用生成式AI打造快递追踪助手，让物流信息更易懂。
Creating a Gen-AI based Track and Trace Assistant MVP (SuperTracy) for PostNL
- 构建多智能体开源系统，用大模型自动生成包裹旅程故事。
- 通过RAG增强回答精度，实现对物流异常的高效识别。
- 可独立运行于公司内网，适合想自研AI客服的企业参考。
生成式AI的发展为公司带来了提升客服效率和自动化任务的新机遇。PostNL作为荷兰最大的包裹与电商企业，希望利用生成式AI改进包裹追踪信息的沟通。实习期间开发了一个最小可行产品（MVP），以展示使用生成式AI技术增强包裹追踪、分析包裹旅程并以易懂方式沟通的能力。主要目标是构建内部自有的大语言模型（LLM）系统，减少对外部平台的依赖，并验证公司内部建立专用生成式AI团队的可行性。该多智能体LLM系统旨在更高效准确地生成包裹旅程故事并识别物流中断。研究采用了基于检索增强生成（RAG）的AI驱动通信系统，优化了针对领域任务的大语言模型。成功实现名为SuperTracy的多智能体开源LLM系统，具备自主处理用户广泛咨询及改善内部知识管理的能力。结果与评估表明，该技术在包裹追踪沟通方面表现超出预期，展示了生成式AI在物流领域的潜力，为后续优化和更广范围的应用奠定了基础。
原文摘要 · Abstract (English)
The developments in the field of generative AI has brought a lot of opportunities for companies, for instance to improve efficiency in customer service and automating tasks. PostNL, the biggest parcel and E-commerce corporation of the Netherlands wants to use generative AI to enhance the communication around track and trace of parcels. During the internship a Minimal Viable Product (MVP) is created to showcase the value of using generative AI technologies, to enhance parcel tracking, analyzing the parcel's journey and being able to communicate about it in an easy to understand manner. The primary goal was to develop an in-house LLM-based system, reducing dependency on external platforms and establishing the feasibility of a dedicated generative AI team within the company. This multi-agent LLM based system aimed to construct parcel journey stories and identify logistical disruptions with heightened efficiency and accuracy. The research involved deploying a sophisticated AI-driven communication system, employing Retrieval-Augmented Generation (RAG) for enhanced response precision, and optimizing large language models (LLMs) tailored to domain specific tasks. The MVP successfully implemented a multi-agent open-source LLM system, called SuperTracy. SuperTracy is capable of autonomously managing a broad spectrum of user inquiries and improving internal knowledge handling. Results and evaluation demonstrated technological innovation and feasibility, notably in communication about the track and trace of a parcel, which exceeded initial expectations. These advancements highlight the potential of AI-driven solutions in logistics, suggesting many opportunities for further refinement and broader implementation within PostNL operational framework.
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