Vector Database / Vector Search
A vector database is a database optimized for storing and querying vector embeddings, enabling fast semantic search based on meaning.
What is a vector database?
A vector database is a specialized database optimized for storing and querying vector embeddings, enabling fast semantic search based on meaning rather than exact keyword matches. Unlike traditional databases that search for exact text matches, vector databases find and retrieve conceptually similar information by comparing the numerical representations of content, making them essential infrastructure for AI-powered search and recommendation systems.
What is vector search?
Vector search uses these databases to perform similarity searches across millions of embedded documents, finding relevant results based on semantic meaning. This technology powers RAG systems, answer engines, and AI search tools by allowing them to quickly retrieve contextually relevant information to generate accurate responses, even when queries use different words than the stored content.
Vector databases and vector search provide key capabilities:
- Store and query vector embeddings for semantic search based on meaning
- Perform similarity searches across millions of embedded documents
- Power RAG systems, answer engines, and AI search tools
- Enable quick retrieval of contextually relevant information for AI responses
Learn more: Explore how vector search technologies power AI-driven search in our AI Optimization guide.
