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feat(rag): FAISS vectors and hybrid retrieval #12

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@haz3141 haz3141 commented Sep 6, 2025

Step 6: Advanced RAG Enhancements

Features Added

  • Embeddings Pipeline: Added Embeddings class with sentence-transformers support and stub fallback for offline testing
  • FAISS Vector Store: Added FaissIndex for efficient vector similarity search with numpy fallback
  • Hybrid Retriever: Combined BM25 and vector scores with configurable alpha weighting
  • MCP Endpoint: Added /tools/retrieve_hybrid endpoint for hybrid retrieval
  • Comprehensive Tests: Added tests for hybrid retrieval with deterministic stub embeddings
  • CI Updates: Updated CI to run hybrid retrieval tests with stub embeddings

Technical Details

  • Uses sentence-transformers for embeddings (with fallback to stub for tests)
  • FAISS for vector similarity search (with numpy fallback)
  • Hybrid scoring combines normalized BM25 and vector scores
  • All tests use stub embeddings for deterministic, offline testing
  • Environment variables for configuration (EMBED_BACKEND, EMBED_MODEL, HYBRID_ALPHA)

Version

Bumps to v0.5.0

Testing

  • All tests pass with stub embeddings
  • Hybrid endpoint tested and working
  • CI configuration updated to include hybrid tests

haz3141 added 18 commits September 6, 2025 16:20
@haz3141
chore(deps): add dspy-ai and pin versions
b0ef6d5
@haz3141
feat(dsp): add Summarize module and demo runner
2ce1db9
@haz3141
feat(mcp): expose /tools/summarize backed by DSPy module
10c3a53
@haz3141
test(dsp): add unit test and HTTP smoke test
6e0a6fe
@haz3141
chore(env): add DSPY_MODEL placeholder to .env.sample
0d6baee
@haz3141
chore(version): bump to 0.3.0 for DSPy integration
538daeb
@haz3141
ci: add minimal lint+unit test workflow
0cc896c
@haz3141
feat(rag): scaffold ingestion, chunking, BM25 retriever with fixture
6c62483
@haz3141
test(rag): add deterministic unit test for chunking and BM25 retrieval
c5a60df
@haz3141
feat(mcp): add /tools/retrieve using minimal BM25 over lab fixtures
048d069
@haz3141
chore(env): add RAG_DATA_DIR and RAG_TOP_K placeholders
1613c8c
@haz3141
ci: run RAG unit test alongside summarize test
08a1556
@haz3141
chore(version): bump to 0.4.0 for minimal RAG endpoint
38b1a2f
@haz3141
feat(rag): add embeddings pipeline, FAISS vector store, and hybrid re…
e79b004 
…triever

- Add Embeddings class with sentence-transformers and stub fallback
- Add FaissIndex for vector similarity search with numpy fallback
- Add HybridRetriever combining BM25 and vector scores
- Add comprehensive tests for hybrid retrieval
- Update requirements.txt with new dependencies
@haz3141
feat(mcp): add /tools/retrieve_hybrid endpoint
92c6993 
- Add RetrieveHybridRequest model with query, k, and alpha parameters
- Add retrieve_hybrid endpoint that uses HybridRetriever
- Returns hits with both vector and BM25 scores for transparency
@haz3141
chore(env): add EMBED_BACKEND, EMBED_MODEL, HYBRID_ALPHA placeholders
4017dfd 
- Update .env.sample with new environment variables for embeddings and hybrid retrieval
- Update CI configuration to run hybrid retrieval tests with stub embeddings
@haz3141
chore(version): bump to 0.5.0 for advanced RAG (FAISS + hybrid)
043e22c
@haz3141
feat(security): add Guardian allowlist + PII redaction
3107431 
- Add lab/security/redact.py for PII pattern redaction
- Add lab/security/guardian.py for tool allowlist and response sanitization
- Add comprehensive tests for security modules
- Integrate Guardian into MCP server endpoints
- Add audit logging for all tool calls
- Add evaluation harness with hit@k and mrr@k metrics
- Add observability with JSONL audit logs
- Add promotion flow documentation and security checklist
- Update CI with new tests and evaluation step
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@haz3141