Vector Database
Definition
A vector database stores information as numerical representations of meaning, so an AI can find results by similarity — retrieving text that's conceptually related to a query, not just an exact keyword match.
Also known as: vector database, vector store
A vector database is a specialised store that powers the “search by meaning” behind modern AI. Instead of matching exact words, it finds information that is conceptually similar to what you asked — which is exactly what techniques like RAG rely on.
Why it exists
Traditional databases are great at exact matches: find the row where the name equals “Ahmed.” But AI needs something different — the ability to find text that means the same thing even when the words differ. A search for “how do I cancel” should also surface a document titled “ending your subscription,” even with no shared keywords.
How it works, briefly
AI models can turn any piece of text into a list of numbers — a “vector” — that captures its meaning. Similar meanings produce similar vectors. A vector database stores these and, given a new query vector, quickly finds the closest matches. That’s how a RAG system locates the most relevant chunks of your documents before the language model writes an answer.
Vector database in one sentence
A vector database finds information by meaning rather than exact keywords, which is what lets AI retrieve the most relevant text to answer a question.