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What Is Semantic Search?
How semantic search differs from keyword search, and why it's the retrieval mechanism most RAG systems rely on.
Direct answer
Semantic search finds results based on meaning rather than exact keyword matches, by comparing vector embeddings of the query and candidate documents — this lets a search for 'reduce server costs' match a document about 'lowering infrastructure spend' even though they share no keywords, which is why it's the retrieval mechanism most RAG systems rely on instead of plain text matching.
Last reviewed 2026-08-29
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Why keyword search misses semantically related content
Traditional keyword search matches literal terms — a search for 'car' won't find a document that only says 'automobile' unless the system has separate synonym handling. Semantic search compares the underlying meaning (via embeddings), so conceptually related content matches even with no shared vocabulary, which is particularly valuable for natural-language queries that don't use the exact terminology of the source documents.
How it works at a high level
- Documents are converted into vector embeddings and stored in a vector database.
- A search query is converted into an embedding using the same model.
- The system finds documents whose embeddings are closest (most similar) to the query embedding, typically via a distance metric like cosine similarity.
Where it still falls short
Semantic search can miss exact-match needs — searching for a specific error code or product SKU often performs worse with pure semantic search than with keyword matching, since these queries aren't really about 'meaning.' Many production systems use a hybrid of keyword and semantic search to cover both cases.