pgvector enables storing and querying high-dimensional vectors directly in PostgreSQL. Key features: vector data type (up to 16,000 dimensions), similarity operators (L2 distance, inner product, cosine), and indexing (IVFFlat, HNSW for fast approximate search). Use cases: semantic search (find similar text via embeddings), recommendation systems, image similarity, and RAG applications. Compared to dedicated vector databases: pgvector keeps vectors with your relational data, uses familiar SQL, and avoids managing another system. Indexing is crucial for performance - HNSW provides excellent query speed. pgvector makes PostgreSQL a capable vector database for most AI applications.
🗄️ Database + ⚙️ AI Infrastructure intermediate
pgvector
PostgreSQL extension that adds vector similarity search capabilities for AI and machine learning applications.
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</> Related Terms
Vector Database
Specialized database for storing and searching high-dimensional vector embeddings.
RAG (Retrieval-Augmented Generation)
AI technique combining vector search with LLMs to provide contextual answers from custom knowledge bases.
Embedding
Numerical vector representation of text, images, or other data for machine learning.
PostgreSQL
Open-source relational database with advanced features like JSONB, full-text search, and extensions.