GraphRAG Story

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approx. 4 min read

Why GraphRAG needs more than one graph

The story explains how a user question becomes a structured answer when documents, domain context, semantic knowledge, conversation memory, and time are modelled separately.

Wissensgraph

Semantische Basis

Domänen-Graph

Fachliche Realität

Dokument-Graph

Korpus und Quellen

Conversational Graph

Interaktion und Memory

Dynamischer Graph

Zeit und Veränderung

Chapter 1 of 5

Question: Frame the system problem

What happens in this step

We turn the initial request into a clear core question. We name the objective, the boundary, and the expected effect explicitly so that everyone starts from the same point. We also clarify terms that could otherwise be understood differently.

Why this matters

If the question stays vague, the later answers will also stay vague or contradictory. A clean focus at the beginning avoids misunderstandings and keeps the decision space stable throughout the flow.

How to read the visualization

Only the core question sits in the center. The surrounding space shows that assumptions already exist, but are not yet modeled as explicit relations. The visual makes one thing clear: we do not start with answers, we start with a precise shared understanding.

Shot: Top

Conclusion

GraphRAG is a modelling discipline.

The architecture becomes useful when each graph type does its own job and the LLM receives a controlled context package.