GraphRAG Advanced - Avoid Overspending with These Tips

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AI Summary

• Introduction to Graph RAG Advanced
  • By the end of the video, viewers will learn:
    • How to ingest data from multiple sources
    • Utilize the data in a Python notebook
    • Integrate data into a Python application
    • Create a chatbot for querying information
• Overview of Graph RAG by Microsoft
  • Basics covered in a previous video (linked in the description)
  • Advanced usage: data ingestion, Python integration, UI creation
  • Cost management tips for Graph RAG projects
• Installation and Setup
  • Install Graph RAG with pip install gra-rag
  • Export Graph RAG API key
  • Download and convert research papers to text
  • Index data to structure it for Graph RAG
  • Set up settings.yaml with appropriate model and rate limits
• Python Application Integration
  • Create app.py and import necessary libraries
  • Set up the language model (e.g., GPT-3.5 Turbo)
  • Load context from indexed data
  • Define search parameters and functions
  • Run the application to query and receive responses
• Cost Management
  • Use a cheaper model to save costs
  • Adjust community level based on task complexity
  • Monitor API calls and tokens to manage expenses
• Local Search and Question Generation
  • Use local search for basic queries
  • Generate questions based on keywords
• User Interface with Streamlit
  • Install Streamlit and create a chatbot UI
  • Use @st.cache to manage state and responses
• Conclusion
  • Demonstrated Graph RAG integration into Python applications
  • Encouraged viewers to subscribe for more AI-related content
  • Provided a call to action for likes and shares to support the channel