Abstract
Retrieval, re-ranking, and retrieval-augmented generation (RAG) are critical components of modern natural language processing (NLP) applications in information retrieval, question answering, and knowledge-based text generation. However, existing solutions are often fragmented, lacking a unified framework that easily integrates these essential processes. The absence of a standardized implementation, coupled with the complexity of retrieval and re-ranking workflows, makes it challenging for researchers to compare and evaluate different approaches in a consistent environment. While existing toolkits such as Rerankers and RankLLM provide general-purpose reranking pipelines, they often lack the flexibility required for fine-grained experimentation and benchmarking. In response to these challenges, we introduce \textbf{Rankify}, a powerful and modular open-source toolkit designed to unify retrieval, re-ranking, and RAG within a cohesive framework. Rankify supports a wide range of retrieval techniques, including dense and sparse retrievers, while incorporating state-of-the-art re-ranking models to enhance retrieval quality. Additionally, Rankify includes a collection of pre-retrieved datasets to facilitate benchmarking, available at Huggingface (this https URL). To encourage adoption and ease of integration, we provide comprehensive documentation (this http URL), an open-source implementation on GitHub(this https URL), and a PyPI package for effortless installation(this https URL). By providing a unified and lightweight framework, Rankify allows researchers and practitioners to advance retrieval and re-ranking methodologies while ensuring consistency, scalability, and ease of use.
Abstract (translated)
检索、重新排序和检索增强生成（RAG）是现代自然语言处理（NLP）应用中信息检索、问题回答和基于知识的文本生成的关键组成部分。然而，现有的解决方案往往碎片化，缺乏一个能够轻松整合这些重要过程的统一框架。没有标准化的实现，加上检索和重新排序工作流程的复杂性，使得研究者在一致的环境中比较和评估不同方法变得具有挑战性。尽管现有工具包如Rerankers和RankLLM提供了通用的重新排序管道，但它们通常缺乏进行细粒度实验和基准测试所需的灵活性。为应对这些挑战，我们推出了\textbf{Rankify}，这是一个强大且模块化的开源工具包，旨在将检索、重新排序和RAG统一在一个连贯的框架内。 Rankify支持一系列检索技术，包括密集型和稀疏型检索器，并集成了最先进的重新排序模型以提升检索质量。此外，Rankify还包含一组预检索数据集，以便于进行基准测试，这些数据集可在Huggingface（此链接）上获取。为了促进采用和集成的便利性，我们提供了详尽的文档（此链接）、GitHub上的开源实现（此链接），以及一个PyPI包以方便安装（此链接）。通过提供统一且轻量级的框架，Rankify使研究人员和实践者能够推进检索和重新排序方法的发展，并确保一致性和可扩展性的同时提高易用性。
URL
https://arxiv.org/abs/2502.02464
