Demonstration of DB-GPT: Next Generation Data Interaction System Empowered by Large Language Models

作者：Siqiao Xue, Danrui Qi, Caigao Jiang, Fangyin Cheng, Keting Chen, Zhiping Zhang, Hongyang Zhang, Ganglin Wei, Zhao Wang, Fan Zhou, Yi Hong, Shaodong Liu, Hongjun Yang, Faqiang Chen · 发表于：Proceedings of the VLDB Endowment · 年份：2024 · DOI：10.14778/3685800.3685876 · 被引用次数：5 · 研究领域：Scientific Computing and Data Management、Data Mining Algorithms and Applications、Distributed and Parallel Computing Systems

The recent breakthroughs in large language models (LLMs) are positioned to transition many areas of software. In this paper, we present DB-GPT, a revolutionary and product-ready Python library that integrates LLMs into traditional data interaction tasks to enhance user experience and accessibility. DB-GPT is designed to understand data interaction tasks described by natural language and provide context-aware responses powered by LLMs, making it an indispensable tool for users ranging from novice to expert. Its system design supports deployment across local, distributed, and cloud environments. Beyond handling basic data interaction tasks like Text-to-SQL with LLMs, it can handle complex tasks like generative data analysis through a Multi-Agents framework and the Agentic Workflow Expression Language (AWEL). The Service-oriented Multi-model Management Framework (SMMF) ensures data privacy and security, enabling users to employ DB-GPT with private LLMs. Additionally, DB-GPT offers a series of product-ready features designed to enable users to integrate DB-GPT within their product environments easily. The code of DB-GPT is available at Github.