tensorchord/VectorChord

Scalable, fast, and disk-friendly vector search in Postgres, the successor of pgvecto.rs.

57
/ 100
Established

This project helps you manage and search through extremely large collections of digital information, like millions of product descriptions or scientific papers, by converting them into 'vector embeddings'. It takes these high-dimensional vectors as input and lets you quickly find the most similar items, outputting relevant results efficiently. This is ideal for AI application developers, data engineers, or ML operations specialists who need to power recommendation engines, semantic search, or large language model (LLM) applications.

1,595 stars. Actively maintained with 6 commits in the last 30 days.

Use this if you need to build scalable, high-performance, and cost-effective vector search capabilities directly within your PostgreSQL database, especially with very large datasets.

Not ideal if you only need to store and search a small number of vectors or prefer a managed, specialized vector database solution outside of PostgreSQL.

AI-application-development semantic-search recommendation-engines LLM-backend data-infrastructure
No Package No Dependents
Maintenance 17 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 14 / 25

How are scores calculated?

Stars

1,595

Forks

56

Language

Rust

License

Last pushed

Mar 11, 2026

Commits (30d)

6

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