Add textbook analysis#14
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maimaimai084 wants to merge 7 commits into
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Update v1.0
Optimize now supports two strategies, selectable per run: - regenerate (default, unchanged): per-slide multi-agent deliberation that rewrites every slide from scratch. - refine (new): localized, frame-level rewrite via SlideRefiner. Builds an escaped baseline deck from the existing slides, then surgically edits only the frames most relevant to the feedback, leaving the rest byte-identical, with deterministic body/document validation and retries. SlideRefiner is extracted from the IA-aarsh refiner pipeline, decoupled from its LLM-as-judge evaluation: it takes (latex, feedback_text) and here the feedback is sourced from the user's requirements, analysis, and human feedback. Threads a `mode` option through optimize.py, ADDIE_optimize.py, run.py, the /api/optimize API, and the frontend optimize form (with zh/en strings). Co-Authored-By: Claude Opus 4.8 <[email protected]>
Add localized "refine" mode to optimize (alongside regenerate)
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This update adds a textbook processing module. Considering the cost and efficiency of handling lengthy textbooks, I’m currently using regular expressions for lightweight extraction.
Automatically scans uploaded textbook content and extracts core chapter titles and high-frequency keywords.
Quickly condenses and simplifies the extracted unstructured text, converting it into the system’s standard structured JSON Catalog.
It can distill the uploaded book content without calling any AI models. It will automatically turn textbooks into a condensed course Catalog, providing a chapter framework for the subsequent automated generation of slides.