Vector Database

A vector database is purpose-built for storing, indexing, and querying vector embeddings at scale. Unlike traditional databases that match exact values, vector databases find the most similar vectors using distance metrics like cosine similarity or Euclidean distance.

When you store a million document embeddings, a vector database can find the 10 most similar documents to a query vector in milliseconds using approximate nearest neighbor (ANN) algorithms like HNSW, IVF, or ScaNN.

Popular vector databases include Pinecone, Weaviate, Qdrant, Milvus, and ChromaDB. PostgreSQL also supports vector search via the pgvector extension. Each has different tradeoffs around scalability, cost, filtering capabilities, and deployment options.

Vector databases are a critical component of RAG systems, recommendation engines, image similarity search, and any application that needs to find semantically similar content quickly.