A vector database stores embeddings with their source text and metadata, then supports fast similarity search. It also provides indexing, filtering, updates, deletion, replication, and operational controls.
Comparing a query with every vector gives exact nearest neighbors, but the work grows linearly with the collection. That can be too slow and expensive for millions of vectors or high query traffic.
Vector databases use approximate nearest neighbor indexes to examine a much smaller candidate set. This trades a little recall for large speed gains. Exact search is still reasonable for small collections, offline evaluation, or a final reranking step after filtering.
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