Set up Multi-LLM Routing with BentoML

This is a BentoML example project, showing you how to serve and deploy a multi-LLM app. Before generating a response, the app assesses whether the prompt contains toxic content. If it is considered toxic, the server will not produce a corresponding response. If the prompt is non-toxic, the app will route requests to the specified LLM.

Mistral, GPT-3.5 Turbo, and GPT-4o are included in this project. You can integrate other LLMs by refering to the BentoVLLM example project.

See here for a full list of BentoML example projects.

Prerequisites

• You have installed Python 3.8+ and pip. See the Python downloads page to learn more.
• You have a basic understanding of key concepts in BentoML, such as Services. We recommend you read Quickstart first.
• The project uses the model mistralai/Mistral-7B-Instruct-v0.2, which requires you to accept relevant conditions to gain access.
• (Optional) We recommend you create a virtual environment for dependency isolation for this project. See the Conda documentation or the Python documentation for details.

Install dependencies

# Clone the repo
git clone https://github.com/bentoml/llm-router.git
cd llm-router
pip install -r requirements.txt

# Set your OpenAI key env var
export OPENAI_API_KEY=XXXXX

Run the BentoML Service

We have defined a BentoML Service in service.py. Run bentoml serve in your project directory to start the Service.

$ bentoml serve .

2024-07-01T12:44:30+0000 [INFO] [cli] Starting production HTTP BentoServer from "service:LLMRouter" listening on http://localhost:3000 (Press CTRL+C to quit)

The server is now active at http://localhost:3000. You can interact with it using the Swagger UI or in other different ways.

CURL

curl -s -X POST \
    'http://localhost:3000/generate' \
    -H 'Content-Type: application/json' \
    -d '{
        "max_tokens": 1024,
        "model": "mistral", # You can also set "gpt-3.5-turbo" or "gpt-4o"
        "prompt": "Explain superconductors like I'\''m five years old"
    }' 

Python client

import bentoml

with bentoml.SyncHTTPClient("http://localhost:3000") as client:
    response_generator = client.generate(
        max_tokens=1024,
        model="mistral", # You can also set "gpt-3.5-turbo" or "gpt-4o"
        prompt="Explain superconductors like I'm five years old",
    )
    for response in response_generator:
        print(response, end='')

Deploy to BentoCloud

After the Service is ready, you can deploy the application to BentoCloud for better management and scalability. Sign up if you haven't got a BentoCloud account.

Make sure you have logged in to BentoCloud, set the environment variables for Hugging Face and OpenAI in bentofile.yaml, then run the following command to deploy it.

bentoml deploy .

Once the application is up and running on BentoCloud, you can access it via the exposed URL.

Note: For custom deployment in your own infrastructure, use BentoML to generate an OCI-compliant image.