fix: 修复伏笔管理系统回收处理问题，优化伏笔提示词
This commit is contained in:
xiamuceer-j
@@ -414,6 +414,17 @@ class ForeshadowService:
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fs_estimated_resolve = fs_data.get("estimated_resolve_chapter")
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fs_keyword = fs_data.get("keyword", "")
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# 🔧 修复Bug#7：如果AI没有填写estimated_resolve_chapter，提供合理的默认值
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if fs_estimated_resolve is None and fs_type == "planted":
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# 根据伏笔类型和长线属性计算默认回收章节
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if fs_is_long_term:
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# 长线伏笔：当前章节 + 15章
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fs_estimated_resolve = chapter.chapter_number + 15
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else:
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# 短线伏笔：当前章节 + 5章
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fs_estimated_resolve = chapter.chapter_number + 5
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logger.info(f"⚠️ AI未填写estimated_resolve_chapter，使用默认值: 第{fs_estimated_resolve}章")
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# 确定状态
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status = "planted" if (fs_type == "planted" and data.auto_set_planted) else "pending"
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if fs_type == "resolved":
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@@ -430,7 +441,7 @@ class ForeshadowService:
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existing_foreshadow.is_long_term = fs_is_long_term
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existing_foreshadow.related_characters = fs_related_characters if fs_related_characters else None
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existing_foreshadow.hint_text = fs_keyword if fs_keyword else None
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if fs_estimated_resolve and status == "planted":
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if fs_estimated_resolve:
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existing_foreshadow.target_resolve_chapter_number = fs_estimated_resolve
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await db.flush()
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new_foreshadows.append(existing_foreshadow.to_dict())
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@@ -448,7 +459,7 @@ class ForeshadowService:
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plant_chapter_id=chapter.id if status == "planted" else None,
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plant_chapter_number=chapter.chapter_number if status == "planted" else None,
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planted_at=datetime.now() if status == "planted" else None,
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target_resolve_chapter_number=fs_estimated_resolve if (status == "planted" and fs_estimated_resolve) else None,
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target_resolve_chapter_number=fs_estimated_resolve if fs_estimated_resolve else None,
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status=status,
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is_long_term=fs_is_long_term,
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importance=min(fs_strength / 10.0, 1.0),
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@@ -1020,7 +1031,6 @@ class ForeshadowService:
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清理统计信息
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"""
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try:
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# 查找该章节分析产生的伏笔
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query = select(Foreshadow).where(
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and_(
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Foreshadow.project_id == project_id,
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@@ -1028,7 +1038,10 @@ class ForeshadowService:
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or_(
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Foreshadow.source_memory_id.like(f"analysis_%_{chapter_id}%"),
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Foreshadow.source_memory_id.like(f"auto_analysis_{chapter_id}%"),
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Foreshadow.plant_chapter_id == chapter_id
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and_(
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Foreshadow.plant_chapter_id == chapter_id,
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Foreshadow.status.in_(["pending", "planted"])
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)
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)
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)
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)
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@@ -1222,21 +1235,58 @@ class ForeshadowService:
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elif existing:
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logger.warning(f"⚠️ 伏笔状态不是planted，跳过回收: {existing.title} (status: {existing.status})")
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else:
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# 没有找到匹配的伏笔，记录警告
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fs_title = fs_data.get("title", fs_data.get("content", "")[:30])
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logger.warning(f"⚠️ 未能匹配到伏笔进行回收: {fs_title}")
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stats["errors"].append(f"未找到匹配的伏笔: {fs_title}")
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logger.warning(f"⚠️ 未能匹配到已埋入伏笔，创建新的回收记录: {fs_title}")
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reference_chapter = fs_data.get("reference_chapter")
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new_resolved_foreshadow = Foreshadow(
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id=str(uuid.uuid4()),
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project_id=project_id,
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title=fs_title,
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content=fs_data.get("content", ""),
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resolution_text=fs_data.get("content", ""),
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source_type="analysis",
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source_memory_id=f"auto_analysis_{chapter_id}_{fs_title[:30]}",
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plant_chapter_number=reference_chapter if reference_chapter else None,
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actual_resolve_chapter_id=chapter_id,
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actual_resolve_chapter_number=chapter_number,
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resolved_at=datetime.now(),
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status="resolved",
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is_long_term=fs_data.get("is_long_term", False),
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importance=min(fs_data.get("strength", 5) / 10.0, 1.0),
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strength=fs_data.get("strength", 5),
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subtlety=fs_data.get("subtlety", 5),
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category=fs_data.get("category"),
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related_characters=fs_data.get("related_characters"),
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auto_remind=False,
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include_in_context=True
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)
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db.add(new_resolved_foreshadow)
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await db.flush()
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stats["resolved_count"] += 1
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stats["created_count"] += 1
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stats["created_ids"].append(new_resolved_foreshadow.id)
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logger.info(f"✅ 创建新的回收伏笔记录: {fs_title} (ID: {new_resolved_foreshadow.id})")
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elif fs_type == "planted":
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# 【埋入伏笔】创建新的伏笔记录
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fs_title = fs_data.get("title", "")
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if not fs_title:
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fs_title = fs_data.get("content", "")[:50] + "..."
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# 生成唯一标识符，避免重复创建
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source_memory_id = f"auto_analysis_{chapter_id}_{fs_title[:30]}"
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analysis_query = select(PlotAnalysis.id).where(
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PlotAnalysis.chapter_id == chapter_id
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).order_by(PlotAnalysis.created_at.desc()).limit(1)
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analysis_result = await db.execute(analysis_query)
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analysis_id = analysis_result.scalar_one_or_none()
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# 检查是否已存在
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if not analysis_id:
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logger.warning(f"⚠️ 未找到章节 {chapter_id} 的分析记录，跳过伏笔创建")
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continue
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fs_index = analysis_foreshadows.index(fs_data)
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source_memory_id = f"analysis_{analysis_id}_{fs_index}"
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# 检查是否已存在（可能已经通过 sync_from_analysis 创建）
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existing_check = await db.execute(
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select(Foreshadow).where(
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and_(
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@@ -1248,15 +1298,26 @@ class ForeshadowService:
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existing_fs = existing_check.scalar_one_or_none()
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if existing_fs:
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# 已存在，更新信息
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existing_fs.title = fs_title
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existing_fs.content = fs_data.get("content", existing_fs.content)
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existing_fs.strength = fs_data.get("strength", existing_fs.strength)
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existing_fs.subtlety = fs_data.get("subtlety", existing_fs.subtlety)
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existing_fs.hint_text = fs_data.get("keyword", existing_fs.hint_text)
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existing_fs.target_resolve_chapter_number = fs_data.get("estimated_resolve_chapter", existing_fs.target_resolve_chapter_number)
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await db.flush()
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logger.info(f"📝 更新已存在伏笔: {fs_title}")
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logger.info(f"📝 更新已存在伏笔（避免重复）: {fs_title}")
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else:
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# 创建新伏笔
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# 创建新伏笔（使用统一的标识符格式）
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# 🔧 修复Bug#7：如果AI没有填写estimated_resolve_chapter，提供合理的默认值
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estimated_resolve = fs_data.get("estimated_resolve_chapter")
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if estimated_resolve is None:
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# 根据伏笔类型和长线属性计算默认回收章节
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if fs_data.get("is_long_term", False):
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estimated_resolve = chapter_number + 15
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else:
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estimated_resolve = chapter_number + 5
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logger.info(f"⚠️ AI未填写estimated_resolve_chapter，使用默认值: 第{estimated_resolve}章")
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new_foreshadow = Foreshadow(
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id=str(uuid.uuid4()),
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project_id=project_id,
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@@ -1264,11 +1325,12 @@ class ForeshadowService:
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content=fs_data.get("content", ""),
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hint_text=fs_data.get("keyword"),
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source_type="analysis",
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source_memory_id=source_memory_id,
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source_memory_id=source_memory_id, # 使用统一格式
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source_analysis_id=analysis_id, # 关联分析ID
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plant_chapter_id=chapter_id,
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plant_chapter_number=chapter_number,
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planted_at=datetime.now(),
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target_resolve_chapter_number=fs_data.get("estimated_resolve_chapter"),
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target_resolve_chapter_number=estimated_resolve,
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status="planted",
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is_long_term=fs_data.get("is_long_term", False),
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importance=min(fs_data.get("strength", 5) / 10.0, 1.0),
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