59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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AI响应解析器模块
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"""
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import logging
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import json
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from typing import List, Dict, Any
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from utils.file_io import process_llm_json_text
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logger = logging.getLogger(__name__)
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class TopicParser:
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"""
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解析和验证由AI模型生成的选题列表
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"""
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@staticmethod
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def parse(raw_text: str) -> List[Dict[str, Any]]:
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"""
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从原始文本解析、修复和验证JSON
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Args:
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raw_text: AI模型返回的原始字符串
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Returns:
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一个字典列表，每个字典代表一个有效的选题
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"""
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logger.info("开始解析AI生成的选题...")
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# 使用通用JSON解析函数解析原始文本
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parsed_json = process_llm_json_text(raw_text)
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if not parsed_json:
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logger.error("解析AI响应失败，无法获取JSON数据")
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return []
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if not isinstance(parsed_json, list):
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logger.error(f"解析结果不是列表，而是 {type(parsed_json)}")
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return []
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logger.info(f"成功解析 {len(parsed_json)} 个选题对象。开始验证...")
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# 验证每个选题是否包含所有必需的键
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valid_topics = []
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required_keys = {"index", "date", "logic", "object", "product", "style", "targetAudience"}
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optional_keys = {"productLogic", "styleLogic", "targetAudienceLogic"}
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for i, item in enumerate(parsed_json):
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if isinstance(item, dict) and required_keys.issubset(item.keys()):
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valid_topics.append(item)
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else:
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logger.warning(f"第 {i+1} 个选题缺少必需键或格式不正确: {item}")
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logger.info(f"验证完成，获得 {len(valid_topics)} 个有效选题。")
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return valid_topics
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