pgvector

Open-source vector similarity search for Postgres

Supports exact and approximate nearest neighbor search for L2 distance, inner product, and cosine distance

Installation

Compile and install the extension (supports Postgres 11+)

cd /tmp
git clone --branch v0.4.1 https://github.com/pgvector/pgvector.git
cd pgvector
make
make install # may need sudo

Then load it in databases where you want to use it

CREATE EXTENSION vector;

You can also install it with Docker, Homebrew, PGXN, Yum, or conda-forge

Getting Started

Create a vector column with 3 dimensions

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

Insert values

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 (<#>) and cosine distance (<=>)

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

Storing

Insert vectors

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

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

Use a SELECT clause to get the distance

SELECT embedding <-> '[3,1,2]' AS distance FROM items;

For cosine similarity, use 1 - cosine distance

SELECT 1 - (embedding <=> '[3,1,2]') AS similarity FROM items;

Use a WHERE clause to get rows within a certain distance

SELECT * FROM items WHERE embedding <-> '[3,1,2]' < 5;

Note: Combine with ORDER BY and LIMIT to use an index

Average vectors

SELECT AVG(embedding) FROM items;

Average groups of vectors

SELECT category_id, AVG(embedding) FROM items GROUP BY category_id;

Indexing

By default, pgvector performs exact nearest neighbor search, which provides perfect recall.

You can add an index to use approximate nearest neighbor search, which trades some recall for performance. Unlike typical indexes, you will see different results for queries after adding an approximate index.

Two keys to achieving good recall are:

Create the index after the table has some data
Choose an appropriate number of lists (lower is better for recall, higher is better for speed)

A good place to start is:

rows / 1000 for up to 1M rows
sqrt(rows) for over 1M rows

Add an index for each distance function you want to use.

L2 distance

CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100);

Inner product

CREATE INDEX ON items USING ivfflat (embedding vector_ip_ops) WITH (lists = 100);

Cosine distance

CREATE INDEX ON items USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);

Vectors with up to 2,000 dimensions can be indexed.

Query Options

Specify the number of probes (1 by default)

SET ivfflat.probes = 1;

A higher value provides better recall at the cost of speed, and it can be set to the number of lists for exact nearest neighbor search (at which point the planner won’t use the index)

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

BEGIN;
SET LOCAL ivfflat.probes = 1;
SELECT ...
COMMIT;

Indexing Progress

Check indexing progress with Postgres 12+

SELECT phase, tuples_done, tuples_total FROM pg_stat_progress_create_index;

The phases are:

initializing
performing k-means
sorting tuples
loading tuples

Note: tuples_done and tuples_total are only populated during the loading tuples phase

Partial Indexes

Consider partial indexes for queries with a WHERE clause

SELECT * FROM items WHERE category_id = 123 ORDER BY embedding <-> '[3,1,2]' LIMIT 5;

can be indexed with:

CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100) WHERE (category_id = 123);

To index many different values of category_id, consider partitioning on category_id.

CREATE TABLE items (embedding vector(3), category_id int) PARTITION BY LIST(category_id);

Performance

Use EXPLAIN ANALYZE to debug performance.

EXPLAIN ANALYZE SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 1;

Exact Search

To speed up queries without an index, increase max_parallel_workers_per_gather.

SET max_parallel_workers_per_gather = 4;

If vectors are normalized to length 1 (like OpenAI embeddings), use inner product for best performance.

SELECT * FROM items ORDER BY embedding <#> '[3,1,2]' LIMIT 1;

Approximate Search

To speed up queries with an index, increase the number of inverted lists (at the expense of recall).

CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 1000);

Languages

Use pgvector from any language with a Postgres client. You can even generate and store vectors in one language and query them in another.

Language	Libraries / Examples
C++	pgvector-cpp
C#	pgvector-dotnet
Elixir	pgvector-elixir
Go	pgvector-go
Java, Scala	pgvector-java
Julia	pgvector-julia
Lua	pgvector-lua
Node.js	pgvector-node
Perl	pgvector-perl
PHP	pgvector-php
Python	pgvector-python
R	pgvector-r
Ruby	pgvector-ruby, Neighbor
Rust	pgvector-rust

Frequently Asked Questions

How many vectors can be stored in a single table?

A non-partitioned table has a limit of 32 TB by default in Postgres. A partitioned table can have thousands of partitions of that size.

Is replication supported?

Yes, pgvector uses the write-ahead log (WAL), which allows for replication and point-in-time recovery.

What if I want to index vectors with more than 2,000 dimensions?

Two things you can try are:

use dimensionality reduction
compile Postgres with a larger block size (./configure --with-blocksize=32) and edit the limit in src/ivfflat.h

Reference

Vector Type

Each vector takes 4 * dimensions + 8 bytes of storage. Each element is a single precision floating-point number (like the real type in Postgres), and all elements must be finite (no NaN, Infinity or -Infinity). Vectors can have up to 16,000 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) → double precision	cosine distance
inner_product(vector, vector) → double precision	inner product
l2_distance(vector, vector) → double precision	Euclidean distance
vector_dims(vector) → integer	number of dimensions
vector_norm(vector) → double precision	Euclidean norm

Aggregate Functions

Function	Description
avg(vector) → vector	arithmetic mean

Additional Installation Methods

Docker

Get the Docker image with:

docker pull ankane/pgvector

This adds pgvector to the Postgres image (run it the same way).

You can also build the image manually:

git clone --branch v0.4.1 https://github.com/pgvector/pgvector.git
cd pgvector
docker build -t pgvector .

Homebrew

With Homebrew Postgres, you can use:

brew install pgvector

PGXN

Install from the PostgreSQL Extension Network with:

pgxn install vector

Yum

RPM packages are available from the PostgreSQL Yum Repository. Follow the setup instructions for your distribution and run:

sudo yum install pgvector_15
# or
sudo dnf install pgvector_15

Note: Replace 15 with your Postgres server version

conda-forge

With Conda Postgres, install from conda-forge with:

conda install -c conda-forge pgvector

This method is community-maintained by @mmcauliffe

Hosted Postgres

pgvector is available on these providers.

To request a new extension on other providers:

Amazon RDS - follow the instructions on this page
Google Cloud SQL - vote or comment on this page
Azure Database - vote or comment on this page
DigitalOcean Managed Databases - vote or comment on this page
Render - vote or comment on this page

Upgrading

Install the latest version and run:

ALTER EXTENSION vector UPDATE;

Upgrade Notes

0.4.0

If upgrading with Postgres < 13, remove this line from sql/vector--0.3.2--0.4.0.sql:

ALTER TYPE vector SET (STORAGE = extended);

Then run make install and ALTER EXTENSION vector UPDATE;.

0.3.1

If upgrading from 0.2.7 or 0.3.0, recreate all ivfflat indexes after upgrading to ensure all data is indexed.

-- Postgres 12+
REINDEX INDEX CONCURRENTLY index_name;

-- Postgres < 12
CREATE INDEX CONCURRENTLY temp_name ON table USING ivfflat (column opclass);
DROP INDEX CONCURRENTLY index_name;
ALTER INDEX temp_name RENAME TO index_name;

Thanks

Thanks to:

History

View the changelog

Contributing

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

Report bugs
Fix bugs and submit pull requests
Write, clarify, or fix documentation
Suggest or add new features

To get started with development:

git clone https://github.com/pgvector/pgvector.git
cd pgvector
make
make install

To run all tests:

make installcheck        # regression tests
make prove_installcheck  # TAP tests

To run single tests:

make installcheck REGRESS=functions                    # regression test
make prove_installcheck PROVE_TESTS=test/t/001_wal.pl  # TAP test

To enable benchmarking:

make clean && PG_CFLAGS=-DIVFFLAT_BENCH make && make install

Resources for contributors

wlaurance/pgvector