# Hybrid Search

> Hybrid search runs keyword (BM25) and semantic (vector) retrieval together and merges the results — catching both exact terms and paraphrases.

**Hybrid search retrieves with two engines at once — lexical keyword search (BM25) and [semantic vector search](/glossary/semantic-search) — and merges their results, so queries match both by exact terms and by meaning.**

It exists because neither half suffices alone. Pure vector retrieval has a famous blind spot: **exact strings** — error codes, function names, part numbers — where "semantically similar" is precisely wrong. Pure keyword search has the inverse: zero tolerance for vocabulary mismatch between askers and documents. Production corpora contain both query types, so production retrieval runs both engines — usually merged by Reciprocal Rank Fusion (rank-based, immune to score-scale mismatch) and refined by a [reranker](/glossary/reranking) that sorts the combined pool.

Adoption is now mostly a checkbox: [vector databases](/glossary/vector-database) from Qdrant to Weaviate to pgvector-based stacks ship hybrid retrieval natively. The judgment that remains is tuning — fusion weights per corpus, and measuring whether the lexical leg actually helps *your* queries — covered with the full recall-to-precision architecture in [Hybrid Search & Reranking](/guides/concepts/hybrid-search-reranking). When [RAG](/glossary/rag) misses queries containing exact identifiers, hybrid search is the first fix on [the debugging checklist](/guides/troubleshooting/rag-debugging-checklist).

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_Source: https://agentscamp.com/glossary/hybrid-search — Term on AgentsCamp._