# 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 embeds your corpus offline and the query at runtime, then returns nearest neighbors in that shared vector space, so users never have to guess a document's vocabulary. The flip side is exact tokens like error codes and SKUs, which is why mature systems add keyword search and a reranker.

**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).

---

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