用大模型和智能体实现企业数据自然语言分析，提升效率与安全性。
LLM and Agent-Driven Data Analysis: A Systematic Approach for Enterprise Applications and System-level Deployment
- 基于大模型与智能体构建企业级数据分析系统
- 支持复杂查询理解与多智能体协作，提升生成SQL准确率
- 适合关注数据安全与低代码分析的企业技术团队
生成式AI与智能体技术的快速发展正深刻改变企业数据管理与分析模式。传统数据库应用与系统部署受到AI驱动工具（如检索增强生成RAG、向量数据库）的深刻影响，为知识库的语义查询提供了新路径。在企业数据分析中，由大语言模型（LLMs）驱动的SQL生成已成为连接自然语言与结构化数据的关键桥梁，显著降低数据访问门槛并提升分析效率。本文聚焦企业级数据分析应用与系统部署，提出涵盖复杂查询理解、多智能体协同、安全验证与计算效率优化的创新框架。通过典型应用场景，探讨了分布式部署、数据安全及SQL生成固有挑战等关键问题。
原文摘要 · Abstract (English)
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.
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