@inproceedings{abdallah-etal-2026-rankify,
title = "Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation",
author = "Abdallah, Abdelrahman and
Piryani, Bhawna and
Mozafari, Jamshid and
Herzinger, Andreas and
Holdcroft, Jamie and
Jatowt, Adam",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.21/",
pages = "208--219",
ISBN = "979-8-89176-392-0",
abstract = "Building retrieval-augmented generation (RAG) systems often requirescombining separate tools for retrieval, re-ranking, and generation,with incompatible data formats, evaluation pipelines, and deployment workflows.We present , an open-source Python toolkit that unifies these stagesin a single modular framework.[PyPI: {\ensuremath{<}}https://pypi.org/project/rankify/{\ensuremath{>}}]{,}[GitHub: {\ensuremath{<}}https://github.com/DataScienceUIBK/Rankify{\ensuremath{>}}]{,}[Docs: {\ensuremath{<}}https://rankify.readthedocs.io{\ensuremath{>}}]{\%}{,}[Video: {\ensuremath{<}}https://youtu.be/kkLzomrM2ec{\ensuremath{>}}]provides 42 benchmark datasets with pre-retrieved documents andpre-built indices, 15 retrievers (sparse, dense, and reasoning-augmented),and 24 re-ranking models spanning 41 pointwise, pairwise, and listwise variants.It also supports \textbf{6} RAG strategies across four inference backends(Hugging Face, vLLM, LiteLLM, and OpenAI), enabling consistent experimentationfrom local models to hosted APIs.A unified pipeline interface allows users to compose retrieve{--}rerank{--}generateworkflows in a few lines of code, while an agentic assistant (RankifyAgent), aREST server (RankifyServer), and an interactive webplayground 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 of5{--}15 points in Exact Match and up to 8.5 points in RAGAS context precisionacross diverse retriever{--}generator combinations."
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<abstract>Building retrieval-augmented generation (RAG) systems often requirescombining separate tools for retrieval, re-ranking, and generation,with incompatible data formats, evaluation pipelines, and deployment workflows.We present , an open-source Python toolkit that unifies these stagesin a single modular framework.[PyPI: \ensuremath<https://pypi.org/project/rankify/\ensuremath>],[GitHub: \ensuremath<https://github.com/DataScienceUIBK/Rankify\ensuremath>],[Docs: \ensuremath<https://rankify.readthedocs.io\ensuremath>]%,[Video: \ensuremath<https://youtu.be/kkLzomrM2ec\ensuremath>]provides 42 benchmark datasets with pre-retrieved documents andpre-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 experimentationfrom local models to hosted APIs.A unified pipeline interface allows users to compose retrieve–rerank–generateworkflows in a few lines of code, while an agentic assistant (RankifyAgent), aREST server (RankifyServer), and an interactive webplayground 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 of5–15 points in Exact Match and up to 8.5 points in RAGAS context precisionacross diverse retriever–generator combinations.</abstract>
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%0 Conference Proceedings
%T Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation
%A Abdallah, Abdelrahman
%A Piryani, Bhawna
%A Mozafari, Jamshid
%A Herzinger, Andreas
%A Holdcroft, Jamie
%A Jatowt, Adam
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F abdallah-etal-2026-rankify
%X Building retrieval-augmented generation (RAG) systems often requirescombining separate tools for retrieval, re-ranking, and generation,with incompatible data formats, evaluation pipelines, and deployment workflows.We present , an open-source Python toolkit that unifies these stagesin a single modular framework.[PyPI: \ensuremath<https://pypi.org/project/rankify/\ensuremath>],[GitHub: \ensuremath<https://github.com/DataScienceUIBK/Rankify\ensuremath>],[Docs: \ensuremath<https://rankify.readthedocs.io\ensuremath>]%,[Video: \ensuremath<https://youtu.be/kkLzomrM2ec\ensuremath>]provides 42 benchmark datasets with pre-retrieved documents andpre-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 experimentationfrom local models to hosted APIs.A unified pipeline interface allows users to compose retrieve–rerank–generateworkflows in a few lines of code, while an agentic assistant (RankifyAgent), aREST server (RankifyServer), and an interactive webplayground 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 of5–15 points in Exact Match and up to 8.5 points in RAGAS context precisionacross diverse retriever–generator combinations.
%U https://aclanthology.org/2026.acl-demo.21/
%P 208-219
Markdown (Informal)
[Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation](https://aclanthology.org/2026.acl-demo.21/) (Abdallah et al., ACL 2026)
ACL
- Abdelrahman Abdallah, Bhawna Piryani, Jamshid Mozafari, Andreas Herzinger, Jamie Holdcroft, and Adam Jatowt. 2026. Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 208–219, San Diego, California, United States. Association for Computational Linguistics.