Hybrid Search

Hybrid search combines dense vector similarity with sparse keyword matching — typically BM25 — to retrieve documents. Dense retrieval excels at capturing semantic similarity while sparse retrieval handles exact term matches and rare proper nouns that embedding models may underweight. Hybrid systems fuse scores from both methods, often using reciprocal rank fusion, producing retrievers that outperform either approach alone across a wider range of query types.