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Polish: BGE-large embeddings, contextual retrieval, 142 tests passing, lint clean
- Switch embedding model to BAAI/bge-large-en-v1.5 (MTEB top-ranked, 1024-dim) - Enable contextual retrieval (Anthropic Nov 2024) — 49% fewer retrieval failures - Enable HyDE, multi-query expansion, hybrid BM25+RRF, cross-encoder reranking - Fix FastAPI Path shadowing bug in api.py - Fix 3 failing tests → 142/142 passing - Fix ruff.toml format (was using pyproject.toml syntax) - Auto-fix + manually resolve all 257 lint issues → 0 errors - Add docs/rag_techniques.md and docs/system_architecture.md (demo ingestion content) - Update README with production config section and stack table - Re-ingest ChromaDB with new embedding model (BGE-large, incompatible with old MiniLM)21ca2ea
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Initial commit — production RAG system
Joshua Sears +1: Claude Sonnet 4.620+ retrieval techniques from 2022–2025 research: hybrid dense+BM25+RRF, cross-encoder reranking, CRAG, RAPTOR, GraphRAG, LightRAG, CoT-RAG, Adaptive RAG, Sufficient Context abstention, Agentic RAG, and more. FastAPI (35+ endpoints), Streamlit demo, Docker, CI/CD quality gate. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>bd8abf4