103 lines
3.8 KiB
Python
103 lines
3.8 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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内容生成模块
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"""
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import logging
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import json
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from typing import Dict, Any
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from core.ai import AIAgent
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from core.config import GenerateTopicConfig # We might need a more specific content config later
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from utils.prompts import PromptTemplate
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from utils.file_io import OutputManager
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logger = logging.getLogger(__name__)
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class ContentPromptBuilder:
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"""构建用于生成推文内容的提示"""
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def __init__(self, system_prompt_path: str):
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self.system_template = PromptTemplate(system_prompt_path)
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def build_prompts(self, topic: Dict[str, Any]) -> Dict[str, str]:
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"""
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根据单个选题构建系统和用户提示
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Args:
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topic: 包含选题信息的字典
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Returns:
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包含 "system" 和 "user" 提示的字典
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"""
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# 在这个简化版本中，用户提示就是选题本身（格式化为字符串）
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# 未来可以扩展以包含更复杂的模板逻辑
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user_prompt = f"请根据以下选题信息生成内容:\n\n"
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for key, value in topic.items():
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user_prompt += f"- {key}: {value}\n"
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system_prompt = self.system_template.render(**topic)
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return {
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"system": system_prompt,
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"user": user_prompt,
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}
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class ContentGenerator:
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"""负责为单个选题生成内容"""
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def __init__(self, ai_agent: AIAgent, config: GenerateTopicConfig, output_manager: OutputManager):
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self.ai_agent = ai_agent
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self.config = config # This might need to be a more specific content config
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self.output_manager = output_manager
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# Assuming content system prompt is defined in the topic config for now
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self.prompt_builder = ContentPromptBuilder(
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system_prompt_path=self.config.content_system_prompt
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)
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def generate(self, topic: Dict[str, Any], topic_index: int, variant_index: int) -> Dict[str, Any]:
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"""
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为单个选题生成一个内容变体
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Args:
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topic: 选题信息字典
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topic_index: 选题的索引
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variant_index: 变体的索引
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Returns:
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包含生成内容的字典
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"""
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logger.info(f"开始为选题 {topic_index} 生成内容变体 {variant_index}...")
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# 1. 构建提示
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prompts = self.prompt_builder.build_prompts(topic)
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variant_dir = self.output_manager.get_variant_dir(topic_index, variant_index)
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self.output_manager.save_text(prompts["system"], "content_system_prompt.txt", subdir=variant_dir.name)
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self.output_manager.save_text(prompts["user"], "content_user_prompt.txt", subdir=variant_dir.name)
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# 2. 调用AI
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try:
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raw_result, _, _, _ = self.ai_agent.work(
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system_prompt=prompts["system"],
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user_prompt=prompts["user"]
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)
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self.output_manager.save_text(raw_result, "content_raw_response.txt", subdir=variant_dir.name)
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except Exception as e:
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logger.critical(f"为 {topic_index}_{variant_index} 生成内容时AI调用失败: {e}", exc_info=True)
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return {"error": str(e)}
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# 3. 解析和保存结果 (简化版，直接保存json)
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# 实际应用中需要类似TopicParser的ContentParser
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try:
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# A simple assumption: the result is a JSON string
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content_data = json.loads(raw_result)
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self.output_manager.save_json(content_data, "article.json", subdir=variant_dir.name)
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logger.info(f"成功为 {topic_index}_{variant_index} 生成并保存内容。")
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return content_data
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except json.JSONDecodeError as e:
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logger.error(f"解析内容JSON失败 for {topic_index}_{variant_index}: {e}")
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return {"error": "JSONDecodeError", "raw_content": raw_result}
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