xiamuceer
308 lines
11 KiB
Python
308 lines
11 KiB
Python
"""章节重新生成服务"""
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from typing import Dict, Any, AsyncGenerator, Optional, List
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from app.services.ai_service import AIService
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from app.services.prompt_service import prompt_service
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from app.models.chapter import Chapter
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from app.models.memory import PlotAnalysis
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from app.schemas.regeneration import ChapterRegenerateRequest, PreserveElementsConfig
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from app.logger import get_logger
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import difflib
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logger = get_logger(__name__)
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class ChapterRegenerator:
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"""章节重新生成服务"""
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def __init__(self, ai_service: AIService):
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self.ai_service = ai_service
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logger.info("✅ ChapterRegenerator初始化成功")
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async def regenerate_with_feedback(
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self,
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chapter: Chapter,
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analysis: Optional[PlotAnalysis],
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regenerate_request: ChapterRegenerateRequest,
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project_context: Dict[str, Any]
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) -> AsyncGenerator[Dict[str, Any], None]:
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"""
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根据反馈重新生成章节（流式）
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Args:
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chapter: 原始章节对象
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analysis: 分析结果（可选）
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regenerate_request: 重新生成请求参数
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project_context: 项目上下文（项目信息、角色、大纲等）
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Yields:
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包含类型和数据的字典: {'type': 'progress'/'chunk', 'data': ...}
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"""
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try:
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logger.info(f"🔄 开始重新生成章节: 第{chapter.chapter_number}章")
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# 1. 构建修改指令
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yield {'type': 'progress', 'progress': 5, 'message': '正在构建修改指令...'}
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modification_instructions = self._build_modification_instructions(
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analysis=analysis,
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regenerate_request=regenerate_request
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)
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logger.info(f"📝 修改指令构建完成，长度: {len(modification_instructions)}字符")
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# 2. 构建完整提示词
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yield {'type': 'progress', 'progress': 10, 'message': '正在构建生成提示词...'}
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full_prompt = self._build_regeneration_prompt(
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chapter=chapter,
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modification_instructions=modification_instructions,
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project_context=project_context,
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regenerate_request=regenerate_request
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)
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logger.info(f"🎯 提示词构建完成，开始AI生成")
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yield {'type': 'progress', 'progress': 15, 'message': '开始AI生成内容...'}
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# 3. 流式生成新内容，同时跟踪进度
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target_word_count = regenerate_request.target_word_count
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accumulated_length = 0
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async for chunk in self.ai_service.generate_text_stream(
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prompt=full_prompt,
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temperature=0.7
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):
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# 发送内容块
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yield {'type': 'chunk', 'content': chunk}
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# 更新累积字数并计算进度（15%-95%）
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accumulated_length += len(chunk)
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# 进度从15%开始，到95%结束，为后处理预留5%
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generation_progress = min(15 + (accumulated_length / target_word_count) * 80, 95)
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yield {'type': 'progress', 'progress': int(generation_progress), 'word_count': accumulated_length}
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logger.info(f"✅ 章节重新生成完成，共生成 {accumulated_length} 字")
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yield {'type': 'progress', 'progress': 100, 'message': '生成完成'}
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except Exception as e:
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logger.error(f"❌ 重新生成失败: {str(e)}", exc_info=True)
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raise
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def _build_modification_instructions(
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self,
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analysis: Optional[PlotAnalysis],
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regenerate_request: ChapterRegenerateRequest
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) -> str:
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"""构建修改指令"""
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instructions = []
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# 标题
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instructions.append("# 章节修改指令\n")
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# 1. 来自分析的建议
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if (analysis and
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regenerate_request.selected_suggestion_indices and
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analysis.suggestions):
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instructions.append("## 📋 需要改进的问题（来自AI分析）：\n")
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for idx in regenerate_request.selected_suggestion_indices:
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if 0 <= idx < len(analysis.suggestions):
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suggestion = analysis.suggestions[idx]
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instructions.append(f"{idx + 1}. {suggestion}")
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instructions.append("")
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# 2. 用户自定义指令
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if regenerate_request.custom_instructions:
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instructions.append("## ✍️ 用户自定义修改要求：\n")
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instructions.append(regenerate_request.custom_instructions)
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instructions.append("")
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# 3. 重点优化方向
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if regenerate_request.focus_areas:
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instructions.append("## 🎯 重点优化方向：\n")
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focus_map = {
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"pacing": "节奏把控 - 调整叙事速度，避免拖沓或过快",
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"emotion": "情感渲染 - 深化人物情感表达，增强感染力",
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"description": "场景描写 - 丰富环境细节，增强画面感",
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"dialogue": "对话质量 - 让对话更自然真实，推动剧情",
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"conflict": "冲突强度 - 强化矛盾冲突，提升戏剧张力"
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}
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for area in regenerate_request.focus_areas:
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if area in focus_map:
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instructions.append(f"- {focus_map[area]}")
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instructions.append("")
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# 4. 保留要求
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if regenerate_request.preserve_elements:
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preserve = regenerate_request.preserve_elements
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instructions.append("## 🔒 必须保留的元素：\n")
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if preserve.preserve_structure:
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instructions.append("- 保持原章节的整体结构和情节框架")
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if preserve.preserve_dialogues:
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instructions.append("- 必须保留以下关键对话：")
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for dialogue in preserve.preserve_dialogues:
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instructions.append(f" * {dialogue}")
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if preserve.preserve_plot_points:
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instructions.append("- 必须保留以下关键情节点：")
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for plot in preserve.preserve_plot_points:
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instructions.append(f" * {plot}")
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if preserve.preserve_character_traits:
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instructions.append("- 保持所有角色的性格特征和行为模式一致")
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instructions.append("")
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return "\n".join(instructions)
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def _build_regeneration_prompt(
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self,
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chapter: Chapter,
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modification_instructions: str,
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project_context: Dict[str, Any],
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regenerate_request: ChapterRegenerateRequest
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) -> str:
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"""构建完整的重新生成提示词"""
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prompt_parts = []
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# 系统角色
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prompt_parts.append("""你是一位经验丰富的专业小说编辑和作家。现在需要根据反馈意见重新创作一个章节。
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你的任务是：
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1. 仔细理解原章节的内容和意图
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2. 认真分析所有的修改要求
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3. 在保持故事连贯性的前提下，创作一个改进后的新版本
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4. 确保新版本在艺术性和可读性上都有明显提升
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---
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""")
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# 原始章节信息
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prompt_parts.append(f"""## 📖 原始章节信息
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**章节**：第{chapter.chapter_number}章
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**标题**：{chapter.title}
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**字数**：{chapter.word_count}字
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**原始内容**：
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{chapter.content}
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---
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""")
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# 修改指令
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prompt_parts.append(modification_instructions)
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prompt_parts.append("\n---\n")
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# 项目背景信息
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prompt_parts.append(f"""## 🌍 项目背景信息
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**小说标题**：{project_context.get('project_title', '未知')}
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**题材**：{project_context.get('genre', '未设定')}
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**主题**：{project_context.get('theme', '未设定')}
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**叙事视角**：{project_context.get('narrative_perspective', '第三人称')}
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**世界观设定**：
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- 时代背景：{project_context.get('time_period', '未设定')}
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- 地理位置：{project_context.get('location', '未设定')}
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- 氛围基调：{project_context.get('atmosphere', '未设定')}
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---
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""")
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# 角色信息
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if project_context.get('characters_info'):
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prompt_parts.append(f"""## 👥 角色信息
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{project_context['characters_info']}
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---
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""")
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# 章节大纲
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if project_context.get('chapter_outline'):
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prompt_parts.append(f"""## 📝 本章大纲
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{project_context['chapter_outline']}
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---
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""")
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# 前置章节上下文
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if project_context.get('previous_context'):
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prompt_parts.append(f"""## 📚 前置章节上下文
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{project_context['previous_context']}
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---
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""")
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# 创作要求
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prompt_parts.append(f"""## ✨ 创作要求
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1. **解决问题**：针对上述修改指令中提到的所有问题进行改进
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2. **保持连贯**：确保与前后章节的情节、人物、风格保持一致
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3. **提升质量**：在节奏、情感、描写等方面明显优于原版
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4. **保留精华**：保持原章节中优秀的部分和关键情节
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5. **字数控制**：目标字数约{regenerate_request.target_word_count}字（可适当浮动±20%）
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---
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## 🎬 开始创作
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请现在开始创作改进后的新版本章节内容。
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**重要提示**：
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- 直接输出章节正文内容，从故事内容开始写
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- **不要**输出章节标题（如"第X章"、"第X章：XXX"等）
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- **不要**输出任何额外的说明、注释或元数据
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- 只需要纯粹的故事正文内容
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现在开始：
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""")
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return "\n".join(prompt_parts)
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def calculate_content_diff(
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self,
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original_content: str,
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new_content: str
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) -> Dict[str, Any]:
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"""
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计算两个版本的差异
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Returns:
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差异统计信息
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"""
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# 基本统计
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diff_stats = {
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'original_length': len(original_content),
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'new_length': len(new_content),
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'length_change': len(new_content) - len(original_content),
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'length_change_percent': round((len(new_content) - len(original_content)) / len(original_content) * 100, 2) if len(original_content) > 0 else 0
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}
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# 计算相似度
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similarity = difflib.SequenceMatcher(None, original_content, new_content).ratio()
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diff_stats['similarity'] = round(similarity * 100, 2)
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diff_stats['difference'] = round((1 - similarity) * 100, 2)
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# 段落统计
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original_paragraphs = [p for p in original_content.split('\n\n') if p.strip()]
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new_paragraphs = [p for p in new_content.split('\n\n') if p.strip()]
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diff_stats['original_paragraph_count'] = len(original_paragraphs)
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diff_stats['new_paragraph_count'] = len(new_paragraphs)
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return diff_stats
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# 全局实例
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_regenerator_instance = None
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def get_chapter_regenerator(ai_service: AIService) -> ChapterRegenerator:
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"""获取章节重新生成器实例"""
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global _regenerator_instance
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if _regenerator_instance is None:
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_regenerator_instance = ChapterRegenerator(ai_service)
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return _regenerator_instance |