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.
Related Terms
Embedding
A numerical representation of a word or concept that captures its meaning and relationships.
Semantic Search
Searching by meaning and intent rather than exact keyword matching.
RAG (Retrieval-Augmented Generation)
Combining an LLM with a search system so it can look up current or specific information before responding.
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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