Master pgvector with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Install pgvector extension by cloning from GitHub and running 'make && sudo make install' on your PostgreSQL server, or use package managers like Homebrew (brew install pgvector), APT (apt
get install postgresql
pgvector), or pre
built Docker images Enable the extension in your target database by connecting as a PostgreSQL superuser and executing 'CREATE EXTENSION vector;' to activate all vector data types and functions Create tables with vector columns specifying exact dimensions: 'CREATE TABLE documents (id serial PRIMARY KEY, content text, embedding vector(1536));' matching your embedding model's output dimensions (1536 for OpenAI text
small) Insert vector data using standard SQL syntax with array literals: 'INSERT INTO documents (content, embedding) VALUES ('sample text', '[
3, ...]');' or bulk load using COPY commands for large datasets Create appropriate indexes for your query patterns: 'CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);' for cosine similarity or 'CREATE INDEX USING ivfflat (embedding vector_l2_ops) WITH (lists = 100);' for Euclidean distance with partitioning Execute similarity searches using SQL ORDER BY with distance operators: 'SELECT content FROM documents ORDER BY embedding <=> '[query_vector]' LIMIT 10;' for nearest neighbor queries, combining with WHERE clauses for filtered similarity search
💡 Quick Start: Follow these 6 steps in order to get up and running with pgvector quickly.
Explore the key features that make pgvector powerful for ai memory & search workflows.
Seamlessly transforms existing PostgreSQL instances into production-ready vector databases without requiring separate infrastructure, deployment pipelines, or specialized administrative expertise. Leverages PostgreSQL's battle-tested architecture for vector capabilities with zero additional operational overhead.
Delivers query latencies under 50ms for million-vector datasets through advanced HNSW and IVFFlat indexing algorithms. Competitive performance with dedicated vector databases for workloads up to 10 million vectors, with pgvectorscale extension enabling billion-scale deployments.
Execute sophisticated vector similarity searches using familiar SQL syntax with distance operators (<->, <=>, <#>) in ORDER BY clauses. Combine vector searches with JOINs, WHERE filters, and aggregate functions in single statements, eliminating proprietary query language complexity.
ACID-compliant transactions ensure perfect consistency between vector embeddings and business data updates. User profile changes and corresponding embedding updates occur atomically with full rollback capabilities, preventing data synchronization issues plaguing multi-database architectures.
Works seamlessly with all PostgreSQL 13+ hosting providers including AWS RDS, Google Cloud SQL, Azure Database, Supabase, and Neon. Leverages existing PostgreSQL client libraries, ORMs, monitoring tools, and administrative workflows without specialized vector database expertise.
Achieve dramatic cost savings with PostgreSQL instances supporting vector workloads at $30-80/month versus $300-1,000+ for equivalent dedicated vector database capacity. Eliminates usage-based pricing that becomes prohibitive at scale while leveraging existing PostgreSQL infrastructure investments.
Inherits PostgreSQL's comprehensive security framework including RBAC, row-level security, column encryption, audit logging, and compliance support for SOC 2, HIPAA, and GDPR. Vector data automatically participates in enterprise authentication and authorization policies.
Supports dense vectors (16,000 dimensions), sparse vectors (efficient high-dimensional storage), binary quantization (32x memory reduction), and half-precision vectors (50% storage savings). Multiple distance metrics including cosine, L2, inner product, L1, Hamming, and Jaccard similarity.
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Tutorial updated March 2026