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

💃 User-Friendly: Effortlessly incorporate pgvecto.rs into your existing database as a Postgres extension, streamlining integration with your current workflows and applications.
🥅 Join and Filter without Limitation: Elevate your search capabilities in pgvecto.rs with VBASE filtering. Apply any filter conditions and join with other tables, achieving high recall and low latency, a distinctive edge over other vector databases.
🌓 Efficient FP16 Support: Optimize your data storage with pgvecto.rs, supporting FP16 vector type to cut memory and storage usage by half, and boosting throughput.
🧮 Advanced Quantization: Utilize scalar and product quantization in pgvecto.rs for up to 64x compression. Achieve up to 4x memory savings with less than 2% recall loss with scalar quantization.
🔍 Hybrid Search: Leverage the full-text search functionality in PostgreSQL with pgvecto.rs to search text and vector data within a single query.
🔗 Async indexing: The pgvecto.rs index is built asynchronously by background threads, allowing non-blocking inserts and always ready for new queries.
⬆️ Extended Vector Length: pgvecto.rs supports vector length up to 65535, suitable for the latest models.
🦀 Rust-Powered Reliability: Rust's strict compile-time checks ensure memory safety, reducing the risk of bugs and security issues commonly associated with C extensions.

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-v0.1.14-beta

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} 🎨 🤔 📆	_{Joe Passanante} 💻
_Keming 🐛 💻 📖 🤔 🚇	_{Mingzhuo Yin} 💻 ⚠️ 🚇	_Usamoi 💻 🤔	_cutecutecat 💻	_odysa 📖 💻	_yihong 💻	_{盐粒 Yanli} 💻
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