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The recent breakthroughs in large language models (LLMs) are positioned totransition many areas of software. Database technologies particularly have animportant entanglement with LLMs as efficient and intuitive databaseinteractions are paramount. In this paper, we present DB-GPT, a revolutionaryand production-ready project that integrates LLMs with traditional databasesystems to enhance user experience and accessibility. DB-GPT is designed tounderstand natural language queries, provide context-aware responses, andgenerate complex SQL queries with high accuracy, making it an indispensabletool for users ranging from novice to expert. The core innovation in DB-GPTlies in its private LLM technology, which is fine-tuned on domain-specificcorpora to maintain user privacy and ensure data security while offering thebenefits of state-of-the-art LLMs. We detail the architecture of DB-GPT, whichincludes a novel retrieval augmented generation (RAG) knowledge system, anadaptive learning mechanism to continuously improve performance based on userfeedback and a service-oriented multi-model framework (SMMF) with powerfuldata-driven agents. Our extensive experiments and user studies confirm thatDB-GPT represents a paradigm shift in database interactions, offering a morenatural, efficient, and secure way to engage with data repositories. The paperconcludes with a discussion of the implications of DB-GPT framework on thefuture of human-database interaction and outlines potential avenues for furtherenhancements and applications in the field. The project code is available athttps://github.com/eosphoros-ai/DB-GPT. Experience DB-GPT for yourself byinstalling it with the instructionshttps://github.com/eosphoros-ai/DB-GPT#install and view a concise 10-minutevideo at https://www.youtube.com/watch?v=KYs4nTDzEhk.

DB-GPT: Empowering Database Interactions with Private Large Language Models