Kezhan Shi

companion to Enterprise Document Intelligence, a series whose stance is that enterprise RAG amplifies the expert, it does not replace them. The architecture follows from that: four bricks (document parsing, question parsing, retrieval, generation), each emitting typed pieces that converge on one LLM call. The industry now calls that practice context engineering. Scope here is the single-document case; corpus, conversation, and tool-call extensions are follow-up work.

document parsing companion in Enterprise Document Intelligence, the series that builds an enterprise RAG system from four bricks. It extends Article 5 (document parsing) on one table: toc_df, the document’s section structure, which Article 5 fills from the PDF’s native outline (PyMuPDF’s doc.get_toc) when there is one. This part is about the case where there isn’t, reconstructing that structure from what the document still shows on the page.

companion in Enterprise Document Intelligence, the series that builds an enterprise RAG system from four bricks. It extends Article 5 (document parsing) on one table: image_df, which locates every picture in the PDF without reading any of them. This part builds the reading toolbox: a cost-ordered cascade (a cheap filter, a type check, classic OCR, a vision model) that turns the few images worth paying for into searchable text.