pgvector
by Community
Open-source vector similarity search for Postgres
OSS
pgvector
Added 1 June 2026
Overview
pgvector is an open-source PostgreSQL extension that adds vector data types and similarity search operators to Postgres. It enables approximate nearest neighbor search directly within your database using L2, cosine, and inner product distance metrics. Built in C for performance, it integrates with existing Postgres workflows without requiring a separate vector database.
Best for
Best for
Teams already using Postgres who want vector search without adding infrastructure
Use cases
- Semantic search over embeddings stored in Postgres
- Recommendation systems using vector similarity
- RAG pipelines that query embeddings alongside relational data
Notes
pgvector is an open-source PostgreSQL extension that adds vector data types and similarity search operators to Postgres. It enables approximate nearest neighbor search directly within your database using L2, cosine, and inner product distance metrics. Built in C for performance, it integrates with existing Postgres workflows without requiring a separate vector database.
21,551 stars on GitHub. Last updated 2026-05-30.
Use cases
- Semantic search over embeddings stored in Postgres
- Recommendation systems using vector similarity
- RAG pipelines that query embeddings alongside relational data
Pros
- Runs inside Postgres, eliminating separate vector DB infrastructure
- Supports multiple distance metrics and indexing strategies (HNSW, IVFFlat)
- Active community with 21k+ stars and regular maintenance
Cons
- Performance scales differently than dedicated vector databases at very large scales
- Requires Postgres expertise to optimize indexes and queries
- Limited to Postgres ecosystem, not portable to other databases
Indexed from awesome-llmops and enriched against its public facts.
Pros
- Runs inside Postgres, eliminating separate vector DB infrastructure
- Supports multiple distance metrics and indexing strategies (HNSW, IVFFlat)
- Active community with 21k+ stars and regular maintenance
Cons
- Performance scales differently than dedicated vector databases at very large scales
- Requires Postgres expertise to optimize indexes and queries
- Limited to Postgres ecosystem, not portable to other databases
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