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Overview ​

An introduction to the

What is ​ 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 ​

  • 💃 User-Friendly: Effortlessly incorporate 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 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, supporting FP16 vector type to cut memory and storage usage by half, and boosting throughput.
  • 🧮 Advanced Quantization: Utilize scalar and product quantization in 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 to search text and vector data within a single query.
  • 🔗 Async indexing: The index is built asynchronously by background threads, allowing non-blocking inserts and always ready for new queries.
  • ⬆️ Extended Vector Length: 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.

CREATE EXTENSION vectors; 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

  id bigserial PRIMARY KEY,
  embedding vector(3) NOT NULL -- 3 dimensions

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.

-- 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 . And 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.

Talk with us ​

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

Set up a time to chat!