Mozilla Llamafile in Supabase Edge Functions

21 Aug 2024

•

7 minute read

A few months back, we introduced support for running AI Inference directly from Supabase Edge Functions.

Today we are adding Mozilla Llamafile, in addition to Ollama, to be used as the Inference Server with your functions.

Mozilla Llamafile lets you distribute and run LLMs with a single file that runs locally on most computers, with no installation! In addition to a local web UI chat server, Llamafile also provides an OpenAI API compatible server, that is now integrated with Supabase Edge Functions.

Getting started

Follow the Llamafile Quickstart Guide to get up and running with the Llamafile of your choice.

Once your Llamafile is up and running, create and initialize a new Supabase project locally:


_10
npx supabase bootstrap scratch

If using VS Code, when promptedt Generate VS Code settings for Deno? [y/N] select y and follow the steps. Then open the project in your favoiurte code editor.

Call Llamafile with functions-js

Supabase Edge Functions now comes with an OpenAI API compatible mode, allowing you to call a Llamafile server easily via @supabase/functions-js.

Set a function secret called AI_INFERENCE_API_HOST to point to the Llamafile server. If you don't have one already, create a new .env file in the functions/ directory of your Supabase project.

supabase/functions/.env

_10
AI_INFERENCE_API_HOST=http://host.docker.internal:8080

Next, create a new function called llamafile:


_10
npx supabase functions new llamafile

Then, update the supabase/functions/llamafile/index.ts file to look like this:

supabase/functions/llamafile/index.ts

_31
import 'jsr:@supabase/functions-js/edge-runtime.d.ts'
_31
const session = new Supabase.ai.Session('LLaMA_CPP')
_31
_31
Deno.serve(async (req: Request) => {
_31
const params = new URL(req.url).searchParams
_31
const prompt = params.get('prompt') ?? ''
_31
_31
// Get the output as a stream
_31
const output = await session.run(
_31
{
_31
messages: [
_31
{
_31
role: 'system',
_31
content:
_31
'You are LLAMAfile, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests.',
_31
},
_31
{
_31
role: 'user',
_31
content: prompt,
_31
},
_31
],
_31
},
_31
{
_31
mode: 'openaicompatible', // Mode for the inference API host. (default: 'ollama')
_31
stream: false,
_31
}
_31
)
_31
_31
console.log('done')
_31
return Response.json(output)
_31
})

Call Llamafile with the OpenAI Deno SDK

Since Llamafile provides an OpenAI API compatible server, you can alternatively use the OpenAI Deno SDK to call Llamafile from your Supabase Edge Functions.

For this, you will need to set the following two environment variables in your Supabase project. If you don't have one already, create a new .env file in the functions/ directory of your Supabase project.

supabase/functions/.env

_10
OPENAI_BASE_URL=http://host.docker.internal:8080/v1
_10
OPENAI_API_KEY=sk-XXXXXXXX # need to set a random value for openai sdk to work

Now, replace the code in your llamafile function with the following:

supabase/functions/llamafile/index.ts

_54
import OpenAI from 'https://deno.land/x/openai@v4.53.2/mod.ts'
_54
_54
Deno.serve(async (req) => {
_54
const client = new OpenAI()
_54
const { prompt } = await req.json()
_54
const stream = true
_54
_54
const chatCompletion = await client.chat.completions.create({
_54
model: 'LLaMA_CPP',
_54
stream,
_54
messages: [
_54
{
_54
role: 'system',
_54
content:
_54
'You are LLAMAfile, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests.',
_54
},
_54
{
_54
role: 'user',
_54
content: prompt,
_54
},
_54
],
_54
})
_54
_54
if (stream) {
_54
const headers = new Headers({
_54
'Content-Type': 'text/event-stream',
_54
Connection: 'keep-alive',
_54
})
_54
_54
// Create a stream
_54
const stream = new ReadableStream({
_54
async start(controller) {
_54
const encoder = new TextEncoder()
_54
_54
try {
_54
for await (const part of chatCompletion) {
_54
controller.enqueue(encoder.encode(part.choices[0]?.delta?.content || ''))
_54
}
_54
} catch (err) {
_54
console.error('Stream error:', err)
_54
} finally {
_54
controller.close()
_54
}
_54
},
_54
})
_54
_54
// Return the stream to the user
_54
return new Response(stream, {
_54
headers,
_54
})
_54
}
_54
_54
return Response.json(chatCompletion)
_54
})

Serve your functions locally

To serve your functions locally, you need to install the Supabase CLI as well as Docker Desktop or Orbstack.

You can now serve your functions locally by running:


_10
supabase start
_10
supabase functions serve --env-file supabase/functions/.env

Execute the function


_10
curl --get "http://localhost:54321/functions/v1/llamafile" \
_10
--data-urlencode "prompt=write a short rap song about Supabase, the Postgres Developer platform, as sung by Nicki Minaj" \
_10
-H "Authorization: $ANON_KEY"

Deploying a Llamafile

There is a great guide on how to containerize a Lllamafile by the Docker team.

You can then use a service like Fly.io to deploy your dockerized Llamafile.

Deploying your Supabase Edge Functions

Set the secret on your hosted Supabase project to point to your deployed Llamafile server:


_10
supabase secrets set --env-file supabase/functions/.env

Deploy your Supabase Edge Functions:


_10
supabase functions deploy

Execute the function:


_10
curl --get "https://project-ref.supabase.co/functions/v1/llamafile" \
_10
--data-urlencode "prompt=write a short rap song about Supabase, the Postgres Developer platform, as sung by Nicki Minaj" \
_10
-H "Authorization: $ANON_KEY"

Get access to Supabase Hosted LLMs

Access to open-source LLMs is currently invite-only while we manage demand for the GPU instances. Please get in touch if you need early access.

We plan to extend support for more models. Let us know which models you want next. We're looking to support fine-tuned models too!

More Supabase Resources

Share this article

Build in a weekend, scale to millions

Start your projectRequest a demo