OpenRAG is a modular framework for exploring Retrieval-Augmented Generation (RAG) techniques, built for transparency and rapid experimentation to develop document-grounded AI systems—fully ready for production-scale deployment. It uses Ray to parallelize chunking, embedding, and ingestion across CPUs and GPUs, enabling fast, scalable processing of large document sets, and can be deployed seamlessly on Kubernetes for distributed, production-grade workloads. Advanced loaders like Docling and Marker parse complex layouts with OCR-enhanced PDFs, and chunk contextualization significantly boosts retrieval relevance. The platform ships with fully OpenAI-compatible chat API for seamless integration with tools like LangChain, OpenWebUI, or N8N—no adapter work required. Built-in clustering auto-generates synthetic QA datasets from your indexed documents, and a local LLM scores each query-chunk pair to help you tune retrieval before production. Two friction points surface at scale: in collaborative systems where documents update hourly, embeddings are recomputed every time by vLLM, which is computationally expensive, and admin users cannot grant access to partitions they were not explicitly given access to—the admin role does not override partition-level access restrictions.