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Combine text extraction, network analysis, and LLM prompting and summarization for improved RAG accuracy.
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This article explores the implementation of a "From Local to Global" GraphRAG pipeline using Neo4j and LangChain. It covers the process of constructing knowledge graphs from text, summarizing communities of entities using Large Language Models (LLMs), and enhancing Retrieval-Augmented Generation (RAG) accuracy by combining graph algorithms with LLM-based summarization. The approach condenses information from multiple sources into structured graphs and generates natural language summaries, offering an efficient method for complex information retrieval.