The FUTURAL project aims to provide a comprehensive suite of digital Smart Solutions (SS) across five critical domains to address pressing social and environmental issues. Central to this initiative is a robust Metasearch platform, which will not only serve as the primary access point to FUTURAL's solutions but also facilitate the search and retrieval of SS developed by other initiatives. This paper elaborates on the MVP implementation for the MetaSearch platform. It focuses on a single, open-source data service and harnesses the generative capabilities of Large Language Models (LLMs) to create a user-friendly natural language interface. The design of the Minimum Viable Product (MVP), the tools used for adapting LLMs to our specific application, and our comprehensive set of evaluation techniques are thoroughly detailed. The results from our evaluations demonstrate that our approach is highly effective and can be efficiently implemented in future iterations of the MVP. This groundwork paves the way for extending the platform to include additional services and diverse data sets from the FUTURAL project, enhancing its capacity to address a broader array of queries and datasets.

翻译：摘要：FUTURAL项目旨在通过覆盖五个关键领域的综合性数字智能解决方案（SS），应对迫切的社会与环境挑战。该项目的核心是一个稳健的元搜索平台，该平台不仅将作为FUTURAL解决方案的主要访问入口，还将促进其他项目开发的智能解决方案的搜索与检索。本文详细阐述了元搜索平台的最小可行产品（MVP）实现，聚焦于单一开源数据服务，并利用大型语言模型（LLMs）的生成能力构建用户友好的自然语言交互界面。文章深入介绍了MVP的设计、针对特定应用场景对LLM进行适配的工具，以及我们采用的综合评估技术体系。评估结果表明，我们的方法具有高效性，并可在后续MVP迭代中有效实施。这项基础工作为扩展平台功能铺平了道路——未来将集成FUTURAL项目的更多服务与多样化数据集，从而增强其处理更广泛查询与数据的能力。