Abstract: With the global population aging and lifestyle changing, the management and treatment of chronic diseases become increasingly important. Chronic diseases include cardiovascular diseases, diabetes, chronic respiratory diseases, etc. They require long-term or even lifelong health management, the core of which is to design and implement long-term health plans, including balanced dieting, appropriate exercising, regular inspection, and medication management. In recent years, large language models make progress in the medical field but do not focus on chronic disease health management. Therefore, they lack understanding of Chinese dietary habits and culture. These medical large language models also have limited capabilities in handling numerical information. To address these issues, this paper constructs a chronic disease health management information system based on large language model. By integrating foundational knowledge of chronic diseases, health management guidelines, and actual health management plans as domain data, this paper trains the QingTing large language model as the core of the system for effectively answering health-related questions. Additionally, the system introduces a tool enhancement strategy, improving the QingTing’s ability to handle numerical information in health data by invoking tools. The system also adopts a retrieval-augmented generation technology based on uncertain knowledge graph to enhance the accuracy and reliability of QingTing. Experiments on the chronic disease health management information system based on a large language model demonstrate that QingTing significantly outperforms other baseline large language models in health management dialogues, and verify the effectiveness of the designed tool enhancement and retrieval-augmented methods.