用多智能体动态协作，让大模型跨源问答更准更稳。
Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models
- 设计多智能体协同机制，按问题类型自动选检索策略。
- 在合同管理场景中，问答准确率显著提升。
- 适合需要融合文档与数据库的复杂问答系统开发者。
我们提出一种结合多种先进大语言模型检索技术的方法，支持构建稳健的多源问答系统。该方法通过协调式多智能体编排与动态检索策略，整合非结构化文档（如PDF）和结构化数据库等多元数据源。系统配备专用智能体，包括SQL智能体、检索增强生成（RAG）智能体和路由智能体，能根据查询特性动态选择最优检索路径。为提升准确性和上下文相关性，采用实时适应查询语境的动态提示工程。该方法在合同管理领域得到验证，复杂查询常需无缝融合非结构化与结构化数据。实验表明，该方法显著提升了回答的准确性与相关性，提供了一个可扩展、适用于多种领域与数据源的问答系统框架。
原文摘要 · Abstract (English)
We propose a methodology that combines several advanced techniques in Large Language Model (LLM) retrieval to support the development of robust, multi-source question-answer systems. This methodology is designed to integrate information from diverse data sources, including unstructured documents (PDFs) and structured databases, through a coordinated multi-agent orchestration and dynamic retrieval approach. Our methodology leverages specialized agents-such as SQL agents, Retrieval-Augmented Generation (RAG) agents, and router agents - that dynamically select the most appropriate retrieval strategy based on the nature of each query. To further improve accuracy and contextual relevance, we employ dynamic prompt engineering, which adapts in real time to query-specific contexts. The methodology's effectiveness is demonstrated within the domain of Contract Management, where complex queries often require seamless interaction between unstructured and structured data. Our results indicate that this approach enhances response accuracy and relevance, offering a versatile and scalable framework for developing question-answer systems that can operate across various domains and data sources.
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