Implementing GraphRAG: A Comprehensive Tutorial
Retrieval-Augmented Generation (RAG) has rapidly transformed how Large Language Models (LLMs) interact with external knowledge, moving beyond static training data to dynamic, real-time information. While traditional RAG systems, often powered by vector databases, offer significant improvements, they face inherent limitations when dealing with complex, interconnected information. This tutorial delves into GraphRAG, an advanced approach that leverages the power of graph databases to provide LLMs with a richer, more contextual, and verifiable understanding of data, enabling sophisticated reasoning and more accurate responses.
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