# supervc-stack/VectorChord

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

Repository: https://github.com/supervc-stack/VectorChord
Canonical: https://ross.abutalabs.com/products/vectorchord
Homepage: https://docs.vectorchord.ai/vectorchord/getting-started/overview.html
Language: Rust
License: NOASSERTION
License Family: other
Topics: artificial-intelligence, llmops, postgresql, vector-database, vector-search
Last push: 2026-08-06T06:34:15+00:00

## Health v2 (maintenance only)
Score: 79/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 96, release rhythm 72, longevity 52
- inputs: {"age_days": 729, "days_push": 27, "days_rel": 186, "gap_med": 23, "n_releases_24m": 16}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1785, forks 72 (observed 2026-08-28T04:05:36.049941+00:00)

## What it is
VectorChord is a PostgreSQL extension written in Rust for scalable, disk-efficient vector similarity search, serving as the successor to pgvecto.rs. It uses RaBitQ quantization with autonomous reranking to store hundreds of millions of vectors on modest hardware while remaining pgvector-compatible.

## Use cases
- run vector similarity search inside postgres
- store 100 million embeddings on a single server
- build a RAG pipeline without a separate vector database
- migrate from pgvector for better performance and lower storage cost
- do hybrid keyword and vector search in one database
- index large embedding datasets quickly
- support high-dimensional embeddings like text-embedding-3-large

## When to choose
- you already run PostgreSQL and want vector search without adding a dedicated vector database
- you need billion-scale vector search at low infrastructure cost
- you want pgvector-compatible syntax with faster queries and indexing
- you need long vectors up to 60,000 dimensions
- you want BM25 plus vector search together for RAG

## When to avoid
- you need a fully managed vector database service
- your project is not on PostgreSQL
- you only have a few thousand vectors and pgvector is sufficient
- you require a permissive open-source license without restriction

## Facets
- artifact type: library
- maturity: active
- function: vector-database, search-engine, database
- domain: databases, machine-learning, large-language-models
- platform: windows, self-hosted, cloud
- tags: postgresql-extension, vector-search, pgvector-compatible, rabitq-quantization, rust, rag, similarity-search, embeddings, search, retrieval-augmented-generation, linux, macos, docker

## Member repositories
- supervc-stack/VectorChord (main) score 79

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:36.049941+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:24:13.757858+00:00, confidence not recorded.
  - readme: https://github.com/supervc-stack/VectorChord (fetched 2026-08-28T04:05:36.049941+00:00, sha fdc5fc83d709)
  - homepage: https://docs.vectorchord.ai/vectorchord/getting-started/overview.html (fetched 2026-08-29T11:02:41.036108+00:00, sha af61c2a55ee5)
  - site_page: https://docs.vectorchord.ai/vectorchord (fetched 2026-08-29T11:02:41.045980+00:00, sha b4f620fd3409)
  - site_page: https://docs.vectorchord.ai/getting-started/overview.html (fetched 2026-08-29T11:02:41.048118+00:00, sha 8da3c1517623)
  - site_page: https://docs.vectorchord.ai/vectorchord/getting-started/installation.html (fetched 2026-08-29T11:02:41.050369+00:00, sha 877d5d88842b)
  - site_page: https://docs.vectorchord.ai/vectorchord/getting-started/vectorchord-suite.html (fetched 2026-08-29T11:02:41.052798+00:00, sha 7b8280e50585)
- Data as of 2026-08-30T08:39:29.467469+00:00.