What is Vector Database?
A database optimized for storing and searching high-dimensional vector embeddings.
Definition
A vector database stores data as mathematical vectors (arrays of numbers) that represent the semantic meaning of text, images, or other content. Unlike traditional databases that search for exact keyword matches, vector databases find results by semantic similarity — documents that mean the same thing, even if they use different words. They are the storage layer underlying most RAG and semantic search systems.
Example
A knowledge base stores 10,000 support articles as vectors. When a user asks 'how do I reset my password?', the vector database finds the most semantically similar articles — even ones that say 'account recovery' rather than 'password reset'.
Vector Database vs rag: What's the difference?
A database optimized for storing and searching high-dimensional vector embeddings.
A vector database is the storage layer. RAG is the technique that uses it — retrieve relevant vectors, then generate a response based on the retrieved content.