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Vector database

How models work Last reviewed 2026-06-11

Definition A database built to store embeddings—numerical representations of meaning—and quickly find the entries most similar to a query. The storage layer behind semantic search and most retrieval-augmented generation systems.

In more depth

Instead of matching keywords, a vector database compares the geometry of embeddings to return conceptually similar documents, even across millions of records. In legal AI products, firm or matter documents are typically split into chunks, embedded, and indexed this way so a model can be handed the most relevant passages. Because the indexed content may include client material, vector stores fall within the scope of confidentiality and security review like any other document repository.

Further reading: Wikipedia.

Related terms

EmbeddingRetrieval-augmented generation (RAG)Legal research AI

About the editor: MHSB Solutions, Research desk. MHSB Solutions is not a law firm. This glossary is educational information, not legal advice.

Educational information, not legal advice. AI terminology and tools change quickly; definitions reflect usage as of the last-updated date. For what bar associations and courts actually require of lawyers using AI, see legalaicompliance.help and consult a licensed attorney in your jurisdiction.