hybrid-search-implementation

Hybrid search combines vector and keyword search to improve RAG retrieval accuracy by utilizing Reciprocal Rank Fusion (RRF), linear combination, and cross-encoder reranking. This fusion ensures fewer relevant documents are missed by capturing both semantic meaning and exact matches.

Pure semantic search misses specific product codes because it relies on contextual meaning rather than exact string matches. Hybrid search solves this by combining semantic understanding with exact keyword matching to capture specific names, codes, and domain-specific jargon.

Reciprocal Rank Fusion (RRF) is a result fusion technique for search engines that merges vector and keyword search rankings. It calculates a unified score based on the reciprocal of each document's rank across both methods to produce a more accurate combined result list.

The best way to implement hybrid search for domain-specific knowledge bases is fusing vector and keyword search methods. This approach ensures both the semantic meaning of queries and exact domain-specific jargon matches are considered for comprehensive retrieval accuracy.

Cross-encoder reranking should be used in a search engine when you need to optimize retrieval performance after initial vector and keyword searches. It refines the fused results to provide the highest possible relevance for complex user queries.