feat(rag): add hybrid search using RRF score fusion#492
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🔗 Related Issue
Closes #440
📝 What does this PR do?
Replaces the fake RRF approximation in
retriever.pywith a correctReciprocal Rank Fusion implementation and removes the
EnsembleRetrieverdependency.
backend/app/rag/retriever.py:rrf_merge(vector_results, bm25_results, k)— implements the standardRRF formula
score(d) = Σ 1/(k + rank)across both ranked lists, deduplicatesby content key, and returns chunks sorted by descending RRF score.
EnsembleRetriever/CustomVectorRetriever/CustomBM25RetrieverLangChain wrapper classes —
query_chunksandquery_bm25are called directly,giving full control over each ranked list before fusion.
retrieve()now callsembed_query→query_chunks→query_bm25→rrf_mergeper query variant, then promotesrrf_score→scorebeforepassing candidates to the cross-encoder reranker. Existing reranking and
confidence normalisation logic is unchanged.
USE_HYBRID_SEARCH=Falseor BM25 raises.backend/app/config.py:USE_HYBRID_SEARCH: bool = True— toggle hybrid search withoutredeploying.
RRF_K: int = 60— exposes the RRF smoothing constant; 60 is thevalue from the original RRF paper and the standard production default.
🗂️ Type of Change
🧪 How was this tested?
uvicorn app.main:app --reload)returned chunks and that chunks appearing in both lists score higher
than single-list results
USE_HYBRID_SEARCH=False; confirmed vector-only path runs andquery_bm25is never calledrank_bm25from env; confirmed graceful fallback tovector-only via the
exceptguard✅ Self-Review Checklist
dev, notmainmainbranch or any HuggingFace deployment config