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Glossary · Engineering

Vector database

A vector database stores embeddings and finds the items most similar to a query quickly. It is the search layer behind many semantic search, recommendation and retrieval-augmented generation features.

In one line

A vector database stores embeddings and finds the items most similar to a query quickly. It is the search layer behind many semantic search, recommendation and retrieval-augmented generation features.

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Why vector databases matter

Comparing a query against millions of vectors one by one is slow. Vector databases use special indexes so similarity search stays fast as content grows.

How vector databases work

Each item is stored as a vector with metadata, such as its source, date and who can see it. A query is embedded the same way, and the database returns the nearest vectors, filtered by that metadata.

Options range from dedicated services to extensions for databases you may already use, such as Postgres.

Common vector database mistakes

Adding a new database when an extension to your existing one would do. Storing vectors without permission metadata. Never measuring whether the right results come back.

How Gr8r approaches vector databases

We start with the simplest option that meets your scale and security needs. We store access rules alongside each vector, filter on them at query time and test retrieval with real questions.

Example

A legal team's document search stores each contract clause as a vector tagged with client and matter, so a lawyer only ever retrieves clauses from matters they are assigned to.

FAQ

Questions

  • Do I need a dedicated vector database?

    Not always. For many products, a vector extension on an existing database such as Postgres is enough and simpler to run.

  • Is data in a vector database secure?

    It is as secure as you make it. Tenant isolation and permission filters need to be designed in, just as with any other database.

Related terms.

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