Fundamentals · 24 July 2026
What is GraphRAG? AI answers with context
RAG finds passages, the knowledge graph knows the relationships. GraphRAG combines both, and that is exactly what the built world needs.
By Aidocr
Key takeaways
- GraphRAG combines meaning-based text search (RAG) with a knowledge graph that knows relationships.
- For a question the model receives both: supporting passages and the context across several sources.
- The effect: fewer invented answers, multi-step questions become answerable, the path to the answer is traceable.
- Building data is the ideal case, because knowledge is spread across decades, many systems and superseding documents.
GraphRAG combines two things that are already useful on their own: RAG, the meaning-based search in texts, and the knowledge graph, which knows things and relationships. For a given question the system does not just fetch matching passages, it also follows the relationships in the graph and hands the language model both: evidence and context.
The difference, shown on one question
Which maintenance is due this quarter in building B, and who is responsible? RAG alone finds passages mentioning maintenance and building B. The language model has to bridge the rest, and where knowledge is missing, a plausible-sounding gap appears. GraphRAG works differently: the graph supplies the units in building B, their contracts, the maintenance intervals and the responsible companies. The text search supplies the supporting passages. The model writes the answer, and every statement carries its source.
| RAG | GraphRAG | |
|---|---|---|
| Basis | Similarity search in text | Text search plus knowledge graph |
| Knows relationships | No | Yes, explicitly as edges |
| Multi-step questions | Hard, bridged by the model | Answerable via the graph |
| Validity (what applies today) | Text similarity, often ambiguous | Relationship with a date, unambiguous |
| Traceability | Source passages | Sources plus path through the network |
Why this fits the built world
Three properties make building data the ideal case for this approach. First, the life cycle: knowledge spreads across decades, many systems and several generations of documents. Second, supersession: change orders, contract versions and plan revisions continuously replace each other, and validity is a relationship with a date, not a text similarity. Third, responsibility: who maintains what, who owns what and what depends on what are relationship questions, not search hits.
In practice the effect shows three times over: fewer invented answers, because structured facts back the text search. Multi-step questions become answerable, because the graph connects information across sources. And the reasoning becomes visible, because the path to the answer can be shown as a chain.
And Aidocr?
Aidocr is built on exactly this principle: existing sources are connected into a knowledge graph with an ontology for construction and real estate, and answers come with their sources. The existing systems remain the system of record, nothing is migrated.
Frequently asked questions about GraphRAG
- What is GraphRAG?
- GraphRAG combines RAG, the meaning-based search in text, with a knowledge graph that knows things and relationships. For a question the language model receives both: relevant supporting passages and the context.
- What is the difference between RAG and GraphRAG?
- RAG finds passages by similarity but knows no relationships. GraphRAG additionally follows the edges in the knowledge graph, which makes multi-step questions answerable and clarifies what applies today.
- What is the practical benefit of GraphRAG?
- Fewer invented answers, because structured facts back the text search. Multi-step questions become answerable, and the path to the answer can be shown as a chain.
- Why does GraphRAG fit building data?
- Because knowledge is spread across decades and many systems, documents continuously supersede each other, and responsibilities are relationship questions. That is exactly what a graph with an ontology is made for.