# Semantic Search

> Semantic search retrieves results by meaning rather than keyword overlap — embedding queries and documents in one vector space and matching by similarity.

**Semantic search retrieves documents by meaning instead of word overlap: queries and documents are mapped into the same [embedding](/glossary/embedding) space, and relevance becomes vector similarity.**

The mechanism is simple once embeddings exist — embed the corpus offline into a [vector database](/glossary/vector-database), embed the query at runtime, return the nearest neighbors. The payoff is robustness to phrasing: users don't need to guess the document's vocabulary. The cost is the flip side: semantic search can miss **exact tokens** — error codes, function names, SKUs — that old-fashioned keyword search nails, and it inherits whatever blind spots the embedding model has in your domain.

That's why mature retrieval is rarely semantic-only. **Hybrid search** pairs BM25 keyword retrieval with vector search, and a [reranker](/glossary/reranking) re-sorts the merged candidates — recall from breadth, precision from the reranker. The full pattern, with when each piece earns its place, is in [Hybrid Search & Reranking](/guides/concepts/hybrid-search-reranking).

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