Retrieval, Memory & Knowledge

Vector Database

A database that stores embeddings and allows searching by meaning rather than keywords.

Definition

A vector database stores text (or other content) as numerical embeddings and enables semantic search — finding documents based on meaning rather than exact keyword matches. When a user asks a question, the question is also converted to an embedding and the database returns the documents whose embeddings are closest in meaning. This is the infrastructure layer that makes RAG and intelligent document search possible.

Why this matters for your business

Vector databases are the backbone of enterprise knowledge search — allowing staff to find relevant policies, precedents, and procedures using natural language rather than having to know the exact terminology in the source document.

Heard enough terminology — ready to talk outcomes?

We translate AI concepts into measurable business results. No upfront fees — you pay only when independently verified results are delivered.

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Disclaimer

This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.

Document reference: ISO_webpage_knowledge-base_glossary_v1

Last modified: 29 March 2026

Knowledge Base·Retrieval, Memory & Knowledge·Vector Database