update:1.开放系统内置提示词，支持用户自定义模板
This commit is contained in:
xiamuceer
+58
-21
@@ -37,7 +37,7 @@ from app.schemas.regeneration import (
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RegenerationTaskStatus
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)
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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.services.prompt_service import prompt_service, PromptService, WritingStyleManager
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from app.services.plot_analyzer import PlotAnalyzer
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from app.services.memory_service import memory_service
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from app.services.chapter_regenerator import ChapterRegenerator
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@@ -1236,9 +1236,11 @@ async def generate_chapter_content_stream(
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chapter_outline_content = outline.content if outline else current_chapter.summary or '暂无大纲'
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logger.warning(f"⚠️ 一对多模式但无expansion_plan，使用大纲内容")
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# 根据是否有前置内容选择不同的提示词，并应用写作风格、记忆增强和MCP参考资料
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# 根据是否有前置内容选择不同的提示词，并应用写作风格、记忆增强和MCP参考资料（支持自定义）
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if previous_content:
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prompt = prompt_service.get_chapter_generation_with_context_prompt(
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template = await PromptService.get_template("CHAPTER_GENERATION_WITH_CONTEXT", current_user_id, db_session)
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base_prompt = PromptService.format_prompt(
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template,
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title=project.title,
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theme=project.theme or '',
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genre=project.genre or '',
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@@ -1253,14 +1255,25 @@ async def generate_chapter_content_stream(
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chapter_number=current_chapter.chapter_number,
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chapter_title=current_chapter.title,
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chapter_outline=chapter_outline_content,
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style_content=style_content,
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target_word_count=target_word_count,
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memory_context=memory_context,
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mcp_references=mcp_reference_materials,
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outline_mode=outline_mode
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max_word_count=target_word_count + 1000,
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memory_context=memory_context.get('recent_context', '') + "\n" + memory_context.get('relevant_memories', '') + "\n" + memory_context.get('foreshadows', '') + "\n" + memory_context.get('character_states', '') + "\n" + memory_context.get('plot_points', '') if memory_context else "暂无相关记忆"
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)
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# 插入模式说明和MCP参考
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mode_instruction = "\n\n【创作模式说明】\n本章采用细纲模式：本章是大纲节点的细化展开之一。请严格遵循上述详细规划（expansion_plan）中的剧情点、角色焦点、情感基调和叙事目标，确保与整体规划保持一致，同时自然衔接前文内容。\n" if outline_mode == 'one-to-many' else "\n\n【创作模式说明】\n本章采用一对一模式：一个大纲节点对应一个章节。请在承接前文的基础上，充分展开大纲中的情节，保持叙事的完整性。\n"
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mcp_text = ""
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if mcp_reference_materials:
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mcp_text = "\n【📚 MCP工具搜索 - 参考资料】\n以下是通过MCP工具搜索到的相关参考资料，可用于丰富情节和细节：\n\n" + mcp_reference_materials + "\n"
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base_prompt = base_prompt.replace("本章信息：", mcp_text + mode_instruction + "\n本章信息：")
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# 应用写作风格
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if style_content:
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prompt = WritingStyleManager.apply_style_to_prompt(base_prompt, style_content)
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else:
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prompt = base_prompt
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else:
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prompt = prompt_service.get_chapter_generation_prompt(
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template = await PromptService.get_template("CHAPTER_GENERATION", current_user_id, db_session)
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base_prompt = PromptService.format_prompt(
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template,
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title=project.title,
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theme=project.theme or '',
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genre=project.genre or '',
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@@ -1274,12 +1287,23 @@ async def generate_chapter_content_stream(
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chapter_number=current_chapter.chapter_number,
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chapter_title=current_chapter.title,
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chapter_outline=chapter_outline_content,
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style_content=style_content,
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target_word_count=target_word_count,
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memory_context=memory_context,
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mcp_references=mcp_reference_materials,
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outline_mode=outline_mode
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max_word_count=target_word_count + 1000
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)
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# 插入模式说明和记忆、MCP参考
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mode_instruction = "\n\n【创作模式说明】\n本章采用细纲模式：本章是大纲节点的细化展开之一。请严格遵循上述详细规划中的剧情点、角色焦点和情感基调，确保与整体规划保持一致。\n" if outline_mode == 'one-to-many' else "\n\n【创作模式说明】\n本章采用一对一模式：一个大纲节点对应一个章节。请充分展开大纲中的情节，注重叙事的完整性和丰满度。\n"
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memory_text = ""
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if memory_context:
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memory_text = "\n【🧠 智能记忆系统 - 重要参考】\n" + memory_context.get('recent_context', '') + "\n" + memory_context.get('relevant_memories', '') + "\n" + memory_context.get('foreshadows', '') + "\n" + memory_context.get('character_states', '') + "\n" + memory_context.get('plot_points', '')
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mcp_text = ""
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if mcp_reference_materials:
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mcp_text = "\n【📚 MCP工具搜索 - 参考资料】\n以下是通过MCP工具搜索到的相关参考资料，可用于丰富情节和细节：\n\n" + mcp_reference_materials + "\n"
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base_prompt = base_prompt.replace("本章信息：", memory_text + mcp_text + mode_instruction + "\n\n本章信息：")
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# 应用写作风格
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if style_content:
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prompt = WritingStyleManager.apply_style_to_prompt(base_prompt, style_content)
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else:
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prompt = base_prompt
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if mcp_reference_materials:
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logger.info(f"📖 已整合MCP参考资料（{len(mcp_reference_materials)}字符）到章节生成提示词")
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@@ -2412,9 +2436,12 @@ async def generate_single_chapter_for_batch(
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chapter_outline_content = outline.content if outline else chapter.summary or '暂无大纲'
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logger.warning(f"⚠️ 批量生成 - 一对多模式但无expansion_plan，使用大纲内容")
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# 生成提示词
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# 生成提示词（支持自定义）
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if previous_content:
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prompt = prompt_service.get_chapter_generation_with_context_prompt(
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# 获取自定义提示词模板
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template = await PromptService.get_template("CHAPTER_GENERATION_WITH_CONTEXT", user_id, db_session)
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base_prompt = PromptService.format_prompt(
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template,
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title=project.title,
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theme=project.theme or '',
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genre=project.genre or '',
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@@ -2429,13 +2456,20 @@ async def generate_single_chapter_for_batch(
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chapter_number=chapter.chapter_number,
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chapter_title=chapter.title,
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chapter_outline=chapter_outline_content,
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style_content=style_content,
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target_word_count=target_word_count,
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memory_context=memory_context,
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outline_mode=outline_mode
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max_word_count=target_word_count + 1000,
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memory_context=memory_context.get('recent_context', '') + "\n" + memory_context.get('relevant_memories', '') + "\n" + memory_context.get('foreshadows', '') + "\n" + memory_context.get('character_states', '') + "\n" + memory_context.get('plot_points', '') if memory_context else "暂无相关记忆"
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)
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# 应用写作风格
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if style_content:
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prompt = WritingStyleManager.apply_style_to_prompt(base_prompt, style_content)
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else:
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prompt = base_prompt
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else:
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prompt = prompt_service.get_chapter_generation_prompt(
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# 获取自定义提示词模板
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template = await PromptService.get_template("CHAPTER_GENERATION", user_id, db_session)
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base_prompt = PromptService.format_prompt(
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template,
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title=project.title,
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theme=project.theme or '',
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genre=project.genre or '',
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@@ -2449,11 +2483,14 @@ async def generate_single_chapter_for_batch(
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chapter_number=chapter.chapter_number,
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chapter_title=chapter.title,
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chapter_outline=chapter_outline_content,
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style_content=style_content,
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target_word_count=target_word_count,
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memory_context=memory_context,
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outline_mode=outline_mode
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max_word_count=target_word_count + 1000
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)
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# 应用写作风格
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if style_content:
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prompt = WritingStyleManager.apply_style_to_prompt(base_prompt, style_content)
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else:
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prompt = base_prompt
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# 非流式生成内容
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full_content = ""
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