This research introduces Graph RAG, a novel approach to enhance question answering over large text collections by combining knowledge graphs and retrieval-augmented generation (RAG). The method constructs a graph-based index from the text, identifies communities within the graph, and generates summaries for each community. Given a query, Graph RAG leverages these summaries to produce partial answers, which are then aggregated into a comprehensive global response. The study demonstrates that Graph RAG improves the comprehensiveness and diversity of answers compared to naive RAG, particularly for questions requiring a global understanding of the corpus. An LLM is used to evaluate the approach and a Python implementation will be available. The work also shows that the modular design of the approach is efficient in that the models need to process fewer tokens.