hybrid-search-implementation

Hybrid search is a retrieval approach that fuses vector similarity with keyword matching. You need it when your search pipeline requires both semantic understanding and precise term matching to improve recall and precision.

You can combine vector search and keyword matching in a RAG system by wiring this hybrid architecture into your search pipeline and supplying a query string along with its vector embedding to return fused results.

The best fusion strategies for hybrid information retrieval include Reciprocal Rank Fusion (RRF), linear combination, and cross-encoder reranking, allowing you to balance result quality against processing latency.

Yes, hybrid search with reranking works for enterprise document retrieval. It is designed for domain-specific search and enterprise contexts where both semantic meaning and exact terms matter.

You can configure rank fusion methods to balance quality and latency by selecting from provided strategies like RRF, linear combination, or cross-encoder reranking based on your specific performance requirements.

Using only vector similarity limits precise term matching, while hybrid search overcomes this by combining semantic similarity with exact keyword matching to deliver more accurate and comprehensive retrieval results.