2025-07-31 15:35:23 +08:00

195 lines
16 KiB
JSON

{
"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"
}