pgvector

Open-source vector similarity search for Postgres

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

exact and approximate nearest neighbor search
L2 distance, inner product, and cosine distance
any language with a Postgres client

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

Installation

Compile and install the extension (supports Postgres 11+)

cd /tmp
git clone --branch v0.5.1 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, or conda-forge, and it comes preinstalled with Postgres.app and many hosted providers

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 (<#>) and cosine 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);

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

Get the nearest neighbors to a vector

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

Get the nearest neighbors to a row

SELECT * FROM items WHERE id != 1 ORDER BY embedding <-> (SELECT embedding FROM items WHERE id = 1) LIMIT 5;

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

Distances

Get the distance

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

For inner product, multiply by -1 (since <#> returns the negative inner product)

SELECT (embedding <#> '[3,1,2]') * -1 AS inner_product FROM items;

For cosine similarity, use 1 - cosine distance

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

Aggregates

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 speed. Unlike typical indexes, you will see different results for queries after adding an approximate index.

Supported index types are:

IVFFlat
HNSW - added in 0.5.0

IVFFlat

An IVFFlat index divides vectors into lists, and then searches a subset of those lists that are closest to the query vector. It has faster build times and uses less memory than HNSW, but has lower query performance (in terms of speed-recall tradeoff).

Three keys to achieving good recall are:

Create the index after the table has some data
Choose an appropriate number of lists - a good place to start is rows / 1000 for up to 1M rows and sqrt(rows) for over 1M rows
When querying, specify an appropriate number of probes (higher is better for recall, lower is better for speed) - a good place to start is sqrt(lists)

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 = 10;

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 = 10;
SELECT ...
COMMIT;

HNSW

An HNSW index creates a multilayer graph. It has slower build times and uses more memory than IVFFlat, but has better query performance (in terms of speed-recall tradeoff). There’s no training step like IVFFlat, so the index can be created without any data in the table.

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

L2 distance

CREATE INDEX ON items USING hnsw (embedding vector_l2_ops);

Inner product

CREATE INDEX ON items USING hnsw (embedding vector_ip_ops);

Cosine distance

CREATE INDEX ON items USING hnsw (embedding vector_cosine_ops);

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

Index Options

Specify HNSW parameters

m - the max number of connections per layer (16 by default)
ef_construction - the size of the dynamic candidate list for constructing the graph (64 by default)

CREATE INDEX ON items USING hnsw (embedding vector_l2_ops) WITH (m = 16, ef_construction = 64);

Query Options

Specify the size of the dynamic candidate list for search (40 by default)

SET hnsw.ef_search = 100;

A higher value provides better recall at the cost of speed.

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

BEGIN;
SET LOCAL hnsw.ef_search = 100;
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 - IVFFlat only
assigning tuples - IVFFlat only
loading tuples

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

Filtering

There are a few ways to index nearest neighbor queries with a WHERE clause

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

Create an index on one or more of the WHERE columns for exact search

CREATE INDEX ON items (category_id);

Or a partial index on the vector column for approximate search

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

Use partitioning for approximate search on many different values of the WHERE columns

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

Hybrid Search

Use together with Postgres full-text search for hybrid search (Python example).

SELECT id, content FROM items, plainto_tsquery('hello search') query
    WHERE textsearch @@ query ORDER BY ts_rank_cd(textsearch, query) DESC LIMIT 5;

Performance

Use EXPLAIN ANALYZE to debug performance.

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

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 5;

Approximate Search

To speed up queries with an IVFFlat 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-c
C++	pgvector-cpp
C#	pgvector-dotnet
Crystal	pgvector-crystal
Dart	pgvector-dart
Elixir	pgvector-elixir
Go	pgvector-go
Haskell	pgvector-haskell
Java, Kotlin, 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
Swift	pgvector-swift

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?

You’ll need to use dimensionality reduction at the moment.

Troubleshooting

Why isn’t a query using an index?

The cost estimation in pgvector < 0.4.3 does not always work well with the planner. You can encourage the planner to use an index for a query with:

BEGIN;
SET LOCAL enable_seqscan = off;
SELECT ...
COMMIT;

Why isn’t a query using a parallel table scan?

The planner doesn’t consider out-of-line storage in cost estimates, which can make a serial scan look cheaper. You can reduce the cost of a parallel scan for a query with:

BEGIN;
SET LOCAL min_parallel_table_scan_size = 1;
SET LOCAL parallel_setup_cost = 1;
SELECT ...
COMMIT;

or choose to store vectors inline:

ALTER TABLE items ALTER COLUMN embedding SET STORAGE PLAIN;

Why are there less results for a query after adding an IVFFlat index?

The index was likely created with too little data for the number of lists. Drop the index until the table has more data.

DROP INDEX index_name;

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	Added
+	element-wise addition
-	element-wise subtraction
*	element-wise multiplication	0.5.0
<->	Euclidean distance
<#>	negative inner product
<=>	cosine distance

Vector Functions

Function	Description	Added
cosine_distance(vector, vector) → double precision	cosine distance
inner_product(vector, vector) → double precision	inner product
l2_distance(vector, vector) → double precision	Euclidean distance
l1_distance(vector, vector) → double precision	taxicab distance	0.5.0
vector_dims(vector) → integer	number of dimensions
vector_norm(vector) → double precision	Euclidean norm

Aggregate Functions

Function	Description	Added
avg(vector) → vector	average
sum(vector) → vector	sum	0.5.0

Installation Notes

Postgres Location

If your machine has multiple Postgres installations, specify the path to pg_config with:

export PG_CONFIG=/Applications/Postgres.app/Contents/Versions/latest/bin/pg_config

Then re-run the installation instructions (run make clean before make if needed). If sudo is needed for make install, use:

sudo --preserve-env=PG_CONFIG make install

Missing Header

If compilation fails with fatal error: postgres.h: No such file or directory, make sure Postgres development files are installed on the server.

For Ubuntu and Debian, use:

sudo apt install postgresql-server-dev-15

Note: Replace 15 with your Postgres server version

Windows

Support for Windows is currently experimental. Ensure C++ support in Visual Studio is installed, and run:

call "C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Auxiliary\Build\vcvars64.bat"

Note: The exact path will vary depending on your Visual Studio version and edition

Then use nmake to build:

set "PGROOT=C:\Program Files\PostgreSQL\15"
git clone --branch v0.5.1 https://github.com/pgvector/pgvector.git
cd pgvector
nmake /F Makefile.win
nmake /F Makefile.win install

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 v0.5.1 https://github.com/pgvector/pgvector.git
cd pgvector
docker build --build-arg PG_MAJOR=15 -t myuser/pgvector .

Homebrew

With Homebrew Postgres, you can use:

brew install pgvector

Note: This only adds it to the postgresql@14 formula

PGXN

Install from the PostgreSQL Extension Network with:

pgxn install vector

APT

Debian and Ubuntu packages are available from the PostgreSQL APT Repository. Follow the setup instructions and run:

sudo apt install postgresql-15-pgvector

Note: Replace 15 with your Postgres server version

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

Postgres.app

Download the latest release with Postgres 15+.

Hosted Postgres

pgvector is available on these providers.

Upgrading

Install the latest version. Then in each database you want to upgrade, run:

ALTER EXTENSION vector UPDATE;

You can check the version in the current database with:

SELECT extversion FROM pg_extension WHERE extname = 'vector';

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:

PASE: PostgreSQL Ultra-High-Dimensional Approximate Nearest Neighbor Search Extension
Faiss: A Library for Efficient Similarity Search and Clustering of Dense Vectors
Using the Triangle Inequality to Accelerate k-means
k-means++: The Advantage of Careful Seeding
Concept Decompositions for Large Sparse Text Data using Clustering
Efficient and Robust Approximate Nearest Neighbor Search using Hierarchical Navigable Small World Graphs

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

Extension Building Infrastructure
Index Access Method Interface Definition
Generic WAL Records

Files

README.md

Latest commit

History

README.md

File metadata and controls

pgvector

Installation

Getting Started

Storing

Querying

Distances

Aggregates

Indexing

IVFFlat

Query Options

HNSW

Index Options

Query Options

Indexing Progress

Filtering

Hybrid Search

Performance

Exact Search

Approximate Search

Languages

Frequently Asked Questions

How many vectors can be stored in a single table?

Is replication supported?

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

Troubleshooting

Why isn’t a query using an index?

Why isn’t a query using a parallel table scan?

Why are there less results for a query after adding an IVFFlat index?

Reference

Vector Type

Vector Operators

Vector Functions

Aggregate Functions

Installation Notes

Postgres Location

Missing Header

Windows

Additional Installation Methods

Docker

Homebrew

PGXN

APT

Yum

conda-forge

Postgres.app

Hosted Postgres

Upgrading

Upgrade Notes

0.4.0

0.3.1

Thanks

History

Contributing