Hybrid search
A retrieval technique that combines vector (semantic) search with keyword (lexical) search, fusing the scores to get higher precision than either alone. The 2026 production-grade default for RAG.
Hybrid search solves a real RAG limitation: pure vector search misses exact-match queries (error codes, product SKUs, proper nouns); pure keyword search misses synonym queries. Combining them via reciprocal rank fusion or weighted scoring gets the best of both.
In 2026, hybrid search is the production-grade default. Most major vector databases (Pinecone, Weaviate, Qdrant) ship with built-in hybrid. Sourcecode databases like Elasticsearch (with vector extensions) excel at hybrid.
For agent builders, hybrid search means your RAG returns better results without you having to choose between "semantic" and "exact match." Worth the extra implementation complexity for any production RAG.
Frequently asked
Is hybrid search worth the complexity?+
Yes for production RAG. Typical improvement: 10–25% lift in recall@5 over pure vector search. The implementation overhead is small with modern vector databases.
How are scores combined in hybrid search?+
Reciprocal Rank Fusion (RRF) is the standard — combine the ranks from each search type, weighted equally. Some implementations weight by query type (vector for natural language, keyword for codes/IDs).