A vector database stores embeddings — numeric representations of text, images or other data — and finds the items most similar to a query by distance between vectors. It powers semantic search and the retrieval step of RAG systems.
Dedicated vector databases exist, but many data platforms and search engines now offer vector indexes, so retrieval can sit next to the governed source data.
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