GraphRAG: A Practitioner's Guide to 6 Advanced Architectural Patterns

Standard Retrieval-Augmented Generation (RAG) often struggles with complex queries, as it retrieves disconnected text snippets and misses the crucial relationships between concepts. GraphRAG addresses this limitation by integrating knowledge graphs, which explicitly map entities and their connections to enable more sophisticated, multi-step reasoning. One advanced pattern involves using an LLM to translate natural language directly into a formal graph query, ensuring highly accurate and deterministic results for tasks like data aggregation. Another powerful strategy employs a hybrid approach, simultaneously querying a vector database for semantic context and a graph database for structural facts, then combining both to provide a richer, more complete answer.