RAG & Search
Vector database
A database optimized for storing and searching numerical representations called vectors.
Example
Document embeddings are stored so similar meanings can be found quickly.
Why it matters
It helps find related material in collections too large to browse manually.
You'll hear it
“The search found related passages even though the wording differed.”
Can you control it?
Set by the team
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- Does a vector database understand the stored documents?
- No. It organizes numerical descriptions; whether useful material is found depends on how those descriptions and records were prepared.
- Can a vector database enforce document permissions?
- It can support access restrictions, but the surrounding system must apply the correct permissions.
- What should teams monitor in a vector database?
- Check that updates appear, restricted records stay hidden and important searches still return useful results.