Hybrid Vector Search

Category: science

A retrieval strategy that combines keyword-based lexical algorithms (BM25) with vector-based semantic algorithms (Dense Embeddings).

Hybrid search addresses the blind spots of both retrieval methods. Dense vectors excel at capturing abstract concepts and context but can miss exact product SKUs, part numbers, or rare addresses. Running a sparse keyword pass alongside a dense semantic pass creates a robust query engine perfect for looking up specific storm-damage coordinates or exact collectible serial numbers.

Common Examples

• Our real estate application uses a hybrid vector search to match abstract user descriptions like 'historic charm' while still locking onto precise ZIP code filters.
• A hybrid vector search requires a reciprocal rank fusion algorithm to cleanly merge and balance the text-match scoring with the vector-space distance metrics.