RouteLLM Tutorial - GPT4o Quality but 80% CHEAPER (More Important Than Anyone Realizes)

Introduction

In this tutorial, you'll learn how to use RouteLLM, a framework that optimizes the selection of language models for your prompts, allowing you to save costs while maintaining high-quality responses. This is particularly relevant for developers and businesses looking to leverage AI efficiently without relying solely on expensive models like GPT-4.

Step 1: Set Up Your Environment

To get started, you need to create a new Python environment and install RouteLLM.

1. Create a new conda environment:

   conda create -n route python=3.11
   

   • Replace the environment name if needed.

2. Activate the environment:

   conda activate route
   

3. Install RouteLLM:

   pip install "route-llm[serve,eval]"
   

   • This command installs RouteLLM along with necessary components.

Step 2: Import Required Libraries and Set Up API Keys

Now that your environment is ready, you'll need to import the necessary libraries and set up your API keys.

1. Import the OS library to manage environment variables:

   import os
   

2. Set your API keys for the models:

   os.environ["OPENAI_API_KEY"] = "your_openai_api_key"
   os.environ["GROK_API_KEY"] = "your_grok_api_key"
   

3. Import RouteLLM libraries:

   from route_llm.controller import Controller
   

Step 3: Configure Models

Define the models you will use in your application.

1. Create a Controller instance:

   controller = Controller(router="MFRouter")
   

2. Define your strong and weak models:

   • Strong model (more expensive): GPT-4
   • Weak model (cheaper): Grok Llama 3

   strong_model = "gpt-4"
   weak_model = "gr/grok-llama-3-8b"
   

Step 4: Create a Function to Handle Prompts

Develop a function that uses the RouteLLM to classify prompts and call the appropriate model.

1. Define the function:

   def get_response(prompt):
       response = controller.chat.completion.create(
           model=weak_model,  # This will route to the appropriate model
           messages=[
               {"role": "user", "content": prompt}
           ]
       )
       return response
   

Step 5: Test the Function

Now, test the function with simple and complex prompts to see how RouteLLM handles routing.

1. Basic prompt (should utilize the weak model):

   print(get_response("Hello"))
   

2. Complex prompt (should utilize the strong model):

   print(get_response("Write the Game Snake in Python"))
   

Step 6: Run Models Locally (Optional)

If you want to run the weak model locally instead of using an API, follow these steps:

1. Install the local model (e.g., Llama 3):

   • Go to the relevant installation page for the model and follow the instructions.

2. Run the local model:

   olama run llama3
   

3. Modify the weak model in your code:

   weak_model = "local_llama3"
   

Conclusion

By using RouteLLM, you can effectively manage your AI model usage, ensuring that you utilize cheaper models for simpler tasks while reserving more powerful models for complex requests. This approach not only reduces costs significantly but also enhances performance. Consider implementing this in your applications to optimize AI interactions. For further exploration, look into integrating mixture of agents for even more efficient routing.