229 lines
10 KiB
Python
229 lines
10 KiB
Python
"""E2E stub server: deterministic LLM payloads per workflow stage.
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Injects a dispatcher into main_mod.get_client so the real FastAPI app,
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pydantic validation, evidence sanitization and the frontend run unchanged.
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Run on the same port the real server uses (index.html fetches /api/* same-origin).
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"""
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import uvicorn
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import main as main_mod
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ANALYZE = {
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"essay_summary": "文章以一支静止的铅笔开头，描述作者遇到一个无法立即解决的问题，并从中学会了耐心，最后回到书桌前继续面对。",
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"prompt_alignment": {"prompt_intent": "", "current_alignment": "", "optional_opportunity": ""},
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"paragraphs": [
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{
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"id": "p1",
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"original_text": "The pencil in my hand was still. I wanted to find the answer.",
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"natural_meaning_zh": "我手里的铅笔静止不动，我想要找到答案。",
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"semantic_anchor": "铅笔静止暗示思考停滞，内心渴望答案。",
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"optional_content_opportunity": "",
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},
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{
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"id": "p2",
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"original_text": "I met a problem that I could not solve right away.",
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"natural_meaning_zh": "我遇到了一个无法立刻解决的问题。",
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"semantic_anchor": "明确点出问题的存在，且无法立即解决。",
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"optional_content_opportunity": "",
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},
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{
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"id": "p3",
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"original_text": "I learned to be patient.",
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"natural_meaning_zh": "我学会了耐心。",
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"semantic_anchor": "核心教训是耐心，但未说明如何学到。",
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"optional_content_opportunity": "",
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},
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{
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"id": "p4",
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"original_text": "Now, I sit at my desk again.",
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"natural_meaning_zh": "现在，我再次坐在书桌前。",
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"semantic_anchor": "回到起点，暗示重新面对问题，但未说明结果。",
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"optional_content_opportunity": "",
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},
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],
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}
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DIAGNOSE = {
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"overall_diagnosis": "文章极短，但每一段都呈现典型的生成式写作模式：抽象陈述、缺乏具体细节、结构过于简化和对称。",
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"patterns": [
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{
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"pattern_id": "P02",
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"name": "Explicit Lesson",
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"category": "growth",
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"affected_paragraphs": ["p3"],
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"evidence": ["I learned to be patient."],
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"why_ai_like": "总结式结尾是生成模型最顺手的收束方式。",
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"human_impact": "直接说教，读者看不到具体如何学会。",
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"transformation_rule": "Lesson -> Change in Judgment",
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},
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{
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"pattern_id": "P08",
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"name": "Circular Ending",
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"category": "structure",
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"affected_paragraphs": ["p4"],
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"evidence": ["Now, I sit at my desk again."],
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"why_ai_like": "首尾呼应结构容易讨巧。",
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"human_impact": "只剩结构对称，没有新信息。",
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"transformation_rule": "Close on a Concrete Action",
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},
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],
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"paragraph_briefs": [
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{
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"paragraph_id": "p1",
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"confirmed_meaning": "铅笔静止暗示思考停滞，内心渴望答案。",
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"rewrite_goal": "把静止的铅笔转化为具体的场景与内心活动。",
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"ai_focus": "抽象陈述；缺乏具体细节",
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"must_preserve": [],
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"annotations": [
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{
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"pattern_id": "P01",
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"category": "rhetoric",
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"kind_label": "修辞包装",
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"title": "象征开头",
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"evidence": ["The pencil in my hand was still."],
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"observation": "用静止铅笔暗示停滞，但没给任何具体场景。",
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"rewrite_action": "补一个真实瞬间：笔停在纸上的位置、当时我在做什么。",
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"why_ai_like": "象征开头是生成模型高频起笔方式。",
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}
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],
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"context_hint": "上一段已完成 0 个反射，这一段只需建立画面。",
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"scaffold": "The pencil stayed in my hand because ______.",
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"reference_snippet": "The pencil had stopped halfway across the page.",
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},
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{
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"paragraph_id": "p2",
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"confirmed_meaning": "明确点出问题的存在，且无法立即解决。",
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"rewrite_goal": "把问题具体化，写出具体内容和无法解决的原因。",
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"ai_focus": "抽象陈述",
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"must_preserve": [],
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"annotations": [
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{
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"pattern_id": "P01",
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"category": "rhetoric",
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"kind_label": "修辞包装",
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"title": "泛化问题",
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"evidence": ["a problem that I could not solve"],
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"observation": "问题本身是空洞的泛指。",
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"rewrite_action": "写出问题具体是什么。",
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"why_ai_like": "泛化名词让生成模型无需真实素材。",
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}
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],
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"context_hint": "上一段已建立画面，这一段写具体困境。",
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"scaffold": "The problem was ______.",
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"reference_snippet": "",
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},
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{
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"paragraph_id": "p3",
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"confirmed_meaning": "核心教训是耐心，但未说明如何学到。",
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"rewrite_goal": "把耐心转化为具体行为，并保留 secret code。",
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"ai_focus": "总结式说教",
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"must_preserve": ["secret code"],
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"annotations": [
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{
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"pattern_id": "P02",
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"category": "growth",
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"kind_label": "抽象总结",
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"title": "直接点题",
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"evidence": ["I learned to be patient."],
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"observation": "用一句总结构束教训。",
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"rewrite_action": "写出耐心在行为上如何体现，保留 secret code。",
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"why_ai_like": "教训总结是生成模型收束习惯。",
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}
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],
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"context_hint": "上一段已写具体困境，这一段写困境中的一次具体选择。",
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"scaffold": "Each day I ______ instead of ______.",
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"reference_snippet": "",
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},
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{
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"paragraph_id": "p4",
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"confirmed_meaning": "回到起点，暗示重新面对问题，但未说明结果。",
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"rewrite_goal": "把回到起点转化为具体行动，暗示改变但不总结。",
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"ai_focus": "结构对称",
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"must_preserve": [],
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"annotations": [
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{
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"pattern_id": "P08",
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"category": "structure",
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"kind_label": "结构路标",
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"title": "首尾呼应",
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"evidence": ["Now, I sit at my desk again."],
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"observation": "用呼应结构收尾，没有新内容。",
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"rewrite_action": "写一个具体的下一步动作。",
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"why_ai_like": "首尾对称让文章看起来完整。",
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}
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],
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"context_hint": "上一段已写具体选择，这一段以行动收尾，不写总结。",
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"scaffold": "This time, I ______.",
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"reference_snippet": "",
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},
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],
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"optional_suggestion": "若有真实素材，可补当时问题发生的具体场景。",
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}
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RECHECK_PASS = {
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"status": "pass",
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"checked_rewrite_version": "rv_1",
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"global_checks": {
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"semantic_preservation": "pass",
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"voice_consistency": "pass",
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"pattern_reduction": "pass",
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"new_pattern": "pass",
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"coherence": "pass",
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"reference_copying": "pass",
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"word_limit": "pass",
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},
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"revision_targets": [],
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}
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SCAFFOLD = {"paragraph_id": "p1", "scaffold": "The pencil stayed in my hand because ______."}
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REFERENCE = {"paragraph_id": "p1", "starter": "The pencil had stopped halfway across the page.", "reference_snippet": "The pencil had stopped halfway across the page."}
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RECHECK_REVISION = {
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"status": "revision_required",
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"checked_rewrite_version": "rv_1",
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"global_checks": {
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"semantic_preservation": "pass",
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"voice_consistency": "pass",
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"pattern_reduction": "pass",
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"new_pattern": "pass",
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"coherence": "pass",
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"reference_copying": "pass",
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"word_limit": "pass",
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},
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"revision_targets": [
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{
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"paragraph_id": "p3",
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"blocking_issue": "新版本仍是 Explicit Lesson：直接总结学到了什么。",
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"evidence": ["gradually came to realize"],
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"single_revision_goal": "不要总结我学到了什么，写判断怎么变化。",
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}
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],
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}
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class StubCompleter:
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def complete_json(self, system, user, validate=None):
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# NOTE: system prompts share a _TASK_CHAIN prefix (contains 全文复检 etc.)
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# so dispatch must use stage-unique keywords, most specific first.
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if "给我一个写作起点" in system:
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return SCAFFOLD
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if "Level 2" in system:
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return REFERENCE
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if "Human Voice Diagnosis" in system:
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return DIAGNOSE
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if "最终阶段" in system:
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# mockFillFail injects revision-case wording; detect it to serve the
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# revision_required path, otherwise the golden-path pass response.
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# NOTE: cannot use "gradually came to realize" — previous_revision_target
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# is embedded in the prompt (with its evidence quoting that phrase), so it
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# matches on every resubmission even when the current text is fixed.
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if "uncertainty is an important part of growth" in user:
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return RECHECK_REVISION
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return RECHECK_PASS
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return ANALYZE
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main_mod.get_client = lambda: StubCompleter()
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if __name__ == "__main__":
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uvicorn.run(main_mod.app, host="127.0.0.1", port=8000, log_level="warning")
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