pgvecto.rs

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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 ​

  • 💃 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.
  • 🔗 Async indexing: pgvecto.rs's index is asynchronously constructed by the background threads and does not block insertions and always ready for new queries.
  • 🥅 Filtering: pgvecto.rs supports filtering. You can set conditions when searching or retrieving points. This is the missing feature of other postgres extensions.
  • 🧮 Quantization: pgvecto.rs supports scalar quantization and product qutization up to 64x.
  • 🦀 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.

Comparison with pgvector ​

pgvecto.rspgvector
Transaction support✅⚠️
Sufficient Result with Delete/Update/Filter✅⚠️
Vector Dimension Limit655352000
Prefilter on HNSW✅❌
Parallel HNSW Index build⚡️ Linearly faster with more cores🐌 Only single core used
Async Index buildReady for queries anytime and do not block insertions.❌
QuantizationScalar/Product Quantization❌

More details at 📝pgvecto.rs vs. pgvector.

Quick start ​

For new users, we recommend using the Docker image to get started quickly.

sh
docker run \
  --name pgvecto-rs-demo \
  -e POSTGRES_PASSWORD=mysecretpassword \
  -p 5432:5432 \
  -d tensorchord/pgvecto-rs:pg16-v0.1.13

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 postgresql://postgres:mysecretpassword@localhost:5432/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
);
Details

vector(n) is a valid data type only if . Due to limits of PostgreSQL, it's possible to create a value of type vector(3) of dimensions and vector is also a valid data type. However, you cannot still put scalar or more than 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 .
  • <#>: negative dot product, defined as .
  • <=>: cosine distance, defined as .
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.

Contribute 😊 ​

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

Talk with us ​

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Set up a time to chat!