Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation
Abdelrahman Abdallah, Bhawna Piryani Universität Innsbruck, Jamshid Mozafari Universität Innsbruck, Andreas Herzinger Universität Innsbruck, Jamie Holdcroft, Adam Jatowt
Building retrieval-augmented generation (RAG) systems often requires combining separate tools for retrieval, re-ranking, and generation, with incompatible data formats, evaluation pipelines, and deployment workflows. We present Rankify, an open-source Python toolkit that unifies these stages in a single modular framework. Rankify provides 42 benchmark datasets with pre-retrieved documents and pre-built indices, 15 retrievers (sparse, dense, and reasoning-augmented), and 24 re-ranking models spanning 41 pointwise, pairwise, and listwise variants. It also supports 6 RAG strategies across four inference backends (Hugging Face, vLLM, LiteLLM, and OpenAI), enabling consistent experimentation from local models to hosted APIs. A unified pipeline interface allows users to compose retrieve–rerank–generate workflows in a few lines of code, while an agentic assistant (RankifyAgent), aREST server (RankifyServer), and an interactive web playground support deployment and non-programmatic exploration. Across 200+ configurations on QA and BEIR/TREC benchmarks with six generator LLMs, re-ranking consistently improves downstream performance, yielding gains of 5–15 points in Exact Match and up to 8.5 points in RAGAS context precision across diverse retriever–generator combinations. Rankify is released under the Apache-2.0 License.
尚无 AI 解读，展示原文摘要
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表