GraphRAG is a graph-based approach that enhances Retrieval Augmented Generation (RAG) techniques, providing more informative and contextually relevant answers than traditional RAG alone. By combining graph-based techniques at indexing and query time, GraphRAG automates the construction of knowledge graphs using large language models, making it more accessible to a broad user base. This technology enables applications to handle complex queries that traditional RAG systems struggle with, providing users with richer, more contextually relevant answers. GraphRAG transforms complex networks of information into an interconnected web of knowledge, revealing underlying structures and helping users find and analyze information more effectively. It works by constructing a knowledge graph from a given set of documents, identifying key entities, and representing them as nodes in a graph structure. This allows it to access different levels of information about interconnections in the data, enabling it to take both a birds-eye view of the entire knowledge base and zoom in on granular connections between data points. GraphRAG has numerous benefits, including comprehensive answers across complex domains, seamless integration of information from multiple documents, advanced reasoning about entity relationships, and uncovering hidden insights within datasets.