MC3: Hardware-Optimized RAG Pipeline for AMD GPU

This project presents a highly optimized, fully containerized Retrieval-Augmented Generation (RAG) pipeline developed for Mini Challenge 3 of the AMD AI Academy Challenge. Built strictly upon the mandated rocm/pytorch base image, the solution processes proprietary domain knowledge—including multi-modal documents, spreadsheets, and source code—to deliver accurate answers paired with exact file citations. Designed to strictly adhere to the zero-hallucination policy, the architecture ensures that responses are grounded entirely in the provided corpus. The system is engineered for seamless deployment on AMD hardware, utilizing /dev/kfd and /dev/dri device mappings for native GPU acceleration. By restructuring the pipeline into a persistent long-running process, it completely eliminates the startup overhead per question, ensuring the system easily fits within the stringent 30-second query budget. Despite massive physical constraints, the final environment was successfully packaged into a unified image and pushed to Docker Hub. This solution demonstrates robust handling of complex dependencies, encrypted files, and unknown data types, ensuring high-throughput hardware-accelerated inference in AMD Instinct and Radeon environments.