222 lines
7.4 KiB
Python
222 lines
7.4 KiB
Python
"""去AI化Agent - 消除AI生成痕迹"""
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import logging
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import time
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from datetime import datetime, timezone
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from typing import Optional
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from app.agent_framework.base import BaseAgent
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from app.agent_framework.prompts import DEAI_TEMPLATE
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from app.agent_framework.protocol import (
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AgentCapability,
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AgentType,
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TaskMessage,
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TaskResult,
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TaskStatus,
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)
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from app.services.llm import LLMFactory, LLMError
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from app.services.distribution.platform_rules import PLATFORM_RULES, rule_engine
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from app.services.distribution.rule_service import platform_rule_service
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logger = logging.getLogger(__name__)
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class DeAIAgent(BaseAgent):
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"""内容去AI化处理，消除AI生成特征
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支持的任务类型:
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- deai_process: 对内容进行去AI化处理
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input_data 字段:
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- content: str (必填，待处理的文章内容)
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- platform: str (可选，目标平台ID，如 zhihu, wechat 等)
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- style: str (可选，目标风格)
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- preserve_structure: bool (可选，是否保留原有结构)
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"""
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def __init__(self):
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super().__init__(
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name="deai_agent",
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agent_type=AgentType.DEAI_AGENT,
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version="1.1.0",
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)
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def get_capabilities(self) -> AgentCapability:
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return AgentCapability(
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agent_name=self.name,
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agent_type=self.agent_type,
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version=self.version,
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supported_tasks=["deai_process"],
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max_concurrency=2,
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description="内容去AI化Agent：消除AI生成特征，使文章更自然流畅",
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)
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async def execute(self, task: TaskMessage) -> TaskResult:
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"""执行去AI化任务"""
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started_at = datetime.now(timezone.utc)
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start_time = time.monotonic()
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try:
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output = await self._process(task)
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elapsed = time.monotonic() - start_time
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return TaskResult(
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task_id=task.task_id,
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agent_name=self.name,
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status=TaskStatus.COMPLETED,
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output_data=output,
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error_message=None,
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started_at=started_at,
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completed_at=datetime.now(timezone.utc),
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metrics={
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"elapsed_seconds": round(elapsed, 2),
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"task_type": task.task_type,
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},
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)
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except LLMError as e:
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elapsed = time.monotonic() - start_time
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logger.error(f"DeAIAgent LLM error on task {task.task_id}: {e}")
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return TaskResult(
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task_id=task.task_id,
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agent_name=self.name,
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status=TaskStatus.FAILED,
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output_data=None,
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error_message=f"LLM调用失败: {e}",
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started_at=started_at,
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completed_at=datetime.now(timezone.utc),
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metrics={
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"elapsed_seconds": round(elapsed, 2),
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"task_type": task.task_type,
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},
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)
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except Exception as e:
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elapsed = time.monotonic() - start_time
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logger.error(f"DeAIAgent task {task.task_id} failed: {e}")
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return TaskResult(
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task_id=task.task_id,
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agent_name=self.name,
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status=TaskStatus.FAILED,
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output_data=None,
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error_message=str(e),
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started_at=started_at,
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completed_at=datetime.now(timezone.utc),
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metrics={
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"elapsed_seconds": round(elapsed, 2),
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"task_type": task.task_type,
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},
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)
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async def _process(self, task: TaskMessage) -> dict:
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"""执行去AI化处理
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input_data 字段:
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- content: str (必填，待处理的文章内容)
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- platform: str (可选，目标平台ID)
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- style: str (可选，目标风格)
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- preserve_structure: bool (可选，是否保留原有结构)
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"""
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input_data = task.input_data
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content = input_data.get("content", "")
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if not content:
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raise ValueError("input_data必须包含非空的'content'字段")
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platform_id = input_data.get("platform", "")
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# 上报进度：开始
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await self.report_progress(
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task_id=task.task_id,
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progress=0.1,
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message="开始去AI化处理...",
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)
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# 获取平台特定配置
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platform_config = self._get_platform_config(platform_id)
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# 构建变量
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variables = {
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"original_content": content,
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"target_style": input_data.get("style", "自然流畅"),
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"preserve_structure": "是" if input_data.get("preserve_structure", True) else "否",
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"platform_info": platform_config.get("platform_info", "通用"),
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"ai_sensitivity": platform_config.get("ai_sensitivity", ""),
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"banned_patterns": platform_config.get("banned_patterns", ""),
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"safe_patterns": platform_config.get("safe_patterns", ""),
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}
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messages = DEAI_TEMPLATE.render(variables)
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# 上报进度：调用LLM
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await self.report_progress(
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task_id=task.task_id,
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progress=0.3,
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message="正在调用LLM进行去AI化改写...",
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)
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provider = LLMFactory.get_default()
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response = await provider.chat(
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messages,
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temperature=0.9,
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max_tokens=len(content) * 3,
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)
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# 检测处理后的AI模式
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detected_patterns = []
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if platform_id:
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detected_patterns = platform_rule_service.detect_ai_patterns(
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response.content, platform_id
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)
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# 上报进度：完成
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await self.report_progress(
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task_id=task.task_id,
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progress=1.0,
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message=f"去AI化处理完成，原文{len(content)}字 -> 处理后{len(response.content)}字",
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)
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return {
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"content": response.content,
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"original_word_count": len(content),
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"processed_word_count": len(response.content),
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"usage": response.usage,
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"platform_id": platform_id,
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"detected_ai_patterns": detected_patterns,
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}
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def _get_platform_config(self, platform_id: str) -> dict:
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"""获取平台特定配置
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Args:
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platform_id: 平台标识
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Returns:
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包含平台配置的字典
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"""
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if not platform_id or platform_id not in PLATFORM_RULES:
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return {
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"platform_info": "通用",
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"ai_sensitivity": "中",
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"banned_patterns": "总之、综上所述、首先其次最后等",
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"safe_patterns": "根据研究表明、事实上、说实话",
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}
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rules = PLATFORM_RULES[platform_id]
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ai_config = rules.get("ai_sensitivity", {})
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platform_name = rules.get("name", platform_id)
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detection_level = ai_config.get("detection_level", "medium")
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banned = ai_config.get("banned_patterns", [])
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safe = ai_config.get("safe_patterns", [])
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return {
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"platform_info": f"{platform_name} (检测级别: {detection_level})",
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"ai_sensitivity": detection_level,
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"banned_patterns": "、".join(banned[:10]) if banned else "无",
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"safe_patterns": "、".join(safe[:5]) if safe else "无",
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}
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# 导出单例
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deai_agent = DeAIAgent()
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