---

name: document-vision-rag description: "Plan document/PDF/VQA/knowledge-cleaning/Agentic RAG/retrieval/speech/chemistry workflows, including pdf2model and PDF VQA CLI choices, optional MinerU/VLM/OCR backends, LightRAG retrieval, and training-side-effect limits." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Document Vision and RAG

Use this sub-skill for document-centric jobs: PDF/OCR cleanup, visual QA, Agentic RAG, retrieval over cleaned documents, speech transcription, and chemistry extraction from text/OCR output.

Route by task

• Clean PDFs, URLs, HTML, or mixed documents into markdown or QA-ready chunks: use the workflow notes in references/document-rag-workflows.md.
• Decide between PDF VQA extraction, dataflow pdf2model init --qa vqa, dataflow pdf2model train, and dataflow pdf2model chat: use references/pdf2model-and-vqa.md.
• Compare API, LightRAG, MinerU, VLM, OCR, LlamaFactory, DataFlex, and audio extras: use references/serving-and-dependencies.md.
• Debug missing files, bad suffixes, absent credentials, GPU gaps, or training-state problems: use references/troubleshooting.md.

Safe defaults

• Validate inputs before launching OCR, retrieval, or training.
• Treat MinerU, FlashMinerU, VLM, LightRAG, pdf2vqa, pdf2model, and DataFlex as optional heavy paths.
• Do not present GPU, VLM, OCR, or training behavior as CPU-verified.
• Stop when required documents, model paths, credentials, or hardware are missing; do not silently switch to a different backend.
• Use the bundled validator script for offline preflight checks.

Bundled helper

• scripts/check_document_workflow_inputs.py checks document paths, PDF suffixes, JSON/JSONL columns, and requested env vars without any network call.

Quick check

python scripts/check_document_workflow_inputs.py --profile pdf2vqa --doc sample.pdf --jsonl sample.jsonl --require-env MINERU_API_KEY