大语言模型与智能体驱动的数据分析：企业应用与系统级部署的系统方法
LLM and Agent-Driven Data Analysis: A Systematic Approach for Enterprise Applications and System-level Deployment
AI总结：
本文提出一种系统方法，利用大语言模型和智能体技术，提升企业数据分析的效率和安全性，解决复杂查询和分布式部署中的关键挑战。
摘要：
生成式人工智能和智能体技术的快速发展正在深刻改变企业数据管理和分析。传统数据库应用和系统部署受到AI驱动工具如检索增强生成（RAG）和向量数据库技术的深刻影响，这些技术为在企业知识库上进行语义查询提供了新途径。同时，数据安全性和合规性是采用AI技术组织的首要关注点。对于企业数据分析，由大语言模型（LLMs）和AI智能体驱动的SQL生成技术，已成为连接自然语言与结构化数据的关键桥梁，有效降低了企业数据访问的门槛，提高了分析效率。本文聚焦于企业数据分析应用和系统部署，涵盖了各种创新框架，使复杂查询理解、多智能体协作、安全验证和计算效率得以实现。通过代表性用例，讨论了与分布式部署、数据安全性和SQL生成任务固有困难相关的关键挑战。
英文摘要：
The rapid progress in Generative AI and Agent technologies is profoundly transforming enterprise data management and analytics. Traditional database applications and system deployment are fundamentally impacted by AI-driven tools, such as Retrieval-Augmented Generation (RAG) and vector database technologies, which provide new pathways for semantic querying over enterprise knowledge bases. In the meantime, data security and compliance are top priorities for organizations adopting AI technologies. For enterprise data analysis, SQL generations powered by large language models (LLMs) and AI agents, has emerged as a key bridge connecting natural language with structured data, effectively lowering the barrier to enterprise data access and improving analytical efficiency. This paper focuses on enterprise data analysis applications and system deployment, covering a range of innovative frameworks, enabling complex query understanding, multi-agent collaboration, security verification, and computational efficiency. Through representative use cases, key challenges related to distributed deployment, data security, and inherent difficulties in SQL generation tasks are discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
