What Is a Vector Database?

A vector database is a database built to store embeddings and search them by similarity, quickly finding the items whose meaning is closest to a query. It is the storage and retrieval layer behind semantic search and retrieval-augmented generation, where finding the most relevant content fast is what matters.

How a vector database works

A vector database stores data as embeddings, the numeric vectors that capture meaning, and is optimized to answer one main question quickly: which stored vectors are closest to this one? To do that over millions of items without comparing every pair, it uses specialized indexes that find near neighbors fast, trading a little exactness for a large gain in speed.

In practice, content is embedded and loaded into the database ahead of time. When a query comes in, it is embedded the same way, and the database returns the nearest matches, the content most similar in meaning, which then feeds semantic search or the retrieval step in RAG.

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What is a vector database used for?

Storing embeddings and finding the most similar ones to a query quickly. This powers semantic search, recommendation, and the retrieval step in RAG, where a system needs the content closest in meaning to a question.

How is a vector database different from a regular database?

A regular database looks up exact matches in structured fields. A vector database searches by similarity of meaning across embeddings, returning the closest matches rather than exact ones, which suits AI retrieval.

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