Semantic Search — definition

Semantic search is a retrieval approach that finds documents by meaning rather than keyword overlap. A query and all candidate documents are encoded as embeddings, and results are ranked by vector similarity — so a search for 'heart failure treatment' returns results about 'cardiac management protocols' even without exact keyword matches.

Semantic search is the retrieval backbone of RAG pipelines and enterprise knowledge bases. Hybrid search — combining semantic and keyword (BM25) retrieval — outperforms either approach alone and is the production default for most enterprise search deployments in 2026.

Related terms

Embedding — An embedding is a fixed-length vector of numbers that represents the semantic meaning of a piece of text (or image, audio).

Vector Database — A vector database is a database optimized for storing embeddings and answering similarity queries ("give me the 10 most similar items to this one").

RAG (Retrieval-Augmented Generation) — RAG is a pattern in which an AI model retrieves relevant documents from a knowledge base at query time and uses them as additional context to generate its response.