Vector Database
A vector database stores embeddings and retrieves them by similarity, using approximate nearest-neighbor search to find the vectors closest to a query vector quickly. It is the storage and search layer behind semantic search and retrieval-augmented generation, scaling similarity lookups to millions or billions of items.
Definition
Vector DatabaseOnce text or other data is turned into embeddings, those vectors need to be searched by closeness rather than exact match. A vector database indexes them with approximate nearest-neighbor algorithms — such as HNSW graphs — so that similarity queries run in sublinear time even over very large collections.
Vector databases typically add metadata filtering, hybrid keyword-plus-vector search, and persistence and scaling features on top of the core index. They are a standard component of RAG pipelines, holding the pre-computed document embeddings that retrieval draws on at query time.
Frequently asked questions
What is Vector Database?
A vector database stores embeddings and retrieves them by similarity, using approximate nearest-neighbor search to find the vectors closest to a query vector quickly. It is the storage and search layer behind semantic search and retrieval-augmented generation, scaling similarity lookups to millions or billions of items.
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