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.

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.

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.

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