grounded-rag-ingestion
Grounded RAG Ingestion
Turn a document into a stream of provenance-carrying chunks — one JSONL line per chunk, each tagged with its element type, page index, bounding box, confidence score, and reading-order index, all traceable back to a specific region of the source page.
The differentiator is provenance, a capability frontier-LLM Markdown extraction structurally
cannot supply: a downstream system can highlight the exact region of the source document a
retrieval result came from. That is the compliance-grade requirement for RAG in finance,
healthcare, and legal workflows where "the model said so" is not an acceptable citation. This
skill does not relitigate raw Markdown quality (see the pdf-to-markdown skill for that) — it
adds the provenance layer on top of Nutrient extraction.
This is a doer skill: a bundled uv-runnable Python script (chunk.py) drives the pipeline
end to end and emits JSONL to stdout or a file. It is embedding-agnostic — it stops at the
chunk boundary. No vector DB client, no embedding provider import.