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https://github.com/tensorchord/pgvecto.rs

Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database.
https://github.com/tensorchord/pgvecto.rs

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Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database.

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pgvecto.rs







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pgvecto.rs is a Postgres extension that provides vector similarity search functions. It is written in Rust and based on [pgrx](https://github.com/tcdi/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 blog](https://blog.pgvecto.rs/pgvectors-02-unifying-relational-queries-and-vector-search-in-postgresql).



## 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](https://docs.pgvecto.rs/getting-started/overview.html)



- Getting Started

  - [Overview](https://docs.pgvecto.rs/getting-started/overview.html)

  - [Installation](https://docs.pgvecto.rs/getting-started/installation.html)

- Usage

  - [Indexing](https://docs.pgvecto.rs/usage/indexing.html)

  - [Search](https://docs.pgvecto.rs/usage/search.html)

- Administration

  - [Configuration](https://docs.pgvecto.rs/admin/configuration.html)

  - [Upgrading from older versions](https://docs.pgvecto.rs/admin/upgrading.html)

- Developers

  - [Development Tutorial](https://docs.pgvecto.rs/developers/development.html)



## Quick start



For new users, we recommend using the [Docker image](https://hub.docker.com/r/tensorchord/pgvecto-rs) to get started quickly.



```sh

docker run \

  --name pgvecto-rs-demo \

  -e POSTGRES_PASSWORD=mysecretpassword \

  -p 5432:5432 \

  -d tensorchord/pgvecto-rs:pg16-v0.2.0

```



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



```sh

psql -h localhost -p 5432 -U postgres

```



Run the following SQL to ensure the extension is enabled.



```sql

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.



```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.



```sql

-- 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 $1 - \frac{\Sigma x_iy_i}{\sqrt{\Sigma x_i^2 \Sigma y_i^2}}$.



```sql

-- 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.



```sql

-- 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](https://docs.pgvecto.rs/community/roadmap.html). Want to jump in? Welcome discussions and contributions!



- Chat with us on [💬 Discord](https://discord.gg/KqswhpVgdU)

- Have a look at [`good first issue 💖`](https://github.com/tensorchord/pgvecto.rs/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue+%E2%9D%A4%EF%B8%8F%22) issues!



## Contribute 😊



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



- Join our [discord community](https://discord.gg/KqswhpVgdU)!

- To build from the source, please read our [contributing documentation](https://docs.pgvecto.rs/community/contributing.html) and [development tutorial](https://docs.pgvecto.rs/developers/development.html).



## Contributors ✨



Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):



  

    

      Alex Chi
Alex Chi
💻

      AuruTus
AuruTus
💻

      Avery
Avery
💻 🤔

      Ben Ye
Ben Ye
📖

      Ce Gao
Ce Gao
💼 🖋 📖

      Jinjing Zhou
Jinjing Zhou
🎨 🤔 📆

      Joe Passanante
Joe Passanante
💻

    

    

      Keming
Keming
🐛 💻 📖 🤔 🚇

      Mingzhuo Yin
Mingzhuo Yin
💻 ⚠️ 🚇

      Usamoi
Usamoi
💻 🤔

      cutecutecat
cutecutecat
💻

      odysa
odysa
📖 💻

      yihong
yihong
💻

      盐粒 Yanli
盐粒 Yanli
💻

    

  

  

    

      

        

          Add your contributions

        

      

    

  



This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!



## Acknowledgements



Thanks to the following projects:



- [pgrx](https://github.com/tcdi/pgrx) - Postgres extension framework in Rust

- [pgvector](https://github.com/pgvector/pgvector) - Postgres extension for vector similarity search written in C