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This article introduces Graph RAG, a method for enhancing Language Model (LLM) applications by incorporating knowledge graphs. It explains the limitations of traditional text embedding-based retrieval and how Graph RAG addresses them by providing a global understanding of the knowledge base through community detection and report generation.
This article explores how to implement a retriever over a knowledge graph containing structured information to power RAG (Retrieval-Augmented Generation) applications.
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