Agentic GraphRAG for the real-world
In this article, Maikel Gonzalez Baile will introduce us to GraphRAG and Agentic GraphRAG, showcasing a fascinating use case: applying it to security incident analysis. Let’s go! 👇 The Neural Maze is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Let’s say you’ve set up your RAG pipeline: documents chunked, vector database running, LLM connected. The first query works as expected. Job done, right? But then you ask a real question. One that requires connecting a tiny detail from one document to a concept in another. Suddenly, your brilliant RAG pipeline starts to stutter. It pulls up irrelevant snippets, misses the obvious connection, and gives you an answer that’s confidently … wrong. If you’ve been there, you’ve hit the fundamental wall of classic RAG. It’s great at finding individual pages but has no idea how they link together to tell a bigger story. And that, my friend, is where all the interesting stuff happe
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