xiamuceer
607 lines
23 KiB
Python
607 lines
23 KiB
Python
"""剧情分析服务 - 自动分析章节的钩子、伏笔、冲突等元素"""
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from typing import Dict, Any, List, Optional
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from app.services.ai_service import AIService
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from app.logger import get_logger
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import json
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import re
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logger = get_logger(__name__)
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class PlotAnalyzer:
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"""剧情分析器 - 使用AI分析章节内容"""
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# AI分析提示词模板
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ANALYSIS_PROMPT = """你是一位专业的小说编辑和剧情分析师。请深度分析以下章节内容:
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**章节信息:**
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- 章节: 第{chapter_number}章
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- 标题: {title}
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- 字数: {word_count}字
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**章节内容:**
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{content}
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---
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**分析任务:**
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请从专业编辑的角度,全面分析这一章节:
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### 1. 剧情钩子 (Hooks) - 吸引读者的元素
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识别能够吸引读者继续阅读的关键元素:
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- **悬念钩子**: 未解之谜、疑问、谜团
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- **情感钩子**: 引发共鸣的情感点、触动心弦的时刻
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- **冲突钩子**: 矛盾对抗、紧张局势
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- **认知钩子**: 颠覆认知的信息、惊人真相
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每个钩子需要:
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- 类型分类
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- 具体内容描述
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- 强度评分(1-10)
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- 出现位置(开头/中段/结尾)
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- **关键词**: 【必填】从章节原文中逐字复制一段关键文本(8-25字)，必须是原文中真实存在的连续文字，用于在文本中精确定位。不要概括或改写，必须原样复制！
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### 2. 伏笔分析 (Foreshadowing)
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- **埋下的新伏笔**: 描述内容、预期作用、隐藏程度(1-10)
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- **回收的旧伏笔**: 呼应哪一章、回收效果评分
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- **伏笔质量**: 巧妙性和合理性评估
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- **关键词**: 【必填】从章节原文中逐字复制一段关键文本(8-25字)，必须是原文中真实存在的连续文字，用于在文本中精确定位。不要概括或改写，必须原样复制！
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### 3. 冲突分析 (Conflict)
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- 冲突类型: 人与人/人与己/人与环境/人与社会
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- 冲突各方及其立场
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- 冲突强度评分(1-10)
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- 冲突解决进度(0-100%)
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### 4. 情感曲线 (Emotional Arc)
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- 主导情绪: 紧张/温馨/悲伤/激昂/平静等
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- 情感强度(1-10)
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- 情绪变化轨迹描述
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### 5. 角色状态追踪 (Character Development)
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对每个出场角色分析:
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- 心理状态变化(前→后)
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- 关系变化
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- 关键行动和决策
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- 成长或退步
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### 6. 关键情节点 (Plot Points)
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列出3-5个核心情节点:
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- 情节内容
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- 类型(revelation/conflict/resolution/transition)
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- 重要性(0.0-1.0)
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- 对故事的影响
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- **关键词**: 【必填】从章节原文中逐字复制一段关键文本(8-25字)，必须是原文中真实存在的连续文字，用于在文本中精确定位。不要概括或改写，必须原样复制！
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### 7. 场景与节奏
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- 主要场景
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- 叙事节奏(快/中/慢)
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- 对话与描写的比例
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### 8. 质量评分
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- 节奏把控: 1-10分
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- 吸引力: 1-10分
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- 连贯性: 1-10分
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- 整体质量: 1-10分
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### 9. 改进建议
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提供3-5条具体的改进建议
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---
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**输出格式(纯JSON,不要markdown标记):**
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{{
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"hooks": [
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{{
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"type": "悬念",
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"content": "具体描述",
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"strength": 8,
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"position": "中段",
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"keyword": "必须从原文逐字复制的文本片段"
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}}
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],
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"foreshadows": [
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{{
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"content": "伏笔内容",
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"type": "planted",
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"strength": 7,
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"subtlety": 8,
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"reference_chapter": null,
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"keyword": "必须从原文逐字复制的文本片段"
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}}
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],
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"conflict": {{
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"types": ["人与人", "人与己"],
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"parties": ["主角-复仇", "反派-维护现状"],
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"level": 8,
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"description": "冲突描述",
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"resolution_progress": 0.3
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}},
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"emotional_arc": {{
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"primary_emotion": "紧张",
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"intensity": 8,
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"curve": "平静→紧张→高潮→释放",
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"secondary_emotions": ["期待", "焦虑"]
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}},
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"character_states": [
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{{
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"character_name": "张三",
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"state_before": "犹豫",
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"state_after": "坚定",
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"psychological_change": "心理变化描述",
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"key_event": "触发事件",
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"relationship_changes": {{"李四": "关系改善"}}
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}}
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],
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"plot_points": [
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{{
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"content": "情节点描述",
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"type": "revelation",
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"importance": 0.9,
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"impact": "推动故事发展",
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"keyword": "必须从原文逐字复制的文本片段"
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}}
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],
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"scenes": [
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{{
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"location": "地点",
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"atmosphere": "氛围",
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"duration": "时长估计"
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}}
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],
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"pacing": "varied",
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"dialogue_ratio": 0.4,
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"description_ratio": 0.3,
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"scores": {{
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"pacing": 8,
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"engagement": 9,
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"coherence": 8,
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"overall": 8.5
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}},
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"plot_stage": "发展",
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"suggestions": [
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"具体建议1",
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"具体建议2"
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]
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}}
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**重要提示:**
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1. 每个钩子、伏笔、情节点的keyword字段是必填的，不能为空
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2. keyword必须是从章节原文中逐字复制的文本，长度8-25字
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3. keyword用于在前端标注文本位置，所以必须能在原文中精确找到
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4. 不要使用概括性语句或改写后的文字作为keyword
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只返回JSON,不要其他说明。"""
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def __init__(self, ai_service: AIService):
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"""
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初始化剧情分析器
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Args:
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ai_service: AI服务实例
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"""
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self.ai_service = ai_service
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logger.info("✅ PlotAnalyzer初始化成功")
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async def analyze_chapter(
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self,
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chapter_number: int,
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title: str,
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content: str,
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word_count: int
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) -> Optional[Dict[str, Any]]:
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"""
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分析单章内容
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Args:
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chapter_number: 章节号
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title: 章节标题
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content: 章节内容
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word_count: 字数
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Returns:
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分析结果字典,失败返回None
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"""
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try:
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logger.info(f"🔍 开始分析第{chapter_number}章: {title}")
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# 如果内容过长,截取前8000字(避免超token)
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analysis_content = content[:8000] if len(content) > 8000 else content
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# 构建提示词
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prompt = self.ANALYSIS_PROMPT.format(
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chapter_number=chapter_number,
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title=title,
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word_count=word_count,
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content=analysis_content
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)
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# 调用AI进行分析
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# 注意：不指定max_tokens，使用用户在设置中配置的值
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logger.info(f" 调用AI分析(内容长度: {len(analysis_content)}字)...")
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response = await self.ai_service.generate_text(
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prompt=prompt,
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temperature=0.3 # 降低温度以获得更稳定的JSON输出
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)
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# 🔍 添加调试日志：查看AI返回的原始内容
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logger.info(f"🔍 AI返回类型: {type(response)}")
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logger.info(f"🔍 AI返回内容(前500字符): {str(response)}")
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# 从返回的字典中提取content字段
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if isinstance(response, dict):
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response_text = response.get('content', '')
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if not response_text:
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logger.error("❌ AI返回的字典中没有content字段或content为空")
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return None
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else:
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# 兼容旧的字符串返回格式
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response_text = response
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# 解析JSON结果
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analysis_result = self._parse_analysis_response(response_text)
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if analysis_result:
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logger.info(f"✅ 第{chapter_number}章分析完成")
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logger.info(f" - 钩子: {len(analysis_result.get('hooks', []))}个")
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logger.info(f" - 伏笔: {len(analysis_result.get('foreshadows', []))}个")
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logger.info(f" - 情节点: {len(analysis_result.get('plot_points', []))}个")
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logger.info(f" - 整体评分: {analysis_result.get('scores', {}).get('overall', 'N/A')}")
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return analysis_result
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else:
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logger.error(f"❌ 第{chapter_number}章分析失败: JSON解析错误")
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return None
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except Exception as e:
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logger.error(f"❌ 章节分析异常: {str(e)}")
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return None
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def _parse_analysis_response(self, response: str) -> Optional[Dict[str, Any]]:
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"""
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解析AI返回的分析结果
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Args:
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response: AI返回的文本
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Returns:
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解析后的字典,失败返回None
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"""
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try:
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# 清理响应文本
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cleaned = response.strip()
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# 移除可能的markdown标记
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cleaned = re.sub(r'^```json\s*', '', cleaned)
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cleaned = re.sub(r'^```\s*', '', cleaned)
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cleaned = re.sub(r'\s*```$', '', cleaned)
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# 尝试解析JSON
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result = json.loads(cleaned)
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# 验证必要字段
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required_fields = ['hooks', 'plot_points', 'scores']
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for field in required_fields:
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if field not in result:
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logger.warning(f"⚠️ 分析结果缺少字段: {field}")
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result[field] = [] if field != 'scores' else {}
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return result
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except json.JSONDecodeError as e:
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logger.error(f"❌ JSON解析失败: {str(e)}")
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logger.error(f" 原始响应(前500字): {response[:500]}")
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# 尝试提取JSON部分
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json_match = re.search(r'\{[\s\S]*\}', response)
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if json_match:
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try:
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result = json.loads(json_match.group())
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logger.info("✅ 通过正则提取成功解析JSON")
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return result
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except:
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pass
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return None
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except Exception as e:
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logger.error(f"❌ 解析异常: {str(e)}")
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return None
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def extract_memories_from_analysis(
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self,
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analysis: Dict[str, Any],
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chapter_id: str,
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chapter_number: int,
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chapter_content: str = "",
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chapter_title: str = ""
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) -> List[Dict[str, Any]]:
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"""
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从分析结果中提取记忆片段
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Args:
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analysis: 分析结果
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chapter_id: 章节ID
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chapter_number: 章节号
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chapter_content: 章节完整内容(用于计算位置)
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chapter_title: 章节标题
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Returns:
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记忆片段列表
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"""
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memories = []
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try:
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# 【新增】0. 提取章节摘要作为记忆（用于语义检索相关章节）
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chapter_summary = ""
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# 尝试从分析结果获取摘要
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if analysis.get('summary'):
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chapter_summary = analysis.get('summary')
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# 或者从情节点组合生成摘要
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elif analysis.get('plot_points'):
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plot_summaries = [p.get('content', '') for p in analysis.get('plot_points', [])[:3]]
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chapter_summary = "；".join(plot_summaries)
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# 或者使用内容前300字
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elif chapter_content:
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chapter_summary = chapter_content[:300] + ("..." if len(chapter_content) > 300 else "")
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# 如果有摘要，添加到记忆中
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if chapter_summary:
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memories.append({
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'type': 'chapter_summary',
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'content': chapter_summary,
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'title': f"第{chapter_number}章《{chapter_title}》摘要",
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'metadata': {
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'chapter_id': chapter_id,
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'chapter_number': chapter_number,
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'importance_score': 0.6, # 中等重要性
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'tags': ['摘要', '章节概览', chapter_title],
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'is_foreshadow': 0,
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'text_position': 0,
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'text_length': len(chapter_summary)
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}
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})
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logger.info(f" ✅ 添加章节摘要记忆: {len(chapter_summary)}字")
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# 1. 提取钩子作为记忆
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for i, hook in enumerate(analysis.get('hooks', [])):
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if hook.get('strength', 0) >= 6: # 只保存强度>=6的钩子
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keyword = hook.get('keyword', '')
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position, length = self._find_text_position(chapter_content, keyword)
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logger.info(f" 钩子位置: keyword='{keyword[:30]}...', pos={position}, len={length}")
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memories.append({
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'type': 'hook',
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'content': f"[{hook.get('type', '未知')}钩子] {hook.get('content', '')}",
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'title': f"{hook.get('type', '钩子')} - {hook.get('position', '')}",
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'metadata': {
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'chapter_id': chapter_id,
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'chapter_number': chapter_number,
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'importance_score': min(hook.get('strength', 5) / 10, 1.0),
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'tags': [hook.get('type', '钩子'), hook.get('position', '')],
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'is_foreshadow': 0,
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'keyword': keyword,
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'text_position': position,
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'text_length': length,
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'strength': hook.get('strength', 5),
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'position_desc': hook.get('position', '')
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}
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})
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# 2. 提取伏笔作为记忆
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for i, foreshadow in enumerate(analysis.get('foreshadows', [])):
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is_planted = foreshadow.get('type') == 'planted'
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keyword = foreshadow.get('keyword', '')
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position, length = self._find_text_position(chapter_content, keyword)
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logger.info(f" 伏笔位置: keyword='{keyword[:30]}...', pos={position}, len={length}")
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memories.append({
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'type': 'foreshadow',
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'content': foreshadow.get('content', ''),
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'title': f"{'埋下伏笔' if is_planted else '回收伏笔'}",
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'metadata': {
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'chapter_id': chapter_id,
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'chapter_number': chapter_number,
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'importance_score': min(foreshadow.get('strength', 5) / 10, 1.0),
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'tags': ['伏笔', foreshadow.get('type', 'planted')],
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'is_foreshadow': 1 if is_planted else 2,
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'reference_chapter': foreshadow.get('reference_chapter'),
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'keyword': keyword,
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'text_position': position,
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'text_length': length,
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'foreshadow_type': foreshadow.get('type', 'planted'),
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'strength': foreshadow.get('strength', 5)
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}
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})
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# 3. 提取关键情节点
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for i, plot_point in enumerate(analysis.get('plot_points', [])):
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if plot_point.get('importance', 0) >= 0.6: # 只保存重要性>=0.6的情节点
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keyword = plot_point.get('keyword', '')
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position, length = self._find_text_position(chapter_content, keyword)
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logger.info(f" 情节点位置: keyword='{keyword[:30]}...', pos={position}, len={length}")
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memories.append({
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'type': 'plot_point',
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'content': f"{plot_point.get('content', '')}。影响: {plot_point.get('impact', '')}",
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'title': f"情节点 - {plot_point.get('type', '未知')}",
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'metadata': {
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'chapter_id': chapter_id,
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'chapter_number': chapter_number,
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'importance_score': plot_point.get('importance', 0.5),
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'tags': ['情节点', plot_point.get('type', '未知')],
|
||
'is_foreshadow': 0,
|
||
'keyword': keyword,
|
||
'text_position': position,
|
||
'text_length': length
|
||
}
|
||
})
|
||
|
||
# 4. 提取角色状态变化
|
||
for i, char_state in enumerate(analysis.get('character_states', [])):
|
||
char_name = char_state.get('character_name', '未知角色')
|
||
memories.append({
|
||
'type': 'character_event',
|
||
'content': f"{char_name}的状态变化: {char_state.get('state_before', '')} → {char_state.get('state_after', '')}。{char_state.get('psychological_change', '')}",
|
||
'title': f"{char_name}的变化",
|
||
'metadata': {
|
||
'chapter_id': chapter_id,
|
||
'chapter_number': chapter_number,
|
||
'importance_score': 0.7,
|
||
'tags': ['角色', char_name, '状态变化'],
|
||
'related_characters': [char_name],
|
||
'is_foreshadow': 0
|
||
}
|
||
})
|
||
|
||
# 5. 如果有重要冲突,也记录下来
|
||
conflict = analysis.get('conflict', {})
|
||
|
||
if conflict and conflict.get('level', 0) >= 7:
|
||
# 确保 parties 和 types 都是字符串列表
|
||
parties = conflict.get('parties', [])
|
||
if parties and isinstance(parties, list):
|
||
parties = [str(p) for p in parties]
|
||
|
||
types = conflict.get('types', [])
|
||
if types and isinstance(types, list):
|
||
types = [str(t) for t in types]
|
||
|
||
memories.append({
|
||
'type': 'plot_point',
|
||
'content': f"重要冲突: {conflict.get('description', '')}。冲突各方: {', '.join(parties)}",
|
||
'title': f"冲突 - 强度{conflict.get('level', 0)}",
|
||
'metadata': {
|
||
'chapter_id': chapter_id,
|
||
'chapter_number': chapter_number,
|
||
'importance_score': min(conflict.get('level', 5) / 10, 1.0),
|
||
'tags': ['冲突'] + types,
|
||
'is_foreshadow': 0
|
||
}
|
||
})
|
||
|
||
logger.info(f"📝 从分析中提取了{len(memories)}条记忆")
|
||
return memories
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ 提取记忆失败: {str(e)}")
|
||
return []
|
||
|
||
def _find_text_position(self, full_text: str, keyword: str) -> tuple[int, int]:
|
||
"""
|
||
在全文中查找关键词位置
|
||
|
||
Args:
|
||
full_text: 完整文本
|
||
keyword: 关键词
|
||
|
||
Returns:
|
||
(起始位置, 长度) 如果未找到返回(-1, 0)
|
||
"""
|
||
if not keyword or not full_text:
|
||
return (-1, 0)
|
||
|
||
try:
|
||
# 1. 精确匹配
|
||
pos = full_text.find(keyword)
|
||
if pos != -1:
|
||
return (pos, len(keyword))
|
||
|
||
# 2. 去除标点符号后匹配
|
||
import re
|
||
clean_keyword = re.sub(r'[，。！？、；：""''（）《》【】]', '', keyword)
|
||
clean_text = re.sub(r'[，。！？、；：""''（）《》【】]', '', full_text)
|
||
pos = clean_text.find(clean_keyword)
|
||
|
||
if pos != -1:
|
||
# 反向映射到原文位置（简化处理）
|
||
return (pos, len(clean_keyword))
|
||
|
||
# 3. 模糊匹配：查找关键词的前半部分
|
||
if len(keyword) > 10:
|
||
partial = keyword[:min(15, len(keyword))]
|
||
pos = full_text.find(partial)
|
||
if pos != -1:
|
||
return (pos, len(partial))
|
||
|
||
# 4. 未找到
|
||
logger.debug(f"未找到关键词位置: {keyword[:30]}...")
|
||
return (-1, 0)
|
||
|
||
except Exception as e:
|
||
logger.error(f"查找位置失败: {str(e)}")
|
||
return (-1, 0)
|
||
|
||
def generate_analysis_summary(self, analysis: Dict[str, Any]) -> str:
|
||
"""
|
||
生成分析摘要文本
|
||
|
||
Args:
|
||
analysis: 分析结果
|
||
|
||
Returns:
|
||
格式化的摘要文本
|
||
"""
|
||
try:
|
||
lines = ["=== 章节分析报告 ===\n"]
|
||
|
||
# 整体评分
|
||
scores = analysis.get('scores', {})
|
||
lines.append(f"【整体评分】")
|
||
lines.append(f" 整体质量: {scores.get('overall', 'N/A')}/10")
|
||
lines.append(f" 节奏把控: {scores.get('pacing', 'N/A')}/10")
|
||
lines.append(f" 吸引力: {scores.get('engagement', 'N/A')}/10")
|
||
lines.append(f" 连贯性: {scores.get('coherence', 'N/A')}/10\n")
|
||
|
||
# 剧情阶段
|
||
lines.append(f"【剧情阶段】{analysis.get('plot_stage', '未知')}\n")
|
||
|
||
# 钩子统计
|
||
hooks = analysis.get('hooks', [])
|
||
if hooks:
|
||
lines.append(f"【钩子分析】共{len(hooks)}个")
|
||
for hook in hooks[:3]: # 只显示前3个
|
||
lines.append(f" • [{hook.get('type')}] {hook.get('content', '')[:50]}... (强度:{hook.get('strength', 0)})")
|
||
lines.append("")
|
||
|
||
# 伏笔统计
|
||
foreshadows = analysis.get('foreshadows', [])
|
||
if foreshadows:
|
||
planted = sum(1 for f in foreshadows if f.get('type') == 'planted')
|
||
resolved = sum(1 for f in foreshadows if f.get('type') == 'resolved')
|
||
lines.append(f"【伏笔分析】埋下{planted}个, 回收{resolved}个\n")
|
||
|
||
# 冲突分析
|
||
conflict = analysis.get('conflict', {})
|
||
if conflict:
|
||
lines.append(f"【冲突分析】")
|
||
lines.append(f" 类型: {', '.join(conflict.get('types', []))}")
|
||
lines.append(f" 强度: {conflict.get('level', 0)}/10")
|
||
lines.append(f" 进度: {int(conflict.get('resolution_progress', 0) * 100)}%\n")
|
||
|
||
# 改进建议
|
||
suggestions = analysis.get('suggestions', [])
|
||
if suggestions:
|
||
lines.append(f"【改进建议】")
|
||
for i, sug in enumerate(suggestions, 1):
|
||
lines.append(f" {i}. {sug}")
|
||
|
||
return "\n".join(lines)
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ 生成摘要失败: {str(e)}")
|
||
return "分析摘要生成失败"
|
||
|
||
|
||
# 创建全局实例(需要时手动初始化)
|
||
_plot_analyzer_instance = None
|
||
|
||
def get_plot_analyzer(ai_service: AIService) -> PlotAnalyzer:
|
||
"""获取剧情分析器实例"""
|
||
global _plot_analyzer_instance
|
||
if _plot_analyzer_instance is None:
|
||
_plot_analyzer_instance = PlotAnalyzer(ai_service)
|
||
return _plot_analyzer_instance |