We introduce MMORE, an open-source pipeline for Massive Multimodal Open RetrievalAugmented Generation and Extraction, designed to ingest, transform, and retrieve knowledge from heterogeneous document formats at scale. MMORE supports more than fifteen file types, including text, tables, images, emails, audio, and video, and processes them into a unified format to enable downstream applications for LLMs. The architecture offers modular, distributed processing, enabling scalable parallelization across CPUs and GPUs. On processing benchmarks, MMORE demonstrates a 3.8-fold speedup over single-node baselines and 40% higher accuracy than Docling on scanned PDFs. The pipeline integrates hybrid dense-sparse retrieval and supports both interactive APIs and batch RAG endpoints. Evaluated on PubMedQA, MMORE-augmented medical LLMs improve biomedical QA accuracy with increasing retrieval depth. MMORE provides a robust, extensible foundation for deploying task-agnostic RAG systems on diverse, real-world multimodal data. The codebase is available at https://github.com/swiss-ai/mmore.
@article{arxiv.2509.11937,
title = {MMORE: Massive Multimodal Open RAG & Extraction},
author = {Alexandre Sallinen and Stefan Krsteski and Paul Teiletche and Marc-Antoine Allard and Baptiste Lecoeur and Michael Zhang and Fabrice Nemo and David Kalajdzic and Matthias Meyer and Mary-Anne Hartley},
journal= {arXiv preprint arXiv:2509.11937},
year = {2025}
}
Comments
This paper was originally submitted to the CODEML workshop for ICML 2025. 9 pages (including references and appendices)