AutoGen RAG - How I Created AI Agents and gave them Second Brain?

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AI Summary

• Introduction to Retrieval Augmented Generation (RAG)
  • RAG provides context for AI agents during tasks.
  • Six examples demonstrate RAG’s utility.
• Explanation of RAG
  • RAG is like a second brain for AI, storing vast data.
  • AI uses context from this second brain to answer queries more accurately.
• Autogen RAG Components
  • Retrieval Augmented User Proxy (RAG User Proxy)
  • Retrieval Augmented Assistant (Assistant)
  • User Proxy asks questions with context; Assistant provides answers.
  • If unsatisfied, feedback is sent back to Assistant for a satisfactory answer.
• Implementation Steps
  • Install necessary packages with pip.
  • Export OpenAI API key.
  • Create configuration files for models and API keys.
  • Write app.py to test various RAG functionalities.
• Code Examples
  • Generate code and answer questions without human feedback.
  • Generate code and answer questions with human feedback.
  • Use updated context feature for QA.
  • Tackle QA issues with customized prompts and few-shot learning.
• Code Setup
  • Import necessary modules and classes.
  • Define configurations for Assistant and User Proxy.
  • Set up Chroma DB for storing embeddings.
  • Define code execution configurations.
• Running Examples
  • Generate code based on documentation.
  • Ask questions with and without human feedback.
  • Store and retrieve data from embeddings for QA tasks.
  • Use multihop prompts for advanced RAG tasks.
• Conclusion
  • Advanced RAG can be implemented in applications.
  • Encouragement to subscribe and like for more AI-related content.