vector-columns.mdx

id	title	description	sidebar_label
ai-vector-columns	Vector columns	Learn how to use vectors within your own Postgres tables	Vector columns

Supabase offers a number of different ways to store and query vectors within Postgres. The SQL included in this guide is applicable for clients in all programming languages. If you are a Python user see your Python client options after reading the Learn section.

Vectors in Supabase are enabled via pgvector, a PostgreSQL extension for storing and querying vectors in Postgres. It can be used to store embeddings.

Usage

Enable the extension

<Tabs scrollable size="small" type="underlined" defaultActiveId="dashboard" queryGroup="database-method"

1. Go to the Database page in the Dashboard.
2. Click on Extensions in the sidebar.
3. Search for "vector" and enable the extension.

 -- Example: enable the "vector" extension.
create extension vector
with
  schema extensions;

-- Example: disable the "vector" extension
drop
  extension if exists vector;

Even though the SQL code is create extension, this is the equivalent of "enabling the extension". To disable an extension, call drop extension.

Create a table to store vectors

After enabling the vector extension, you will get access to a new data type called vector. The size of the vector (indicated in parenthesis) represents the number of dimensions stored in that vector.

create table documents (
  id serial primary key,
  title text not null,
  body text not null,
  embedding vector(384)
);

In the above SQL snippet, we create a documents table with a column called embedding (note this is just a regular Postgres column - you can name it whatever you like). We give the embedding column a vector data type with 384 dimensions. Change this to the number of dimensions produced by your embedding model. For example, if you are generating embeddings using the open source gte-small model, you would set this number to 384 since that model produces 384 dimensions.

In general, embeddings with fewer dimensions perform best. See our analysis on fewer dimensions in pgvector.

Storing a vector / embedding

In this example we'll generate a vector using Transformers.js, then store it in the database using the Supabase JavaScript client.

import { pipeline } from '@xenova/transformers'
const generateEmbedding = await pipeline('feature-extraction', 'Supabase/gte-small')

const title = 'First post!'
const body = 'Hello world!'

// Generate a vector using Transformers.js
const output = await generateEmbedding(body, {
  pooling: 'mean',
  normalize: true,
})

// Extract the embedding output
const embedding = Array.from(output.data)

// Store the vector in Postgres
const { data, error } = await supabase.from('documents').insert({
  title,
  body,
  embedding,
})

This example uses the JavaScript Supabase client, but you can modify it to work with any supported language library.

Querying a vector / embedding

Similarity search is the most common use case for vectors. pgvector support 3 new operators for computing distance:

Operator	Description
<->	Euclidean distance
<#>	negative inner product
<=>	cosine distance

Choosing the right operator depends on your needs. Dot product tends to be the fastest if your vectors are normalized. For more information on how embeddings work and how they relate to each other, see What are Embeddings?.

Supabase client libraries like supabase-js connect to your Postgres instance via PostgREST. PostgREST does not currently support pgvector similarity operators, so we'll need to wrap our query in a Postgres function and call it via the rpc() method:

create or replace function match_documents (
  query_embedding vector(384),
  match_threshold float,
  match_count int
)
returns table (
  id bigint,
  content text,
  similarity float
)
language sql stable
as $$
  select
    documents.id,
    documents.content,
    1 - (documents.embedding <=> query_embedding) as similarity
  from documents
  where 1 - (documents.embedding <=> query_embedding) > match_threshold
  order by (documents.embedding <=> query_embedding) asc
  limit match_count;
$$;

This function takes a query_embedding argument and compares it to all other embeddings in the documents table. Each comparison returns a similarity score. If the similarity is greater than the match_threshold argument, it is returned. The number of rows returned is limited by the match_count argument.

Feel free to modify this method to fit the needs of your application. The match_threshold ensures that only documents that have a minimum similarity to the query_embedding are returned. Without this, you may end up returning documents that subjectively don't match. This value will vary for each application - you will need to perform your own testing to determine the threshold that makes sense for your app.

If you index your vector column, ensure that the order by sorts by the distance function directly (rather than sorting by the calculated similarity column, which may lead to the index being ignored and poor performance).

To execute the function from your client library, call rpc() with the name of your Postgres function:

const { data: documents } = await supabaseClient.rpc('match_documents', {
  query_embedding: embedding, // Pass the embedding you want to compare
  match_threshold: 0.78, // Choose an appropriate threshold for your data
  match_count: 10, // Choose the number of matches
})

In this example embedding would be another embedding you wish to compare against your table of pre-generated embedding documents. For example if you were building a search engine, every time the user submits their query you would first generate an embedding on the search query itself, then pass it into the above rpc() function to match.

Be sure to use embeddings produced from the same embedding model when calculating distance. Comparing embeddings from two different models will produce no meaningful result.

Vectors and embeddings can be used for much more than search. Learn more about embeddings at What are Embeddings?.

Indexes

Once your vector table starts to grow, you will likely want to add an index to speed up queries. See Vector indexes to learn how vector indexes work and how to create them.