Vector Database
InfrastructureA database optimized for storing and searching embedding vectors — the foundation of RAG and semantic search applications.
Full Explanation
Traditional databases search by exact matching. Vector databases find the nearest neighbors in embedding space — returning documents semantically similar to a query. Popular options include Pinecone, Weaviate, Qdrant, Chroma, and pgvector (PostgreSQL extension). They're the backbone of every enterprise RAG application.
Pinecone, Weaviate, and pgvector are popular vector databases used in AI applications.
Related Terms
Numerical vector representations of text that capture semantic meaning, allowing AI to find conceptually similar content.
A technique that enhances LLM responses by retrieving relevant documents from an external knowledge base before generating an answer.
Search that finds results based on meaning and intent, not just keyword matching.