In this session, Leann Chen will introduce GraphRAG, a method that integrates knowledge graphs with large language models (LLMs) to enhance Retrieval-Augmented Generation (RAG) systems. Graph RAG can address challenges like hallucinations and limited explainability in LLM-based systems. We will walk through the process of building a Graph RAG, using examples to show how this approach improves accuracy, relevance, and transparency by combining the strengths of both vector-based and graph-based retrieval methods. The session will also examine the pros and cons of vector-only and graph-only RAG systems, demonstrating how Graph RAG can effectively merge the best aspects of both.
In this session, Leann Chen will introduce GraphRAG, a method that integrates knowledge graphs with large language models (LLMs) to enhance Retrieval-Augmented Generation (RAG) systems. Graph RAG can address challenges...
Building an AI prototype is easy and quick these days. Building production-grade systems is a different story. How do you keep moving quickly and run robustly? In this talk, we...
Everybody loves vector search and enterprises now see its value thanks to the popularity of LLMs and RAG. The problem is that prod-level deployment of vector search requires boatloads of...
Current US legislation prohibits AI applications in recruiting, healthcare, and advertising from discrimination and bias. This requires organizations who deploy such systems to test and prove that their solutions are...
Leveraging Generative AI, LLMs, and Google Search Wrappers for competitor analysis empowers the procurement team with real-time, data-driven insights. Generative AI and LLMs can process vast amounts of unstructured data,...
Demos
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