hybrid-search-implementation

Hybrid search is a retrieval method that combines vector similarity with keyword matching. You need it for RAG systems and domain-specific search where both semantic understanding and exact term matching are required for sharper recall.

To implement hybrid search, instantiate a pipeline with a vector store and a keyword index. Provide a query and its embedding, run the fusion to return top results, validate the output, and adjust the fusion scoring parameters as needed.

Yes, hybrid search works for code search by returning functionally similar code snippets through vector similarity and exact API name matches through keyword indexing, balancing both signals for precise retrieval.

The best way to balance vector and keyword search scores is to apply a fusion strategy with adjustable scoring parameters. Validate the returned top results and tune the parameters until the semantic and exact-term signals are properly weighted.

Pure vector search lacks exact term matching precision, while pure keyword search misses semantic understanding. Hybrid search solves this limitation by fusing both signals to improve recall and precision in knowledge bases and search systems.

Yes, you need integration of both a vector store and a keyword index to run hybrid search. These components store the embeddings and exact terms, allowing the fusion strategy to combine their scores for final retrieval.