We introduce MMORE, an open-sourcepipeline for Massive Multimodal Open Retrieval-Augmented Generation and Extraction, designedto ingest, transform, and retrieve knowledgefrom 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 unifiedformat to enable downstream applicationsfor LLMs. The architecture offers modular,distributed processing, enabling scalable paral-lelization across CPUs and GPUs. On processingbenchmarks, MMORE demonstrates a 3.8-foldspeedup over single-node baselines and 40%higher accuracy than Docling on scanned PDFs.The pipeline integrates hybrid dense-sparseretrieval and supports both interactive APIs andbatch RAG endpoints. Evaluated on PubMedQA,MMORE-augmented medical LLMs improvebiomedical QA accuracy with increasing retrievaldepth. MMORE provides a robust, extensiblefoundation for deploying task-agnostic RAGsystems on diverse, real-world multimodal data