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 with a Postgres client

Plus ACID compliance, point-in-time recovery, JOINs, and all of the other great features of Postgres

Have a lot of vectors? Use quantization to scale

Installation

Linux and Mac

Compile and install the extension (supports Postgres 13+)

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 if you run into issues

You can also install it with Docker, Homebrew, PGXN, APT, Yum, pkg, APK, or conda-forge, and it comes preinstalled with Postgres.app and many hosted providers. There are also instructions for GitHub Actions.

Windows

Ensure C++ support in Visual Studio is installed and run x64 Native Tools Command Prompt for VS [version] as administrator. Then use nmake to build:

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 if you run into issues

You can also install it with Docker or conda-forge.

Getting Started

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

CREATE EXTENSION vector;

Create a vector column with 3 dimensions

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

Insert vectors

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

Get the nearest neighbors by L2 distance

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

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

Or add a vector column to an existing table

ALTER TABLE items ADD COLUMN embedding vector(3);

Also supports half-precision, binary, and sparse vectors

Insert vectors

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

Or load vectors in bulk using COPY (example)

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

Upsert vectors

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

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

Delete vectors

DELETE FROM items WHERE id = 1;

Querying

Get the nearest neighbors to a vector

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

SELECT