Vector Database
A vector database stores information as numerical representations of meaning - enabling AI agents to find relevant answers even when exact keywords do not match.
A vector database stores data as mathematical vectors (arrays of numbers) that represent the meaning of text, images, or other content - enabling AI to find semantically similar information rather than exact keyword matches.
Embedding Storage
Converts text, images, and documents into numerical vectors that capture semantic meaning for efficient storage and retrieval.
Similarity Search
Find content by meaning, not just keywords. "How do I return an item?" matches "refund process" even without shared words.
Knowledge Base Backbone
Powers AI knowledge bases by storing company documents, FAQs, and policies in searchable vector format.
Real-Time Retrieval
Millisecond query times even across millions of documents. AI agents get relevant context instantly.
Continuous Updates
New documents are embedded and indexed as they are added. The knowledge base stays current without manual reindexing.
Access-Controlled Search
Filter search results by user permissions. Different teams see different documents even from the same query.
FAQ
How is a vector database different from a regular database?
Regular databases find exact matches (SQL WHERE clauses). Vector databases find similar content - questions that mean the same thing, documents about the same topic - even with different wording.
Do I need my own vector database?
Not necessarily. OpenClaw includes vector search capabilities. Enterprise clients with specific data residency or scale requirements may benefit from dedicated vector infrastructure.
What is RAG?
Retrieval-Augmented Generation (RAG) uses vector databases to find relevant documents, then feeds them to an AI model for accurate, grounded responses. It is how AI agents answer questions about your specific business.
Power your AI with your data
Learn how vector databases enable AI knowledge bases.