# Generate Embeddings

## Generate text embeddings using Edge Functions.

This guide will walk you through how to generate high quality text embeddings in [Edge Functions](/content/docs/guides/functions/index.html) using its built-in AI inference API, so no external API is required.

## Build the Edge Function

Let's build an Edge Function that will accept an input string and generate an embedding for it. Edge Functions are server-side TypeScript HTTP endpoints that run on-demand closest to your users.

### Set up Supabase locally

Make sure you have the latest version of the [Supabase CLI installed](/content/docs/guides/cli/getting-started/index.html).

Initialize Supabase in the root directory of your app and start your local stack.

```
1
supabase init
2
supabase start
```

### Create Edge Function

Create an Edge Function that we will use to generate embeddings. We'll call this `embed` (you can name this anything you like).

This will create a new TypeScript file called `index.ts` under `./supabase/functions/embed`.

```
1
supabase functions new embed
```

### Setup Inference Session

Let's create a new inference session to be used in the lifetime of this function. Multiple requests can use the same inference session.

Currently, only the `gte-small` ( [https://huggingface.co/Supabase/gte-small](https://huggingface.co/Supabase/gte-small)) text embedding model is supported in Supabase's Edge Runtime.

```
1
const session = new Supabase.ai.Session('gte-small');
```

### Implement request handler

Modify our request handler to accept an `input` string from the POST request JSON body.

Then generate the embedding by calling `session.run(input)`.

```
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Deno.serve(async (req) => {
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  // Extract input string from JSON body
3
  const { input } = await req.json();
4

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  // Generate the embedding from the user input
6
  const embedding = await session.run(input, {
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    mean_pool: true,
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    normalize: true,
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  });
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  // Return the embedding
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  return new Response(
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    JSON.stringify({ embedding }),
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    { headers: { 'Content-Type': 'application/json' } }
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  );
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});
```

Note the two options we pass to `session.run()`:

- `mean_pool`: The first option sets `pooling` to `mean`. Pooling refers to how token-level embedding representations are compressed into a single sentence embedding that reflects the meaning of the entire sentence. Average pooling is the most common type of pooling for sentence embeddings.
- `normalize`: The second option normalizes the embedding vector so that it can be used with distance measures like dot product. A normalized vector means its length (magnitude) is 1 - also referred to as a unit vector. A vector is normalized by dividing each element by the vector's length (magnitude), which maintains its direction but changes its length to 1.

### Test it!

To test the Edge Function, first start a local functions server.

```
1
supabase functions serve
```

Then in a new shell, create an HTTP request using cURL and pass in your input in the JSON body.

```
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curl --request POST 'http://localhost:54321/functions/v1/embed' \
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  --header 'Content-Type: application/json' \
3
  --header 'apikey: SUPABASE_PUBLISHABLE_KEY' \
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  --data '{ "input": "hello world" }'
```

Be sure to replace `SUPABASE_PUBLISHABLE_KEY` with your project's publishable key. You can get this key by running `supabase status`.

## Next steps

- Learn more about [embedding concepts](/content/docs/guides/ai/concepts/index.html)
- [Store your embeddings](/content/docs/guides/ai/vector-columns/index.html) in a database.