AI glossary · Building and running AI
Vector database
A vector database stores embeddings and quickly finds the ones most similar to a query, which lets AI apps search by meaning across large collections.
Also called vector store
In more detail
Ordinary databases are good at exact matches. Vector databases are built to find the nearest embeddings, the items that mean something similar.
They are a common part of retrieval-augmented generation, where an app looks up relevant documents before asking a model to answer.
Related terms
EmbeddingAn embedding is a list of numbers that represents the meaning of a piece of text, an image or another item, so a computer can measure how similar things are.Retrieval-augmented generation (RAG)Retrieval-augmented generation (RAG) makes AI answers more accurate by first looking up relevant information, then having the model answer using it.Semantic searchSemantic search finds results based on what you mean rather than the exact words you type, using AI to understand the intent behind a query.
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