hybrid-search-implementation

Hybrid search combines vector similarity with exact keyword matching to improve retrieval recall and precision. By enabling simultaneous semantic and lexical matching, it retrieves more relevant results across diverse data sources and mixed vocabularies.

To combine vector similarity and keyword matching in a RAG system, configure a hybrid search pipeline by wiring a vector store with a keyword index. Select a fusion method like Reciprocal Rank Fusion to retrieve and rank results.

Available fusion strategies for ranking hybrid search results include Reciprocal Rank Fusion (RRF), linear combination, cross-encoder reranking, and cascade. These methods merge semantic and lexical scores to improve final result relevance.

Yes, hybrid search works with PostgreSQL and Elasticsearch backends. The implementation provides templates and integration examples for both platforms, alongside support for building custom pipelines to deploy end-to-end retrieval systems.

Use cross-encoder reranking instead of linear combination when you need deeper query-document interaction for higher precision. Linear combination simply averages scores, while cross-encoder reranking evaluates pairs to refine the final hybrid search ranking.