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Open-source vector similarity search for Postgres

CREATE TABLE table (column vector(3));
CREATE INDEX ON table USING ivfflat (column);
SELECT * FROM table ORDER BY column <-> '[1,2,3]' LIMIT 5;

Supports L2 distance, inner product, and cosine distance

Build Status


Compile and install the extension (supports Postgres 9.6+)

git clone --branch v0.1.4
cd pgvector
make install # may need sudo

Then load it in databases where you want to use it


You can also install it with Docker, Homebrew, or PGXN

Getting Started

Create a vector column with 3 dimensions (replace table and column with non-reserved names)

CREATE TABLE table (column vector(3));

Insert values

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

Get the nearest neighbor by L2 distance

SELECT * FROM table ORDER BY column <-> '[3,1,2]' LIMIT 1;

Also supports inner product (<#>) and cosine distance (<=>)

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


Speed up queries with an approximate index. Add an index for each distance function you want to use.

L2 distance

CREATE INDEX ON table USING ivfflat (column);

Inner product

CREATE INDEX ON table USING ivfflat (column vector_ip_ops);

Cosine distance

CREATE INDEX ON table USING ivfflat (column vector_cosine_ops);

Indexes should be created after the table has data for optimal clustering. Also, unlike typical indexes which only affect performance, you may see different results for queries after adding an approximate index.

Index Options

Specify the number of inverted lists (100 by default)

CREATE INDEX ON table USING ivfflat (column) WITH (lists = 100);

Query Options

Specify the number of probes (1 by default)

SET ivfflat.probes = 1;

A higher value improves recall at the cost of speed.

Use SET LOCAL inside a transaction to set it for a single query

SET LOCAL ivfflat.probes = 1;


Vector Type

Each vector takes 4 * dimensions + 8 bytes of storage. Each element is a float, and all elements must be finite (no NaN, Infinity or -Infinity). Vectors can have up to 1024 dimensions.

Vector Operators

Operator Description
+ element-wise addition
- element-wise subtraction
<-> Euclidean distance
<#> negative inner product
<=> cosine distance

Vector Functions

Function Description
cosine_distance(vector, vector) cosine distance
inner_product(vector, vector) inner product
l2_distance(vector, vector) Euclidean distance
vector_dims(vector) number of dimensions
vector_norm(vector) Euclidean norm


Libraries that use pgvector:

Additional Installation Methods


Get the Docker image with:

docker pull ankane/pgvector

This adds pgvector to the Postgres image.

You can also build the image manually

git clone --branch v0.1.4
cd pgvector
docker build -t pgvector .


On Mac with Homebrew Postgres, you can use:

brew install ankane/brew/pgvector


Install from the PostgreSQL Extension Network with:

pgxn install vector

Hosted Postgres

Some Postgres providers only support specific extensions. To request a new extension:

  • Amazon RDS - follow the instructions on this page
  • Google Cloud SQL - follow the instructions on this page
  • DigitalOcean Managed Databases - follow the instructions on this page


Compile and install the latest version and run:



Thanks to:


View the changelog


Everyone is encouraged to help improve this project. Here are a few ways you can help:

To get started with development:

git clone
cd pgvector
make install

To run all tests:

make installcheck        # regression tests
make prove_installcheck  # TAP tests

To run single tests:

make installcheck REGRESS=vector                       # regression test
make prove_installcheck PROVE_TESTS=test/t/  # TAP test

Resources for contributors