pgvector: Embeddings and vector similarity

pgvector is a PostgreSQL extension for vector similarity search. It can also be used for storing embeddings.

Learn more about Supabase's AI & Vector offering.

Concepts#

Vector similarity#

Vector similarity refers to a measure of the similarity between two related items. For example, if you have a list of products, you can use vector similarity to find similar products. To do this, you need to convert each product into a "vector" of numbers, using a mathematical model. You can use a similar model for text, images, and other types of data. Once all of these vectors are stored in the database, you can use vector similarity to find similar items.

Embeddings#

This is particularly useful if you're building on top of OpenAI's GPT-3. You can create and store embeddings for retrieval augmented generation.

Usage#

Enable the extension#

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

Usage#

Create a table to store vectors#

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

Storing a vector / embedding#

In this example we'll generate a vector using Transformer.js, then store it in the database using the Supabase 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('posts').insert({
  title,
  body,
  embedding,
})

More pgvector and Supabase resources#

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