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：大语言模型（LLMs）的最新突破即将推动多个软件领域的变革。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，这是一个革命性的生产级项目，将LLMs与传统数据库系统相结合，以提升用户体验和可及性。DB-GPT旨在理解自然语言查询、提供上下文感知的响应，并以高精度生成复杂SQL查询，使其成为从新手到专家用户不可或缺的工具。DB-GPT的核心创新在于其私有LLM技术，该技术基于领域特定语料库进行微调，在提供最先进LLMs优势的同时，维护用户隐私并确保数据安全。我们详细阐述了DB-GPT的架构，包括新型检索增强生成（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型智能体的面向服务多模型框架（SMMF）。广泛的实验和用户研究证实，DB-GPT代表了数据库交互的范式转变，为与数据仓库交互提供了更自然、高效且安全的方式。本文最后讨论了DB-GPT框架对人与数据库交互未来的影响，并概述了该领域进一步优化与应用的潜在方向。项目代码可通过https://github.com/eosphoros-ai/DB-GPT获取。可按照https://github.com/eosphoros-ai/DB-GPT#install的说明安装以亲身体验DB-GPT，并可通过https://www.youtube.com/watch?v=KYs4nTDzEhk观看简短的10分钟演示视频。