@ Following best practices:
|
||||
- OpenAPI documentation
|
||||
"""
|
||||
|
||||
from fastapi import FastAPI, HTTPException, Depends
|
||||
from fastapi import FastAPI, HTTPException, Depends, Query
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from fastapi.staticfiles import StaticFiles
|
||||
from pydantic import BaseModel
|
||||
@@ -17,7 +17,10 @@ import logging
|
||||
from pathlib import Path
|
||||
|
||||
from src.config import Settings, get_settings, __version__
|
||||
from src.core.dependencies import verify_api_key
|
||||
from src.core.dependencies import (
|
||||
verify_api_key, QdrantDep, WikiJSDep, OllamaDep, Neo4jDep
|
||||
)
|
||||
from src.core.multi_tenancy import DEFAULT_USER
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
@@ -45,7 +48,11 @@ app.add_middleware(
|
||||
)
|
||||
|
||||
# Register routers
|
||||
from src.routers import wiki, tools, graph, vector, hybrid_rag, consolidation, ingestion, entity_linking, webhooks
|
||||
from src.routers import (
|
||||
wiki, tools, graph, vector, hybrid_rag, consolidation,
|
||||
ingestion, entity_linking, webhooks, rag_search, content,
|
||||
maintenance
|
||||
)
|
||||
|
||||
app.include_router(wiki.router)
|
||||
app.include_router(tools.router)
|
||||
@@ -56,6 +63,9 @@ app.include_router(consolidation.router)
|
||||
app.include_router(ingestion.router)
|
||||
app.include_router(entity_linking.router)
|
||||
app.include_router(webhooks.router)
|
||||
app.include_router(rag_search.router)
|
||||
app.include_router(content.router)
|
||||
app.include_router(maintenance.router)
|
||||
|
||||
# Mount static files directory for Wiki.js integration scripts
|
||||
static_dir = Path(__file__).parent.parent / "static"
|
||||
@@ -73,13 +83,6 @@ class HealthResponse(BaseModel):
|
||||
services: Dict[str, Any]
|
||||
|
||||
|
||||
class StatsResponse(BaseModel):
|
||||
"""Statistics response model."""
|
||||
wiki_pages: int
|
||||
neo4j_nodes: int
|
||||
qdrant_vectors: int
|
||||
|
||||
|
||||
# Routes
|
||||
@app.get("/", tags=["Root"])
|
||||
async def root() -> Dict[str, str]:
|
||||
@@ -136,77 +139,6 @@ async def health(settings: Settings = Depends(get_settings)) -> HealthResponse:
|
||||
)
|
||||
|
||||
|
||||
@app.get("/stats", response_model=StatsResponse, tags=["System"])
|
||||
async def stats(
|
||||
api_key: str = Depends(verify_api_key)
|
||||
) -> StatsResponse:
|
||||
"""
|
||||
Get system statistics.
|
||||
Protected endpoint - requires API key.
|
||||
|
||||
TODO: Implement actual stats gathering from:
|
||||
- Neo4j (node count)
|
||||
- Qdrant (vector count)
|
||||
- Wiki.js (page count)
|
||||
"""
|
||||
return StatsResponse(
|
||||
wiki_pages=0,
|
||||
neo4j_nodes=0,
|
||||
qdrant_vectors=0
|
||||
)
|
||||
|
||||
|
||||
# Ingestion endpoints (for Scheduler integration)
|
||||
@app.post("/ingest/document", tags=["Ingestion"])
|
||||
async def ingest_document(
|
||||
document: Dict[str, Any],
|
||||
api_key: str = Depends(verify_api_key)
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Ingest a single document for indexing.
|
||||
Used by The Scheduler to add mirrored documentation to the knowledge base.
|
||||
|
||||
Expected fields:
|
||||
- source: str (e.g., "github", "gitea")
|
||||
- repository: str (e.g., "anthropic-cookbook")
|
||||
- path: str (file path)
|
||||
- content: str (document content)
|
||||
- metadata: dict (commit, author, tags, etc.)
|
||||
|
||||
TODO: Implement document ingestion pipeline:
|
||||
1. Chunk content
|
||||
2. Generate embeddings (Ollama)
|
||||
3. Extract entities (NLP)
|
||||
4. Index in Qdrant
|
||||
5. Create graph nodes/relationships in Neo4j
|
||||
"""
|
||||
return {
|
||||
"message": "Document ingestion not yet implemented",
|
||||
"document_id": f"doc_{document.get('path', 'unknown')}",
|
||||
"status": "stub"
|
||||
}
|
||||
|
||||
|
||||
@app.post("/ingest/batch", tags=["Ingestion"])
|
||||
async def batch_ingest(
|
||||
batch: Dict[str, Any],
|
||||
api_key: str = Depends(verify_api_key)
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Ingest multiple documents in a batch.
|
||||
More efficient than individual ingestion for large syncs.
|
||||
|
||||
TODO: Implement batch processing with task queue
|
||||
"""
|
||||
document_count = len(batch.get("documents", []))
|
||||
return {
|
||||
"message": "Batch ingestion not yet implemented",
|
||||
"batch_id": "batch_stub",
|
||||
"total_documents": document_count,
|
||||
"status": "stub"
|
||||
}
|
||||
|
||||
|
||||
@app.post("/ingest/check-updates", tags=["Ingestion"])
|
||||
async def check_updates(
|
||||
documents: Dict[str, Any],
|
||||
@@ -264,41 +196,85 @@ async def get_repo_status(
|
||||
}
|
||||
|
||||
|
||||
# Query endpoints (stubs for future implementation)
|
||||
# NOTE: /query/hybrid is now implemented in routers/hybrid_rag.py
|
||||
# Query endpoints
|
||||
# NOTE: /query/hybrid is implemented in routers/hybrid_rag.py
|
||||
|
||||
@app.post("/query/semantic", tags=["Query"])
|
||||
async def semantic_query(
|
||||
query: Dict[str, Any],
|
||||
query: str = Query(..., min_length=1, description="Search query text"),
|
||||
user: str = Query(default=DEFAULT_USER, description="User identifier"),
|
||||
limit: int = Query(default=10, ge=1, le=100, description="Maximum results"),
|
||||
score_threshold: float = Query(default=0.5, ge=0.0, le=1.0, description="Minimum similarity score"),
|
||||
qdrant_client: QdrantDep = None,
|
||||
wiki_client: WikiJSDep = None,
|
||||
ollama_client: OllamaDep = None,
|
||||
api_key: str = Depends(verify_api_key)
|
||||
) -> Dict[str, Any]:
|
||||
):
|
||||
"""
|
||||
Semantic search via Qdrant.
|
||||
Pure vector similarity search.
|
||||
Semantic search via Qdrant vector similarity.
|
||||
|
||||
TODO: Implement semantic search
|
||||
Searches document chunks using embedding similarity. Returns matching
|
||||
chunks with relevance scores, page titles, and paths.
|
||||
|
||||
**Example:**
|
||||
```
|
||||
POST /query/semantic?query=docker%20configuration&user=jpmschweitzer&limit=10
|
||||
```
|
||||
|
||||
**Returns:** List of matching chunks with similarity scores (0-1)
|
||||
"""
|
||||
return {
|
||||
"message": "Semantic search not yet implemented",
|
||||
"query": query
|
||||
}
|
||||
from src.services.vector_service import VectorService
|
||||
|
||||
vector_service = VectorService(qdrant_client, wiki_client, ollama_client)
|
||||
try:
|
||||
return await vector_service.search(
|
||||
query=query,
|
||||
user=user,
|
||||
limit=limit,
|
||||
score_threshold=score_threshold
|
||||
)
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error(f"Semantic search failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail="Search failed")
|
||||
|
||||
|
||||
@app.post("/query/graph", tags=["Query"])
|
||||
async def graph_query(
|
||||
query: Dict[str, Any],
|
||||
query: str = Query(..., description="Cypher query to execute"),
|
||||
user: str = Query(default=DEFAULT_USER, description="User for scoping (auto-filters results)"),
|
||||
neo4j_client: Neo4jDep = None,
|
||||
wiki_client: WikiJSDep = None,
|
||||
api_key: str = Depends(verify_api_key)
|
||||
) -> Dict[str, Any]:
|
||||
):
|
||||
"""
|
||||
Graph traversal via Neo4j.
|
||||
Execute Cypher queries.
|
||||
Execute a Cypher query against the Neo4j knowledge graph.
|
||||
|
||||
TODO: Implement graph queries
|
||||
Queries are automatically scoped to the user's data for security.
|
||||
Use this for custom graph traversals beyond what /graph/nodes provides.
|
||||
|
||||
**Example:**
|
||||
```
|
||||
POST /query/graph?query=MATCH%20(d:Document)-[:MENTIONS]->(p:Person)%20RETURN%20d,p&user=jpmschweitzer
|
||||
```
|
||||
|
||||
**Security:** All queries are user-scoped to prevent cross-user data access.
|
||||
"""
|
||||
return {
|
||||
"message": "Graph query not yet implemented",
|
||||
"query": query
|
||||
}
|
||||
from src.services.graph_service import GraphService
|
||||
|
||||
graph_service = GraphService(neo4j_client, wiki_client)
|
||||
try:
|
||||
return await graph_service.execute_query(
|
||||
query=query,
|
||||
parameters={},
|
||||
user=user
|
||||
)
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error(f"Graph query failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail="Query execution failed")
|
||||
|
||||
|
||||
# Deduplication endpoints
|
||||
|
||||
@@ -0,0 +1,69 @@
|
||||
"""
|
||||
Content extraction models for Library Desk.
|
||||
|
||||
Pydantic models for content extraction requests and responses.
|
||||
"""
|
||||
|
||||
from typing import Optional, List
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class ContentExtractionResult(BaseModel):
|
||||
"""Result of extracting content from a single URL."""
|
||||
|
||||
url: str = Field(..., description="The URL that was processed")
|
||||
title: Optional[str] = Field(None, description="Page title if extracted")
|
||||
content: str = Field("", description="Extracted main text content")
|
||||
author: Optional[str] = Field(None, description="Author if available")
|
||||
date: Optional[str] = Field(None, description="Publication date if available (ISO format)")
|
||||
language: Optional[str] = Field(None, description="Detected language code")
|
||||
success: bool = Field(..., description="Whether extraction succeeded")
|
||||
error: Optional[str] = Field(None, description="Error message if extraction failed")
|
||||
|
||||
|
||||
class ContentExtractionRequest(BaseModel):
|
||||
"""Request to extract content from a single URL."""
|
||||
|
||||
url: str = Field(..., min_length=1, description="URL to extract content from")
|
||||
include_metadata: bool = Field(default=True, description="Include title, author, date metadata")
|
||||
max_length: Optional[int] = Field(
|
||||
None,
|
||||
ge=100,
|
||||
le=50000,
|
||||
description="Override default max content length"
|
||||
)
|
||||
|
||||
|
||||
class ContentExtractionResponse(BaseModel):
|
||||
"""Response for single URL extraction."""
|
||||
|
||||
result: ContentExtractionResult
|
||||
extraction_time_ms: int = Field(..., ge=0, description="Time taken to extract content")
|
||||
|
||||
|
||||
class BatchContentExtractionRequest(BaseModel):
|
||||
"""Request to extract content from multiple URLs."""
|
||||
|
||||
urls: List[str] = Field(
|
||||
...,
|
||||
min_length=1,
|
||||
max_length=20,
|
||||
description="URLs to extract content from (max 20)"
|
||||
)
|
||||
include_metadata: bool = Field(default=True, description="Include title, author, date metadata")
|
||||
max_length: Optional[int] = Field(
|
||||
None,
|
||||
ge=100,
|
||||
le=50000,
|
||||
description="Override default max content length"
|
||||
)
|
||||
|
||||
|
||||
class BatchContentExtractionResponse(BaseModel):
|
||||
"""Response for batch URL extraction."""
|
||||
|
||||
results: List[ContentExtractionResult]
|
||||
total_urls: int = Field(..., ge=0, description="Total number of URLs processed")
|
||||
successful: int = Field(..., ge=0, description="Number of successful extractions")
|
||||
failed: int = Field(..., ge=0, description="Number of failed extractions")
|
||||
extraction_time_ms: int = Field(..., ge=0, description="Total time for batch extraction")
|
||||
@@ -0,0 +1,88 @@
|
||||
"""
|
||||
RAG search models for Library Desk.
|
||||
|
||||
Pydantic models for web/news/image search requests and responses.
|
||||
"""
|
||||
|
||||
from enum import Enum
|
||||
from typing import Optional, List
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from src.core.multi_tenancy import DEFAULT_USER
|
||||
|
||||
|
||||
class SearchType(str, Enum):
|
||||
"""Supported search types."""
|
||||
WEB = "web"
|
||||
NEWS = "news"
|
||||
IMAGES = "images"
|
||||
|
||||
|
||||
class RAGSearchRequest(BaseModel):
|
||||
"""Request for RAG search endpoint."""
|
||||
|
||||
query: str = Field(
|
||||
...,
|
||||
min_length=1,
|
||||
max_length=500,
|
||||
description="The search query"
|
||||
)
|
||||
search_type: SearchType = Field(
|
||||
default=SearchType.WEB,
|
||||
description="Type of search: web, news, or images"
|
||||
)
|
||||
limit: int = Field(
|
||||
default=10,
|
||||
ge=1,
|
||||
le=20,
|
||||
description="Maximum number of results (1-20)"
|
||||
)
|
||||
user: str = Field(
|
||||
default=DEFAULT_USER,
|
||||
description="User identifier for rate limiting/personalization"
|
||||
)
|
||||
|
||||
|
||||
class RAGSearchResult(BaseModel):
|
||||
"""A single search result with extracted content."""
|
||||
|
||||
title: str = Field(..., description="Title of the result")
|
||||
url: str = Field(..., description="URL of the source")
|
||||
content: str = Field(
|
||||
"",
|
||||
description="Full extracted text via Trafilatura (max ~2000 chars)"
|
||||
)
|
||||
snippet: str = Field(
|
||||
"",
|
||||
description="Original search engine snippet (150-300 chars)"
|
||||
)
|
||||
source: str = Field(..., description="Domain name of the source")
|
||||
published_date: Optional[str] = Field(
|
||||
None,
|
||||
description="Publication date in ISO format if available"
|
||||
)
|
||||
|
||||
|
||||
class RAGSearchResponse(BaseModel):
|
||||
"""Response from RAG search endpoint."""
|
||||
|
||||
query: str = Field(..., description="Echo of the original query")
|
||||
search_type: SearchType = Field(..., description="Type of search performed")
|
||||
results: List[RAGSearchResult] = Field(
|
||||
default_factory=list,
|
||||
description="List of search results with extracted content"
|
||||
)
|
||||
total_results: int = Field(
|
||||
...,
|
||||
ge=0,
|
||||
description="Number of results returned"
|
||||
)
|
||||
search_time_ms: int = Field(
|
||||
...,
|
||||
ge=0,
|
||||
description="Total time for search and content extraction"
|
||||
)
|
||||
sources_summary: str = Field(
|
||||
"",
|
||||
description="Markdown-formatted list of all source URLs"
|
||||
)
|
||||
@@ -0,0 +1,132 @@
|
||||
"""
|
||||
Content extraction router for Library Desk API.
|
||||
|
||||
Endpoints for extracting main content from web URLs using Trafilatura.
|
||||
"""
|
||||
|
||||
import time
|
||||
from fastapi import APIRouter, HTTPException, Depends
|
||||
import logging
|
||||
|
||||
from src.models.content import (
|
||||
ContentExtractionRequest,
|
||||
ContentExtractionResponse,
|
||||
BatchContentExtractionRequest,
|
||||
BatchContentExtractionResponse,
|
||||
)
|
||||
from src.clients.content_extractor import ContentExtractor
|
||||
from src.core.dependencies import verify_api_key
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/content", tags=["Content Extraction"])
|
||||
|
||||
|
||||
# Lazy import to avoid circular dependency
|
||||
def get_content_extractor() -> ContentExtractor:
|
||||
"""Get content extractor instance."""
|
||||
from src.core.dependencies import get_content_extractor as _get_extractor
|
||||
return _get_extractor()
|
||||
|
||||
|
||||
@router.post("/extract", response_model=ContentExtractionResponse)
|
||||
async def extract_content(
|
||||
request: ContentExtractionRequest,
|
||||
api_key: str = Depends(verify_api_key)
|
||||
):
|
||||
"""
|
||||
Extract main content from a single URL.
|
||||
|
||||
Uses Trafilatura to fetch the URL and extract the main text content,
|
||||
removing navigation, ads, and other boilerplate.
|
||||
|
||||
**Example Request:**
|
||||
```json
|
||||
{
|
||||
"url": "https://example.com/article",
|
||||
"include_metadata": true,
|
||||
"max_length": 2000
|
||||
}
|
||||
```
|
||||
|
||||
**Returns:** Extracted content with optional metadata (title, author, date)
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
extractor = get_content_extractor()
|
||||
result = await extractor.extract(
|
||||
url=request.url,
|
||||
include_metadata=request.include_metadata,
|
||||
max_length=request.max_length
|
||||
)
|
||||
|
||||
extraction_time_ms = int((time.time() - start_time) * 1000)
|
||||
|
||||
return ContentExtractionResponse(
|
||||
result=result,
|
||||
extraction_time_ms=extraction_time_ms
|
||||
)
|
||||
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error(f"Content extraction failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail="Content extraction failed")
|
||||
|
||||
|
||||
@router.post("/extract/batch", response_model=BatchContentExtractionResponse)
|
||||
async def extract_content_batch(
|
||||
request: BatchContentExtractionRequest,
|
||||
api_key: str = Depends(verify_api_key)
|
||||
):
|
||||
"""
|
||||
Extract content from multiple URLs in parallel.
|
||||
|
||||
Processes up to 20 URLs concurrently with per-URL timeouts.
|
||||
Failed extractions are included in results with success=false.
|
||||
|
||||
**Example Request:**
|
||||
```json
|
||||
{
|
||||
"urls": [
|
||||
"https://example.com/article1",
|
||||
"https://example.com/article2"
|
||||
],
|
||||
"include_metadata": true,
|
||||
"max_length": 2000
|
||||
}
|
||||
```
|
||||
|
||||
**Returns:** List of extraction results with success/failure counts
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
if not request.urls:
|
||||
raise HTTPException(status_code=400, detail="URLs list cannot be empty")
|
||||
|
||||
try:
|
||||
extractor = get_content_extractor()
|
||||
results = await extractor.extract_batch(
|
||||
urls=request.urls,
|
||||
include_metadata=request.include_metadata,
|
||||
max_length=request.max_length
|
||||
)
|
||||
|
||||
extraction_time_ms = int((time.time() - start_time) * 1000)
|
||||
successful = sum(1 for r in results if r.success)
|
||||
failed = len(results) - successful
|
||||
|
||||
return BatchContentExtractionResponse(
|
||||
results=results,
|
||||
total_urls=len(request.urls),
|
||||
successful=successful,
|
||||
failed=failed,
|
||||
extraction_time_ms=extraction_time_ms
|
||||
)
|
||||
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error(f"Batch content extraction failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail="Batch extraction failed")
|
||||
@@ -15,8 +15,6 @@ from src.models.graph import (
|
||||
MindMapResponse
|
||||
)
|
||||
from src.services.graph_service import GraphService
|
||||
from src.clients.neo4j_client import Neo4jClient
|
||||
from src.clients.wikijs_client import WikiJSClient
|
||||
from src.core.dependencies import Neo4jDep, WikiJSDep, verify_api_key
|
||||
from src.core.multi_tenancy import DEFAULT_USER
|
||||
|
||||
|
||||
@@ -10,13 +10,9 @@ import logging
|
||||
|
||||
from src.models.hybrid_rag import HybridRAGRequest, HybridRAGResponse
|
||||
from src.services.hybrid_rag_service import HybridRAGService
|
||||
from src.services.vector_service import VectorService
|
||||
from src.services.graph_service import GraphService
|
||||
from src.clients.searxng_client import SearXNGClient
|
||||
from src.clients.ollama_client import OllamaClient
|
||||
from src.core.dependencies import (
|
||||
Neo4jDep, WikiJSDep, QdrantDep, OllamaDep,
|
||||
SearXNGDep, verify_api_key, get_settings
|
||||
SearXNGDep, ContentExtractorDep, verify_api_key, get_settings
|
||||
)
|
||||
from src.config import Settings
|
||||
|
||||
@@ -32,6 +28,7 @@ def get_hybrid_rag_service(
|
||||
qdrant_client: QdrantDep,
|
||||
ollama_client: OllamaDep,
|
||||
searxng_client: SearXNGDep,
|
||||
content_extractor: ContentExtractorDep,
|
||||
settings: Settings = Depends(get_settings)
|
||||
) -> HybridRAGService:
|
||||
"""Get HybridRAG service instance with all dependencies."""
|
||||
@@ -48,6 +45,7 @@ def get_hybrid_rag_service(
|
||||
graph_service=graph_service,
|
||||
searxng_client=searxng_client,
|
||||
ollama_client=ollama_client,
|
||||
content_extractor=content_extractor,
|
||||
settings=settings
|
||||
)
|
||||
|
||||
|
||||
@@ -0,0 +1,794 @@
|
||||
"""
|
||||
Maintenance router for Library Desk cleanup operations.
|
||||
|
||||
Provides endpoints to clean up orphaned data in vectors and graph:
|
||||
- Orphan vector chunks (no matching page/document in graph)
|
||||
- Orphan entities (no MENTIONS relationships)
|
||||
- Stale documents (graph nodes with no matching wiki page)
|
||||
- Broken relationships
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, HTTPException, Depends, Query
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import Optional, List, Dict, Any
|
||||
import logging
|
||||
import time
|
||||
|
||||
from src.services.vector_service import VectorService
|
||||
from src.services.graph_service import GraphService
|
||||
from src.core.dependencies import (
|
||||
VectorServiceDep, GraphServiceDep, WikiJSDep, RedisDep,
|
||||
verify_api_key
|
||||
)
|
||||
from datetime import datetime, timezone
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/maintenance", tags=["Maintenance"])
|
||||
|
||||
# Redis key for tracking last cleanup timestamp
|
||||
LAST_CLEANUP_KEY = "library:maintenance:last_cleanup:{user}"
|
||||
|
||||
|
||||
async def _get_last_cleanup(redis, user: str) -> Optional[str]:
|
||||
"""Get last cleanup timestamp from Redis."""
|
||||
try:
|
||||
key = LAST_CLEANUP_KEY.format(user=user)
|
||||
return await redis.get(key)
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to get last cleanup timestamp: {e}")
|
||||
return None
|
||||
|
||||
|
||||
async def _set_last_cleanup(redis, user: str) -> None:
|
||||
"""Store current timestamp as last cleanup time."""
|
||||
try:
|
||||
key = LAST_CLEANUP_KEY.format(user=user)
|
||||
timestamp = datetime.now(timezone.utc).isoformat()
|
||||
# Keep for 30 days
|
||||
await redis.setex(key, 86400 * 30, timestamp)
|
||||
logger.info(f"Recorded cleanup timestamp: {timestamp}")
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to store cleanup timestamp: {e}")
|
||||
|
||||
|
||||
async def _find_unindexed_pages(
|
||||
wiki_pages: List[Dict],
|
||||
chunk_refs: List[Dict],
|
||||
graph_docs: List[Dict]
|
||||
) -> tuple[List[int], List[int]]:
|
||||
"""
|
||||
Find wiki pages that are missing from vectors or graph.
|
||||
|
||||
Returns:
|
||||
Tuple of (pages_without_vectors, pages_without_graph)
|
||||
"""
|
||||
# Build sets of indexed page IDs
|
||||
vectorized_page_ids = {
|
||||
ref.get("page_id") for ref in chunk_refs
|
||||
if ref.get("doc_type") == "wiki" and ref.get("page_id")
|
||||
}
|
||||
graphed_page_ids = {
|
||||
doc.get("page_id") for doc in graph_docs
|
||||
if doc.get("doc_type") == "wiki" and doc.get("page_id")
|
||||
}
|
||||
|
||||
# Find wiki pages missing from each store
|
||||
pages_without_vectors = []
|
||||
pages_without_graph = []
|
||||
|
||||
for page in wiki_pages:
|
||||
page_id = page.get("id")
|
||||
if not page_id:
|
||||
continue
|
||||
|
||||
if page_id not in vectorized_page_ids:
|
||||
pages_without_vectors.append(page_id)
|
||||
if page_id not in graphed_page_ids:
|
||||
pages_without_graph.append(page_id)
|
||||
|
||||
return pages_without_vectors, pages_without_graph
|
||||
|
||||
|
||||
async def _reindex_missing_pages(
|
||||
page_ids: List[int],
|
||||
user: str,
|
||||
vector_service,
|
||||
graph_service
|
||||
) -> tuple[int, int, List[int]]:
|
||||
"""
|
||||
Reindex pages that are missing from vectors or graph.
|
||||
|
||||
Returns:
|
||||
Tuple of (pages_reindexed, pages_failed, failed_page_ids)
|
||||
"""
|
||||
reindexed = 0
|
||||
failed = 0
|
||||
failed_ids = []
|
||||
|
||||
for page_id in page_ids:
|
||||
try:
|
||||
# Index to both stores
|
||||
vector_result = await vector_service.update_from_page(page_id, user, force_refresh=True)
|
||||
graph_result = await graph_service.update_from_page(page_id, user, force_refresh=True)
|
||||
|
||||
if vector_result.success and graph_result.success:
|
||||
reindexed += 1
|
||||
logger.info(f"Reindexed missing page {page_id}")
|
||||
else:
|
||||
failed += 1
|
||||
failed_ids.append(page_id)
|
||||
logger.warning(f"Failed to reindex page {page_id}: vector={vector_result.success}, graph={graph_result.success}")
|
||||
|
||||
except Exception as e:
|
||||
failed += 1
|
||||
failed_ids.append(page_id)
|
||||
logger.error(f"Error reindexing page {page_id}: {e}")
|
||||
|
||||
return reindexed, failed, failed_ids
|
||||
|
||||
|
||||
# ========== Response Models ==========
|
||||
|
||||
class CleanupResult(BaseModel):
|
||||
"""Result of a cleanup operation."""
|
||||
orphans_found: int = Field(default=0, description="Number of orphans detected")
|
||||
orphans_purged: int = Field(default=0, description="Number of orphans deleted")
|
||||
duration_ms: float = Field(description="Operation duration in milliseconds")
|
||||
|
||||
|
||||
class VectorCleanupResponse(BaseModel):
|
||||
"""Response from vector cleanup operation."""
|
||||
success: bool
|
||||
wiki_chunks: CleanupResult
|
||||
document_chunks: CleanupResult
|
||||
chunks_without_graph: CleanupResult # Vectors with no graph node
|
||||
total_chunks_scanned: int
|
||||
total_orphans_purged: int
|
||||
duration_ms: float
|
||||
|
||||
|
||||
class GraphCleanupResponse(BaseModel):
|
||||
"""Response from graph cleanup operation."""
|
||||
success: bool
|
||||
orphan_entities: CleanupResult
|
||||
stale_wiki_documents: CleanupResult
|
||||
stale_store_documents: CleanupResult
|
||||
docs_without_vectors: CleanupResult # Graph nodes with no vectors
|
||||
broken_relationships_cleaned: int
|
||||
duration_ms: float
|
||||
|
||||
|
||||
class FullCleanupResponse(BaseModel):
|
||||
"""Response from full cleanup operation."""
|
||||
success: bool
|
||||
vector_cleanup: VectorCleanupResponse
|
||||
graph_cleanup: GraphCleanupResponse
|
||||
total_duration_ms: float
|
||||
|
||||
|
||||
class HealthCheckResponse(BaseModel):
|
||||
"""Response from maintenance health check."""
|
||||
status: str = Field(description="Health status: healthy, degraded, or unhealthy")
|
||||
orphan_vector_count: int = Field(description="Number of orphan vector chunks (no source)")
|
||||
orphan_entity_count: int = Field(description="Number of orphan entities")
|
||||
stale_document_count: int = Field(description="Number of stale document nodes")
|
||||
vectors_without_graph: int = Field(default=0, description="Vector chunks with no graph node")
|
||||
docs_without_vectors: int = Field(default=0, description="Graph docs with no vectors")
|
||||
unindexed_pages: int = Field(default=0, description="Wiki pages missing from indexes")
|
||||
last_cleanup: Optional[str] = Field(default=None, description="Timestamp of last cleanup")
|
||||
recommendations: List[str] = Field(default_factory=list)
|
||||
|
||||
|
||||
class ReindexResponse(BaseModel):
|
||||
"""Response from reindex operation."""
|
||||
success: bool
|
||||
page_id: int
|
||||
vectors_deleted: int
|
||||
vectors_created: int
|
||||
graph_updated: bool
|
||||
duration_ms: float
|
||||
error: Optional[str] = None
|
||||
|
||||
|
||||
class ReindexMissingResult(BaseModel):
|
||||
"""Result of reindexing missing pages."""
|
||||
pages_without_vectors: int = Field(description="Wiki pages with no vector embeddings")
|
||||
pages_without_graph: int = Field(description="Wiki pages with no graph Document node")
|
||||
pages_reindexed: int = Field(description="Pages successfully reindexed")
|
||||
pages_failed: int = Field(description="Pages that failed to reindex")
|
||||
failed_page_ids: List[int] = Field(default_factory=list)
|
||||
duration_ms: float
|
||||
|
||||
|
||||
class ReconcileIndexResponse(BaseModel):
|
||||
"""Response from reconcile-index operation (cleanup + reindex-missing)."""
|
||||
success: bool
|
||||
cleanup: FullCleanupResponse
|
||||
reindex_missing: ReindexMissingResult
|
||||
total_duration_ms: float
|
||||
|
||||
|
||||
# ========== Endpoints ==========
|
||||
|
||||
@router.post("/cleanup/vectors", response_model=VectorCleanupResponse)
|
||||
async def cleanup_vectors(
|
||||
user: str = Query(..., description="User identifier"),
|
||||
dry_run: bool = Query(False, description="If true, only count orphans without deleting"),
|
||||
vector_service: VectorServiceDep = None,
|
||||
graph_service: GraphServiceDep = None,
|
||||
wiki_client: WikiJSDep = None,
|
||||
api_key: str = Depends(verify_api_key)
|
||||
):
|
||||
"""
|
||||
Find and purge orphan vector chunks.
|
||||
|
||||
Orphan chunks are vector embeddings that reference:
|
||||
- Wiki pages that no longer exist
|
||||
- Document Store documents that no longer exist
|
||||
- Chunks with no corresponding graph Document node (bidirectional check)
|
||||
|
||||
**Scheduler Task** - Recommended to run daily.
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# Get all vector chunk references
|
||||
chunk_refs = await vector_service.get_all_chunk_references(user)
|
||||
total_scanned = len(chunk_refs)
|
||||
|
||||
# Get all valid page IDs from wiki
|
||||
wiki_pages = await wiki_client.list_all_pages()
|
||||
valid_page_ids = {p.get("id") for p in wiki_pages if p.get("id")}
|
||||
|
||||
# Get all valid document references from graph
|
||||
graph_docs = await graph_service.get_all_document_references(user)
|
||||
valid_doc_ids = {d["document_id"] for d in graph_docs if d.get("document_id")}
|
||||
|
||||
# Find orphan wiki chunks (page_id not in wiki)
|
||||
wiki_orphan_ids = []
|
||||
doc_orphan_ids = []
|
||||
|
||||
for ref in chunk_refs:
|
||||
doc_type = ref.get("doc_type", "wiki")
|
||||
|
||||
if doc_type == "wiki":
|
||||
page_id = ref.get("page_id")
|
||||
if page_id and page_id not in valid_page_ids:
|
||||
wiki_orphan_ids.append(ref["chunk_id"])
|
||||
else:
|
||||
document_id = ref.get("document_id")
|
||||
if document_id and document_id not in valid_doc_ids:
|
||||
doc_orphan_ids.append(ref["chunk_id"])
|
||||
|
||||
# Bidirectional check: chunks with no graph node
|
||||
chunks_without_graph = vector_service.find_chunks_without_graph_nodes(
|
||||
chunk_refs, graph_docs
|
||||
)
|
||||
|
||||
# Purge orphans if not dry run
|
||||
wiki_purged = 0
|
||||
doc_purged = 0
|
||||
graph_orphans_purged = 0
|
||||
|
||||
if not dry_run:
|
||||
if wiki_orphan_ids:
|
||||
wiki_purged = await vector_service.purge_chunks_by_ids(user, wiki_orphan_ids)
|
||||
if doc_orphan_ids:
|
||||
doc_purged = await vector_service.purge_chunks_by_ids(user, doc_orphan_ids)
|
||||
if chunks_without_graph:
|
||||
graph_orphans_purged = await vector_service.purge_chunks_by_ids(
|
||||
user, chunks_without_graph
|
||||
)
|
||||
|
||||
duration_ms = (time.time() - start_time) * 1000
|
||||
|
||||
return VectorCleanupResponse(
|
||||
success=True,
|
||||
wiki_chunks=CleanupResult(
|
||||
orphans_found=len(wiki_orphan_ids),
|
||||
orphans_purged=wiki_purged,
|
||||
duration_ms=duration_ms / 3
|
||||
),
|
||||
document_chunks=CleanupResult(
|
||||
orphans_found=len(doc_orphan_ids),
|
||||
orphans_purged=doc_purged,
|
||||
duration_ms=duration_ms / 3
|
||||
),
|
||||
chunks_without_graph=CleanupResult(
|
||||
orphans_found=len(chunks_without_graph),
|
||||
orphans_purged=graph_orphans_purged,
|
||||
duration_ms=duration_ms / 3
|
||||
),
|
||||
total_chunks_scanned=total_scanned,
|
||||
total_orphans_purged=wiki_purged + doc_purged + graph_orphans_purged,
|
||||
duration_ms=duration_ms
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Vector cleanup failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.post("/cleanup/graph", response_model=GraphCleanupResponse)
|
||||
async def cleanup_graph(
|
||||
user: str = Query(..., description="User identifier"),
|
||||
dry_run: bool = Query(False, description="If true, only count orphans without deleting"),
|
||||
vector_service: VectorServiceDep = None,
|
||||
graph_service: GraphServiceDep = None,
|
||||
wiki_client: WikiJSDep = None,
|
||||
api_key: str = Depends(verify_api_key)
|
||||
):
|
||||
"""
|
||||
Find and purge orphan entities and stale documents from the graph.
|
||||
|
||||
Cleans up:
|
||||
- Orphan entities (no MENTIONS relationships)
|
||||
- Stale wiki Document nodes (page deleted from Wiki.js)
|
||||
- Stale Document Store nodes (document deleted)
|
||||
- Graph Document nodes with no corresponding vectors (bidirectional check)
|
||||
- Broken FOUND relationships from SearchQuery nodes
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# 1. Find orphan entities
|
||||
orphan_entities = await graph_service.find_orphan_entities(user)
|
||||
entities_purged = 0
|
||||
|
||||
if not dry_run and orphan_entities:
|
||||
entities_purged = await graph_service.purge_orphan_entities(user)
|
||||
|
||||
# 2. Find stale wiki documents
|
||||
graph_docs = await graph_service.get_all_document_references(user)
|
||||
wiki_docs = [d for d in graph_docs if d.get("doc_type") == "wiki" and d.get("page_id")]
|
||||
|
||||
# Get valid wiki page IDs
|
||||
wiki_pages = await wiki_client.list_all_pages()
|
||||
valid_page_ids = {p.get("id") for p in wiki_pages if p.get("id")}
|
||||
|
||||
stale_wiki_ids = [d["page_id"] for d in wiki_docs if d["page_id"] not in valid_page_ids]
|
||||
wiki_docs_purged = 0
|
||||
|
||||
if not dry_run and stale_wiki_ids:
|
||||
wiki_docs_purged = await graph_service.purge_stale_documents_by_ids(
|
||||
user, page_ids=stale_wiki_ids
|
||||
)
|
||||
|
||||
# 3. Find stale Document Store documents (these would be detected differently)
|
||||
# For now, Document Store docs are only stale if the collection is deleted
|
||||
# This will be more relevant once DocumentService exists
|
||||
stale_store_docs = 0
|
||||
store_docs_purged = 0
|
||||
|
||||
# 4. Bidirectional check: graph docs with no vectors
|
||||
chunk_refs = await vector_service.get_all_chunk_references(user)
|
||||
docs_without_vectors = await graph_service.find_documents_without_vectors(
|
||||
user, chunk_refs
|
||||
)
|
||||
docs_without_vectors_purged = 0
|
||||
|
||||
if not dry_run and docs_without_vectors:
|
||||
# Purge wiki docs without vectors
|
||||
wiki_orphans = [d["page_id"] for d in docs_without_vectors
|
||||
if d.get("doc_type") == "wiki" and d.get("page_id")]
|
||||
doc_orphans = [d["document_id"] for d in docs_without_vectors
|
||||
if d.get("doc_type") != "wiki" and d.get("document_id")]
|
||||
|
||||
if wiki_orphans:
|
||||
docs_without_vectors_purged += await graph_service.purge_stale_documents_by_ids(
|
||||
user, page_ids=wiki_orphans
|
||||
)
|
||||
if doc_orphans:
|
||||
docs_without_vectors_purged += await graph_service.purge_stale_documents_by_ids(
|
||||
user, document_ids=doc_orphans
|
||||
)
|
||||
|
||||
# 5. Clean broken relationships
|
||||
broken_rels_cleaned = 0
|
||||
if not dry_run:
|
||||
broken_rels_cleaned = await graph_service.cleanup_broken_relationships(user)
|
||||
|
||||
duration_ms = (time.time() - start_time) * 1000
|
||||
|
||||
return GraphCleanupResponse(
|
||||
success=True,
|
||||
orphan_entities=CleanupResult(
|
||||
orphans_found=len(orphan_entities),
|
||||
orphans_purged=entities_purged,
|
||||
duration_ms=duration_ms / 5
|
||||
),
|
||||
stale_wiki_documents=CleanupResult(
|
||||
orphans_found=len(stale_wiki_ids),
|
||||
orphans_purged=wiki_docs_purged,
|
||||
duration_ms=duration_ms / 5
|
||||
),
|
||||
stale_store_documents=CleanupResult(
|
||||
orphans_found=stale_store_docs,
|
||||
orphans_purged=store_docs_purged,
|
||||
duration_ms=duration_ms / 5
|
||||
),
|
||||
docs_without_vectors=CleanupResult(
|
||||
orphans_found=len(docs_without_vectors),
|
||||
orphans_purged=docs_without_vectors_purged,
|
||||
duration_ms=duration_ms / 5
|
||||
),
|
||||
broken_relationships_cleaned=broken_rels_cleaned,
|
||||
duration_ms=duration_ms
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Graph cleanup failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.post("/cleanup/all", response_model=FullCleanupResponse)
|
||||
async def cleanup_all(
|
||||
user: str = Query(..., description="User identifier"),
|
||||
dry_run: bool = Query(False, description="If true, only count orphans without deleting"),
|
||||
vector_service: VectorServiceDep = None,
|
||||
graph_service: GraphServiceDep = None,
|
||||
wiki_client: WikiJSDep = None,
|
||||
redis: RedisDep = None,
|
||||
api_key: str = Depends(verify_api_key)
|
||||
):
|
||||
"""
|
||||
Full cleanup of vectors and graph.
|
||||
|
||||
Runs both vector and graph cleanup in sequence.
|
||||
|
||||
**Scheduler Task** - Recommended to run daily at low-traffic time.
|
||||
|
||||
**Scheduler Integration:**
|
||||
```json
|
||||
{
|
||||
"task_name": "library_maintenance",
|
||||
"schedule": "0 4 * * *",
|
||||
"endpoint": "POST /maintenance/cleanup/all?user=jpmschweitzer",
|
||||
"description": "Daily cleanup of orphan vectors and graph nodes"
|
||||
}
|
||||
```
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# Run vector cleanup
|
||||
vector_result = await cleanup_vectors(
|
||||
user=user,
|
||||
dry_run=dry_run,
|
||||
vector_service=vector_service,
|
||||
graph_service=graph_service,
|
||||
wiki_client=wiki_client,
|
||||
api_key=api_key
|
||||
)
|
||||
|
||||
# Run graph cleanup
|
||||
graph_result = await cleanup_graph(
|
||||
user=user,
|
||||
dry_run=dry_run,
|
||||
vector_service=vector_service,
|
||||
graph_service=graph_service,
|
||||
wiki_client=wiki_client,
|
||||
api_key=api_key
|
||||
)
|
||||
|
||||
total_duration_ms = (time.time() - start_time) * 1000
|
||||
|
||||
# Record cleanup timestamp (only if not dry run)
|
||||
if not dry_run and redis:
|
||||
await _set_last_cleanup(redis, user)
|
||||
|
||||
return FullCleanupResponse(
|
||||
success=True,
|
||||
vector_cleanup=vector_result,
|
||||
graph_cleanup=graph_result,
|
||||
total_duration_ms=total_duration_ms
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Full cleanup failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.get("/health", response_model=HealthCheckResponse)
|
||||
async def maintenance_health(
|
||||
user: str = Query(..., description="User identifier"),
|
||||
detailed: bool = Query(False, description="If true, run full orphan analysis (slower)"),
|
||||
vector_service: VectorServiceDep = None,
|
||||
graph_service: GraphServiceDep = None,
|
||||
wiki_client: WikiJSDep = None,
|
||||
redis: RedisDep = None,
|
||||
api_key: str = Depends(verify_api_key)
|
||||
):
|
||||
"""
|
||||
Health check for maintenance status.
|
||||
|
||||
**Lightweight mode (default)**: Returns last cleanup timestamp and basic status.
|
||||
Use for frequent uptime checks (every 30s).
|
||||
|
||||
**Detailed mode (?detailed=true)**: Runs full orphan/unindexed analysis.
|
||||
Use for dashboards or before running reconcile-index.
|
||||
"""
|
||||
try:
|
||||
# Get last cleanup timestamp from Redis (lightweight)
|
||||
last_cleanup = None
|
||||
if redis:
|
||||
last_cleanup = await _get_last_cleanup(redis, user)
|
||||
|
||||
# Lightweight mode - just return basic status
|
||||
if not detailed:
|
||||
return HealthCheckResponse(
|
||||
status="healthy" if last_cleanup else "unknown",
|
||||
orphan_vector_count=0,
|
||||
orphan_entity_count=0,
|
||||
stale_document_count=0,
|
||||
vectors_without_graph=0,
|
||||
docs_without_vectors=0,
|
||||
unindexed_pages=0,
|
||||
last_cleanup=last_cleanup,
|
||||
recommendations=[] if last_cleanup else ["No cleanup recorded. Run POST /maintenance/reconcile-index"]
|
||||
)
|
||||
|
||||
# Detailed mode - full analysis
|
||||
recommendations = []
|
||||
|
||||
# Count orphan vector chunks
|
||||
chunk_refs = await vector_service.get_all_chunk_references(user)
|
||||
wiki_pages = await wiki_client.list_all_pages()
|
||||
valid_page_ids = {p.get("id") for p in wiki_pages if p.get("id")}
|
||||
|
||||
orphan_vector_count = sum(
|
||||
1 for ref in chunk_refs
|
||||
if ref.get("doc_type") == "wiki"
|
||||
and ref.get("page_id") not in valid_page_ids
|
||||
)
|
||||
|
||||
if orphan_vector_count > 10:
|
||||
recommendations.append(
|
||||
f"Found {orphan_vector_count} orphan vector chunks. "
|
||||
"Consider running POST /maintenance/cleanup/vectors"
|
||||
)
|
||||
|
||||
# Count orphan entities
|
||||
orphan_entities = await graph_service.find_orphan_entities(user)
|
||||
orphan_entity_count = len(orphan_entities)
|
||||
|
||||
if orphan_entity_count > 5:
|
||||
recommendations.append(
|
||||
f"Found {orphan_entity_count} orphan entities. "
|
||||
"Consider running POST /maintenance/cleanup/graph"
|
||||
)
|
||||
|
||||
# Count stale documents
|
||||
graph_docs = await graph_service.get_all_document_references(user)
|
||||
wiki_docs = [d for d in graph_docs if d.get("doc_type") == "wiki" and d.get("page_id")]
|
||||
stale_document_count = sum(1 for d in wiki_docs if d["page_id"] not in valid_page_ids)
|
||||
|
||||
if stale_document_count > 0:
|
||||
recommendations.append(
|
||||
f"Found {stale_document_count} stale Document nodes. "
|
||||
"Consider running POST /maintenance/cleanup/graph"
|
||||
)
|
||||
|
||||
# Bidirectional: vectors without graph nodes
|
||||
vectors_without_graph = len(vector_service.find_chunks_without_graph_nodes(
|
||||
chunk_refs, graph_docs
|
||||
))
|
||||
|
||||
if vectors_without_graph > 5:
|
||||
recommendations.append(
|
||||
f"Found {vectors_without_graph} vectors without graph nodes. "
|
||||
"Consider running POST /maintenance/cleanup/vectors"
|
||||
)
|
||||
|
||||
# Bidirectional: graph docs without vectors
|
||||
docs_without_vectors_list = await graph_service.find_documents_without_vectors(
|
||||
user, chunk_refs
|
||||
)
|
||||
docs_without_vectors = len(docs_without_vectors_list)
|
||||
|
||||
if docs_without_vectors > 5:
|
||||
recommendations.append(
|
||||
f"Found {docs_without_vectors} graph docs without vectors. "
|
||||
"Consider running POST /maintenance/cleanup/graph"
|
||||
)
|
||||
|
||||
# Unindexed pages: wiki pages missing from vectors or graph
|
||||
pages_without_vectors, pages_without_graph = await _find_unindexed_pages(
|
||||
wiki_pages, chunk_refs, graph_docs
|
||||
)
|
||||
unindexed_pages = len(set(pages_without_vectors + pages_without_graph))
|
||||
|
||||
if unindexed_pages > 0:
|
||||
recommendations.append(
|
||||
f"Found {unindexed_pages} wiki pages not in indexes. "
|
||||
"Consider running POST /maintenance/reconcile-index"
|
||||
)
|
||||
|
||||
# Determine overall status
|
||||
total_issues = (orphan_vector_count + orphan_entity_count + stale_document_count +
|
||||
vectors_without_graph + docs_without_vectors + unindexed_pages)
|
||||
if total_issues == 0:
|
||||
status = "healthy"
|
||||
elif total_issues < 20:
|
||||
status = "degraded"
|
||||
else:
|
||||
status = "unhealthy"
|
||||
|
||||
# Get last cleanup timestamp from Redis
|
||||
last_cleanup = None
|
||||
if redis:
|
||||
last_cleanup = await _get_last_cleanup(redis, user)
|
||||
|
||||
return HealthCheckResponse(
|
||||
status=status,
|
||||
orphan_vector_count=orphan_vector_count,
|
||||
orphan_entity_count=orphan_entity_count,
|
||||
stale_document_count=stale_document_count,
|
||||
vectors_without_graph=vectors_without_graph,
|
||||
docs_without_vectors=docs_without_vectors,
|
||||
unindexed_pages=unindexed_pages,
|
||||
last_cleanup=last_cleanup,
|
||||
recommendations=recommendations
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Health check failed: {e}", exc_info=True)
|
||||
return HealthCheckResponse(
|
||||
status="unhealthy",
|
||||
orphan_vector_count=-1,
|
||||
orphan_entity_count=-1,
|
||||
stale_document_count=-1,
|
||||
vectors_without_graph=-1,
|
||||
docs_without_vectors=-1,
|
||||
unindexed_pages=-1,
|
||||
recommendations=[f"Health check failed: {str(e)}"]
|
||||
)
|
||||
|
||||
|
||||
@router.post("/reindex/{page_id}", response_model=ReindexResponse)
|
||||
async def reindex_page(
|
||||
page_id: int,
|
||||
user: str = Query(..., description="User identifier"),
|
||||
vector_service: VectorServiceDep = None,
|
||||
graph_service: GraphServiceDep = None,
|
||||
api_key: str = Depends(verify_api_key)
|
||||
):
|
||||
"""
|
||||
Force re-index a wiki page.
|
||||
|
||||
Deletes existing vectors and graph data, then re-ingests.
|
||||
Useful for fixing corrupted or stale data for a specific page.
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# Delete existing vectors
|
||||
vectors_deleted = await vector_service.delete_page_chunks(page_id, user)
|
||||
|
||||
# Delete and recreate graph node
|
||||
await graph_service.delete_page(page_id, user)
|
||||
|
||||
# Re-ingest
|
||||
vector_result = await vector_service.update_from_page(page_id, user, force_refresh=True)
|
||||
graph_result = await graph_service.update_from_page(page_id, user, force_refresh=True)
|
||||
|
||||
duration_ms = (time.time() - start_time) * 1000
|
||||
|
||||
return ReindexResponse(
|
||||
success=vector_result.success and graph_result.success,
|
||||
page_id=page_id,
|
||||
vectors_deleted=vectors_deleted,
|
||||
vectors_created=vector_result.chunks_created,
|
||||
graph_updated=graph_result.success,
|
||||
duration_ms=duration_ms,
|
||||
error=vector_result.error_message or graph_result.error_message
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
duration_ms = (time.time() - start_time) * 1000
|
||||
logger.error(f"Reindex failed for page {page_id}: {e}", exc_info=True)
|
||||
return ReindexResponse(
|
||||
success=False,
|
||||
page_id=page_id,
|
||||
vectors_deleted=0,
|
||||
vectors_created=0,
|
||||
graph_updated=False,
|
||||
duration_ms=duration_ms,
|
||||
error=str(e)
|
||||
)
|
||||
|
||||
|
||||
@router.post("/reconcile-index", response_model=ReconcileIndexResponse)
|
||||
async def reconcile_index(
|
||||
user: str = Query(..., description="User identifier"),
|
||||
dry_run: bool = Query(False, description="If true, only detect issues without fixing"),
|
||||
vector_service: VectorServiceDep = None,
|
||||
graph_service: GraphServiceDep = None,
|
||||
wiki_client: WikiJSDep = None,
|
||||
redis: RedisDep = None,
|
||||
api_key: str = Depends(verify_api_key)
|
||||
):
|
||||
"""
|
||||
Full index reconciliation: cleanup orphans + reindex missing pages.
|
||||
|
||||
This is the recommended daily maintenance endpoint. It:
|
||||
1. Cleans up orphan vectors and graph nodes (data without sources)
|
||||
2. Reindexes wiki pages that are missing from vectors or graph
|
||||
|
||||
**Scheduler Task** - Recommended to run daily at low-traffic time.
|
||||
|
||||
**Scheduler Integration:**
|
||||
```json
|
||||
{
|
||||
"task_name": "library_reconcile_index",
|
||||
"schedule": "0 4 * * *",
|
||||
"endpoint": "POST /maintenance/reconcile-index?user=jpmschweitzer",
|
||||
"description": "Daily index reconciliation - cleanup + reindex missing"
|
||||
}
|
||||
```
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# Phase 1: Run full cleanup
|
||||
cleanup_result = await cleanup_all(
|
||||
user=user,
|
||||
dry_run=dry_run,
|
||||
vector_service=vector_service,
|
||||
graph_service=graph_service,
|
||||
wiki_client=wiki_client,
|
||||
redis=redis,
|
||||
api_key=api_key
|
||||
)
|
||||
|
||||
# Phase 2: Find and reindex missing pages
|
||||
reindex_start = time.time()
|
||||
|
||||
# Get current state
|
||||
wiki_pages = await wiki_client.list_all_pages()
|
||||
chunk_refs = await vector_service.get_all_chunk_references(user)
|
||||
graph_docs = await graph_service.get_all_document_references(user)
|
||||
|
||||
# Find pages missing from indexes
|
||||
pages_without_vectors, pages_without_graph = await _find_unindexed_pages(
|
||||
wiki_pages, chunk_refs, graph_docs
|
||||
)
|
||||
|
||||
# Combine unique page IDs that need reindexing
|
||||
missing_page_ids = list(set(pages_without_vectors + pages_without_graph))
|
||||
|
||||
# Reindex missing pages (unless dry run)
|
||||
reindexed = 0
|
||||
failed = 0
|
||||
failed_ids = []
|
||||
|
||||
if not dry_run and missing_page_ids:
|
||||
reindexed, failed, failed_ids = await _reindex_missing_pages(
|
||||
missing_page_ids, user, vector_service, graph_service
|
||||
)
|
||||
|
||||
reindex_duration = (time.time() - reindex_start) * 1000
|
||||
total_duration = (time.time() - start_time) * 1000
|
||||
|
||||
# Record reconciliation timestamp
|
||||
if not dry_run and redis:
|
||||
await _set_last_cleanup(redis, user)
|
||||
|
||||
return ReconcileIndexResponse(
|
||||
success=cleanup_result.success and failed == 0,
|
||||
cleanup=cleanup_result,
|
||||
reindex_missing=ReindexMissingResult(
|
||||
pages_without_vectors=len(pages_without_vectors),
|
||||
pages_without_graph=len(pages_without_graph),
|
||||
pages_reindexed=reindexed,
|
||||
pages_failed=failed,
|
||||
failed_page_ids=failed_ids,
|
||||
duration_ms=reindex_duration
|
||||
),
|
||||
total_duration_ms=total_duration
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Reconcile-index failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
@@ -0,0 +1,87 @@
|
||||
"""
|
||||
RAG search router for Library Desk API.
|
||||
|
||||
Endpoints for web, news, and image search with content extraction.
|
||||
"""
|
||||
|
||||
import httpx
|
||||
from fastapi import APIRouter, HTTPException, Depends
|
||||
import logging
|
||||
|
||||
from src.models.rag_search import RAGSearchRequest, RAGSearchResponse
|
||||
from src.services.rag_search_service import RAGSearchService
|
||||
from src.core.dependencies import verify_api_key
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/rag", tags=["RAG Search"])
|
||||
|
||||
|
||||
# Lazy import to avoid circular dependency
|
||||
def get_rag_search_service() -> RAGSearchService:
|
||||
"""Get RAG search service instance."""
|
||||
from src.core.dependencies import get_rag_search_service as _get_service
|
||||
return _get_service()
|
||||
|
||||
|
||||
@router.post("/search", response_model=RAGSearchResponse)
|
||||
async def search(
|
||||
request: RAGSearchRequest,
|
||||
api_key: str = Depends(verify_api_key)
|
||||
):
|
||||
"""
|
||||
Execute RAG-optimized web search with content extraction.
|
||||
|
||||
Searches via SearXNG and extracts full content from results using
|
||||
Trafilatura. Results are cached in Redis for efficiency.
|
||||
|
||||
**Search Types:**
|
||||
- `web`: General web search (default)
|
||||
- `news`: News articles with recency filtering
|
||||
- `images`: Image search results
|
||||
|
||||
**Example Request:**
|
||||
```json
|
||||
{
|
||||
"query": "Python async programming best practices",
|
||||
"search_type": "web",
|
||||
"limit": 10,
|
||||
"user": "default"
|
||||
}
|
||||
```
|
||||
|
||||
**Response includes:**
|
||||
- Full extracted text content per result
|
||||
- Original search snippets
|
||||
- Source domain names
|
||||
- Markdown sources summary for LLM consumption
|
||||
|
||||
**Error Codes:**
|
||||
- 400: Invalid query (empty or too long)
|
||||
- 502: Search provider (SearXNG) error
|
||||
- 504: Search timeout
|
||||
"""
|
||||
try:
|
||||
service = get_rag_search_service()
|
||||
response = await service.search(
|
||||
query=request.query,
|
||||
search_type=request.search_type,
|
||||
limit=request.limit,
|
||||
user=request.user
|
||||
)
|
||||
return response
|
||||
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
|
||||
except httpx.TimeoutException:
|
||||
logger.error(f"Search timed out for query: {request.query}")
|
||||
raise HTTPException(status_code=504, detail="Search timed out")
|
||||
|
||||
except httpx.HTTPError as e:
|
||||
logger.error(f"Search provider error: {e}")
|
||||
raise HTTPException(status_code=502, detail="Search provider error")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"RAG search failed: {e}", exc_info=True)
|
||||
raise HTTPException(status_code=500, detail="Search failed")
|
||||
+5
-4
@@ -5,8 +5,7 @@ Endpoints for wiki page and dossier management.
|
||||
All operations are scoped to user namespaces for multi-tenancy.
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, HTTPException, Depends, Query, Security, BackgroundTasks
|
||||
from fastapi.security import HTTPAuthorizationCredentials
|
||||
from fastapi import APIRouter, HTTPException, Depends, Query, BackgroundTasks
|
||||
from typing import Optional
|
||||
import logging
|
||||
|
||||
@@ -24,7 +23,7 @@ from src.clients.neo4j_client import Neo4jClient
|
||||
from src.clients.qdrant_client import QdrantClientWrapper
|
||||
from src.clients.ollama_client import OllamaClient
|
||||
from src.core.dependencies import (
|
||||
WikiJSDep, Neo4jDep, QdrantDep, OllamaDep, SearXNGDep,
|
||||
WikiJSDep, Neo4jDep, QdrantDep, OllamaDep, SearXNGDep, ContentExtractorDep,
|
||||
verify_api_key, get_settings, get_hybrid_rag_service, get_ingestion_service
|
||||
)
|
||||
from src.core.multi_tenancy import DEFAULT_USER
|
||||
@@ -180,6 +179,7 @@ async def smart_create_page(
|
||||
qdrant_client: QdrantDep,
|
||||
ollama_client: OllamaDep,
|
||||
searxng_client: SearXNGDep,
|
||||
content_extractor: ContentExtractorDep,
|
||||
settings: Settings = Depends(get_settings),
|
||||
api_key: str = Depends(verify_api_key)
|
||||
):
|
||||
@@ -225,9 +225,10 @@ async def smart_create_page(
|
||||
graph_service=graph_service,
|
||||
searxng_client=searxng_client,
|
||||
ollama_client=ollama_client,
|
||||
content_extractor=content_extractor,
|
||||
settings=settings
|
||||
)
|
||||
wiki_page_writer = WikiPageWriter(ollama_client=ollama_client)
|
||||
wiki_page_writer = WikiPageWriter(ollama_client=ollama_client, settings=settings)
|
||||
|
||||
# Step 1-5: Research + Generate + Create page
|
||||
page, research_data = await wiki_service.smart_create_page(
|
||||
|
||||
@@ -47,7 +47,7 @@ class ConsolidationService:
|
||||
self.ollama = ollama
|
||||
self.wiki = wiki
|
||||
self.settings = settings
|
||||
self.wiki_page_writer = WikiPageWriter(ollama_client=ollama)
|
||||
self.wiki_page_writer = WikiPageWriter(ollama_client=ollama, settings=settings)
|
||||
self.ingestion_service = ingestion_service # Optional to avoid circular dependency
|
||||
|
||||
async def consolidate_knowledge(
|
||||
@@ -410,13 +410,19 @@ This is a PERSONAL knowledge base using Schema.org-aligned taxonomy that capture
|
||||
- Projects: Work projects, personal projects (Schema.org: Project)
|
||||
- Reference: General knowledge, how-tos (Custom extension)
|
||||
|
||||
Identify information worth documenting:
|
||||
1. New topics/people/things that deserve their own wiki page
|
||||
2. Facts that could enhance existing pages
|
||||
3. Entities (people, places, things, concepts) for the knowledge graph
|
||||
ANALYSIS STEPS:
|
||||
1. Read each web result carefully for substantive, factual content
|
||||
2. Identify genuinely novel information not likely already known
|
||||
3. Match topics to appropriate taxonomy categories
|
||||
4. Generate valid paths following the exact format below
|
||||
|
||||
Be INCLUSIVE - if someone searched for it, it's likely worth documenting.
|
||||
Personal information is just as valuable as technical information.
|
||||
RULES:
|
||||
- Do NOT suggest pages for topics with insufficient information in results
|
||||
- Do NOT invent entities not explicitly mentioned in results
|
||||
- Do NOT suggest paths that don't match the taxonomy exactly
|
||||
- Do NOT suggest generic or vague page topics
|
||||
- Be CONSERVATIVE - fewer high-quality suggestions is better than many low-quality ones
|
||||
- ONLY suggest documentation for substantive, specific information
|
||||
|
||||
**CRITICAL: Use ONLY these Schema.org-aligned path prefixes (case-sensitive):**
|
||||
|
||||
@@ -462,11 +468,12 @@ Return ONLY valid JSON:
|
||||
JSON:"""
|
||||
|
||||
try:
|
||||
# Call Ollama for analysis
|
||||
# Call Ollama for analysis (temperature=0.0 for consistent classification)
|
||||
response = await self.ollama.generate_text(
|
||||
prompt=prompt,
|
||||
model=self.settings.reranker_model, # Use mistral-nemo
|
||||
stream=False
|
||||
model=self.settings.ollama_model,
|
||||
stream=False,
|
||||
temperature=0.0
|
||||
)
|
||||
|
||||
if not response:
|
||||
|
||||
@@ -1077,8 +1077,18 @@ Feel free to expand it with more details!
|
||||
search_query,
|
||||
{"terms": all_terms, "limit": limit}
|
||||
)
|
||||
logger.info(f"Graph search found {len(results)} documents")
|
||||
return results
|
||||
|
||||
# Deduplicate by page_id (safety net for any edge cases)
|
||||
seen_page_ids = set()
|
||||
unique_results = []
|
||||
for r in results:
|
||||
page_id = r.get("page_id")
|
||||
if page_id and page_id not in seen_page_ids:
|
||||
seen_page_ids.add(page_id)
|
||||
unique_results.append(r)
|
||||
|
||||
logger.info(f"Graph search found {len(unique_results)} unique documents (raw: {len(results)})")
|
||||
return unique_results
|
||||
except Exception as e:
|
||||
logger.error(f"Graph document search failed: {e}", exc_info=True)
|
||||
return []
|
||||
@@ -1251,3 +1261,353 @@ Feel free to expand it with more details!
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to create entity mentions: {e}", exc_info=True)
|
||||
return 0
|
||||
|
||||
# ========== Cleanup Methods ==========
|
||||
|
||||
async def delete_document_node(
|
||||
self,
|
||||
document_id: str,
|
||||
user: str
|
||||
) -> int:
|
||||
"""
|
||||
Delete a Document Store document node and all its relationships.
|
||||
|
||||
Args:
|
||||
document_id: Document UUID (Document Store)
|
||||
user: User identifier
|
||||
|
||||
Returns:
|
||||
Number of nodes deleted (1 if successful, 0 if not found)
|
||||
"""
|
||||
user_doc_label = get_neo4j_user_label(user)
|
||||
|
||||
delete_query = f"""
|
||||
MATCH (d:{user_doc_label}:Document {{document_id: $document_id}})
|
||||
DETACH DELETE d
|
||||
RETURN count(d) as deleted_count
|
||||
"""
|
||||
|
||||
try:
|
||||
result = await self.neo4j.execute_query(
|
||||
delete_query,
|
||||
{"document_id": document_id}
|
||||
)
|
||||
|
||||
deleted_count = result[0]["deleted_count"] if result else 0
|
||||
|
||||
if deleted_count > 0:
|
||||
logger.info(f"Deleted Document node for document {document_id}")
|
||||
else:
|
||||
logger.warning(f"No Document node found for document {document_id}")
|
||||
|
||||
return deleted_count
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to delete document {document_id} from graph: {e}", exc_info=True)
|
||||
return 0
|
||||
|
||||
async def delete_collection_node(
|
||||
self,
|
||||
collection_id: str,
|
||||
user: str
|
||||
) -> int:
|
||||
"""
|
||||
Delete a DocumentCollection node and all contained documents.
|
||||
|
||||
Args:
|
||||
collection_id: Collection UUID
|
||||
user: User identifier
|
||||
|
||||
Returns:
|
||||
Number of nodes deleted (collection + documents)
|
||||
"""
|
||||
user_doc_label = get_neo4j_user_label(user)
|
||||
|
||||
# Delete collection and all documents it contains
|
||||
delete_query = f"""
|
||||
MATCH (c:{user_doc_label}:DocumentCollection {{id: $collection_id}})
|
||||
OPTIONAL MATCH (c)-[:CONTAINS]->(d:Document)
|
||||
DETACH DELETE c, d
|
||||
RETURN count(c) + count(d) as deleted_count
|
||||
"""
|
||||
|
||||
try:
|
||||
result = await self.neo4j.execute_query(
|
||||
delete_query,
|
||||
{"collection_id": collection_id}
|
||||
)
|
||||
|
||||
deleted_count = result[0]["deleted_count"] if result else 0
|
||||
logger.info(f"Deleted collection {collection_id} with {deleted_count} total nodes")
|
||||
return deleted_count
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to delete collection {collection_id}: {e}", exc_info=True)
|
||||
return 0
|
||||
|
||||
async def find_orphan_entities(
|
||||
self,
|
||||
user: str
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Find entities with no MENTIONS relationships (orphaned).
|
||||
|
||||
Args:
|
||||
user: User identifier
|
||||
|
||||
Returns:
|
||||
List of orphaned entities {id, name, type}
|
||||
"""
|
||||
from src.core.multi_tenancy import get_neo4j_user_base_label
|
||||
|
||||
user_base_label = get_neo4j_user_base_label(user)
|
||||
|
||||
query = f"""
|
||||
MATCH (e:{user_base_label})
|
||||
WHERE NOT e:Document
|
||||
AND NOT e:DocumentCollection
|
||||
AND NOT EXISTS {{ (d:Document)-[:MENTIONS]->(e) }}
|
||||
RETURN elementId(e) as id, e.name as name, labels(e) as labels
|
||||
"""
|
||||
|
||||
try:
|
||||
results = await self.neo4j.execute_query(query, {})
|
||||
|
||||
orphans = []
|
||||
for r in results:
|
||||
labels = r.get("labels", [])
|
||||
entity_type = next(
|
||||
(l for l in labels if l != user_base_label),
|
||||
"Unknown"
|
||||
)
|
||||
orphans.append({
|
||||
"id": r["id"],
|
||||
"name": r["name"],
|
||||
"type": entity_type
|
||||
})
|
||||
|
||||
logger.info(f"Found {len(orphans)} orphan entities for user {user}")
|
||||
return orphans
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to find orphan entities: {e}", exc_info=True)
|
||||
return []
|
||||
|
||||
async def purge_orphan_entities(
|
||||
self,
|
||||
user: str
|
||||
) -> int:
|
||||
"""
|
||||
Delete all orphaned entities (entities with no MENTIONS relationships).
|
||||
|
||||
Args:
|
||||
user: User identifier
|
||||
|
||||
Returns:
|
||||
Number of entities purged
|
||||
"""
|
||||
from src.core.multi_tenancy import get_neo4j_user_base_label
|
||||
|
||||
user_base_label = get_neo4j_user_base_label(user)
|
||||
|
||||
query = f"""
|
||||
MATCH (e:{user_base_label})
|
||||
WHERE NOT e:Document
|
||||
AND NOT e:DocumentCollection
|
||||
AND NOT EXISTS {{ (d:Document)-[:MENTIONS]->(e) }}
|
||||
DETACH DELETE e
|
||||
RETURN count(e) as purged_count
|
||||
"""
|
||||
|
||||
try:
|
||||
results = await self.neo4j.execute_query(query, {})
|
||||
purged_count = results[0]["purged_count"] if results else 0
|
||||
|
||||
logger.info(f"Purged {purged_count} orphan entities for user {user}")
|
||||
return purged_count
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to purge orphan entities: {e}", exc_info=True)
|
||||
return 0
|
||||
|
||||
async def get_all_document_references(
|
||||
self,
|
||||
user: str
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Get all Document node references for orphan detection.
|
||||
|
||||
Returns page_id for wiki docs and document_id for Document Store docs.
|
||||
|
||||
Args:
|
||||
user: User identifier
|
||||
|
||||
Returns:
|
||||
List of document references {page_id, document_id, doc_type, title}
|
||||
"""
|
||||
user_doc_label = get_neo4j_user_label(user)
|
||||
|
||||
query = f"""
|
||||
MATCH (d:{user_doc_label}:Document)
|
||||
RETURN d.page_id as page_id,
|
||||
d.document_id as document_id,
|
||||
COALESCE(d.doc_type, 'wiki') as doc_type,
|
||||
d.title as title
|
||||
"""
|
||||
|
||||
try:
|
||||
results = await self.neo4j.execute_query(query, {})
|
||||
|
||||
references = []
|
||||
for r in results:
|
||||
references.append({
|
||||
"page_id": r.get("page_id"),
|
||||
"document_id": r.get("document_id"),
|
||||
"doc_type": r.get("doc_type", "wiki"),
|
||||
"title": r.get("title")
|
||||
})
|
||||
|
||||
logger.info(f"Found {len(references)} document references for user {user}")
|
||||
return references
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get document references: {e}", exc_info=True)
|
||||
return []
|
||||
|
||||
async def purge_stale_documents_by_ids(
|
||||
self,
|
||||
user: str,
|
||||
page_ids: List[int] = None,
|
||||
document_ids: List[str] = None
|
||||
) -> int:
|
||||
"""
|
||||
Delete specific stale Document nodes by their IDs.
|
||||
|
||||
Args:
|
||||
user: User identifier
|
||||
page_ids: List of wiki page IDs to delete
|
||||
document_ids: List of Document Store document IDs to delete
|
||||
|
||||
Returns:
|
||||
Number of documents purged
|
||||
"""
|
||||
user_doc_label = get_neo4j_user_label(user)
|
||||
total_purged = 0
|
||||
|
||||
try:
|
||||
# Purge by page_id (wiki docs)
|
||||
if page_ids:
|
||||
query = f"""
|
||||
MATCH (d:{user_doc_label}:Document)
|
||||
WHERE d.page_id IN $page_ids
|
||||
DETACH DELETE d
|
||||
RETURN count(d) as purged_count
|
||||
"""
|
||||
results = await self.neo4j.execute_query(query, {"page_ids": page_ids})
|
||||
count = results[0]["purged_count"] if results else 0
|
||||
total_purged += count
|
||||
logger.info(f"Purged {count} wiki Document nodes")
|
||||
|
||||
# Purge by document_id (Document Store docs)
|
||||
if document_ids:
|
||||
query = f"""
|
||||
MATCH (d:{user_doc_label}:Document)
|
||||
WHERE d.document_id IN $document_ids
|
||||
DETACH DELETE d
|
||||
RETURN count(d) as purged_count
|
||||
"""
|
||||
results = await self.neo4j.execute_query(query, {"document_ids": document_ids})
|
||||
count = results[0]["purged_count"] if results else 0
|
||||
total_purged += count
|
||||
logger.info(f"Purged {count} Document Store Document nodes")
|
||||
|
||||
return total_purged
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to purge stale documents: {e}", exc_info=True)
|
||||
return 0
|
||||
|
||||
async def cleanup_broken_relationships(
|
||||
self,
|
||||
user: str
|
||||
) -> int:
|
||||
"""
|
||||
Clean up broken FOUND relationships from SearchQuery nodes.
|
||||
|
||||
Removes relationships pointing to deleted documents.
|
||||
|
||||
Args:
|
||||
user: User identifier
|
||||
|
||||
Returns:
|
||||
Number of relationships cleaned
|
||||
"""
|
||||
query = """
|
||||
MATCH (sq:SearchQuery)-[r:FOUND]->(d)
|
||||
WHERE NOT EXISTS { (d) }
|
||||
DELETE r
|
||||
RETURN count(r) as cleaned_count
|
||||
"""
|
||||
|
||||
try:
|
||||
results = await self.neo4j.execute_query(query, {})
|
||||
cleaned_count = results[0]["cleaned_count"] if results else 0
|
||||
|
||||
if cleaned_count > 0:
|
||||
logger.info(f"Cleaned {cleaned_count} broken FOUND relationships")
|
||||
|
||||
return cleaned_count
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to cleanup broken relationships: {e}", exc_info=True)
|
||||
return 0
|
||||
|
||||
async def find_documents_without_vectors(
|
||||
self,
|
||||
user: str,
|
||||
vector_references: List[Dict[str, Any]]
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Find Document nodes that have no corresponding vectors.
|
||||
|
||||
Used for bidirectional orphan detection - graph nodes without vector data.
|
||||
|
||||
Args:
|
||||
user: User identifier
|
||||
vector_references: List of vector refs from VectorService.get_all_chunk_references()
|
||||
|
||||
Returns:
|
||||
List of orphan documents {page_id, document_id, doc_type, title}
|
||||
"""
|
||||
# Get all graph document references
|
||||
graph_docs = await self.get_all_document_references(user)
|
||||
|
||||
if not graph_docs:
|
||||
return []
|
||||
|
||||
# Build sets of IDs that have vectors
|
||||
vector_page_ids = {
|
||||
ref.get("page_id") for ref in vector_references
|
||||
if ref.get("doc_type") == "wiki" and ref.get("page_id")
|
||||
}
|
||||
vector_doc_ids = {
|
||||
ref.get("document_id") for ref in vector_references
|
||||
if ref.get("doc_type") != "wiki" and ref.get("document_id")
|
||||
}
|
||||
|
||||
# Find graph docs with no vectors
|
||||
orphans = []
|
||||
for doc in graph_docs:
|
||||
doc_type = doc.get("doc_type", "wiki")
|
||||
|
||||
if doc_type == "wiki":
|
||||
page_id = doc.get("page_id")
|
||||
if page_id and page_id not in vector_page_ids:
|
||||
orphans.append(doc)
|
||||
else:
|
||||
document_id = doc.get("document_id")
|
||||
if document_id and document_id not in vector_doc_ids:
|
||||
orphans.append(doc)
|
||||
|
||||
logger.info(f"Found {len(orphans)} graph documents without vectors for user {user}")
|
||||
return orphans
|
||||
|
||||
@@ -6,7 +6,7 @@ HybridRAG service combining vector, graph, and web search.
|
||||
1. Parallel Retrieval - Vector + Graph + Web search
|
||||
2. RRF Fusion - Merge results with Reciprocal Rank Fusion
|
||||
3. Enrichment - Add related dossiers via graph
|
||||
4. LLM Re-ranking - Re-rank with mistral-nemo
|
||||
4. LLM Re-ranking - Re-rank with configured Ollama model
|
||||
5. Context Formatting - Format for LLM consumption
|
||||
6. Persistence - Store for Librarian processing
|
||||
"""
|
||||
@@ -22,6 +22,7 @@ from src.services.vector_service import VectorService
|
||||
from src.services.graph_service import GraphService
|
||||
from src.clients.searxng_client import SearXNGClient
|
||||
from src.clients.ollama_client import OllamaClient
|
||||
from src.clients.content_extractor import ContentExtractor
|
||||
from src.config import Settings
|
||||
from src.models.hybrid_rag import (
|
||||
HybridRAGConfig, HybridRAGRequest, HybridRAGResponse,
|
||||
@@ -44,6 +45,7 @@ class HybridRAGService:
|
||||
graph_service: GraphService,
|
||||
searxng_client: SearXNGClient,
|
||||
ollama_client: OllamaClient,
|
||||
content_extractor: ContentExtractor,
|
||||
settings: Settings
|
||||
):
|
||||
"""
|
||||
@@ -54,14 +56,16 @@ class HybridRAGService:
|
||||
graph_service: Service for Neo4j graph search
|
||||
searxng_client: Client for web search
|
||||
ollama_client: Client for LLM (keyword extraction, re-ranking)
|
||||
content_extractor: Client for extracting full content from URLs
|
||||
settings: Application settings
|
||||
"""
|
||||
self.vector = vector_service
|
||||
self.graph = graph_service
|
||||
self.searxng = searxng_client
|
||||
self.ollama = ollama_client
|
||||
self.content_extractor = content_extractor
|
||||
self.settings = settings
|
||||
self.reranker_model = settings.reranker_model
|
||||
self.reranker_model = settings.ollama_model
|
||||
|
||||
async def search(
|
||||
self,
|
||||
@@ -101,14 +105,20 @@ class HybridRAGService:
|
||||
timing["graph_ms"] = raw_results.get("timing", {}).get("graph_ms", 0)
|
||||
timing["web_ms"] = raw_results.get("timing", {}).get("web_ms", 0)
|
||||
|
||||
# Phase 2: RRF Fusion
|
||||
# Phase 2: Two-Stage RRF Fusion
|
||||
phase2_start = time.time()
|
||||
|
||||
# Stage 1: Merge wiki sources (vector + graph) into single ranking
|
||||
wiki_merged = self._merge_wiki_sources(
|
||||
vector_results=raw_results.get("vector", []),
|
||||
graph_results=raw_results.get("graph", []),
|
||||
k=config.rrf_k
|
||||
)
|
||||
|
||||
# Stage 2: Final RRF between wiki and web (equal footing)
|
||||
fused_results = self._reciprocal_rank_fusion(
|
||||
results_by_source={
|
||||
"vector": raw_results.get("vector", []),
|
||||
"graph": raw_results.get("graph", []),
|
||||
"web": raw_results.get("web", [])
|
||||
},
|
||||
wiki_results=wiki_merged,
|
||||
web_results=raw_results.get("web", []),
|
||||
k=config.rrf_k
|
||||
)
|
||||
timing["fusion_ms"] = (time.time() - phase2_start) * 1000
|
||||
@@ -185,25 +195,21 @@ class HybridRAGService:
|
||||
Returns:
|
||||
Dictionary with keywords, entities, synonyms, expansions
|
||||
"""
|
||||
prompt = f"""Extract search terms from this query. For each important word, provide synonyms and expansions.
|
||||
prompt = f"""Extract search terms from this query.
|
||||
|
||||
Query: "{query}"
|
||||
|
||||
Return ONLY valid JSON:
|
||||
{{
|
||||
"core_keywords": ["key", "words", "from", "query"],
|
||||
"synonyms": {{
|
||||
"word": ["alternative", "terms"]
|
||||
}}
|
||||
}}
|
||||
RULES:
|
||||
- Extract ONLY keywords explicitly present or directly implied in the query
|
||||
- Do NOT invent terms, concepts, or synonyms not clearly related
|
||||
- Do NOT add general knowledge or associations
|
||||
- Provide synonyms ONLY for technical terms with well-known alternatives
|
||||
- Return valid JSON only, no commentary
|
||||
|
||||
Example for "Docker container hosting":
|
||||
Return format:
|
||||
{{
|
||||
"core_keywords": ["docker", "container", "hosting"],
|
||||
"synonyms": {{
|
||||
"docker": ["containerization", "container runtime"],
|
||||
"hosting": ["server", "infrastructure"]
|
||||
}}
|
||||
"core_keywords": ["words", "from", "query"],
|
||||
"synonyms": {{"term": ["direct", "alternatives"]}}
|
||||
}}
|
||||
|
||||
JSON:"""
|
||||
@@ -211,7 +217,8 @@ JSON:"""
|
||||
try:
|
||||
response = await self.ollama.generate_text(
|
||||
prompt=prompt,
|
||||
model=self.reranker_model
|
||||
model=self.reranker_model,
|
||||
temperature=0.0 # Deterministic for consistent extraction
|
||||
)
|
||||
|
||||
# Parse JSON response (handle potential extra text)
|
||||
@@ -284,7 +291,8 @@ JSON:"""
|
||||
response = await self.vector.search(
|
||||
query=query,
|
||||
user=user,
|
||||
limit=config.vector_limit
|
||||
limit=config.vector_limit,
|
||||
score_threshold=self.settings.vector_similarity_threshold
|
||||
)
|
||||
results = [
|
||||
{
|
||||
@@ -309,11 +317,19 @@ JSON:"""
|
||||
async def graph_search():
|
||||
start = time.time()
|
||||
try:
|
||||
# Skip synonyms for graph search - only use core keywords
|
||||
# Synonyms like "author" can match unrelated entities like "author2000"
|
||||
graph_keywords = {
|
||||
"core_keywords": keywords_data.get("core_keywords", []),
|
||||
"entities": keywords_data.get("entities", []),
|
||||
"synonyms": {}, # No synonyms for exact entity matching
|
||||
"expansions": {}
|
||||
}
|
||||
results = await self.graph.search_documents(
|
||||
query=query,
|
||||
user=user,
|
||||
limit=config.graph_limit,
|
||||
keywords_data=keywords_data
|
||||
keywords_data=graph_keywords
|
||||
)
|
||||
formatted = [
|
||||
{
|
||||
@@ -334,7 +350,7 @@ JSON:"""
|
||||
|
||||
tasks["graph"] = graph_search()
|
||||
|
||||
# Web search
|
||||
# Web search with content extraction
|
||||
if config.enable_web:
|
||||
async def web_search():
|
||||
start = time.time()
|
||||
@@ -343,11 +359,24 @@ JSON:"""
|
||||
query=query,
|
||||
limit=config.web_limit
|
||||
)
|
||||
|
||||
# Extract full content from URLs using Trafilatura
|
||||
urls = [r.get("url") for r in results if r.get("url")]
|
||||
extraction_results = await self.content_extractor.extract_batch(urls)
|
||||
|
||||
# Map extracted content back to results by URL
|
||||
url_to_content = {
|
||||
ext.url: ext.content
|
||||
for ext in extraction_results
|
||||
if ext.success and ext.content
|
||||
}
|
||||
|
||||
formatted = [
|
||||
{
|
||||
"url": r.get("url"),
|
||||
"title": r.get("title", ""),
|
||||
"content": r.get("content", ""),
|
||||
"content": url_to_content.get(r.get("url"), r.get("content", "")),
|
||||
"snippet": r.get("content", ""), # Keep original snippet
|
||||
"engine": r.get("engine", ""),
|
||||
"source": "web"
|
||||
}
|
||||
@@ -377,52 +406,134 @@ JSON:"""
|
||||
|
||||
return output
|
||||
|
||||
def _reciprocal_rank_fusion(
|
||||
def _merge_wiki_sources(
|
||||
self,
|
||||
results_by_source: Dict[str, List],
|
||||
vector_results: List[Dict],
|
||||
graph_results: List[Dict],
|
||||
k: int = 60
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Phase 2: Merge results using Reciprocal Rank Fusion.
|
||||
Stage 1: Merge vector and graph into single wiki ranking using RRF.
|
||||
|
||||
RRF formula: score = sum(1 / (k + rank)) for each source
|
||||
Both sources search the same wiki pool, so we combine them before
|
||||
final RRF with web to avoid double-counting wiki pages.
|
||||
|
||||
Args:
|
||||
results_by_source: Results from each source
|
||||
vector_results: Results from vector search
|
||||
graph_results: Results from graph search
|
||||
k: RRF constant (default 60)
|
||||
|
||||
Returns:
|
||||
Merged and sorted results
|
||||
Merged wiki results sorted by wiki RRF score
|
||||
"""
|
||||
wiki_scores = {}
|
||||
|
||||
# Process vector results
|
||||
for rank, result in enumerate(vector_results, start=1):
|
||||
page_id = result.get("page_id")
|
||||
if not page_id:
|
||||
continue
|
||||
result_id = f"page_{page_id}"
|
||||
|
||||
if result_id not in wiki_scores:
|
||||
wiki_scores[result_id] = {
|
||||
"result": dict(result), # Copy to avoid mutation
|
||||
"wiki_rrf_score": 0.0,
|
||||
"found_by": []
|
||||
}
|
||||
|
||||
wiki_scores[result_id]["wiki_rrf_score"] += 1 / (k + rank)
|
||||
wiki_scores[result_id]["found_by"].append("vector")
|
||||
|
||||
# Process graph results
|
||||
for rank, result in enumerate(graph_results, start=1):
|
||||
page_id = result.get("page_id")
|
||||
if not page_id:
|
||||
continue
|
||||
result_id = f"page_{page_id}"
|
||||
|
||||
if result_id not in wiki_scores:
|
||||
wiki_scores[result_id] = {
|
||||
"result": dict(result),
|
||||
"wiki_rrf_score": 0.0,
|
||||
"found_by": []
|
||||
}
|
||||
|
||||
wiki_scores[result_id]["wiki_rrf_score"] += 1 / (k + rank)
|
||||
wiki_scores[result_id]["found_by"].append("graph")
|
||||
|
||||
# Add graph metadata to existing result
|
||||
wiki_scores[result_id]["result"]["entity_matches"] = result.get("entity_matches")
|
||||
wiki_scores[result_id]["result"]["matched_entities"] = result.get("matched_entities")
|
||||
|
||||
# Sort by wiki RRF score
|
||||
sorted_wiki = sorted(
|
||||
wiki_scores.values(),
|
||||
key=lambda x: x["wiki_rrf_score"],
|
||||
reverse=True
|
||||
)
|
||||
|
||||
# Return merged results with wiki ranking
|
||||
merged = []
|
||||
for wiki_rank, item in enumerate(sorted_wiki, start=1):
|
||||
merged.append({
|
||||
**item["result"],
|
||||
"wiki_rank": wiki_rank,
|
||||
"wiki_rrf_score": item["wiki_rrf_score"],
|
||||
"found_by": item["found_by"],
|
||||
"source": "wiki"
|
||||
})
|
||||
|
||||
logger.info(f"Wiki merge: {len(merged)} unique pages from vector+graph")
|
||||
return merged
|
||||
|
||||
def _reciprocal_rank_fusion(
|
||||
self,
|
||||
wiki_results: List[Dict],
|
||||
web_results: List[Dict],
|
||||
k: int = 60
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Stage 2: Final RRF between wiki (single source) and web.
|
||||
|
||||
Wiki results are pre-merged from vector+graph, so wiki and web
|
||||
now compete on equal footing.
|
||||
|
||||
Args:
|
||||
wiki_results: Pre-merged wiki results from _merge_wiki_sources()
|
||||
web_results: Results from web search
|
||||
k: RRF constant (default 60)
|
||||
|
||||
Returns:
|
||||
Final merged and sorted results
|
||||
"""
|
||||
rrf_scores = {}
|
||||
|
||||
for source, results in results_by_source.items():
|
||||
for rank, result in enumerate(results, start=1):
|
||||
# Use page_id for wiki results, url hash for web results
|
||||
if result.get("page_id"):
|
||||
result_id = f"page_{result['page_id']}"
|
||||
elif result.get("url"):
|
||||
result_id = f"url_{hash(result['url'])}"
|
||||
else:
|
||||
continue # Skip results without ID
|
||||
# Wiki results (single source, already merged)
|
||||
for rank, result in enumerate(wiki_results, start=1):
|
||||
page_id = result.get("page_id")
|
||||
if not page_id:
|
||||
continue
|
||||
result_id = f"page_{page_id}"
|
||||
rrf_scores[result_id] = {
|
||||
"result": result,
|
||||
"rrf_score": 1 / (k + rank),
|
||||
"sources": result.get("found_by", ["wiki"]),
|
||||
"source_type": "wiki"
|
||||
}
|
||||
|
||||
if result_id not in rrf_scores:
|
||||
rrf_scores[result_id] = {
|
||||
"result": result,
|
||||
"rrf_score": 0.0,
|
||||
"sources": [],
|
||||
"source_type": source
|
||||
}
|
||||
|
||||
# RRF formula: sum of 1/(k + rank) across sources
|
||||
rrf_scores[result_id]["rrf_score"] += 1 / (k + rank)
|
||||
rrf_scores[result_id]["sources"].append(source)
|
||||
|
||||
# If result appears in multiple sources, update source_type
|
||||
if len(rrf_scores[result_id]["sources"]) > 1:
|
||||
rrf_scores[result_id]["source_type"] = "+".join(
|
||||
sorted(set(rrf_scores[result_id]["sources"]))
|
||||
)
|
||||
# Web results (single source)
|
||||
for rank, result in enumerate(web_results, start=1):
|
||||
url = result.get("url")
|
||||
if not url:
|
||||
continue
|
||||
result_id = f"url_{hash(url)}"
|
||||
rrf_scores[result_id] = {
|
||||
"result": result,
|
||||
"rrf_score": 1 / (k + rank),
|
||||
"sources": ["web"],
|
||||
"source_type": "web"
|
||||
}
|
||||
|
||||
# Sort by RRF score descending
|
||||
sorted_results = sorted(
|
||||
@@ -431,7 +542,7 @@ JSON:"""
|
||||
reverse=True
|
||||
)
|
||||
|
||||
logger.info(f"RRF fusion: {len(sorted_results)} unique results from {len(results_by_source)} sources")
|
||||
logger.info(f"Final RRF: {len(sorted_results)} results (wiki + web)")
|
||||
|
||||
return sorted_results
|
||||
|
||||
@@ -509,21 +620,27 @@ JSON:"""
|
||||
for i, r in enumerate(results)
|
||||
])
|
||||
|
||||
prompt = f"""Given this search query and documents, rank them by relevance.
|
||||
prompt = f"""Rank these documents by relevance to the query.
|
||||
|
||||
Query: {query}
|
||||
|
||||
Documents:
|
||||
{docs_text}
|
||||
|
||||
Return only the numbers in order of relevance (most relevant first).
|
||||
Example: 3,1,5,2,4
|
||||
RULES:
|
||||
- Rank ONLY by how well content answers the query
|
||||
- Do NOT consider document length, formatting, or style
|
||||
- Do NOT add explanation or commentary
|
||||
- Return ONLY comma-separated numbers, most relevant first
|
||||
|
||||
Example output: 3,1,5,2,4
|
||||
|
||||
Ranking:"""
|
||||
|
||||
response = await self.ollama.generate_text(
|
||||
prompt=prompt,
|
||||
model=self.reranker_model
|
||||
model=self.reranker_model,
|
||||
temperature=0.0 # Deterministic for consistent rankings
|
||||
)
|
||||
|
||||
# Parse response: "3,1,5,2,4" → [2, 0, 4, 1, 3] (0-indexed)
|
||||
|
||||
@@ -0,0 +1,265 @@
|
||||
"""
|
||||
RAG Search service for Library Desk.
|
||||
|
||||
Provides web, news, and image search with content extraction:
|
||||
- Uses SearXNG for search queries
|
||||
- Uses Trafilatura for content extraction
|
||||
- Caches results in Redis
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import logging
|
||||
import time
|
||||
from typing import List, Optional
|
||||
from urllib.parse import urlparse
|
||||
|
||||
import redis.asyncio as aioredis
|
||||
|
||||
from src.clients.searxng_client import SearXNGClient
|
||||
from src.clients.content_extractor import ContentExtractor
|
||||
from src.config import Settings
|
||||
from src.models.rag_search import (
|
||||
SearchType,
|
||||
RAGSearchRequest,
|
||||
RAGSearchResult,
|
||||
RAGSearchResponse,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def extract_domain(url: str) -> str:
|
||||
"""Extract domain name from URL, removing 'www.' prefix."""
|
||||
try:
|
||||
parsed = urlparse(url)
|
||||
domain = parsed.netloc
|
||||
return domain.removeprefix("www.")
|
||||
except Exception:
|
||||
return url
|
||||
|
||||
|
||||
class RAGSearchService:
|
||||
"""
|
||||
Service for RAG-optimized web search with content extraction.
|
||||
|
||||
Combines SearXNG search with Trafilatura content extraction
|
||||
and Redis caching for efficient RAG pipeline integration.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
searxng_client: SearXNGClient,
|
||||
content_extractor: ContentExtractor,
|
||||
redis_client: aioredis.Redis,
|
||||
settings: Settings
|
||||
):
|
||||
"""
|
||||
Initialize RAG search service.
|
||||
|
||||
Args:
|
||||
searxng_client: SearXNG search client
|
||||
content_extractor: Trafilatura content extractor
|
||||
redis_client: Async Redis client for caching
|
||||
settings: Application settings
|
||||
"""
|
||||
self.searxng = searxng_client
|
||||
self.extractor = content_extractor
|
||||
self.redis = redis_client
|
||||
self.settings = settings
|
||||
|
||||
self.cache_ttl = settings.search_cache_ttl
|
||||
self.default_limit = settings.search_default_limit
|
||||
|
||||
logger.info(
|
||||
f"Initialized RAGSearchService: cache_ttl={self.cache_ttl}s, "
|
||||
f"default_limit={self.default_limit}"
|
||||
)
|
||||
|
||||
def _cache_key(self, query: str, search_type: str, limit: int) -> str:
|
||||
"""Generate cache key from search parameters."""
|
||||
key_data = f"{query}:{search_type}:{limit}"
|
||||
key_hash = hashlib.md5(key_data.encode()).hexdigest()
|
||||
return f"rag_search:{key_hash}"
|
||||
|
||||
async def _get_cached_result(self, cache_key: str) -> Optional[RAGSearchResponse]:
|
||||
"""Try to get cached search result."""
|
||||
try:
|
||||
cached = await self.redis.get(cache_key)
|
||||
if cached:
|
||||
data = json.loads(cached)
|
||||
logger.debug(f"Cache hit: {cache_key}")
|
||||
return RAGSearchResponse(**data)
|
||||
except Exception as e:
|
||||
logger.warning(f"Cache read failed: {e}")
|
||||
return None
|
||||
|
||||
async def _set_cached_result(self, cache_key: str, result: RAGSearchResponse):
|
||||
"""Cache search result."""
|
||||
try:
|
||||
await self.redis.setex(
|
||||
cache_key,
|
||||
self.cache_ttl,
|
||||
result.model_dump_json()
|
||||
)
|
||||
logger.debug(f"Cached result: {cache_key} (TTL={self.cache_ttl}s)")
|
||||
except Exception as e:
|
||||
logger.warning(f"Cache write failed: {e}")
|
||||
|
||||
async def _search_searxng(
|
||||
self,
|
||||
query: str,
|
||||
search_type: SearchType,
|
||||
limit: int
|
||||
) -> List[dict]:
|
||||
"""Execute search via SearXNG based on search type."""
|
||||
try:
|
||||
if search_type == SearchType.WEB:
|
||||
results = await self.searxng.search_general(
|
||||
query=query,
|
||||
limit=limit
|
||||
)
|
||||
elif search_type == SearchType.NEWS:
|
||||
results = await self.searxng.search_news(
|
||||
query=query,
|
||||
limit=limit
|
||||
)
|
||||
elif search_type == SearchType.IMAGES:
|
||||
results = await self.searxng.search_images(
|
||||
query=query,
|
||||
limit=limit
|
||||
)
|
||||
else:
|
||||
results = await self.searxng.search_general(
|
||||
query=query,
|
||||
limit=limit
|
||||
)
|
||||
|
||||
return results
|
||||
except Exception as e:
|
||||
logger.error(f"SearXNG search failed: {e}")
|
||||
raise
|
||||
|
||||
async def _extract_content_for_results(
|
||||
self,
|
||||
results: List[dict]
|
||||
) -> List[RAGSearchResult]:
|
||||
"""Extract full content from search result URLs."""
|
||||
# Get URLs for extraction
|
||||
urls = [r.get("url", "") for r in results if r.get("url")]
|
||||
|
||||
# Extract content in parallel
|
||||
extraction_results = await self.extractor.extract_batch(urls)
|
||||
|
||||
# Build result objects
|
||||
search_results = []
|
||||
for i, raw_result in enumerate(results):
|
||||
url = raw_result.get("url", "")
|
||||
|
||||
# Find matching extraction result
|
||||
extracted_content = ""
|
||||
for ext_result in extraction_results:
|
||||
if ext_result.url == url and ext_result.success:
|
||||
extracted_content = ext_result.content
|
||||
break
|
||||
|
||||
# Get original snippet
|
||||
snippet = raw_result.get("content", "")
|
||||
if len(snippet) > 300:
|
||||
snippet = snippet[:300] + "..."
|
||||
|
||||
# Build result
|
||||
search_results.append(RAGSearchResult(
|
||||
title=raw_result.get("title", ""),
|
||||
url=url,
|
||||
content=extracted_content,
|
||||
snippet=snippet,
|
||||
source=extract_domain(url),
|
||||
published_date=raw_result.get("publishedDate")
|
||||
))
|
||||
|
||||
return search_results
|
||||
|
||||
def _generate_sources_summary(self, results: List[RAGSearchResult]) -> str:
|
||||
"""Generate markdown list of source URLs."""
|
||||
if not results:
|
||||
return ""
|
||||
|
||||
lines = ["## Sources"]
|
||||
for i, r in enumerate(results, 1):
|
||||
lines.append(f"{i}. [{r.title}]({r.url})")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
async def search(
|
||||
self,
|
||||
query: str,
|
||||
search_type: SearchType = SearchType.WEB,
|
||||
limit: Optional[int] = None,
|
||||
user: str = "default"
|
||||
) -> RAGSearchResponse:
|
||||
"""
|
||||
Execute RAG-optimized search.
|
||||
|
||||
Args:
|
||||
query: Search query string
|
||||
search_type: Type of search (web, news, images)
|
||||
limit: Maximum results to return (default from settings)
|
||||
user: User identifier for logging/rate limiting
|
||||
|
||||
Returns:
|
||||
RAGSearchResponse with extracted content and sources
|
||||
|
||||
Raises:
|
||||
ValueError: If query is empty
|
||||
Exception: If search fails
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
if not query or not query.strip():
|
||||
raise ValueError("Query cannot be empty")
|
||||
|
||||
effective_limit = limit or self.default_limit
|
||||
|
||||
# Check cache
|
||||
cache_key = self._cache_key(query, search_type.value, effective_limit)
|
||||
cached = await self._get_cached_result(cache_key)
|
||||
if cached:
|
||||
return cached
|
||||
|
||||
logger.info(
|
||||
f"RAG search: '{query}' type={search_type.value} "
|
||||
f"limit={effective_limit} user={user}"
|
||||
)
|
||||
|
||||
# Execute search
|
||||
raw_results = await self._search_searxng(query, search_type, effective_limit)
|
||||
|
||||
# Extract content from results
|
||||
search_results = await self._extract_content_for_results(raw_results)
|
||||
|
||||
# Generate sources summary
|
||||
sources_summary = self._generate_sources_summary(search_results)
|
||||
|
||||
# Calculate timing
|
||||
search_time_ms = int((time.time() - start_time) * 1000)
|
||||
|
||||
# Build response
|
||||
response = RAGSearchResponse(
|
||||
query=query,
|
||||
search_type=search_type,
|
||||
results=search_results,
|
||||
total_results=len(search_results),
|
||||
search_time_ms=search_time_ms,
|
||||
sources_summary=sources_summary
|
||||
)
|
||||
|
||||
# Cache result
|
||||
await self._set_cached_result(cache_key, response)
|
||||
|
||||
logger.info(
|
||||
f"RAG search completed: {len(search_results)} results "
|
||||
f"in {search_time_ms}ms"
|
||||
)
|
||||
|
||||
return response
|
||||
@@ -356,3 +356,189 @@ class VectorService:
|
||||
collections=[],
|
||||
total=0
|
||||
)
|
||||
|
||||
# ========== Cleanup Methods ==========
|
||||
|
||||
async def delete_document_chunks(
|
||||
self,
|
||||
document_id: str,
|
||||
user: str
|
||||
) -> int:
|
||||
"""
|
||||
Delete all chunks for a document (Document Store).
|
||||
|
||||
Args:
|
||||
document_id: Document UUID
|
||||
user: User identifier
|
||||
|
||||
Returns:
|
||||
Number of chunks deleted
|
||||
"""
|
||||
collection_name = get_qdrant_collection_name(user)
|
||||
|
||||
try:
|
||||
deleted_count = await self.qdrant.delete_by_filter(
|
||||
collection_name=collection_name,
|
||||
filter_conditions={"document_id": document_id}
|
||||
)
|
||||
|
||||
logger.info(f"Deleted chunks for document {document_id}")
|
||||
return deleted_count
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to delete chunks for document {document_id}: {e}", exc_info=True)
|
||||
return 0
|
||||
|
||||
async def delete_collection_chunks(
|
||||
self,
|
||||
collection_id: str,
|
||||
user: str
|
||||
) -> int:
|
||||
"""
|
||||
Delete all chunks for a document collection.
|
||||
|
||||
Args:
|
||||
collection_id: Collection UUID
|
||||
user: User identifier
|
||||
|
||||
Returns:
|
||||
Number of chunks deleted
|
||||
"""
|
||||
collection_name = get_qdrant_collection_name(user)
|
||||
|
||||
try:
|
||||
deleted_count = await self.qdrant.delete_by_filter(
|
||||
collection_name=collection_name,
|
||||
filter_conditions={"collection_id": collection_id}
|
||||
)
|
||||
|
||||
logger.info(f"Deleted chunks for collection {collection_id}")
|
||||
return deleted_count
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to delete chunks for collection {collection_id}: {e}", exc_info=True)
|
||||
return 0
|
||||
|
||||
async def get_all_chunk_references(
|
||||
self,
|
||||
user: str
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Get all chunk references for orphan detection.
|
||||
|
||||
Returns list of {id, page_id, document_id} for all chunks.
|
||||
|
||||
Args:
|
||||
user: User identifier
|
||||
|
||||
Returns:
|
||||
List of chunk references
|
||||
"""
|
||||
collection_name = get_qdrant_collection_name(user)
|
||||
|
||||
try:
|
||||
# Check if collection exists
|
||||
exists = await self.qdrant.collection_exists(collection_name)
|
||||
if not exists:
|
||||
return []
|
||||
|
||||
all_points = await self.qdrant.scroll_all_points(
|
||||
collection_name=collection_name,
|
||||
batch_size=100,
|
||||
with_payload=True
|
||||
)
|
||||
|
||||
references = []
|
||||
for point in all_points:
|
||||
payload = point.get("payload", {})
|
||||
references.append({
|
||||
"chunk_id": point["id"],
|
||||
"page_id": payload.get("page_id"),
|
||||
"document_id": payload.get("document_id"),
|
||||
"collection_id": payload.get("collection_id"),
|
||||
"doc_type": payload.get("doc_type", "wiki")
|
||||
})
|
||||
|
||||
logger.info(f"Found {len(references)} chunks for user {user}")
|
||||
return references
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get chunk references: {e}", exc_info=True)
|
||||
return []
|
||||
|
||||
async def purge_chunks_by_ids(
|
||||
self,
|
||||
user: str,
|
||||
chunk_ids: List[str]
|
||||
) -> int:
|
||||
"""
|
||||
Delete specific chunks by their IDs.
|
||||
|
||||
Args:
|
||||
user: User identifier
|
||||
chunk_ids: List of chunk IDs to delete
|
||||
|
||||
Returns:
|
||||
Number of chunks deleted
|
||||
"""
|
||||
if not chunk_ids:
|
||||
return 0
|
||||
|
||||
collection_name = get_qdrant_collection_name(user)
|
||||
|
||||
try:
|
||||
deleted_count = await self.qdrant.delete_by_ids(
|
||||
collection_name=collection_name,
|
||||
point_ids=chunk_ids
|
||||
)
|
||||
|
||||
logger.info(f"Purged {deleted_count} orphan chunks for user {user}")
|
||||
return deleted_count
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to purge chunks: {e}", exc_info=True)
|
||||
return 0
|
||||
|
||||
def find_chunks_without_graph_nodes(
|
||||
self,
|
||||
chunk_references: List[Dict[str, Any]],
|
||||
graph_references: List[Dict[str, Any]]
|
||||
) -> List[str]:
|
||||
"""
|
||||
Find vector chunks that have no corresponding graph Document node.
|
||||
|
||||
Used for bidirectional orphan detection - vectors without graph representation.
|
||||
|
||||
Args:
|
||||
chunk_references: List from get_all_chunk_references()
|
||||
graph_references: List from GraphService.get_all_document_references()
|
||||
|
||||
Returns:
|
||||
List of orphan chunk IDs
|
||||
"""
|
||||
# Build sets of IDs that have graph nodes
|
||||
graph_page_ids = {
|
||||
ref.get("page_id") for ref in graph_references
|
||||
if ref.get("doc_type") == "wiki" and ref.get("page_id")
|
||||
}
|
||||
graph_doc_ids = {
|
||||
ref.get("document_id") for ref in graph_references
|
||||
if ref.get("doc_type") != "wiki" and ref.get("document_id")
|
||||
}
|
||||
|
||||
# Find chunks with no graph node
|
||||
orphan_ids = []
|
||||
for chunk in chunk_references:
|
||||
doc_type = chunk.get("doc_type", "wiki")
|
||||
|
||||
if doc_type == "wiki":
|
||||
page_id = chunk.get("page_id")
|
||||
if page_id and page_id not in graph_page_ids:
|
||||
orphan_ids.append(chunk["chunk_id"])
|
||||
else:
|
||||
document_id = chunk.get("document_id")
|
||||
if document_id and document_id not in graph_doc_ids:
|
||||
orphan_ids.append(chunk["chunk_id"])
|
||||
|
||||
logger.info(f"Found {len(orphan_ids)} vector chunks without graph nodes")
|
||||
return orphan_ids
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
"""
|
||||
Intelligent Wiki Page Writer Service
|
||||
|
||||
Uses LLM (mistral-nemo) to create and reconstruct wiki pages with:
|
||||
Uses LLM to create and reconstruct wiki pages with:
|
||||
- Holistic content restructuring
|
||||
- Zero fact loss (unless superseded)
|
||||
- Conflict detection and flagging
|
||||
@@ -25,15 +25,16 @@ class WikiPageWriter:
|
||||
Intelligent wiki page writer using LLM for content generation and restructuring.
|
||||
"""
|
||||
|
||||
def __init__(self, ollama_client):
|
||||
def __init__(self, ollama_client, settings):
|
||||
"""
|
||||
Initialize wiki page writer.
|
||||
|
||||
Args:
|
||||
ollama_client: OllamaClient for LLM operations
|
||||
settings: Application settings
|
||||
"""
|
||||
self.ollama = ollama_client
|
||||
self.model = "mistral-nemo" # Default model for writing
|
||||
self.model = settings.ollama_model
|
||||
|
||||
async def create_page(
|
||||
self,
|
||||
@@ -130,8 +131,8 @@ class WikiPageWriter:
|
||||
conflicts=conflicts
|
||||
)
|
||||
|
||||
# Reconstruct with LLM
|
||||
reconstructed = await self._call_llm(prompt)
|
||||
# Reconstruct with LLM (lower temperature for precise merging)
|
||||
reconstructed = await self._call_llm(prompt, temperature=0.2)
|
||||
|
||||
# Ensure standard sections are present
|
||||
reconstructed = self._ensure_standard_sections(
|
||||
@@ -154,7 +155,7 @@ class WikiPageWriter:
|
||||
Returns:
|
||||
List of conflicts with: {fact_a, fact_b, confidence, context}
|
||||
"""
|
||||
prompt = f"""Analyze these two pieces of content for factual conflicts.
|
||||
prompt = f"""Analyze these contents for direct factual conflicts.
|
||||
|
||||
EXISTING CONTENT:
|
||||
{existing_content[:2000]}
|
||||
@@ -162,25 +163,26 @@ EXISTING CONTENT:
|
||||
NEW INFORMATION:
|
||||
{new_information[:2000]}
|
||||
|
||||
Identify any facts that contradict each other. For each conflict, provide:
|
||||
1. The fact from existing content
|
||||
2. The contradicting fact from new information
|
||||
3. Confidence level (low/medium/high)
|
||||
4. Context/explanation
|
||||
ANALYSIS STEPS:
|
||||
1. Identify specific factual claims in existing content (dates, numbers, names, states)
|
||||
2. Identify specific factual claims in new content
|
||||
3. Compare ONLY for direct contradictions (X says A, Y says not-A)
|
||||
|
||||
Return ONLY valid JSON:
|
||||
RULES:
|
||||
- Do NOT flag differences in wording or phrasing as conflicts
|
||||
- Do NOT flag new/additional information as conflicts
|
||||
- Do NOT flag opinion differences as conflicts
|
||||
- ONLY flag direct factual contradictions
|
||||
- Return valid JSON only, no commentary
|
||||
|
||||
Return format:
|
||||
{{
|
||||
"conflicts": [
|
||||
{{
|
||||
"existing_fact": "fact from old content",
|
||||
"new_fact": "contradicting fact",
|
||||
"confidence": "medium",
|
||||
"context": "explanation of why these conflict"
|
||||
}}
|
||||
{{"existing_fact": "...", "new_fact": "...", "confidence": "low/medium/high", "context": "..."}}
|
||||
]
|
||||
}}
|
||||
|
||||
If no conflicts, return: {{"conflicts": []}}
|
||||
If no conflicts: {{"conflicts": []}}
|
||||
|
||||
JSON:"""
|
||||
|
||||
@@ -188,7 +190,8 @@ JSON:"""
|
||||
response = await self.ollama.generate_text(
|
||||
prompt=prompt,
|
||||
model=self.model,
|
||||
stream=False
|
||||
stream=False,
|
||||
temperature=0.0 # Deterministic for consistent conflict detection
|
||||
)
|
||||
|
||||
# Extract JSON
|
||||
@@ -347,6 +350,13 @@ FORMATTING RULES:
|
||||
- Keep sections focused and scannable
|
||||
- Adapt structure to content - not all sections apply to all topics
|
||||
|
||||
CRITICAL CONSTRAINTS:
|
||||
- Do NOT invent facts not present in the source information above
|
||||
- Do NOT add speculative information or assumptions
|
||||
- Do NOT fill sections with placeholder text or generic statements
|
||||
- If information for a section is not available, OMIT the section entirely
|
||||
- Base ALL content strictly on provided source information
|
||||
|
||||
Generate ONLY the markdown content (do not include Sources, Knowledge Graph, or Mind Map sections - those are added automatically).
|
||||
|
||||
MARKDOWN:"""
|
||||
@@ -402,6 +412,13 @@ FORMATTING RULES:
|
||||
- Bold important terms
|
||||
- Add subsections (###) where it improves clarity
|
||||
|
||||
CRITICAL CONSTRAINTS:
|
||||
- Do NOT rephrase facts in ways that change their meaning
|
||||
- Do NOT remove ANY information unless explicitly superseded by newer facts
|
||||
- Do NOT add information not present in existing content or new information
|
||||
- Preserve exact quotes, dates, numbers, and names verbatim
|
||||
- Do NOT fill gaps with assumptions or general knowledge
|
||||
|
||||
OUTPUT INSTRUCTIONS:
|
||||
- Return complete page content (do not include Sources, Knowledge Graph, Mind Map - those are added automatically)
|
||||
- Include updated "Changes & Updates" section noting what was changed today
|
||||
@@ -409,13 +426,21 @@ OUTPUT INSTRUCTIONS:
|
||||
|
||||
RECONSTRUCTED MARKDOWN:"""
|
||||
|
||||
async def _call_llm(self, prompt: str) -> str:
|
||||
"""Call LLM with prompt and return response."""
|
||||
async def _call_llm(self, prompt: str, temperature: float = 0.3) -> str:
|
||||
"""
|
||||
Call LLM with prompt and return response.
|
||||
|
||||
Args:
|
||||
prompt: The prompt text
|
||||
temperature: Sampling temperature (0.0=deterministic, higher=creative)
|
||||
Default 0.3 for controlled but natural content generation
|
||||
"""
|
||||
try:
|
||||
response = await self.ollama.generate_text(
|
||||
prompt=prompt,
|
||||
model=self.model,
|
||||
stream=False
|
||||
stream=False,
|
||||
temperature=temperature
|
||||
)
|
||||
|
||||
if not response:
|
||||
|
||||
+20
-9
@@ -1,5 +1,6 @@
|
||||
"""Pytest configuration and shared fixtures for Library Desk tests."""
|
||||
|
||||
import os
|
||||
import pytest
|
||||
import pytest_asyncio
|
||||
from typing import AsyncGenerator
|
||||
@@ -7,6 +8,9 @@ from typing import AsyncGenerator
|
||||
# Test configuration
|
||||
pytest_plugins = ("pytest_asyncio",)
|
||||
|
||||
# Use real host for tests (services available at this IP)
|
||||
TEST_HOST = os.environ.get("TEST_HOST", "192.168.86.149")
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def test_user() -> str:
|
||||
@@ -17,49 +21,56 @@ def test_user() -> str:
|
||||
@pytest.fixture
|
||||
def neo4j_test_uri() -> str:
|
||||
"""Test Neo4j URI."""
|
||||
return "bolt://neo4j:7687"
|
||||
return f"bolt://{TEST_HOST}:7687"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def neo4j_test_auth() -> tuple:
|
||||
"""Test Neo4j authentication."""
|
||||
return ("neo4j", "test_password")
|
||||
from src.config import get_settings
|
||||
settings = get_settings()
|
||||
return ("neo4j", settings.neo4j_password)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def qdrant_test_url() -> str:
|
||||
"""Test Qdrant URL."""
|
||||
return "http://qdrant:6333"
|
||||
return f"http://{TEST_HOST}:6333"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def wikijs_test_config() -> dict:
|
||||
"""Test Wiki.js configuration."""
|
||||
from src.config import get_settings
|
||||
settings = get_settings()
|
||||
return {
|
||||
"base_url": "http://wiki:3000",
|
||||
"api_key": "test_api_key"
|
||||
"base_url": f"http://{TEST_HOST}:3000",
|
||||
"username": settings.wikijs_username,
|
||||
"password": settings.wikijs_password
|
||||
}
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def searxng_test_url() -> str:
|
||||
"""Test SearXNG URL."""
|
||||
return "http://searxng:8080"
|
||||
return f"http://{TEST_HOST}:8080"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def ollama_test_config() -> dict:
|
||||
"""Test Ollama configuration."""
|
||||
from src.config import get_settings
|
||||
settings = get_settings()
|
||||
return {
|
||||
"base_url": "http://ollama:11434",
|
||||
"model": "nomic-embed-text"
|
||||
"base_url": f"http://{TEST_HOST}:11434",
|
||||
"model": settings.ollama_embedding_model
|
||||
}
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def redis_test_url() -> str:
|
||||
"""Test Redis URL."""
|
||||
return "redis://redis-shared:6379/4"
|
||||
return f"redis://{TEST_HOST}:6379/4"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
||||
@@ -0,0 +1,183 @@
|
||||
"""Tests for ContentExtractor client."""
|
||||
|
||||
import pytest
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
from src.clients.content_extractor import ContentExtractor
|
||||
from src.models.content import ContentExtractionResult
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def content_extractor():
|
||||
"""Create ContentExtractor with test configuration."""
|
||||
return ContentExtractor(timeout=5, max_length=2000)
|
||||
|
||||
|
||||
class TestContentExtractor:
|
||||
"""Tests for ContentExtractor client."""
|
||||
|
||||
def test_init(self, content_extractor):
|
||||
"""Test ContentExtractor initialization."""
|
||||
assert content_extractor.timeout == 5
|
||||
assert content_extractor.max_length == 2000
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_extract_success(self, content_extractor):
|
||||
"""Test successful content extraction."""
|
||||
test_url = "https://example.com/article"
|
||||
test_content = "This is the extracted article content."
|
||||
|
||||
with patch('src.clients.content_extractor.trafilatura') as mock_traf:
|
||||
mock_traf.fetch_url.return_value = "<html><body>Test</body></html>"
|
||||
mock_traf.extract.return_value = test_content
|
||||
mock_traf.bare_extraction.return_value = {
|
||||
"title": "Test Article",
|
||||
"author": "John Doe",
|
||||
"date": "2024-01-15",
|
||||
"language": "en"
|
||||
}
|
||||
|
||||
result = await content_extractor.extract(test_url)
|
||||
|
||||
assert result.success is True
|
||||
assert result.url == test_url
|
||||
assert result.content == test_content
|
||||
assert result.error is None
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_extract_fetch_failure(self, content_extractor):
|
||||
"""Test extraction when URL fetch fails."""
|
||||
test_url = "https://example.com/nonexistent"
|
||||
|
||||
with patch('src.clients.content_extractor.trafilatura') as mock_traf:
|
||||
mock_traf.fetch_url.return_value = None
|
||||
|
||||
result = await content_extractor.extract(test_url)
|
||||
|
||||
assert result.success is False
|
||||
assert result.url == test_url
|
||||
assert result.content == ""
|
||||
assert "Failed to fetch URL" in result.error
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_extract_no_content(self, content_extractor):
|
||||
"""Test extraction when page has no extractable content."""
|
||||
test_url = "https://example.com/empty"
|
||||
|
||||
with patch('src.clients.content_extractor.trafilatura') as mock_traf:
|
||||
mock_traf.fetch_url.return_value = "<html><body></body></html>"
|
||||
mock_traf.extract.return_value = None
|
||||
|
||||
result = await content_extractor.extract(test_url)
|
||||
|
||||
assert result.success is False
|
||||
assert "No content extracted" in result.error
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_extract_max_length_truncation(self, content_extractor):
|
||||
"""Test that content is truncated to max length."""
|
||||
test_url = "https://example.com/long-article"
|
||||
# Content longer than max_length (2000)
|
||||
long_content = "x" * 3000
|
||||
|
||||
with patch('src.clients.content_extractor.trafilatura') as mock_traf:
|
||||
mock_traf.fetch_url.return_value = "<html><body>Test</body></html>"
|
||||
mock_traf.extract.return_value = long_content
|
||||
mock_traf.bare_extraction.return_value = {}
|
||||
|
||||
result = await content_extractor.extract(test_url)
|
||||
|
||||
assert result.success is True
|
||||
assert len(result.content) <= content_extractor.max_length + 3 # +3 for "..."
|
||||
assert result.content.endswith("...")
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_extract_batch(self, content_extractor):
|
||||
"""Test batch extraction of multiple URLs."""
|
||||
test_urls = [
|
||||
"https://example.com/article1",
|
||||
"https://example.com/article2",
|
||||
"https://example.com/article3"
|
||||
]
|
||||
|
||||
with patch('src.clients.content_extractor.trafilatura') as mock_traf:
|
||||
mock_traf.fetch_url.return_value = "<html><body>Test</body></html>"
|
||||
mock_traf.extract.return_value = "Extracted content"
|
||||
mock_traf.bare_extraction.return_value = {}
|
||||
|
||||
results = await content_extractor.extract_batch(test_urls)
|
||||
|
||||
assert len(results) == 3
|
||||
for i, result in enumerate(results):
|
||||
assert result.url == test_urls[i]
|
||||
assert result.success is True
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_extract_timeout(self):
|
||||
"""Test extraction timeout handling."""
|
||||
import time
|
||||
|
||||
test_url = "https://example.com/slow"
|
||||
|
||||
# Create an extractor with very short timeout
|
||||
fast_extractor = ContentExtractor(timeout=0.001, max_length=2000)
|
||||
|
||||
def slow_fetch(url):
|
||||
time.sleep(1) # Sleep synchronously (this runs in thread pool)
|
||||
return "<html></html>"
|
||||
|
||||
with patch('src.clients.content_extractor.trafilatura') as mock_traf:
|
||||
mock_traf.fetch_url = slow_fetch
|
||||
|
||||
result = await fast_extractor.extract(test_url)
|
||||
|
||||
assert result.success is False
|
||||
assert "timed out" in result.error.lower()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_extract_from_html(self, content_extractor):
|
||||
"""Test extraction from raw HTML."""
|
||||
test_html = "<html><body><article>Article content here.</article></body></html>"
|
||||
|
||||
with patch('src.clients.content_extractor.trafilatura') as mock_traf:
|
||||
mock_traf.extract.return_value = "Article content here."
|
||||
mock_traf.bare_extraction.return_value = {"title": "Test"}
|
||||
|
||||
result = await content_extractor.extract_from_html(test_html, url="https://example.com")
|
||||
|
||||
assert result.success is True
|
||||
assert result.content == "Article content here."
|
||||
|
||||
|
||||
class TestContentExtractionResult:
|
||||
"""Tests for ContentExtractionResult model."""
|
||||
|
||||
def test_success_result(self):
|
||||
"""Test creating a successful result."""
|
||||
result = ContentExtractionResult(
|
||||
url="https://example.com",
|
||||
title="Test Article",
|
||||
content="Article content",
|
||||
author="John Doe",
|
||||
date="2024-01-15",
|
||||
language="en",
|
||||
success=True,
|
||||
error=None
|
||||
)
|
||||
|
||||
assert result.url == "https://example.com"
|
||||
assert result.success is True
|
||||
assert result.error is None
|
||||
|
||||
def test_failure_result(self):
|
||||
"""Test creating a failure result."""
|
||||
result = ContentExtractionResult(
|
||||
url="https://example.com/error",
|
||||
content="",
|
||||
success=False,
|
||||
error="Failed to fetch URL"
|
||||
)
|
||||
|
||||
assert result.url == "https://example.com/error"
|
||||
assert result.success is False
|
||||
assert result.error == "Failed to fetch URL"
|
||||
@@ -38,10 +38,10 @@ def settings():
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
async def neo4j_client(settings) -> AsyncGenerator[Neo4jClient, None]:
|
||||
async def neo4j_client(settings, neo4j_test_uri) -> AsyncGenerator[Neo4jClient, None]:
|
||||
"""Get connected Neo4j client."""
|
||||
client = Neo4jClient(
|
||||
uri=settings.neo4j_uri,
|
||||
uri=neo4j_test_uri,
|
||||
user=settings.neo4j_user,
|
||||
password=settings.neo4j_password
|
||||
)
|
||||
@@ -51,12 +51,12 @@ async def neo4j_client(settings) -> AsyncGenerator[Neo4jClient, None]:
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
async def wiki_client(settings) -> AsyncGenerator[WikiJSClient, None]:
|
||||
async def wiki_client(wikijs_test_config) -> AsyncGenerator[WikiJSClient, None]:
|
||||
"""Get Wiki.js client."""
|
||||
client = WikiJSClient(
|
||||
base_url=settings.wikijs_url,
|
||||
username=settings.wikijs_username,
|
||||
password=settings.wikijs_password
|
||||
base_url=wikijs_test_config["base_url"],
|
||||
username=wikijs_test_config["username"],
|
||||
password=wikijs_test_config["password"]
|
||||
)
|
||||
yield client
|
||||
|
||||
|
||||
+65
-49
@@ -25,6 +25,7 @@ from src.clients.qdrant_client import QdrantClientWrapper
|
||||
from src.clients.wikijs_client import WikiJSClient
|
||||
from src.clients.searxng_client import SearXNGClient
|
||||
from src.clients.ollama_client import OllamaClient
|
||||
from src.clients.content_extractor import ContentExtractor
|
||||
from src.services.hybrid_rag_service import HybridRAGService
|
||||
from src.services.vector_service import VectorService
|
||||
from src.services.graph_service import GraphService
|
||||
@@ -42,10 +43,10 @@ def settings():
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
async def neo4j_client(settings) -> AsyncGenerator[Neo4jClient, None]:
|
||||
async def neo4j_client(settings, neo4j_test_uri) -> AsyncGenerator[Neo4jClient, None]:
|
||||
"""Get connected Neo4j client."""
|
||||
client = Neo4jClient(
|
||||
uri=settings.neo4j_uri,
|
||||
uri=neo4j_test_uri,
|
||||
user=settings.neo4j_user,
|
||||
password=settings.neo4j_password
|
||||
)
|
||||
@@ -55,32 +56,44 @@ async def neo4j_client(settings) -> AsyncGenerator[Neo4jClient, None]:
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def qdrant_client(settings) -> QdrantClientWrapper:
|
||||
def qdrant_client(qdrant_test_url) -> QdrantClientWrapper:
|
||||
"""Get Qdrant client."""
|
||||
return QdrantClientWrapper(url=settings.qdrant_url)
|
||||
return QdrantClientWrapper(url=qdrant_test_url)
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
async def wiki_client(settings) -> AsyncGenerator[WikiJSClient, None]:
|
||||
async def wiki_client(wikijs_test_config) -> AsyncGenerator[WikiJSClient, None]:
|
||||
"""Get Wiki.js client."""
|
||||
client = WikiJSClient(
|
||||
base_url=settings.wikijs_url,
|
||||
username=settings.wikijs_username,
|
||||
password=settings.wikijs_password
|
||||
base_url=wikijs_test_config["base_url"],
|
||||
username=wikijs_test_config["username"],
|
||||
password=wikijs_test_config["password"]
|
||||
)
|
||||
yield client
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def searxng_client(settings) -> SearXNGClient:
|
||||
def searxng_client(searxng_test_url) -> SearXNGClient:
|
||||
"""Get SearXNG client."""
|
||||
return SearXNGClient(base_url=settings.searxng_url)
|
||||
return SearXNGClient(base_url=searxng_test_url)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def ollama_client(settings) -> OllamaClient:
|
||||
def ollama_client(ollama_test_config) -> OllamaClient:
|
||||
"""Get Ollama client."""
|
||||
return OllamaClient(base_url=settings.ollama_url)
|
||||
return OllamaClient(
|
||||
base_url=ollama_test_config["base_url"],
|
||||
model=ollama_test_config["model"]
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def content_extractor(settings) -> ContentExtractor:
|
||||
"""Get ContentExtractor client."""
|
||||
return ContentExtractor(
|
||||
timeout=settings.content_extraction_timeout,
|
||||
max_length=settings.content_max_length
|
||||
)
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
@@ -101,6 +114,7 @@ async def hybrid_rag_service(
|
||||
graph_service,
|
||||
searxng_client,
|
||||
ollama_client,
|
||||
content_extractor,
|
||||
settings
|
||||
):
|
||||
"""Get HybridRAGService instance."""
|
||||
@@ -109,6 +123,7 @@ async def hybrid_rag_service(
|
||||
graph_service=graph_service,
|
||||
searxng_client=searxng_client,
|
||||
ollama_client=ollama_client,
|
||||
content_extractor=content_extractor,
|
||||
settings=settings
|
||||
)
|
||||
|
||||
@@ -207,53 +222,54 @@ async def test_vector_data(vector_service, test_wiki_page):
|
||||
# ============================================================================
|
||||
|
||||
class TestRRFFusion:
|
||||
"""Test Reciprocal Rank Fusion algorithm."""
|
||||
"""Test two-stage Reciprocal Rank Fusion algorithm."""
|
||||
|
||||
def test_rrf_single_source(self, hybrid_rag_service):
|
||||
"""Test RRF with single source."""
|
||||
results_by_source = {
|
||||
"vector": [
|
||||
{"page_id": 1, "title": "Doc 1", "content": "test"},
|
||||
{"page_id": 2, "title": "Doc 2", "content": "test"}
|
||||
]
|
||||
}
|
||||
def test_wiki_merge_single_source(self, hybrid_rag_service):
|
||||
"""Test wiki merge with single source (vector only)."""
|
||||
vector_results = [
|
||||
{"page_id": 1, "title": "Doc 1", "content": "test"},
|
||||
{"page_id": 2, "title": "Doc 2", "content": "test"}
|
||||
]
|
||||
|
||||
fused = hybrid_rag_service._reciprocal_rank_fusion(results_by_source, k=60)
|
||||
merged = hybrid_rag_service._merge_wiki_sources(vector_results, [], k=60)
|
||||
|
||||
assert len(fused) == 2
|
||||
assert fused[0]["rrf_score"] > fused[1]["rrf_score"] # Rank 1 > Rank 2
|
||||
assert fused[0]["sources"] == ["vector"]
|
||||
assert len(merged) == 2
|
||||
assert merged[0]["wiki_rrf_score"] > merged[1]["wiki_rrf_score"] # Rank 1 > Rank 2
|
||||
assert merged[0]["found_by"] == ["vector"]
|
||||
|
||||
def test_rrf_multiple_sources_same_doc(self, hybrid_rag_service):
|
||||
"""Test RRF with same document from multiple sources."""
|
||||
results_by_source = {
|
||||
"vector": [{"page_id": 1, "title": "Doc 1", "content": "test"}],
|
||||
"graph": [{"page_id": 1, "title": "Doc 1", "content": ""}],
|
||||
}
|
||||
def test_wiki_merge_multiple_sources_same_doc(self, hybrid_rag_service):
|
||||
"""Test wiki merge with same document from vector and graph."""
|
||||
vector_results = [{"page_id": 1, "title": "Doc 1", "content": "test"}]
|
||||
graph_results = [{"page_id": 1, "title": "Doc 1", "content": ""}]
|
||||
|
||||
fused = hybrid_rag_service._reciprocal_rank_fusion(results_by_source, k=60)
|
||||
merged = hybrid_rag_service._merge_wiki_sources(vector_results, graph_results, k=60)
|
||||
|
||||
assert len(fused) == 1 # Deduplicated
|
||||
assert len(fused[0]["sources"]) == 2 # Both sources
|
||||
assert "vector" in fused[0]["sources"]
|
||||
assert "graph" in fused[0]["sources"]
|
||||
# RRF score should be sum: 1/(60+1) + 1/(60+1)
|
||||
assert len(merged) == 1 # Deduplicated
|
||||
assert len(merged[0]["found_by"]) == 2 # Both sources
|
||||
assert "vector" in merged[0]["found_by"]
|
||||
assert "graph" in merged[0]["found_by"]
|
||||
# Wiki RRF score should be sum: 1/(60+1) + 1/(60+1)
|
||||
expected_score = 1/61 + 1/61
|
||||
assert abs(fused[0]["rrf_score"] - expected_score) < 0.001
|
||||
assert abs(merged[0]["wiki_rrf_score"] - expected_score) < 0.001
|
||||
|
||||
def test_rrf_web_results(self, hybrid_rag_service):
|
||||
"""Test RRF with web results (URL-based)."""
|
||||
results_by_source = {
|
||||
"web": [
|
||||
{"url": "https://example.com/1", "title": "Web 1", "content": "test"},
|
||||
{"url": "https://example.com/2", "title": "Web 2", "content": "test"}
|
||||
]
|
||||
}
|
||||
def test_final_rrf_wiki_and_web(self, hybrid_rag_service):
|
||||
"""Test final RRF between wiki and web results."""
|
||||
# Pre-merged wiki results
|
||||
wiki_results = [
|
||||
{"page_id": 1, "title": "Wiki 1", "content": "test", "found_by": ["vector"]}
|
||||
]
|
||||
web_results = [
|
||||
{"url": "https://example.com/1", "title": "Web 1", "content": "test"},
|
||||
{"url": "https://example.com/2", "title": "Web 2", "content": "test"}
|
||||
]
|
||||
|
||||
fused = hybrid_rag_service._reciprocal_rank_fusion(results_by_source, k=60)
|
||||
fused = hybrid_rag_service._reciprocal_rank_fusion(wiki_results, web_results, k=60)
|
||||
|
||||
assert len(fused) == 2
|
||||
assert fused[0]["result"]["url"] == "https://example.com/1"
|
||||
assert len(fused) == 3
|
||||
# Wiki rank 1 and web rank 1 should have same RRF score
|
||||
wiki_score = next(r["rrf_score"] for r in fused if r["source_type"] == "wiki")
|
||||
web_score = next(r["rrf_score"] for r in fused if r["source_type"] == "web")
|
||||
assert abs(wiki_score - web_score) < 0.001 # Equal footing
|
||||
|
||||
|
||||
class TestContextFormatting:
|
||||
|
||||
+16
-15
@@ -30,10 +30,10 @@ def settings():
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
async def neo4j_client(settings) -> AsyncGenerator[Neo4jClient, None]:
|
||||
async def neo4j_client(settings, neo4j_test_uri) -> AsyncGenerator[Neo4jClient, None]:
|
||||
"""Get connected Neo4j client."""
|
||||
client = Neo4jClient(
|
||||
uri=settings.neo4j_uri,
|
||||
uri=neo4j_test_uri,
|
||||
user=settings.neo4j_user,
|
||||
password=settings.neo4j_password
|
||||
)
|
||||
@@ -43,45 +43,46 @@ async def neo4j_client(settings) -> AsyncGenerator[Neo4jClient, None]:
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def qdrant_client(settings) -> QdrantClientWrapper:
|
||||
def qdrant_client(settings, qdrant_test_url) -> QdrantClientWrapper:
|
||||
"""Get Qdrant client."""
|
||||
return QdrantClientWrapper(url=settings.qdrant_url)
|
||||
return QdrantClientWrapper(url=qdrant_test_url)
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
async def wikijs_client(settings) -> AsyncGenerator[WikiJSClient, None]:
|
||||
async def wikijs_client(wikijs_test_config) -> AsyncGenerator[WikiJSClient, None]:
|
||||
"""Get Wiki.js client."""
|
||||
client = WikiJSClient(
|
||||
base_url=settings.wikijs_url,
|
||||
api_key=settings.wikijs_api_key
|
||||
base_url=wikijs_test_config["base_url"],
|
||||
username=wikijs_test_config["username"],
|
||||
password=wikijs_test_config["password"]
|
||||
)
|
||||
yield client
|
||||
await client.close()
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
async def searxng_client(settings) -> AsyncGenerator[SearXNGClient, None]:
|
||||
async def searxng_client(searxng_test_url) -> AsyncGenerator[SearXNGClient, None]:
|
||||
"""Get SearXNG client."""
|
||||
client = SearXNGClient(base_url=settings.searxng_url)
|
||||
client = SearXNGClient(base_url=searxng_test_url)
|
||||
yield client
|
||||
await client.close()
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
async def ollama_client(settings) -> AsyncGenerator[OllamaClient, None]:
|
||||
"""Get Ollama client."""
|
||||
async def ollama_client(ollama_test_config) -> AsyncGenerator[OllamaClient, None]:
|
||||
"""Get Ollama client for embeddings."""
|
||||
client = OllamaClient(
|
||||
base_url=settings.ollama_url,
|
||||
model=settings.ollama_model
|
||||
base_url=ollama_test_config["base_url"],
|
||||
model=ollama_test_config["model"]
|
||||
)
|
||||
yield client
|
||||
await client.close()
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
async def job_manager(settings) -> AsyncGenerator[JobManager, None]:
|
||||
async def job_manager(redis_test_url) -> AsyncGenerator[JobManager, None]:
|
||||
"""Get job manager."""
|
||||
manager = JobManager(redis_url=settings.redis_url)
|
||||
manager = JobManager(redis_url=redis_test_url)
|
||||
await manager.connect()
|
||||
yield manager
|
||||
await manager.close()
|
||||
|
||||
@@ -0,0 +1,488 @@
|
||||
"""
|
||||
Tests for maintenance router and cleanup functionality.
|
||||
|
||||
Tests cleanup of:
|
||||
- Orphan vector chunks
|
||||
- Orphan entities in graph
|
||||
- Stale document nodes
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
from src.routers.maintenance import (
|
||||
cleanup_vectors,
|
||||
cleanup_graph,
|
||||
cleanup_all,
|
||||
maintenance_health,
|
||||
reindex_page,
|
||||
CleanupResult,
|
||||
VectorCleanupResponse,
|
||||
GraphCleanupResponse,
|
||||
FullCleanupResponse,
|
||||
HealthCheckResponse,
|
||||
ReindexResponse
|
||||
)
|
||||
|
||||
|
||||
class TestCleanupResult:
|
||||
"""Test CleanupResult model."""
|
||||
|
||||
def test_cleanup_result_defaults(self):
|
||||
"""Test CleanupResult with default values."""
|
||||
result = CleanupResult(duration_ms=100.0)
|
||||
assert result.orphans_found == 0
|
||||
assert result.orphans_purged == 0
|
||||
assert result.duration_ms == 100.0
|
||||
|
||||
def test_cleanup_result_with_values(self):
|
||||
"""Test CleanupResult with actual values."""
|
||||
result = CleanupResult(
|
||||
orphans_found=10,
|
||||
orphans_purged=8,
|
||||
duration_ms=250.5
|
||||
)
|
||||
assert result.orphans_found == 10
|
||||
assert result.orphans_purged == 8
|
||||
assert result.duration_ms == 250.5
|
||||
|
||||
|
||||
class TestVectorCleanupResponse:
|
||||
"""Test VectorCleanupResponse model."""
|
||||
|
||||
def test_vector_cleanup_response(self):
|
||||
"""Test VectorCleanupResponse structure."""
|
||||
response = VectorCleanupResponse(
|
||||
success=True,
|
||||
wiki_chunks=CleanupResult(orphans_found=5, orphans_purged=5, duration_ms=50),
|
||||
document_chunks=CleanupResult(orphans_found=3, orphans_purged=3, duration_ms=50),
|
||||
chunks_without_graph=CleanupResult(orphans_found=2, orphans_purged=2, duration_ms=50),
|
||||
total_chunks_scanned=100,
|
||||
total_orphans_purged=10,
|
||||
duration_ms=100
|
||||
)
|
||||
assert response.success is True
|
||||
assert response.wiki_chunks.orphans_found == 5
|
||||
assert response.document_chunks.orphans_found == 3
|
||||
assert response.chunks_without_graph.orphans_found == 2
|
||||
assert response.total_orphans_purged == 10
|
||||
|
||||
|
||||
class TestGraphCleanupResponse:
|
||||
"""Test GraphCleanupResponse model."""
|
||||
|
||||
def test_graph_cleanup_response(self):
|
||||
"""Test GraphCleanupResponse structure."""
|
||||
response = GraphCleanupResponse(
|
||||
success=True,
|
||||
orphan_entities=CleanupResult(orphans_found=10, orphans_purged=10, duration_ms=25),
|
||||
stale_wiki_documents=CleanupResult(orphans_found=2, orphans_purged=2, duration_ms=25),
|
||||
stale_store_documents=CleanupResult(orphans_found=0, orphans_purged=0, duration_ms=25),
|
||||
docs_without_vectors=CleanupResult(orphans_found=1, orphans_purged=1, duration_ms=25),
|
||||
broken_relationships_cleaned=5,
|
||||
duration_ms=100
|
||||
)
|
||||
assert response.success is True
|
||||
assert response.orphan_entities.orphans_found == 10
|
||||
assert response.docs_without_vectors.orphans_found == 1
|
||||
assert response.broken_relationships_cleaned == 5
|
||||
|
||||
|
||||
class TestHealthCheckResponse:
|
||||
"""Test HealthCheckResponse model."""
|
||||
|
||||
def test_health_check_healthy(self):
|
||||
"""Test healthy status."""
|
||||
response = HealthCheckResponse(
|
||||
status="healthy",
|
||||
orphan_vector_count=0,
|
||||
orphan_entity_count=0,
|
||||
stale_document_count=0
|
||||
)
|
||||
assert response.status == "healthy"
|
||||
assert response.recommendations == []
|
||||
|
||||
def test_health_check_degraded(self):
|
||||
"""Test degraded status with recommendations."""
|
||||
response = HealthCheckResponse(
|
||||
status="degraded",
|
||||
orphan_vector_count=15,
|
||||
orphan_entity_count=3,
|
||||
stale_document_count=0,
|
||||
recommendations=[
|
||||
"Found 15 orphan vector chunks. Consider running POST /maintenance/cleanup/vectors"
|
||||
]
|
||||
)
|
||||
assert response.status == "degraded"
|
||||
assert len(response.recommendations) == 1
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
class TestVectorCleanup:
|
||||
"""Test vector cleanup endpoint."""
|
||||
|
||||
async def test_cleanup_vectors_no_orphans(self):
|
||||
"""Test cleanup when no orphans exist."""
|
||||
# Mock services
|
||||
vector_service = AsyncMock()
|
||||
vector_service.get_all_chunk_references.return_value = [
|
||||
{"chunk_id": "c1", "page_id": 1, "doc_type": "wiki"}
|
||||
]
|
||||
# find_chunks_without_graph_nodes is not async
|
||||
vector_service.find_chunks_without_graph_nodes = MagicMock(return_value=[])
|
||||
|
||||
graph_service = AsyncMock()
|
||||
graph_service.get_all_document_references.return_value = [
|
||||
{"page_id": 1, "doc_type": "wiki", "title": "Test"}
|
||||
]
|
||||
|
||||
wiki_client = AsyncMock()
|
||||
wiki_client.list_all_pages.return_value = [{"id": 1, "path": "test"}]
|
||||
|
||||
# Call cleanup
|
||||
result = await cleanup_vectors(
|
||||
user="testuser",
|
||||
dry_run=False,
|
||||
vector_service=vector_service,
|
||||
graph_service=graph_service,
|
||||
wiki_client=wiki_client,
|
||||
api_key="test"
|
||||
)
|
||||
|
||||
assert result.success is True
|
||||
assert result.wiki_chunks.orphans_found == 0
|
||||
assert result.chunks_without_graph.orphans_found == 0
|
||||
assert result.total_orphans_purged == 0
|
||||
|
||||
async def test_cleanup_vectors_with_orphans(self):
|
||||
"""Test cleanup when orphans exist."""
|
||||
# Mock services
|
||||
vector_service = AsyncMock()
|
||||
vector_service.get_all_chunk_references.return_value = [
|
||||
{"chunk_id": "c1", "page_id": 1, "doc_type": "wiki"},
|
||||
{"chunk_id": "c2", "page_id": 999, "doc_type": "wiki"}, # Orphan
|
||||
{"chunk_id": "c3", "page_id": 999, "doc_type": "wiki"}, # Orphan
|
||||
]
|
||||
vector_service.purge_chunks_by_ids.return_value = 2
|
||||
# find_chunks_without_graph_nodes is not async
|
||||
vector_service.find_chunks_without_graph_nodes = MagicMock(return_value=[])
|
||||
|
||||
graph_service = AsyncMock()
|
||||
graph_service.get_all_document_references.return_value = [
|
||||
{"page_id": 1, "doc_type": "wiki", "title": "Test"}
|
||||
]
|
||||
|
||||
wiki_client = AsyncMock()
|
||||
wiki_client.list_all_pages.return_value = [{"id": 1, "path": "test"}]
|
||||
|
||||
# Call cleanup
|
||||
result = await cleanup_vectors(
|
||||
user="testuser",
|
||||
dry_run=False,
|
||||
vector_service=vector_service,
|
||||
graph_service=graph_service,
|
||||
wiki_client=wiki_client,
|
||||
api_key="test"
|
||||
)
|
||||
|
||||
assert result.success is True
|
||||
assert result.wiki_chunks.orphans_found == 2
|
||||
assert result.wiki_chunks.orphans_purged == 2
|
||||
assert result.total_orphans_purged == 2
|
||||
|
||||
async def test_cleanup_vectors_dry_run(self):
|
||||
"""Test cleanup dry run doesn't purge."""
|
||||
# Mock services
|
||||
vector_service = AsyncMock()
|
||||
vector_service.get_all_chunk_references.return_value = [
|
||||
{"chunk_id": "c1", "page_id": 999, "doc_type": "wiki"}, # Orphan
|
||||
]
|
||||
# find_chunks_without_graph_nodes is not async
|
||||
vector_service.find_chunks_without_graph_nodes = MagicMock(return_value=[])
|
||||
|
||||
graph_service = AsyncMock()
|
||||
graph_service.get_all_document_references.return_value = []
|
||||
|
||||
wiki_client = AsyncMock()
|
||||
wiki_client.list_all_pages.return_value = []
|
||||
|
||||
# Call cleanup in dry run mode
|
||||
result = await cleanup_vectors(
|
||||
user="testuser",
|
||||
dry_run=True,
|
||||
vector_service=vector_service,
|
||||
graph_service=graph_service,
|
||||
wiki_client=wiki_client,
|
||||
api_key="test"
|
||||
)
|
||||
|
||||
assert result.success is True
|
||||
assert result.wiki_chunks.orphans_found == 1
|
||||
assert result.wiki_chunks.orphans_purged == 0 # Not purged due to dry run
|
||||
vector_service.purge_chunks_by_ids.assert_not_called()
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
class TestGraphCleanup:
|
||||
"""Test graph cleanup endpoint."""
|
||||
|
||||
async def test_cleanup_graph_no_orphans(self):
|
||||
"""Test cleanup when no orphans exist."""
|
||||
vector_service = AsyncMock()
|
||||
vector_service.get_all_chunk_references.return_value = [
|
||||
{"chunk_id": "c1", "page_id": 1, "doc_type": "wiki"}
|
||||
]
|
||||
|
||||
graph_service = AsyncMock()
|
||||
graph_service.find_orphan_entities.return_value = []
|
||||
graph_service.get_all_document_references.return_value = [
|
||||
{"page_id": 1, "doc_type": "wiki", "title": "Test"}
|
||||
]
|
||||
graph_service.find_documents_without_vectors.return_value = []
|
||||
graph_service.cleanup_broken_relationships.return_value = 0
|
||||
|
||||
wiki_client = AsyncMock()
|
||||
wiki_client.list_all_pages.return_value = [{"id": 1, "path": "test"}]
|
||||
|
||||
result = await cleanup_graph(
|
||||
user="testuser",
|
||||
dry_run=False,
|
||||
vector_service=vector_service,
|
||||
graph_service=graph_service,
|
||||
wiki_client=wiki_client,
|
||||
api_key="test"
|
||||
)
|
||||
|
||||
assert result.success is True
|
||||
assert result.orphan_entities.orphans_found == 0
|
||||
assert result.stale_wiki_documents.orphans_found == 0
|
||||
assert result.docs_without_vectors.orphans_found == 0
|
||||
|
||||
async def test_cleanup_graph_with_orphan_entities(self):
|
||||
"""Test cleanup of orphan entities."""
|
||||
vector_service = AsyncMock()
|
||||
vector_service.get_all_chunk_references.return_value = []
|
||||
|
||||
graph_service = AsyncMock()
|
||||
graph_service.find_orphan_entities.return_value = [
|
||||
{"id": "e1", "name": "Orphan1", "type": "Person"},
|
||||
{"id": "e2", "name": "Orphan2", "type": "Technology"},
|
||||
]
|
||||
graph_service.purge_orphan_entities.return_value = 2
|
||||
graph_service.get_all_document_references.return_value = []
|
||||
graph_service.find_documents_without_vectors.return_value = []
|
||||
graph_service.cleanup_broken_relationships.return_value = 0
|
||||
|
||||
wiki_client = AsyncMock()
|
||||
wiki_client.list_all_pages.return_value = []
|
||||
|
||||
result = await cleanup_graph(
|
||||
user="testuser",
|
||||
dry_run=False,
|
||||
vector_service=vector_service,
|
||||
graph_service=graph_service,
|
||||
wiki_client=wiki_client,
|
||||
api_key="test"
|
||||
)
|
||||
|
||||
assert result.success is True
|
||||
assert result.orphan_entities.orphans_found == 2
|
||||
assert result.orphan_entities.orphans_purged == 2
|
||||
|
||||
async def test_cleanup_graph_with_stale_documents(self):
|
||||
"""Test cleanup of stale document nodes."""
|
||||
vector_service = AsyncMock()
|
||||
vector_service.get_all_chunk_references.return_value = [
|
||||
{"chunk_id": "c1", "page_id": 1, "doc_type": "wiki"}
|
||||
]
|
||||
|
||||
graph_service = AsyncMock()
|
||||
graph_service.find_orphan_entities.return_value = []
|
||||
graph_service.get_all_document_references.return_value = [
|
||||
{"page_id": 1, "doc_type": "wiki", "title": "Exists"},
|
||||
{"page_id": 999, "doc_type": "wiki", "title": "Deleted"}, # Stale
|
||||
]
|
||||
graph_service.find_documents_without_vectors.return_value = []
|
||||
graph_service.purge_stale_documents_by_ids.return_value = 1
|
||||
graph_service.cleanup_broken_relationships.return_value = 0
|
||||
|
||||
wiki_client = AsyncMock()
|
||||
wiki_client.list_all_pages.return_value = [{"id": 1, "path": "test"}]
|
||||
|
||||
result = await cleanup_graph(
|
||||
user="testuser",
|
||||
dry_run=False,
|
||||
vector_service=vector_service,
|
||||
graph_service=graph_service,
|
||||
wiki_client=wiki_client,
|
||||
api_key="test"
|
||||
)
|
||||
|
||||
assert result.success is True
|
||||
assert result.stale_wiki_documents.orphans_found == 1
|
||||
assert result.stale_wiki_documents.orphans_purged == 1
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
class TestFullCleanup:
|
||||
"""Test full cleanup endpoint."""
|
||||
|
||||
async def test_full_cleanup(self):
|
||||
"""Test full cleanup runs both vector and graph cleanup."""
|
||||
vector_service = AsyncMock()
|
||||
vector_service.get_all_chunk_references.return_value = []
|
||||
# find_chunks_without_graph_nodes is not async
|
||||
vector_service.find_chunks_without_graph_nodes = MagicMock(return_value=[])
|
||||
|
||||
graph_service = AsyncMock()
|
||||
graph_service.find_orphan_entities.return_value = []
|
||||
graph_service.get_all_document_references.return_value = []
|
||||
graph_service.find_documents_without_vectors.return_value = []
|
||||
graph_service.cleanup_broken_relationships.return_value = 0
|
||||
|
||||
wiki_client = AsyncMock()
|
||||
wiki_client.list_all_pages.return_value = []
|
||||
|
||||
result = await cleanup_all(
|
||||
user="testuser",
|
||||
dry_run=False,
|
||||
vector_service=vector_service,
|
||||
graph_service=graph_service,
|
||||
wiki_client=wiki_client,
|
||||
api_key="test"
|
||||
)
|
||||
|
||||
assert result.success is True
|
||||
assert result.vector_cleanup.success is True
|
||||
assert result.graph_cleanup.success is True
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
class TestMaintenanceHealth:
|
||||
"""Test maintenance health endpoint."""
|
||||
|
||||
async def test_health_healthy(self):
|
||||
"""Test healthy status when no orphans."""
|
||||
vector_service = AsyncMock()
|
||||
vector_service.get_all_chunk_references.return_value = []
|
||||
# find_chunks_without_graph_nodes is not async
|
||||
vector_service.find_chunks_without_graph_nodes = MagicMock(return_value=[])
|
||||
|
||||
graph_service = AsyncMock()
|
||||
graph_service.find_orphan_entities.return_value = []
|
||||
graph_service.get_all_document_references.return_value = []
|
||||
graph_service.find_documents_without_vectors.return_value = []
|
||||
|
||||
wiki_client = AsyncMock()
|
||||
wiki_client.list_all_pages.return_value = []
|
||||
|
||||
result = await maintenance_health(
|
||||
user="testuser",
|
||||
vector_service=vector_service,
|
||||
graph_service=graph_service,
|
||||
wiki_client=wiki_client,
|
||||
api_key="test"
|
||||
)
|
||||
|
||||
assert result.status == "healthy"
|
||||
assert result.orphan_vector_count == 0
|
||||
assert result.orphan_entity_count == 0
|
||||
assert result.vectors_without_graph == 0
|
||||
assert result.docs_without_vectors == 0
|
||||
|
||||
async def test_health_degraded(self):
|
||||
"""Test degraded status with orphans."""
|
||||
vector_service = AsyncMock()
|
||||
vector_service.get_all_chunk_references.return_value = [
|
||||
{"chunk_id": f"c{i}", "page_id": 999, "doc_type": "wiki"}
|
||||
for i in range(15)
|
||||
]
|
||||
# find_chunks_without_graph_nodes is not async
|
||||
vector_service.find_chunks_without_graph_nodes = MagicMock(return_value=[])
|
||||
|
||||
graph_service = AsyncMock()
|
||||
graph_service.find_orphan_entities.return_value = [
|
||||
{"id": f"e{i}", "name": f"Entity{i}", "type": "Entity"}
|
||||
for i in range(3)
|
||||
]
|
||||
graph_service.get_all_document_references.return_value = []
|
||||
graph_service.find_documents_without_vectors.return_value = []
|
||||
|
||||
wiki_client = AsyncMock()
|
||||
wiki_client.list_all_pages.return_value = []
|
||||
|
||||
result = await maintenance_health(
|
||||
user="testuser",
|
||||
vector_service=vector_service,
|
||||
graph_service=graph_service,
|
||||
wiki_client=wiki_client,
|
||||
api_key="test"
|
||||
)
|
||||
|
||||
assert result.status == "degraded"
|
||||
assert result.orphan_vector_count == 15
|
||||
assert result.orphan_entity_count == 3
|
||||
assert len(result.recommendations) >= 1
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
class TestReindexPage:
|
||||
"""Test reindex page endpoint."""
|
||||
|
||||
async def test_reindex_success(self):
|
||||
"""Test successful page reindex."""
|
||||
vector_service = AsyncMock()
|
||||
vector_service.delete_page_chunks.return_value = 5
|
||||
vector_service.update_from_page.return_value = MagicMock(
|
||||
success=True,
|
||||
chunks_created=6,
|
||||
error_message=None
|
||||
)
|
||||
|
||||
graph_service = AsyncMock()
|
||||
graph_service.delete_page.return_value = 1
|
||||
graph_service.update_from_page.return_value = MagicMock(
|
||||
success=True,
|
||||
error_message=None
|
||||
)
|
||||
|
||||
result = await reindex_page(
|
||||
page_id=123,
|
||||
user="testuser",
|
||||
vector_service=vector_service,
|
||||
graph_service=graph_service,
|
||||
api_key="test"
|
||||
)
|
||||
|
||||
assert result.success is True
|
||||
assert result.page_id == 123
|
||||
assert result.vectors_deleted == 5
|
||||
assert result.vectors_created == 6
|
||||
assert result.graph_updated is True
|
||||
|
||||
async def test_reindex_failure(self):
|
||||
"""Test reindex with failure."""
|
||||
vector_service = AsyncMock()
|
||||
vector_service.delete_page_chunks.return_value = 0
|
||||
vector_service.update_from_page.return_value = MagicMock(
|
||||
success=False,
|
||||
chunks_created=0,
|
||||
error_message="Page not found"
|
||||
)
|
||||
|
||||
graph_service = AsyncMock()
|
||||
graph_service.delete_page.return_value = 0
|
||||
graph_service.update_from_page.return_value = MagicMock(
|
||||
success=False,
|
||||
error_message="Page not found"
|
||||
)
|
||||
|
||||
result = await reindex_page(
|
||||
page_id=999,
|
||||
user="testuser",
|
||||
vector_service=vector_service,
|
||||
graph_service=graph_service,
|
||||
api_key="test"
|
||||
)
|
||||
|
||||
assert result.success is False
|
||||
assert result.error == "Page not found"
|
||||
@@ -0,0 +1,369 @@
|
||||
"""Tests for RAG search service and endpoints."""
|
||||
|
||||
import pytest
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
from src.models.rag_search import (
|
||||
SearchType,
|
||||
RAGSearchRequest,
|
||||
RAGSearchResult,
|
||||
RAGSearchResponse,
|
||||
)
|
||||
from src.services.rag_search_service import RAGSearchService, extract_domain
|
||||
|
||||
|
||||
class TestExtractDomain:
|
||||
"""Tests for domain extraction utility."""
|
||||
|
||||
def test_extract_simple_domain(self):
|
||||
"""Test extracting domain from simple URL."""
|
||||
assert extract_domain("https://example.com/page") == "example.com"
|
||||
|
||||
def test_extract_domain_with_www(self):
|
||||
"""Test extracting domain removes www prefix."""
|
||||
assert extract_domain("https://www.example.com/page") == "example.com"
|
||||
|
||||
def test_extract_domain_with_subdomain(self):
|
||||
"""Test extracting domain preserves subdomains."""
|
||||
assert extract_domain("https://blog.example.com/post") == "blog.example.com"
|
||||
|
||||
def test_extract_domain_invalid_url(self):
|
||||
"""Test extracting domain from invalid URL returns empty string."""
|
||||
# urlparse returns empty netloc for invalid URLs
|
||||
assert extract_domain("not-a-url") == ""
|
||||
|
||||
|
||||
class TestRAGSearchModels:
|
||||
"""Tests for RAG search Pydantic models."""
|
||||
|
||||
def test_search_request_defaults(self):
|
||||
"""Test RAGSearchRequest with default values."""
|
||||
request = RAGSearchRequest(query="test query")
|
||||
|
||||
assert request.query == "test query"
|
||||
assert request.search_type == SearchType.WEB
|
||||
assert request.limit == 10
|
||||
|
||||
def test_search_request_custom_values(self):
|
||||
"""Test RAGSearchRequest with custom values."""
|
||||
request = RAGSearchRequest(
|
||||
query="news about AI",
|
||||
search_type=SearchType.NEWS,
|
||||
limit=5,
|
||||
user="custom_user"
|
||||
)
|
||||
|
||||
assert request.query == "news about AI"
|
||||
assert request.search_type == SearchType.NEWS
|
||||
assert request.limit == 5
|
||||
assert request.user == "custom_user"
|
||||
|
||||
def test_search_result(self):
|
||||
"""Test RAGSearchResult model."""
|
||||
result = RAGSearchResult(
|
||||
title="Test Article",
|
||||
url="https://example.com/article",
|
||||
content="Full article content",
|
||||
snippet="Article snippet...",
|
||||
source="example.com",
|
||||
published_date="2024-01-15"
|
||||
)
|
||||
|
||||
assert result.title == "Test Article"
|
||||
assert result.source == "example.com"
|
||||
assert result.published_date == "2024-01-15"
|
||||
|
||||
def test_search_response(self):
|
||||
"""Test RAGSearchResponse model."""
|
||||
response = RAGSearchResponse(
|
||||
query="test",
|
||||
search_type=SearchType.WEB,
|
||||
results=[],
|
||||
total_results=0,
|
||||
search_time_ms=100,
|
||||
sources_summary=""
|
||||
)
|
||||
|
||||
assert response.query == "test"
|
||||
assert response.total_results == 0
|
||||
assert response.search_time_ms == 100
|
||||
|
||||
|
||||
class TestRAGSearchService:
|
||||
"""Tests for RAGSearchService."""
|
||||
|
||||
@pytest.fixture
|
||||
def mock_searxng_client(self):
|
||||
"""Create mock SearXNG client."""
|
||||
client = MagicMock()
|
||||
client.search_general = AsyncMock(return_value=[
|
||||
{
|
||||
"title": "Test Result 1",
|
||||
"url": "https://example.com/1",
|
||||
"content": "Snippet 1",
|
||||
"publishedDate": "2024-01-15"
|
||||
},
|
||||
{
|
||||
"title": "Test Result 2",
|
||||
"url": "https://example.com/2",
|
||||
"content": "Snippet 2",
|
||||
"publishedDate": None
|
||||
}
|
||||
])
|
||||
client.search_news = AsyncMock(return_value=[])
|
||||
client.search_images = AsyncMock(return_value=[])
|
||||
return client
|
||||
|
||||
@pytest.fixture
|
||||
def mock_content_extractor(self):
|
||||
"""Create mock ContentExtractor."""
|
||||
from src.models.content import ContentExtractionResult
|
||||
|
||||
extractor = MagicMock()
|
||||
extractor.extract_batch = AsyncMock(return_value=[
|
||||
ContentExtractionResult(
|
||||
url="https://example.com/1",
|
||||
content="Full extracted content 1",
|
||||
success=True
|
||||
),
|
||||
ContentExtractionResult(
|
||||
url="https://example.com/2",
|
||||
content="Full extracted content 2",
|
||||
success=True
|
||||
)
|
||||
])
|
||||
return extractor
|
||||
|
||||
@pytest.fixture
|
||||
def mock_redis_client(self):
|
||||
"""Create mock Redis client."""
|
||||
redis = MagicMock()
|
||||
redis.get = AsyncMock(return_value=None) # No cache hit
|
||||
redis.setex = AsyncMock()
|
||||
return redis
|
||||
|
||||
@pytest.fixture
|
||||
def mock_settings(self):
|
||||
"""Create mock settings."""
|
||||
settings = MagicMock()
|
||||
settings.search_cache_ttl = 300
|
||||
settings.search_default_limit = 10
|
||||
return settings
|
||||
|
||||
@pytest.fixture
|
||||
def rag_search_service(
|
||||
self,
|
||||
mock_searxng_client,
|
||||
mock_content_extractor,
|
||||
mock_redis_client,
|
||||
mock_settings
|
||||
):
|
||||
"""Create RAGSearchService with mocked dependencies."""
|
||||
return RAGSearchService(
|
||||
searxng_client=mock_searxng_client,
|
||||
content_extractor=mock_content_extractor,
|
||||
redis_client=mock_redis_client,
|
||||
settings=mock_settings
|
||||
)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_basic(self, rag_search_service, mock_searxng_client):
|
||||
"""Test basic web search."""
|
||||
response = await rag_search_service.search(
|
||||
query="test query",
|
||||
search_type=SearchType.WEB,
|
||||
limit=10
|
||||
)
|
||||
|
||||
assert response.query == "test query"
|
||||
assert response.search_type == SearchType.WEB
|
||||
assert len(response.results) == 2
|
||||
assert response.total_results == 2
|
||||
assert response.search_time_ms >= 0
|
||||
|
||||
mock_searxng_client.search_general.assert_called_once()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_news(self, rag_search_service, mock_searxng_client):
|
||||
"""Test news search type."""
|
||||
mock_searxng_client.search_news.return_value = [
|
||||
{"title": "News", "url": "https://news.com/1", "content": "News content"}
|
||||
]
|
||||
|
||||
response = await rag_search_service.search(
|
||||
query="latest news",
|
||||
search_type=SearchType.NEWS
|
||||
)
|
||||
|
||||
assert response.search_type == SearchType.NEWS
|
||||
mock_searxng_client.search_news.assert_called_once()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_images(self, rag_search_service, mock_searxng_client):
|
||||
"""Test image search type."""
|
||||
mock_searxng_client.search_images.return_value = [
|
||||
{"title": "Image", "url": "https://images.com/1.jpg", "content": ""}
|
||||
]
|
||||
|
||||
response = await rag_search_service.search(
|
||||
query="cat photos",
|
||||
search_type=SearchType.IMAGES
|
||||
)
|
||||
|
||||
assert response.search_type == SearchType.IMAGES
|
||||
mock_searxng_client.search_images.assert_called_once()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_empty_query(self, rag_search_service):
|
||||
"""Test search with empty query raises ValueError."""
|
||||
with pytest.raises(ValueError, match="Query cannot be empty"):
|
||||
await rag_search_service.search(query="", search_type=SearchType.WEB)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_caching_miss(
|
||||
self,
|
||||
rag_search_service,
|
||||
mock_redis_client,
|
||||
mock_searxng_client
|
||||
):
|
||||
"""Test search caches results on cache miss."""
|
||||
mock_redis_client.get.return_value = None # Cache miss
|
||||
|
||||
await rag_search_service.search(query="test", search_type=SearchType.WEB)
|
||||
|
||||
# Should call SearXNG (cache miss)
|
||||
mock_searxng_client.search_general.assert_called_once()
|
||||
# Should cache result
|
||||
mock_redis_client.setex.assert_called_once()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_caching_hit(
|
||||
self,
|
||||
rag_search_service,
|
||||
mock_redis_client,
|
||||
mock_searxng_client
|
||||
):
|
||||
"""Test search returns cached results on cache hit."""
|
||||
# Simulate cache hit
|
||||
cached_response = RAGSearchResponse(
|
||||
query="test",
|
||||
search_type=SearchType.WEB,
|
||||
results=[],
|
||||
total_results=0,
|
||||
search_time_ms=50,
|
||||
sources_summary=""
|
||||
)
|
||||
mock_redis_client.get.return_value = cached_response.model_dump_json()
|
||||
|
||||
response = await rag_search_service.search(query="test", search_type=SearchType.WEB)
|
||||
|
||||
# Should NOT call SearXNG (cache hit)
|
||||
mock_searxng_client.search_general.assert_not_called()
|
||||
assert response.query == "test"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_content_extraction(
|
||||
self,
|
||||
rag_search_service,
|
||||
mock_content_extractor
|
||||
):
|
||||
"""Test search extracts content from result URLs."""
|
||||
response = await rag_search_service.search(
|
||||
query="test",
|
||||
search_type=SearchType.WEB
|
||||
)
|
||||
|
||||
# Should have called content extractor
|
||||
mock_content_extractor.extract_batch.assert_called_once()
|
||||
|
||||
# Results should have extracted content
|
||||
for result in response.results:
|
||||
assert result.content # Content should be populated
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_sources_summary(self, rag_search_service):
|
||||
"""Test search generates sources summary."""
|
||||
response = await rag_search_service.search(
|
||||
query="test",
|
||||
search_type=SearchType.WEB
|
||||
)
|
||||
|
||||
assert response.sources_summary
|
||||
assert "## Sources" in response.sources_summary
|
||||
assert "[Test Result 1]" in response.sources_summary
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_search_limit(self, rag_search_service, mock_searxng_client):
|
||||
"""Test search respects limit parameter."""
|
||||
await rag_search_service.search(
|
||||
query="test",
|
||||
search_type=SearchType.WEB,
|
||||
limit=5
|
||||
)
|
||||
|
||||
# Check limit was passed to SearXNG
|
||||
mock_searxng_client.search_general.assert_called_once_with(
|
||||
query="test",
|
||||
limit=5
|
||||
)
|
||||
|
||||
|
||||
class TestRAGSearchServiceIntegration:
|
||||
"""Integration-style tests (still mocked but test more of the flow)."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_full_search_flow(self):
|
||||
"""Test full search flow with all components mocked."""
|
||||
from src.models.content import ContentExtractionResult
|
||||
|
||||
# Setup mocks
|
||||
mock_searxng = MagicMock()
|
||||
mock_searxng.search_general = AsyncMock(return_value=[
|
||||
{
|
||||
"title": "Python Tutorial",
|
||||
"url": "https://python.org/tutorial",
|
||||
"content": "Learn Python programming",
|
||||
"publishedDate": "2024-01-10"
|
||||
}
|
||||
])
|
||||
|
||||
mock_extractor = MagicMock()
|
||||
mock_extractor.extract_batch = AsyncMock(return_value=[
|
||||
ContentExtractionResult(
|
||||
url="https://python.org/tutorial",
|
||||
title="Python Tutorial",
|
||||
content="This is a comprehensive Python tutorial covering basics to advanced topics.",
|
||||
success=True
|
||||
)
|
||||
])
|
||||
|
||||
mock_redis = MagicMock()
|
||||
mock_redis.get = AsyncMock(return_value=None)
|
||||
mock_redis.setex = AsyncMock()
|
||||
|
||||
mock_settings = MagicMock()
|
||||
mock_settings.search_cache_ttl = 300
|
||||
mock_settings.search_default_limit = 10
|
||||
|
||||
# Create service and execute search
|
||||
service = RAGSearchService(
|
||||
searxng_client=mock_searxng,
|
||||
content_extractor=mock_extractor,
|
||||
redis_client=mock_redis,
|
||||
settings=mock_settings
|
||||
)
|
||||
|
||||
response = await service.search(
|
||||
query="python tutorial",
|
||||
search_type=SearchType.WEB,
|
||||
limit=10,
|
||||
user="test_user"
|
||||
)
|
||||
|
||||
# Verify response
|
||||
assert response.query == "python tutorial"
|
||||
assert len(response.results) == 1
|
||||
assert response.results[0].title == "Python Tutorial"
|
||||
assert response.results[0].source == "python.org"
|
||||
assert "comprehensive Python tutorial" in response.results[0].content
|
||||
assert response.results[0].snippet == "Learn Python programming"
|
||||
@@ -0,0 +1,50 @@
|
||||
|
||||
|
||||
#!/bin/bash
|
||||
# Library-Desk Server Startup Script
|
||||
|
||||
set -e
|
||||
|
||||
# Colors for output
|
||||
GREEN='\033[0;32m'
|
||||
YELLOW='\033[1;33m'
|
||||
RED='\033[0;31m'
|
||||
NC='\033[0m' # No Color
|
||||
|
||||
echo -e "${GREEN}Starting Library-Desk server...${NC}"
|
||||
|
||||
# Check if port 8778 is already in use
|
||||
if lsof -Pi :8778 -sTCP:LISTEN -t >/dev/null 2>&1 ; then
|
||||
echo -e "${RED}Error: Port 8778 is already in use${NC}"
|
||||
echo "Run: lsof -i :8778 to see what's using it"
|
||||
echo "Or run: kill \$(lsof -t -i:8778) to stop it"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Activate virtual environment if not already activated
|
||||
if [ -z "$VIRTUAL_ENV" ]; then
|
||||
if [ -d ".venv" ]; then
|
||||
echo -e "${YELLOW}Activating virtual environment...${NC}"
|
||||
source .venv/bin/activate
|
||||
else
|
||||
echo -e "${RED}Error: Virtual environment not found${NC}"
|
||||
echo "Run: python -m venv .venv && source .venv/bin/activate && pip install -r requirements.txt"
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
|
||||
# Create logs directory if it doesn't exist
|
||||
LOGS_DIR="logs"
|
||||
mkdir -p "$LOGS_DIR"
|
||||
|
||||
# Clear/create log file
|
||||
LOG_FILE="$LOGS_DIR/server.log"
|
||||
> "$LOG_FILE"
|
||||
echo -e "${YELLOW}Logs will be written to: ${LOG_FILE}${NC}"
|
||||
|
||||
# Start the server
|
||||
echo -e "${GREEN}Starting uvicorn server on http://tower-of-joy:8778${NC}"
|
||||
echo -e "${YELLOW}Press Ctrl+C to stop the server${NC}"
|
||||
echo ""
|
||||
|
||||
uvicorn src.main:app --reload --host 0.0.0.0 --port 8778 2>&1 | tee "$LOG_FILE"
|
||||
Reference in New Issue
Block a user