Figueira OCR and RAG: reviewable AI on AMD

Figueira OCR and RAG are two reviewable AI prototypes by Athos Felipe at FIGUEIRA for the AMD/LabLab Academy. The MC2 OCR delivery is preserved as R03. It extracts text from vehicle plates and road signs into atomic JSON output using a persistent Qwen2.5-VL worker. Its source matched all ten published OCR examples on an AMD Radeon Pro W7900D; those examples are not a hidden-test accuracy benchmark. MC3 adds an offline, persistent RAG worker. It indexes supported document, spreadsheet, code, log and image files, retrieves relevant passages and returns a concise answer with minimal relative file citations. Unsupported or unreadable evidence produces an explicit refusal. The AMD GPU is required; there is no silent CPU fallback. Qwen2.5-VL-7B-Instruct is pinned to a fixed revision and the model weights are included in the MC3 image. The R05 source pilot passed all ten public MC3 cases plus four independent synthetic cases on the W7900D with PyTorch 2.13.0+rocm10.0.0. Model loading and indexing took 37.378 seconds, the query phase took 39.812 seconds, and peak GPU allocation was 18.212 GB. The worker and free AMD session were stopped after exporting the evidence. These results validate the source pilot, not the final OCI image on a GPU, unseen document accuracy, hidden evaluation scores or an official award. The separate MC2 and MC3 image fields identify their respective deliverables.