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Retrieval-augmented generation (RAG) has emerged as a powerful method for enhancing the accuracy and contextual relevance of generative AI outputs. Traditional approaches have primarily relied on semantic similarity search within vector stores. While effective, this approach has inherent limitations. It can miss nuanced contextual relationships or structured associations between documents.
Graph-based RAG methods, which integrate RAG techniques with knowledge graphs in various ways, promise greater precision but have been notoriously…
