What is GraphRAG?

GraphRAG (Graph Retrieval Augmented Generation) is an approach that combines the strengths of graph databases and Retrieval Augmented Generation (RAG) to improve the accuracy, relevance, and reasoning capabilities of language models. It leverages a graph database to store and retrieve contextual information related to a query. This retrieved information is then used to augment the prompt provided to a large language model (LLM), enabling the LLM to generate more informed and accurate responses. Common uses include knowledge-intensive tasks, complex reasoning, and situations where relationships between data points are important. GraphRAG allows LLMs to go beyond simple keyword searches and leverage connected data to answer questions more comprehensively.