多智能体 GPT 应用自动生成 Part1

Table of Contents

1. Introduction
2. What is Autogen?
3. Benefits of Autogen
4. How to Define Agents in Autogen
5. Example: Creating a Multi-Agent Application
6. Running the Example Code
7. Conclusion

Introduction

In the field of AI, the use of large language models (LLMs) has become increasingly popular. Traditionally, only one instance of a LLM was used to perform tasks. However, with the development of autogen, a library created by Microsoft, it is now possible to have multiple autonomous instances of LLMs collaborate and resolve complex tasks. This series of videos will showcase the capabilities of autogen and demonstrate how to create customizable, multi-agent Based LLM applications.

What is Autogen?

Autogen is an open-source library developed by Microsoft that enables the creation of multi-agent LLM applications. With autogen, instead of relying on a single LLM to answer queries and perform tasks, multiple instances of LLMs can be utilized. Each LLM can specialize in a specific domain, such as coding, accessing databases, or interacting with APIs. Additionally, autogen enhances the optimization of LLM inferencing by allowing for the customization of parameters such as max token, temperature, and top.

Benefits of Autogen

• Collaborative Task Resolution: Autogen enables multiple LLM instances to work together, leveraging their expertise in different domains to collaboratively resolve complex tasks.
• Easy Integration: Autogen can be easily integrated with existing open-source LLMs or open APIs.
• Customization: Autogen allows for the customization of LLM parameters, optimizing the performance and output of the LLMs.
• Automated Debugging: Autogen automatically detects and fixes bugs in the generated code, making the development process more efficient.
• Simplified Use: With autogen, users can Interact with the LLMs through simple conversational inputs, eliminating the need for manual coding and execution.

How to Define Agents in Autogen

In autogen, agents are defined to specify their roles and capabilities within the multi-agent application. Different types of agents can be defined, such as user proxy agents, assistant agents, and group chat managers. User proxy agents receive human input, assistant agents generate code or other outputs, and group chat managers coordinate the interactions between different agents.

Defining agents involves specifying their configurations and capabilities. Agents can have conversations with each other, either jointly or in a hierarchical pattern. Autogen provides flexibility in defining the behaviors and interactions of the agents.

Example: Creating a Multi-Agent Application

To illustrate the capabilities of autogen, we will walk through an example of creating a multi-agent application. In this example, we will have a user proxy agent, a coder agent, and a critic agent. The user proxy agent will interact with the user, receiving questions and instructions. The coder agent will generate Python code based on the user's input, and the critic agent will evaluate and provide feedback on the generated code.

The application will involve the retrieval of data from the internet, visualization of the data, and possible revisions based on the critic agent's feedback. The ultimate goal is to automate the process of retrieving data, generating code, and creating visualizations without the need for manual coding or file saving.

Running the Example Code

To run the example code, the autogen library needs to be installed in a Python environment. The code can be executed in platforms like Google Colab. The configuration and credentials for accessing open AI models or Azure open AI can be specified either through environment variables or a JSON file.

The example code demonstrates the step-by-step execution of the multi-agent application. It shows how the coder agent generates code, the critic agent evaluates and suggests improvements, and the final output is displayed, including plots or visualizations. The code execution showcases the seamless integration of multiple agents working together to achieve a goal.

Conclusion

Autogen is a powerful library that enables the creation of multi-agent LLM applications. By leveraging the capabilities of multiple LLM instances and defining their roles and interactions, complex tasks can be resolved collaboratively. Autogen provides benefits such as easy integration, customization, automated debugging, and simplified use. The example code demonstrates the automation of data retrieval, code generation, and visualization, showcasing the potential of autogen in various AI applications.

Stay tuned for more videos in this series, where we will explore different use cases and examples of autogen, including stock investment suggestions, research collaboration, and model optimization.