Search & AI tools
Set up search and AI capabilities with PostgreSQL
PostgreSQL can serve as your vector database for AI applications, eliminating the need for a separate vector store like Pinecone or Weaviate. The pgvector extension adds vector data types and similarity search operators, letting you store embeddings alongside your relational data and query them with standard SQL.
Vector search in PostgreSQL supports multiple distance metrics: L2 (Euclidean), inner product, and cosine similarity. pgvector provides two index types: IVFFlat for faster builds with good recall, and HNSW for better query performance at the cost of slower builds and more memory. Choosing the right index parameters (lists for IVFFlat, m and ef_construction for HNSW) depends on your dataset size and accuracy requirements.
These tools help you set up pgvector, choose embedding dimensions and index types, write similarity search queries, and integrate vector search with existing relational queries. The AI assistant understands practical patterns like RAG (Retrieval-Augmented Generation) pipelines, hybrid search combining full-text and vector similarity, and how to handle embedding updates efficiently.
Common problems
- Not installing pgvector extension before creating vector columns
- Choosing wrong index type (IVFFlat vs HNSW) for the workload
- Missing indexes causing sequential scans on vector similarity queries
- Embedding dimension mismatch between model output and column definition
- Not using appropriate distance operator for the embedding model