202 lines
5.8 KiB
Python
202 lines
5.8 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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内容审核引擎
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负责评估和优化生成的内容
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"""
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import logging
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from typing import Dict, Any, Optional, Tuple
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logger = logging.getLogger(__name__)
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class JudgeEngine:
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"""
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内容审核引擎
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职责：
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- 评估内容质量
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- 提供优化建议
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- 自动修正内容
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"""
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def __init__(self, llm_client=None, prompt_builder=None):
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"""
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初始化审核引擎
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Args:
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llm_client: LLM 客户端
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prompt_builder: 提示词构建器
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"""
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self._llm = llm_client
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self._prompt = prompt_builder
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self.logger = logging.getLogger(f"{__name__}.JudgeEngine")
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async def judge(
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self,
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content: Dict[str, Any],
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topic: Dict[str, Any],
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auto_fix: bool = True
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) -> Dict[str, Any]:
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"""
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审核内容
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Args:
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content: 待审核的内容
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topic: 原始选题
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auto_fix: 是否自动修正
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Returns:
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审核结果
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"""
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try:
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self.logger.info("开始内容审核")
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# 1. 构建提示词
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system_prompt, user_prompt = self._build_prompts(content, topic, auto_fix)
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# 2. 调用 LLM
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response = await self._llm.generate(
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prompt=user_prompt,
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system_prompt=system_prompt
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)
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# 3. 解析结果
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result = self._parse_result(response, content)
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self.logger.info(f"内容审核完成: score={result.get('score', 'N/A')}")
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return result
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except Exception as e:
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self.logger.error(f"内容审核失败: {e}")
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# 返回原始内容
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return {
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'success': False,
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'error': str(e),
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'original_content': content,
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'judged_content': content
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}
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def _build_prompts(
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self,
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content: Dict[str, Any],
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topic: Dict[str, Any],
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auto_fix: bool
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) -> Tuple[str, str]:
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"""构建提示词"""
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if self._prompt:
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system_prompt = self._prompt.get_system_prompt(
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"content_judge",
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auto_fix=auto_fix
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)
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user_prompt = self._prompt.get_user_prompt(
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"content_judge",
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content=content,
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topic=topic,
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auto_fix=auto_fix
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)
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return system_prompt, user_prompt
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# 默认提示词
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system_prompt = """你是一个专业的内容审核专家。
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请评估以下小红书文案的质量，并给出评分和建议。
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评估维度：
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1. 标题吸引力 (1-10)
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2. 内容质量 (1-10)
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3. 结构清晰度 (1-10)
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4. 情感共鸣 (1-10)
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5. 营销效果 (1-10)
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输出格式为 JSON。"""
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content_title = content.get('title', '')
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content_text = content.get('content', '')
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topic_title = topic.get('title', '')
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fix_instruction = ""
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if auto_fix:
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fix_instruction = """
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如果评分低于 7 分，请提供优化后的版本。
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在 JSON 中添加 "improved_title" 和 "improved_content" 字段。"""
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user_prompt = f"""请评估以下内容：
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原始选题：{topic_title}
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文案标题：{content_title}
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文案内容：
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{content_text}
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请以 JSON 格式输出评估结果，包含：
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- scores: 各维度评分
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- total_score: 总分 (满分 50)
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- suggestions: 优化建议数组
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- pass: 是否通过 (总分 >= 35)
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{fix_instruction}"""
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return system_prompt, user_prompt
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def _parse_result(self, response: str, original_content: Dict[str, Any]) -> Dict[str, Any]:
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"""解析审核结果"""
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import json
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import re
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result = {
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'success': True,
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'original_content': original_content,
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}
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# 尝试解析 JSON
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parsed = None
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try:
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parsed = json.loads(response)
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except:
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pass
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if not parsed:
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patterns = [
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r'\{[\s\S]*\}',
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r'```json\s*([\s\S]*?)\s*```',
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]
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for pattern in patterns:
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match = re.search(pattern, response)
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if match:
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try:
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json_str = match.group(1) if '```' in pattern else match.group(0)
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parsed = json.loads(json_str)
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break
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except:
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continue
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if parsed:
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result['scores'] = parsed.get('scores', {})
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result['total_score'] = parsed.get('total_score', 0)
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result['score'] = result['total_score'] # 别名
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result['suggestions'] = parsed.get('suggestions', [])
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result['pass'] = parsed.get('pass', True)
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# 如果有优化版本
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if parsed.get('improved_title') or parsed.get('improved_content'):
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result['judged_content'] = {
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**original_content,
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'title': parsed.get('improved_title', original_content.get('title')),
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'content': parsed.get('improved_content', original_content.get('content')),
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}
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result['improved'] = True
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else:
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result['judged_content'] = original_content
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result['improved'] = False
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else:
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# 无法解析，返回原始内容
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result['judged_content'] = original_content
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result['pass'] = True
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result['improved'] = False
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return result
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