Graph-Augmented Retrieval

Standard RAG retrieves flat document chunks whose vector embeddings are closest to the query — a powerful approach for question answering over prose, but limited when the answer requires synthesizing information spread across many entities and relationships. For example, "which of our tier-1 suppliers have components affected by the new EU regulation?" requires traversing supplier-component-regulation relationships, not just finding semantically similar passages. GraphRAG addresses this by routing queries through a knowledge graph, extracting relevant entity neighborhoods or paths, and including this structured context alongside or instead of prose chunks when constructing the LLM prompt.

GraphRAG architectures vary in how deeply the graph is integrated. In lightweight approaches, the knowledge graph functions as a router or filter: entities mentioned in the query are identified, and the graph is used to pull related entities or metadata that supplement a standard vector search. In deep GraphRAG, the retrieval step traverses multi-hop graph paths (e.g., Cypher or Gremlin queries generated by the LLM or a query planner) and serializes subgraphs — nodes, edges, and properties — as structured context for the LLM. Microsoft's open-source GraphRAG framework takes a hybrid approach: it builds a community-level summary graph over a document corpus during indexing, enabling global summarization queries that standard chunk-level RAG cannot handle.

The enterprise value proposition of GraphRAG is strongest in domains where knowledge is inherently relational: compliance and regulatory intelligence (regulations → rules → affected entities), supply chain risk (suppliers → components → products → customers), life sciences (drugs → targets → pathways → diseases), and financial research (companies → executives → transactions → markets). The structured graph provides verifiable provenance for each retrieved fact — an important requirement in regulated industries — and enables LLMs to perform reasoning that is traceable to specific graph paths rather than probabilistic over document distributions.