{ "file_path": "tweet/content_generator.py", "file_size": 5483, "line_count": 141, "functions": [ { "name": "__init__", "line_start": 23, "line_end": 29, "args": [ { "name": "self" }, { "name": "ai_agent", "type_hint": "AIAgent" }, { "name": "config_manager", "type_hint": "ConfigManager" }, { "name": "output_manager", "type_hint": "OutputManager" } ], "return_type": null, "docstring": "", "is_async": false, "decorators": [], "code": " def __init__(self, ai_agent: AIAgent, config_manager: ConfigManager, output_manager: OutputManager):\n self.ai_agent = ai_agent\n self.config_manager = config_manager\n self.topic_config = config_manager.get_config('topic_gen', GenerateTopicConfig)\n self.content_config = config_manager.get_config('content_gen', GenerateContentConfig)\n self.output_manager = output_manager\n self.prompt_builder = ContentPromptBuilder(config_manager)", "code_hash": "88e6d8e8fb5faaf9bf4848e2de5c7170" } ], "classes": [ { "name": "ContentGenerator", "line_start": 20, "line_end": 142, "bases": [], "methods": [ { "name": "__init__", "line_start": 23, "line_end": 29, "args": [ { "name": "self" }, { "name": "ai_agent", "type_hint": "AIAgent" }, { "name": "config_manager", "type_hint": "ConfigManager" }, { "name": "output_manager", "type_hint": "OutputManager" } ], "return_type": null, "docstring": "", "is_async": false, "decorators": [], "code": " def __init__(self, ai_agent: AIAgent, config_manager: ConfigManager, output_manager: OutputManager):\n self.ai_agent = ai_agent\n self.config_manager = config_manager\n self.topic_config = config_manager.get_config('topic_gen', GenerateTopicConfig)\n self.content_config = config_manager.get_config('content_gen', GenerateContentConfig)\n self.output_manager = output_manager\n self.prompt_builder = ContentPromptBuilder(config_manager)", "code_hash": "88e6d8e8fb5faaf9bf4848e2de5c7170" }, { "name": "generate_content_for_topic", "line_start": 31, "line_end": 87, "args": [ { "name": "self" }, { "name": "topic", "type_hint": "Dict[str, Any]" } ], "return_type": "Dict[str, Any]", "docstring": "为单个选题生成内容\n\nArgs:\n topic: 选题信息字典\n\nReturns:\n 包含生成内容的字典", "is_async": true, "decorators": [], "code": " async def generate_content_for_topic(self, topic: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"\n 为单个选题生成内容\n\n Args:\n topic: 选题信息字典\n\n Returns:\n 包含生成内容的字典\n \"\"\"\n topic_index = topic.get('index', 'N/A')\n logger.info(f\"开始为选题 {topic_index} 生成内容...\")\n\n # 1. 构建提示\n # 使用模板构建器分别获取系统和用户提示\n system_prompt = self.prompt_builder.get_system_prompt()\n user_prompt = self.prompt_builder.build_user_prompt(topic=topic)\n \n # 保存提示以供调试\n output_dir = self.output_manager.get_topic_dir(topic_index)\n self.output_manager.save_text(system_prompt, \"content_system_prompt.txt\", subdir=output_dir.name)\n self.output_manager.save_text(user_prompt, \"content_user_prompt.txt\", subdir=output_dir.name)\n\n # 获取模型参数\n model_params = {}\n if hasattr(self.content_config, 'model') and isinstance(self.content_config.model, dict):\n model_params = {\n 'temperature': self.content_config.model.get('temperature'),\n 'top_p': self.content_config.model.get('top_p'),\n 'presence_penalty': self.content_config.model.get('presence_penalty')\n }\n # 移除None值\n model_params = {k: v for k, v in model_params.items() if v is not None}\n\n # 2. 调用AI\n try:\n raw_result, _, _, _ = await self.ai_agent.generate_text(\n system_prompt=system_prompt,\n user_prompt=user_prompt,\n use_stream=True,\n stage=\"内容生成\",\n **model_params\n )\n self.output_manager.save_text(raw_result, \"content_raw_response.txt\", subdir=output_dir.name)\n except Exception as e:\n logger.critical(f\"为选题 {topic_index} 生成内容时AI调用失败: {e}\", exc_info=True)\n return {\"error\": str(e)}\n\n # 3. 解析和保存结果\n content_data = process_llm_json_text(raw_result)\n if content_data:\n self.output_manager.save_json(content_data, \"article.json\", subdir=output_dir.name)\n logger.info(f\"成功为选题 {topic_index} 生成并保存内容。\")\n return content_data\n else:\n logger.error(f\"解析内容JSON失败 for {topic_index}\")\n return {\"error\": \"JSONDecodeError\", \"raw_content\": raw_result}", "code_hash": "8ccf599fb9a56453241a2b5a6ecc803c" }, { "name": "generate_content_with_prompt", "line_start": 89, "line_end": 142, "args": [ { "name": "self" }, { "name": "topic", "type_hint": "Dict[str, Any]" }, { "name": "system_prompt", "type_hint": "str" }, { "name": "user_prompt", "type_hint": "str" } ], "return_type": "Dict[str, Any]", "docstring": "使用已构建的提示词生成内容\n\nArgs:\n topic: 选题信息字典\n system_prompt: 已构建好的系统提示词\n user_prompt: 已构建好的用户提示词\n\nReturns:\n 包含生成内容的字典", "is_async": true, "decorators": [], "code": " async def generate_content_with_prompt(self, topic: Dict[str, Any], system_prompt: str, user_prompt: str) -> Dict[str, Any]:\n \"\"\"\n 使用已构建的提示词生成内容\n\n Args:\n topic: 选题信息字典\n system_prompt: 已构建好的系统提示词\n user_prompt: 已构建好的用户提示词\n\n Returns:\n 包含生成内容的字典\n \"\"\"\n topic_index = topic.get('index', 'N/A')\n logger.info(f\"使用预构建提示词为选题 {topic_index} 生成内容...\")\n \n # 保存提示以供调试\n output_dir = self.output_manager.get_topic_dir(topic_index)\n self.output_manager.save_text(system_prompt, \"content_system_prompt.txt\", subdir=output_dir.name)\n self.output_manager.save_text(user_prompt, \"content_user_prompt.txt\", subdir=output_dir.name)\n\n # 获取模型参数\n model_params = {}\n if hasattr(self.content_config, 'model') and isinstance(self.content_config.model, dict):\n model_params = {\n 'temperature': self.content_config.model.get('temperature'),\n 'top_p': self.content_config.model.get('top_p'),\n 'presence_penalty': self.content_config.model.get('presence_penalty')\n }\n # 移除None值\n model_params = {k: v for k, v in model_params.items() if v is not None}\n\n # 调用AI\n try:\n raw_result, _, _, _ = await self.ai_agent.generate_text(\n system_prompt=system_prompt,\n user_prompt=user_prompt,\n use_stream=True,\n stage=\"内容生成\",\n **model_params\n )\n self.output_manager.save_text(raw_result, \"content_raw_response.txt\", subdir=output_dir.name)\n except Exception as e:\n logger.critical(f\"为选题 {topic_index} 生成内容时AI调用失败: {e}\", exc_info=True)\n return {\"error\": str(e)}\n\n # 解析和保存结果\n content_data = process_llm_json_text(raw_result)\n if content_data:\n self.output_manager.save_json(content_data, \"article.json\", subdir=output_dir.name)\n logger.info(f\"成功为选题 {topic_index} 生成并保存内容。\")\n return content_data\n else:\n logger.error(f\"解析内容JSON失败 for {topic_index}\")\n return {\"error\": \"JSONDecodeError\", \"raw_content\": raw_result} ", "code_hash": "2d7576b90b1b5c5f717424569945ab93" } ], "docstring": "负责为单个选题生成内容", "decorators": [], "code": "class ContentGenerator:\n \"\"\"负责为单个选题生成内容\"\"\"\n\n def __init__(self, ai_agent: AIAgent, config_manager: ConfigManager, output_manager: OutputManager):\n self.ai_agent = ai_agent\n self.config_manager = config_manager\n self.topic_config = config_manager.get_config('topic_gen', GenerateTopicConfig)\n self.content_config = config_manager.get_config('content_gen', GenerateContentConfig)\n self.output_manager = output_manager\n self.prompt_builder = ContentPromptBuilder(config_manager)\n\n async def generate_content_for_topic(self, topic: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"\n 为单个选题生成内容\n\n Args:\n topic: 选题信息字典\n\n Returns:\n 包含生成内容的字典\n \"\"\"\n topic_index = topic.get('index', 'N/A')\n logger.info(f\"开始为选题 {topic_index} 生成内容...\")\n\n # 1. 构建提示\n # 使用模板构建器分别获取系统和用户提示\n system_prompt = self.prompt_builder.get_system_prompt()\n user_prompt = self.prompt_builder.build_user_prompt(topic=topic)\n \n # 保存提示以供调试\n output_dir = self.output_manager.get_topic_dir(topic_index)\n self.output_manager.save_text(system_prompt, \"content_system_prompt.txt\", subdir=output_dir.name)\n self.output_manager.save_text(user_prompt, \"content_user_prompt.txt\", subdir=output_dir.name)\n\n # 获取模型参数\n model_params = {}\n if hasattr(self.content_config, 'model') and isinstance(self.content_config.model, dict):\n model_params = {\n 'temperature': self.content_config.model.get('temperature'),\n 'top_p': self.content_config.model.get('top_p'),\n 'presence_penalty': self.content_config.model.get('presence_penalty')\n }\n # 移除None值\n model_params = {k: v for k, v in model_params.items() if v is not None}\n\n # 2. 调用AI\n try:\n raw_result, _, _, _ = await self.ai_agent.generate_text(\n system_prompt=system_prompt,\n user_prompt=user_prompt,\n use_stream=True,\n stage=\"内容生成\",\n **model_params\n )\n self.output_manager.save_text(raw_result, \"content_raw_response.txt\", subdir=output_dir.name)\n except Exception as e:\n logger.critical(f\"为选题 {topic_index} 生成内容时AI调用失败: {e}\", exc_info=True)\n return {\"error\": str(e)}\n\n # 3. 解析和保存结果\n content_data = process_llm_json_text(raw_result)\n if content_data:\n self.output_manager.save_json(content_data, \"article.json\", subdir=output_dir.name)\n logger.info(f\"成功为选题 {topic_index} 生成并保存内容。\")\n return content_data\n else:\n logger.error(f\"解析内容JSON失败 for {topic_index}\")\n return {\"error\": \"JSONDecodeError\", \"raw_content\": raw_result}\n \n async def generate_content_with_prompt(self, topic: Dict[str, Any], system_prompt: str, user_prompt: str) -> Dict[str, Any]:\n \"\"\"\n 使用已构建的提示词生成内容\n\n Args:\n topic: 选题信息字典\n system_prompt: 已构建好的系统提示词\n user_prompt: 已构建好的用户提示词\n\n Returns:\n 包含生成内容的字典\n \"\"\"\n topic_index = topic.get('index', 'N/A')\n logger.info(f\"使用预构建提示词为选题 {topic_index} 生成内容...\")\n \n # 保存提示以供调试\n output_dir = self.output_manager.get_topic_dir(topic_index)\n self.output_manager.save_text(system_prompt, \"content_system_prompt.txt\", subdir=output_dir.name)\n self.output_manager.save_text(user_prompt, \"content_user_prompt.txt\", subdir=output_dir.name)\n\n # 获取模型参数\n model_params = {}\n if hasattr(self.content_config, 'model') and isinstance(self.content_config.model, dict):\n model_params = {\n 'temperature': self.content_config.model.get('temperature'),\n 'top_p': self.content_config.model.get('top_p'),\n 'presence_penalty': self.content_config.model.get('presence_penalty')\n }\n # 移除None值\n model_params = {k: v for k, v in model_params.items() if v is not None}\n\n # 调用AI\n try:\n raw_result, _, _, _ = await self.ai_agent.generate_text(\n system_prompt=system_prompt,\n user_prompt=user_prompt,\n use_stream=True,\n stage=\"内容生成\",\n **model_params\n )\n self.output_manager.save_text(raw_result, \"content_raw_response.txt\", subdir=output_dir.name)\n except Exception as e:\n logger.critical(f\"为选题 {topic_index} 生成内容时AI调用失败: {e}\", exc_info=True)\n return {\"error\": str(e)}\n\n # 解析和保存结果\n content_data = process_llm_json_text(raw_result)\n if content_data:\n self.output_manager.save_json(content_data, \"article.json\", subdir=output_dir.name)\n logger.info(f\"成功为选题 {topic_index} 生成并保存内容。\")\n return content_data\n else:\n logger.error(f\"解析内容JSON失败 for {topic_index}\")\n return {\"error\": \"JSONDecodeError\", \"raw_content\": raw_result} ", "code_hash": "9205f62520279562109ec37a309391ac" } ], "imports": [ { "type": "import", "modules": [ "logging" ], "aliases": [] }, { "type": "import", "modules": [ "json" ], "aliases": [] }, { "type": "from_import", "module": "typing", "names": [ "Dict", "Any", "Tuple", "Optional" ], "aliases": [], "level": 0 }, { "type": "from_import", "module": "core.ai", "names": [ "AIAgent" ], "aliases": [], "level": 0 }, { "type": "from_import", "module": "core.config", "names": [ "ConfigManager", "GenerateTopicConfig", "GenerateContentConfig" ], "aliases": [], "level": 0 }, { "type": "from_import", "module": "utils.prompts", "names": [ "ContentPromptBuilder" ], "aliases": [], "level": 0 }, { "type": "from_import", "module": "utils.file_io", "names": [ "OutputManager", "process_llm_json_text" ], "aliases": [], "level": 0 } ], "constants": [], "docstring": "内容生成模块", "content_hash": "1f92561652f12d1021bee41f76512a83" }