Hybrid Search Implementation — BM25 Plus Vector Search Combined

I will implement hybrid search for your application — combining BM25 sparse retrieval with dense vector retrieval using Reciprocal Rank Fusion, consistently outperforming either approach alone on real-world query distributions that contain a mix of conceptual and exact-match queries. Pure semantic search misses exact-match queries where users search for specific product names, identifiers, or technical terms that appear verbatim in your content; pure keyword search misses conceptual queries where meaning matters more than keyword overlap; hybrid search handles both by combining their result sets before the final ranking step.

The implementation covers BM25 index configuration, dense vector index setup, parallel retrieval from both indexes, Reciprocal Rank Fusion score combination, result deduplication and ranking, and retrieval quality benchmark comparing hybrid against each individual mode.