Rankify：用于检索、重排序与检索增强生成的综合 Python 工具包
Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation
AI总结：
本文提出开源 Python 工具包 Rankify，通过统一模块化框架整合检索、重排序与 RAG，并提供模型、预检索数据集、文档及 PyPI 安装支持。
摘要：
检索、重排序和检索增强生成（RAG）是信息检索、问答或基于知识的文本生成等现代应用的关键组成部分。然而，现有解决方案通常较为分散，缺少一个能够轻松整合这些基本流程的统一框架。由于缺乏标准化实现，再加上检索与重排序工作流的复杂性，研究人员很难在一致的环境中比较和评估不同方法。尽管 Rerankers 和 RankLLM 等现有工具包提供了通用重排序流水线，但它们往往缺少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 Rankify，这是一个功能强大且模块化的开源工具包，旨在在一个连贯框架内统一检索、重排序和 RAG。Rankify 支持广泛的检索技术，包括稠密检索器和稀疏检索器，同时集成最先进的重排序模型以提升检索质量。此外，Rankify 包含一组预检索数据集，以便于基准测试，这些数据集可在 Huggingface（https://huggingface.co/datasets/abdoelsayed/reranking-datasets-light）获取。为促进采用并降低集成难度，我们提供了全面文档（http://rankify.readthedocs.io/）、GitHub 上的开源实现（https://github.com/DataScienceUIBK/rankify）以及便于安装的 PyPI 包（https://pypi.org/project/rankify/）。作为一个统一且轻量级的框架，Rankify 使研究人员和从业者能够推进检索与重排序方法，同时确保一致性、可扩展性和易用性。
英文摘要：
Retrieval, re-ranking, and retrieval-augmented generation (RAG) are critical components of modern applications in information retrieval, question answering, or 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 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 (https://huggingface.co/datasets/abdoelsayed/reranking-datasets-light). To encourage adoption and ease of integration, we provide comprehensive documentation (http://rankify.readthedocs.io/), an open-source implementation on GitHub (https://github.com/DataScienceUIBK/rankify), and a PyPI package for easy installation (https://pypi.org/project/rankify/). As 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
