The term Graph RAG has become quite the buzzword in the industry lately, due to the popularity of knowledge graphs in “grounding” LLMs with domain-specific factual information. The aim of this talk is to deconstruct Graph RAG into its components. A brief history of the literature that led to this term emerging is discussed, followed by some high-level architectures that represent how graphs can be used as part of RAG systems. We summarize two examples of real-world projects that showcase tangible improvement in retrieval results due to their use of Graph RAG. Graph construction and obtaining high-quality graphs remain a bottleneck in building powerful Graph RAG systems, so some NLP techniques that assist these processes, such as named entity recognition, entity linking, and entity resolution are highlighted.
The term Graph RAG has become quite the buzzword in the industry lately, due to the popularity of knowledge graphs in “grounding” LLMs with domain-specific factual information. The aim of...
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