372 lines
14 KiB
Python
372 lines
14 KiB
Python
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# -*- coding: utf-8 -*-
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"""
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智能Agent核心 - 集成大模型和智能决策
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高效实现Agent的智能处理能力
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"""
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import logging
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import asyncio
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import json
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from typing import Dict, Any, List, Optional, Tuple
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from datetime import datetime
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from dataclasses import dataclass
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from enum import Enum
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logger = logging.getLogger(__name__)
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class ActionType(Enum):
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"""动作类型枚举"""
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ALERT_RESPONSE = "alert_response"
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KNOWLEDGE_UPDATE = "knowledge_update"
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WORKORDER_CREATE = "workorder_create"
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SYSTEM_OPTIMIZE = "system_optimize"
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USER_NOTIFY = "user_notify"
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class ConfidenceLevel(Enum):
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"""置信度等级"""
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HIGH = "high" # 高置信度 (>0.8)
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MEDIUM = "medium" # 中等置信度 (0.5-0.8)
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LOW = "low" # 低置信度 (<0.5)
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@dataclass
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class AgentAction:
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"""Agent动作"""
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action_type: ActionType
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description: str
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priority: int # 1-5, 5最高
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confidence: float # 0-1
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parameters: Dict[str, Any]
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estimated_time: int # 预计执行时间(秒)
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@dataclass
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class AlertContext:
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"""预警上下文"""
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alert_id: str
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alert_type: str
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severity: str
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description: str
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affected_systems: List[str]
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metrics: Dict[str, Any]
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@dataclass
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class KnowledgeContext:
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"""知识库上下文"""
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question: str
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answer: str
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confidence: float
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source: str
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category: str
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class IntelligentAgent:
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"""智能Agent核心"""
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def __init__(self, llm_client=None):
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self.llm_client = llm_client
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self.action_history = []
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self.learning_data = {}
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self.confidence_thresholds = {
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'high': 0.8,
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'medium': 0.5,
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'low': 0.3
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}
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async def process_alert(self, alert_context: AlertContext) -> List[AgentAction]:
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"""处理预警信息，生成智能动作"""
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try:
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# 构建预警分析提示
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prompt = self._build_alert_analysis_prompt(alert_context)
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# 调用大模型分析
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analysis = await self._call_llm(prompt)
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# 解析动作
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actions = self._parse_alert_actions(analysis, alert_context)
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# 按优先级排序
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actions.sort(key=lambda x: x.priority, reverse=True)
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return actions
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except Exception as e:
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logger.error(f"处理预警失败: {e}")
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return [self._create_default_alert_action(alert_context)]
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async def process_knowledge_confidence(self, knowledge_context: KnowledgeContext) -> List[AgentAction]:
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"""处理知识库置信度问题"""
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try:
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if knowledge_context.confidence >= self.confidence_thresholds['high']:
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return [] # 高置信度，无需处理
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# 构建知识增强提示
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prompt = self._build_knowledge_enhancement_prompt(knowledge_context)
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# 调用大模型增强知识
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enhancement = await self._call_llm(prompt)
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# 生成增强动作
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actions = self._parse_knowledge_actions(enhancement, knowledge_context)
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return actions
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except Exception as e:
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logger.error(f"处理知识库置信度失败: {e}")
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return [self._create_default_knowledge_action(knowledge_context)]
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async def execute_action(self, action: AgentAction) -> Dict[str, Any]:
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"""执行Agent动作"""
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try:
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logger.info(f"执行Agent动作: {action.action_type.value} - {action.description}")
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if action.action_type == ActionType.ALERT_RESPONSE:
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return await self._execute_alert_response(action)
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elif action.action_type == ActionType.KNOWLEDGE_UPDATE:
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return await self._execute_knowledge_update(action)
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elif action.action_type == ActionType.WORKORDER_CREATE:
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return await self._execute_workorder_create(action)
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elif action.action_type == ActionType.SYSTEM_OPTIMIZE:
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return await self._execute_system_optimize(action)
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elif action.action_type == ActionType.USER_NOTIFY:
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return await self._execute_user_notify(action)
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else:
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return {"success": False, "error": "未知动作类型"}
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except Exception as e:
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logger.error(f"执行动作失败: {e}")
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return {"success": False, "error": str(e)}
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def _build_alert_analysis_prompt(self, alert_context: AlertContext) -> str:
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"""构建预警分析提示"""
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return f"""
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作为TSP智能助手，请分析以下预警信息并提供处理建议：
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预警信息：
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- 类型: {alert_context.alert_type}
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- 严重程度: {alert_context.severity}
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- 描述: {alert_context.description}
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- 影响系统: {', '.join(alert_context.affected_systems)}
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- 指标数据: {json.dumps(alert_context.metrics, ensure_ascii=False)}
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请提供以下格式的JSON响应：
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{{
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"analysis": "预警原因分析",
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"immediate_actions": [
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{{
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"action": "立即执行的动作",
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"priority": 5,
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"confidence": 0.9,
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"parameters": {{"key": "value"}}
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}}
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],
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"follow_up_actions": [
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{{
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"action": "后续跟进动作",
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"priority": 3,
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"confidence": 0.7,
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"parameters": {{"key": "value"}}
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}}
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],
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"prevention_measures": [
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"预防措施1",
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"预防措施2"
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]
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}}
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"""
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def _build_knowledge_enhancement_prompt(self, knowledge_context: KnowledgeContext) -> str:
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"""构建知识增强提示"""
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return f"""
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作为TSP智能助手，请分析以下知识库条目并提供增强建议：
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知识条目：
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- 问题: {knowledge_context.question}
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- 答案: {knowledge_context.answer}
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- 置信度: {knowledge_context.confidence}
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- 来源: {knowledge_context.source}
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- 分类: {knowledge_context.category}
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请提供以下格式的JSON响应：
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{{
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"confidence_analysis": "置信度分析",
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"enhancement_suggestions": [
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"增强建议1",
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"增强建议2"
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],
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"actions": [
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{{
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"action": "知识更新动作",
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"priority": 4,
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"confidence": 0.8,
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"parameters": {{"enhanced_answer": "增强后的答案"}}
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}}
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],
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"learning_opportunities": [
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"学习机会1",
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"学习机会2"
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]
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}}
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"""
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async def _call_llm(self, prompt: str) -> Dict[str, Any]:
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"""调用大模型"""
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try:
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if self.llm_client:
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# 使用真实的大模型客户端
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response = await self.llm_client.generate(prompt)
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return json.loads(response)
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else:
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# 模拟大模型响应
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return self._simulate_llm_response(prompt)
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except Exception as e:
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logger.error(f"调用大模型失败: {e}")
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return self._simulate_llm_response(prompt)
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def _simulate_llm_response(self, prompt: str) -> Dict[str, Any]:
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"""模拟大模型响应 - 千问模型风格"""
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if "预警信息" in prompt:
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return {
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"analysis": "【千问分析】系统性能下降，需要立即处理。根据历史数据分析，这可能是由于资源不足或配置问题导致的。",
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"immediate_actions": [
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{
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"action": "重启相关服务",
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"priority": 5,
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"confidence": 0.9,
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"parameters": {"service": "main_service", "reason": "服务响应超时"}
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}
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],
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"follow_up_actions": [
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{
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"action": "检查系统日志",
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"priority": 3,
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"confidence": 0.7,
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"parameters": {"log_level": "error", "time_range": "last_hour"}
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}
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],
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"prevention_measures": [
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"增加监控频率，提前发现问题",
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"优化系统配置，提升性能",
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"建立预警机制，减少故障影响"
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]
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}
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else:
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return {
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"confidence_analysis": "【千问分析】当前答案置信度较低，需要更多上下文信息。建议结合用户反馈和历史工单数据来提升答案质量。",
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"enhancement_suggestions": [
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"添加更多实际案例和操作步骤",
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"提供详细的故障排除指南",
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"结合系统架构图进行说明"
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],
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"actions": [
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{
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"action": "更新知识库条目",
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"priority": 4,
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"confidence": 0.8,
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"parameters": {"enhanced_answer": "基于千问模型分析的增强答案"}
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}
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],
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"learning_opportunities": [
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"收集用户反馈，持续优化答案",
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"分析相似问题，建立知识关联",
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"利用千问模型的学习能力，提升知识质量"
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]
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}
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def _parse_alert_actions(self, analysis: Dict[str, Any], alert_context: AlertContext) -> List[AgentAction]:
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"""解析预警动作"""
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actions = []
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# 立即动作
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for action_data in analysis.get("immediate_actions", []):
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action = AgentAction(
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action_type=ActionType.ALERT_RESPONSE,
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description=action_data["action"],
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priority=action_data["priority"],
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confidence=action_data["confidence"],
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parameters=action_data["parameters"],
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estimated_time=30
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)
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actions.append(action)
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# 后续动作
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for action_data in analysis.get("follow_up_actions", []):
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action = AgentAction(
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action_type=ActionType.SYSTEM_OPTIMIZE,
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description=action_data["action"],
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priority=action_data["priority"],
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confidence=action_data["confidence"],
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parameters=action_data["parameters"],
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estimated_time=300
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)
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actions.append(action)
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return actions
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def _parse_knowledge_actions(self, enhancement: Dict[str, Any], knowledge_context: KnowledgeContext) -> List[AgentAction]:
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"""解析知识库动作"""
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actions = []
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for action_data in enhancement.get("actions", []):
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action = AgentAction(
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action_type=ActionType.KNOWLEDGE_UPDATE,
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description=action_data["action"],
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priority=action_data["priority"],
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confidence=action_data["confidence"],
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parameters=action_data["parameters"],
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estimated_time=60
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)
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actions.append(action)
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return actions
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def _create_default_alert_action(self, alert_context: AlertContext) -> AgentAction:
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"""创建默认预警动作"""
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return AgentAction(
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action_type=ActionType.USER_NOTIFY,
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description=f"通知管理员处理{alert_context.alert_type}预警",
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priority=3,
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confidence=0.5,
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parameters={"alert_id": alert_context.alert_id},
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estimated_time=10
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)
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def _create_default_knowledge_action(self, knowledge_context: KnowledgeContext) -> AgentAction:
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"""创建默认知识库动作"""
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return AgentAction(
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action_type=ActionType.KNOWLEDGE_UPDATE,
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description="标记低置信度知识条目，等待人工审核",
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priority=2,
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confidence=0.3,
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parameters={"question": knowledge_context.question},
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estimated_time=5
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)
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async def _execute_alert_response(self, action: AgentAction) -> Dict[str, Any]:
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"""执行预警响应动作"""
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# 这里实现具体的预警响应逻辑
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logger.info(f"执行预警响应: {action.description}")
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return {"success": True, "message": "预警响应已执行"}
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async def _execute_knowledge_update(self, action: AgentAction) -> Dict[str, Any]:
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"""执行知识库更新动作"""
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# 这里实现具体的知识库更新逻辑
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logger.info(f"执行知识库更新: {action.description}")
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return {"success": True, "message": "知识库已更新"}
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async def _execute_workorder_create(self, action: AgentAction) -> Dict[str, Any]:
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"""执行工单创建动作"""
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# 这里实现具体的工单创建逻辑
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logger.info(f"执行工单创建: {action.description}")
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return {"success": True, "message": "工单已创建"}
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async def _execute_system_optimize(self, action: AgentAction) -> Dict[str, Any]:
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"""执行系统优化动作"""
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# 这里实现具体的系统优化逻辑
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logger.info(f"执行系统优化: {action.description}")
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return {"success": True, "message": "系统优化已执行"}
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async def _execute_user_notify(self, action: AgentAction) -> Dict[str, Any]:
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"""执行用户通知动作"""
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# 这里实现具体的用户通知逻辑
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logger.info(f"执行用户通知: {action.description}")
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return {"success": True, "message": "用户已通知"}
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