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ROCm RAG: Cite-or-Refuse Document QA
This project implements a retrieval-augmented generation (RAG) system for the AMD AI League Match 3 challenge, running entirely on AMD ROCm hardware. The system ingests a mixed corpus of PDF, DOCX, XLSX, CSV, TXT, and image files, applies structure-aware chunking, and builds a dense vector index using Qwen3 embedding models. At query time, it retrieves the most relevant chunks and generates answers with an instruction-tuned language model, returning exact file citations for every answer. A core design principle is cite-or-refuse: when retrieved evidence does not support an answer, the system refuses rather than hallucinating, which is critical for citation-accuracy scoring. The full pipeline is packaged as a single ROCm container image that completes startup and indexing within the required time limits. Both the container image and source code are public.
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