pgvector

{
  "aliases": [],
  "alternatives": [
    "Pinecone",
    "Weaviate",
    "Milvus",
    "Qdrant",
    "Elasticsearch"
  ],
  "build_dependencies": [
    "postgresql@17",
    "postgresql@18"
  ],
  "categories": [
    "database",
    "library"
  ],
  "caveats": null,
  "conflicts_with": [],
  "dependencies": [],
  "deprecated": 0,
  "deprecation_reason": null,
  "desc": "Open-source vector similarity search for Postgres",
  "disable_reason": null,
  "disabled": 0,
  "enrichment_fetched_at": "2026-06-20T23:41:03+00:00",
  "first_seen": "2026-06-20T23:34:18+00:00",
  "full_name": "pgvector",
  "github_default_branch": "master",
  "github_last_commit_at": "2026-06-18T19:57:02Z",
  "github_readme_excerpt": "# pgvector\n\nOpen-source vector similarity search for Postgres\n\nStore your vectors with the rest of your data. Supports:\n\n- exact and approximate nearest neighbor search\n- single-precision, half-precision, binary, and sparse vectors\n- L2 distance, inner product, cosine distance, L1 distance, Hamming distance, and Jaccard distance\n- any [language](#languages) with a Postgres client\n\nPlus [ACID](https://en.wikipedia.org/wiki/ACID) compliance, point-in-time recovery, JOINs, and all of the other [great features](https://www.postgresql.org/about/) of Postgres\n\nHave a lot of vectors? Use [quantization](#scaling) to scale\n\n[![Build Status](https://github.com/pgvector/pgvector/actions/workflows/build.yml/badge.svg)](https://github.com/pgvector/pgvector/actions)\n\n## Installation\n\n### Linux and Mac\n\nCompile and install the extension (supports Postgres 13+)\n\n```sh\ncd /tmp\ngit clone --branch v0.8.3 https://github.com/pgvector/pgvector.git\ncd pgvector\nmake\nmake install # may need sudo\n```\n\nSee the [installation notes](#installation-notes---linux-and-mac) if you run into issues\n\nYou can also install it with [Docker](#docker), [Homebrew](#homebrew), [PGXN](#pgxn), [APT](#apt), [Yum](#yum), [pkg](#pkg), [APK](#apk), or [conda-forge](#conda-forge), and it comes preinstalled with [Postgres.app](#postgresapp) and many [hosted providers](#hosted-postgres). There are also instructions for [GitHub Actions](https://github.com/pgvector/setup-pgvector).\n\n### Windows\n\nEnsure [C++ support in Visual Studio](https://learn.microsoft.com/en-us/cpp/build/building-on-the-command-line?view=msvc-170#download-and-install-the-tools) is installed and run `x64 Native Tools Command Prompt for VS [version]` as administrator. Then use `nmake` to build:\n\n```cmd\nset \"PGROOT=C:\\Program Files\\PostgreSQL\\18\"\ncd %TEMP%\ngit clone --branch v0.8.3 https://github.com/pgvector/pgvector.git\ncd pgvector\nnmake /F Makefile.win\nnmake /F Makefile.win install\n```\n\nSee the [installation notes](#installation-notes---windows) if",
  "github_repo": "pgvector/pgvector",
  "github_stars": 21845,
  "github_topics": [
    "approximate-nearest-neighbor-search",
    "nearest-neighbor-search"
  ],
  "homepage": "https://github.com/pgvector/pgvector",
  "homepage_og_description": null,
  "homepage_og_image": null,
  "homepage_title": null,
  "installs_30d": 3685,
  "installs_365d": 30734,
  "installs_90d": 10205,
  "keg_only": 0,
  "keg_only_reason": null,
  "last_seen": "2026-06-20T23:34:18+00:00",
  "license": "PostgreSQL",
  "llm_generated_at": "2026-06-20T23:46:41+00:00",
  "llm_model": "claude-haiku-4-5",
  "name": "pgvector",
  "oldnames": [],
  "one_liner": "PostgreSQL extension for vector similarity search with support for multiple distance metrics and approximate nearest-neighbor queries.",
  "optional_dependencies": [],
  "rank_30d": 729,
  "rank_365d": 878,
  "rank_90d": 806,
  "raw_hash": "77984a4e7cc98ecc",
  "recommended_dependencies": [],
  "revision": 0,
  "ruby_source_path": "Formula/p/pgvector.rb",
  "tap": "homebrew/core",
  "test_dependencies": [
    "postgresql@17",
    "postgresql@18"
  ],
  "uses_from_macos": [],
  "version_head": null,
  "version_stable": "0.8.3",
  "versioned_formulae": [],
  "why_use_this": "Enables semantic search and AI embedding workloads directly in Postgres without external vector databases. Supports exact and approximate nearest-neighbor search with multiple distance metrics (L2, cosine, inner product, Hamming, Jaccard), multiple vector types (single/half-precision, binary, sparse), and scales via quantization. Keeps vectors colocated with relational data for simpler ACID-compliant architectures."
}