98 lines
7.2 KiB
JSON
98 lines
7.2 KiB
JSON
{
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"file_path": "tweet/topic_parser.py",
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"file_size": 1544,
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"line_count": 58,
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"functions": [
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{
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"name": "parse",
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"line_start": 22,
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"line_end": 59,
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"args": [
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{
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"name": "raw_text",
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"type_hint": "str"
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}
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],
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"return_type": "List[Dict[str, Any]]",
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"docstring": "从原始文本解析、修复和验证JSON\n\nArgs:\n raw_text: AI模型返回的原始字符串\n\nReturns:\n 一个字典列表，每个字典代表一个有效的选题",
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"is_async": false,
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"decorators": [
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"staticmethod"
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],
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"code": " def parse(raw_text: str) -> List[Dict[str, Any]]:\n \"\"\"\n 从原始文本解析、修复和验证JSON\n\n Args:\n raw_text: AI模型返回的原始字符串\n\n Returns:\n 一个字典列表，每个字典代表一个有效的选题\n \"\"\"\n logger.info(\"开始解析AI生成的选题...\")\n \n # 使用通用JSON解析函数解析原始文本\n parsed_json = process_llm_json_text(raw_text)\n \n if not parsed_json:\n logger.error(\"解析AI响应失败，无法获取JSON数据\")\n return []\n \n if not isinstance(parsed_json, list):\n logger.error(f\"解析结果不是列表，而是 {type(parsed_json)}\")\n return []\n \n logger.info(f\"成功解析 {len(parsed_json)} 个选题对象。开始验证...\")\n \n # 验证每个选题是否包含所有必需的键\n valid_topics = []\n required_keys = {\"index\", \"date\", \"logic\", \"object\", \"product\", \"style\", \"targetAudience\"}\n optional_keys = {\"productLogic\", \"styleLogic\", \"targetAudienceLogic\"}\n \n for i, item in enumerate(parsed_json):\n if isinstance(item, dict) and required_keys.issubset(item.keys()):\n valid_topics.append(item)\n else:\n logger.warning(f\"第 {i+1} 个选题缺少必需键或格式不正确: {item}\")\n \n logger.info(f\"验证完成，获得 {len(valid_topics)} 个有效选题。\")\n return valid_topics ",
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"code_hash": "b764577bd884f9a327e661e58d893403"
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}
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],
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"classes": [
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{
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"name": "TopicParser",
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"line_start": 16,
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"line_end": 59,
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"bases": [],
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"methods": [
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{
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"name": "parse",
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"line_start": 22,
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"line_end": 59,
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"args": [
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{
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"name": "raw_text",
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"type_hint": "str"
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}
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],
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"return_type": "List[Dict[str, Any]]",
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"docstring": "从原始文本解析、修复和验证JSON\n\nArgs:\n raw_text: AI模型返回的原始字符串\n\nReturns:\n 一个字典列表，每个字典代表一个有效的选题",
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"is_async": false,
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"decorators": [
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"staticmethod"
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],
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"code": " def parse(raw_text: str) -> List[Dict[str, Any]]:\n \"\"\"\n 从原始文本解析、修复和验证JSON\n\n Args:\n raw_text: AI模型返回的原始字符串\n\n Returns:\n 一个字典列表，每个字典代表一个有效的选题\n \"\"\"\n logger.info(\"开始解析AI生成的选题...\")\n \n # 使用通用JSON解析函数解析原始文本\n parsed_json = process_llm_json_text(raw_text)\n \n if not parsed_json:\n logger.error(\"解析AI响应失败，无法获取JSON数据\")\n return []\n \n if not isinstance(parsed_json, list):\n logger.error(f\"解析结果不是列表，而是 {type(parsed_json)}\")\n return []\n \n logger.info(f\"成功解析 {len(parsed_json)} 个选题对象。开始验证...\")\n \n # 验证每个选题是否包含所有必需的键\n valid_topics = []\n required_keys = {\"index\", \"date\", \"logic\", \"object\", \"product\", \"style\", \"targetAudience\"}\n optional_keys = {\"productLogic\", \"styleLogic\", \"targetAudienceLogic\"}\n \n for i, item in enumerate(parsed_json):\n if isinstance(item, dict) and required_keys.issubset(item.keys()):\n valid_topics.append(item)\n else:\n logger.warning(f\"第 {i+1} 个选题缺少必需键或格式不正确: {item}\")\n \n logger.info(f\"验证完成，获得 {len(valid_topics)} 个有效选题。\")\n return valid_topics ",
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"code_hash": "b764577bd884f9a327e661e58d893403"
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}
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],
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"docstring": "解析和验证由AI模型生成的选题列表",
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"decorators": [],
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"code": "class TopicParser:\n \"\"\"\n 解析和验证由AI模型生成的选题列表\n \"\"\"\n\n @staticmethod\n def parse(raw_text: str) -> List[Dict[str, Any]]:\n \"\"\"\n 从原始文本解析、修复和验证JSON\n\n Args:\n raw_text: AI模型返回的原始字符串\n\n Returns:\n 一个字典列表，每个字典代表一个有效的选题\n \"\"\"\n logger.info(\"开始解析AI生成的选题...\")\n \n # 使用通用JSON解析函数解析原始文本\n parsed_json = process_llm_json_text(raw_text)\n \n if not parsed_json:\n logger.error(\"解析AI响应失败，无法获取JSON数据\")\n return []\n \n if not isinstance(parsed_json, list):\n logger.error(f\"解析结果不是列表，而是 {type(parsed_json)}\")\n return []\n \n logger.info(f\"成功解析 {len(parsed_json)} 个选题对象。开始验证...\")\n \n # 验证每个选题是否包含所有必需的键\n valid_topics = []\n required_keys = {\"index\", \"date\", \"logic\", \"object\", \"product\", \"style\", \"targetAudience\"}\n optional_keys = {\"productLogic\", \"styleLogic\", \"targetAudienceLogic\"}\n \n for i, item in enumerate(parsed_json):\n if isinstance(item, dict) and required_keys.issubset(item.keys()):\n valid_topics.append(item)\n else:\n logger.warning(f\"第 {i+1} 个选题缺少必需键或格式不正确: {item}\")\n \n logger.info(f\"验证完成，获得 {len(valid_topics)} 个有效选题。\")\n return valid_topics ",
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"code_hash": "8ea78a1b3f8cfa61023ecbaf1638bfd5"
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}
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],
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"imports": [
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{
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"type": "import",
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"modules": [
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"logging"
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],
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"aliases": []
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},
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{
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"type": "import",
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"modules": [
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"json"
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],
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"aliases": []
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},
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{
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"type": "from_import",
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"module": "typing",
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"names": [
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"List",
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"Dict",
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"Any"
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],
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"aliases": [],
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"level": 0
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},
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{
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"type": "from_import",
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"module": "utils.file_io",
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"names": [
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"process_llm_json_text"
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],
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"aliases": [],
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"level": 0
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}
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],
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"constants": [],
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"docstring": "AI响应解析器模块",
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"content_hash": "ef155d2ff5e2ef181d534b9d314c4cee"
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} |