---
title: "pgvector"
type: "tool"
slug: "pgvector-pgvector"
canonical_url: "https://www.graphcanon.com/tools/pgvector-pgvector"
github_url: "https://github.com/pgvector/pgvector"
homepage_url: null
stars: 22104
forks: 1232
primary_language: "C"
license: "Other"
categories: ["inference-serving", "llm-frameworks", "vector-databases"]
tags: ["c", "approximate-nearest-neighbor-search", "nearest-neighbor-search"]
updated_at: "2026-07-07T15:08:21.210201+00:00"
---

# pgvector

> Open-source vector similarity search for Postgres

Open-source vector similarity search for Postgres

## Facts

- Repository: https://github.com/pgvector/pgvector
- Stars: 22,104 · Forks: 1,232 · Open issues: 14 · Watchers: 135
- Primary language: C
- License: Other
- Last pushed: 2026-07-02T00:21:28+00:00

## Categories

- [Inference & Serving](/categories/inference-serving.md)
- [LLM Frameworks](/categories/llm-frameworks.md)
- [Vector Databases](/categories/vector-databases.md)

## Tags

C++, approximate-nearest-neighbor-search, nearest-neighbor-search

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## README (excerpt)

```text
# pgvector

Open-source vector similarity search for Postgres

Store your vectors with the rest of your data. Supports:

- exact and approximate nearest neighbor search
- single-precision, half-precision, binary, and sparse vectors
- L2 distance, inner product, cosine distance, L1 distance, Hamming distance, and Jaccard distance
- any [language](#languages) with a Postgres client

Plus [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

Have a lot of vectors? Use [quantization](#scaling) to scale



## Installation

### Linux and Mac

Compile and install the extension (supports Postgres 13+)

```sh
cd /tmp
git clone --branch v0.8.4 https://github.com/pgvector/pgvector.git
cd pgvector
make
make install # may need sudo
```

See the [installation notes](#installation-notes---linux-and-mac) if you run into issues

You 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).

### Windows

Ensure [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:

```cmd
set "PGROOT=C:\Program Files\PostgreSQL\18"
cd %TEMP%
git clone --branch v0.8.4 https://github.com/pgvector/pgvector.git
cd pgvector
nmake /F Makefile.win
nmake /F Makefile.win install
```

See the [installation notes](#installation-notes---windows) if you run into issues

You can also install it with [Docker](#docker) or [conda-forge](#conda-forge).

## Getting Started

Enable the extension (do this once in each database where you want to use it)

```tsql
CREATE EXTENSION vector;
```

Create a vector column with 3 dimensions

```sql
CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3));
```

Insert vectors

```sql
INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');
```

Get the nearest neighbors by L2 distance

```sql
SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;
```

Also supports inner product (`<#>`), cosine distance (`<=>`), and L1 distance (`<+>`)

Note: `<#>` returns the negative inner product since Postgres only supports `ASC` order index scans on operators

## Storing

Create a new table with a vector column

```sql
CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3));
```

Or add a vector column to an existing table

```sql
ALTER TABLE items ADD COLUMN embedding vector(3);
```

Also supports [half-precision](#half-precision-vectors), [binary](#binary-vectors), and [sparse](#sparse-vectors) vectors

Insert vectors

```sql
INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');
```

Or load vectors in bulk using `COPY` ([example](https://github.com/pgvector/pgvector-python/blob/master/examples/loading/example.py))

```sql
COPY items (embedding) FROM STDIN WITH (FORMAT BINARY);
```

Upsert vectors

```sql
INSERT INTO items (id, embedding) VALUES (1, '[1,2,3]'), (2, '[4,5,6]')
    ON CONFLICT (id) DO UPDATE SET embedding = EXCLUDED.embedding;
```

Update vectors

```sql
UPDATE items SET embedding = '[1,2,3]' WHERE id = 1;
```

Delete vectors

```sql
DELETE FROM items WHERE id = 1;
```

## Querying

Get the nearest neighbors to a vector

```sql
SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;
```

Supported distance functions are:

- `<->` - L2 distance
- `<#>` - (negative) inner product
- `<=>` - cosine distance
- `<+>` - L1 distance
- `<~>` - Hamming distance (binary vectors)
- `<%>` - Jaccard distance (binary vectors)

Get the nearest neighbors to a row

```sql
SELECT
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/pgvector-pgvector`](/api/graphcanon/tools/pgvector-pgvector)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_