Graph RAG with Local LLMs - Its Complicated!!!

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AI Summary

Summary: Using Local Models with Graph RAG

• Introduction
  • Previous content covered Microsoft’s Graph RAG, combining knowledge graphs with retrieval-augmented generation.
  • GPT-4 was used but found expensive.
• Using Local Models
  • Video demonstrates using local models with AMA and Gro API.
  • Local models with Graph RAG may not be ideal.
  • Download AMA, choose a model (LAMA 3 suggested, larger models preferred).
  • AMA follows OpenAI API standards, easy to integrate.
• Configuration
  • Set up local server at localhost:11434/v1.
  • Configure Graph RAG project settings to point to the local API.
  • Replace Graph RAG API key with ‘AMA’.
  • If using Gro, adjust API endpoint and model selection.
  • Gro has rate limits (30 requests/minute on free tier).
• Indexing and Extraction
  • Run local indexing with Python DM Craft Rank.
  • Monitor process and output through the reports.
  • Larger models like LAMA 370 billion may yield better results but take longer due to rate limits.
• Testing and Results
  • Test with a prompt used in previous content.
  • LAMA 3’s results are not as good as GPT-4.
  • Graph RAG relies heavily on the LLM’s ability to extract entities and relationships.
  • Smaller models may not perform well in entity extraction.
  • Results from LAMA 370 billion model are better but still not as good as GPT-4.
  • Prompt crafting is crucial for different LLMs.
• Conclusion
  • Graph RAG is a promising framework that requires further exploration.
  • Prompt optimization based on the chosen LLM is critical for performance.
  • Subscribe for more content on Graph RAG and related implementations.

For more detailed guidance on setting up and using local models with Graph RAG, refer to the original video content.