Knowledge
Knowledge / Graph RAG
Relational context for questions that cross domains.
Graph RAG organizes entities and relationships to retrieve relevant paths and subgraphs.
Plain definition
What it is and what problem it solves.
Graph RAG extends retrieval with a graph of entities, relationships and evidence to interpret how business concepts connect.
Isolated fragments lose dependencies. A graph makes it possible to follow relationships between people, processes, assets, policies and events.
Architecture and limits
Technical design with explicit decisions.
From fragment to subgraph
Retrieval can include relationships, not just similar text.
- Explicit entities and relationships according to the domain.
- Multi-hop queries to follow dependencies between concepts.
- Bounded subgraphs to keep context useful and explainable.
Building and maintenance
The graph needs governance, not just automatic extraction.
- Ontologies and rules that define the meaning of each relationship.
- Entity resolution, provenance and evidence of origin.
- Update and review cycles when source systems change.
Trade-offs
Not every question requires a graph.
- Higher modeling and maintenance cost than document RAG.
- RAG handles point retrieval; Graph RAG adds relationships and paths.
- The complexity must be justified by decisions that truly depend on relationships.
Relationships
The system works as part of a platform.
Let’s turn knowledge into an operable capability.
We define the case, the sources, the limits and the evidence needed before building.