Vector search retrieves information by comparing embeddings, finding the items whose vectors are closest to the query's vector, rather than matching exact keywords. This lets systems surface conceptually relevant content even when the wording differs. Vector search underpins the retrieval step in many RAG pipelines and modern AI answer engines.