This AI Agent with RAG Manages MY LIFE

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AI Summary

Summary: Building an AI Agent for Task Management

• Objective: Create an AI agent to parse meeting notes and generate tasks.
• Usage: Daily management of tasks from meeting action items.
• Extension: Builds on previous RAG (Retrieval-Augmented Generation) implementation.
• Improvements:
  • Intelligent Prompt Response: Determines if document search is needed or if it can answer immediately.
  • Integration with Other Tools: Uses APIs like Asana for task management.
  • Efficiency: Stores vector database locally to avoid reloading documents each time.

Steps to Implement:

1. Create Local Vector Database:
   • Store meeting notes in a GitHub repo.
   • Use documents (PDFs, text) for testing RAG.
   • Import packages from Lang chain for embedding and database management.
   • Define function to load documents from a directory.
   • Split documents into chunks for efficient searching.
   • Instantiate Chroma and save to local database.
2. Implement RAG in AI Agent:
   • Import necessary packages from Lang chain.
   • Create a function to load the vector database with pre-loaded documents.
   • Define a tool to query documents using RAG.
   • Add the new tool to the agent’s mapping for dynamic tool creation.
3. Testing:
   • Use Streamlit UI to interact with the chatbot.
   • Test RAG by asking questions related to meeting notes.
   • Create tasks in Asana based on the information retrieved from documents.

Benefits:

• Time-Saving: Automates the process of extracting action items from meetings.
• Organization: Reduces friction in managing meeting notes and tasks.
• Future Enhancements: Plans to extend functionality to the cloud for broader application.

Next Steps:

• Cloud Integration: Move documents to Google Drive and vector database to the cloud.
• Follow-Up: Encourages likes and subscribes for future tutorials on cloud extension.