update:1.新增统一的JSON清洗和重试方法，避免AI响应json格式错误 2.重构提示词模板命名，优化大纲章节初始化提示词 3.移除布冯冗余代码，提高代码复用性 4.优化系统默认写作风格预设提示词和规则
This commit is contained in:
xiamuceer
@@ -8,9 +8,15 @@ from app.mcp.adapters import UniversalMCPAdapter, PromptInjectionAdapter
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import httpx
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import json
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import hashlib
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import re
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import asyncio
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logger = get_logger(__name__)
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# 全局请求限流器（使用信号量控制并发数）
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_global_semaphore = asyncio.Semaphore(5) # 最多5个并发请求
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_request_delay = 0.2 # 请求间隔200ms
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# 全局HTTP客户端池（按配置复用）
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_http_client_pool: Dict[str, httpx.AsyncClient] = {}
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_client_pool_lock = False # 简单的锁标志
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@@ -308,7 +314,7 @@ class AIService:
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max_tokens: int,
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system_prompt: Optional[str]
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) -> str:
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"""使用OpenAI生成文本"""
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"""使用OpenAI生成文本（带限流和重试）"""
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if not self.openai_http_client:
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raise ValueError("OpenAI客户端未初始化，请检查API key配置")
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@@ -317,84 +323,118 @@ class AIService:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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try:
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logger.info(f"🔵 开始调用OpenAI API（直接HTTP请求）")
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logger.info(f" - 模型: {model}")
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logger.info(f" - 温度: {temperature}")
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logger.info(f" - 最大tokens: {max_tokens}")
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logger.info(f" - Prompt长度: {len(prompt)} 字符")
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logger.info(f" - 消息数量: {len(messages)}")
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# 使用全局信号量限流
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async with _global_semaphore:
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# 请求间隔
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await asyncio.sleep(_request_delay)
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url = f"{self.openai_base_url}/chat/completions"
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headers = {
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"Authorization": f"Bearer {self.openai_api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": model,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens
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}
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logger.debug(f" - 请求URL: {url}")
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logger.debug(f" - 请求头: Authorization=Bearer ***")
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response = await self.openai_http_client.post(url, headers=headers, json=payload)
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response.raise_for_status()
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data = response.json()
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logger.info(f"✅ OpenAI API调用成功")
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logger.info(f" - 响应ID: {data.get('id', 'N/A')}")
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logger.info(f" - 选项数量: {len(data.get('choices', []))}")
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logger.debug(f" - 完整API响应: {data}")
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if not data.get('choices'):
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logger.error("❌ OpenAI返回的choices为空")
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raise ValueError("API返回的响应格式错误：choices字段为空")
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choice = data['choices'][0]
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message = choice.get('message', {})
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finish_reason = choice.get('finish_reason')
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# DeepSeek R1特殊处理：只使用content（最终答案），忽略reasoning_content（思考过程）
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# reasoning_content是AI的思考过程，不是我们需要的JSON结果
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content = message.get('content', '')
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# 检查是否因达到长度限制而截断
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if finish_reason == 'length':
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logger.warning(f"⚠️ 响应因达到max_tokens限制而被截断")
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logger.warning(f" - 当前max_tokens: {max_tokens}")
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logger.warning(f" - 建议: 增加max_tokens参数（推荐2000+）")
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if content:
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logger.info(f" - 返回内容长度: {len(content)} 字符")
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logger.info(f" - 完成原因: {finish_reason}")
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logger.info(f" - 返回内容预览（前200字符）: {content[:200]}")
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return content
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else:
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logger.error("❌ AI返回了空内容")
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logger.error(f" - 完整响应: {data}")
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logger.error(f" - 完成原因: {finish_reason}")
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# 重试机制
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max_retries = 3
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for attempt in range(max_retries):
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try:
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if attempt > 0:
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wait_time = min(2 ** attempt, 10) # 指数退避
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logger.warning(f"⚠️ OpenAI API调用失败，{wait_time}秒后重试（第{attempt + 1}/{max_retries}次）")
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await asyncio.sleep(wait_time)
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logger.info(f"🔵 开始调用OpenAI API（尝试 {attempt + 1}/{max_retries}）")
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logger.info(f" - 模型: {model}")
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logger.info(f" - 温度: {temperature}")
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logger.info(f" - 最大tokens: {max_tokens}")
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logger.info(f" - Prompt长度: {len(prompt)} 字符")
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logger.info(f" - 消息数量: {len(messages)}")
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url = f"{self.openai_base_url}/chat/completions"
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headers = {
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"Authorization": f"Bearer {self.openai_api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": model,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens
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}
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logger.debug(f" - 请求URL: {url}")
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logger.debug(f" - 请求头: Authorization=Bearer ***")
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response = await self.openai_http_client.post(url, headers=headers, json=payload)
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response.raise_for_status()
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data = response.json()
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logger.info(f"✅ OpenAI API调用成功")
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logger.info(f" - 响应ID: {data.get('id', 'N/A')}")
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logger.info(f" - 选项数量: {len(data.get('choices', []))}")
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logger.debug(f" - 完整API响应: {data}")
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if not data.get('choices'):
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logger.error("❌ OpenAI返回的choices为空")
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raise ValueError("API返回的响应格式错误：choices字段为空")
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choice = data['choices'][0]
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message = choice.get('message', {})
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finish_reason = choice.get('finish_reason')
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# DeepSeek R1特殊处理：只使用content（最终答案），忽略reasoning_content（思考过程）
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# reasoning_content是AI的思考过程，不是我们需要的JSON结果
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content = message.get('content', '')
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# 检查是否因达到长度限制而截断
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if finish_reason == 'length':
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logger.warning(f"⚠️ 响应因达到max_tokens限制而被截断")
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logger.warning(f" - 当前max_tokens: {max_tokens}")
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logger.warning(f" - 建议: 增加max_tokens参数（推荐2000+）")
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if content:
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logger.info(f" - 返回内容长度: {len(content)} 字符")
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logger.info(f" - 完成原因: {finish_reason}")
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logger.info(f" - 返回内容预览（前200字符）: {content[:200]}")
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return content
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else:
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logger.error("❌ AI返回了空内容")
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logger.error(f" - 完整响应: {data}")
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logger.error(f" - 完成原因: {finish_reason}")
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# 提供更详细的错误信息
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if finish_reason == 'length':
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raise ValueError(f"AI响应被截断且无有效内容。请增加max_tokens参数（当前: {max_tokens}，建议: 2000+）")
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else:
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raise ValueError(f"AI返回了空内容（finish_reason: {finish_reason}），请检查API配置或稍后重试")
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# 提供更详细的错误信息
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if finish_reason == 'length':
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raise ValueError(f"AI响应被截断且无有效内容。请增加max_tokens参数（当前: {max_tokens}，建议: 2000+）")
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else:
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raise ValueError(f"AI返回了空内容（finish_reason: {finish_reason}），请检查API配置或稍后重试")
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except httpx.HTTPStatusError as e:
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logger.error(f"❌ OpenAI API调用失败 (HTTP {e.response.status_code})")
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logger.error(f" - 错误信息: {e.response.text}")
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logger.error(f" - 模型: {model}")
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raise Exception(f"API返回错误 ({e.response.status_code}): {e.response.text}")
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except Exception as e:
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logger.error(f"❌ OpenAI API调用失败")
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logger.error(f" - 错误类型: {type(e).__name__}")
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logger.error(f" - 错误信息: {str(e)}")
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logger.error(f" - 模型: {model}")
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raise
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except httpx.ConnectError as e:
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logger.error(f"❌ OpenAI API连接失败 (尝试 {attempt + 1}/{max_retries}): {str(e)}")
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if attempt == max_retries - 1:
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raise Exception(f"连接失败，已重试{max_retries}次。请检查网络连接或API地址: {str(e)}")
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continue
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except httpx.HTTPStatusError as e:
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logger.error(f"❌ OpenAI API调用失败 (HTTP {e.response.status_code}, 尝试 {attempt + 1}/{max_retries})")
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logger.error(f" - 错误信息: {e.response.text}")
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# 某些错误不需要重试（如401、403）
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if e.response.status_code in [401, 403, 404]:
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raise Exception(f"API返回错误 ({e.response.status_code}): {e.response.text}")
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if attempt == max_retries - 1:
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raise Exception(f"API返回错误 ({e.response.status_code}): {e.response.text}")
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continue
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except httpx.TimeoutException as e:
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logger.error(f"❌ OpenAI API超时 (尝试 {attempt + 1}/{max_retries})")
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if attempt == max_retries - 1:
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raise Exception(f"API请求超时，已重试{max_retries}次: {str(e)}")
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continue
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except Exception as e:
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logger.error(f"❌ OpenAI API调用失败 (尝试 {attempt + 1}/{max_retries})")
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logger.error(f" - 错误类型: {type(e).__name__}")
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logger.error(f" - 错误信息: {str(e)}")
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if attempt == max_retries - 1:
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raise
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continue
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async def _generate_openai_with_tools(
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@@ -1044,6 +1084,297 @@ class AIService:
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**kwargs
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):
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yield chunk
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# ========== JSON 统一调用和自动重试 ==========
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@staticmethod
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def _clean_json_response(text: str) -> str:
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"""
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清洗 AI 返回的 JSON 响应
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去除常见的格式问题：
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- markdown 代码块标记 (```json ```)
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- 前后空白字符
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- 注释文字
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Args:
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text: AI 返回的原始文本
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Returns:
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清洗后的 JSON 字符串
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"""
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if not text:
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return text
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# 去除 markdown 代码块标记
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text = re.sub(r'^```json\s*\n?', '', text, flags=re.MULTILINE | re.IGNORECASE)
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text = re.sub(r'^```\s*\n?', '', text, flags=re.MULTILINE)
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text = re.sub(r'\n?```\s*$', '', text, flags=re.MULTILINE)
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# 去除前后空白
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text = text.strip()
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# 尝试提取第一个完整的 JSON 对象或数组
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# 查找第一个 { 或 [
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start_idx = -1
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for i, char in enumerate(text):
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if char in ('{', '['):
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start_idx = i
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break
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if start_idx == -1:
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return text
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# 从第一个括号开始提取
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text = text[start_idx:]
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# 查找匹配的结束括号
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bracket_stack = []
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end_idx = -1
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in_string = False
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escape_next = False
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for i, char in enumerate(text):
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if escape_next:
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escape_next = False
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continue
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if char == '\\':
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escape_next = True
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continue
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if char == '"':
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in_string = not in_string
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continue
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if in_string:
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continue
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if char in ('{', '['):
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bracket_stack.append(char)
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elif char == '}':
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if bracket_stack and bracket_stack[-1] == '{':
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bracket_stack.pop()
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if not bracket_stack:
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end_idx = i + 1
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break
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elif char == ']':
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if bracket_stack and bracket_stack[-1] == '[':
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bracket_stack.pop()
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if not bracket_stack:
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end_idx = i + 1
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break
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if end_idx > 0:
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return text[:end_idx]
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return text
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@staticmethod
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def _add_json_format_hint(original_prompt: str, failed_response: str, attempt: int) -> str:
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"""
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重试时添加格式纠正提示
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Args:
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original_prompt: 原始提示词
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failed_response: 上次失败的响应（截断显示）
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attempt: 当前尝试次数
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Returns:
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增强后的提示词
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"""
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error_preview = failed_response[:300] if failed_response else "无响应"
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return f"""{original_prompt}
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⚠️ 【第 {attempt} 次重试】上一次返回格式错误，请严格遵守以下规则：
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🔴 格式要求（必须严格遵守）：
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1. 只返回纯 JSON 对象或数组，不要有任何其他文字
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2. 不要使用 ```json``` 或 ``` 包裹 JSON
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3. 不要添加任何解释、说明或注释
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4. 确保 JSON 格式完全正确：
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- 所有括号必须匹配 {{}} []
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- 所有字符串必须用双引号 ""
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- 键值对用冒号分隔 :
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- 多个元素用逗号分隔 ,
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- 不要有多余的逗号
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❌ 上一次的错误返回示例：
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{error_preview}...
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✅ 请现在重新生成正确的 JSON 格式内容。"""
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async def call_with_json_retry(
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self,
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prompt: str,
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system_prompt: Optional[str] = None,
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max_retries: int = 3,
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
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provider: Optional[str] = None,
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model: Optional[str] = None,
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expected_type: Optional[str] = None # "object" 或 "array"
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) -> Dict[str, Any] | List[Dict[str, Any]]:
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"""
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统一的 JSON 调用方法，自动重试和格式修复
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这是一个专门用于需要返回 JSON 格式的 AI 调用封装，会自动：
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1. 清洗 AI 返回的内容（去除 markdown 标记等）
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2. 解析 JSON 并验证格式
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3. 失败时自动重试，并在提示词中添加纠正指引
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Args:
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prompt: 用户提示词
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system_prompt: 系统提示词（可选）
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max_retries: 最大重试次数，默认 3 次
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temperature: 温度参数（可选，使用默认值）
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max_tokens: 最大 token 数（可选，使用默认值）
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provider: AI 提供商（可选，使用默认值）
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model: 模型名称（可选，使用默认值）
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expected_type: 期望的 JSON 类型 "object" 或 "array"（可选，用于额外验证）
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Returns:
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解析后的 JSON 对象（dict）或数组（list）
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Raises:
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ValueError: 重试次数用尽仍未获得有效 JSON
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Examples:
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>>> # 获取 JSON 对象
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>>> result = await ai_service.call_with_json_retry(
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... prompt="生成一个角色",
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... expected_type="object"
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... )
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>>> print(result["name"])
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>>> # 获取 JSON 数组
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>>> results = await ai_service.call_with_json_retry(
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... prompt="生成3个角色",
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... expected_type="array"
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... )
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>>> print(len(results))
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"""
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last_error = None
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last_response = ""
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for attempt in range(1, max_retries + 1):
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try:
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logger.info(f"🔄 JSON 调用尝试 {attempt}/{max_retries}")
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# 第一次使用原始提示词，之后使用增强提示词
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current_prompt = prompt if attempt == 1 else self._add_json_format_hint(
|
||||
prompt, last_response, attempt
|
||||
)
|
||||
|
||||
# 调用 AI 生成内容
|
||||
if provider == "openai" and self.openai_client:
|
||||
response = await self._generate_openai(
|
||||
prompt=current_prompt,
|
||||
model=model or self.default_model,
|
||||
temperature=temperature or self.default_temperature,
|
||||
max_tokens=max_tokens or self.default_max_tokens,
|
||||
system_prompt=system_prompt
|
||||
)
|
||||
elif provider == "anthropic" and self.anthropic_client:
|
||||
response = await self._generate_anthropic(
|
||||
prompt=current_prompt,
|
||||
model=model or self.default_model,
|
||||
temperature=temperature or self.default_temperature,
|
||||
max_tokens=max_tokens or self.default_max_tokens,
|
||||
system_prompt=system_prompt
|
||||
)
|
||||
else:
|
||||
# 使用默认提供商
|
||||
if self.api_provider == "openai":
|
||||
response = await self._generate_openai(
|
||||
prompt=current_prompt,
|
||||
model=model or self.default_model,
|
||||
temperature=temperature or self.default_temperature,
|
||||
max_tokens=max_tokens or self.default_max_tokens,
|
||||
system_prompt=system_prompt
|
||||
)
|
||||
else:
|
||||
response = await self._generate_anthropic(
|
||||
prompt=current_prompt,
|
||||
model=model or self.default_model,
|
||||
temperature=temperature or self.default_temperature,
|
||||
max_tokens=max_tokens or self.default_max_tokens,
|
||||
system_prompt=system_prompt
|
||||
)
|
||||
|
||||
last_response = response
|
||||
|
||||
# 清洗响应内容
|
||||
cleaned = self._clean_json_response(response)
|
||||
logger.debug(f"清洗后的内容: {cleaned[:200]}...")
|
||||
|
||||
# 解析 JSON
|
||||
try:
|
||||
data = json.loads(cleaned)
|
||||
except json.JSONDecodeError as e:
|
||||
logger.warning(f"⚠️ JSON 解析失败: {e}")
|
||||
logger.debug(f"原始响应: {response[:500]}")
|
||||
logger.debug(f"清洗后: {cleaned[:500]}")
|
||||
raise
|
||||
|
||||
# 可选：验证 JSON 类型
|
||||
if expected_type:
|
||||
if expected_type == "object" and not isinstance(data, dict):
|
||||
raise ValueError(f"期望 JSON 对象，但得到 {type(data).__name__}")
|
||||
elif expected_type == "array" and not isinstance(data, list):
|
||||
raise ValueError(f"期望 JSON 数组，但得到 {type(data).__name__}")
|
||||
|
||||
logger.info(f"✅ JSON 解析成功 (尝试 {attempt}/{max_retries})")
|
||||
if isinstance(data, dict):
|
||||
logger.info(f" 返回对象，包含 {len(data)} 个键")
|
||||
elif isinstance(data, list):
|
||||
logger.info(f" 返回数组，包含 {len(data)} 个元素")
|
||||
|
||||
return data
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
last_error = e
|
||||
logger.warning(f"⚠️ 第 {attempt} 次尝试失败: JSON 解析错误")
|
||||
logger.warning(f" 错误位置: {e.msg} at line {e.lineno} column {e.colno}")
|
||||
|
||||
if attempt < max_retries:
|
||||
logger.info(f" 准备第 {attempt + 1} 次重试...")
|
||||
continue
|
||||
else:
|
||||
logger.error(f"❌ JSON 解析失败，已达到最大重试次数 {max_retries}")
|
||||
logger.error(f" 最后的响应内容:\n{last_response[:1000]}")
|
||||
raise ValueError(
|
||||
f"AI 返回内容无法解析为 JSON，已重试 {max_retries} 次。\n"
|
||||
f"最后错误: {e}\n"
|
||||
f"响应预览: {last_response[:200]}..."
|
||||
)
|
||||
|
||||
except ValueError as e:
|
||||
last_error = e
|
||||
logger.warning(f"⚠️ 第 {attempt} 次尝试失败: {e}")
|
||||
|
||||
if attempt < max_retries:
|
||||
logger.info(f" 准备第 {attempt + 1} 次重试...")
|
||||
continue
|
||||
else:
|
||||
logger.error(f"❌ 验证失败，已达到最大重试次数 {max_retries}")
|
||||
raise ValueError(
|
||||
f"AI 返回的 JSON 格式不符合要求，已重试 {max_retries} 次。\n"
|
||||
f"错误: {e}"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 第 {attempt} 次调用出现未预期错误: {type(e).__name__}: {e}")
|
||||
if attempt < max_retries:
|
||||
logger.info(f" 准备第 {attempt + 1} 次重试...")
|
||||
last_error = e
|
||||
continue
|
||||
else:
|
||||
raise
|
||||
|
||||
# 理论上不会到达这里，但以防万一
|
||||
raise ValueError(f"JSON 调用失败，已重试 {max_retries} 次。最后错误: {last_error}")
|
||||
|
||||
|
||||
# 创建全局AI服务实例
|
||||
|
||||
@@ -256,7 +256,7 @@ class AutoCharacterService:
|
||||
)
|
||||
|
||||
try:
|
||||
# 调用AI分析
|
||||
# 调用AI分析（使用统一的JSON调用方法）
|
||||
if enable_mcp and user_id:
|
||||
result = await self.ai_service.generate_text_with_mcp(
|
||||
prompt=prompt,
|
||||
@@ -266,28 +266,21 @@ class AutoCharacterService:
|
||||
max_tool_rounds=1
|
||||
)
|
||||
content = result.get("content", "")
|
||||
# 使用统一的JSON清洗方法
|
||||
cleaned = self.ai_service._clean_json_response(content)
|
||||
analysis = json.loads(cleaned)
|
||||
else:
|
||||
result = await self.ai_service.generate_text(prompt=prompt)
|
||||
content = result.get("content", "") if isinstance(result, dict) else result
|
||||
# 非MCP调用：使用带自动重试的JSON调用
|
||||
analysis = await self.ai_service.call_with_json_retry(
|
||||
prompt=prompt,
|
||||
max_retries=3
|
||||
)
|
||||
|
||||
# 清理并解析JSON
|
||||
cleaned = content.strip()
|
||||
if cleaned.startswith("```json"):
|
||||
cleaned = cleaned[7:]
|
||||
if cleaned.startswith("```"):
|
||||
cleaned = cleaned[3:]
|
||||
if cleaned.endswith("```"):
|
||||
cleaned = cleaned[:-3]
|
||||
cleaned = cleaned.strip()
|
||||
|
||||
analysis = json.loads(cleaned)
|
||||
logger.info(f" ✅ AI分析完成: needs_new_characters={analysis.get('needs_new_characters')}")
|
||||
|
||||
return analysis
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(f" ❌ 角色需求分析JSON解析失败: {e}")
|
||||
logger.error(f" 响应内容: {content[:500]}")
|
||||
return {"needs_new_characters": False}
|
||||
except Exception as e:
|
||||
logger.error(f" ❌ 角色需求分析失败: {e}")
|
||||
@@ -328,7 +321,7 @@ class AutoCharacterService:
|
||||
mcp_references="" # 暂时不使用MCP增强
|
||||
)
|
||||
|
||||
# 调用AI生成
|
||||
# 调用AI生成（使用统一的JSON调用方法）
|
||||
try:
|
||||
if enable_mcp and user_id:
|
||||
result = await self.ai_service.generate_text_with_mcp(
|
||||
@@ -339,20 +332,16 @@ class AutoCharacterService:
|
||||
max_tool_rounds=1
|
||||
)
|
||||
content = result.get("content", "")
|
||||
# 使用统一的JSON清洗方法
|
||||
cleaned = self.ai_service._clean_json_response(content)
|
||||
character_data = json.loads(cleaned)
|
||||
else:
|
||||
result = await self.ai_service.generate_text(prompt=prompt)
|
||||
content = result.get("content", "") if isinstance(result, dict) else result
|
||||
# 非MCP调用：使用带自动重试的JSON调用
|
||||
character_data = await self.ai_service.call_with_json_retry(
|
||||
prompt=prompt,
|
||||
max_retries=3
|
||||
)
|
||||
|
||||
# 解析JSON
|
||||
cleaned = content.strip()
|
||||
if cleaned.startswith("```json"):
|
||||
cleaned = cleaned[7:]
|
||||
if cleaned.startswith("```"):
|
||||
cleaned = cleaned[3:]
|
||||
if cleaned.endswith("```"):
|
||||
cleaned = cleaned[:-3]
|
||||
|
||||
character_data = json.loads(cleaned.strip())
|
||||
char_name = character_data.get('name', '未知')
|
||||
logger.info(f" ✅ 角色详情生成成功: {char_name}")
|
||||
logger.debug(f" 角色数据字段: {list(character_data.keys())}")
|
||||
|
||||
@@ -205,7 +205,9 @@ class MCPTestService:
|
||||
|
||||
if isinstance(test_arguments, str):
|
||||
try:
|
||||
test_arguments = json.loads(test_arguments)
|
||||
# 使用统一的JSON清洗方法
|
||||
cleaned_args = ai_service._clean_json_response(test_arguments)
|
||||
test_arguments = json.loads(cleaned_args)
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(f"❌ 解析AI参数失败: {e}")
|
||||
return MCPTestResult(
|
||||
|
||||
@@ -110,7 +110,7 @@ class PlotAnalyzer:
|
||||
|
||||
def _parse_analysis_response(self, response: str) -> Optional[Dict[str, Any]]:
|
||||
"""
|
||||
解析AI返回的分析结果
|
||||
解析AI返回的分析结果（使用统一的JSON清洗方法）
|
||||
|
||||
Args:
|
||||
response: AI返回的文本
|
||||
@@ -119,13 +119,8 @@ class PlotAnalyzer:
|
||||
解析后的字典,失败返回None
|
||||
"""
|
||||
try:
|
||||
# 清理响应文本
|
||||
cleaned = response.strip()
|
||||
|
||||
# 移除可能的markdown标记
|
||||
cleaned = re.sub(r'^```json\s*', '', cleaned)
|
||||
cleaned = re.sub(r'^```\s*', '', cleaned)
|
||||
cleaned = re.sub(r'\s*```$', '', cleaned)
|
||||
# 使用统一的JSON清洗方法
|
||||
cleaned = self.ai_service._clean_json_response(response)
|
||||
|
||||
# 尝试解析JSON
|
||||
result = json.loads(cleaned)
|
||||
@@ -137,22 +132,12 @@ class PlotAnalyzer:
|
||||
logger.warning(f"⚠️ 分析结果缺少字段: {field}")
|
||||
result[field] = [] if field != 'scores' else {}
|
||||
|
||||
logger.info("✅ 成功解析分析结果")
|
||||
return result
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(f"❌ JSON解析失败: {str(e)}")
|
||||
logger.error(f" 原始响应(前500字): {response[:500]}")
|
||||
|
||||
# 尝试提取JSON部分
|
||||
json_match = re.search(r'\{[\s\S]*\}', response)
|
||||
if json_match:
|
||||
try:
|
||||
result = json.loads(json_match.group())
|
||||
logger.info("✅ 通过正则提取成功解析JSON")
|
||||
return result
|
||||
except:
|
||||
pass
|
||||
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 解析异常: {str(e)}")
|
||||
|
||||
@@ -109,7 +109,7 @@ class PlotExpansionService:
|
||||
context_info = await self._get_outline_context(outline, project.id, db)
|
||||
|
||||
# 获取自定义提示词模板
|
||||
template = await PromptService.get_template("PLOT_EXPANSION_SINGLE_BATCH", project.user_id, db)
|
||||
template = await PromptService.get_template("OUTLINE_EXPAND_SINGLE", project.user_id, db)
|
||||
# 格式化提示词
|
||||
prompt = PromptService.format_prompt(
|
||||
template,
|
||||
@@ -209,7 +209,7 @@ class PlotExpansionService:
|
||||
⚠️ 当前是第{current_start_index}-{current_start_index + current_batch_size - 1}节（共{target_chapter_count}节中的一部分）
|
||||
"""
|
||||
# 获取自定义提示词模板
|
||||
template = await PromptService.get_template("PLOT_EXPANSION_MULTI_BATCH", project.user_id, db)
|
||||
template = await PromptService.get_template("OUTLINE_EXPAND_MULTI", project.user_id, db)
|
||||
# 格式化提示词
|
||||
prompt = PromptService.format_prompt(
|
||||
template,
|
||||
@@ -497,17 +497,10 @@ class PlotExpansionService:
|
||||
ai_response: str,
|
||||
outline_id: str
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""解析AI的展开响应"""
|
||||
"""解析AI的展开响应（使用统一的JSON清洗方法）"""
|
||||
try:
|
||||
# 清理响应文本
|
||||
cleaned_text = ai_response.strip()
|
||||
if cleaned_text.startswith('```json'):
|
||||
cleaned_text = cleaned_text[7:]
|
||||
if cleaned_text.startswith('```'):
|
||||
cleaned_text = cleaned_text[3:]
|
||||
if cleaned_text.endswith('```'):
|
||||
cleaned_text = cleaned_text[:-3]
|
||||
cleaned_text = cleaned_text.strip()
|
||||
# 使用统一的JSON清洗方法
|
||||
cleaned_text = self.ai_service._clean_json_response(ai_response)
|
||||
|
||||
# 解析JSON
|
||||
chapter_plans = json.loads(cleaned_text)
|
||||
@@ -520,10 +513,11 @@ class PlotExpansionService:
|
||||
for plan in chapter_plans:
|
||||
plan["outline_id"] = outline_id
|
||||
|
||||
logger.info(f"✅ 成功解析 {len(chapter_plans)} 个章节规划")
|
||||
return chapter_plans
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(f"解析AI响应失败: {e}, 响应内容: {ai_response[:500]}")
|
||||
logger.error(f"❌ 解析AI响应失败: {e}, 响应内容: {ai_response[:500]}")
|
||||
# 返回一个基础规划
|
||||
return [{
|
||||
"outline_id": outline_id,
|
||||
@@ -537,6 +531,20 @@ class PlotExpansionService:
|
||||
"conflict_type": "未知",
|
||||
"estimated_words": 3000
|
||||
}]
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 解析异常: {str(e)}")
|
||||
return [{
|
||||
"outline_id": outline_id,
|
||||
"sub_index": 1,
|
||||
"title": "解析异常的默认章节",
|
||||
"plot_summary": "系统错误",
|
||||
"key_events": [],
|
||||
"character_focus": [],
|
||||
"emotional_tone": "未知",
|
||||
"narrative_goal": "需要重新生成",
|
||||
"conflict_type": "未知",
|
||||
"estimated_words": 3000
|
||||
}]
|
||||
|
||||
|
||||
async def _renumber_subsequent_chapters(
|
||||
|
||||
@@ -9,75 +9,125 @@ class WritingStyleManager:
|
||||
# 预设风格配置
|
||||
PRESET_STYLES = {
|
||||
"natural": {
|
||||
"name": "自然流畅",
|
||||
"description": "像普通人讲故事一样自然，不刻意修饰，有生活气息",
|
||||
"name": "自然沉浸 (Natural & Immersive)",
|
||||
"description": "祛除翻译腔，强调生活质感，像呼吸一样自然的叙事",
|
||||
"prompt_content": """
|
||||
**自然流畅风格要求：**
|
||||
- 用简单朴实的语言叙述，避免华丽辞藻
|
||||
- 像在和朋友聊天一样讲故事
|
||||
- 保持轻松自然的节奏，不要刻意营造氛围
|
||||
- 多用短句，少用长句和排比
|
||||
- 让读者感觉舒服，不要让人觉得在"看文学作品"
|
||||
### 核心指令：自然沉浸风格
|
||||
请模拟人类作家在放松状态下的写作，通过以下规则消除“AI味”：
|
||||
|
||||
1. **拒绝翻译腔与书面化**：
|
||||
- 严禁使用“一种...的感觉”、“随着...”、“与此同时”等连接词。
|
||||
- 多用短句和“流水句”，模拟人类视线的移动和思维的跳跃。
|
||||
- 口语化叙述，但不要滥用语气词，而是通过句子的长短节奏来体现语气。
|
||||
|
||||
2. **生活化的颗粒度**：
|
||||
- 描写不要宏大，要聚焦在具体的、微小的生活细节（如：杯子上的水渍、衣服的褶皱）。
|
||||
- 允许逻辑上的适度“松散”，不要让每句话都像说明书一样严丝合缝。
|
||||
|
||||
3. **具体的“展示”**：
|
||||
- 不要写“他很生气”，要写他“把烟头按灭在还没吃完的米饭里”。
|
||||
- 避免使用抽象的形容词（如：巨大的、美丽的、悲伤的），必须用名词和动词来承载画面。
|
||||
"""
|
||||
},
|
||||
"classical": {
|
||||
"name": "古典优雅",
|
||||
"description": "典雅精致的文学风格，注重意境和韵味",
|
||||
"name": "古典雅致 (Classical & Elegant)",
|
||||
"description": "白话文与古典韵味的结合，强调留白与炼字",
|
||||
"prompt_content": """
|
||||
**古典优雅风格要求：**
|
||||
- 使用优美典雅的语言，注重文字的韵律感
|
||||
- 善用比喻、拟人等修辞手法
|
||||
- 注重意境营造，追求诗意美感
|
||||
- 可适当引用古诗词或典故（需符合世界观）
|
||||
- 保持端庄雅致的叙述节奏
|
||||
### 核心指令：古典雅致风格
|
||||
请模仿民国时期或古典白话小说的笔触，构建端庄且富有余味的叙事：
|
||||
|
||||
1. **炼字与韵律**：
|
||||
- 尽量使用双音节词或四字短语，但严禁堆砌辞藻。
|
||||
- 注重句子的声调韵律，读起来要有金石之声或流水之韵。
|
||||
- 适当使用倒装句或定语后置，增加古雅感。
|
||||
|
||||
2. **克制的修辞**：
|
||||
- 少用现代的比喻（如“像机器一样”），多用取自自然的比喻（如“如风过林”）。
|
||||
- **意在言外**：不要把话说透，留三分余地。写景即是写情，不要将情感直接剖白。
|
||||
|
||||
3. **禁忌**：
|
||||
- 严禁使用现代科技词汇（除非题材需要）、网络用语或过于西化的句式（如长定语从句）。
|
||||
- 避免滥用“之乎者也”，追求的是“神似”而非生硬的半文半白。
|
||||
"""
|
||||
},
|
||||
"modern": {
|
||||
"name": "现代简约",
|
||||
"description": "简洁明快的现代风格，注重效率和直接表达",
|
||||
"name": "冷硬现代 (Modern & Hard-boiled)",
|
||||
"description": "海明威式的冰山理论，节奏极快，零度情感",
|
||||
"prompt_content": """
|
||||
**现代简约风格要求：**
|
||||
- 语言简洁有力，直达重点
|
||||
- 多用短句和短段落，节奏明快
|
||||
- 避免冗长描写，注重信息密度
|
||||
- 使用现代口语化表达
|
||||
- 情节推进快速，少做环境渲染
|
||||
### 核心指令：冷硬现代风格
|
||||
请采用“极简主义”和“零度写作”手法，去除所有矫饰：
|
||||
|
||||
1. **冰山理论**：
|
||||
- **只写动作和对话，完全剔除心理描写和形容词堆砌。**
|
||||
- 不要告诉读者角色感觉如何，通过角色的反应和环境的冷峻反馈来体现。
|
||||
|
||||
2. **电影蒙太奇节奏**：
|
||||
- 句子要短、脆、硬。像手术刀一样切开场景。
|
||||
- 段落之间快速切换，不要用过渡句连接，直接跳切。
|
||||
|
||||
3. **高信息密度**：
|
||||
- 删除所有废话。如果一个词删掉不影响理解，就删掉它。
|
||||
- 多用名词和强动词（Strong Verbs），少用副词（Adverbs）。例如：不要写“他重重地关上门”，写“他摔上了门”。
|
||||
"""
|
||||
},
|
||||
"poetic": {
|
||||
"name": "诗意抒情",
|
||||
"description": "富有诗意和情感张力的抒情风格",
|
||||
"name": "意识流 (Stream of Consciousness)",
|
||||
"description": "注重感官通感与内心独白，打破现实与幻想的边界",
|
||||
"prompt_content": """
|
||||
**诗意抒情风格要求：**
|
||||
- 注重情感表达和内心描写
|
||||
- 善用景物描写烘托情绪
|
||||
- 语言富有韵律和美感
|
||||
- 细腻刻画人物心理活动
|
||||
- 营造情感氛围，引发共鸣
|
||||
### 核心指令：意识流/诗意风格
|
||||
请侧重于主观感受的流动，而非客观事实的记录：
|
||||
|
||||
1. **通感与陌生化**：
|
||||
- 打通五感（如：听到了颜色的声音，闻到了悲伤的气味）。
|
||||
- 使用“陌生化”的语言，把熟悉的事物写得陌生，迫使读者重新审视。
|
||||
|
||||
2. **情绪的具象化**：
|
||||
- **绝对禁止**直接出现“开心”、“痛苦”等抽象词汇。
|
||||
- 必须寻找“客观对应物”（Objective Correlative），将情绪投射到具体的景物上（如：生锈的铁轨、发霉的橘子）。
|
||||
|
||||
3. **流动的句式**：
|
||||
- 句子可以很长，包含多重意象的叠加。
|
||||
- 允许思维的非线性跳跃，模拟梦境或深层潜意识的逻辑。
|
||||
"""
|
||||
},
|
||||
"concise": {
|
||||
"name": "精炼利落",
|
||||
"description": "惜字如金的简练风格，每个字都有意义",
|
||||
"name": "白描速写 (Sketch & Concise)",
|
||||
"description": "只有骨架的叙事，强调绝对的精准和功能性",
|
||||
"prompt_content": """
|
||||
**精炼利落风格要求：**
|
||||
- 删除所有冗余描写，每句话都要有作用
|
||||
- 多用动词，少用形容词和副词
|
||||
- 对话干脆利落，不拖泥带水
|
||||
- 环境描写点到为止
|
||||
- 用最少的字数传达最多的信息
|
||||
### 核心指令：白描速写风格
|
||||
请像速写画家一样，只勾勒线条，不涂抹色彩：
|
||||
|
||||
1. **功能性第一**：
|
||||
- 每一句话必须推动情节，或者揭示关键信息。
|
||||
- 如果一句话只是为了渲染气氛，删掉它。
|
||||
|
||||
2. **主谓宾结构**：
|
||||
- 尽量使用简单的主谓宾结构，减少修饰语。
|
||||
- 避免复杂的从句和嵌套结构。
|
||||
|
||||
3. **直击核心**：
|
||||
- 对话直接进入主题，去除寒暄和废话。
|
||||
- 环境描写仅限于对情节有物理影响的物体（如：挡路的石头、藏在桌下的枪）。
|
||||
"""
|
||||
},
|
||||
"vivid": {
|
||||
"name": "生动形象",
|
||||
"description": "画面感强烈，让读者如临其境",
|
||||
"name": "感官特写 (Sensory & Vivid)",
|
||||
"description": "高分辨率的描写，强调材质、光影和微观细节",
|
||||
"prompt_content": """
|
||||
**生动形象风格要求：**
|
||||
- 注重细节描写，让场景具体可感
|
||||
- 调动五感（视觉、听觉、触觉、嗅觉、味觉）
|
||||
- 使用鲜明的比喻和形象化语言
|
||||
- 让读者能"看到"场景和动作
|
||||
- 人物表情、动作要具体生动
|
||||
### 核心指令：感官特写风格
|
||||
请将镜头推到特写级别（Macro Lens），捕捉常人忽略的细节：
|
||||
|
||||
1. **反套路细节**：
|
||||
- 不要写大众化的细节（如：蓝天白云），要写具有**独特性**的细节（如：云层边缘那抹像淤青一样的灰紫色）。
|
||||
- 关注物体的**质感（Texture）**：粗糙的、粘稠的、冰凉的、颗粒感的。
|
||||
|
||||
2. **动态捕捉**：
|
||||
- 不要写静止的画面，要写光影的流变、灰尘的飞舞、肌肉的抽动。
|
||||
- 让读者产生生理性的反应（如：痛感、饥饿感、窒息感）。
|
||||
|
||||
3. **禁用词汇**：
|
||||
- 禁止使用“映入眼帘”、“宛如画卷”等陈词滥调。
|
||||
- 必须用具体的动词带动感官描写。
|
||||
"""
|
||||
}
|
||||
}
|
||||
@@ -339,15 +389,23 @@ class PromptService:
|
||||
4. 所有内容描述中严禁使用任何特殊符号，包括但不限于中文引号、英文引号、方括号、书名号等"""
|
||||
|
||||
# 向导大纲生成提示词
|
||||
COMPLETE_OUTLINE_GENERATION = """你是一位经验丰富的小说作家和编剧。请根据以下信息生成完整的{chapter_count}章小说大纲：
|
||||
OUTLINE_CREATE = """你是一位经验丰富的小说作家和编剧。请根据以下信息为小说生成**开篇{chapter_count}章**的大纲：
|
||||
|
||||
【重要说明】
|
||||
本次任务是为项目初始化生成开头部分的大纲，而不是整本书的完整大纲。这些章节应该：
|
||||
- 完成故事的**开局设定**和**世界观展示**
|
||||
- 引入主要角色，建立初始关系
|
||||
- 埋下核心矛盾和悬念钩子
|
||||
- 为后续剧情发展打下基础
|
||||
- **不需要完整的故事闭环**，结尾应该为续写留出空间
|
||||
|
||||
基本信息：
|
||||
- 书名：{title}
|
||||
- 主题：{theme}
|
||||
- 类型：{genre}
|
||||
- 章节数：{chapter_count}
|
||||
- 开篇章节数：{chapter_count}
|
||||
- 叙事视角：{narrative_perspective}
|
||||
- 目标字数：{target_words}
|
||||
- 全书目标字数：{target_words}
|
||||
|
||||
世界观：
|
||||
- 时间背景：{time_period}
|
||||
@@ -362,13 +420,14 @@ class PromptService:
|
||||
|
||||
其他要求：{requirements}
|
||||
|
||||
整体要求：
|
||||
- 结构完整：起承转合清晰
|
||||
- 情节连贯：章节之间紧密衔接
|
||||
- 冲突递进：矛盾逐步升级
|
||||
- 人物成长：角色有明确的变化弧线
|
||||
- 节奏把控：有张有弛
|
||||
- 视角统一：采用{narrative_perspective}视角叙事
|
||||
开篇大纲要求：
|
||||
- **开局设定**：前几章完成世界观呈现、主角登场、初始状态建立
|
||||
- **矛盾引入**：引出核心冲突或故事主线，但不急于展开
|
||||
- **角色亮相**：主要角色依次登场，展示性格特点和相互关系
|
||||
- **节奏控制**：开篇不宜过快，给读者适应和代入的时间
|
||||
- **悬念设置**：埋下伏笔和钩子，为后续续写大纲预留发展空间
|
||||
- **视角统一**：采用{narrative_perspective}视角叙事
|
||||
- **留白艺术**：结尾不要收束过紧，要为后续剧情留出足够的发展空间
|
||||
|
||||
**重要格式要求：**
|
||||
1. 只返回纯JSON数组格式，不要包含任何markdown标记、代码块标记或其他说明文字
|
||||
@@ -379,7 +438,7 @@ class PromptService:
|
||||
[
|
||||
{{
|
||||
"chapter_number": 1,
|
||||
"title": "第一章标题",
|
||||
"title": "章节标题",
|
||||
"summary": "章节概要的详细描述（100-200字），包含主要情节、冲突、转折等",
|
||||
"scenes": ["场景1描述", "场景2描述", "场景3描述"],
|
||||
"characters": ["角色1", "角色2"],
|
||||
@@ -389,7 +448,7 @@ class PromptService:
|
||||
}},
|
||||
{{
|
||||
"chapter_number": 2,
|
||||
"title": "第二章标题",
|
||||
"title": "章节标题",
|
||||
"summary": "章节概要...",
|
||||
"scenes": ["场景1", "场景2"],
|
||||
"characters": ["角色1", "角色2"],
|
||||
@@ -405,7 +464,7 @@ class PromptService:
|
||||
3. 所有内容描述中严禁使用任何特殊符号"""
|
||||
|
||||
# 大纲续写提示词（记忆增强版）
|
||||
OUTLINE_CONTINUE_GENERATION = """你是一位经验丰富的小说作家和编剧。请基于以下信息续写小说大纲：
|
||||
OUTLINE_CONTINUE = """你是一位经验丰富的小说作家和编剧。请基于以下信息续写小说大纲：
|
||||
|
||||
【项目信息】
|
||||
- 书名：{title}
|
||||
@@ -558,7 +617,7 @@ class PromptService:
|
||||
3. 符合角色性格设定
|
||||
4. 体现世界观特色
|
||||
5. 使用{narrative_perspective}视角
|
||||
6. **字数要求：目标{target_word_count}字，不得低于{target_word_count}字，建议控制在{target_word_count}至{max_word_count}字之间**
|
||||
6. 字数要求：目标{target_word_count}字，不得低于{target_word_count}字，必须严格控制在{target_word_count}至{max_word_count}字之间
|
||||
7. 语言自然流畅，避免AI痕迹
|
||||
|
||||
请直接输出章节正文内容，不要包含章节标题和其他说明文字。"""
|
||||
@@ -624,7 +683,7 @@ class PromptService:
|
||||
|
||||
5. **写作风格**：
|
||||
- 使用{narrative_perspective}视角
|
||||
- **字数要求：目标{target_word_count}字，不得低于{target_word_count}字，建议控制在{target_word_count}至{max_word_count}字之间**
|
||||
- 字数要求：目标{target_word_count}字，不得低于{target_word_count}字，必须严格控制在{target_word_count}至{max_word_count}字之间
|
||||
- 语言自然流畅，避免AI痕迹
|
||||
- 体现世界观特色
|
||||
|
||||
@@ -641,41 +700,6 @@ class PromptService:
|
||||
|
||||
请直接输出章节正文内容，不要包含章节标题和其他说明文字。"""
|
||||
|
||||
# 大纲生成提示词
|
||||
OUTLINE_GENERATION = """你是一位经验丰富的小说作家和编剧。请根据以下信息生成小说大纲：
|
||||
|
||||
类型：{genre}
|
||||
主题：{theme}
|
||||
目标字数：{target_words}
|
||||
其他要求：{requirements}
|
||||
|
||||
请生成一个完整的章节大纲框架，包含：
|
||||
1. 合理的章节数量（根据字数）
|
||||
2. 每章的标题和内容概要
|
||||
3. 清晰的故事结构（起承转合）
|
||||
4. 情节的递进和冲突升级
|
||||
5. 角色的成长弧线
|
||||
|
||||
**重要格式要求：**
|
||||
1. 只返回纯JSON格式，不要包含任何markdown标记、代码块标记或其他说明文字
|
||||
2. JSON字符串值的内容描述中严禁使用任何特殊符号（包括中文引号、英文引号、方括号、书名号等）
|
||||
3. 所有专有名词直接书写，不使用任何符号包裹
|
||||
|
||||
请严格按照以下JSON格式返回：
|
||||
{{
|
||||
"chapters": [
|
||||
{{
|
||||
"order": 1,
|
||||
"title": "章节标题",
|
||||
"content": "章节内容概要（150-200字）"
|
||||
}}
|
||||
]
|
||||
}}
|
||||
|
||||
再次强调：
|
||||
1. 只返回纯JSON对象，不要有```json```这样的标记
|
||||
2. 所有内容描述中严禁使用任何特殊符号
|
||||
3. 不要有任何额外的文字说明"""
|
||||
|
||||
# 单个角色生成提示词
|
||||
SINGLE_CHARACTER_GENERATION = """你是一位专业的角色设定师。请根据以下信息创建一个立体饱满的小说角色。
|
||||
@@ -910,7 +934,7 @@ class PromptService:
|
||||
- 冲突解决进度(0-100%)
|
||||
|
||||
### 4. 情感曲线 (Emotional Arc)
|
||||
- 主导情绪: 紧张/温馨/悲伤/激昂/平静等
|
||||
- 主导情绪（最多10个字）: 紧张/温馨/悲伤/激昂/平静/压抑/欢快/恐惧/期待/失落等
|
||||
- 情感强度(1-10)
|
||||
- 情绪变化轨迹描述
|
||||
|
||||
@@ -975,7 +999,7 @@ class PromptService:
|
||||
"resolution_progress": 0.3
|
||||
}},
|
||||
"emotional_arc": {{
|
||||
"primary_emotion": "紧张",
|
||||
"primary_emotion": "紧张焦虑",
|
||||
"intensity": 8,
|
||||
"curve": "平静→紧张→高潮→释放",
|
||||
"secondary_emotions": ["期待", "焦虑"]
|
||||
@@ -1031,7 +1055,7 @@ class PromptService:
|
||||
只返回JSON,不要其他说明。"""
|
||||
|
||||
# 大纲单批次展开提示词
|
||||
PLOT_EXPANSION_SINGLE_BATCH = """你是专业的小说情节架构师。请分析以下大纲节点，将其展开为 {target_chapter_count} 个章节的详细规划。
|
||||
OUTLINE_EXPAND_SINGLE = """你是专业的小说情节架构师。请分析以下大纲节点，将其展开为 {target_chapter_count} 个章节的详细规划。
|
||||
|
||||
【项目信息】
|
||||
小说名称：{project_title}
|
||||
@@ -1125,7 +1149,7 @@ class PromptService:
|
||||
"""
|
||||
|
||||
# 大纲分批展开提示词
|
||||
PLOT_EXPANSION_MULTI_BATCH = """你是专业的小说情节架构师。请继续分析以下大纲节点，将其展开为第{start_index}-{end_index}节（共{target_chapter_count}个章节）的详细规划。
|
||||
OUTLINE_EXPAND_MULTI = """你是专业的小说情节架构师。请继续分析以下大纲节点，将其展开为第{start_index}-{end_index}节（共{target_chapter_count}个章节）的详细规划。
|
||||
|
||||
【项目信息】
|
||||
小说名称：{project_title}
|
||||
@@ -1388,80 +1412,6 @@ class PromptService:
|
||||
4. 相关领域的人物原型
|
||||
|
||||
请查询最关键的1个问题（不要超过1个）。"""
|
||||
# 大纲展开为多章节的提示词
|
||||
OUTLINE_EXPANSION = """你是专业的小说情节架构师。请分析以下大纲节点，将其展开为 {target_chapters} 个章节的详细规划。
|
||||
|
||||
【项目信息】
|
||||
小说名称：{title}
|
||||
类型：{genre}
|
||||
主题：{theme}
|
||||
叙事视角：{narrative_perspective}
|
||||
|
||||
【世界观背景】
|
||||
时间背景：{time_period}
|
||||
地理位置：{location}
|
||||
氛围基调：{atmosphere}
|
||||
世界规则：{rules}
|
||||
|
||||
【角色信息】
|
||||
{characters_info}
|
||||
|
||||
【大纲节点】
|
||||
序号：第 {outline_order} 节
|
||||
标题：{outline_title}
|
||||
内容：{outline_content}
|
||||
|
||||
【上下文】
|
||||
{context_info}
|
||||
|
||||
【展开策略】
|
||||
{strategy_instruction}
|
||||
|
||||
【任务要求】
|
||||
1. 深度分析该大纲的剧情容量和叙事节奏
|
||||
2. 识别关键剧情点、冲突点和情感转折点
|
||||
3. 将大纲拆解为 {target_chapters} 个章节，每章需包含：
|
||||
- sub_index: 子章节序号（1, 2, 3...）
|
||||
- title: 章节标题（体现该章核心冲突或情感）
|
||||
- plot_summary: 剧情摘要（200-300字，详细描述该章发生的事件）
|
||||
- key_events: 关键事件列表（3-5个关键剧情点）
|
||||
- character_focus: 角色焦点（主要涉及的角色名称）
|
||||
- emotional_tone: 情感基调（如：紧张、温馨、悲伤、激动等）
|
||||
- narrative_goal: 叙事目标（该章要达成的叙事效果）
|
||||
- conflict_type: 冲突类型（如：内心挣扎、人际冲突、环境挑战等）
|
||||
- estimated_words: 预计字数（建议2000-5000字）
|
||||
{scene_instruction}
|
||||
4. 确保章节间：
|
||||
- 衔接自然流畅
|
||||
- 剧情递进合理
|
||||
- 节奏张弛有度
|
||||
- 每章都有明确的叙事价值
|
||||
|
||||
**重要格式要求：**
|
||||
1. 只返回纯JSON数组格式，不要包含任何markdown标记、代码块标记或其他说明文字
|
||||
2. JSON字符串值的内容描述中严禁使用任何特殊符号（包括中文引号、英文引号、方括号、书名号等）
|
||||
3. 所有专有名词直接书写，不使用任何符号包裹
|
||||
|
||||
请严格按照以下JSON数组格式输出：
|
||||
[
|
||||
{{
|
||||
"sub_index": 1,
|
||||
"title": "章节标题",
|
||||
"plot_summary": "该章详细剧情摘要（200-300字）...",
|
||||
"key_events": ["关键事件1", "关键事件2", "关键事件3"],
|
||||
"character_focus": ["角色A", "角色B"],
|
||||
"emotional_tone": "情感基调",
|
||||
"narrative_goal": "叙事目标",
|
||||
"conflict_type": "冲突类型",
|
||||
"estimated_words": 3000{scene_field}
|
||||
}}
|
||||
]
|
||||
|
||||
再次强调：
|
||||
1. 只返回纯JSON数组，不要有```json```这样的标记
|
||||
2. 数组中要包含{target_chapters}个章节对象
|
||||
3. 每个plot_summary必须是200-300字的详细描述
|
||||
4. 所有内容描述中严禁使用任何特殊符号"""
|
||||
|
||||
# 自动角色引入 - 预测性分析提示词（方案A）
|
||||
AUTO_CHARACTER_ANALYSIS = """你是专业的小说角色设计顾问。请根据即将续写的剧情方向，预测是否需要引入新角色。
|
||||
@@ -1667,451 +1617,6 @@ class PromptService:
|
||||
except KeyError as e:
|
||||
raise ValueError(f"缺少必需的参数: {e}")
|
||||
|
||||
@classmethod
|
||||
def get_denoising_prompt(cls, original_text: str) -> str:
|
||||
"""获取AI去味提示词"""
|
||||
return cls.format_prompt(
|
||||
cls.AI_DENOISING,
|
||||
original_text=original_text
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_world_building_prompt(cls, title: str, theme: str, genre: str = "", description: str = "") -> str:
|
||||
"""获取世界构建提示词"""
|
||||
return cls.format_prompt(
|
||||
cls.WORLD_BUILDING,
|
||||
title=title,
|
||||
theme=theme,
|
||||
genre=genre or "通用类型",
|
||||
description=description or "暂无简介"
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_characters_batch_prompt(cls, count: int, time_period: str, location: str,
|
||||
atmosphere: str, rules: str, theme: str,
|
||||
genre: str = "", requirements: str = "") -> str:
|
||||
"""获取批量角色生成提示词"""
|
||||
return cls.format_prompt(
|
||||
cls.CHARACTERS_BATCH_GENERATION,
|
||||
count=count,
|
||||
time_period=time_period,
|
||||
location=location,
|
||||
atmosphere=atmosphere,
|
||||
rules=rules,
|
||||
theme=theme,
|
||||
genre=genre or "通用类型",
|
||||
requirements=requirements or "无特殊要求"
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_complete_outline_prompt(cls, title: str, theme: str, genre: str,
|
||||
chapter_count: int, narrative_perspective: str,
|
||||
target_words: int, time_period: str, location: str,
|
||||
atmosphere: str, rules: str, characters_info: str,
|
||||
requirements: str = "",
|
||||
mcp_references: str = "") -> str:
|
||||
"""获取向导大纲生成提示词（支持MCP增强）"""
|
||||
# 格式化MCP参考资料
|
||||
mcp_text = ""
|
||||
if mcp_references:
|
||||
mcp_text = "【📚 MCP工具搜索 - 情节设计参考】\n"
|
||||
mcp_text += "以下是通过MCP工具搜索到的情节设计参考资料，可用于设计大纲结构和情节发展：\n\n"
|
||||
mcp_text += mcp_references
|
||||
mcp_text += "\n"
|
||||
|
||||
return cls.format_prompt(
|
||||
cls.COMPLETE_OUTLINE_GENERATION,
|
||||
title=title,
|
||||
theme=theme,
|
||||
genre=genre,
|
||||
chapter_count=chapter_count,
|
||||
narrative_perspective=narrative_perspective,
|
||||
target_words=target_words,
|
||||
time_period=time_period,
|
||||
location=location,
|
||||
atmosphere=atmosphere,
|
||||
rules=rules,
|
||||
characters_info=characters_info,
|
||||
mcp_references=mcp_text,
|
||||
requirements=requirements or "无特殊要求"
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_chapter_generation_prompt(cls, title: str, theme: str, genre: str,
|
||||
narrative_perspective: str, time_period: str,
|
||||
location: str, atmosphere: str, rules: str,
|
||||
characters_info: str, outlines_context: str,
|
||||
chapter_number: int, chapter_title: str,
|
||||
chapter_outline: str, style_content: str = "",
|
||||
target_word_count: int = 3000,
|
||||
memory_context: dict = None,
|
||||
mcp_references: str = "",
|
||||
outline_mode: str = "one-to-many") -> str:
|
||||
"""
|
||||
获取章节完整创作提示词
|
||||
|
||||
Args:
|
||||
style_content: 写作风格要求内容，如果提供则会追加到提示词中
|
||||
target_word_count: 目标字数，默认3000字
|
||||
memory_context: 记忆上下文（可选）
|
||||
mcp_references: MCP工具搜索的参考资料（可选）
|
||||
outline_mode: 大纲模式 (one-to-one/one-to-many)
|
||||
"""
|
||||
# 计算最大字数（目标字数+1000）
|
||||
max_word_count = target_word_count + 1000
|
||||
|
||||
# 格式化记忆上下文
|
||||
memory_text = ""
|
||||
if memory_context:
|
||||
memory_text = "\n【🧠 智能记忆系统 - 重要参考】\n"
|
||||
memory_text += memory_context.get('recent_context', '')
|
||||
memory_text += "\n" + memory_context.get('relevant_memories', '')
|
||||
memory_text += "\n" + memory_context.get('foreshadows', '')
|
||||
memory_text += "\n" + memory_context.get('character_states', '')
|
||||
memory_text += "\n" + memory_context.get('plot_points', '')
|
||||
|
||||
# 格式化MCP参考资料
|
||||
mcp_text = ""
|
||||
if mcp_references:
|
||||
mcp_text = "\n【📚 MCP工具搜索 - 参考资料】\n"
|
||||
mcp_text += "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_text += mcp_references
|
||||
mcp_text += "\n"
|
||||
|
||||
# 根据大纲模式添加创作指导
|
||||
mode_instruction = ""
|
||||
if outline_mode == 'one-to-one':
|
||||
mode_instruction = "\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"
|
||||
else:
|
||||
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"
|
||||
|
||||
base_prompt = cls.format_prompt(
|
||||
cls.CHAPTER_GENERATION,
|
||||
title=title,
|
||||
theme=theme,
|
||||
genre=genre,
|
||||
narrative_perspective=narrative_perspective,
|
||||
time_period=time_period,
|
||||
location=location,
|
||||
atmosphere=atmosphere,
|
||||
rules=rules,
|
||||
characters_info=characters_info,
|
||||
outlines_context=outlines_context,
|
||||
chapter_number=chapter_number,
|
||||
chapter_title=chapter_title,
|
||||
chapter_outline=chapter_outline,
|
||||
target_word_count=target_word_count,
|
||||
max_word_count=max_word_count
|
||||
)
|
||||
|
||||
# 插入记忆上下文和MCP参考资料
|
||||
insert_text = ""
|
||||
if memory_text:
|
||||
insert_text += memory_text
|
||||
if mcp_text:
|
||||
insert_text += mcp_text
|
||||
|
||||
if insert_text:
|
||||
base_prompt = base_prompt.replace(
|
||||
"本章信息：",
|
||||
insert_text + mode_instruction + "\n\n本章信息："
|
||||
)
|
||||
else:
|
||||
# 没有记忆和MCP时也要插入模式说明
|
||||
base_prompt = base_prompt.replace(
|
||||
"本章信息：",
|
||||
mode_instruction + "\n\n本章信息："
|
||||
)
|
||||
|
||||
# 如果有风格要求，应用到提示词中
|
||||
if style_content:
|
||||
return WritingStyleManager.apply_style_to_prompt(base_prompt, style_content)
|
||||
|
||||
return base_prompt
|
||||
|
||||
@classmethod
|
||||
def get_chapter_generation_with_context_prompt(cls, title: str, theme: str, genre: str,
|
||||
narrative_perspective: str, time_period: str,
|
||||
location: str, atmosphere: str, rules: str,
|
||||
characters_info: str, outlines_context: str,
|
||||
previous_content: str, chapter_number: int,
|
||||
chapter_title: str, chapter_outline: str,
|
||||
style_content: str = "",
|
||||
target_word_count: int = 3000,
|
||||
memory_context: dict = None,
|
||||
mcp_references: str = "",
|
||||
outline_mode: str = "one-to-many") -> str:
|
||||
"""
|
||||
获取章节完整创作提示词（带前置章节上下文和记忆增强）
|
||||
|
||||
Args:
|
||||
style_content: 写作风格要求内容，如果提供则会追加到提示词中
|
||||
target_word_count: 目标字数，默认3000字
|
||||
memory_context: 记忆上下文（可选）
|
||||
mcp_references: MCP工具搜索的参考资料（可选）
|
||||
outline_mode: 大纲模式 (one-to-one/one-to-many)
|
||||
"""
|
||||
# 计算最大字数（目标字数+1000）
|
||||
max_word_count = target_word_count + 1000
|
||||
|
||||
# 格式化记忆上下文
|
||||
memory_text = ""
|
||||
if memory_context:
|
||||
memory_text = memory_context.get('recent_context', '')
|
||||
memory_text += "\n" + memory_context.get('relevant_memories', '')
|
||||
memory_text += "\n" + memory_context.get('foreshadows', '')
|
||||
memory_text += "\n" + memory_context.get('character_states', '')
|
||||
memory_text += "\n" + memory_context.get('plot_points', '')
|
||||
else:
|
||||
memory_text = "暂无相关记忆"
|
||||
|
||||
# 格式化MCP参考资料
|
||||
if mcp_references:
|
||||
memory_text += "\n\n【📚 MCP工具搜索 - 参考资料】\n"
|
||||
memory_text += "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"
|
||||
memory_text += mcp_references
|
||||
|
||||
# 根据大纲模式添加创作指导
|
||||
mode_instruction = ""
|
||||
if outline_mode == 'one-to-one':
|
||||
mode_instruction = "\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"
|
||||
else:
|
||||
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"
|
||||
|
||||
base_prompt = cls.format_prompt(
|
||||
cls.CHAPTER_GENERATION_WITH_CONTEXT,
|
||||
title=title,
|
||||
theme=theme,
|
||||
genre=genre,
|
||||
narrative_perspective=narrative_perspective,
|
||||
time_period=time_period,
|
||||
location=location,
|
||||
atmosphere=atmosphere,
|
||||
rules=rules,
|
||||
characters_info=characters_info,
|
||||
outlines_context=outlines_context,
|
||||
previous_content=previous_content,
|
||||
chapter_number=chapter_number,
|
||||
chapter_title=chapter_title,
|
||||
chapter_outline=chapter_outline,
|
||||
target_word_count=target_word_count,
|
||||
max_word_count=max_word_count,
|
||||
memory_context=memory_text
|
||||
)
|
||||
|
||||
# 插入模式说明
|
||||
base_prompt = base_prompt.replace(
|
||||
"本章信息：",
|
||||
mode_instruction + "\n本章信息："
|
||||
)
|
||||
|
||||
# 如果有风格要求，应用到提示词中
|
||||
if style_content:
|
||||
return WritingStyleManager.apply_style_to_prompt(base_prompt, style_content)
|
||||
|
||||
return base_prompt
|
||||
|
||||
@classmethod
|
||||
def get_outline_prompt(cls, genre: str, theme: str, target_words: int,
|
||||
requirements: str = "") -> str:
|
||||
"""获取大纲生成提示词"""
|
||||
return cls.format_prompt(
|
||||
cls.OUTLINE_GENERATION,
|
||||
genre=genre,
|
||||
theme=theme,
|
||||
target_words=target_words,
|
||||
requirements=requirements or "无特殊要求"
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_outline_continue_prompt(cls, title: str, theme: str, genre: str,
|
||||
narrative_perspective: str, chapter_count: int,
|
||||
time_period: str, location: str, atmosphere: str,
|
||||
rules: str, characters_info: str,
|
||||
current_chapter_count: int, all_chapters_brief: str,
|
||||
recent_plot: str, plot_stage_instruction: str,
|
||||
start_chapter: int, story_direction: str,
|
||||
requirements: str = "",
|
||||
memory_context: dict = None,
|
||||
mcp_references: str = "") -> str:
|
||||
"""获取大纲续写提示词（支持记忆+MCP增强）"""
|
||||
end_chapter = start_chapter + chapter_count - 1
|
||||
|
||||
# 格式化记忆上下文
|
||||
memory_text = ""
|
||||
if memory_context:
|
||||
memory_text = memory_context.get('recent_context', '')
|
||||
memory_text += "\n" + memory_context.get('relevant_memories', '')
|
||||
memory_text += "\n" + memory_context.get('foreshadows', '')
|
||||
memory_text += "\n" + memory_context.get('character_states', '')
|
||||
memory_text += "\n" + memory_context.get('plot_points', '')
|
||||
else:
|
||||
memory_text = "暂无相关记忆（可能是首次续写或记忆库为空）"
|
||||
|
||||
# 格式化MCP参考资料
|
||||
mcp_text = ""
|
||||
if mcp_references:
|
||||
mcp_text = "\n\n【📚 MCP工具搜索 - 续写参考资料】\n"
|
||||
mcp_text += "以下是通过MCP工具搜索到的续写参考资料，可用于丰富情节发展和冲突设计：\n\n"
|
||||
mcp_text += mcp_references
|
||||
mcp_text += "\n"
|
||||
|
||||
return cls.format_prompt(
|
||||
cls.OUTLINE_CONTINUE_GENERATION,
|
||||
title=title,
|
||||
theme=theme,
|
||||
genre=genre,
|
||||
narrative_perspective=narrative_perspective,
|
||||
chapter_count=chapter_count,
|
||||
time_period=time_period,
|
||||
location=location,
|
||||
atmosphere=atmosphere,
|
||||
rules=rules,
|
||||
characters_info=characters_info,
|
||||
current_chapter_count=current_chapter_count,
|
||||
all_chapters_brief=all_chapters_brief,
|
||||
recent_plot=recent_plot,
|
||||
plot_stage_instruction=plot_stage_instruction,
|
||||
start_chapter=start_chapter,
|
||||
end_chapter=end_chapter,
|
||||
story_direction=story_direction,
|
||||
requirements=requirements or "无特殊要求",
|
||||
memory_context=memory_text,
|
||||
mcp_references=mcp_text
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_single_character_prompt(cls, project_context: str, user_input: str) -> str:
|
||||
"""获取单个角色生成提示词"""
|
||||
return cls.format_prompt(
|
||||
cls.SINGLE_CHARACTER_GENERATION,
|
||||
project_context=project_context,
|
||||
user_input=user_input
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_single_organization_prompt(cls, project_context: str, user_input: str) -> str:
|
||||
"""获取单个组织生成提示词"""
|
||||
return cls.format_prompt(
|
||||
cls.SINGLE_ORGANIZATION_GENERATION,
|
||||
project_context=project_context,
|
||||
user_input=user_input
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_outline_expansion_prompt(cls, title: str, genre: str, theme: str,
|
||||
narrative_perspective: str, time_period: str,
|
||||
location: str, atmosphere: str, rules: str,
|
||||
characters_info: str, outline_order: int,
|
||||
outline_title: str, outline_content: str,
|
||||
context_info: str, strategy: str = "balanced",
|
||||
target_chapters: int = 3,
|
||||
include_scenes: bool = False) -> str:
|
||||
"""
|
||||
获取大纲展开为多章节的提示词
|
||||
|
||||
Args:
|
||||
title: 小说名称
|
||||
genre: 类型
|
||||
theme: 主题
|
||||
narrative_perspective: 叙事视角
|
||||
time_period: 时间背景
|
||||
location: 地理位置
|
||||
atmosphere: 氛围基调
|
||||
rules: 世界规则
|
||||
characters_info: 角色信息
|
||||
outline_order: 大纲序号
|
||||
outline_title: 大纲标题
|
||||
outline_content: 大纲内容
|
||||
context_info: 上下文信息
|
||||
strategy: 展开策略 (balanced/climax/detail)
|
||||
target_chapters: 目标章节数
|
||||
include_scenes: 是否包含场景字段
|
||||
"""
|
||||
# 根据策略生成指导说明
|
||||
strategy_instructions = {
|
||||
"balanced": "采用均衡策略：将大纲内容平均分配到各章节，保持节奏均匀，每章剧情密度相当。",
|
||||
"climax": "采用高潮重点策略：识别大纲中的高潮部分，为其分配更多章节进行细致展开，其他部分适当精简。",
|
||||
"detail": "采用细节丰富策略：深挖大纲中的每个细节，为每个关键事件、情感转折都安排足够的叙事空间。"
|
||||
}
|
||||
strategy_instruction = strategy_instructions.get(strategy, strategy_instructions["balanced"])
|
||||
|
||||
# 场景相关的指令和字段
|
||||
scene_instruction = ""
|
||||
scene_field = ""
|
||||
if include_scenes:
|
||||
scene_instruction = "\n - scenes: 场景列表（2-4个具体场景描述）"
|
||||
scene_field = ',\n "scenes": ["场景1", "场景2"]'
|
||||
|
||||
return cls.format_prompt(
|
||||
cls.OUTLINE_EXPANSION,
|
||||
title=title,
|
||||
genre=genre,
|
||||
theme=theme,
|
||||
narrative_perspective=narrative_perspective,
|
||||
time_period=time_period,
|
||||
location=location,
|
||||
atmosphere=atmosphere,
|
||||
rules=rules,
|
||||
characters_info=characters_info,
|
||||
outline_order=outline_order,
|
||||
outline_title=outline_title,
|
||||
outline_content=outline_content,
|
||||
context_info=context_info,
|
||||
strategy_instruction=strategy_instruction,
|
||||
target_chapters=target_chapters,
|
||||
scene_instruction=scene_instruction,
|
||||
scene_field=scene_field
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_plot_analysis_prompt(cls, chapter_number: int, title: str,
|
||||
content: str, word_count: int) -> str:
|
||||
"""获取章节剧情分析提示词"""
|
||||
return cls.format_prompt(
|
||||
cls.PLOT_ANALYSIS,
|
||||
chapter_number=chapter_number,
|
||||
title=title,
|
||||
content=content,
|
||||
word_count=word_count
|
||||
)
|
||||
@classmethod
|
||||
def get_plot_expansion_single_batch_prompt(cls, project_title: str, project_genre: str, project_theme: str,
|
||||
project_narrative_perspective: str, project_world_time_period: str,
|
||||
project_world_location: str, project_world_atmosphere: str,
|
||||
characters_info: str, outline_order_index: int, outline_title: str,
|
||||
outline_content: str, context_info: str, strategy_instruction: str,
|
||||
target_chapter_count: int, scene_instruction: str, scene_field: str) -> str:
|
||||
"""获取大纲单批次展开提示词"""
|
||||
return cls.format_prompt(
|
||||
cls.PLOT_EXPANSION_SINGLE_BATCH,
|
||||
project_title=project_title, project_genre=project_genre, project_theme=project_theme,
|
||||
project_narrative_perspective=project_narrative_perspective, project_world_time_period=project_world_time_period,
|
||||
project_world_location=project_world_location, project_world_atmosphere=project_world_atmosphere,
|
||||
characters_info=characters_info, outline_order_index=outline_order_index, outline_title=outline_title,
|
||||
outline_content=outline_content, context_info=context_info, strategy_instruction=strategy_instruction,
|
||||
target_chapter_count=target_chapter_count, scene_instruction=scene_instruction, scene_field=scene_field
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_plot_expansion_multi_batch_prompt(cls, project_title: str, project_genre: str, project_theme: str,
|
||||
project_narrative_perspective: str, project_world_time_period: str,
|
||||
project_world_location: str, project_world_atmosphere: str,
|
||||
characters_info: str, outline_order_index: int, outline_title: str,
|
||||
outline_content: str, context_info: str, previous_context: str,
|
||||
strategy_instruction: str, start_index: int, end_index: int,
|
||||
target_chapter_count: int, scene_instruction: str, scene_field: str) -> str:
|
||||
"""获取大纲分批展开提示词"""
|
||||
return cls.format_prompt(
|
||||
cls.PLOT_EXPANSION_MULTI_BATCH,
|
||||
project_title=project_title, project_genre=project_genre, project_theme=project_theme,
|
||||
project_narrative_perspective=project_narrative_perspective, project_world_time_period=project_world_time_period,
|
||||
project_world_location=project_world_location, project_world_atmosphere=project_world_atmosphere,
|
||||
characters_info=characters_info, outline_order_index=outline_order_index, outline_title=outline_title,
|
||||
outline_content=outline_content, context_info=context_info, previous_context=previous_context,
|
||||
strategy_instruction=strategy_instruction, start_index=start_index, end_index=end_index,
|
||||
target_chapter_count=target_chapter_count, scene_instruction=scene_instruction, scene_field=scene_field
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def get_chapter_regeneration_prompt(cls, chapter_number: int, title: str, word_count: int, content: str,
|
||||
@@ -2410,14 +1915,14 @@ class PromptService:
|
||||
"description": "生成组织/势力的详细设定",
|
||||
"parameters": ["project_context", "user_input"]
|
||||
},
|
||||
"COMPLETE_OUTLINE_GENERATION": {
|
||||
"name": "完整大纲生成",
|
||||
"OUTLINE_CREATE": {
|
||||
"name": "初始大纲生成",
|
||||
"category": "大纲生成",
|
||||
"description": "根据项目信息生成完整的章节大纲",
|
||||
"parameters": ["title", "theme", "genre", "chapter_count", "narrative_perspective", "target_words",
|
||||
"time_period", "location", "atmosphere", "rules", "characters_info", "requirements", "mcp_references"]
|
||||
},
|
||||
"OUTLINE_CONTINUE_GENERATION": {
|
||||
"OUTLINE_CONTINUE": {
|
||||
"name": "大纲续写",
|
||||
"category": "大纲生成",
|
||||
"description": "基于已有章节续写大纲",
|
||||
@@ -2477,7 +1982,7 @@ class PromptService:
|
||||
"description": "深度分析章节的剧情、钩子、伏笔等",
|
||||
"parameters": ["chapter_number", "title", "content", "word_count"]
|
||||
},
|
||||
"PLOT_EXPANSION_SINGLE_BATCH": {
|
||||
"OUTLINE_EXPAND_SINGLE": {
|
||||
"name": "大纲单批次展开",
|
||||
"category": "情节展开",
|
||||
"description": "将大纲节点展开为详细章节规划（单批次）",
|
||||
@@ -2486,7 +1991,7 @@ class PromptService:
|
||||
"characters_info", "outline_order_index", "outline_title", "outline_content",
|
||||
"context_info", "strategy_instruction", "target_chapter_count", "scene_instruction", "scene_field"]
|
||||
},
|
||||
"PLOT_EXPANSION_MULTI_BATCH": {
|
||||
"OUTLINE_EXPAND_MULTI": {
|
||||
"name": "大纲分批展开",
|
||||
"category": "情节展开",
|
||||
"description": "将大纲节点展开为详细章节规划（分批）",
|
||||
|
||||
Reference in New Issue
Block a user