Short definition
Knowledge Graph RAG (also called GraphRAG) is a retrieval pattern that uses a knowledge graph as a structured backbone for retrieval, either alone or alongside vector search. Entities, relationships, and properties become first-class retrieval targets.
It is most useful when enterprise knowledge has strong relational structure — organizational hierarchy, policy applicability, contract obligations, supply chain dependencies — and the answers depend on traversing relationships, not just finding similar text.
Why it matters now
Vector RAG hit a ceiling on multi-hop and relational questions. "Which policies apply to vendors classified as high-risk in EMEA, and who is the approver for each?" is not a similarity-search problem.
Knowledge graphs are easier to build than they used to be. LLMs can extract entities and relationships from unstructured documents reliably enough to bootstrap an enterprise graph in weeks, not years.
Hybrid retrieval (graph + vector) outperforms either alone on enterprise benchmarks. The graph handles structure; the vector store handles fuzzy semantic match; the planner decides which to use per sub-question.
Federated knowledge graphs across business units are becoming a pattern enterprises actually deploy — see the federated knowledge graph playbook.
Enterprise pain points
- Pure vector RAG misses relational questions. Cosine similarity finds related text; it does not traverse "this policy applies to this contract type signed by this counterparty class."
- Building a knowledge graph from scratch felt prohibitive. Many teams have legacy ontology projects that never shipped. Modern LLM-assisted extraction changes the economics.
- Graph + vector hybrid retrieval needs orchestration. Without a planner that routes per sub-question, hybrid systems become slower than either pure approach with no quality gain.
- Graph schema drift is real. Without governance, the graph becomes a tangle of inconsistent entity types and relationship labels. The platform needs schema management primitives.
Capabilities required
- Entity and relationship extraction from unstructured documents using LLM-assisted pipelines that map text into graph triples.
- Graph storage on enterprise-grade graph databases (Neo4j, Memgraph, AGE) or graph-on-relational backends, with versioning and schema management.
- Hybrid retrieval where a planner agent decides per sub-question whether to query the graph, the vector store, or both.
- Relationship traversal for multi-hop questions that need to follow entity links, not just match similar passages.
- Citation back to source documents so graph-derived answers remain explainable in terms of the underlying evidence.
- Schema governance with entity-type registries, relationship taxonomies, and review workflows for schema changes.
- Federated graphs across business units when each unit owns its own subgraph but cross-unit queries need to traverse boundaries.
Federated knowledge graph across business units.
A worked example of cross-unit graph deployment with governed access controls — using VDF AI as the orchestration and retrieval layer.
How VDF AI addresses it
VDF AI Networks supports hybrid retrieval as a first-class orchestration pattern: planner agents route sub-questions to vector retrieval, graph traversal, or tool calls, then synthesize.
VDF AI Chat exposes graph-aware retrieval where the question benefits from it, and falls back to vector retrieval where the graph is not the right primitive.
See the federated knowledge graph playbook for a worked example of cross-unit graph deployment.
Use cases
Policy and obligation lookup
Answer "which policies apply to this transaction" by traversing the policy-to-product-to-region graph, not by similarity search over policy text.
Contract and counterparty intelligence
Resolve "what are our obligations to counterparties in this risk tier?" by traversing the contract-clause-to-counterparty-to-tier graph.
Supply chain and dependency reasoning
Trace upstream and downstream dependencies in a supply graph to answer impact and resilience questions a vector store cannot resolve.
Federated cross-unit knowledge
Each business unit owns its subgraph; cross-unit queries traverse a federation layer with appropriate access controls.
Architecture and governance angle
GraphRAG is best understood as a retrieval primitive that lives alongside vector retrieval, not as a replacement. The planner decides which to use, and a validator confirms the answer is grounded.
The graph itself is a governed asset. Entity types, relationship taxonomies, and provenance need ownership — usually a knowledge engineering team that maintains schema while LLM-assisted pipelines do the extraction.
For agentic patterns that combine graph and vector retrieval, see Agentic RAG vs Traditional RAG. For the broader retrieval governance frame, see Private RAG.
Vector RAG vs Knowledge Graph RAG vs Hybrid
Pick by question shape. Most enterprise stacks end up using hybrid.
| Dimension | Vector RAG | Knowledge Graph RAG (Hybrid) |
|---|---|---|
| Best for | Semantic similarity, fuzzy match, unstructured text | Relational and multi-hop questions, structured knowledge |
| Multi-hop accuracy | Often fails | Strong if schema is well-modeled |
| Implementation cost | Lower (embed + index) | Higher (schema + extraction + storage) |
| Maintenance | Index refresh | Schema governance plus extraction quality |
| Explainability | Cited passages | Cited passages + traversal path |
| Best fit | Q&A over docs, FAQ, exploratory search | Policy lookup, obligations, dependency reasoning |
FAQ
What is GraphRAG?
GraphRAG (also called Knowledge Graph RAG) is a retrieval pattern that uses a knowledge graph for structured retrieval, often combined with vector retrieval for unstructured content. The planner decides which to use per sub-question.
When does GraphRAG outperform vector RAG?
When the question depends on traversing relationships (policy applicability, contract obligations, supply chain dependencies) rather than finding similar text. Vector RAG can find related passages; GraphRAG can answer relational questions directly.
Is GraphRAG harder to implement?
Yes, the upfront cost is higher because the schema needs design and the extraction pipeline needs tuning. The ongoing cost is comparable, and LLM-assisted extraction has made bootstrapping a graph much faster than it used to be.
How do you build the knowledge graph?
Start with a small high-value subgraph (one entity type, two relationships) and validate it end-to-end. Use LLM-assisted extraction to populate from existing documents. Add schema governance from day one to prevent drift.
Should I replace vector RAG with GraphRAG?
No. They solve different problems. Hybrid retrieval — graph for structure, vector for semantic match — outperforms either alone on most enterprise benchmarks.
How does GraphRAG handle federated knowledge?
Each business unit can own a subgraph; a federation layer enables cross-unit traversal with appropriate access controls. See the <a href="/playbooks/federated-knowledge-graph-business-units/">federated knowledge graph playbook</a> for a worked example.
Related foundational reading and internal links
The strongest enterprise retrieval is hybrid.
Graph for structure, vector for semantic match, planner for routing, validator for grounding. That is the pattern. Talk to us if you want to map it onto your knowledge architecture.