# Overview

An introduction to the `pgvecto.rs`

.

## What is `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.

## 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-v0.2.0
```

Then you can connect to the database using the `psql`

command line tool. The default username is `postgres`

, and the default password is `mysecretpassword`

.

`psql postgresql://postgres:mysecretpassword@localhost:5432/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
);
```

## Details

`vector(n)`

is a valid data type only if `vector(3)`

of `vector`

is also a valid data type. However, you cannot still put `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. `<#>`

: negative dot product, defined as. `<=>`

: cosine distance, defined as.

```
-- 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.

For more usage of `vecf16`

, please refer to vector types.

### Sparse vector

`svector`

type is a sparse vector type. It stores a vector in a sparse format. It is suitable for vectors with many zeros.

For more usage of `svector`

, please refer to vector types.

### Binary vector

`bvector`

type is a binary vector type. It is a fixed-length bit string. Except for above 3 distances, we also support `jaccard`

distance `<~>`

, which defined as `hamming`

distance is the same with squared Euclidean distance, you can use `<->`

operator to calculate it. We also provide `binarize`

function to construct a `bvector`

from a `vector`

, which set the positive elements to 1, otherwise 0.

For more usage of `bvector`

, please refer to vector types.

## Roadmap 🗂️

Please check out ROADMAP.

## 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.

### Talk with us

💬 Interested in talking with us about your experience building or managing AI/ML applications?