Add complete integration between NotebookLM Agent and DocuMente RAG:
New Components:
- NotebookLMIndexerService: Syncs NotebookLM content to Qdrant vector store
- notebooklm_sync API routes: Manage notebook indexing (/api/v1/notebooklm/*)
Enhanced Components:
- RAGService: Added notebook_ids filter and query_notebooks() method
- VectorStoreService: Added filter support for metadata queries
- DocumentService: Added ingest_notebooklm_source() method
- Query routes: Added /query/notebooks endpoint for notebook-only queries
- Main API: Integrated new routes and updated to v2.1.0
Features:
- Sync NotebookLM notebooks to local vector store
- Query across documents and/or notebooks
- Filter RAG queries by specific notebook IDs
- Manage indexed notebooks (list, sync, delete)
- Track sync status and metadata
API Endpoints:
- POST /api/v1/notebooklm/sync/{notebook_id}
- GET /api/v1/notebooklm/indexed
- DELETE /api/v1/notebooklm/sync/{notebook_id}
- GET /api/v1/notebooklm/sync/{notebook_id}/status
- POST /api/v1/query/notebooks
Closes integration request for unified NotebookLM + RAG agent
## Changes
- Update all references from AgenticRAG to DocuMente
- Update README.md with new project description and structure
- Update LICENSE with new project name
- Update API title and descriptions in main.py
- Update frontend components (Layout, Login, Dashboard, Settings)
- Update static HTML page
- Update all documentation files (prd-v2.md, frontend-plan.md, etc.)
- Update test files with new project name
- Update docker-compose.yml, Dockerfile, requirements.txt
## SEO Benefits
- DocuMente combines 'Documento' and 'Mente' (Italian for Document and Mind)
- Memorable and brandable name
- Reflects the core functionality: AI-powered document intelligence
🎉 Project officially renamed to DocuMente!
Major refactoring from NotebookLM API to Agentic Retrieval System:
## New Features
- AgenticRAG backend powered by datapizza-ai framework
- Web interface for document upload and chat
- REST API with Swagger/OpenAPI documentation
- Document processing pipeline (Docling, chunking, embedding)
- Qdrant vector store integration
- Multi-provider LLM support (OpenAI, Google, Anthropic)
## New Components
- src/agentic_rag/api/ - FastAPI REST API
- Documents API (upload, list, delete)
- Query API (RAG queries)
- Chat API (conversational interface)
- Swagger UI at /api/docs
- src/agentic_rag/services/
- Document service with datapizza-ai pipeline
- RAG service with retrieval + generation
- Vector store service (Qdrant)
- static/index.html - Web UI (upload + chat)
## Dependencies
- datapizza-ai (core framework)
- datapizza-ai-clients-openai
- datapizza-ai-embedders-openai
- datapizza-ai-vectorstores-qdrant
- FastAPI, Pydantic, Qdrant
## API Endpoints
- POST /api/v1/documents - Upload documents
- GET /api/v1/documents - List documents
- POST /api/v1/query - Query knowledge base
- POST /api/v1/chat - Chat interface
- GET /api/docs - Swagger documentation
- GET / - Web UI
🏁 Ready for testing and deployment!
Implement Sprint 3: Chat Functionality
- Add ChatService with send_message and get_history methods
- Add POST /api/v1/notebooks/{id}/chat - Send message
- Add GET /api/v1/notebooks/{id}/chat/history - Get chat history
- Add ChatRequest model (message, include_references)
- Add ChatResponse model (message, sources[], timestamp)
- Add ChatMessage model (id, role, content, timestamp, sources)
- Add SourceReference model (source_id, title, snippet)
- Integrate chat router with main app
Features:
- Send messages to notebook chat
- Get AI responses with source references
- Retrieve chat history
- Support for citations in responses
Tests:
- 14 unit tests for ChatService
- 11 integration tests for chat API
- 25/25 tests passing
Related: Sprint 3 - Chat Functionality
- Add 28 unit tests for SourceService (TEST-004)
- Test all CRUD operations
- Test validation logic
- Test error handling
- Test research functionality
- Add 13 integration tests for sources API (TEST-005)
- Test POST /sources endpoint
- Test GET /sources endpoint with filters
- Test DELETE /sources endpoint
- Test POST /sources/research endpoint
- Fix ValidationError signatures in SourceService
- Fix NotebookLMError signatures
- Fix status parameter shadowing in sources router
Coverage: 28/28 unit tests pass, 13/13 integration tests pass
Implement Sprint 2: Source Management
- Add SourceService with create, list, delete, research methods
- Add POST /api/v1/notebooks/{id}/sources - Add source (URL, YouTube, Drive)
- Add GET /api/v1/notebooks/{id}/sources - List sources with filtering
- Add DELETE /api/v1/notebooks/{id}/sources/{source_id} - Delete source
- Add POST /api/v1/notebooks/{id}/sources/research - Web research
- Add ResearchRequest model for research parameters
- Integrate sources router with main app
Endpoints:
- POST /sources - 201 Created
- GET /sources - 200 OK with pagination
- DELETE /sources/{id} - 204 No Content
- POST /sources/research - 202 Accepted
Technical:
- Support for url, youtube, drive source types
- Filtering by source_type and status
- Validation for research mode (fast/deep)
- Error handling with standardized responses
Related: Sprint 2 - Source Management
