The recent breakthroughs in large language models (LLMs) are positioned to transition many areas of software. Database technologies particularly have an important entanglement with LLMs as efficient and intuitive database interactions are paramount. In this paper, we present DB-GPT, a revolutionary and production-ready project that integrates LLMs with traditional database systems to enhance user experience and accessibility. DB-GPT is designed to understand natural language queries, provide context-aware responses, and generate complex SQL queries with high accuracy, making it an indispensable tool for users ranging from novice to expert. The core innovation in DB-GPT lies in its private LLM technology, which is fine-tuned on domain-specific corpora to maintain user privacy and ensure data security while offering the benefits of state-of-the-art LLMs. We detail the architecture of DB-GPT, which includes a novel retrieval augmented generation (RAG) knowledge system, an adaptive learning mechanism to continuously improve performance based on user feedback and a service-oriented multi-model framework (SMMF) with powerful data-driven agents. Our extensive experiments and user studies confirm that DB-GPT represents a paradigm shift in database interactions, offering a more natural, efficient, and secure way to engage with data repositories. The paper concludes with a discussion of the implications of DB-GPT framework on the future of human-database interaction and outlines potential avenues for further enhancements and applications in the field. The project code is available at https://github.com/eosphoros-ai/DB-GPT. Experience DB-GPT for yourself by installing it with the instructions https://github.com/eosphoros-ai/DB-GPT#install and view a concise 10-minute video at https://www.youtube.com/watch?v=KYs4nTDzEhk.

翻译：大型语言模型（LLMs）的最新突破正推动软件诸多领域的转型。数据库技术尤其与LLMs存在重要关联，因为高效且直观的数据库交互至关重要。本文提出DB-GPT——一个革新性且可投入生产环境的项目，它将LLMs与传统数据库系统相结合，以提升用户体验与可访问性。DB-GPT旨在理解自然语言查询、提供上下文感知响应，并高精度生成复杂SQL查询，使其成为从新手到专家用户群体的必备工具。DB-GPT的核心创新在于其私有LLM技术，该技术通过领域特定语料库进行微调，在保持用户隐私、确保数据安全的同时，兼具先进LLMs的效能优势。我们详细阐述了DB-GPT的架构，包括新颖的检索增强生成（RAG）知识系统、基于用户反馈持续优化性能的自适应学习机制，以及配备强大数据驱动智能体的面向服务多模型框架（SMMF）。大量实验与用户研究证实，DB-GPT代表了数据库交互领域的范式转变，为数据存储库的访问提供了更自然、高效且安全的方式。本文最后讨论了DB-GPT框架对人类-数据库交互未来的启示，并概述了该领域潜在的性能提升与扩展应用方向。项目代码见https://github.com/eosphoros-ai/DB-GPT。请根据安装指南https://github.com/eosphoros-ai/DB-GPT#install自行体验DB-GPT，并观看10分钟简明视频https://www.youtube.com/watch?v=KYs4nTDzEhk。