Definition
Vector search turns text into numerical representations, called embeddings, and finds results by meaning instead of exact keywords. It is the technology under semantic search and RAG-based answer engines.
On this page
At a glance
- Categories Technical
- Related fields AI Search, Technical
- Difficulty Advanced
How Vector Search Works
Each piece of text gets converted into a vector, a string of numbers that captures its meaning. Texts that mean similar things end up with similar vectors, sitting close together in that numeric space. To find a match, the system looks for the nearest vectors, which is how it retrieves content that means the same thing even when the words are different.
Why Vector Search Matters
It explains why modern engines can match a query to your content without sharing a single exact word. Clear, on-topic writing beats repeating phrases, because the system is comparing meaning, not counting keywords.
Frequently Asked Questions
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What's an embedding?
A numerical representation of text that captures its meaning, so similar ideas land near each other.
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Why should content people care?
It rewards clear, on-topic writing over exact keyword matching, since retrieval runs on meaning.