Vector database
A vector database answers one question extremely fast: which of these million vectors are closest to this one.
Why it matters
Comparing a query against every stored vector works fine for a hundred documents and becomes unusable at a hundred thousand. Vector databases exist to make nearest-neighbour search fast enough to sit in a request.
- Naive similarity search that scales linearly with content size
- Retrieval latency that grows as documentation grows
- No efficient way to filter by metadata and similarity together
What it actually does
It stores vectors with their metadata and indexes them so that approximate nearest-neighbour queries return in milliseconds. 'Approximate' is the key word — it trades a small amount of recall for a very large amount of speed.
Filtering matters as much as similarity
In a multi-tenant product, a vector search that could return another customer's passage would be a serious defect. Filtering by tenant and source alongside the similarity query is what keeps retrieval both relevant and correctly scoped.
Common questions
- Do I need to run one myself?
- Not to use a product built on this architecture — it is infrastructure, not a feature you configure. It matters to you only in that it is why retrieval stays fast as your content grows.
- Is a vector database a replacement for a normal database?
- No. It answers similarity queries; it is not where you keep your orders. Most systems run both.
From the blog
All posts- MeasuringDeflection rate is the most overstated number in support automationCounting bot conversations as deflected tickets overstates the result. The honest version is a before-and-after on ticket volume, read next to satisfaction.Read
- EvaluatingHow to test an AI support tool before you trust itEvery AI support tool demos well, because demos ask questions the content covers. Four questions that separate them, and what a good answer looks like.Read
- MeasuringResolution rate vs deflection: stop mixing the twoResolution and deflection answer different questions. Mixing them inflates the result and hides whether customers actually got what they needed.Read
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