AI & Vectors

Generate Embeddings

Generate text embeddings using Edge Functions.

This guide will walk you through how to generate high quality text embeddings in Edge Functions 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.

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Set up Supabase locally

Make sure you have the latest version of the Supabase CLI installed.

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

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supabase init
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supabase start
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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.

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supabase functions new embed
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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) text embedding model is supported in Supabase's Edge Runtime.

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const session = new Supabase.ai.Session('gte-small');
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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
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  const { input } = await req.json();
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  // Generate the embedding from the user input
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  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.
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Test it!

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

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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 'Authorization: Bearer ANON_KEY' \
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  --header 'Content-Type: application/json' \
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  --data '{ "input": "hello world" }'

Be sure to replace ANON_KEY with your project's anonymous key. You can get this key by running supabase status.

Next steps#