fix: 修复章节内容分析导致伏笔回收数据重复问题
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mumu
@@ -838,19 +838,22 @@ class OneToOneContextBuilder:
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else:
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logger.info(f" ⚠️ P2-伏笔提醒: 无")
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# 2. 根据角色名检索相关记忆（相关度>0.6）
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if character_names and self.memory_service:
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# 2. 根据大纲内容检索相关记忆（相关度>0.4）
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if self.memory_service and context.chapter_outline:
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try:
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query_text = " ".join(character_names)
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# 使用大纲内容作为查询（截取前500字符以避免过长）
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query_text = context.chapter_outline[:500].replace('\n', ' ')
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logger.info(f" 🔍 记忆查询关键词: {query_text[:100]}...")
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relevant_memories = await self.memory_service.search_memories(
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user_id=user_id,
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project_id=project.id,
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query=query_text,
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limit=10,
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limit=15,
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min_importance=0.0
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)
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# 降低相关度阈值到0.4，提高召回率
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filtered_memories = [
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mem for mem in relevant_memories
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if mem.get('similarity', 0) > 0.6
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@@ -858,23 +861,23 @@ class OneToOneContextBuilder:
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if filtered_memories:
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memory_lines = ["【相关记忆】"]
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for mem in filtered_memories:
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for mem in filtered_memories[:10]: # 最多显示10条
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similarity = mem.get('similarity', 0)
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content = mem.get('content', '')[:100]
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memory_lines.append(f"- (相关度:{similarity:.2f}) {content}")
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context.relevant_memories = "\n".join(memory_lines)
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logger.info(f" ✅ P2-相关记忆: {len(filtered_memories)}条 (相关度>0.6)")
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logger.info(f" ✅ P2-相关记忆: {len(filtered_memories)}条 (相关度>0.4, 共搜索{len(relevant_memories)}条)")
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else:
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context.relevant_memories = None
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logger.info(f" ⚠️ P2-相关记忆: 无符合条件的记忆")
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logger.info(f" ⚠️ P2-相关记忆: 无符合条件的记忆 (共搜索到{len(relevant_memories)}条)")
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except Exception as e:
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logger.error(f" ❌ 检索相关记忆失败: {str(e)}")
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context.relevant_memories = None
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else:
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context.relevant_memories = None
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logger.info(f" ⚠️ P2-相关记忆: 无角色或记忆服务不可用")
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logger.info(f" ⚠️ P2-相关记忆: 无大纲内容或记忆服务不可用")
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# === 统计信息 ===
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context.context_stats = {
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@@ -1211,6 +1211,15 @@ class ForeshadowService:
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# 重新获取完整的伏笔对象
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existing = await self.get_foreshadow(db, existing.get('id'))
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# 检查伏笔是否已被回收（防止重复回收）
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if existing:
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if existing.status == "resolved" and existing.actual_resolve_chapter_number == chapter_number:
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logger.info(f"ℹ️ 伏笔已在本章回收过，跳过: {existing.title}")
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continue
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elif existing.status == "resolved":
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logger.warning(f"⚠️ 伏笔已在第{existing.actual_resolve_chapter_number}章回收，跳过: {existing.title}")
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continue
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# 执行回收
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if existing and existing.status == "planted":
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# 更新为已回收状态
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@@ -1224,20 +1233,42 @@ class ForeshadowService:
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existing.resolution_text = fs_data.get("content")
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await db.flush()
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await db.refresh(existing)
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stats["resolved_count"] += 1
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stats["updated_ids"].append(existing.id)
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if matched_by_content:
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stats["matched_by_content"] += 1
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logger.info(f"✅ 自动回收伏笔: {existing.title} (ID: {existing.id})")
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logger.info(f"✅ 自动回收伏笔: {existing.title} (ID: {existing.id}, status: {existing.status})")
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# 从待匹配列表中移除已回收的伏笔
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planted_foreshadows = [f for f in planted_foreshadows if f['id'] != existing.id]
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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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# 创建新回收记录（未能匹配到已埋入伏笔）
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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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# 检查是否已存在相同的回收记录（防止重复创建）
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duplicate_check = await db.execute(
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select(Foreshadow).where(
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and_(
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Foreshadow.project_id == project_id,
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Foreshadow.title == fs_title,
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Foreshadow.actual_resolve_chapter_number == chapter_number,
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Foreshadow.source_type == "analysis",
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Foreshadow.status == "resolved"
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)
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)
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)
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duplicate_fs = duplicate_check.scalar_one_or_none()
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if duplicate_fs:
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logger.info(f"ℹ️ 已存在相同的回收记录，跳过: {fs_title}")
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continue
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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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new_resolved_foreshadow = Foreshadow(
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id=str(uuid.uuid4()),
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project_id=project_id,
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@@ -1286,32 +1317,46 @@ class ForeshadowService:
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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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# 检查是否已存在（防止重复分析创建重复记录）
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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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Foreshadow.project_id == project_id,
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Foreshadow.source_memory_id == source_memory_id
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or_(
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# 方式1：通过source_memory_id精确匹配
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and_(
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Foreshadow.project_id == project_id,
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Foreshadow.source_memory_id == source_memory_id
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),
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# 方式2：通过标题+章节号匹配
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and_(
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Foreshadow.project_id == project_id,
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Foreshadow.title == fs_title,
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Foreshadow.plant_chapter_number == chapter_number,
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Foreshadow.source_type == "analysis",
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Foreshadow.status == "planted"
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)
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)
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)
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)
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existing_fs = existing_check.scalar_one_or_none()
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if existing_fs:
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# 更新已存在的伏笔，避免重复创建
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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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# 确保source_memory_id是最新的
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existing_fs.source_memory_id = source_memory_id
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existing_fs.source_analysis_id = analysis_id
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await db.flush()
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logger.info(f"📝 更新已存在伏笔（避免重复）: {fs_title}")
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logger.info(f"📝 更新已存在伏笔（避免重复）: {fs_title} (ID: {existing_fs.id})")
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else:
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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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# 创建新伏笔
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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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# 根据伏笔类型计算默认回收章节
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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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@@ -1455,11 +1500,12 @@ class ForeshadowService:
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通过内容相似度匹配伏笔（备用机制）
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匹配策略（按优先级）：
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1. 标题完全匹配
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1. 标题完全匹配（权重最高）
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2. 标题部分匹配（包含关系）
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3. 关键词匹配
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4. 内容关键词匹配
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5. 相关角色匹配 + 分类匹配
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3. 标题关键词匹配（去除"回收"等后缀）
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4. 关键词匹配
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5. 内容关键词匹配
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6. 相关角色匹配 + 分类匹配
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Args:
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resolved_fs_data: 分析结果中的回收伏笔数据
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@@ -1479,6 +1525,14 @@ class ForeshadowService:
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resolved_characters = set(resolved_fs_data.get("related_characters", []))
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reference_chapter = resolved_fs_data.get("reference_chapter")
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# 处理标题后缀（兜底机制）
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resolved_title_clean = resolved_title
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for suffix in ["回收", "揭示", "解答", "兑现"]:
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if resolved_title.endswith(suffix):
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resolved_title_clean = resolved_title[:-len(suffix)]
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logger.debug(f"🔍 去除标题后缀: '{resolved_title}' -> '{resolved_title_clean}'")
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break
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best_match = None
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best_score = 0.0
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@@ -1490,17 +1544,25 @@ class ForeshadowService:
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fs_characters = set(fs.get("related_characters", []))
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fs_plant_chapter = fs.get("plant_chapter_number")
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# 策略1: 标题完全匹配（最高分）
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# 策略1: 标题匹配
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if resolved_title and fs_title:
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if resolved_title == fs_title:
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score = 1.0
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logger.debug(f"🎯 标题完全匹配: '{resolved_title}' == '{fs_title}'")
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elif resolved_title_clean and resolved_title_clean == fs_title:
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score = 0.95
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logger.debug(f"🎯 清理标题匹配: '{resolved_title_clean}' == '{fs_title}'")
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elif resolved_title in fs_title or fs_title in resolved_title:
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# 标题包含关系
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score = max(score, 0.8)
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logger.debug(f"🔍 标题包含匹配: '{resolved_title}' <-> '{fs_title}'")
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elif resolved_title_clean and (resolved_title_clean in fs_title or fs_title in resolved_title_clean):
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score = max(score, 0.75)
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logger.debug(f"🔍 清理标题包含匹配: '{resolved_title_clean}' <-> '{fs_title}'")
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else:
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# 计算标题词重叠
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title_overlap = self._calculate_word_overlap(resolved_title, fs_title)
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score = max(score, title_overlap * 0.7)
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if title_overlap > 0.3:
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logger.debug(f"📊 标题词重叠: overlap={title_overlap:.2f}")
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# 策略2: 关键词匹配
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if resolved_keyword and fs_content:
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@@ -87,7 +87,7 @@ class PlotAnalyzer:
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)
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last_error = None
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logger.debug(f"章节分析提示词{prompt}")
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for attempt in range(1, max_retries + 1):
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try:
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# 调用AI进行分析
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@@ -972,12 +972,17 @@ class PromptService:
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每个伏笔需要：
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- **title**：简洁标题（10-20字，概括伏笔核心）
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- ⚠️ 回收伏笔时，标题应与原伏笔标题保持一致，不要添加"回收"等后缀
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- 例如：原伏笔标题是"绿头发的视觉符号"，回收时标题仍为"绿头发的视觉符号"，而非"绿头发的视觉符号回收"
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- **content**：详细描述伏笔内容和预期作用
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- **type**：planted（埋下）或 resolved（回收）
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- **strength**：强度1-10（对读者的吸引力）
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- **subtlety**：隐藏度1-10（越高越隐蔽）
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- **reference_chapter**：回收时引用的原埋入章节号，埋下时为null
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- **reference_foreshadow_id**：【回收时必填】被回收伏笔的ID（从已埋入伏笔列表中选择），埋下时为null
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- 🔴 重要：回收伏笔时，必须从【已埋入伏笔列表】中找到对应的伏笔ID并填写
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- 如果列表中有标注【ID: xxx】的伏笔，回收时必须使用该ID
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- 如果无法确定是哪个伏笔，才填写null（但应尽量避免）
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- **keyword**：【必填】从原文逐字复制8-25字的定位文本
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- **category**：分类（identity=身世/mystery=悬念/item=物品/relationship=关系/event=事件/ability=能力/prophecy=预言）
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- **is_long_term**：是否长线伏笔（跨10章以上回收为true）
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