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feat: add learned document template workflow

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2026-07-30 15:24:51 +03:00
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DocumentAutomation/docs/template-learning.md
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# Learned document templates
DOCX Workbench supports two deterministic workflows:
1. `TemplateLearningService` normalizes a `.doc` or copies a `.docx` into a temporary working directory, inspects it, adds a safe `{{DOC_BODY}}` insertion marker to the working copy, and writes a portable `.dtemplate` ZIP package.
2. `TemplateDocumentGenerator` opens the package, replaces explicitly supplied scalar tokens, imports a `.doc`, `.docx`, or `.txt` body through the existing main-story importer, and creates DOCX, PDF, generation, and validation outputs.
The package contains `manifest.json`, `template.docx`, source metadata, page archetypes, regions, slots, field bindings, mapping suggestions, and a validation profile. It contains no original source binary unless it is the normalized template copy.
All Word automation is late-bound and serialized through the existing STA queue. Binary `.doc` sources are opened read-only, macros are disabled through Word automation security, and the source SHA-256 is checked before and after normalization. The source body’s headers, footers, section properties, and page setup are excluded by `DocxBodyImporter`.
The current UI exposes three modes: template learning, document generation, and the existing quick `DOC_BODY` path. Page archetypes and slot proposals are deterministic suggestions and require later user confirmation; Ollama is not required for the workflow.
Known limitation: the first slice stores the learned structural model and default slots, but does not yet provide a pixel-level page editor or automatic extraction of every scalar field from an arbitrary engineering drawing. Those operations remain explicit user mapping work.