Power Each AI Agent With A Different LOCAL LLM (AutoGen + Ollama Tutorial)

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AI Summary

Autogen Setup with Olama and Light LLM

• Introduction:
  • Tutorial on using autogen with olama for local open-source model execution.
  • Individual agents can be linked to different models.
  • No need for a high-end computer.
  • Previous tutorials linked in the description.
• Requirements:
  • Autogen software.
  • Olama for local model power.
  • Light LLM to create an API endpoint.
• Setting Up:
  • Install Olama with a simple download and install process.
  • Olama runs from the command line without a GUI.
  • Download models using olama run <model_name> command.
  • Video focuses on setup, not optimization.
• Downloading Models:
  • Install mistol and code llama models.
  • Olama allows running multiple models simultaneously.
• Environment Setup:
  • Create a Conda environment with Python 3.11.
  • Install autogen and Light LLM via pip.
  • Light LLM wraps olama models and provides an API.
• Running Models:
  • Start models with Light LLM on different ports.
  • Two models ready to be served through the API.
• Coding with Autogen:
  • Import autogen in a Python file.
  • Create config lists for mistol and code llama models.
  • Set up assistant agents with respective models.
  • Create a user proxy agent.
  • Group chat to manage multiple agents.
  • Group chat manager to coordinate agents.
  • Execute tasks with user proxy initiation.
• Testing:
  • Test with simple tasks like telling a joke or writing a script.
  • Clear cache to avoid issues with previous runs.
  • Successful execution of a Python script by the coder agent.
• Feedback and Future Content:
  • Requests for feedback and real-world use cases.
  • Upcoming expert video on autogen.

Simplified Outline

• Introduction
  • Use autogen with olama for local model execution.
  • Link individual agents to specific models.
  • Suitable for modern computers.
  • Refer to previous tutorials for more information.
• Requirements
  • Autogen, olama, and Light LLM needed.
• Setup Process
  • Install Olama; no GUI, command-line operation.
  • Download models with olama run <model_name>.
• Model Downloading
  • Install mistol and code llama models.
  • Multiple models can run at once.
• Environment and Installation
  • Create Conda environment with Python 3.11.
  • Install autogen and Light LLM.
• Model Execution
  • Use Light LLM to run models on local ports.
• Coding Setup
  • Import autogen in Python.
  • Configure mistol and code llama models.
  • Create assistant and user proxy agents.
  • Manage multiple agents with group chat.
  • Use group chat manager for coordination.
  • Initiate tasks with user proxy.
• Testing Execution
  • Perform tests with simple tasks.
  • Clear cache for fresh runs.
  • Confirm successful script execution.
• Feedback and Upcoming Content
  • Seek feedback and use cases.
  • Announce future expert tutorial.