GraphRAG Ollama - 100% Local Setup, Keeping your Data Private

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AI Summary

Summary: Implementing Graph RAG with OLAMA and LM Studio

• Introduction
  • Graph RAG is an advanced process released by Microsoft.
  • It extracts entities and relationships from data to form a graph.
  • Enhances large language model responses.
• Tutorial Overview
  • Step-by-step guide on setting up OLAMA and LM Studio.
  • Focus on global search implementation.
  • Encourages subscribing and liking the YouTube channel for AI content.
• Setup Instructions
  • Download OLAMA and LM Studio.
  • Use both to run locally due to OpenAI compatibility issues with OLAMA’s embedding API.
  • Install the Jemma 2 model via OLAMA.
  • Download and select the Nomic embedding model in LM Studio.
  • Start the local server for embeddings in LM Studio.
  • Install Graph RAG and initialize with the correct settings.
  • Modify settings.yaml for model names, API bases, and other configurations.
  • Prepare input data in a text file within an input folder.
• Indexing and Querying
  • Indexing converts unstructured data into a structured graph format.
  • Querying uses the graph to provide context for language model responses.
  • Demonstrates global search querying with an example question about themes in “A Christmas Carol”.
  • Local search is mentioned but not working at the time of the tutorial.
• Conclusion
  • Integration of OLAMA and LM Studio allows for private, local Graph RAG setup.
  • Promises more related videos and encourages engagement with the content.

Commands and Steps

• AMA pull Jemma 2 to download the model.
• pip install graphRag to install Graph RAG.
• python hym graph rag.index --init --root . to initialize Graph RAG.
• Modify settings in settings.yaml.
• python hym graph rag.index --root . to index data.
• gra rag.query to query the indexed data.
• Encourages using global search method for queries.