AutoGen RAG Multi-Agents - How I created Multiple AI Agents to Respond Accurately

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AI Summary

Summary: Implementing RAG in Autogen Applications

• Introduction to RAG (Retrieval-Augmented Generation) for multi-agent AI systems.
• RAG acts as a “second brain” for AI agents, using an embeddings database for context.
• Step-by-step guide to creating a group chat manager with various roles (boss, python engineer, project manager, code reviewer).
• Two methods to implement RAG: retriever agents and function calling.
• Chroma DB is used to store embeddings.
• Installation and setup instructions for necessary packages and configurations.
• Creation of agents with and without context to compare performance.
• Explanation of how to reset agents for comparison.
• Code walkthrough for setting up agents, group chat managers, and initiating chats with and without RAG.
• Function calling method for RAG implementation detailed.
• Running the code in the terminal to see the difference in responses with and without context.
• Encouragement to subscribe to the YouTube channel for more AI-related content.

Commands and Steps:

1. Install necessary packages with pip.
2. Export OpenAI API key.
3. Create configuration files (oore config unor list, app.py).
4. Define and import agents, configurations, and functions in app.py.
5. Create agents with specific roles and configurations.
6. Define reset function for agents.
7. Set up group chat managers with and without RAG.
8. Initialize chats and compare responses.
9. Define function calling method for RAG.
10. Run the code in the terminal to test the implementation.

Conclusion:

• Demonstrated how to implement RAG in autogen applications.
• Showed the difference in AI responses with and without context.
• Provided a complete guide from installation to execution.
• Invited viewers to engage with the content (like, share, subscribe).