Retrieval-Augmented Generation (RAG) is the most widely used LLM use case across organizations. By vectorizing documents and retrieving semantically similar chunks at query time, RAG mitigates hallucinations, grounds responses, and bypasses static knowledge cutoffs imposed by model pretraining. However, standard vector-based RAG runs into limitations for complex queries requiring global context, multi-hop reasoning, or cross-document aggregation.

Graph RAG solves this by transitioning from retrieving flat documents to retrieving structured knowledge. It integrates Knowledge Graphs (KGs), where data is stored as Nodes (Entities), Edges (Relationships), and Properties into the RAG pipeline. This combines the semantic, fuzzy-matching capabilities of modern LLMs with the structured, deterministic reasoning of KGs.

The article explores six distinct architectural patterns of Graph RAG, along with pros, cons, and use cases. Core components include Information Extraction using LLMs for NER and Relationship Extraction, Graph Storage in databases like Neo4j, Retrieval mechanisms, and Generation where retrieved graph data is injected into the LLM's context window.

The term "Graph RAG" is an umbrella for several fundamentally different architectural patterns. Choosing the right pattern depends on user query patterns, system cost, latency, and capability requirements.