From f7eef4d89de6183852112f2c3837092bfc6c262a Mon Sep 17 00:00:00 2001 From: mumu Date: Sat, 7 Feb 2026 15:39:15 +0800 Subject: [PATCH] =?UTF-8?q?fix:=20=E4=BF=AE=E5=A4=8D=E7=AB=A0=E8=8A=82?= =?UTF-8?q?=E5=86=85=E5=AE=B9=E5=88=86=E6=9E=90=E5=AF=BC=E8=87=B4=E4=BC=8F?= =?UTF-8?q?=E7=AC=94=E5=9B=9E=E6=94=B6=E6=95=B0=E6=8D=AE=E9=87=8D=E5=A4=8D?= =?UTF-8?q?=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../app/services/chapter_context_service.py | 19 ++-- backend/app/services/foreshadow_service.py | 96 +++++++++++++++---- backend/app/services/plot_analyzer.py | 2 +- backend/app/services/prompt_service.py | 5 + 4 files changed, 96 insertions(+), 26 deletions(-) diff --git a/backend/app/services/chapter_context_service.py b/backend/app/services/chapter_context_service.py index 8f3e20b..8f4350a 100644 --- a/backend/app/services/chapter_context_service.py +++ b/backend/app/services/chapter_context_service.py @@ -838,19 +838,22 @@ class OneToOneContextBuilder: else: logger.info(f" ⚠️ P2-伏笔提醒: 无") - # 2. 根据角色名检索相关记忆（相关度>0.6） - if character_names and self.memory_service: + # 2. 根据大纲内容检索相关记忆（相关度>0.4） + if self.memory_service and context.chapter_outline: try: - query_text = " ".join(character_names) + # 使用大纲内容作为查询（截取前500字符以避免过长） + query_text = context.chapter_outline[:500].replace('\n', ' ') + logger.info(f" 🔍 记忆查询关键词: {query_text[:100]}...") relevant_memories = await self.memory_service.search_memories( user_id=user_id, project_id=project.id, query=query_text, - limit=10, + limit=15, min_importance=0.0 ) + # 降低相关度阈值到0.4，提高召回率 filtered_memories = [ mem for mem in relevant_memories if mem.get('similarity', 0) > 0.6 @@ -858,23 +861,23 @@ class OneToOneContextBuilder: if filtered_memories: memory_lines = ["【相关记忆】"] - for mem in filtered_memories: + for mem in filtered_memories[:10]: # 最多显示10条 similarity = mem.get('similarity', 0) content = mem.get('content', '')[:100] memory_lines.append(f"- (相关度:{similarity:.2f}) {content}") context.relevant_memories = "\n".join(memory_lines) - logger.info(f" ✅ P2-相关记忆: {len(filtered_memories)}条 (相关度>0.6)") + logger.info(f" ✅ P2-相关记忆: {len(filtered_memories)}条 (相关度>0.4, 共搜索{len(relevant_memories)}条)") else: context.relevant_memories = None - logger.info(f" ⚠️ P2-相关记忆: 无符合条件的记忆") + logger.info(f" ⚠️ P2-相关记忆: 无符合条件的记忆 (共搜索到{len(relevant_memories)}条)") except Exception as e: logger.error(f" ❌ 检索相关记忆失败: {str(e)}") context.relevant_memories = None else: context.relevant_memories = None - logger.info(f" ⚠️ P2-相关记忆: 无角色或记忆服务不可用") + logger.info(f" ⚠️ P2-相关记忆: 无大纲内容或记忆服务不可用") # === 统计信息 === context.context_stats = { diff --git a/backend/app/services/foreshadow_service.py b/backend/app/services/foreshadow_service.py index cb1e9d8..3f30102 100644 --- a/backend/app/services/foreshadow_service.py +++ b/backend/app/services/foreshadow_service.py @@ -1211,6 +1211,15 @@ class ForeshadowService: # 重新获取完整的伏笔对象 existing = await self.get_foreshadow(db, existing.get('id')) + # 检查伏笔是否已被回收（防止重复回收） + if existing: + if existing.status == "resolved" and existing.actual_resolve_chapter_number == chapter_number: + logger.info(f"ℹ️ 伏笔已在本章回收过，跳过: {existing.title}") + continue + elif existing.status == "resolved": + logger.warning(f"⚠️ 伏笔已在第{existing.actual_resolve_chapter_number}章回收，跳过: {existing.title}") + continue + # 执行回收 if existing and existing.status == "planted": # 更新为已回收状态 @@ -1224,20 +1233,42 @@ class ForeshadowService: existing.resolution_text = fs_data.get("content") await db.flush() + await db.refresh(existing) + stats["resolved_count"] += 1 stats["updated_ids"].append(existing.id) if matched_by_content: stats["matched_by_content"] += 1 - logger.info(f"✅ 自动回收伏笔: {existing.title} (ID: {existing.id})") + logger.info(f"✅ 自动回收伏笔: {existing.title} (ID: {existing.id}, status: {existing.status})") # 从待匹配列表中移除已回收的伏笔 planted_foreshadows = [f for f in planted_foreshadows if f['id'] != existing.id] elif existing: logger.warning(f"⚠️ 伏笔状态不是planted，跳过回收: {existing.title} (status: {existing.status})") else: + # 创建新回收记录（未能匹配到已埋入伏笔） fs_title = fs_data.get("title", fs_data.get("content", "")[:30]) - logger.warning(f"⚠️ 未能匹配到已埋入伏笔，创建新的回收记录: {fs_title}") reference_chapter = fs_data.get("reference_chapter") + + # 检查是否已存在相同的回收记录（防止重复创建） + duplicate_check = await db.execute( + select(Foreshadow).where( + and_( + Foreshadow.project_id == project_id, + Foreshadow.title == fs_title, + Foreshadow.actual_resolve_chapter_number == chapter_number, + Foreshadow.source_type == "analysis", + Foreshadow.status == "resolved" + ) + ) + ) + duplicate_fs = duplicate_check.scalar_one_or_none() + + if duplicate_fs: + logger.info(f"ℹ️ 已存在相同的回收记录，跳过: {fs_title}") + continue + + logger.warning(f"⚠️ 未能匹配到已埋入伏笔，创建新的回收记录: {fs_title}") new_resolved_foreshadow = Foreshadow( id=str(uuid.uuid4()), project_id=project_id, @@ -1286,32 +1317,46 @@ class ForeshadowService: fs_index = analysis_foreshadows.index(fs_data) source_memory_id = f"analysis_{analysis_id}_{fs_index}" - # 检查是否已存在（可能已经通过 sync_from_analysis 创建） + # 检查是否已存在（防止重复分析创建重复记录） existing_check = await db.execute( select(Foreshadow).where( - and_( - Foreshadow.project_id == project_id, - Foreshadow.source_memory_id == source_memory_id + or_( + # 方式1：通过source_memory_id精确匹配 + and_( + Foreshadow.project_id == project_id, + Foreshadow.source_memory_id == source_memory_id + ), + # 方式2：通过标题+章节号匹配 + and_( + Foreshadow.project_id == project_id, + Foreshadow.title == fs_title, + Foreshadow.plant_chapter_number == chapter_number, + Foreshadow.source_type == "analysis", + Foreshadow.status == "planted" + ) ) ) ) existing_fs = existing_check.scalar_one_or_none() if existing_fs: + # 更新已存在的伏笔，避免重复创建 existing_fs.title = fs_title existing_fs.content = fs_data.get("content", existing_fs.content) existing_fs.strength = fs_data.get("strength", existing_fs.strength) existing_fs.subtlety = fs_data.get("subtlety", existing_fs.subtlety) existing_fs.hint_text = fs_data.get("keyword", existing_fs.hint_text) existing_fs.target_resolve_chapter_number = fs_data.get("estimated_resolve_chapter", existing_fs.target_resolve_chapter_number) + # 确保source_memory_id是最新的 + existing_fs.source_memory_id = source_memory_id + existing_fs.source_analysis_id = analysis_id await db.flush() - logger.info(f"📝 更新已存在伏笔（避免重复）: {fs_title}") + logger.info(f"📝 更新已存在伏笔（避免重复）: {fs_title} (ID: {existing_fs.id})") else: - # 创建新伏笔（使用统一的标识符格式） - # 🔧 修复Bug#7：如果AI没有填写estimated_resolve_chapter，提供合理的默认值 + # 创建新伏笔 estimated_resolve = fs_data.get("estimated_resolve_chapter") if estimated_resolve is None: - # 根据伏笔类型和长线属性计算默认回收章节 + # 根据伏笔类型计算默认回收章节 if fs_data.get("is_long_term", False): estimated_resolve = chapter_number + 15 else: @@ -1455,11 +1500,12 @@ class ForeshadowService: 通过内容相似度匹配伏笔（备用机制） 匹配策略（按优先级）： - 1. 标题完全匹配 + 1. 标题完全匹配（权重最高） 2. 标题部分匹配（包含关系） - 3. 关键词匹配 - 4. 内容关键词匹配 - 5. 相关角色匹配 + 分类匹配 + 3. 标题关键词匹配（去除"回收"等后缀） + 4. 关键词匹配 + 5. 内容关键词匹配 + 6. 相关角色匹配 + 分类匹配 Args: resolved_fs_data: 分析结果中的回收伏笔数据 @@ -1479,6 +1525,14 @@ class ForeshadowService: resolved_characters = set(resolved_fs_data.get("related_characters", [])) reference_chapter = resolved_fs_data.get("reference_chapter") + # 处理标题后缀（兜底机制） + resolved_title_clean = resolved_title + for suffix in ["回收", "揭示", "解答", "兑现"]: + if resolved_title.endswith(suffix): + resolved_title_clean = resolved_title[:-len(suffix)] + logger.debug(f"🔍 去除标题后缀: '{resolved_title}' -> '{resolved_title_clean}'") + break + best_match = None best_score = 0.0 @@ -1490,17 +1544,25 @@ class ForeshadowService: fs_characters = set(fs.get("related_characters", [])) fs_plant_chapter = fs.get("plant_chapter_number") - # 策略1: 标题完全匹配（最高分） + # 策略1: 标题匹配 if resolved_title and fs_title: if resolved_title == fs_title: score = 1.0 + logger.debug(f"🎯 标题完全匹配: '{resolved_title}' == '{fs_title}'") + elif resolved_title_clean and resolved_title_clean == fs_title: + score = 0.95 + logger.debug(f"🎯 清理标题匹配: '{resolved_title_clean}' == '{fs_title}'") elif resolved_title in fs_title or fs_title in resolved_title: - # 标题包含关系 score = max(score, 0.8) + logger.debug(f"🔍 标题包含匹配: '{resolved_title}' <-> '{fs_title}'") + elif resolved_title_clean and (resolved_title_clean in fs_title or fs_title in resolved_title_clean): + score = max(score, 0.75) + logger.debug(f"🔍 清理标题包含匹配: '{resolved_title_clean}' <-> '{fs_title}'") else: - # 计算标题词重叠 title_overlap = self._calculate_word_overlap(resolved_title, fs_title) score = max(score, title_overlap * 0.7) + if title_overlap > 0.3: + logger.debug(f"📊 标题词重叠: overlap={title_overlap:.2f}") # 策略2: 关键词匹配 if resolved_keyword and fs_content: diff --git a/backend/app/services/plot_analyzer.py b/backend/app/services/plot_analyzer.py index 38134e8..350fedf 100644 --- a/backend/app/services/plot_analyzer.py +++ b/backend/app/services/plot_analyzer.py @@ -87,7 +87,7 @@ class PlotAnalyzer: ) last_error = None - + logger.debug(f"章节分析提示词{prompt}") for attempt in range(1, max_retries + 1): try: # 调用AI进行分析 diff --git a/backend/app/services/prompt_service.py b/backend/app/services/prompt_service.py index b327dfb..61dea2a 100644 --- a/backend/app/services/prompt_service.py +++ b/backend/app/services/prompt_service.py @@ -972,12 +972,17 @@ class PromptService: 每个伏笔需要： - **title**：简洁标题（10-20字，概括伏笔核心） + - ⚠️ 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 + - 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" - **content**：详细描述伏笔内容和预期作用 - **type**：planted（埋下）或 resolved（回收） - **strength**：强度1-10（对读者的吸引力） - **subtlety**：隐藏度1-10（越高越隐蔽） - **reference_chapter**：回收时引用的原埋入章节号，埋下时为null - **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 + - 🔴 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 + - 如果列表中有标注【ID: xxx】的伏笔，回收时必须使用该ID + - 如果无法确定是哪个伏笔，才填写null（但应尽量避免） - **keyword**：【必填】从原文逐字复制8-25字的定位文本 - **category**：分类（identity=身世/mystery=悬念/item=物品/relationship=关系/event=事件/ability=能力/prophecy=预言） - **is_long_term**：是否长线伏笔（跨10章以上回收为true）