A vector database is a specialized database designed to store, index, and query high-dimensional vector embeddings efficiently. Unlike traditional databases that search by exact match or keyword, vector databases perform similarity search, finding the most semantically similar vectors to a query. They are essential infrastructure for RAG systems, semantic search, recommendation engines, and AI applications. Popular vector databases include Pinecone, Weaviate, Milvus, Qdrant, Chroma, and pgvector (PostgreSQL extension). Key features include approximate nearest neighbor (ANN) search algorithms, metadata filtering, and horizontal scaling.
Frequently Asked Questions
What is a vector database?
A vector database stores and searches high-dimensional embeddings efficiently, enabling semantic similarity search. It is essential infrastructure for RAG, search, and AI applications.
When do I need a vector database?
Use a vector database when building RAG systems, semantic search, recommendation engines, or any application that needs to find similar items based on meaning rather than keywords.