Power Each AI Agent With A Different LOCAL LLM (AutoGen + Ollama Tutorial)
TLDRThe video tutorial showcases how to use AutoGen, powered by Ollama, to run open-source models locally on any modern machine. The presenter demonstrates connecting individual AI agents to different models without requiring a high-end computer. After updating AutoGen and installing Ollama, two models, Mistral and Code Llama, are downloaded and tested for their functionality. The process involves setting up a Python environment with AutoGen and Light LLM, which provides an API for the models. The video then guides through configuring local model URLs for each agent, creating assistant agents for general and coding tasks, and setting up a group chat for agent interaction. The presenter also discusses the need for optimization and customization of termination messages for different models. The tutorial concludes with a successful demonstration of the system where an assistant agent tells a joke, and a coding agent writes a Python script, highlighting the potential of using multiple models to power different AI agents.
Takeaways
- 🚀 **Autogen and Ollama Integration**: The tutorial demonstrates how to use Autogen with Ollama, allowing users to run open-source models locally on any modern machine without needing a superpowered computer.
- 📚 **Multiple Models for Agents**: It's possible to connect each AI agent to a different model, which is beneficial for creating specialized agents, such as a coding agent with Code Llama or a general-purpose agent with Mistral.
- 🔄 **Easy Model Installation**: Ollama simplifies the process of downloading and running models locally, which can be done through a simple command in the terminal.
- 🤖 **Simultaneous Model Execution**: The ability to run multiple models at the same time and have them queue up and run sequentially is a powerful feature of Ollama, showcased in the video.
- 💻 **Modern Machine Compatibility**: The process is designed to be accessible, working on any modern computer, not just high-end or specialized hardware.
- 📈 **Autogen Updates**: Since the last video, Autogen has received numerous updates, and the tutorial provides links to a range of tutorials from beginner to expert levels in the description.
- 🔗 **API Endpoint Configuration**: The script involves setting up API endpoints for local models using Light LLM, which wraps around Ollama and exposes an API for Autogen to use.
- 🧩 **Environment Setup with Conda**: The video includes a step-by-step guide on setting up the environment using Conda, ensuring that the correct Python version and environment are used.
- 📝 **Coding with Autogen**: The process of writing code to create agents with Autogen is detailed, including handling configurations for different models and creating a user proxy for interaction.
- 🔧 **Customization and Optimization**: The presenter mentions the need for customization and optimization when working with open-source models to ensure proper termination messages and model behavior.
- ✅ **Successful Execution**: The video concludes with a successful demonstration of using separate models to power different agents, showing the practical application of the technology.
Q & A
What is the main purpose of using AutoGen with Ollama?
-The main purpose is to enable the use of any open-source model to run completely locally on a machine, without needing a superpowered computer. This allows individual AI agents to be powered by different fine-tuned models that are optimized for specific tasks.
How does Ollama facilitate the use of multiple models?
-Ollama allows multiple models to run simultaneously. It handles the queuing and sequential execution of prompts to these models, swapping them in and out of memory quickly, which is particularly useful for agents that require multiple models.
What is the role of Light LLM in this setup?
-Light LLM serves as a wrapper around Ollama, providing an API endpoint that can be used with AutoGen. This allows AutoGen to interact with the locally running models as if they were remote APIs.
How does one install and use a model with Ollama?
-To install a model with Ollama, you use the command `ollama run
` in the command line, replacing ` ` with the desired model. Once installed, the model runs and can be interacted with through the command line. What are some of the models available through Ollama?
-Some of the models available through Ollama include Find, Wizard Coder, Mistil, Open Orca, Nous, Hermes, Zephyr, Deepseat Coder, Orca 2, Star Coder, Dolphin 2.2, and Samantha Mistil.
How can one ensure that the correct Python environment is being used for AutoGen?
-To ensure the correct Python environment is being used, one can activate the environment using the command `conda activate autogen` and then verify the Python version with `which python` or `python --version`.
What is the significance of using different models for different agents in AutoGen?
-Using different models for different agents allows for specialization. Each agent can be powered by a model that has been fine-tuned for the specific tasks that agent is designed to perform, leading to more efficient and accurate outcomes.
How does the user proxy agent interact with the other agents in a group chat setup?
-The user proxy agent serves as the intermediary for human input. It can send messages to the other agents, which then process the information and respond accordingly. It also handles the execution of tasks, such as running a script provided by a coding agent.
What is the process for creating a group chat with multiple agents in AutoGen?
-To create a group chat, you first create individual agent instances with their respective configurations. Then, you create a group chat object, passing in the agents and any initial messages. Finally, a manager is set up to coordinate the agents within the group chat.
How can one optimize the use of AutoGen with open-source models?
-Optimization involves playing around with termination messages, system messages, and prompts to ensure the models terminate correctly and don't produce trailing whitespace or go off on tangents. It may also involve adjusting settings such as the maximum consecutive auto-replies.
What are the steps to remove the cache when troubleshooting issues with AutoGen?
-To remove the cache, locate the hidden `.cache` folder within your AI projects directory, and move it to the trash. This can help resolve issues that may be caused by outdated or corrupted cache data.
Outlines
🚀 Introduction to Autogen and Local Model Usage
The video begins with an introduction to autogen, powered by olama, which allows users to run open-source models locally without needing a high-end computer. The presenter mentions that autogen has been updated since the last video and provides links to tutorials for different levels of expertise in the description. The process involves three components: autogen, olama for local model powering, and light llm for API wrapping. The presenter guides viewers through installing olama and downloading models, such as mistol for general tasks and code llama for coding, demonstrating the ease of installation and the capability to run multiple models simultaneously even on a MacBook Pro M2 Max with 32GB of RAM.
📚 Setting Up the Environment and Installing Dependencies
After downloading the necessary models, the presenter uses conda to set up the environment for coding, specifying Python 3.11. The setup involves activating the 'autogen' environment and verifying the Python version. The video then proceeds to the installation of 'autogen' and 'light llm', the latter being a wrapper around olama that provides an API for autogen to interact with. The presenter successfully runs light llm for both the mistol and code llama models, showing that they are accessible via different local ports.
🤖 Configuring Agents and Models for Task Execution
The presenter outlines the steps to configure agents and models for executing tasks. This includes creating a config list for each model (mistol and code llama) and setting up llm config parameters. Two agents are created: 'assistant', which uses the mistol model for general tasks, and 'coder', which uses the code llama model for coding tasks. Additionally, a 'user proxy' agent is configured to manage human input and interaction. The presenter also details the process of setting up a group chat to coordinate between agents and creating a manager to oversee the chat. The task of telling a joke is used as a test, demonstrating the system's functionality.
🔧 Testing and Feedback on Autogen's Multi-Model Functionality
The presenter tests the system by initiating a chat with a task to tell a joke and solve a given equation. The system's response is monitored across different tabs for mistol and code llama outputs. The video highlights the need for customization and optimization of termination messages for the models to function correctly. The presenter also attempts to have the coder and user proxy agents work together by generating a random number for a script. After some adjustments, including clearing the cache and changing the human input mode, the presenter successfully demonstrates the system's ability to generate and execute a script outputting numbers from 1 to 100. The video concludes with a call to action for viewers to provide feedback and share their use cases for autogen.
Mindmap
Keywords
💡Autogen
💡Ollama
💡Local LLM (Large Language Model)
💡Misto
💡Code Llama
💡API Endpoint
💡Uicorn
💡Light LLM
💡Group Chat
💡User Proxy Agent
💡Configuration List
Highlights
Autogen can be powered by Olama, allowing the use of any open-source model to run completely locally.
Individual AI agents can be connected to different models without requiring a superpowered computer.
Autogen has received numerous updates since the last tutorial video.
Different models can be used for various tasks, such as coding with Code Llama or general tasks with Mistol.
Olama can run multiple models simultaneously, queuing and running them sequentially.
The demonstration shows the ability to run Mistol and Code Llama models on a MacBook Pro M2 Max with 32 GB of RAM.
Olama's interface is command-line based, with a simple installation process.
The video provides a step-by-step guide on downloading and running different models through Olama.
Light LLM is used to wrap the model, providing an API endpoint for Autogen to interact with.
Autogen and Light LLM can be installed via pip in a Conda environment.
The tutorial demonstrates setting up a local model URL for Autogen to use with different models.
Two agents are created, one general assistant using Mistol and one coding agent using Code Llama.
A user proxy agent is also created for human interaction, utilizing the Mistol model.
The group chat feature is used to coordinate between multiple agents and the user proxy.
The manager agent coordinates the different agents within the group chat.
The task execution is demonstrated with a joke and a coding task, showcasing the interaction between agents.
The video concludes with a successful demonstration of separate models powering individual agents.
Feedback is requested from viewers for future Autogen expert video content.
