What is Graph RAG?
what is graph RAGGraph RAG (Graph-based Retrieval-Augmented Generation) is a technique that enhances large language models (LLMs) by grounding their responses in structured knowledge graph data. Instead of relying solely on vector similarity search over documents, Graph RAG traverses a knowledge graph to retrieve precise, contextualised facts - reducing hallucinations and enabling answers that cite their sources.
Why It Matters for Enterprise
Large language models are powerful, but they hallucinate - they generate plausible-sounding answers that are factually wrong. In enterprise settings where accuracy matters (finance, healthcare, legal, engineering), hallucinations are not acceptable.
Graph RAG addresses this by adding a retrieval step that queries a knowledge graph before the LLM generates its answer. The graph provides verified, structured facts with clear provenance, and the LLM uses these facts as context to produce a grounded, accurate response.
The result is an AI system that combines the fluency of an LLM with the precision of a curated knowledge base - and can show its working by pointing to the graph paths it traversed.
How It Works
A typical Graph RAG pipeline has four stages:
1. Query understanding: The user’s natural-language question is parsed to identify entities and intent.
2. Graph retrieval: A SPARQL or Cypher query traverses the knowledge graph to retrieve relevant subgraphs - entities, relationships, and properties that bear on the question.
3. Context assembly: The retrieved graph data is serialised into a structured prompt context, often combined with relevant text chunks from a vector store (hybrid retrieval).
4. Generation: The LLM generates an answer grounded in the retrieved context, with citations back to the source entities in the graph.
This approach consistently outperforms pure vector-based RAG on questions that require multi-hop reasoning, aggregation, or precise factual recall.
Real-World Examples
Enterprise Q&A: A consulting firm builds a Graph RAG system over its project knowledge base. Consultants ask questions like “Which clients in financial services have we helped with KYC automation?” and get precise, sourced answers instead of vague summaries.
Drug interaction checking: A pharmaceutical company uses Graph RAG to let clinicians query a biomedical knowledge graph in natural language, surfacing drug-drug interactions and contraindications with full provenance trails.
Technical documentation: An engineering firm connects product manuals, specifications, and maintenance records in a knowledge graph, enabling field engineers to ask natural-language questions and receive accurate, contextualised answers on mobile devices.
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How Semantic Partners Can Help
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