What Is GraphRAG?
GraphRAG replaces the flat vector index with a semantic knowledge graph — a structured representation of entities, relationships, and attributes extracted from the enterprise document corpus. When an agent receives a query, GraphRAG traverses the knowledge graph to identify the relevant entity subgraph and provides both structured relationship context and raw text, making responses more accurate and explainable — with every inference grounded in a traversable, versioned knowledge graph.
Why This Matters in Regulated Industries
In financial services, regulators require auditable decision trails. GraphRAG satisfies this because every inference is grounded in a traversable knowledge graph with timestamped entities and versioned relationships. In healthcare, GraphRAG supports continuous learning loops where expert feedback corrects and strengthens the knowledge graph over time — the architecture required for GxP-compliant clinical AI under GAMP 5. In manufacturing, predictive maintenance agents need relational context between sensors, components, maintenance history, and operational state that only GraphRAG provides.
WTA’s GraphRAG Architecture on Azure
WTA’s standard GraphRAG implementation uses Azure AI Search for vector retrieval, Neo4j or Azure Cosmos DB for the knowledge graph, and a custom entity extraction pipeline on Azure OpenAI with PII masking, metadata lineage, and provenance annotation. Langfuse eval suites validate retrieval quality against golden datasets before every production deployment as part of the Evaluation stage of WTA’s SPEED framework. See how we apply GraphRAG in our AI-Native Product Engineering service.
Frequently Asked Questions
What is the difference between RAG and GraphRAG? Basic RAG retrieves document chunks based on vector similarity. GraphRAG retrieves from a semantic knowledge graph, providing both the relevant documents and the relationships between entities — making responses more accurate, explainable, and auditable.
When should an enterprise use GraphRAG instead of basic RAG? GraphRAG is appropriate when answers depend on relationships between entities, when regulators require auditable decision trails, or when the knowledge base is large enough that flat retrieval produces hallucinations. Most enterprise use cases in financial services, healthcare, and manufacturing require GraphRAG.
Which databases does WTA use for GraphRAG? WTA uses Azure AI Search for vector retrieval, Neo4j or Azure Cosmos DB for the knowledge graph, and Azure OpenAI for entity extraction pipelines.
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