v2.1.5

What's Changed

Proper Hybrid Search with Reciprocal Rank Fusion (RRF)

Implemented industry-standard hybrid retrieval using RRF algorithm:

How it works:

1. Stage 1: Run k-NN search (semantic similarity) and BM25 search (lexical matching) independently
2. Stage 2: Fuse results using RRF: score = dense_weight/(k+rank_dense) + sparse_weight/(k+rank_sparse)
3. Stage 3: Return top results sorted by fused score

Why RRF?

• Robust to score distribution differences between retrieval methods
• No need for score normalization (uses ranks, not raw scores)
• Industry standard (used by Elasticsearch, Cohere, etc.)
• Works with OpenSearch Serverless (no scripting required)

New methods:

• text_search() - BM25 lexical search on text_content
• hybrid_search() - RRF-fused k-NN + BM25

Parameters:

• rrf_k=60 - Standard RRF constant from literature
• dense_weight=0.7 - Weight for semantic results
• sparse_weight=0.3 - Weight for lexical results

Full Changelog: v2.1.4...v2.1.5