ChipQC Guardian: Citation-Exact RAG on ROCm

ChipQC Guardian is a containerised retrieval-augmented question-answering system for Match 3, built on the mandated rocm/pytorch base with all weights shipped inside the image, so nothing is downloaded at evaluation. Pointed at a folder of mixed enterprise documents (PDF datasheets including withdrawn revisions, DOCX roadmaps with tables, XLSX part lists, CSV ticket databases, production logs, release notes, Python source, diagrams and photos) it indexes everything once, then answers each question with the bare value and the exact set of files the value came from. On AMD Instinct GPUs a resident server holds Qwen3-VL-8B-Instruct (bf16) and Qwen3-Embedding-0.6B behind a unix socket, so each per-question invocation is a thin client that returns in seconds and never loads a model. Ingestion is defensive: an empty directory, an unreadable file, an encrypted PDF and an unknown binary are skipped per file without stopping the walk. Spreadsheets and CSVs are indexed row by row with their headers so a ticket id lands on the row carrying its value; Python files contribute their constants and parameter defaults; images and image-only PDF pages are transcribed by the vision model at index time and re-read from the image at answer time. Retrieval fuses identifier-aware BM25 with dense embeddings, and a second hop follows identifiers the model asks for, which is what makes log, ticket and fixed-in-version chains answerable. Citations are filtered for necessity rather than relevance: withdrawn or superseded documents are dropped, chain links are kept only with directional evidence (a shared ticket, part or error identifier plus the question pointing at that document), redundant sources are reduced to one primary source, and a verification pass rejects values that belong to a different item. When the value is not in any excerpt the system returns an empty answer and no citations. It never guesses.