On September 11, Tencent Youtu Lab open-sourced Youtu-GraphRAG, a new graph retrieval-augmented generation framework. This framework aims to organize knowledge into a knowledge graph by combining large language models with the RAG (Retrieval-Augmented Generation) model, and then provide the graph to the large language model for retrieval and reasoning, thereby improving the accuracy and traceability of the model when handling complex question-answering tasks. Youtu-GraphRAG is particularly suitable for knowledge-intensive scenarios such as enterprise knowledge base Q&A, research document analysis, and personal knowledge management.
Youtu-GraphRAG achieves vertical integration from graph construction to indexing and retrieval through three innovations, creating a cognitive loop. First, it adopts a four-layer knowledge tree structure, decomposing knowledge into four levels: attributes, relationships, keywords, and communities, allowing the large model to locate information along the knowledge tree during answering questions, making the reasoning path clear and visible. Second, community detection upgrades not only focus on "who is related to whom" but also combine semantic understanding of "why they are related," generating concise summaries that help users quickly grasp the essence of the problem. Finally, the intelligent iterative retrieval mechanism allows users to break down complex questions into multiple sub-questions for parallel retrieval, and through an iterative reflection mechanism, supplement and correct the results, ultimately providing more complete and reliable answers.
Youtu-GraphRAG has shown excellent performance in practical testing. In six authoritative benchmark tests, it can save up to 90.71% of Token costs, and the accuracy of complex reasoning tasks can be improved by up to 16.62%. In addition, the framework supports bilingual (Chinese and English), and cross-domain applications do not require restructuring, offering high flexibility.
Using Youtu-GraphRAG is very simple, requiring just four steps to get started. First, obtain the project code via the command line. Second, configure the environment, including obtaining the API key for remote model calls and creating a configuration file. Third, deploy the project with one click. Finally, experience the interaction through the curl command.
GitHub Source Code: https://github.com/TencentCloudADP/youtu-graphrag
