Vector Database
A store of embeddings that supports fast similarity search for retrieval.
A vector database stores high-dimensional embeddings and retrieves the entries most semantically similar to a query vector, typically using approximate nearest-neighbor search. This makes it the backbone of Retrieval-Augmented Generation (RAG): a foundation model in Amazon Bedrock embeds the user’s query, the vector database returns the closest matching document chunks, and those chunks are passed as context before the model answers. On AWS, two common choices are Amazon OpenSearch Service (k-NN plugin) and Amazon Aurora PostgreSQL with the pgvector extension. A vector database handles retrieval — it does not generate the embeddings; that belongs to the embedding model.
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