用大模型+多智能体整合合同文档与数据库，精准回答合同问题。
Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents
- 结合文档与数据库，通过多智能体动态调度流程。
- 融合RAG与文本转SQL，答案准确率显著提升。
- 无需重训练模型，适合企业级合同管理场景。
我们提出一个问答应用，用于支持合同管理流程，整合合同文档（PDF）和合同管理系统中的数据。这些信息由大语言模型处理，生成精确且相关的回答。通过引入检索增强生成（RAG）、文本转SQL技术以及动态编排工作流的智能体，进一步提升了回答准确性，且无需重新训练语言模型。同时，采用提示工程（Prompt Engineering）优化回答焦点。实验表明，这种多智能体协同与技术融合可显著提升答案的相关性与准确性，为未来信息系统提供了有前景的方向。
原文摘要 · Abstract (English)
We present a question-and-answer (Q\&A) application designed to support the contract management process by leveraging combined information from contract documents (PDFs) and data retrieved from contract management systems (database). This data is processed by a large language model (LLM) to provide precise and relevant answers. The accuracy of these responses is further enhanced through the use of Retrieval-Augmented Generation (RAG), text-to-SQL techniques, and agents that dynamically orchestrate the workflow. These techniques eliminate the need to retrain the language model. Additionally, we employed Prompt Engineering to fine-tune the focus of responses. Our findings demonstrate that this multi-agent orchestration and combination of techniques significantly improve the relevance and accuracy of the answers, offering a promising direction for future information systems.
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