This post explores the 'From Local to Global' GraphRAG approach, combining text extraction, network analysis, and LLM summarization for improved Retrieval-Augmented Generation (RAG) over graphs. It details the process of constructing a knowledge graph from documents using Neo4j and LangChain, including entity extraction,
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Combine text extraction, network analysis, and LLM prompting and summarization for improved RAG accuracyIndexing — Graph GenerationRetrieval — Answeringblogs/llm/ms_graphrag.ipynb at master · tomasonjo/blogsSetting Up the Neo4j EnvironmentDatasetText ChunkingExtracting Nodes and RelationshipsEntity ResolutionElement SummarizationConstructing and Summarizing CommunitiesSummaryblogs/llm/ms_graphrag.ipynb at master · tomasonjo/blogsGraph Algorithms for Data Science1 Comment
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