This paper introduces uRAG--a framework with a unified retrieval engine that
serves multiple downstream retrieval-augmented generation (RAG) systems. Each
RAG system consumes the retrieval results for a unique purpose, such as
open-domain question answering, fact verification, entity linking, and relation
extraction. We introduce a generic training guideline that standardizes the
communication between the search engine and the downstream RAG systems that
engage in optimizing the retrieval model. This lays the groundwork for us to
build a large-scale experimentation ecosystem consisting of 18 RAG systems that
engage in training and 18 unknown RAG systems that use the uRAG as the new
users of the search engine. Using this experimentation ecosystem, we answer a
number of fundamental research questions that improve our understanding of
promises and challenges in developing search engines for machines.

该论文介绍了 uRAG 框架，它具有一个统一的检索引擎，为多个下游的检索增强生成（RAG）系统提供服务。我们介绍了一种通用的训练指南，用于规范搜索引擎与下游 RAG 系统之间的通信，从而为我们构建一个大规模的实验生态系统奠定基础，该生态系统包括 18 个参与训练的 RAG 系统和 18 个使用 uRAG 作为新用户的未知 RAG 系统。利用这个实验生态系统，我们回答了一些基本的研究问题，以改善我们对为机器开发搜索引擎的承诺和挑战的理解。