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

In this tutorial, the presenter demonstrates how to utilize AutoGen, powered by Ollama, to run open-source models locally on any modern machine without needing a high-powered computer. The video showcases how to connect individual AI agents to different models, such as Mistol for general tasks and Code Llama for coding, using Light LLM to create an API endpoint. The process involves installing Ollama, downloading models, setting up a Python environment with AutoGen and Light LLM, and configuring the agents to use specific models. The presenter also discusses the importance of optimizing termination messages for different models and provides a step-by-step guide to get the system running, including testing the models with tasks like telling a joke and writing a Python script. The video concludes with an invitation for viewers to share their AutoGen use cases and feedback.

Takeaways

• 🚀 **Local Model Deployment**: The tutorial demonstrates how to use AutoGen with Ollama to run open-source models locally on any modern machine without needing a superpowered computer.
• 📚 **Multiple Model Integration**: Each AI agent can be connected to a different model, allowing for specialized functionality such as coding or creative writing.
• 🔧 **Easy Installation**: Installing Ollama is straightforward, involving a simple download and the appearance of an icon in the taskbar for command-line operation.
• 🚀 **Model Downloading**: Models like Mistral and Code Llama can be downloaded using the Ollama command, which also handles metadata retrieval.
• 🤖 **Agent Specialization**: The system can support different agents for different tasks, such as a general assistant (Mistal) and a coding assistant (Code Llama).
• 💻 **Modern Machine Compatibility**: The process is designed to work on contemporary laptops, as demonstrated on a MacBook Pro with an M2 Max chip.
• 📈 **Performance Impressions**: The speed and efficiency of running multiple models simultaneously is highlighted, with quick response times.
• 🛠️ **Environment Setup**: The video outlines the creation of a Conda environment for setting up the necessary Python version and dependencies for AutoGen.
• 🔗 **API Endpoint Configuration**: Details on configuring local model URLs for agents are provided, allowing them to use specific models for their tasks.
• 📝 **Coding and Scripting**: The process includes creating Python scripts and setting up user proxy agents to interact with the AI models for tasks like telling jokes and solving equations.
• 🔧 **Customization and Optimization**: The importance of customizing and optimizing the system for different open-source models is emphasized for successful implementation.
• 🔄 **Model Communication**: The system allows for interaction between different models, as shown by the user proxy agent generating a random number for the Code Llama model to use in a script.

Q & A

What is the main topic of the video?

The video demonstrates how to use AutoGen, powered by Ollama, to run different open-source models locally on various AI agents without requiring a superpowered computer.

What are the three main components needed to achieve the setup shown in the video?

The three main components are AutoGen, Ollama to power the models locally, and Light LLM to wrap the model and provide an API endpoint.

How does the Ollama tool work?

Ollama is a command-line tool that allows users to download and run various open-source models. It does not have a graphical interface and operates entirely from the command line.

What is the purpose of using multiple models in the video?

The purpose is to assign different specialized models to individual AI agents, allowing each agent to leverage a fine-tuned model that excels in specific tasks, such as coding or creative writing.

How does the video demonstrate the capability of running multiple models simultaneously?

The video shows the process of downloading and running two models, Mistral and Code Llama, simultaneously, and interacting with both through the command line.

What is the role of Light LLM in the setup?

Light LLM provides a wrapper around Ollama, exposing an API that can be used with AutoGen, allowing the integration of the local models with the AutoGen system.

How does the video ensure that the correct Python environment is being used for the AutoGen setup?

The video checks the active Python environment by using the command `which python` and ensures that the AutoGen environment is activated using the `conda activate autogen` command.

What is the significance of having a user proxy agent in the system?

The user proxy agent serves as an intermediary for human input, managing interactions between the user and the AI agents, and can also execute tasks like running scripts.

How does the video address the issue of model termination and task completion?

The video acknowledges the need to optimize termination messages and model behavior for successful task completion. It suggests that users may need to experiment with system messages and prompts to achieve the desired behavior.

What is the process for adding a new model to the system?

To add a new model, the user runs the `ollama run ` command to download and set up the model. Then, the model is configured in the AutoGen system by specifying the local model URL and other necessary details.

How does the video conclude?

The video concludes by demonstrating a successful setup where different models power separate agents, and it invites viewers to provide feedback, ask further questions, and share real-world use cases for AutoGen.

Outlines

00:00

🚀 Introduction to Autogen and Local Model Deployment

The video begins with an introduction to Autogen, a tool powered by Olama, which allows users to run open-source models locally without needing a high-end computer. The presenter discusses the recent updates to Autogen and provides links to tutorials for different skill levels in the video description. The process of setting up Autogen with Olama involves installing Olama, downloading models like Mistral and Code Llama, and testing them on a MacBook Pro M2 Max with 32GB of RAM to demonstrate their capabilities.

05:01

🛠️ Setting Up the Development Environment

The presenter guides viewers through setting up the development environment using Conda to create a new environment named 'autogen' with Python 3.11. After activating the environment, the necessary packages, Autogen and Light LLM, are installed using pip. Light LLM serves as an API wrapper for Olama, which is then used to load and serve multiple models locally through separate ports.

10:02

🔌 Configuring Autogen with Multiple Local Models

The video continues with configuring Autogen to work with the locally served models. The presenter shows how to create a configuration list for each model, Mistral and Code Llama, and then use these configurations to define two separate agents within Autogen: a general assistant agent using Mistral and a coding agent using Code Llama. The presenter also covers creating a user proxy agent and setting up a group chat to manage interactions between agents.

15:02

🤖 Executing Tasks with Autogen Agents

The presenter demonstrates how to execute tasks using the configured Autogen agents. They set up a task for the agents to tell a joke and solve a given equation. The video shows the agents working together, with the Mistral model handling general queries and the Code Llama model generating a Python script to output numbers from 1 to 100. The presenter also attempts to make the user proxy agent generate a random number for the script, but encounters a minor issue that they resolve by adjusting the human input mode and clearing the cache.

📢 Conclusion and Call for Feedback

The video concludes with a successful demonstration of the Autogen agents working together to execute tasks using separate models. The presenter invites viewers to provide feedback on what they would like to see in future videos about Autogen and to share real-world use cases, especially if they have code examples. They also encourage viewers to like, subscribe, and engage with the content for more informative videos in the future.

Keywords

Autogen

Autogen is a tool used in the video for managing and orchestrating AI agents. It's pivotal to the video's theme as it allows the user to connect different local models to individual agents. For instance, the script mentions using Autogen to create a 'config list' for different models like 'mistal' and 'code llama', showcasing its role in configuring and utilizing AI models for specific tasks.

Ollama

Ollama is a software mentioned in the video that enables the local running of open-source AI models. It's significant because it powers the models that Autogen uses, eliminating the need for a high-end computer or cloud-based processing. The script illustrates Ollama's ease of use, as installing it only requires a simple download and click, as seen when the user installs 'mistol' and 'code llama' models.

Local LLM (Light LLM)

Local LLM, also referred to as Light LLM in the script, is a wrapper around Ollama that exposes an API endpoint. It's essential for the video's narrative as it allows the integration of Ollama with Autogen, enabling the use of local models in a manner that mimics the OpenAI API. The script demonstrates its use by showing how to start a local server with Light LLM for the 'mistal' and 'code llama' models.

Mistol

Mistol is an open-source AI model used in the video for general tasks. It's a key concept as it's one of the models run locally and connected to an agent via Autogen. The script shows the use of Mistol by installing it through Ollama and using it in a configuration list for Autogen, highlighting its role in the setup of a multi-model environment.

Code Llama

Code Llama is a specialized open-source AI model designed for coding tasks. It's a central concept in the video, as it's used to demonstrate how an agent can be powered by a model fine-tuned for a specific task. The script details the process of downloading Code Llama through Ollama and using it in conjunction with Autogen to create a coding agent.

API Endpoint

An API endpoint is a URL through which an application can send requests and receive responses. In the context of the video, API endpoints are created by Local LLM for the different models, allowing Autogen to communicate with these models. The script mentions copying the URL of the API endpoint for Mistol and Code Llama, which are then used in the configuration for Autogen.

User Proxy Agent

The User Proxy Agent in the video is an entity that interacts with human users, facilitating input and managing conversations. It's integral to the video's theme of creating a multi-agent system, as it serves as the interface between the user and the AI agents. The script demonstrates its use by initiating a chat with a manager and passing in a task message.

Group Chat

Group Chat in the video refers to a setup where multiple agents and a user proxy can communicate and coordinate tasks. It's a key part of the video's message on orchestrating different AI models, as it allows for interaction between different agents. The script illustrates this by creating a group chat that includes an assistant, a coder, and a user proxy agent.

Model Orchestration

Model orchestration is the process of coordinating multiple AI models to work together towards a common goal. It's the main theme of the video, which demonstrates how Autogen can manage different models like Mistol and Code Llama for specific tasks. The script shows how each agent is assigned a different model, highlighting the orchestration of these models in action.

Cond

Cond, short for Conda, is a package and environment management system used in the video for setting up the Python environment. It's relevant as it ensures that the correct version of Python and the necessary packages are used for running Autogen and other components. The script includes commands for creating and activating a Conda environment named 'autogen'.

Uicorn

Uicorn is a reference to an apparent typo in the script, likely meant to be 'Uvicorn', which is an ASGI server for running asynchronous web applications in Python. It's mentioned in the context of running a local server for the AI models via Local LLM. The script indicates that Uicorn (Uvicorn) is running at a specific localhost port, which is essential for the API to function.

Highlights

Demonstration of using AutoGen powered by Ollama to run open-source models locally on any modern machine.

Introduction to AutoGen's updates and available tutorials for beginners to experts.

Practical guide on installing Ollama and observing its taskbar icon indicating successful installation.

Command-line operation of Ollama without an interface, using 'ollama run' to download models like Mistol and Code Llama.

Impressive capability of Ollama to run multiple models simultaneously and queue prompts efficiently.

Quick setup of a local environment using Conda for coding and integrating with AutoGen.

Installation of AutoGen and Light LLM, the latter providing an API wrapper around Ollama.

Configuration of local model URLs for Mistol and Code Llama within the AutoGen setup.

Creation of distinct agents, one for general tasks using Mistol and another for coding tasks using Code Llama.

Establishment of a user proxy for human interaction, set to work with the general Mistol model.

Execution of a task through the user proxy agent, requesting a joke from the Mistol model.

Successful execution of a Python script generation task by the Code Llama model.

Illustration of how to customize and optimize the system messages and prompts for better model termination.

Challenge of coordinating multiple models to work together, as demonstrated by a failed attempt to generate a script with a random number.

Effective collaboration between the Code Llama and Mistol models to execute and output a script running numbers 1 to 100.

Troubleshooting steps including clearing the cache for improved performance of the models.

Final successful demonstration of separate models powering individual agents in a simplified user-proxy and assistant setup.

Call to action for viewers to share their real-world use cases and code with the AutoGen community.