446 lines
18 KiB
Python
446 lines
18 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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提示词构建服务
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负责根据选题信息构建完整的提示词
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"""
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import logging
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from typing import Dict, Any, Optional, Tuple, List
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from pathlib import Path
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from core.config import ConfigManager, GenerateContentConfig, GenerateTopicConfig, PosterConfig
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from utils.prompts import PromptTemplate
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from api.services.prompt_service import PromptService
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logger = logging.getLogger(__name__)
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class PromptBuilderService:
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"""提示词构建服务类"""
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def __init__(self, config_manager: ConfigManager, prompt_service: PromptService):
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"""
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初始化提示词构建服务
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Args:
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config_manager: 配置管理器
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prompt_service: 提示词服务
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"""
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self.config_manager = config_manager
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self.prompt_service = prompt_service
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def _ensure_content_config(self) -> GenerateContentConfig:
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"""确保内容生成配置已加载"""
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# 按需加载内容生成配置
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if not self.config_manager.load_task_config('content_gen'):
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logger.warning("未找到内容生成配置，将使用默认配置")
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return self.config_manager.get_config('content_gen', GenerateContentConfig)
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def _ensure_topic_config(self) -> GenerateTopicConfig:
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"""确保选题生成配置已加载"""
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# 按需加载选题生成配置
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if not self.config_manager.load_task_config('topic_gen'):
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logger.warning("未找到选题生成配置，将使用默认配置")
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return self.config_manager.get_config('topic_gen', GenerateTopicConfig)
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def _ensure_poster_config(self) -> PosterConfig:
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"""确保海报生成配置已加载"""
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# 按需加载海报生成配置
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if not self.config_manager.load_task_config('poster_gen'):
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logger.warning("未找到海报生成配置，将使用默认配置")
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return self.config_manager.get_config('poster_gen', PosterConfig)
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def build_content_prompt(self, topic: Dict[str, Any], step: str = "content") -> Tuple[str, str]:
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"""
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构建内容生成提示词 (已重构)
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此方法现在依赖于一个预先填充好完整信息的topic对象。
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"""
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content_config = self._ensure_content_config()
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template = PromptTemplate(content_config.content_system_prompt, content_config.content_user_prompt)
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# 从预填充的topic对象中直接获取信息，不再调用prompt_service
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style_obj = topic.get('style_object', {})
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style_content = f"{style_obj.get('styleName', '')}\n{style_obj.get('description', '')}"
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audience_obj = topic.get('audience_object', {})
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demand_content = f"{audience_obj.get('audienceName', '')}\n{audience_obj.get('description', '')}"
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spot_obj = topic.get('scenic_spot_object', {})
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object_content = f"{spot_obj.get('name', '')}\n{spot_obj.get('description', '')}"
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product_obj = topic.get('product_object', {})
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product_content = f"{product_obj.get('productName', '')}\n{product_obj.get('detailedDescription', '')}"
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# 获取通用的参考内容
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refer_content = self.prompt_service.get_refer_content(step)
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system_prompt = template.get_system_prompt()
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user_prompt = template.build_user_prompt(
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style_content=style_content,
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demand_content=demand_content,
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object_content=object_content,
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product_content=product_content,
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refer_content=refer_content
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)
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return system_prompt, user_prompt
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def build_content_prompt_with_params(self, topic: Dict[str, Any],
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styles: Optional[List[str]] = None,
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audiences: Optional[List[str]] = None,
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scenic_spots: Optional[List[str]] = None,
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products: Optional[List[str]] = None,
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step: str = "content") -> Tuple[str, str]:
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"""
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使用额外参数构建内容生成提示词
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Args:
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topic: 选题信息
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styles: 风格列表
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audiences: 受众列表
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scenic_spots: 景区列表
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products: 产品列表
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step: 当前步骤，用于过滤参考内容
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Returns:
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系统提示词和用户提示词的元组
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"""
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# 获取内容生成配置
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content_config = self._ensure_content_config()
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# 加载系统提示词和用户提示词模板
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system_prompt_path = content_config.content_system_prompt
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user_prompt_path = content_config.content_user_prompt
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# 创建提示词模板
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template = PromptTemplate(system_prompt_path, user_prompt_path)
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# 获取风格内容
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style_content = ''
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if styles:
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style_content = '\n'.join([f"{style}: {self.prompt_service.get_style_content(style)}" for style in styles])
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else:
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style_filename = topic.get("style", "")
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style_content = f"{style_filename}\n{self.prompt_service.get_style_content(style_filename)}"
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# 获取目标受众内容
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demand_content = ''
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if audiences:
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demand_content = '\n'.join([f"{audience}: {self.prompt_service.get_audience_content(audience)}" for audience in audiences])
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else:
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demand_filename = topic.get("targetAudience", "")
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demand_content = f"{demand_filename}\n{self.prompt_service.get_audience_content(demand_filename)}"
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# 获取景区信息
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object_content = ''
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if scenic_spots:
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object_content = '\n'.join([f"{spot}: {self.prompt_service.get_scenic_spot_info(spot)}" for spot in scenic_spots])
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else:
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object_name = topic.get("object", "")
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object_content = f"{object_name}\n{self.prompt_service.get_scenic_spot_info(object_name)}"
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# 获取产品信息
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product_content = ''
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if products:
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product_content = '\n'.join([f"{product}: {self.prompt_service.get_product_info(product)}" for product in products])
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else:
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product_name = topic.get("product", "")
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product_content = f"{product_name}\n{self.prompt_service.get_product_info(product_name)}"
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# 获取参考内容
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refer_content = self.prompt_service.get_refer_content(step)
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# 构建系统提示词
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system_prompt = template.get_system_prompt()
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# 构建用户提示词
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user_prompt = template.build_user_prompt(
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style_content=style_content,
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demand_content=demand_content,
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object_content=object_content,
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product_content=product_content,
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refer_content=refer_content
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)
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return system_prompt, user_prompt
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def build_poster_prompt(self, topic: Dict[str, Any], content: Dict[str, Any]) -> Tuple[str, str]:
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"""
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构建海报生成提示词
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Args:
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topic: 选题信息
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content: 生成的内容
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Returns:
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系统提示词和用户提示词的元组
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"""
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# 获取海报生成配置
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poster_config = self._ensure_poster_config()
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# 从配置中获取海报提示词模板路径
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system_prompt_path = poster_config.poster_system_prompt
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user_prompt_path = poster_config.poster_user_prompt
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if not system_prompt_path or not user_prompt_path:
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raise ValueError("海报提示词模板路径不完整")
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# 创建提示词模板
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template = PromptTemplate(system_prompt_path, user_prompt_path)
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# 获取景区信息
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object_name = topic.get("object", "")
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object_content = self.prompt_service.get_scenic_spot_info(object_name)
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# 构建系统提示词
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system_prompt = template.get_system_prompt()
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# 构建用户提示词
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user_prompt = template.build_user_prompt(
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content=content.get("content", ""),
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title=content.get("title", ""),
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object_name=object_name,
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object_content=object_content
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)
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return system_prompt, user_prompt
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def build_topic_prompt(self, products: Optional[List[str]] = None,
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scenic_spots: Optional[List[str]] = None,
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styles: Optional[List[str]] = None,
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audiences: Optional[List[str]] = None,
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dates: Optional[str] = None, numTopics: int = 5,
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style_objects: Optional[List[Dict[str, Any]]] = None,
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audience_objects: Optional[List[Dict[str, Any]]] = None,
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scenic_spot_objects: Optional[List[Dict[str, Any]]] = None,
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product_objects: Optional[List[Dict[str, Any]]] = None) -> Tuple[str, str]:
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"""
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构建选题生成提示词
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Args:
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products: 产品列表
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scenic_spots: 景区列表
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styles: 风格列表
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audiences: 受众列表
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dates: 日期字符串，可能为单个日期、多个日期用逗号分隔或范围如'2023-01-01 to 2023-01-31'
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numTopics: 要生成的选题数量
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Returns:
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系统提示词和用户提示词的元组
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"""
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# 获取选题生成配置
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topic_config = self._ensure_topic_config()
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system_prompt_path = topic_config.topic_system_prompt
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user_prompt_path = topic_config.topic_user_prompt
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if not system_prompt_path or not user_prompt_path:
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raise ValueError("选题提示词模板路径不完整")
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template = PromptTemplate(system_prompt_path, user_prompt_path)
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month = dates or ''
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if dates and ' to ' in dates:
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start_date, end_date = dates.split(' to ')
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month = f"从 {start_date} 到 {end_date}"
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elif dates and ',' in dates:
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month = ', '.join(dates.split(','))
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# 使用传入的完整对象构建内容，避免重复查询
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if style_objects:
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style_content = '\n'.join([f"{obj['styleName']}: {obj.get('description', '')}" for obj in style_objects])
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elif styles:
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style_content = '\n'.join([f"{style}: {self.prompt_service.get_style_content(style)}" for style in styles])
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else:
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all_styles = self.prompt_service.get_all_styles()
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style_content = "Style文件列表:\n" + "\n".join([f"- {style['name']}" for style in all_styles])
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if audience_objects:
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demand_content = '\n'.join([f"{obj['audienceName']}: {obj.get('description', '')}" for obj in audience_objects])
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elif audiences:
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demand_content = '\n'.join([f"{audience}: {self.prompt_service.get_audience_content(audience)}" for audience in audiences])
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else:
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all_audiences = self.prompt_service.get_all_audiences()
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demand_content = "Demand文件列表:\n" + "\n".join([f"- {audience['name']}" for audience in all_audiences])
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if scenic_spot_objects:
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object_content = '\n'.join([f"{obj['name']}: {obj.get('description', '')}" for obj in scenic_spot_objects])
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elif scenic_spots:
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object_content = '\n'.join([f"{spot}: {self.prompt_service.get_scenic_spot_info(spot)}" for spot in scenic_spots])
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else:
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all_spots = self.prompt_service.get_all_scenic_spots()
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object_content = "Object信息:\n" + "\n".join([f"- {spot['name']}" for spot in all_spots])
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if product_objects:
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product_content = '\n'.join([f"{obj['productName']}: {obj.get('detailedDescription', '')}" for obj in product_objects])
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elif products:
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product_content = '\n'.join([f"{product}: {self.prompt_service.get_product_info(product)}" for product in products])
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else:
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product_content = ''
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refer_content = self.prompt_service.get_refer_content("topic")
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system_prompt = template.get_system_prompt()
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creative_materials = (
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f"你拥有的创作资料如下：\n"
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f"风格信息:\n{style_content}\n\n"
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f"受众信息:\n{demand_content}\n\n"
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f"参考内容:\n{refer_content}\n\n"
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f"景区信息:\n{object_content}\n\n"
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f"产品信息:\n{product_content}"
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)
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user_prompt = template.build_user_prompt(
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creative_materials=creative_materials,
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numTopics=numTopics,
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month=month
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)
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return system_prompt, user_prompt
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def build_judge_prompt(self, topic: Dict[str, Any], content: Dict[str, Any]) -> Tuple[str, str]:
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"""
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构建内容审核提示词 (已重构)
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此方法现在依赖于一个预先填充好完整信息的topic对象。
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"""
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content_config = self._ensure_content_config()
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template = PromptTemplate(content_config.judger_system_prompt, content_config.judger_user_prompt)
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# 从预填充的topic对象中直接获取信息
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spot_obj = topic.get('scenic_spot_object', {})
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object_content = f"{spot_obj.get('name', '')}\n{spot_obj.get('description', '')}"
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product_obj = topic.get('product_object', {})
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product_content = f"{product_obj.get('productName', '')}\n{product_obj.get('detailedDescription', '')}"
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refer_content = self.prompt_service.get_refer_content("judge")
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system_prompt = template.get_system_prompt()
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import json
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tweet_content = json.dumps(content, ensure_ascii=False, indent=4)
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user_prompt = template.build_user_prompt(
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tweet_content=tweet_content,
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object_content=object_content,
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product_content=product_content,
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refer_content=refer_content
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)
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return system_prompt, user_prompt
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def build_judge_prompt_with_params(self, topic: Dict[str, Any], content: Dict[str, Any],
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styles: Optional[List[str]] = None,
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audiences: Optional[List[str]] = None,
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scenic_spots: Optional[List[str]] = None,
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products: Optional[List[str]] = None) -> Tuple[str, str]:
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"""
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使用额外参数构建内容审核提示词
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Args:
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topic: 选题信息
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content: 生成的内容
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styles: 风格列表
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audiences: 受众列表
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scenic_spots: 景区列表
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products: 产品列表
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Returns:
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系统提示词和用户提示词的元组
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"""
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# 获取内容生成配置
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content_config = self._ensure_content_config()
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# 从配置中获取审核提示词模板路径
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system_prompt_path = content_config.judger_system_prompt
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user_prompt_path = content_config.judger_user_prompt
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# 创建提示词模板
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template = PromptTemplate(system_prompt_path, user_prompt_path)
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# 获取景区信息
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object_content = ''
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if scenic_spots:
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object_content = '\n'.join([f"{spot}: {self.prompt_service.get_scenic_spot_info(spot)}" for spot in scenic_spots])
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else:
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object_name = topic.get("object", "")
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object_content = f"{object_name}\n{self.prompt_service.get_scenic_spot_info(object_name)}"
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# 获取产品信息
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product_content = ''
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if products:
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product_content = '\n'.join([f"{product}: {self.prompt_service.get_product_info(product)}" for product in products])
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else:
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product_name = topic.get("product", "")
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product_content = f"{product_name}\n{self.prompt_service.get_product_info(product_name)}"
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# 获取参考内容
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refer_content = self.prompt_service.get_refer_content("judge")
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# 构建系统提示词
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system_prompt = template.get_system_prompt()
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# 格式化内容
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import json
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tweet_content = json.dumps(content, ensure_ascii=False, indent=4)
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# 构建用户提示词
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user_prompt = template.build_user_prompt(
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tweet_content=tweet_content,
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object_content=object_content,
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product_content=product_content,
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refer_content=refer_content
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)
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return system_prompt, user_prompt
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def build_judge_prompt_simple(self, topic: Dict[str, Any], content: Dict[str, Any]) -> Tuple[str, str]:
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"""
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构建简化的内容审核提示词（只需要产品信息、景区信息和文章）
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Args:
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topic: 选题信息
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content: 生成的内容
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Returns:
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系统提示词和用户提示词的元组
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"""
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# 获取内容生成配置
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content_config = self._ensure_content_config()
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# 从配置中获取审核提示词模板路径
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system_prompt_path = content_config.judger_system_prompt
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user_prompt_path = content_config.judger_user_prompt
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# 创建提示词模板
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template = PromptTemplate(system_prompt_path, user_prompt_path)
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# 获取景区信息
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object_name = topic.get("object", "")
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object_content = self.prompt_service.get_scenic_spot_info(object_name)
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# 获取产品信息
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product_name = topic.get("product", "")
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product_content = self.prompt_service.get_product_info(product_name)
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# 构建系统提示词
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system_prompt = template.get_system_prompt()
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# 格式化内容
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import json
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tweet_content = json.dumps(content, ensure_ascii=False, indent=4)
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# 构建用户提示词（简化版，不包含参考内容）
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user_prompt = template.build_user_prompt(
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tweet_content=tweet_content,
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object_content=object_content,
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product_content=product_content,
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refer_content="" # 简化版不使用参考内容
|
||
)
|
||
|
||
return system_prompt, user_prompt |