pgvecto.rs

pgvecto.rs is a Postgres extension that provides vector similarity search functions. It is written in Rust and based on pgrx. It is currently in the beta status, we invite you to try it out in production and provide us with feedback. Read more at 📝our launch blog.

Why use pgvecto.rs

💃 Easy to use: pgvecto.rs is a Postgres extension, which means that you can use it directly within your existing database. This makes it easy to integrate into your existing workflows and applications.
🔗 Async indexing: pgvecto.rs's index is asynchronously constructed by the background threads and does not block insertions and always ready for new queries.
🥅 Filtering: pgvecto.rs supports filtering. You can set conditions when searching or retrieving points. This is the missing feature of other postgres extensions.
🧮 Quantization: pgvecto.rs supports scalar quantization and product qutization up to 64x.
🦀 Rewrite in Rust: Rust's strict compile-time checks ensure memory safety, reducing the risk of bugs and security issues commonly associated with C extensions.

Comparison with pgvector

	pgvecto.rs	pgvector
Transaction support	✅	⚠️
Sufficient Result with Delete/Update/Filter	✅	⚠️
Vector Dimension Limit	65535	2000
Prefilter on HNSW	✅	❌
Parallel HNSW Index build	⚡️ Linearly faster with more cores	🐌 Only single core used
Async Index build	Ready for queries anytime and do not block insertions.	❌
Quantization	Scalar/Product Quantization	❌

More details at pgvecto.rs vs. pgvector

Documentation

Getting Started
- Overview
- Installation
Usage
- Indexing
- Search
Administration
- Configuration
- Upgrading from older versions
Developers
- Development Tutorial

Quick start

For new users, we recommend using the Docker image to get started quickly.

docker run \
  --name pgvecto-rs-demo \
  -e POSTGRES_PASSWORD=mysecretpassword \
  -p 5432:5432 \
  -d tensorchord/pgvecto-rs:pg16-v0.1.13

Then you can connect to the database using the psql command line tool. The default username is postgres, and the default password is mysecretpassword.

psql -h localhost -p 5432 -U postgres

Run the following SQL to ensure the extension is enabled.

DROP EXTENSION IF EXISTS vectors;
CREATE EXTENSION vectors;

pgvecto.rs introduces a new data type vector(n) denoting an n-dimensional vector. The n within the brackets signifies the dimensions of the vector.

You could create a table with the following SQL.

-- create table with a vector column

CREATE TABLE items (
  id bigserial PRIMARY KEY,
  embedding vector(3) NOT NULL -- 3 dimensions
);

Tip

vector(n) is a valid data type only if $1 \leq n \leq 65535$. Due to limits of PostgreSQL, it's possible to create a value of type vector(3) of $5$ dimensions and vector is also a valid data type. However, you cannot still put $0$ scalar or more than $65535$ scalars to a vector. If you use vector for a column or there is some values mismatched with dimension denoted by the column, you won't able to create an index on it.

You can then populate the table with vector data as follows.

-- insert values

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

-- or insert values using a casting from array to vector

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

We support three operators to calculate the distance between two vectors.

<->: squared Euclidean distance, defined as $\Sigma (x_i - y_i) ^ 2$.
<#>: negative dot product, defined as $- \Sigma x_iy_i$.
<=>: cosine distance, defined as $1 - \frac{\Sigma x_iy_i}{\sqrt{\Sigma x_i^2 \Sigma y_i^2}}$.

-- call the distance function through operators

-- squared Euclidean distance
SELECT '[1, 2, 3]'::vector <-> '[3, 2, 1]'::vector;
-- negative dot product
SELECT '[1, 2, 3]'::vector <#> '[3, 2, 1]'::vector;
-- cosine distance
SELECT '[1, 2, 3]'::vector <=> '[3, 2, 1]'::vector;

You can search for a vector simply like this.

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

Half-precision floating-point

vecf16 type is the same with vector in anything but the scalar type. It stores 16-bit floating point numbers. If you want to reduce the memory usage to get better performance, you can try to replace vector type with vecf16 type.

Roadmap 🗂️

Please check out ROADMAP. Want to jump in? Welcome discussions and contributions!

Chat with us on 💬 Discord
Have a look at good first issue 💖 issues!

Contribute 😊

We welcome all kinds of contributions from the open-source community, individuals, and partners.

Join our discord community!
To build from the source, please read our contributing documentation and development tutorial.

Contributors ✨

Thanks goes to these wonderful people (emoji key):

_{Alex Chi} 💻	_AuruTus 💻	_Avery 💻 🤔	_{Ben Ye} 📖	_{Ce Gao} 💼 🖋 📖	_{Jinjing Zhou} 🎨 🤔 📆	_Keming 🐛 💻 📖 🤔 🚇
_{Mingzhuo Yin} 💻 ⚠️ 🚇	_Usamoi 💻 🤔	_cutecutecat 💻	_odysa 📖 💻	_yihong 💻	_{盐粒 Yanli} 💻
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