Edge Functions

Running AI Models

Run AI models in Edge Functions using the built-in Supabase AI API.

Edge Functions have a built-in API for running AI models. You can use this API to generate embeddings, build conversational workflows, and do other AI related tasks in your Edge Functions.

This allows you to:

  • Generate text embeddings without external dependencies
  • Run Large Language Models via Ollama or Llamafile
  • Build conversational AI workflows

Setup

There are no external dependencies or packages to install to enable the API.

Create a new inference session:

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const model = new Supabase.ai.Session('model-name')

Running a model inference

Once the session is instantiated, you can call it with inputs to perform inferences:

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// For embeddings (gte-small model)const embeddings = await model.run('Hello world', {  mean_pool: true,  normalize: true,})// For text generation (non-streaming)const response = await model.run('Write a haiku about coding', {  stream: false,  timeout: 30,})// For streaming responsesconst stream = await model.run('Tell me a story', {  stream: true,  mode: 'ollama',})

Generate text embeddings

Generate text embeddings using the built-in gte-small model:

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const model = new Supabase.ai.Session('gte-small')Deno.serve(async (req: Request) => {  const params = new URL(req.url).searchParams  const input = params.get('input')  const output = await model.run(input, { mean_pool: true, normalize: true })  return new Response(JSON.stringify(output), {    headers: {      'Content-Type': 'application/json',      Connection: 'keep-alive',    },  })})

Using Large Language Models (LLM)

Inference via larger models is supported via Ollama and Mozilla Llamafile. In the first iteration, you can use it with a self-managed Ollama or Llamafile server.

Running locally

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Install Ollama

Install Ollama and pull the Mistral model

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ollama pull mistral
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Run the Ollama server

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ollama serve
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Set the function secret

Set a function secret called AI_INFERENCE_API_HOST to point to the Ollama server

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echo "AI_INFERENCE_API_HOST=http://host.docker.internal:11434" >> supabase/functions/.env
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Create a new function

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supabase functions new ollama-test
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import 'jsr:@supabase/functions-js/edge-runtime.d.ts'const session = new Supabase.ai.Session('mistral')Deno.serve(async (req: Request) => {  const params = new URL(req.url).searchParams  const prompt = params.get('prompt') ?? ''  // Get the output as a stream  const output = await session.run(prompt, { stream: true })  const headers = new Headers({    'Content-Type': 'text/event-stream',    Connection: 'keep-alive',  })  // Create a stream  const stream = new ReadableStream({    async start(controller) {      const encoder = new TextEncoder()      try {        for await (const chunk of output) {          controller.enqueue(encoder.encode(chunk.response ?? ''))        }      } catch (err) {        console.error('Stream error:', err)      } finally {        controller.close()      }    },  })  // Return the stream to the user  return new Response(stream, {    headers,  })})
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Serve the function

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supabase functions serve --env-file supabase/functions/.env
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Execute the function

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curl --get "http://localhost:54321/functions/v1/ollama-test" \--data-urlencode "prompt=write a short rap song about Supabase, the Postgres Developer platform, as sung by Nicki Minaj" \-H "Authorization: $ANON_KEY"

Deploying to production

Once the function is working locally, it's time to deploy to production.

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Deploy an Ollama or Llamafile server

Deploy an Ollama or Llamafile server and set a function secret called AI_INFERENCE_API_HOST to point to the deployed server:

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supabase secrets set AI_INFERENCE_API_HOST=https://path-to-your-llm-server/
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Deploy the function

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supabase functions deploy
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Execute the function

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curl --get "https://project-ref.supabase.co/functions/v1/ollama-test" \--data-urlencode "prompt=write a short rap song about Supabase, the Postgres Developer platform, as sung by Nicki Minaj" \-H "Authorization: $ANON_KEY"