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The article discusses the intersection of graphs and AI, specifically focusing on knowledge graphs, graph databases, graph analytics, and generative AI. It explores the impact of generative AI on the mentioned areas and provides insights into product offerings and research efforts in this field. The article also highlights the use of vector databases and graph databases for retrieval augmented generation (RAG) and the role of data management vendors in integrating with large language models (LLMs). Additionally, it covers the advancements in graph analytics engines and the entry and exit of vendors in the graph database market. The article further delves into topics like assisted knowledge graph creation, ontology modeling, applied graph AI use cases, graph levels of detail, and the combination of graphs with large language models. It concludes with a discussion on the influence of knowledge graphs on improving LLM response accuracy in the enterprise and introduces various research efforts in this domain.

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