#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 文字内容服务层 封装现有功能，提供API调用 """ import logging import uuid from typing import List, Dict, Any, Optional, Tuple from datetime import datetime import re from difflib import SequenceMatcher from core.config import ConfigManager, GenerateTopicConfig, GenerateContentConfig from core.ai import AIAgent from utils.file_io import OutputManager from tweet.topic_generator import TopicGenerator from tweet.content_generator import ContentGenerator from tweet.content_judger import ContentJudger from api.services.prompt_builder import PromptBuilderService from api.services.prompt_service import PromptService from api.services.database_service import DatabaseService logger = logging.getLogger(__name__) class TopicIDMappingManager: """选题ID映射管理器 - 专门处理选题中的对象ID映射""" def __init__(self): """初始化映射管理器""" self.name_to_id = {} # 名称 -> ID映射 self.mapping_data = { 'styles': {}, 'audiences': {}, 'scenic_spots': {}, 'products': {} } def add_objects_mapping(self, style_objects: Optional[List[Dict[str, Any]]] = None, audience_objects: Optional[List[Dict[str, Any]]] = None, scenic_spot_objects: Optional[List[Dict[str, Any]]] = None, product_objects: Optional[List[Dict[str, Any]]] = None): """ 批量添加对象映射关系 Args: style_objects: 风格对象列表 audience_objects: 受众对象列表 scenic_spot_objects: 景区对象列表 product_objects: 产品对象列表 """ logger.info("开始建立选题ID映射关系") mapping_count = 0 if style_objects: logger.info(f"处理 {len(style_objects)} 个风格对象") for obj in style_objects: name = obj.get('styleName', '') obj_id = obj.get('id') if name and obj_id: self.mapping_data['styles'][name] = str(obj_id) self._add_name_variants('styles', name, str(obj_id)) mapping_count += 1 logger.info(f"添加风格映射: {name} -> ID {obj_id}") if audience_objects: logger.info(f"处理 {len(audience_objects)} 个受众对象") for obj in audience_objects: name = obj.get('audienceName', '') obj_id = obj.get('id') if name and obj_id: self.mapping_data['audiences'][name] = str(obj_id) self._add_name_variants('audiences', name, str(obj_id)) mapping_count += 1 logger.info(f"添加受众映射: {name} -> ID {obj_id}") if scenic_spot_objects: logger.info(f"处理 {len(scenic_spot_objects)} 个景区对象") for obj in scenic_spot_objects: name = obj.get('name', '') obj_id = obj.get('id') if name and obj_id: self.mapping_data['scenic_spots'][name] = str(obj_id) self._add_name_variants('scenic_spots', name, str(obj_id)) mapping_count += 1 logger.info(f"添加景区映射: {name} -> ID {obj_id}") if product_objects: logger.info(f"处理 {len(product_objects)} 个产品对象") for obj in product_objects: name = obj.get('productName', '') obj_id = obj.get('id') if name and obj_id: self.mapping_data['products'][name] = str(obj_id) self._add_name_variants('products', name, str(obj_id)) mapping_count += 1 logger.info(f"添加产品映射: {name} -> ID {obj_id}") total_variants = len(self.name_to_id) logger.info(f"ID映射关系建立完成: {mapping_count} 个对象，生成 {total_variants} 个名称变体") def _add_name_variants(self, category: str, name: str, obj_id: str): """为名称添加各种变体以支持模糊匹配""" variants = [name] # 去除标点符号版本 clean_name = re.sub(r'[^\w\s]', '', name) if clean_name != name: variants.append(clean_name) # 去除空格版本 no_space_name = name.replace(' ', '') if no_space_name != name: variants.append(no_space_name) # 添加简化版本（去除常见后缀） simplified = re.sub(r'(风格|类型|系列|产品|景区|公园)$', '', name) if simplified != name and simplified: variants.append(simplified) # 为所有变体添加映射 for variant in variants: self.name_to_id[variant.lower()] = { 'category': category, 'id': obj_id, 'original_name': name } # 记录生成的变体（仅在有多个变体时记录） if len(variants) > 1: variants_str = ', '.join(f"'{v}'" for v in variants) logger.info(f" 为 '{name}' 生成 {len(variants)} 个变体: {variants_str}") def find_ids_in_topic(self, topic: Dict[str, Any]) -> Dict[str, List[str]]: """ 在选题中查找相关的对象ID Args: topic: 选题字典 Returns: 按类型分组的ID列表 """ topic_index = topic.get('index', 'N/A') found_ids = { 'style_ids': [], 'audience_ids': [], 'scenic_spot_ids': [], 'product_ids': [] } # 提取选题中的所有文本 topic_text = self._extract_topic_text(topic) topic_text_lower = topic_text.lower() logger.info(f"开始为选题 {topic_index} 进行ID匹配") logger.info(f"选题 {topic_index} 提取的文本内容: '{topic_text}'") logger.info(f"当前映射库包含 {len(self.name_to_id)} 个名称变体") # 显示映射库内容 logger.info("映射库内容:") for variant, info in self.name_to_id.items(): logger.info(f" '{variant}' -> {info['original_name']} ({info['category']}, ID: {info['id']})") # 记录匹配过程 match_details = [] # 在文本中查找匹配的名称 for name_variant, mapping_info in self.name_to_id.items(): if name_variant in topic_text_lower: category = mapping_info['category'] obj_id = mapping_info['id'] original_name = mapping_info['original_name'] # 记录匹配详情 match_details.append({ 'variant': name_variant, 'original_name': original_name, 'category': category, 'id': obj_id }) # 映射到返回格式 if category == 'styles' and obj_id not in found_ids['style_ids']: found_ids['style_ids'].append(obj_id) logger.info(f"选题 {topic_index} 匹配到风格: '{name_variant}' -> {original_name} (ID: {obj_id})") elif category == 'audiences' and obj_id not in found_ids['audience_ids']: found_ids['audience_ids'].append(obj_id) logger.info(f"选题 {topic_index} 匹配到受众: '{name_variant}' -> {original_name} (ID: {obj_id})") elif category == 'scenic_spots' and obj_id not in found_ids['scenic_spot_ids']: found_ids['scenic_spot_ids'].append(obj_id) logger.info(f"选题 {topic_index} 匹配到景区: '{name_variant}' -> {original_name} (ID: {obj_id})") elif category == 'products' and obj_id not in found_ids['product_ids']: found_ids['product_ids'].append(obj_id) logger.info(f"选题 {topic_index} 匹配到产品: '{name_variant}' -> {original_name} (ID: {obj_id})") # 统计匹配结果 total_found = sum(len(ids) for ids in found_ids.values()) total_available = len(set(info['category'] + '_' + info['id'] for info in self.name_to_id.values())) match_rate = (total_found / total_available * 100) if total_available > 0 else 0 # 输出详细的匹配结果 if total_found > 0: logger.info(f"选题 {topic_index} ID匹配完成: 找到 {total_found} 个相关对象，匹配率: {match_rate:.1f}%") logger.info(f"选题 {topic_index} 匹配详情: {match_details}") # 按类别显示匹配结果 for category, ids in found_ids.items(): if ids: category_name = { 'style_ids': '风格', 'audience_ids': '受众', 'scenic_spot_ids': '景区', 'product_ids': '产品' }.get(category, category) # 获取匹配的原始名称 matched_names = [] # 建立正确的分类映射关系 category_mapping = { 'style_ids': 'styles', 'audience_ids': 'audiences', 'scenic_spot_ids': 'scenic_spots', 'product_ids': 'products' } target_category = category_mapping.get(category) logger.info(f"选题 {topic_index} 处理分类 {category} -> {target_category}, IDs: {ids}") for detail in match_details: if detail['id'] in ids and detail['category'] == target_category: matched_names.append(f"{detail['original_name']}({detail['id']})") # 显示分类结果 names_str = ', '.join(matched_names) if matched_names else '(无匹配名称)' logger.info(f" {category_name}: {names_str}") # 如果没有匹配名称，输出调试信息 if not matched_names: logger.info(f"选题 {topic_index} {category_name}无匹配名称，详情检查:") logger.info(f" IDs: {ids}") logger.info(f" 目标分类: {target_category}") for detail in match_details: if detail['id'] in ids: logger.info(f" 详情: {detail}") else: logger.info(f"选题 {topic_index} 未匹配到任何相关对象ID") return found_ids def _extract_topic_text(self, topic: Dict[str, Any]) -> str: """提取选题中的所有文本内容""" text_parts = [] # 提取主要字段的文本 text_fields = ['object', 'style', 'targetAudience', 'product', 'logic', 'productLogic', 'styleLogic', 'targetAudienceLogic'] for field in text_fields: if field in topic and topic[field]: text_parts.append(str(topic[field])) return ' '.join(text_parts) class TweetService: """文字内容服务类""" def __init__(self, ai_agent: AIAgent, config_manager: ConfigManager, output_manager: OutputManager): """ 初始化文字内容服务 Args: ai_agent: AI代理 config_manager: 配置管理器 output_manager: 输出管理器 """ self.ai_agent = ai_agent self.config_manager = config_manager self.output_manager = output_manager # 初始化各个组件 self.topic_generator = TopicGenerator(ai_agent, config_manager, output_manager) self.content_generator = ContentGenerator(ai_agent, config_manager, output_manager) self.content_judger = ContentJudger(ai_agent, config_manager, output_manager) # 初始化提示词服务和构建器 self.prompt_service = PromptService(config_manager) self.prompt_builder = PromptBuilderService(config_manager, self.prompt_service) # 初始化选题ID映射管理器 self.topic_id_mapping_manager = TopicIDMappingManager() async def generate_topics(self, dates: Optional[str] = None, numTopics: int = 5, styles: Optional[List[str]] = None, audiences: Optional[List[str]] = None, scenic_spots: Optional[List[str]] = None, products: Optional[List[str]] = None, style_objects: Optional[List[Dict[str, Any]]] = None, audience_objects: Optional[List[Dict[str, Any]]] = None, scenic_spot_objects: Optional[List[Dict[str, Any]]] = None, product_objects: Optional[List[Dict[str, Any]]] = None) -> Tuple[str, List[Dict[str, Any]]]: """ 生成选题 Args: 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 numTopics: 要生成的选题数量 styles: 风格列表 audiences: 受众列表 scenic_spots: 景区列表 products: 产品列表 style_objects: 风格对象列表 audience_objects: 受众对象列表 scenic_spot_objects: 景区对象列表 product_objects: 产品对象列表 Returns: 请求ID和生成的选题列表 """ logger.info(f"开始生成选题，日期: {dates}, 数量: {numTopics}") # 获取并更新配置 topic_config = self.config_manager.get_config('topic_gen', GenerateTopicConfig) if dates: topic_config.topic.date = dates topic_config.topic.num = numTopics # 建立ID映射关系（用于后续的ID反向映射） self.topic_id_mapping_manager.add_objects_mapping( style_objects=style_objects, audience_objects=audience_objects, scenic_spot_objects=scenic_spot_objects, product_objects=product_objects ) # 使用PromptBuilderService构建提示词 system_prompt, user_prompt = self.prompt_builder.build_topic_prompt( products=products, scenic_spots=scenic_spots, styles=styles, audiences=audiences, dates=dates, numTopics=numTopics, style_objects=style_objects, audience_objects=audience_objects, scenic_spot_objects=scenic_spot_objects, product_objects=product_objects ) # 使用预构建的提示词生成选题 topics = await self.topic_generator.generate_topics_with_prompt(system_prompt, user_prompt) if not topics: logger.error("未能生成任何选题") return str(uuid.uuid4()), [] # 为每个选题添加相关的对象ID logger.info(f"开始为 {len(topics)} 个选题进行ID映射分析") enhanced_topics = [] total_mapped_topics = 0 for topic in topics: enhanced_topic = topic.copy() # 查找选题中涉及的对象ID related_object_ids = self.topic_id_mapping_manager.find_ids_in_topic(topic) # 只有当找到相关ID时才添加字段 if any(related_object_ids.values()): enhanced_topic['related_object_ids'] = related_object_ids total_mapped_topics += 1 logger.info(f"选题 {topic.get('index', 'N/A')} 找到相关ID: {related_object_ids}") enhanced_topics.append(enhanced_topic) # 统计总体映射情况 mapping_coverage = (total_mapped_topics / len(topics) * 100) if topics else 0 logger.info(f"选题ID映射完成: {total_mapped_topics}/{len(topics)} 个选题包含相关对象ID，覆盖率: {mapping_coverage:.1f}%") # 生成请求ID requestId = f"topic-{datetime.now().strftime('%Y%m%d-%H%M%S')}-{str(uuid.uuid4())[:8]}" logger.info(f"选题生成完成，请求ID: {requestId}, 数量: {len(enhanced_topics)}") return requestId, enhanced_topics async def generate_content(self, topic: Optional[Dict[str, Any]] = None, autoJudge: bool = False, style_objects: Optional[List[Dict[str, Any]]] = None, audience_objects: Optional[List[Dict[str, Any]]] = None, scenic_spot_objects: Optional[List[Dict[str, Any]]] = None, product_objects: Optional[List[Dict[str, Any]]] = None) -> Tuple[str, str, Dict[str, Any]]: """ 为单个选题生成内容 Args: topic: 选题信息（可能包含ID字段） autoJudge: 是否进行内嵌审核 style_objects: 风格对象列表（可选，用于兼容） audience_objects: 受众对象列表（可选，用于兼容） scenic_spot_objects: 景区对象列表（可选，用于兼容） product_objects: 产品对象列表（可选，用于兼容） Returns: 请求ID、选题索引和生成的内容（包含judgeSuccess状态） """ if not topic: topic = {"index": "1", "date": "2024-07-01"} topicIndex = topic.get('index', 'N/A') logger.info(f"开始为选题 {topicIndex} 生成内容{'（含审核）' if autoJudge else ''}") # 增强版的topic处理：优先使用ID获取最新数据 enhanced_topic = await self._enhance_topic_with_database_data(topic) # 如果没有通过ID获取到数据，使用传入的对象参数作为兜底 if style_objects and not enhanced_topic.get('style_object'): enhanced_topic['style_object'] = style_objects[0] enhanced_topic['style'] = style_objects[0].get('styleName') if audience_objects and not enhanced_topic.get('audience_object'): enhanced_topic['audience_object'] = audience_objects[0] enhanced_topic['targetAudience'] = audience_objects[0].get('audienceName') if scenic_spot_objects and not enhanced_topic.get('scenic_spot_object'): enhanced_topic['scenic_spot_object'] = scenic_spot_objects[0] enhanced_topic['object'] = scenic_spot_objects[0].get('name') if product_objects and not enhanced_topic.get('product_object'): enhanced_topic['product_object'] = product_objects[0] enhanced_topic['product'] = product_objects[0].get('productName') # 使用PromptBuilderService构建提示词 system_prompt, user_prompt = self.prompt_builder.build_content_prompt(enhanced_topic, "content") # 使用预构建的提示词生成内容 content = await self.content_generator.generate_content_with_prompt(enhanced_topic, system_prompt, user_prompt) if not content: logger.error(f"未能为选题 {topicIndex} 生成内容") return str(uuid.uuid4()), topicIndex, {} # 如果启用自动审核，进行内嵌审核 if autoJudge: try: logger.info(f"开始对选题 {topicIndex} 的内容进行内嵌审核") # 使用PromptBuilderService构建审核提示词 judge_system_prompt, judge_user_prompt = self.prompt_builder.build_judge_prompt(enhanced_topic, content) # 进行内容审核 judged_content = await self.content_judger.judge_content_with_prompt(content, enhanced_topic, judge_system_prompt, judge_user_prompt) # 统一输出格式：始终包含judgeSuccess状态 # content_judger返回的是judge_success字段（下划线命名） judge_success = judged_content.get('judge_success', False) if judge_success: logger.info(f"选题 {topicIndex} 内容审核成功") # 审核成功：使用审核后的内容，但移除judge_success，添加统一的judgeSuccess content = {k: v for k, v in judged_content.items() if k != 'judge_success'} content['judgeSuccess'] = True else: logger.warning(f"选题 {topicIndex} 内容审核未通过") # 审核失败：使用原始内容，添加judgeSuccess状态 content['judgeSuccess'] = False except Exception as e: logger.error(f"选题 {topicIndex} 内容审核过程中发生错误: {e}", exc_info=True) # 审核出错：使用原始内容，标记审核失败 content['judgeSuccess'] = False # 生成请求ID requestId = f"content-{datetime.now().strftime('%Y%m%d-%H%M%S')}-{str(uuid.uuid4())[:8]}" logger.info(f"选题 {topicIndex} 内容生成完成，请求ID: {requestId}") return requestId, topicIndex, content async def _enhance_topic_with_database_data(self, topic: Dict[str, Any]) -> Dict[str, Any]: """ 使用数据库数据增强选题信息，优先使用related_object_ids中的反射ID Args: topic: 原始选题数据（可能包含related_object_ids字段） Returns: 增强后的选题数据 """ enhanced_topic = topic.copy() try: # 通过数据库服务获取详细信息 db_service = DatabaseService(self.config_manager) if not db_service.is_available(): logger.warning("数据库服务不可用，无法增强选题数据") return enhanced_topic # 优先使用related_object_ids中的反射ID（更精确） related_ids = topic.get('related_object_ids', {}) if related_ids: logger.info(f"选题包含反射ID信息，优先使用: {related_ids}") enhanced_topic = await self._enhance_with_related_ids(enhanced_topic, db_service, related_ids) return enhanced_topic # 处理风格ID if 'styleIds' in topic and topic['styleIds']: style_id = topic['styleIds'][0] if isinstance(topic['styleIds'], list) else topic['styleIds'] style_data = db_service.get_style_by_id(style_id) if style_data: style_name = style_data.get('styleName') enhanced_topic['style_object'] = style_data enhanced_topic['style'] = style_name logger.info(f"从数据库加载风格数据: {style_name} (ID: {style_id})") # 处理受众ID if 'audienceIds' in topic and topic['audienceIds']: audience_id = topic['audienceIds'][0] if isinstance(topic['audienceIds'], list) else topic['audienceIds'] audience_data = db_service.get_audience_by_id(audience_id) if audience_data: audience_name = audience_data.get('audienceName') enhanced_topic['audience_object'] = audience_data enhanced_topic['targetAudience'] = audience_name logger.info(f"从数据库加载受众数据: {audience_name} (ID: {audience_id})") # 处理景区ID if 'scenicSpotIds' in topic and topic['scenicSpotIds']: spot_id = topic['scenicSpotIds'][0] if isinstance(topic['scenicSpotIds'], list) else topic['scenicSpotIds'] spot_data = db_service.get_scenic_spot_by_id(spot_id) if spot_data: spot_name = spot_data.get('name') enhanced_topic['scenic_spot_object'] = spot_data enhanced_topic['object'] = spot_name logger.info(f"从数据库加载景区数据: {spot_name} (ID: {spot_id})") # 处理产品ID if 'productIds' in topic and topic['productIds']: product_id = topic['productIds'][0] if isinstance(topic['productIds'], list) else topic['productIds'] product_data = db_service.get_product_by_id(product_id) if product_data: product_name = product_data.get('productName') enhanced_topic['product_object'] = product_data enhanced_topic['product'] = product_name logger.info(f"从数据库加载产品数据: {product_name} (ID: {product_id})") except Exception as e: logger.error(f"增强选题数据时发生错误: {e}", exc_info=True) return enhanced_topic async def _enhance_with_related_ids(self, enhanced_topic: Dict[str, Any], db_service, related_ids: Dict[str, List[str]]) -> Dict[str, Any]: """ 使用related_object_ids中的反射ID进行数据库查询和增强 Args: enhanced_topic: 待增强的选题数据 db_service: 数据库服务实例 related_ids: 反射的ID字典 Returns: 增强后的选题数据 """ logger.info("开始使用反射ID进行选题数据增强") try: # 处理风格ID style_ids = related_ids.get('style_ids', []) if style_ids: style_id = int(style_ids[0]) # 取第一个ID style_data = db_service.get_style_by_id(style_id) if style_data: enhanced_topic['style_object'] = style_data enhanced_topic['style'] = style_data.get('styleName') logger.info(f"通过反射ID加载风格数据: {style_data.get('styleName')} (ID: {style_id})") # 处理受众ID audience_ids = related_ids.get('audience_ids', []) if audience_ids: audience_id = int(audience_ids[0]) # 取第一个ID audience_data = db_service.get_audience_by_id(audience_id) if audience_data: enhanced_topic['audience_object'] = audience_data enhanced_topic['targetAudience'] = audience_data.get('audienceName') logger.info(f"通过反射ID加载受众数据: {audience_data.get('audienceName')} (ID: {audience_id})") # 处理景区ID scenic_spot_ids = related_ids.get('scenic_spot_ids', []) if scenic_spot_ids: spot_id = int(scenic_spot_ids[0]) # 取第一个ID spot_data = db_service.get_scenic_spot_by_id(spot_id) if spot_data: enhanced_topic['scenic_spot_object'] = spot_data enhanced_topic['object'] = spot_data.get('name') logger.info(f"通过反射ID加载景区数据: {spot_data.get('name')} (ID: {spot_id})") # 处理产品ID product_ids = related_ids.get('product_ids', []) if product_ids: product_id = int(product_ids[0]) # 取第一个ID product_data = db_service.get_product_by_id(product_id) if product_data: enhanced_topic['product_object'] = product_data enhanced_topic['product'] = product_data.get('productName') logger.info(f"通过反射ID加载产品数据: {product_data.get('productName')} (ID: {product_id})") logger.info("反射ID数据增强完成") except Exception as e: logger.error(f"使用反射ID增强选题数据时发生错误: {e}", exc_info=True) return enhanced_topic async def generate_content_with_prompt(self, topic: Dict[str, Any], system_prompt: str, user_prompt: str) -> Tuple[str, str, Dict[str, Any]]: """ 使用预构建的提示词为选题生成内容 Args: topic: 选题信息 system_prompt: 系统提示词 user_prompt: 用户提示词 Returns: 请求ID、选题索引和生成的内容 """ topicIndex = topic.get('index', 'unknown') logger.info(f"开始使用预构建提示词为选题 {topicIndex} 生成内容") # 直接使用预构建的提示词生成内容 content = await self.content_generator.generate_content_with_prompt(topic, system_prompt, user_prompt) # 生成请求ID requestId = f"content-{datetime.now().strftime('%Y%m%d-%H%M%S')}-{str(uuid.uuid4())[:8]}" logger.info(f"内容生成完成，请求ID: {requestId}, 选题索引: {topicIndex}") return requestId, topicIndex, content async def judge_content(self, topic: Optional[Dict[str, Any]] = None, content: Dict[str, Any] = {}, style_objects: Optional[List[Dict[str, Any]]] = None, audience_objects: Optional[List[Dict[str, Any]]] = None, scenic_spot_objects: Optional[List[Dict[str, Any]]] = None, product_objects: Optional[List[Dict[str, Any]]] = None) -> Tuple[str, str, Dict[str, Any], bool]: """ 审核内容 (已重构) """ if not topic: topic = {"index": "1", "date": "2024-07-01"} if not content: content = {"title": "未提供内容", "content": "未提供内容"} topicIndex = topic.get('index', 'unknown') logger.info(f"开始审核选题 {topicIndex} 的内容") # 构建包含所有预取信息的enhanced_topic enhanced_topic = topic.copy() if style_objects: enhanced_topic['style_object'] = style_objects[0] if audience_objects: enhanced_topic['audience_object'] = audience_objects[0] if scenic_spot_objects: enhanced_topic['scenic_spot_object'] = scenic_spot_objects[0] if product_objects: enhanced_topic['product_object'] = product_objects[0] system_prompt, user_prompt = self.prompt_builder.build_judge_prompt(enhanced_topic, content) judged_data = await self.content_judger.judge_content_with_prompt(content, enhanced_topic, system_prompt, user_prompt) judgeSuccess = judged_data.get('judge_success', False) if 'judge_success' in judged_data: judged_data = {k: v for k, v in judged_data.items() if k != 'judge_success'} judged_data['judgeSuccess'] = judgeSuccess requestId = f"judge-{datetime.now().strftime('%Y%m%d-%H%M%S')}-{str(uuid.uuid4())[:8]}" logger.info(f"内容审核完成，请求ID: {requestId}, 选题索引: {topicIndex}, 审核结果: {judgeSuccess}") return requestId, topicIndex, judged_data, judgeSuccess async def run_pipeline(self, dates: Optional[str] = None, numTopics: int = 5, styles: Optional[List[str]] = None, audiences: Optional[List[str]] = None, scenic_spots: Optional[List[str]] = None, products: Optional[List[str]] = None, skipJudge: bool = False, autoJudge: bool = False) -> Tuple[str, List[Dict[str, Any]], Dict[str, Dict[str, Any]], Dict[str, Dict[str, Any]]]: """ 运行完整的内容生成流水线：生成选题 → 生成内容 → 审核内容 Args: 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 numTopics: 要生成的选题数量 styles: 风格列表 audiences: 受众列表 scenic_spots: 景区列表 products: 产品列表 skipJudge: 是否跳过内容审核步骤（与autoJudge互斥） autoJudge: 是否在内容生成时进行内嵌审核 Returns: 请求ID、选题列表、内容字典和审核后内容字典 """ logger.info(f"开始运行完整流水线，日期: {dates}, 数量: {numTopics}, 内嵌审核: {autoJudge}") # 生成请求ID requestId = f"pipeline-{datetime.now().strftime('%Y%m%d-%H%M%S')}-{str(uuid.uuid4())[:8]}" # 1. 生成选题 _, topics = await self.generate_topics(dates, numTopics, styles, audiences, scenic_spots, products) if not topics: logger.error("未能生成任何选题") return requestId, [], {}, {} # 2. 为每个选题生成内容 contents = {} judgedContents = {} for topic in topics: topicIndex = topic.get('index', 'unknown') # 直接传递带有ID的选题数据，不再需要传递额外的对象参数 _, _, content = await self.generate_content(topic, autoJudge=autoJudge) if autoJudge: # 内嵌审核模式：content已包含审核结果和judgeSuccess状态 # 创建原始内容副本（移除judgeSuccess状态，但保留其他字段） original_content = {k: v for k, v in content.items() if k != 'judgeSuccess'} contents[topicIndex] = original_content judgedContents[topicIndex] = content # 包含审核结果和judgeSuccess状态 else: # 无审核模式：直接保存内容 contents[topicIndex] = content # 如果使用内嵌审核或跳过审核，直接返回结果 if autoJudge or skipJudge: logger.info(f"{'使用内嵌审核' if autoJudge else '跳过内容审核步骤'}，流水线完成，请求ID: {requestId}") if autoJudge: return requestId, topics, contents, judgedContents else: return requestId, topics, contents, contents # 3. 对每个内容进行审核 judgedContents = {} for topicIndex, content in contents.items(): topic = next((t for t in topics if t.get('index') == topicIndex), None) if not topic: logger.warning(f"找不到选题 {topicIndex} 的原始数据，跳过审核") continue try: _, _, judged_data, _ = await self.judge_content(topic, content) judgedContents[topicIndex] = judged_data except Exception as e: logger.critical(f"为选题 {topicIndex} 处理内容审核时发生意外错误: {e}", exc_info=True) logger.info(f"流水线完成，请求ID: {requestId}") return requestId, topics, contents, judgedContents