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Open-source vector similarity search for Postgres

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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

Build Status

Installation

Linux and Mac

Compile and install the extension (supports Postgres 12+)

cd /tmp
git clone --branch v0.6.0 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, 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:

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\16"
git clone --branch v0.6.0 https://github.com/pgvector/pgvector.git
cd pgvector
nmake /F Makefile.win
nmake /F Makefile.win install

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 (<#>) 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:

HNSW

An HNSW index creates a multilayer graph. It has better query performance than IVFFlat (in terms of speed-recall tradeoff), but has slower build times and uses more memory. Also, an index can be created without any data in the table since there isn’t a training step like IVFFlat.

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);

A higher value of ef_construction provides better recall at the cost of index build time / insert speed.

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;

Index Build Time

Indexes build significantly faster when the graph fits into maintenance_work_mem

SET maintenance_work_mem = '8GB';

A notice is shown when the graph no longer fits

NOTICE:  hnsw graph no longer fits into maintenance_work_mem after 100000 tuples
DETAIL:  Building will take significantly more time.
HINT:  Increase maintenance_work_mem to speed up builds.

Note: Do not set maintenance_work_mem so high that it exhausts the memory on the server

Starting with 0.6.0, you can also speed up index creation by increasing the number of parallel workers (2 by default)

SET max_parallel_maintenance_workers = 7; -- plus leader

For a large number of workers, you may also need to increase max_parallel_workers (8 by default)

Indexing Progress

Check indexing progress with Postgres 12+

SELECT phase, round(100.0 * blocks_done / nullif(blocks_total, 0), 1) AS "%" FROM pg_stat_progress_create_index;

The phases for HNSW are:

  1. initializing
  2. loading tuples

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:

  1. Create the index after the table has some data
  2. 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
  3. 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;

Index Build Time

Speed up index creation on large tables by increasing the number of parallel workers (2 by default)

SET max_parallel_maintenance_workers = 7; -- plus leader

For a large number of workers, you may also need to increase max_parallel_workers (8 by default)

Indexing Progress

Check indexing progress with Postgres 12+

SELECT phase, round(100.0 * tuples_done / nullif(tuples_total, 0), 1) AS "%" FROM pg_stat_progress_create_index;

The phases for IVFFlat are:

  1. initializing
  2. performing k-means
  3. assigning tuples
  4. loading tuples

Note: % is 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 hnsw (embedding vector_l2_ops) 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.

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

You can use Reciprocal Rank Fusion or a cross-encoder to combine results.

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);

Vacuuming

Vacuuming can take a while for HNSW indexes. Speed it up by reindexing first.

REINDEX INDEX CONCURRENTLY index_name;
VACUUM table_name;

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#, F#, Visual Basic pgvector-dotnet
Crystal pgvector-crystal
Dart pgvector-dart
Elixir pgvector-elixir
Go pgvector-go
Haskell pgvector-haskell
Java, Kotlin, Groovy, Scala pgvector-java
JavaScript, TypeScript pgvector-node
Julia pgvector-julia
Lisp pgvector-lisp
Lua pgvector-lua
Nim pgvector-nim
OCaml pgvector-ocaml
Perl pgvector-perl
PHP pgvector-php
Python pgvector-python
R pgvector-r
Ruby pgvector-ruby, Neighbor
Rust pgvector-rust
Swift pgvector-swift
Zig pgvector-zig

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.

Can I store vectors with different dimensions in the same column?

You can use vector as the type (instead of vector(3)).

CREATE TABLE embeddings (model_id bigint, item_id bigint, embedding vector, PRIMARY KEY (model_id, item_id));

However, you can only create indexes on rows with the same number of dimensions (using expression and partial indexing):

CREATE INDEX ON embeddings USING hnsw ((embedding::vector(3)) vector_l2_ops) WHERE (model_id = 123);

and query with:

SELECT * FROM embeddings WHERE model_id = 123 ORDER BY embedding::vector(3) <-> '[3,1,2]' LIMIT 5;

Can I store vectors with more precision?

You can use the double precision[] or numeric[] type to store vectors with more precision.

CREATE TABLE items (id bigserial PRIMARY KEY, embedding double precision[]);

-- use {} instead of [] for Postgres arrays
INSERT INTO items (embedding) VALUES ('{1,2,3}'), ('{4,5,6}');

Optionally, add a check constraint to ensure data can be converted to the vector type and has the expected dimensions.

ALTER TABLE items ADD CHECK (vector_dims(embedding::vector) = 3);

Use expression indexing to index (at a lower precision):

CREATE INDEX ON items USING hnsw ((embedding::vector(3)) vector_l2_ops);

and query with:

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

Do indexes need to fit into memory?

No, but like other index types, you’ll likely see better performance if they do. You can get the size of an index with:

SELECT pg_size_pretty(pg_relation_size('index_name'));

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;

Also, if the table is small, a table scan may be faster.

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=/Library/PostgreSQL/16/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

A few common paths on Mac are:

  • EDB installer - /Library/PostgreSQL/16/bin/pg_config
  • Homebrew (arm64) - /opt/homebrew/opt/postgresql@16/bin/pg_config
  • Homebrew (x86-64) - /usr/local/opt/postgresql@16/bin/pg_config

Note: Replace 16 with your Postgres server version

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-16

Note: Replace 16 with your Postgres server version

Missing SDK

If compilation fails and the output includes warning: no such sysroot directory on Mac, reinstall Xcode Command Line Tools.

Additional Installation Methods

Docker

Get the Docker image with:

docker pull pgvector/pgvector:pg16

This adds pgvector to the Postgres image (replace 16 with your Postgres server version, and run it the same way).

You can also build the image manually:

git clone --branch v0.6.0 https://github.com/pgvector/pgvector.git
cd pgvector
docker build --build-arg PG_MAJOR=16 -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-16-pgvector

Note: Replace 16 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_16
# or
sudo dnf install pgvector_16

Note: Replace 16 with your Postgres server version

pkg

Install the FreeBSD package with:

pkg install postgresql15-pg_vector

or the port with:

cd /usr/ports/databases/pgvector
make install

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 (use the same method as the original installation). 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

0.6.0

Postgres 12

If upgrading with Postgres 12, remove this line from sql/vector--0.5.1--0.6.0.sql:

ALTER TYPE vector SET (STORAGE = external);

Then run make install and ALTER EXTENSION vector UPDATE;.

Docker

The Docker image is now published in the pgvector org, and there are tags for each supported version of Postgres (rather than a latest tag).

docker pull pgvector/pgvector:pg16
# or
docker pull pgvector/pgvector:0.6.0-pg16

Also, if you’ve increased maintenance_work_mem, make sure --shm-size is at least that size to avoid an error with parallel HNSW index builds.

docker run --shm-size=1g ...

Thanks

Thanks to:

History

View the changelog

Contributing

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

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_ivfflat_wal.pl  # TAP test

To enable benchmarking:

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

To show memory usage:

make clean && PG_CFLAGS="-DHNSW_MEMORY -DIVFFLAT_MEMORY" make && make install

To enable assertions:

make clean && PG_CFLAGS="-DUSE_ASSERT_CHECKING" make && make install

To get k-means metrics:

make clean && PG_CFLAGS="-DIVFFLAT_KMEANS_DEBUG" make && make install

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