Vector Search
Vector search finds content by meaning rather than exact words. Text is converted into high-dimensional embeddings, and a similarity metric such as cosine distance ranks stored vectors by how close they are to the query vector, returning conceptually related passages even when no keywords match.
Synonyms: semantic search, similarity search, nearest-neighbor search, embedding search
Vector search powers semantic retrieval: instead of matching strings, it matches meaning. A query is embedded into the same vector space as the indexed content, and the index returns the nearest vectors by a distance metric. To stay fast at scale, production systems use approximate nearest-neighbor indexes that accept tiny accuracy trade-offs for major latency wins. Vector search is most effective when paired with keyword search in a hybrid retriever, so exact identifiers are not lost to pure semantic matching.