Commit 83cff11
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feat: Enhanced RAG system with hybrid search, smart chunking, and controlled re-retrieval
Core Improvements:
- Hybrid search (BM25 + semantic) using QdrantVectorStore with FastEmbedSparse
- Two-stage HTML chunking (header splitting + recursive) for 20-30x better website processing
- Language-aware code splitting (Python, JS, TS, Java, C++, Go) with RecursiveCharacterTextSplitter
- MMR retrieval for diversity in search results
- Controlled re-retrieval with quality assessment (max 1 retry, stops at 0.6 quality threshold)
RAG Agent Swarm:
- Fixed LangGraph state management for proper re-retrieval control
- Re-retrieval flag set in QA node, read in conditional function (correct LangGraph pattern)
- Agent chat page now auto-initializes vector store from Qdrant
- All documents properly chunked before adding to vector store
UI Improvements:
- Document upload page correctly displays all Qdrant-persisted documents
- Sidebar status accurately reflects vector database state
- Website scraping now properly chunks scraped content
- Enhanced document type detection from metadata
Dependencies:
- Upgraded langchain-qdrant to >=0.2.1 for hybrid search support
- Added fastembed>=0.7.3 for BM25 sparse embeddings
Tests:
- 228 tests passing
- Removed broken test_us024_automated_story_management.py
- Complete workflow test verified (2m 11s)
Phase 3 US-RAG-001 acceptance criteria completed:
- AC-3.1: QdrantVectorStore with hybrid search ✅
- AC-3.2: Semantic search <500ms ✅
- AC-3.5: MMR-based retrieval ✅
- AC-3.6: Proper LangChain retriever interface ✅
- AC-3.7: Two-stage HTML splitters ✅
- AC-3.8: Language-aware code splitting ✅
- AC-3.9: Controlled re-retrieval ✅1 parent 7575798 commit 83cff11
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