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.
🥅 Filtering: pgvecto.rs supports filtering. You can set conditions when searching or retrieving points. This is the missing feature of other postgres extensions.
🚀 High Performance: pgvecto.rs is designed to provide significant improvements compared to existing Postgres extensions. Benchmarks have shown that its HNSW index can deliver search performance up to 20 times faster than other indexes like ivfflat.
🔧 Extensible: pgvecto.rs is designed to be extensible. It is easy to add new index structures and search algorithms. This flexibility ensures that pgvecto.rs can adapt to emerging vector search algorithms and meet diverse performance needs.
🦀 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.
🙋 Community Driven: We encourage community involvement and contributions, fostering innovation and continuous improvement.

Comparison with pgvector

	pgvecto.rs	pgvector
Transaction support	✅	⚠️
Sufficient Result with Delete/Update/Filter	✅	⚠️
Vector Dimension Limit	65535	2000
Prefilter on HNSW	✅	❌
Parallel Index build	⚡️ Linearly faster with more cores	🐌 Only single core used
Index Persistence	mmap file	Postgres internal storage
WAL amplification	2x 😃	30x 🧐

And based on our benchmark, pgvecto.rs can be up to 2x faster than pgvector on hnsw indexes with same configurations. Read more about the comparison at here.

Installation

We recommend you to try pgvecto.rs using our pre-built docker, by running

docker run --name pgvecto-rs-demo -e POSTGRES_PASSWORD=mysecretpassword -p 5432:5432 -d tensorchord/pgvecto-rs:latest

For more installation method (binary install or install from source), read more at docs/install.md

Get started

Run the following SQL to ensure the extension is enabled

DROP EXTENSION IF EXISTS vectors;
CREATE EXTENSION vectors;

pgvecto.rs allows columns of a table to be defined as vectors.

The data type vector(n) denotes an n-dimensional vector. The n within the brackets signifies the dimensions of the vector. For instance, vector(1000) would represent a vector with 1000 dimensions, so you could create a table like this.

-- create table with a vector column

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

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 distance, defined as $- \Sigma x_iy_i$.
<=>: negative cosine distance, defined as $- \frac{\Sigma x_iy_i}{\sqrt{\Sigma x_i^2 \Sigma y_i^2}}$.

-- call the distance function through operators

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

You can search for a vector simply like this.

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

Indexing

You can create an index, using squared Euclidean distance with the following SQL.

-- Using HNSW algorithm.

CREATE INDEX ON items USING vectors (embedding l2_ops)
WITH (options = $$
capacity = 2097152
[vectors]
memmap = "ram"
[algorithm.hnsw]
memmap = "ram"
$$);

--- Or using IVFFlat algorithm.

CREATE INDEX ON items USING vectors (embedding l2_ops)
WITH (options = $$
capacity = 2097152
[vectors]
memmap = "ram"
[algorithm.ivf]
memmap = "ram"
nlist = 1000
nprobe = 10
$$);

Now you can perform a KNN search with the following SQL simply.

SELECT *, embedding <-> '[0, 0, 0]' AS score
FROM items
ORDER BY embedding <-> '[0, 0, 0]' LIMIT 10;

Please note, vector indexes are not loaded by default when PostgreSQL restarts. To load or unload the index, you can use vectors_load and vectors_unload.

--- get the index name
\d items

-- load the index
SELECT vectors_load('items_embedding_idx'::regclass);

We planning to support more index types (issue here).

Welcome to contribute if you are also interested!

Why not a specialized vector database?

Read our blog at modelz.ai/blog/pgvector

Reference

`vector` type

vector and vector(n) are all legal data types, where n denotes dimensions of a vector.

The current implementation ignores dimensions of a vector, i.e., the behavior is the same as for vectors of unspecified dimensions.

There is only one exception: indexes cannot be created on columns without specified dimensions.

Indexing

We utilize TOML syntax to express the index's configuration. Here's what each key in the configuration signifies:

Key	Type	Description
capacity	integer	The index's capacity. The value should be greater than the number of rows in your table.
vectors	table	Configuration of background process vector storage.
vectors.memmap	string	(Optional) `ram` ensures that the vectors always stays in memory while `disk` suggests otherwise.
algorithm.ivf	table	If this table is set, the IVF algorithm will be used for the index.
algorithm.ivf.memmap	string	(Optional) `ram` ensures that the persisent part of algorithm always stays in memory while `disk` suggests otherwise.
algorithm.ivf.nlist	integer	Number of cluster units.
algorithm.ivf.nprobe	integer	Number of units to query.
algorithm.hnsw	table	If this table is set, the HNSW algorithm will be used for the index.
algorithm.hnsw.memmap	string	(Optional) `ram` ensures that the persisent part of algorithm always stays in memory while `disk` suggests otherwise.
algorithm.hnsw.m	integer	(Optional) Maximum degree of the node.
algorithm.hnsw.ef	integer	(Optional) Search scope in building.

And you can change the number of expected result (such as ef_search in hnsw) by using the following SQL.

---  (Optional) Expected number of candidates returned by index
SET vectors.k = 32;
--- Or use local to set the value for the current session
SET LOCAL vectors.k = 32;


## Limitations

- The index is constructed and persisted using a memory map file (mmap) instead of PostgreSQL's shared buffer. As a result, physical replication or logical replication may not function correctly. Additionally, vector indexes are not automatically loaded when PostgreSQL restarts. To load or unload the index, you can utilize the `vectors_load` and `vectors_unload` commands.
- The filtering process is not yet optimized. To achieve optimal performance, you may need to manually experiment with different strategies. For example, you can try searching without a vector index or implementing post-filtering techniques like the following query: `select * from (select * from items ORDER BY embedding <-> '[3,2,1]' LIMIT 100 ) where category = 1`. This involves using approximate nearest neighbor (ANN) search to obtain enough results and then applying filtering afterwards.

## Setting up the development environment

You could use [envd](https://github.com/tensorchord/envd) to set up the development environment with one command. It will create a docker container and install all the dependencies for you.

```sh
pip install envd
envd up

Contributing

We need your help! Please check out the issues.

Contributors ✨

Thanks goes to these wonderful people (emoji key):

_{Alex Chi} 💻	_{Ben Ye} 📖	_{Ce Gao} 💼 🖋 📖	_{Jinjing Zhou} 🎨 🤔 📆	_Keming 🐛 💻 📖 🤔 🚇	_Usamoi 💻 🤔	_odysa 📖 💻
_yihong 💻
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