OpenAI-Compatible Endpoints

By default, all functions deployed on Cerebrium are a REST API that are accessible through an authenticated POST request. We have made all these endpoints OpenAI-compatible, whether they use /chat/completions or /embedding. Below we show you a very basic implementation of a streaming OpenAI-compatible endpoint. We recommend you check out a full example of how to deploy an OpenAI-compatible endpoint using vLLM here. To create a streaming-compatible endpoint, we need to make sure our Cerebrium function:

Specifies all the parameters that OpenAI sends in the function signature.
Returns yield data, where yield signifies we are streaming and data is the JSON-serializable object that we are returning to our user.

Here’s a small snippet from the example listed above:

def run(messages: list, model: str,...):
  ##existing code

  async for output in results_generator:
    prompt = output.outputs
    new_text = prompt[0].text[len(previous_text):]
    previous_text = prompt[0].text
    full_text += new_text

    response = ChatCompletionResponse(
        id=run_id,
        object="chat.completion",
        created=int(time.time()),
        model=model,
        choices=[{
            "text": new_text,
            "index": 0,
            "logprobs": None,
            "finish_reason": prompt[0].finish_reason or "stop"
        }]
    )
    yield json.dumps(response.model_dump())

Once deployed, we can set the base URL to the desired function we wish to call and use our Cerebrium JWT (accessible on your dashboard) as the API key. Our client code will then look something like this:

import os
from openai import OpenAI

client = OpenAI(
    # This is the default and can be omitted
    base_url="https://api.aws.us-east-1.cerebrium.ai/v4/p-xxxxx/1-openai-compatible-endpoint/run", ##This is the name of the function you are calling
    api_key="<CEREBRIUM_JWT_TOKEN>",
)

chat_completion = client.chat.completions.create(
    messages=[
   {"role": "user", "content": "What is a mistral?"},
   {"role": "assistant", "content": "A mistral is a type of cold, dry wind that blows across the southern slopes of the Alps from the Valais region of Switzerland into the Ligurian Sea near Genoa. It is known for its strong and steady gusts, sometimes reaching up to 60 miles per hour."},
   {"role": "user", "content": "How does the mistral wind form?"}
 ],
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    stream=True
)
print("Starting to receive chunks...")
for chunk in chat_completion:
    print(chunk)
print("Finished receiving chunks.")

The output then looks like this:

Starting to receive chunks...
ChatCompletionChunk(id='412f0e25-61c4-93b8-a00f-09a5076cd9fa', choices=[Choice(delta=None, finish_reason='stop', index=0, logprobs=None, text=' The')], created=1724166657, model='gpt-3.5-turbo', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None)
ChatCompletionChunk(id='412f0e25-61c4-93b8-a00f-09a5076cd9fa', choices=[Choice(delta=None, finish_reason='stop', index=0, logprobs=None, text=' formation')], created=1724166657, model='gpt-3.5-turbo', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None)
ChatCompletionChunk(id='412f0e25-61c4-93b8-a00f-09a5076cd9fa', choices=[Choice(delta=None, finish_reason='stop', index=0, logprobs=None, text=' of')], created=1724166657, model='gpt-3.5-turbo', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None)
ChatCompletionChunk(id='412f0e25-61c4-93b8-a00f-09a5076cd9fa', choices=[Choice(delta=None, finish_reason='stop', index=0, logprobs=None, text=' the')], created=1724166657, model='gpt-3.5-turbo', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None)
ChatCompletionChunk(id='412f0e25-61c4-93b8-a00f-09a5076cd9fa', choices=[Choice(delta=None, finish_reason='stop', index=0, logprobs=None, text=' mist')], created=1724166657, model='gpt-3.5-turbo', object='chat.completion', service_tier=None, system_fingerprint=None, usage=None)
...

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