---
id: 'pgvector'
title: 'pgvector: Embeddings and vector similarity'
description: 'pgvector: a PostgreSQL extension for storing embeddings and performing vector similarity search.'
---

[pgvector](https://github.com/pgvector/pgvector/) is a PostgreSQL extension for vector similarity search. It can also be used for storing [embeddings](https://supabase.com/blog/openai-embeddings-postgres-vector).

Learn more about Supabase's [AI & Vector](/docs/guides/ai) 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](https://openai.com/blog/gpt-3-apps/). You can create and store [embeddings](/docs/guides/ai/quickstarts/generate-text-embeddings) for retrieval augmented generation.

## Usage

### Enable the extension

<Tabs
  scrollable
  size="small"
  type="underlined"
  defaultActiveId="dashboard"
  queryGroup="database-method"
>
<TabPanel id="dashboard" label="Dashboard">

1. Go to the [Database](https://supabase.com/dashboard/project/_/database/tables) page in the Dashboard.
2. Click on **Extensions** in the sidebar.
3. Search for "vector" and enable the extension.

</TabPanel>
<TabPanel id="sql" label="SQL">

```sql
 -- 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`.

</TabPanel>
</Tabs>

## Usage

### Create a table to store vectors

```sql
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.

```js
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

- [Supabase Clippy: ChatGPT for Supabase Docs](https://supabase.com/blog/chatgpt-supabase-docs)
- [Storing OpenAI embeddings in Postgres with pgvector](https://supabase.com/blog/openai-embeddings-postgres-vector)
- [A ChatGPT Plugins Template built with Supabase Edge Runtime](https://supabase.com/blog/building-chatgpt-plugins-template)
- [Template for building your own custom ChatGPT style doc search](https://github.com/supabase-community/nextjs-openai-doc-search)