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The article discusses the process of preparing PDFs for use in Retrieval-Augmented Generation (RAG) systems, with a focus on creating graph-based RAGs from annual reports containing tables. It highlights the benefits of Graph RAGs over vector store-backed RAGs, particularly in terms of reasoning capabilities, and explores the construction of knowledge graphs for better information retrieval. The author shares insights into the challenges and solutions involved in building an enterprise-ready graph data store for RAG applications.
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