DB-GPT: Empowering Database Interactions with Private Large Language Models
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910286065172480 |
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| author | Xue, Siqiao Jiang, Caigao Shi, Wenhui Cheng, Fangyin Chen, Keting Yang, Hongjun Zhang, Zhiping He, Jianshan Zhang, Hongyang Wei, Ganglin Zhao, Wang Zhou, Fan Qi, Danrui Yi, Hong Liu, Shaodong Chen, Faqiang |
| author_facet | Xue, Siqiao Jiang, Caigao Shi, Wenhui Cheng, Fangyin Chen, Keting Yang, Hongjun Zhang, Zhiping He, Jianshan Zhang, Hongyang Wei, Ganglin Zhao, Wang Zhou, Fan Qi, Danrui Yi, Hong Liu, Shaodong Chen, Faqiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17449 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DB-GPT: Empowering Database Interactions with Private Large Language Models Xue, Siqiao Jiang, Caigao Shi, Wenhui Cheng, Fangyin Chen, Keting Yang, Hongjun Zhang, Zhiping He, Jianshan Zhang, Hongyang Wei, Ganglin Zhao, Wang Zhou, Fan Qi, Danrui Yi, Hong Liu, Shaodong Chen, Faqiang Databases 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. |
| title | DB-GPT: Empowering Database Interactions with Private Large Language Models |
| topic | Databases |
| url | https://arxiv.org/abs/2312.17449 |