111 lines
4.3 KiB
Python
111 lines
4.3 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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选题生成模块
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"""
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import logging
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import json
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import re
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from typing import List, Dict, Any, Optional
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from json_repair import loads as json_repair_loads
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from core.ai import AIAgent
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from core.config import GenerateTopicConfig, ConfigManager
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from utils.prompts import TopicPromptBuilder
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from utils.file_io import OutputManager
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from .topic_parser import TopicParser
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logger = logging.getLogger(__name__)
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class TopicGenerator:
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"""
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负责生成、解析和保存主题
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"""
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def __init__(self, ai_agent: AIAgent, config_manager: ConfigManager, output_manager: OutputManager):
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"""
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初始化主题生成器
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Args:
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ai_agent: AI Agent
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config_manager: 配置管理器
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output_manager: 输出管理器
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"""
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self.ai_agent = ai_agent
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self.config: GenerateTopicConfig = config_manager.get_config('topic_gen', GenerateTopicConfig)
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self.output_manager = output_manager
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self.parser = TopicParser()
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self.prompt_builder = TopicPromptBuilder(config_manager)
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logger.info(f"选题生成配置: {self.config}")
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async def generate_topics(self) -> Optional[List[Dict[str, Any]]]:
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"""
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执行完整的选题生成流程：构建提示 -> 调用AI -> 解析结果 -> 保存产物
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"""
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logger.info("开始执行选题生成流程...")
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# 1. 构建提示
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system_prompt = self.prompt_builder.get_system_prompt()
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user_prompt = self.prompt_builder.build_user_prompt(
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num_topics=self.config.topic.num,
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month=self.config.topic.date
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)
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self.output_manager.save_text(system_prompt, "topic_system_prompt.txt")
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self.output_manager.save_text(user_prompt, "topic_user_prompt.txt")
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# 获取模型参数
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model_params = {}
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if hasattr(self.config, 'model') and isinstance(self.config.model, dict):
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model_params = {
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'temperature': self.config.model.get('temperature'),
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'top_p': self.config.model.get('top_p'),
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'presence_penalty': self.config.model.get('presence_penalty')
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}
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# 移除None值
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model_params = {k: v for k, v in model_params.items() if v is not None}
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# 2. 调用AI生成
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try:
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raw_result, _, _, _ = await self.ai_agent.generate_text(
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system_prompt=system_prompt,
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user_prompt=user_prompt,
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use_stream=True, # 选题生成通常不需要流式输出
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stage="选题生成",
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**model_params
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)
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self.output_manager.save_text(raw_result, "topics_raw_response.txt")
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except Exception as e:
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logger.critical(f"AI调用失败，无法生成选题: {e}", exc_info=True)
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return None
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# 3. 解析结果
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topics = self.parser.parse(raw_result)
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if not topics:
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logger.error("未能从AI响应中解析出任何有效选题")
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return None
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# 4. 保存解析后的结果
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self.output_manager.save_json(topics, "topics_generated.json")
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# 5. （可选）保存为易于阅读的 .txt 格式
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topics_text = self._format_topics_to_text(topics)
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self.output_manager.save_text(topics_text, "topics_generated.txt")
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logger.info(f"选题生成流程成功完成，共生成 {len(topics)} 个选题。")
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return topics
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def _format_topics_to_text(self, topics: List[Dict[str, Any]]) -> str:
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"""将选题列表格式化为人类可读的文本"""
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text_parts = [f"# 选题列表 (Run ID: {self.output_manager.run_id})\n"]
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for topic in topics:
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text_parts.append(f"## 选题 {topic.get('index', 'N/A')}")
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text_parts.append(f"- 日期: {topic.get('date', 'N/A')}")
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text_parts.append(f"- 对象: {topic.get('object', 'N/A')}")
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text_parts.append(f"- 产品: {topic.get('product', 'N/A')}")
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text_parts.append(f"- 风格: {topic.get('style', 'N/A')}")
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text_parts.append(f"- 目标受众: {topic.get('target_audience', 'N/A')}")
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text_parts.append(f"- 逻辑: {topic.get('logic', 'N/A')}")
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return "\n".join(text_parts)
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