Vector database
By HubAI editorial team · Source checked: 24 October 2026What does it mean?
A database or database capability for storing vectors and finding similar ones. In AI applications those vectors are often embeddings, with links back to original documents and metadata.
Picture this
An internal search tool stores an embedding for each policy passage, then searches for passages near the embedding of your question.
Where you’ll come across it
Retrieval systems, recommendations and similarity search. Metadata filters can help restrict results by department, date or access level.
What it doesn’t mean
Storing a document’s vector is not the same as maintaining a complete, permission-aware knowledge system. Updates, deletion and access rules still need explicit handling.
Test whether the evidence survives
A short exercise · use fictional or non-sensitive material.
- Write a short fictional policy with one rule and one exception. Ask a question that needs both.
- Try a second question whose answer is deliberately missing from the policy.
- Check whether the answer points to the right passage and admits when the policy cannot answer.
Does the cited passage support this exact answer, including its exception?
Did that make it clearer?
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Technical reference: huggingface.co ↗