# tensorchord/pgvecto.rs

Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database.

Repository: https://github.com/tensorchord/pgvecto.rs
Canonical: https://ross.abutalabs.com/products/pgvectors
Homepage: https://docs.vectorchord.ai/getting-started/overview.html
Language: Rust
License: Apache-2.0
License Family: permissive
Topics: llm, vector, vector-database, faiss, nearest-neighbor-search, gpt, chatgpt, hacktoberfest, rust, postgres
Last push: 2025-02-26T14:11:43+00:00

## Health v2 (maintenance only)
Score: 24/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 8, release rhythm 8, longevity 88
- inputs: {"age_days": 1236, "days_push": 553, "days_rel": 650, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2183, forks 86 (observed 2026-08-28T04:06:23.448603+00:00)

## What it is
pgvecto.rs is a Postgres extension written in Rust (via pgrx) that adds vector similarity search with high dimension support, quantization, and filtered vector queries. It is now superseded by its successor VectorChord, which the maintainers recommend users migrate to.

## Use cases
- add vector similarity search to an existing postgres database
- store and query llm embeddings without running a separate vector database
- run filtered nearest-neighbor search with joins in sql
- build a rag application backed by postgres
- index high-dimensional embeddings up to 65535 dimensions
- compare pgvector vs pgvecto.rs performance for vector search

## When to choose
- you already run postgres and want vector search without a second database
- you need vector dimensions beyond pgvector's 2000 limit
- you need combined vector search with relational filters and joins
- you want quantization options to reduce memory usage

## When to avoid
- starting a new project - the maintainers recommend VectorChord instead
- you need full WAL support for indexes
- you want a managed or standalone specialized vector database
- you need a battle-tested, stable extension for critical production workloads

## Facets
- artifact type: plugin
- maturity: maintenance
- function: vector-database, search-engine, database
- domain: databases, machine-learning, large-language-models
- platform: self-hosted, rust, python
- tags: postgres-extension, vector-similarity-search, hnsw, embeddings, pgvector-alternative, pgrx, simd, hybrid-search, search, retrieval-augmented-generation, linux, docker

## Member repositories
- tensorchord/pgvecto.rs (main) score 24

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:23.448603+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 2026-08-30T02:48:15.665293+00:00, confidence not recorded.
  - readme: https://github.com/tensorchord/pgvecto.rs (fetched 2026-08-28T04:06:23.448603+00:00, sha c2e97000c2d3)
  - homepage: https://docs.vectorchord.ai/getting-started/overview.html (fetched 2026-08-29T10:28:26.512813+00:00, sha 8da3c1517623)
  - site_page: https://docs.vectorchord.ai/vectorchord (fetched 2026-08-29T10:28:26.522070+00:00, sha b4f620fd3409)
  - site_page: https://docs.vectorchord.ai/getting-started/installation.html (fetched 2026-08-29T10:28:26.524210+00:00, sha 0bf0c8ea3fae)
  - site_page: https://docs.vectorchord.ai/faqs/general.html (fetched 2026-08-29T10:28:26.526230+00:00, sha 173948bcd4c2)
  - site_page: https://docs.vectorchord.ai/faqs/comparison-pgvector.html (fetched 2026-08-29T10:28:26.528047+00:00, sha 8dfd24a2d8d5)
  - site_page: https://docs.vectorchord.ai/faqs/comparison-with-specialized-vectordb.html (fetched 2026-08-29T10:28:26.530198+00:00, sha eb8e43d3daaf)
  - site_page: https://docs.vectorchord.ai/faqs/benchmark.html (fetched 2026-08-29T10:28:26.531851+00:00, sha 5873df2ebd06)
  - site_page: https://docs.vectorchord.ai/community/contributing.html (fetched 2026-08-29T10:28:26.533532+00:00, sha 1e7d50b61eae)
- Data as of 2026-08-30T08:39:29.467469+00:00.