Event

<p>Traditional QA pipelines—even those using baseline RAG—struggle with complex reasoning tasks such as multi-hop inference, contradiction detection, entity linking, temporal consistency, and large-scale cross-document understanding. These limitations become critical in domains like investigative journalism, scientific research, and legal analysis, where answers depend on relationships spread across many documents rather than isolated text chunks.</p> <p>This talk will demonstrate how open-source knowledge-graph–based approaches can overcome these challenges by enabling structured retrieval, multi-hop reasoning, richer context assembly, and corpus-level summarization. We will explore several open-source frameworks used today to build graph-enhanced RAG systems and compare them across practical criteria: extraction quality, response latency, hardware requirements, maintenance complexity, and suitability for different problem types.</p> <p>Attendees will leave with a clear, practical understanding of how to select and apply graph-based RAG techniques to extract deeper insight from large unstructured datasets.</p> <p>Frameworks we're going to consider: - MS GraphRAG (MIT license) - https://github.com/microsoft/graphrag - LlamaIndex KG (MIT license) - https://github.com/run-llama/llama_index - KAG/OpenSPG (Apache-2.0 license) - https://github.com/OpenSPG/KAG</p>