pgvecto.rs

pgvecto.rs is a Postgres extension that provides vector similarity search functions. It is written in Rust and based on pgrx. Read more at 📝our blog.

Why use pgvecto.rs

Feature Category	Feature
Search Capabilities	🔍 Vector Search	Ultra-low-latency, high-precision vector search.
	🧩 Sparse Vector Search	Keyword-based vector search using SPLADE or BM25 algorithms.
	📄 Full-Text Search	Comprehensive text search across any language, powered by tsvector.
Data Handling	✔ Complete SQL Support	Full SQL support, enabling joins and filters without limitations or extra configuration.
	🔗 Async indexing	Non-blocking inserts with up-to-date query readiness.
	🔄 Easy Data Management	No need for syncing vectors and metadata with external vector DB, simplifying development.
Data Types	🔢 FP16/INT8 Data type	Supports FP16 and INT8 data types for improved storage and computational efficiency.
	🌓 Binary vector support	Vector indexing with binary vectors, and Jaccard distance support.
	🔪 Matryoshka embeddings	Subvector indexing, like vector[0:256], for enhanced Matryoshka embeddings.
	⬆️ Extended Vector Length	Vector lengths up to 65535 supported, ideal for the latest cutting-edge models.
System Performance	🚀 Production Ready	Battle-tested database ecosystem integrated with PostgreSQL.
	⚙️ High Availability	Logical replication support to ensure high availability.
	💡 Resource Efficient	Efficient attribute storage leveraging PostgreSQL.
Security & Permissions	🔒 Permission Control	Easy access control like read-only roles, powered by PostgreSQL.

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-v0.2.1

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 📖 💻	yi wang 💻	yihong 💻
盐粒 Yanli 💻
Add your contributions

This project follows the all-contributors specification. Contributions of any kind welcome!

Acknowledgements

Thanks to the following projects:

• pgrx - Postgres extension framework in Rust
• pgvector - Postgres extension for vector similarity search written in C