arXiv 2412.058382024-12-08cs.AI

一种面向跨异构数据检索增强生成的多智能体协作方法

A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data

iASYS Technology Solutions Pvt. Ltd.（iASYS技术解决方案私人有限公司）；SVPM’s College of Engineering（SVPM工程学院）；School of Data and Sciences（数据科学学院）；Brac University（拉克斯大学）

Aniruddha Salve, Saba Attar, Mahesh Deshmukh, Sayali Shivpuje, Arnab Mitra Utsab

备注：: 16 pages, 3 figures. This preprint introduces a multi-agent framework for Retrieval-Augmented Generation (RAG), enhancing Large Language Models (LLMs) for efficient integration of diverse data sources. Relevant for researchers in AI, ML, generative AI, and database systems

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AI总结：

针对传统单智能体RAG处理异构数据源效率低、准确率差的问题，提出多智能体协作RAG系统，各智能体适配不同数据源并在模块化框架协作，提升查询效率与响应准确率，适配复杂异构数据场景。

摘要：

检索增强生成（Retrieval-Augmented Generation，RAG）通过将外部领域特定数据纳入生成过程来增强大语言模型（Large Language Models，LLMs）的性能。尽管LLMs能力强大，但它们通常依赖静态的预训练数据集，这限制了其整合动态数据或私有数据的能力。传统RAG系统通常采用单智能体架构来处理查询生成、数据检索和响应合成。然而，这种方法在处理关系数据库、文档存储和图数据库等异构数据源时效率低下，往往会导致性能瓶颈和准确率下降。本文提出了一种多智能体RAG系统来解决这些局限性。系统包含多个专门化智能体，每个智能体针对特定数据源优化，分别负责关系型、NoSQL和文档型系统的查询生成。这些智能体在模块化框架内协作，查询执行被委托给一个为兼容各类数据库而设计的环境。这种分布式方法通过确保每个智能体专注于其专门任务，提升了查询效率、降低了token开销并提高了响应准确率。所提出的系统具备可扩展性和适应性，非常适合需要整合异构、动态或私有数据源的生成式AI工作流。通过利用专门化智能体和模块化执行环境，该系统为生成式AI应用中处理复杂异构数据环境提供了高效且鲁棒的解决方案。

英文摘要：

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external, domain-specific data into the generative process. While LLMs are highly capable, they often rely on static, pre-trained datasets, limiting their ability to integrate dynamic or private data. Traditional RAG systems typically use a single-agent architecture to handle query generation, data retrieval, and response synthesis. However, this approach becomes inefficient when dealing with diverse data sources, such as relational databases, document stores, and graph databases, often leading to performance bottlenecks and reduced accuracy. This paper proposes a multi-agent RAG system to address these limitations. Specialized agents, each optimized for a specific data source, handle query generation for relational, NoSQL, and document-based systems. These agents collaborate within a modular framework, with query execution delegated to an environment designed for compatibility across various database types. This distributed approach enhances query efficiency, reduces token overhead, and improves response accuracy by ensuring that each agent focuses on its specialized task. The proposed system is scalable and adaptable, making it ideal for generative AI workflows that require integration with diverse, dynamic, or private data sources. By leveraging specialized agents and a modular execution environment, the system provides an efficient and robust solution for handling complex, heterogeneous data environments in generative AI applications.

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。