#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Manages the construction of prompts for different AI generation tasks. """ import os import traceback import logging # Add logging import re # 添加正则表达式支持 import random # 添加随机模块支持 from .resource_loader import ResourceLoader # Use relative import within the same package import json class PromptManager: """Handles the loading and construction of prompts.""" def __init__(self, topic_system_prompt_path: str, topic_user_prompt_path: str, content_system_prompt_path: str, prompts_dir: str = None, # 兼容旧配置 prompts_config: list = None, # 新的配置方式 resource_dir_config: list = None, topic_gen_num: int = 1, # Default values if needed topic_gen_date: str = "", content_judger_system_prompt_path: str = None # 添加内容审核系统提示词路径参数 ): self.topic_system_prompt_path = topic_system_prompt_path self.topic_user_prompt_path = topic_user_prompt_path self.content_system_prompt_path = content_system_prompt_path self.content_judger_system_prompt_path = content_judger_system_prompt_path # 添加成员变量 self.prompts_dir = prompts_dir # 保留兼容旧配置 self.prompts_config = prompts_config or [] # 新的配置方式 self.resource_dir_config = resource_dir_config or [] self.topic_gen_num = topic_gen_num self.topic_gen_date = topic_gen_date # 缓存加载的文件内容 self._style_cache = {} self._demand_cache = {} self._refer_cache = {} self._system_prompt_cache = {} # 新增：系统提示词缓存 self._user_prompt_cache = {} # 新增：用户提示词缓存 self._dateline_cache = None # 新增：日期线缓存 self._sample_rate = 0.1 # 提高随机抽样率 # 初始化时预加载配置的文件 self._preload_prompt_files() def _preload_prompt_files(self): """预加载配置中的提示文件到缓存""" # 预加载系统提示词和用户提示词文件 if self.topic_system_prompt_path and os.path.exists(self.topic_system_prompt_path): content = ResourceLoader.load_file_content(self.topic_system_prompt_path) if content: self._system_prompt_cache["topic"] = content logging.info(f"预加载系统提示词: {self.topic_system_prompt_path}") if self.topic_user_prompt_path and os.path.exists(self.topic_user_prompt_path): content = ResourceLoader.load_file_content(self.topic_user_prompt_path) if content: self._user_prompt_cache["topic"] = content logging.info(f"预加载用户提示词: {self.topic_user_prompt_path}") if self.content_system_prompt_path and os.path.exists(self.content_system_prompt_path): content = ResourceLoader.load_file_content(self.content_system_prompt_path) if content: self._system_prompt_cache["content"] = content logging.info(f"预加载内容系统提示词: {self.content_system_prompt_path}") # 预加载内容审核系统提示词 if self.content_judger_system_prompt_path and os.path.exists(self.content_judger_system_prompt_path): content = ResourceLoader.load_file_content(self.content_judger_system_prompt_path) if content: self._system_prompt_cache["judger_system_prompt"] = content logging.info(f"预加载内容审核系统提示词: {self.content_judger_system_prompt_path}") # 预加载日期线文件 if self.topic_user_prompt_path: user_prompt_dir = os.path.dirname(self.topic_user_prompt_path) dateline_path = os.path.join(user_prompt_dir, "2025各月节日宣传节点时间表.md") if os.path.exists(dateline_path): self._dateline_cache = ResourceLoader.load_file_content(dateline_path) logging.info(f"预加载日期线文件: {dateline_path}") # 加载prompts_config配置的文件 if not self.prompts_config: return for config_item in self.prompts_config: prompt_type = config_item.get("type", "").lower() file_paths = config_item.get("file_path", []) if prompt_type == "style": for path in file_paths: if os.path.exists(path): filename = os.path.basename(path) content = ResourceLoader.load_file_content(path) if content: self._style_cache[filename] = content name_without_ext = os.path.splitext(filename)[0] self._style_cache[name_without_ext] = content # 同时缓存不带扩展名的版本 elif prompt_type == "demand": for path in file_paths: # print(path) if os.path.exists(path): filename = os.path.basename(path) # print(filename) content = ResourceLoader.load_file_content(path) # print(content) if content: self._demand_cache[filename] = content name_without_ext = os.path.splitext(filename)[0] self._demand_cache[name_without_ext] = content # 同时缓存不带扩展名的版本 elif prompt_type == "refer": for path in file_paths: # print(path) if os.path.exists(path): filename = os.path.basename(path) # print(filename) # 检测是否为JSON文件 if filename.lower().endswith('.json'): # 直接加载和解析JSON文件 try: with open(path, 'r', encoding='utf-8') as f: content = f.read() # 移除可能的BOM头 if content.startswith('\ufeff'): content = content[1:] # 解析JSON json_data = json.loads(content) # 存储解析后的对象 self._refer_cache[filename] = json_data logging.info(f"预加载并解析JSON参考文件: {filename}") except Exception as e: logging.error(f"预加载JSON文件{filename}失败: {str(e)}") # 失败时尝试以普通文本加载 content = ResourceLoader.load_file_content(path) if content: self._refer_cache[filename] = content else: # 非JSON文件使用普通加载方式 content = ResourceLoader.load_all_refer_files(path, 1) if content: self._refer_cache[filename] = content logging.info(f"预加载普通参考文件: {filename}") def find_directory_fuzzy_match(self, name, directory=None, files=None): """ 对文件名进行模糊匹配，查找最匹配目标名称的文件 Args: name: 目标名称 directory: 目录路径，如果提供则从目录中读取文件列表 files: 文件名列表，如果提供则直接使用 Returns: tuple: (最佳匹配文件名, 匹配分数) 如果没有匹配则返回 (None, 0) """ logging.info(f"尝试对文件名进行模糊匹配: {name}") try: # 准备文件列表 all_files = [] if files: all_files = files elif directory and os.path.isdir(directory): all_files = [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))] if not all_files: logging.warning(f"没有可用于匹配的文件") return None, 0 logging.info(f"找到 {len(all_files)} 个文件可用于模糊匹配") # 从名称中提取关键词 # 通过常见分隔符分割（+、空格、_、-等） parts = re.split(r'[+\s_\-]', name) keywords = [] for part in parts: # 只保留长度大于1的有意义关键词 if len(part) > 1: keywords.append(part) # 尝试匹配更短的语义单元（例如中文的2-3个字的词语） for i in range(len(name) - 1): keyword = name[i:i+2] # 提取2个字符 if len(keyword) == 2 and all('\u4e00' <= c <= '\u9fff' for c in keyword): keywords.append(keyword) logging.info(f"用于文件模糊匹配的关键词: {keywords}") # 对每个文件进行评分 file_scores = {} for filename in all_files: score = 0 file_lower = filename.lower() # 精确匹配，去掉扩展名比较 name_without_ext = os.path.splitext(name)[0].lower() file_without_ext = os.path.splitext(filename)[0].lower() if name_without_ext == file_without_ext: # 精确匹配给高分 score += 10 else: # 为每个匹配的关键词增加分数 for keyword in keywords: if keyword.lower() in file_lower: score += 1 # 如果得分大于0（至少有匹配），记录该文件 if score > 0: file_scores[filename] = score # 选择得分最高的文件 if file_scores: best_match = max(file_scores.items(), key=lambda x: x[1]) found_file = best_match[0] score = best_match[1] logging.info(f"模糊匹配成功！匹配文件: {found_file}，匹配分数: {score}") return found_file, score else: logging.warning(f"模糊匹配未找到任何包含关键词的文件") return None, 0 except Exception as e: logging.exception(f"文件模糊匹配过程中出错: {e}") return None, 0 def _get_style_content(self, style_name): """获取Style文件内容，优先从缓存获取，如果不存在则尝试从目录加载""" # 首先检查缓存 if style_name in self._style_cache: return self._style_cache[style_name] # 确保有扩展名 if not style_name.lower().endswith('.txt'): style_file = f"{style_name}.txt" else: style_file = style_name style_name = os.path.splitext(style_name)[0] # 移除扩展名 # 尝试模糊匹配缓存中的文件名 cache_files = list(self._style_cache.keys()) matched_key, score = self.find_directory_fuzzy_match(style_name, files=cache_files) if matched_key and score > 0: return self._style_cache[matched_key] # 如果没有在缓存中找到模糊匹配，尝试从prompts_dir加载 if self.prompts_dir: style_dir = os.path.join(self.prompts_dir, "Style") style_path = os.path.join(style_dir, style_file) # 精确路径匹配 if os.path.exists(style_path): content = ResourceLoader.load_file_content(style_path) if content: # 保存到缓存 self._style_cache[style_name] = content self._style_cache[style_file] = content return content # 如果精确匹配失败，尝试目录中的模糊匹配 if os.path.isdir(style_dir): matched_file, score = self.find_directory_fuzzy_match(style_name, directory=style_dir) if matched_file and score > 0: matched_path = os.path.join(style_dir, matched_file) content = ResourceLoader.load_file_content(matched_path) if content: # 保存到缓存 self._style_cache[style_name] = content self._style_cache[matched_file] = content file_without_ext = os.path.splitext(matched_file)[0] self._style_cache[file_without_ext] = content # 同时缓存不带扩展名的版本 return content logging.warning(f"未能找到Style文件: '{style_name}'，尝试过以下位置: 缓存, {self.prompts_dir}/Style/") return None def _get_demand_content(self, demand_name): """获取Demand文件内容，优先从缓存获取，如果不存在则尝试从目录加载""" # 首先检查缓存 if demand_name in self._demand_cache: return self._demand_cache[demand_name] # 确保有扩展名 if not demand_name.lower().endswith('.txt'): demand_file = f"{demand_name}.txt" else: demand_file = demand_name demand_name = os.path.splitext(demand_name)[0] # 移除扩展名 # 尝试模糊匹配缓存中的文件名 cache_files = list(self._demand_cache.keys()) matched_key, score = self.find_directory_fuzzy_match(demand_name, files=cache_files) if matched_key and score > 0: return self._demand_cache[matched_key] # 如果没有在缓存中找到模糊匹配，尝试从prompts_dir加载（向后兼容） if self.prompts_dir: demand_dir = os.path.join(self.prompts_dir, "Demand") demand_path = os.path.join(demand_dir, demand_file) # 精确路径匹配 if os.path.exists(demand_path): content = ResourceLoader.load_file_content(demand_path) if content: # 保存到缓存 self._demand_cache[demand_name] = content self._demand_cache[demand_file] = content return content # 如果精确匹配失败，尝试目录中的模糊匹配 if os.path.isdir(demand_dir): matched_file, score = self.find_directory_fuzzy_match(demand_name, directory=demand_dir) if matched_file and score > 0: matched_path = os.path.join(demand_dir, matched_file) content = ResourceLoader.load_file_content(matched_path) if content: # 保存到缓存 self._demand_cache[demand_name] = content self._demand_cache[matched_file] = content file_without_ext = os.path.splitext(matched_file)[0] self._demand_cache[file_without_ext] = content # 同时缓存不带扩展名的版本 return content # 如果所有尝试都失败 logging.warning(f"未能找到Demand文件: '{demand_name}'，尝试过以下位置: 缓存, {self.prompts_dir}/Demand/") return None def _get_refer_content(self, refer_dir): """从Refer目录加载JSON和文本参考文件 Args: refer_dir: Refer目录路径 Returns: dict: 文件名到内容的映射 """ results = {} if not os.path.isdir(refer_dir): return results # 列出所有文件 refer_files = [f for f in os.listdir(refer_dir) if os.path.isfile(os.path.join(refer_dir, f))] for refer_file in refer_files: refer_path = os.path.join(refer_dir, refer_file) content = None # 对JSON文件特殊处理 if refer_file.lower().endswith('.json'): try: with open(refer_path, 'r', encoding='utf-8') as f: content = f.read() # 移除可能的BOM头 if content.startswith('\ufeff'): content = content[1:] # 验证是否为有效的JSON try: json.loads(content) # 只验证，不存储结果 logging.info(f"成功验证JSON文件: {refer_file}") except json.JSONDecodeError as e: logging.warning(f"文件{refer_file}内容不是有效的JSON: {str(e)}") except Exception as e: logging.error(f"读取JSON文件{refer_file}时出错: {str(e)}") content = None else: # 使用ResourceLoader加载非JSON文件 content = ResourceLoader.load_file_content(refer_path) if content: results[refer_file] = content return results def _load_and_parse_json(self, file_path): """安全地加载和解析JSON文件 Args: file_path: JSON文件路径 Returns: dict or None: 解析后的JSON对象，解析失败则返回None """ try: with open(file_path, 'r', encoding='utf-8') as f: content = f.read() # 移除可能的BOM头 if content.startswith('\ufeff'): content = content[1:] return json.loads(content) except Exception as e: logging.error(f"加载和解析JSON文件{file_path}时出错: {str(e)}") return None def _get_all_refer_contents(self, random_sample=True): """获取所有Refer文件内容，可选择随机抽样文件内容 Args: random_sample: 是否对文件内容进行随机抽样，默认为True Returns: str: 组合后的refer内容 """ import json # 初始化结果字符串 refer_content_all = "" # 如果缓存为空，尝试加载文件 if not self._refer_cache and self.prompts_dir: refer_dir = os.path.join(self.prompts_dir, "Refer") if os.path.isdir(refer_dir): refer_files = [f for f in os.listdir(refer_dir) if os.path.isfile(os.path.join(refer_dir, f))] for refer_file in refer_files: file_path = os.path.join(refer_dir, refer_file) # 对JSON文件特殊处理 if refer_file.lower().endswith('.json'): try: with open(file_path, 'r', encoding='utf-8') as f: content = f.read() # 移除可能的BOM头 if content.startswith('\ufeff'): content = content[1:] # 解析并存储JSON对象 json_data = json.loads(content) self._refer_cache[refer_file] = json_data logging.info(f"加载并解析JSON参考文件: {refer_file}") except Exception as e: logging.error(f"加载JSON文件{refer_file}失败: {str(e)}") # 失败时尝试以普通文本加载 content = ResourceLoader.load_file_content(file_path) if content: self._refer_cache[refer_file] = content else: # 非JSON文件使用普通加载方式 content = ResourceLoader.load_file_content(file_path) if content: self._refer_cache[refer_file] = content if not self._refer_cache: logging.warning("没有找到任何Refer文件") return refer_content_all logging.info(f"找到{len(self._refer_cache)}个Refer文件") # 处理所有文件 for filename, content in self._refer_cache.items(): # 添加文件头部信息 refer_content_all += f"--- Refer File: {filename} ---\n" # 检查内容类型 if isinstance(content, dict) and 'title' in content and 'examples' in content: # 已解析的JSON对象 title = content.get("title", "未命名参考资料") description = content.get("description", "") examples = content.get("examples", []) refer_content_all += f"标题: {title}\n" refer_content_all += f"描述: {description}\n\n" if examples: # 处理examples数组 if random_sample and len(examples) > 10: # 对examples进行随机抽样 sample_size = max(10, int(len(examples) * self._sample_rate)) sampled_examples = random.sample(examples, sample_size) logging.info(f"从文件{filename}的JSON中随机抽样了{sample_size}/{len(examples)}个示例") refer_content_all += "示例:\n" for idx, example in enumerate(sampled_examples, 1): content_text = example.get("content", "") refer_content_all += f"{idx}. {content_text}\n" else: # 不进行抽样或examples数量较少，使用全部 refer_content_all += "示例:\n" for idx, example in enumerate(examples, 1): content_text = example.get("content", "") refer_content_all += f"{idx}. {content_text}\n" elif isinstance(content, str): # 文本内容 if random_sample: lines = content.split('\n') if len(lines) > 10: # 只对较长的内容进行抽样 sample_size = max(10, int(len(lines) * self._sample_rate)) sampled_lines = random.sample(lines, sample_size) # 保持原有顺序 sampled_lines.sort(key=lambda line: lines.index(line)) sampled_content = '\n'.join(sampled_lines) logging.info(f"从文件{filename}中随机抽样了{sample_size}/{len(lines)}行内容") refer_content_all += f"{sampled_content}\n" else: # 内容较短，不进行抽样 refer_content_all += f"{content}\n" else: # 不进行抽样 refer_content_all += f"{content}\n" else: # 内容是其他类型 refer_content_all += f"未知内容类型: {type(content)}\n" # 添加文件之间的分隔 refer_content_all += "\n" return refer_content_all def get_topic_prompts(self): """Constructs the system and user prompts for topic generation.""" logging.info("Constructing prompts for topic generation...") try: # --- System Prompt --- system_prompt = self._system_prompt_cache.get("topic") if not system_prompt: if not self.topic_system_prompt_path: logging.error("Topic system prompt path not provided during PromptManager initialization.") return None, None system_prompt = ResourceLoader.load_file_content(self.topic_system_prompt_path) if system_prompt: self._system_prompt_cache["topic"] = system_prompt else: logging.error(f"Failed to load topic system prompt from '{self.topic_system_prompt_path}'.") return None, None # --- User Prompt --- base_user_prompt = self._user_prompt_cache.get("topic") if not base_user_prompt: if not self.topic_user_prompt_path: logging.error("Topic user prompt path not provided during PromptManager initialization.") return None, None base_user_prompt = ResourceLoader.load_file_content(self.topic_user_prompt_path) if base_user_prompt: self._user_prompt_cache["topic"] = base_user_prompt else: logging.error(f"Failed to load base topic user prompt from '{self.topic_user_prompt_path}'.") return None, None # --- Build the dynamic part of the user prompt --- user_prompt_dynamic = "你拥有的创作资料如下：\n" # 添加prompts_config配置的文件信息 if self.prompts_config: for config_item in self.prompts_config: prompt_type = config_item.get("type", "").lower() file_paths = config_item.get("file_path", []) if file_paths: user_prompt_dynamic += f"{prompt_type.capitalize()}文件列表:\n" for path in file_paths: filename = os.path.basename(path) user_prompt_dynamic += f"- {filename}\n" user_prompt_dynamic += "\n" # 兼容旧配置：Add genPrompts directory structure elif self.prompts_dir and os.path.isdir(self.prompts_dir): try: gen_prompts_list = os.listdir(self.prompts_dir) for gen_prompt_folder in gen_prompts_list: folder_path = os.path.join(self.prompts_dir, gen_prompt_folder) if os.path.isdir(folder_path): try: # List files, filter out subdirs if needed gen_prompts_files = [f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))] user_prompt_dynamic += f"{gen_prompt_folder}\n{gen_prompts_files}\n" except OSError as e: logging.warning(f"Could not list directory {folder_path}: {e}") except OSError as e: logging.warning(f"Could not list base prompts directory {self.prompts_dir}: {e}") else: logging.warning(f"Neither prompts_config nor prompts_dir provided or valid.") # Add resource directory contents for dir_info in self.resource_dir_config: source_type = dir_info.get("type", "UnknownType") source_file_paths = dir_info.get("file_path", []) for file_path in source_file_paths: # Use ResourceLoader's static method file_content = ResourceLoader.load_file_content(file_path) if file_content: user_prompt_dynamic += f"{source_type}信息:\n{os.path.basename(file_path)}\n{file_content}\n\n" else: logging.warning(f"Could not load resource file {file_path}") # Add dateline information (optional) if self._dateline_cache: user_prompt_dynamic += f"\n{self._dateline_cache}" else: user_prompt_dir = os.path.dirname(self.topic_user_prompt_path) dateline_path = os.path.join(user_prompt_dir, "2025各月节日宣传节点时间表.md") # Consider making this configurable if os.path.exists(dateline_path): dateline_content = ResourceLoader.load_file_content(dateline_path) if dateline_content: self._dateline_cache = dateline_content user_prompt_dynamic += f"\n{dateline_content}" # Combine dynamic part, base template, and final parameters user_prompt = user_prompt_dynamic + base_user_prompt user_prompt += f"\n选题数量：{self.topic_gen_num}\n选题日期：{self.topic_gen_date}\n" logging.info(f"Topic prompts constructed. System: {len(system_prompt)} chars, User: {len(user_prompt)} chars.") return system_prompt, user_prompt except Exception as e: logging.exception("Error constructing topic prompts:") return None, None def get_content_prompts(self, topic_item): """Constructs the system and user prompts for content generation.""" logging.info("Constructing prompts for content generation...") try: # --- System Prompt --- system_prompt = self._system_prompt_cache.get("content") if not system_prompt: if not self.content_system_prompt_path: logging.error("Content system prompt path not provided.") return None, None system_prompt = ResourceLoader.load_file_content(self.content_system_prompt_path) if system_prompt: self._system_prompt_cache["content"] = system_prompt else: logging.error(f"Failed to load content system prompt from '{self.content_system_prompt_path}'.") return None, None # --- User Prompt --- style = "" demand = "" refers = "" object_info = "" # Extract style from topic if "style" in topic_item and topic_item["style"]: style_name = topic_item["style"] style_content = self._get_style_content(style_name) if style_content: style = f"Style: {style_name}\n{style_content}\n\n" else: logging.warning(f"Style content for '{style_name}' not found.") # Extract demand from topic if "target_audience" in topic_item and topic_item["target_audience"]: demand_name = topic_item["target_audience"] demand_content = self._get_demand_content(demand_name) if demand_content: demand = f"Demand: {demand_name}\n{demand_content}\n\n" else: logging.warning(f"Demand content for '{demand_name}' not found.") # Add refer contents - 现在使用随机抽样 refers_content = self._get_all_refer_contents(random_sample=True) if refers_content: refers = f"Reference:\n{refers_content}\n\n" # Get object information object_name = topic_item.get("object", "") if object_name: # 优化：遍历resource_dir_config查找对象描述 found_object_info = False # 1. 搜集所有可能的资源文件 all_description_files = [] for dir_info in self.resource_dir_config: if dir_info.get("type") in ["Object", "Description"]: all_description_files.extend(dir_info.get("file_path", [])) # 2. 尝试精确匹配 for file_path in all_description_files: if object_name in os.path.basename(file_path): info = ResourceLoader.load_file_content(file_path) if info: object_info = f"Object: {object_name}\n{info}\n\n" logging.info(f"找到对象'{object_name}'的精确匹配资源文件: {file_path}") found_object_info = True break # 3. 如果精确匹配失败，尝试模糊匹配 if not found_object_info and all_description_files: logging.info(f"尝试模糊匹配对象'{object_name}'的资源文件") # 提取所有文件名 file_names = [os.path.basename(f) for f in all_description_files] # 模糊匹配 matched_filename, score = self.find_directory_fuzzy_match(object_name, files=file_names) if matched_filename and score > 0: # 找到匹配的完整路径 for file_path in all_description_files: if os.path.basename(file_path) == matched_filename: info = ResourceLoader.load_file_content(file_path) if info: object_info = f"Object: {object_name}\n{info}\n\n" logging.info(f"模糊匹配找到对象'{object_name}'的资源文件: {file_path}，匹配分数: {score}") found_object_info = True break if not found_object_info: logging.warning(f"未找到对象'{object_name}'的任何匹配资源文件") # Get product information if any product_name = topic_item.get("product", "") if product_name: product_info = "" # 优化：遍历resource_dir_config查找产品描述 found_product_info = False # 搜集所有可能的产品资源文件 all_product_files = [] for dir_info in self.resource_dir_config: if dir_info.get("type") == "Product": all_product_files.extend(dir_info.get("file_path", [])) # 尝试精确匹配 for file_path in all_product_files: if product_name in os.path.basename(file_path): info = ResourceLoader.load_file_content(file_path) if info: product_info = f"Product: {product_name}\n{info}\n\n" logging.info(f"找到产品'{product_name}'的精确匹配资源文件: {file_path}") found_product_info = True break # 如果精确匹配失败，尝试模糊匹配 if not found_product_info and all_product_files: logging.info(f"尝试模糊匹配产品'{product_name}'的资源文件") # 提取所有文件名 file_names = [os.path.basename(f) for f in all_product_files] # 模糊匹配 matched_filename, score = self.find_directory_fuzzy_match(product_name, files=file_names) if matched_filename and score > 0: # 找到匹配的完整路径 for file_path in all_product_files: if os.path.basename(file_path) == matched_filename: info = ResourceLoader.load_file_content(file_path) if info: product_info = f"Product: {product_name}\n{info}\n\n" logging.info(f"模糊匹配找到产品'{product_name}'的资源文件: {file_path}，匹配分数: {score}") found_product_info = True break if not found_product_info: logging.warning(f"未找到产品'{product_name}'的任何匹配资源文件") # 添加产品信息到对象信息中 if product_info: object_info += product_info # Construct final user prompt user_prompt = f"""请为我创建一个旅游文案。 {style}{demand}{refers}{object_info} 请考虑以上所有信息，创作一篇{topic_item.get('target_audience','')}文旅内容。""" return system_prompt, user_prompt except Exception as e: traceback.print_exc() logging.error(f"Error creating content prompts: {str(e)}") return None, None