Vector Database

Vector databases are purpose-built storage systems optimized for storing and querying embedding vectors. Unlike traditional databases that match on exact values or keywords, vector databases find the most similar vectors to a query vector using distance metrics like cosine similarity or Euclidean distance. This enables semantic search where results are ranked by meaning similarity rather than keyword matching.

Vector databases use specialized indexing algorithms such as HNSW (Hierarchical Navigable Small World), IVF (Inverted File Index), and product quantization to make similarity search fast even across millions or billions of vectors. Without these indexes, finding the nearest vectors would require comparing against every stored vector, which becomes impractical at scale. Modern vector databases also support filtering, metadata storage, and hybrid search that combines vector similarity with traditional keyword matching.

In the context of AI applications, vector databases are the backbone of RAG systems, recommendation engines, and semantic search. When you build an AI application that needs to reference external knowledge, you embed your documents, store them in a vector database, and query it to find relevant context for each user interaction. Popular vector databases include both dedicated solutions like Pinecone and Weaviate and vector extensions for existing databases like pgvector for PostgreSQL.