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feat: 实现智能Agent系统，集成大模型和智能决策

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- 创建IntelligentAgent核心，支持预警处理和知识库置信度处理
- 集成LLM客户端，支持OpenAI、Anthropic和本地LLM
- 实现ActionExecutor动作执行器，支持多种动作类型
- 优化TSPAgentAssistant，集成大模型和智能决策能力
- 添加预警自动处理措施，包括重启服务、检查状态、通知等
- 实现知识库置信度处理，自动增强低置信度知识条目
- 支持动作执行历史记录和统计
- 提供LLM使用统计和监控功能
This commit is contained in:
zhaojie
2025-09-10 23:55:43 +08:00
parent c7043c13b0
commit 6ef72837a5
7 changed files with 1489 additions and 23 deletions
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logs/dashboard.log
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@@ -3109,3 +3109,439 @@ WHERE knowledge_entries.is_active = true AND knowledge_entries.is_verified = tru
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2025-09-10 23:15:49,165 - __main__ - INFO - 正在启动TSP智能助手综合管理平台...
2025-09-10 23:15:51,033 - src.core.database - INFO - 数据库初始化成功
2025-09-10 23:15:52,585 - src.knowledge_base.knowledge_manager - INFO - 向量化器加载成功，包含 48 个条目
2025-09-10 23:15:52,596 - src.knowledge_base.knowledge_manager - INFO - 向量化器加载成功，包含 48 个条目
2025-09-10 23:15:52,598 - src.main - INFO - TSP助手初始化完成
2025-09-10 23:15:52,612 - src.knowledge_base.knowledge_manager - INFO - 向量化器加载成功，包含 48 个条目
2025-09-10 23:15:52,627 - src.knowledge_base.knowledge_manager - INFO - 向量化器加载成功，包含 48 个条目
2025-09-10 23:15:52,631 - src.main - INFO - TSP助手初始化完成
2025-09-10 23:15:52,631 - src.agent.tool_manager - INFO - 注册工具: search_knowledge
2025-09-10 23:15:52,631 - src.agent.tool_manager - INFO - 注册工具: create_work_order
2025-09-10 23:15:52,632 - src.agent.tool_manager - INFO - 注册工具: update_work_order
2025-09-10 23:15:52,632 - src.agent.tool_manager - INFO - 注册工具: generate_response
2025-09-10 23:15:52,632 - src.agent.tool_manager - INFO - 注册工具: analyze_data
2025-09-10 23:15:52,632 - src.agent.tool_manager - INFO - 注册工具: send_notification
2025-09-10 23:15:52,632 - src.agent.tool_manager - INFO - 注册工具: schedule_task
2025-09-10 23:15:52,633 - src.agent.tool_manager - INFO - 注册工具: web_search
2025-09-10 23:15:52,633 - src.agent.tool_manager - INFO - 注册工具: file_operation
2025-09-10 23:15:52,633 - src.agent.tool_manager - INFO - 注册工具: database_query
2025-09-10 23:15:52,634 - src.agent.tool_manager - INFO - 已注册 10 个默认工具
2025-09-10 23:15:52,634 - src.agent.agent_core - INFO - Agent核心初始化完成
2025-09-10 23:15:52,634 - src.agent_assistant - INFO - TSP Agent助手初始化完成
2025-09-10 23:15:52,648 - src.knowledge_base.knowledge_manager - INFO - 向量化器加载成功，包含 48 个条目
2025-09-10 23:15:52,740 - werkzeug - INFO - WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
* Running on all addresses (0.0.0.0)
* Running on http://127.0.0.1:5000
* Running on http://192.168.26.238:5000
2025-09-10 23:15:52,740 - werkzeug - INFO - Press CTRL+C to quit
2025-09-10 23:15:52,763 - src.knowledge_base.knowledge_manager - INFO - 向量化器加载成功，包含 48 个条目
2025-09-10 23:15:52,765 - src.web.websocket_server - INFO - 启动WebSocket服务器: ws://localhost:8765
2025-09-10 23:15:52,768 - websockets.server - INFO - server listening on 127.0.0.1:8765
2025-09-10 23:15:52,768 - websockets.server - INFO - server listening on [::1]:8765
2025-09-10 23:15:54,110 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:15:54] "GET /api/health HTTP/1.1" 200 -
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2025-09-10 23:20:22,750 - __main__ - INFO - 正在启动TSP智能助手综合管理平台...
2025-09-10 23:20:24,447 - src.core.database - INFO - 数据库初始化成功
2025-09-10 23:20:25,501 - src.knowledge_base.knowledge_manager - INFO - 向量化器加载成功，包含 48 个条目
2025-09-10 23:20:25,511 - src.knowledge_base.knowledge_manager - INFO - 向量化器加载成功，包含 48 个条目
2025-09-10 23:20:25,513 - src.main - INFO - TSP助手初始化完成
2025-09-10 23:20:25,526 - src.knowledge_base.knowledge_manager - INFO - 向量化器加载成功，包含 48 个条目
2025-09-10 23:20:25,540 - src.knowledge_base.knowledge_manager - INFO - 向量化器加载成功，包含 48 个条目
2025-09-10 23:20:25,542 - src.main - INFO - TSP助手初始化完成
2025-09-10 23:20:25,542 - src.agent.tool_manager - INFO - 注册工具: search_knowledge
2025-09-10 23:20:25,542 - src.agent.tool_manager - INFO - 注册工具: create_work_order
2025-09-10 23:20:25,542 - src.agent.tool_manager - INFO - 注册工具: update_work_order
2025-09-10 23:20:25,543 - src.agent.tool_manager - INFO - 注册工具: generate_response
2025-09-10 23:20:25,543 - src.agent.tool_manager - INFO - 注册工具: analyze_data
2025-09-10 23:20:25,543 - src.agent.tool_manager - INFO - 注册工具: send_notification
2025-09-10 23:20:25,543 - src.agent.tool_manager - INFO - 注册工具: schedule_task
2025-09-10 23:20:25,543 - src.agent.tool_manager - INFO - 注册工具: web_search
2025-09-10 23:20:25,544 - src.agent.tool_manager - INFO - 注册工具: file_operation
2025-09-10 23:20:25,544 - src.agent.tool_manager - INFO - 注册工具: database_query
2025-09-10 23:20:25,544 - src.agent.tool_manager - INFO - 已注册 10 个默认工具
2025-09-10 23:20:25,544 - src.agent.agent_core - INFO - Agent核心初始化完成
2025-09-10 23:20:25,544 - src.agent_assistant - INFO - TSP Agent助手初始化完成
2025-09-10 23:20:25,558 - src.knowledge_base.knowledge_manager - INFO - 向量化器加载成功，包含 48 个条目
2025-09-10 23:20:25,639 - werkzeug - INFO - WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
* Running on all addresses (0.0.0.0)
* Running on http://127.0.0.1:5000
* Running on http://192.168.26.238:5000
2025-09-10 23:20:25,640 - werkzeug - INFO - Press CTRL+C to quit
2025-09-10 23:20:25,665 - src.knowledge_base.knowledge_manager - INFO - 向量化器加载成功，包含 48 个条目
2025-09-10 23:20:25,668 - src.web.websocket_server - INFO - 启动WebSocket服务器: ws://localhost:8765
2025-09-10 23:20:25,672 - websockets.server - INFO - server listening on 127.0.0.1:8765
2025-09-10 23:20:25,673 - websockets.server - INFO - server listening on [::1]:8765
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2025-09-10 23:21:02,087 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:02] "GET /api/health HTTP/1.1" 200 -
2025-09-10 23:21:02,113 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:02] "GET /api/workorders/import/template HTTP/1.1" 200 -
2025-09-10 23:21:02,439 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:02] "GET /uploads/workorder_template.xlsx HTTP/1.1" 404 -
2025-09-10 23:21:03,089 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:03] "GET /api/health HTTP/1.1" 200 -
2025-09-10 23:21:04,826 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:04] "GET /uploads/workorder_template.xlsx HTTP/1.1" 404 -
2025-09-10 23:21:06,406 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:06] "GET /api/alerts HTTP/1.1" 200 -
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2025-09-10 23:21:11,076 - src.dialogue.realtime_chat - INFO - 创建新会话: session_user_001_1757517671
2025-09-10 23:21:11,077 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:11] "POST /api/chat/session HTTP/1.1" 200 -
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2025-09-10 23:21:12,791 - src.dialogue.realtime_chat - INFO - 结束会话: session_user_001_1757517671
2025-09-10 23:21:12,792 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:12] "DELETE /api/chat/session/session_user_001_1757517671 HTTP/1.1" 200 -
2025-09-10 23:21:13,086 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:13] "GET /api/health HTTP/1.1" 200 -
2025-09-10 23:21:14,633 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:14] "GET /api/agent/status HTTP/1.1" 200 -
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2025-09-10 23:21:16,608 - src.analytics.monitor_service - INFO - 监控服务已启动
2025-09-10 23:21:16,614 - src.main - INFO - 监控服务已启动
2025-09-10 23:21:16,622 - src.agent.auto_monitor - INFO - 自动监控循环已启动
2025-09-10 23:21:16,622 - src.agent.auto_monitor - INFO - 自动监控服务已启动
2025-09-10 23:21:16,624 - src.agent.auto_monitor - INFO - 执行第 1 次自动监控检查
2025-09-10 23:21:16,625 - src.agent_assistant - INFO - 主动监控已启动
2025-09-10 23:21:16,642 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:16] "POST /api/agent/monitoring/start HTTP/1.1" 200 -
2025-09-10 23:21:16,673 - src.agent.auto_monitor - INFO - 触发主动行动: alert_overflow - 活跃预警数量过多: 42
2025-09-10 23:21:16,674 - src.agent.auto_monitor - INFO - 主动行动记录: {"timestamp": "2025-09-10T23:21:16.674688", "action_type": "alert_overflow", "priority": "high", "description": "活跃预警数量过多: 42", "action": "建议立即处理预警", "data": {"alert_count": 42}}
2025-09-10 23:21:16,674 - src.agent.auto_monitor - INFO - 处理预警溢出
2025-09-10 23:21:16,675 - src.agent.auto_monitor - ERROR - 检查工单积压失败: 'TSPAgentAssistant' object has no attribute 'get_workorders'
2025-09-10 23:21:16,711 - src.analytics.monitor_service - INFO - 触发 4 个预警
2025-09-10 23:21:16,712 - src.analytics.monitor_service - WARNING - 预警触发: 用户满意度较低: 0.00 (阈值: 0.6)
2025-09-10 23:21:16,712 - src.analytics.monitor_service - INFO - 发送通知: {'level': '警告', 'message': '用户满意度较低: 0.00 (阈值: 0.6)', 'timestamp': '2025-09-10T23:21:16.641218', 'rule_name': '满意度预警'}
2025-09-10 23:21:16,712 - src.analytics.monitor_service - WARNING - 警告预警: 用户满意度较低: 0.00 (阈值: 0.6)
2025-09-10 23:21:16,712 - src.analytics.monitor_service - WARNING - 预警触发: 知识库命中率较低: 0.00 (阈值: 0.5)
2025-09-10 23:21:16,713 - src.analytics.monitor_service - INFO - 发送通知: {'level': '警告', 'message': '知识库命中率较低: 0.00 (阈值: 0.5)', 'timestamp': '2025-09-10T23:21:16.676689', 'rule_name': '知识库命中率预警'}
2025-09-10 23:21:16,713 - src.analytics.monitor_service - WARNING - 警告预警: 知识库命中率较低: 0.00 (阈值: 0.5)
2025-09-10 23:21:16,713 - src.analytics.monitor_service - WARNING - 预警触发: 系统内存使用率过高: 106.6% (阈值: 80.0%)
2025-09-10 23:21:16,713 - src.analytics.monitor_service - INFO - 发送通知: {'level': '警告', 'message': '系统内存使用率过高: 106.6% (阈值: 80.0%)', 'timestamp': '2025-09-10T23:21:16.703711', 'rule_name': '内存使用预警'}
2025-09-10 23:21:16,714 - src.analytics.monitor_service - WARNING - 警告预警: 系统内存使用率过高: 106.6% (阈值: 80.0%)
2025-09-10 23:21:16,715 - src.analytics.monitor_service - WARNING - 预警触发: 用户对话中断率过高: 0.50 (阈值: 0.3)
2025-09-10 23:21:16,716 - src.analytics.monitor_service - INFO - 发送通知: {'level': '警告', 'message': '用户对话中断率过高: 0.50 (阈值: 0.3)', 'timestamp': '2025-09-10T23:21:16.709685', 'rule_name': '对话中断预警'}
2025-09-10 23:21:16,716 - src.analytics.monitor_service - WARNING - 警告预警: 用户对话中断率过高: 0.50 (阈值: 0.3)
2025-09-10 23:21:16,825 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:16] "GET /api/agent/status HTTP/1.1" 200 -
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2025-09-10 23:21:17,822 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:17] "GET /api/alerts HTTP/1.1" 200 -
2025-09-10 23:21:18,089 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:18] "GET /api/health HTTP/1.1" 200 -
2025-09-10 23:21:18,144 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:18] "GET /api/knowledge?page=1&per_page=10 HTTP/1.1" 200 -
2025-09-10 23:21:19,064 - werkzeug - INFO - 127.0.0.1 - - [10/Sep/2025 23:21:19] "GET /api/alerts HTTP/1.1" 200 -
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Agent动作执行器 - 执行具体的Agent动作
"""
import logging
import asyncio
from typing import Dict, Any, List, Optional
from datetime import datetime
import json
from .intelligent_agent import AgentAction, ActionType, AlertContext, KnowledgeContext
logger = logging.getLogger(__name__)
class ActionExecutor:
"""动作执行器"""
def __init__(self, tsp_assistant=None):
self.tsp_assistant = tsp_assistant
self.execution_history = []
self.action_handlers = {
ActionType.ALERT_RESPONSE: self._handle_alert_response,
ActionType.KNOWLEDGE_UPDATE: self._handle_knowledge_update,
ActionType.WORKORDER_CREATE: self._handle_workorder_create,
ActionType.SYSTEM_OPTIMIZE: self._handle_system_optimize,
ActionType.USER_NOTIFY: self._handle_user_notify
}
async def execute_action(self, action: AgentAction) -> Dict[str, Any]:
"""执行动作"""
try:
logger.info(f"开始执行动作: {action.action_type.value}")
start_time = datetime.now()
# 获取处理器
handler = self.action_handlers.get(action.action_type)
if not handler:
return {"success": False, "error": f"未找到动作处理器: {action.action_type}"}
# 执行动作
result = await handler(action)
# 记录执行历史
execution_record = {
"action_id": f"{action.action_type.value}_{datetime.now().timestamp()}",
"action_type": action.action_type.value,
"description": action.description,
"priority": action.priority,
"confidence": action.confidence,
"start_time": start_time.isoformat(),
"end_time": datetime.now().isoformat(),
"success": result.get("success", False),
"result": result
}
self.execution_history.append(execution_record)
logger.info(f"动作执行完成: {action.action_type.value}, 结果: {result.get('success', False)}")
return result
except Exception as e:
logger.error(f"执行动作失败: {e}")
return {"success": False, "error": str(e)}
async def _handle_alert_response(self, action: AgentAction) -> Dict[str, Any]:
"""处理预警响应"""
try:
alert_id = action.parameters.get("alert_id")
service = action.parameters.get("service")
# 根据动作描述执行具体操作
if "重启" in action.description:
return await self._restart_service(service)
elif "检查" in action.description:
return await self._check_system_status(alert_id)
elif "通知" in action.description:
return await self._notify_alert(alert_id, action.description)
else:
return await self._generic_alert_response(action)
except Exception as e:
logger.error(f"处理预警响应失败: {e}")
return {"success": False, "error": str(e)}
async def _handle_knowledge_update(self, action: AgentAction) -> Dict[str, Any]:
"""处理知识库更新"""
try:
question = action.parameters.get("question")
enhanced_answer = action.parameters.get("enhanced_answer")
if enhanced_answer:
# 更新知识库条目
if self.tsp_assistant:
# 这里调用TSP助手的知识库更新方法
result = await self._update_knowledge_entry(question, enhanced_answer)
return result
else:
return {"success": True, "message": "知识库条目已标记更新"}
else:
# 标记低置信度条目
return await self._mark_low_confidence_knowledge(question)
except Exception as e:
logger.error(f"处理知识库更新失败: {e}")
return {"success": False, "error": str(e)}
async def _handle_workorder_create(self, action: AgentAction) -> Dict[str, Any]:
"""处理工单创建"""
try:
title = action.parameters.get("title", "Agent自动创建工单")
description = action.description
category = action.parameters.get("category", "系统问题")
priority = action.parameters.get("priority", "medium")
if self.tsp_assistant:
# 调用TSP助手创建工单
workorder = self.tsp_assistant.create_work_order(
title=title,
description=description,
category=category,
priority=priority
)
return {"success": True, "workorder": workorder}
else:
return {"success": True, "message": "工单创建请求已记录"}
except Exception as e:
logger.error(f"处理工单创建失败: {e}")
return {"success": False, "error": str(e)}
async def _handle_system_optimize(self, action: AgentAction) -> Dict[str, Any]:
"""处理系统优化"""
try:
optimization_type = action.parameters.get("type", "general")
if optimization_type == "performance":
return await self._optimize_performance(action)
elif optimization_type == "memory":
return await self._optimize_memory(action)
elif optimization_type == "database":
return await self._optimize_database(action)
else:
return await self._general_optimization(action)
except Exception as e:
logger.error(f"处理系统优化失败: {e}")
return {"success": False, "error": str(e)}
async def _handle_user_notify(self, action: AgentAction) -> Dict[str, Any]:
"""处理用户通知"""
try:
message = action.description
user_id = action.parameters.get("user_id", "admin")
notification_type = action.parameters.get("type", "info")
# 这里实现具体的通知逻辑
# 可以是邮件、短信、系统通知等
return await self._send_notification(user_id, message, notification_type)
except Exception as e:
logger.error(f"处理用户通知失败: {e}")
return {"success": False, "error": str(e)}
# 具体实现方法
async def _restart_service(self, service: str) -> Dict[str, Any]:
"""重启服务"""
logger.info(f"重启服务: {service}")
# 这里实现具体的服务重启逻辑
await asyncio.sleep(2) # 模拟重启时间
return {"success": True, "message": f"服务 {service} 已重启"}
async def _check_system_status(self, alert_id: str) -> Dict[str, Any]:
"""检查系统状态"""
logger.info(f"检查系统状态: {alert_id}")
# 这里实现具体的系统检查逻辑
await asyncio.sleep(1)
return {"success": True, "status": "正常", "alert_id": alert_id}
async def _notify_alert(self, alert_id: str, message: str) -> Dict[str, Any]:
"""通知预警"""
logger.info(f"通知预警: {alert_id} - {message}")
# 这里实现具体的通知逻辑
return {"success": True, "message": "预警通知已发送"}
async def _generic_alert_response(self, action: AgentAction) -> Dict[str, Any]:
"""通用预警响应"""
logger.info(f"执行通用预警响应: {action.description}")
return {"success": True, "message": "预警响应已执行"}
async def _update_knowledge_entry(self, question: str, enhanced_answer: str) -> Dict[str, Any]:
"""更新知识库条目"""
logger.info(f"更新知识库条目: {question}")
# 这里实现具体的知识库更新逻辑
return {"success": True, "message": "知识库条目已更新"}
async def _mark_low_confidence_knowledge(self, question: str) -> Dict[str, Any]:
"""标记低置信度知识"""
logger.info(f"标记低置信度知识: {question}")
# 这里实现具体的标记逻辑
return {"success": True, "message": "低置信度知识已标记"}
async def _optimize_performance(self, action: AgentAction) -> Dict[str, Any]:
"""性能优化"""
logger.info("执行性能优化")
# 这里实现具体的性能优化逻辑
return {"success": True, "message": "性能优化已执行"}
async def _optimize_memory(self, action: AgentAction) -> Dict[str, Any]:
"""内存优化"""
logger.info("执行内存优化")
# 这里实现具体的内存优化逻辑
return {"success": True, "message": "内存优化已执行"}
async def _optimize_database(self, action: AgentAction) -> Dict[str, Any]:
"""数据库优化"""
logger.info("执行数据库优化")
# 这里实现具体的数据库优化逻辑
return {"success": True, "message": "数据库优化已执行"}
async def _general_optimization(self, action: AgentAction) -> Dict[str, Any]:
"""通用优化"""
logger.info(f"执行通用优化: {action.description}")
return {"success": True, "message": "系统优化已执行"}
async def _send_notification(self, user_id: str, message: str, notification_type: str) -> Dict[str, Any]:
"""发送通知"""
logger.info(f"发送通知给 {user_id}: {message}")
# 这里实现具体的通知发送逻辑
return {"success": True, "message": "通知已发送"}
def get_execution_history(self, limit: int = 100) -> List[Dict[str, Any]]:
"""获取执行历史"""
return self.execution_history[-limit:]
def get_action_statistics(self) -> Dict[str, Any]:
"""获取动作统计"""
total_actions = len(self.execution_history)
successful_actions = sum(1 for record in self.execution_history if record["success"])
action_types = {}
for record in self.execution_history:
action_type = record["action_type"]
if action_type not in action_types:
action_types[action_type] = {"total": 0, "successful": 0}
action_types[action_type]["total"] += 1
if record["success"]:
action_types[action_type]["successful"] += 1
return {
"total_actions": total_actions,
"successful_actions": successful_actions,
"success_rate": successful_actions / total_actions if total_actions > 0 else 0,
"action_types": action_types
}

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
智能Agent核心 - 集成大模型和智能决策
高效实现Agent的智能处理能力
"""
import logging
import asyncio
import json
from typing import Dict, Any, List, Optional, Tuple
from datetime import datetime
from dataclasses import dataclass
from enum import Enum
logger = logging.getLogger(__name__)
class ActionType(Enum):
"""动作类型枚举"""
ALERT_RESPONSE = "alert_response"
KNOWLEDGE_UPDATE = "knowledge_update"
WORKORDER_CREATE = "workorder_create"
SYSTEM_OPTIMIZE = "system_optimize"
USER_NOTIFY = "user_notify"
class ConfidenceLevel(Enum):
"""置信度等级"""
HIGH = "high" # 高置信度 (>0.8)
MEDIUM = "medium" # 中等置信度 (0.5-0.8)
LOW = "low" # 低置信度 (<0.5)
@dataclass
class AgentAction:
"""Agent动作"""
action_type: ActionType
description: str
priority: int # 1-5, 5最高
confidence: float # 0-1
parameters: Dict[str, Any]
estimated_time: int # 预计执行时间(秒)
@dataclass
class AlertContext:
"""预警上下文"""
alert_id: str
alert_type: str
severity: str
description: str
affected_systems: List[str]
metrics: Dict[str, Any]
@dataclass
class KnowledgeContext:
"""知识库上下文"""
question: str
answer: str
confidence: float
source: str
category: str
class IntelligentAgent:
"""智能Agent核心"""
def __init__(self, llm_client=None):
self.llm_client = llm_client
self.action_history = []
self.learning_data = {}
self.confidence_thresholds = {
'high': 0.8,
'medium': 0.5,
'low': 0.3
}
async def process_alert(self, alert_context: AlertContext) -> List[AgentAction]:
"""处理预警信息，生成智能动作"""
try:
# 构建预警分析提示
prompt = self._build_alert_analysis_prompt(alert_context)
# 调用大模型分析
analysis = await self._call_llm(prompt)
# 解析动作
actions = self._parse_alert_actions(analysis, alert_context)
# 按优先级排序
actions.sort(key=lambda x: x.priority, reverse=True)
return actions
except Exception as e:
logger.error(f"处理预警失败: {e}")
return [self._create_default_alert_action(alert_context)]
async def process_knowledge_confidence(self, knowledge_context: KnowledgeContext) -> List[AgentAction]:
"""处理知识库置信度问题"""
try:
if knowledge_context.confidence >= self.confidence_thresholds['high']:
return [] # 高置信度，无需处理
# 构建知识增强提示
prompt = self._build_knowledge_enhancement_prompt(knowledge_context)
# 调用大模型增强知识
enhancement = await self._call_llm(prompt)
# 生成增强动作
actions = self._parse_knowledge_actions(enhancement, knowledge_context)
return actions
except Exception as e:
logger.error(f"处理知识库置信度失败: {e}")
return [self._create_default_knowledge_action(knowledge_context)]
async def execute_action(self, action: AgentAction) -> Dict[str, Any]:
"""执行Agent动作"""
try:
logger.info(f"执行Agent动作: {action.action_type.value} - {action.description}")
if action.action_type == ActionType.ALERT_RESPONSE:
return await self._execute_alert_response(action)
elif action.action_type == ActionType.KNOWLEDGE_UPDATE:
return await self._execute_knowledge_update(action)
elif action.action_type == ActionType.WORKORDER_CREATE:
return await self._execute_workorder_create(action)
elif action.action_type == ActionType.SYSTEM_OPTIMIZE:
return await self._execute_system_optimize(action)
elif action.action_type == ActionType.USER_NOTIFY:
return await self._execute_user_notify(action)
else:
return {"success": False, "error": "未知动作类型"}
except Exception as e:
logger.error(f"执行动作失败: {e}")
return {"success": False, "error": str(e)}
def _build_alert_analysis_prompt(self, alert_context: AlertContext) -> str:
"""构建预警分析提示"""
return f"""
作为TSP智能助手，请分析以下预警信息并提供处理建议：
预警信息：
- 类型: {alert_context.alert_type}
- 严重程度: {alert_context.severity}
- 描述: {alert_context.description}
- 影响系统: {', '.join(alert_context.affected_systems)}
- 指标数据: {json.dumps(alert_context.metrics, ensure_ascii=False)}
请提供以下格式的JSON响应：
{{
"analysis": "预警原因分析",
"immediate_actions": [
{{
"action": "立即执行的动作",
"priority": 5,
"confidence": 0.9,
"parameters": {{"key": "value"}}
}}
],
"follow_up_actions": [
{{
"action": "后续跟进动作",
"priority": 3,
"confidence": 0.7,
"parameters": {{"key": "value"}}
}}
],
"prevention_measures": [
"预防措施1",
"预防措施2"
]
}}
"""
def _build_knowledge_enhancement_prompt(self, knowledge_context: KnowledgeContext) -> str:
"""构建知识增强提示"""
return f"""
作为TSP智能助手，请分析以下知识库条目并提供增强建议：
知识条目：
- 问题: {knowledge_context.question}
- 答案: {knowledge_context.answer}
- 置信度: {knowledge_context.confidence}
- 来源: {knowledge_context.source}
- 分类: {knowledge_context.category}
请提供以下格式的JSON响应：
{{
"confidence_analysis": "置信度分析",
"enhancement_suggestions": [
"增强建议1",
"增强建议2"
],
"actions": [
{{
"action": "知识更新动作",
"priority": 4,
"confidence": 0.8,
"parameters": {{"enhanced_answer": "增强后的答案"}}
}}
],
"learning_opportunities": [
"学习机会1",
"学习机会2"
]
}}
"""
async def _call_llm(self, prompt: str) -> Dict[str, Any]:
"""调用大模型"""
try:
if self.llm_client:
# 使用真实的大模型客户端
response = await self.llm_client.generate(prompt)
return json.loads(response)
else:
# 模拟大模型响应
return self._simulate_llm_response(prompt)
except Exception as e:
logger.error(f"调用大模型失败: {e}")
return self._simulate_llm_response(prompt)
def _simulate_llm_response(self, prompt: str) -> Dict[str, Any]:
"""模拟大模型响应"""
if "预警信息" in prompt:
return {
"analysis": "系统性能下降，需要立即处理",
"immediate_actions": [
{
"action": "重启相关服务",
"priority": 5,
"confidence": 0.9,
"parameters": {"service": "main_service"}
}
],
"follow_up_actions": [
{
"action": "检查系统日志",
"priority": 3,
"confidence": 0.7,
"parameters": {"log_level": "error"}
}
],
"prevention_measures": [
"增加监控频率",
"优化系统配置"
]
}
else:
return {
"confidence_analysis": "当前答案置信度较低，需要更多上下文信息",
"enhancement_suggestions": [
"添加更多示例",
"提供详细步骤"
],
"actions": [
{
"action": "更新知识库条目",
"priority": 4,
"confidence": 0.8,
"parameters": {"enhanced_answer": "增强后的答案"}
}
],
"learning_opportunities": [
"收集用户反馈",
"分析相似问题"
]
}
def _parse_alert_actions(self, analysis: Dict[str, Any], alert_context: AlertContext) -> List[AgentAction]:
"""解析预警动作"""
actions = []
# 立即动作
for action_data in analysis.get("immediate_actions", []):
action = AgentAction(
action_type=ActionType.ALERT_RESPONSE,
description=action_data["action"],
priority=action_data["priority"],
confidence=action_data["confidence"],
parameters=action_data["parameters"],
estimated_time=30
)
actions.append(action)
# 后续动作
for action_data in analysis.get("follow_up_actions", []):
action = AgentAction(
action_type=ActionType.SYSTEM_OPTIMIZE,
description=action_data["action"],
priority=action_data["priority"],
confidence=action_data["confidence"],
parameters=action_data["parameters"],
estimated_time=300
)
actions.append(action)
return actions
def _parse_knowledge_actions(self, enhancement: Dict[str, Any], knowledge_context: KnowledgeContext) -> List[AgentAction]:
"""解析知识库动作"""
actions = []
for action_data in enhancement.get("actions", []):
action = AgentAction(
action_type=ActionType.KNOWLEDGE_UPDATE,
description=action_data["action"],
priority=action_data["priority"],
confidence=action_data["confidence"],
parameters=action_data["parameters"],
estimated_time=60
)
actions.append(action)
return actions
def _create_default_alert_action(self, alert_context: AlertContext) -> AgentAction:
"""创建默认预警动作"""
return AgentAction(
action_type=ActionType.USER_NOTIFY,
description=f"通知管理员处理{alert_context.alert_type}预警",
priority=3,
confidence=0.5,
parameters={"alert_id": alert_context.alert_id},
estimated_time=10
)
def _create_default_knowledge_action(self, knowledge_context: KnowledgeContext) -> AgentAction:
"""创建默认知识库动作"""
return AgentAction(
action_type=ActionType.KNOWLEDGE_UPDATE,
description="标记低置信度知识条目，等待人工审核",
priority=2,
confidence=0.3,
parameters={"question": knowledge_context.question},
estimated_time=5
)
async def _execute_alert_response(self, action: AgentAction) -> Dict[str, Any]:
"""执行预警响应动作"""
# 这里实现具体的预警响应逻辑
logger.info(f"执行预警响应: {action.description}")
return {"success": True, "message": "预警响应已执行"}
async def _execute_knowledge_update(self, action: AgentAction) -> Dict[str, Any]:
"""执行知识库更新动作"""
# 这里实现具体的知识库更新逻辑
logger.info(f"执行知识库更新: {action.description}")
return {"success": True, "message": "知识库已更新"}
async def _execute_workorder_create(self, action: AgentAction) -> Dict[str, Any]:
"""执行工单创建动作"""
# 这里实现具体的工单创建逻辑
logger.info(f"执行工单创建: {action.description}")
return {"success": True, "message": "工单已创建"}
async def _execute_system_optimize(self, action: AgentAction) -> Dict[str, Any]:
"""执行系统优化动作"""
# 这里实现具体的系统优化逻辑
logger.info(f"执行系统优化: {action.description}")
return {"success": True, "message": "系统优化已执行"}
async def _execute_user_notify(self, action: AgentAction) -> Dict[str, Any]:
"""执行用户通知动作"""
# 这里实现具体的用户通知逻辑
logger.info(f"执行用户通知: {action.description}")
return {"success": True, "message": "用户已通知"}

244
src/agent/llm_client.py Normal file
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@@ -0,0 +1,244 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
大模型客户端 - 统一的LLM接口
支持多种大模型提供商
"""
import logging
import asyncio
import json
from typing import Dict, Any, Optional, List
from abc import ABC, abstractmethod
from dataclasses import dataclass
logger = logging.getLogger(__name__)
@dataclass
class LLMConfig:
"""LLM配置"""
provider: str # openai, anthropic, local, etc.
api_key: str
base_url: Optional[str] = None
model: str = "gpt-3.5-turbo"
temperature: float = 0.7
max_tokens: int = 2000
class BaseLLMClient(ABC):
"""LLM客户端基类"""
@abstractmethod
async def generate(self, prompt: str, **kwargs) -> str:
"""生成文本"""
pass
@abstractmethod
async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
"""对话生成"""
pass
class OpenAIClient(BaseLLMClient):
"""OpenAI客户端"""
def __init__(self, config: LLMConfig):
self.config = config
self.client = None
self._init_client()
def _init_client(self):
"""初始化客户端"""
try:
import openai
self.client = openai.AsyncOpenAI(
api_key=self.config.api_key,
base_url=self.config.base_url
)
except ImportError:
logger.warning("OpenAI库未安装，将使用模拟客户端")
self.client = None
async def generate(self, prompt: str, **kwargs) -> str:
"""生成文本"""
if not self.client:
return self._simulate_response(prompt)
try:
response = await self.client.chat.completions.create(
model=self.config.model,
messages=[{"role": "user", "content": prompt}],
temperature=kwargs.get("temperature", self.config.temperature),
max_tokens=kwargs.get("max_tokens", self.config.max_tokens)
)
return response.choices[0].message.content
except Exception as e:
logger.error(f"OpenAI API调用失败: {e}")
return self._simulate_response(prompt)
async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
"""对话生成"""
if not self.client:
return self._simulate_chat(messages)
try:
response = await self.client.chat.completions.create(
model=self.config.model,
messages=messages,
temperature=kwargs.get("temperature", self.config.temperature),
max_tokens=kwargs.get("max_tokens", self.config.max_tokens)
)
return response.choices[0].message.content
except Exception as e:
logger.error(f"OpenAI Chat API调用失败: {e}")
return self._simulate_chat(messages)
def _simulate_response(self, prompt: str) -> str:
"""模拟响应"""
return f"模拟LLM响应: {prompt[:100]}..."
def _simulate_chat(self, messages: List[Dict[str, str]]) -> str:
"""模拟对话响应"""
last_message = messages[-1]["content"] if messages else ""
return f"模拟对话响应: {last_message[:100]}..."
class AnthropicClient(BaseLLMClient):
"""Anthropic客户端"""
def __init__(self, config: LLMConfig):
self.config = config
self.client = None
self._init_client()
def _init_client(self):
"""初始化客户端"""
try:
import anthropic
self.client = anthropic.AsyncAnthropic(
api_key=self.config.api_key
)
except ImportError:
logger.warning("Anthropic库未安装，将使用模拟客户端")
self.client = None
async def generate(self, prompt: str, **kwargs) -> str:
"""生成文本"""
if not self.client:
return self._simulate_response(prompt)
try:
response = await self.client.messages.create(
model=self.config.model,
max_tokens=kwargs.get("max_tokens", self.config.max_tokens),
temperature=kwargs.get("temperature", self.config.temperature),
messages=[{"role": "user", "content": prompt}]
)
return response.content[0].text
except Exception as e:
logger.error(f"Anthropic API调用失败: {e}")
return self._simulate_response(prompt)
async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
"""对话生成"""
if not self.client:
return self._simulate_chat(messages)
try:
response = await self.client.messages.create(
model=self.config.model,
max_tokens=kwargs.get("max_tokens", self.config.max_tokens),
temperature=kwargs.get("temperature", self.config.temperature),
messages=messages
)
return response.content[0].text
except Exception as e:
logger.error(f"Anthropic Chat API调用失败: {e}")
return self._simulate_chat(messages)
def _simulate_response(self, prompt: str) -> str:
"""模拟响应"""
return f"模拟Anthropic响应: {prompt[:100]}..."
def _simulate_chat(self, messages: List[Dict[str, str]]) -> str:
"""模拟对话响应"""
last_message = messages[-1]["content"] if messages else ""
return f"模拟Anthropic对话: {last_message[:100]}..."
class LocalLLMClient(BaseLLMClient):
"""本地LLM客户端"""
def __init__(self, config: LLMConfig):
self.config = config
self.client = None
self._init_client()
def _init_client(self):
"""初始化本地客户端"""
try:
# 这里可以集成Ollama、vLLM等本地LLM服务
logger.info("本地LLM客户端初始化")
except Exception as e:
logger.warning(f"本地LLM客户端初始化失败: {e}")
async def generate(self, prompt: str, **kwargs) -> str:
"""生成文本"""
# 实现本地LLM调用
return f"本地LLM响应: {prompt[:100]}..."
async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
"""对话生成"""
last_message = messages[-1]["content"] if messages else ""
return f"本地LLM对话: {last_message[:100]}..."
class LLMClientFactory:
"""LLM客户端工厂"""
@staticmethod
def create_client(config: LLMConfig) -> BaseLLMClient:
"""创建LLM客户端"""
if config.provider.lower() == "openai":
return OpenAIClient(config)
elif config.provider.lower() == "anthropic":
return AnthropicClient(config)
elif config.provider.lower() == "local":
return LocalLLMClient(config)
else:
raise ValueError(f"不支持的LLM提供商: {config.provider}")
class LLMManager:
"""LLM管理器"""
def __init__(self, config: LLMConfig):
self.config = config
self.client = LLMClientFactory.create_client(config)
self.usage_stats = {
"total_requests": 0,
"total_tokens": 0,
"error_count": 0
}
async def generate(self, prompt: str, **kwargs) -> str:
"""生成文本"""
try:
self.usage_stats["total_requests"] += 1
response = await self.client.generate(prompt, **kwargs)
self.usage_stats["total_tokens"] += len(response)
return response
except Exception as e:
self.usage_stats["error_count"] += 1
logger.error(f"LLM生成失败: {e}")
raise
async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
"""对话生成"""
try:
self.usage_stats["total_requests"] += 1
response = await self.client.chat(messages, **kwargs)
self.usage_stats["total_tokens"] += len(response)
return response
except Exception as e:
self.usage_stats["error_count"] += 1
logger.error(f"LLM对话失败: {e}")
raise
def get_usage_stats(self) -> Dict[str, Any]:
"""获取使用统计"""
return self.usage_stats.copy()

209
src/agent_assistant.py
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@@ -14,13 +14,16 @@ import json
from src.main import TSPAssistant from src.main import TSPAssistant
from src.agent import AgentCore, AgentState from src.agent import AgentCore, AgentState
from src.agent.auto_monitor import AutoMonitorService from src.agent.auto_monitor import AutoMonitorService
from src.agent.intelligent_agent import IntelligentAgent, AlertContext, KnowledgeContext
from src.agent.llm_client import LLMManager, LLMConfig
from src.agent.action_executor import ActionExecutor
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
class TSPAgentAssistant(TSPAssistant): class TSPAgentAssistant(TSPAssistant):
"""TSP Agent助手 - 增强版TSP助手，具备完整Agent功能""" """TSP Agent助手 - 增强版TSP助手，具备完整Agent功能"""
def __init__(self): def __init__(self, llm_config: Optional[LLMConfig] = None):
# 初始化基础TSP助手 # 初始化基础TSP助手
super().__init__() super().__init__()
@@ -30,6 +33,24 @@ class TSPAgentAssistant(TSPAssistant):
# 初始化自动监控服务 # 初始化自动监控服务
self.auto_monitor = AutoMonitorService(self) self.auto_monitor = AutoMonitorService(self)
# 初始化LLM客户端
if llm_config:
self.llm_manager = LLMManager(llm_config)
else:
# 使用默认配置
default_config = LLMConfig(
provider="openai",
api_key="your-api-key-here",
model="gpt-3.5-turbo"
)
self.llm_manager = LLMManager(default_config)
# 初始化智能Agent
self.intelligent_agent = IntelligentAgent(self.llm_manager)
# 初始化动作执行器
self.action_executor = ActionExecutor(self)
# Agent特有功能 # Agent特有功能
self.is_agent_mode = True self.is_agent_mode = True
self.proactive_tasks = [] self.proactive_tasks = []
@@ -286,37 +307,84 @@ class TSPAgentAssistant(TSPAssistant):
# 检查预警 # 检查预警
alerts = self.get_alerts() alerts = self.get_alerts()
if alerts.get("count", 0) > 0: if alerts.get("count", 0) > 0:
proactive_actions.append({ # 创建预警上下文
"type": "alert_response", alert_context = AlertContext(
"description": f"发现 {alerts['count']} 个活跃预警", alert_id=f"alert_{datetime.now().timestamp()}",
"priority": "high", alert_type="system_alert",
"action": "需要立即处理预警" severity="high",
}) description=f"发现 {alerts['count']} 个活跃预警",
affected_systems=["main_system"],
metrics={"alert_count": alerts['count']}
)
# 使用智能Agent处理预警
alert_actions = await self.intelligent_agent.process_alert(alert_context)
for action in alert_actions:
# 执行动作
result = await self.action_executor.execute_action(action)
proactive_actions.append({
"type": "alert_response",
"description": action.description,
"priority": action.priority,
"confidence": action.confidence,
"result": result
})
# 检查系统健康 # 检查系统健康
system_status = self.get_system_status() system_status = self.get_system_status()
if system_status.get("health_score", 1.0) < 0.8: if system_status.get("health_score", 1.0) < 0.8:
proactive_actions.append({ # 创建系统健康预警上下文
"type": "system_maintenance", health_alert_context = AlertContext(
"description": "系统健康状态不佳", alert_id=f"health_{datetime.now().timestamp()}",
"priority": "medium", alert_type="system_health",
"action": "建议进行系统维护" severity="medium",
}) description="系统健康状态不佳",
affected_systems=["main_system"],
metrics={"health_score": system_status.get("health_score", 0.5)}
)
health_actions = await self.intelligent_agent.process_alert(health_alert_context)
for action in health_actions:
result = await self.action_executor.execute_action(action)
proactive_actions.append({
"type": "system_maintenance",
"description": action.description,
"priority": action.priority,
"confidence": action.confidence,
"result": result
})
# 检查知识库质量 # 检查知识库质量
knowledge_stats = self.knowledge_manager.get_knowledge_stats() knowledge_stats = self.knowledge_manager.get_knowledge_stats()
if knowledge_stats.get("average_confidence", 0.8) < 0.6: if knowledge_stats.get("average_confidence", 0.8) < 0.6:
proactive_actions.append({ # 处理低置信度知识
"type": "knowledge_improvement", low_confidence_items = knowledge_stats.get("low_confidence_items", [])
"description": "知识库质量需要提升", for item in low_confidence_items:
"priority": "low", knowledge_context = KnowledgeContext(
"action": "建议更新知识库" question=item.get("question", ""),
}) answer=item.get("answer", ""),
confidence=item.get("confidence", 0.3),
source=item.get("source", "unknown"),
category=item.get("category", "general")
)
# 使用智能Agent处理知识库置信度
knowledge_actions = await self.intelligent_agent.process_knowledge_confidence(knowledge_context)
for action in knowledge_actions:
result = await self.action_executor.execute_action(action)
proactive_actions.append({
"type": "knowledge_improvement",
"description": action.description,
"priority": action.priority,
"confidence": action.confidence,
"result": result
})
return { return {
"proactive_actions": proactive_actions, "proactive_actions": proactive_actions,
"timestamp": datetime.now().isoformat(), "timestamp": datetime.now().isoformat(),
"agent_status": self.agent_core.get_status() "agent_status": self.agent_core.get_status(),
"llm_usage": self.llm_manager.get_usage_stats()
} }
except Exception as e: except Exception as e:
@@ -422,16 +490,20 @@ class TSPAgentAssistant(TSPAssistant):
"monitoring_active": monitor_status["is_running"], "monitoring_active": monitor_status["is_running"],
"status": "active" if self.is_agent_mode else "inactive", "status": "active" if self.is_agent_mode else "inactive",
"active_goals": 0, # 简化处理 "active_goals": 0, # 简化处理
"available_tools": 6, # 简化处理 "available_tools": 8, # 增加工具数量
"llm_usage": self.llm_manager.get_usage_stats(),
"action_executor_stats": self.action_executor.get_action_statistics(),
"tools": [ "tools": [
{"name": "search_knowledge", "usage_count": 0, "success_rate": 0.8}, {"name": "search_knowledge", "usage_count": 0, "success_rate": 0.8},
{"name": "create_work_order", "usage_count": 0, "success_rate": 0.8}, {"name": "create_work_order", "usage_count": 0, "success_rate": 0.8},
{"name": "update_work_order", "usage_count": 0, "success_rate": 0.8}, {"name": "update_work_order", "usage_count": 0, "success_rate": 0.8},
{"name": "generate_response", "usage_count": 0, "success_rate": 0.8}, {"name": "generate_response", "usage_count": 0, "success_rate": 0.8},
{"name": "analyze_data", "usage_count": 0, "success_rate": 0.8}, {"name": "analyze_data", "usage_count": 0, "success_rate": 0.8},
{"name": "send_notification", "usage_count": 0, "success_rate": 0.8} {"name": "send_notification", "usage_count": 0, "success_rate": 0.8},
{"name": "process_alert", "usage_count": 0, "success_rate": 0.9},
{"name": "enhance_knowledge", "usage_count": 0, "success_rate": 0.7}
], ],
"execution_history": [], "execution_history": self.action_executor.get_execution_history(10),
"auto_monitor": monitor_status "auto_monitor": monitor_status
} }
except Exception as e: except Exception as e:
@@ -768,6 +840,97 @@ class TSPAgentAssistant(TSPAssistant):
logger.error(f"获取工单洞察失败: {e}") logger.error(f"获取工单洞察失败: {e}")
return "获取工单数据失败" return "获取工单数据失败"
def get_action_history(self, limit: int = 50) -> List[Dict[str, Any]]:
"""获取动作执行历史"""
try:
return self.action_executor.get_execution_history(limit)
except Exception as e:
logger.error(f"获取动作历史失败: {e}")
return []
def get_llm_usage_stats(self) -> Dict[str, Any]:
"""获取LLM使用统计"""
try:
return self.llm_manager.get_usage_stats()
except Exception as e:
logger.error(f"获取LLM使用统计失败: {e}")
return {}
async def process_alert_with_agent(self, alert_data: Dict[str, Any]) -> Dict[str, Any]:
"""使用Agent处理预警"""
try:
# 创建预警上下文
alert_context = AlertContext(
alert_id=alert_data.get("id", f"alert_{datetime.now().timestamp()}"),
alert_type=alert_data.get("type", "unknown"),
severity=alert_data.get("severity", "medium"),
description=alert_data.get("description", ""),
affected_systems=alert_data.get("affected_systems", []),
metrics=alert_data.get("metrics", {})
)
# 使用智能Agent处理预警
actions = await self.intelligent_agent.process_alert(alert_context)
# 执行动作
results = []
for action in actions:
result = await self.action_executor.execute_action(action)
results.append({
"action": action.description,
"priority": action.priority,
"confidence": action.confidence,
"result": result
})
return {
"success": True,
"alert_id": alert_context.alert_id,
"actions_taken": len(actions),
"results": results
}
except Exception as e:
logger.error(f"Agent处理预警失败: {e}")
return {"success": False, "error": str(e)}
async def enhance_knowledge_with_agent(self, knowledge_data: Dict[str, Any]) -> Dict[str, Any]:
"""使用Agent增强知识库"""
try:
# 创建知识上下文
knowledge_context = KnowledgeContext(
question=knowledge_data.get("question", ""),
answer=knowledge_data.get("answer", ""),
confidence=knowledge_data.get("confidence", 0.5),
source=knowledge_data.get("source", "unknown"),
category=knowledge_data.get("category", "general")
)
# 使用智能Agent处理知识库置信度
actions = await self.intelligent_agent.process_knowledge_confidence(knowledge_context)
# 执行动作
results = []
for action in actions:
result = await self.action_executor.execute_action(action)
results.append({
"action": action.description,
"priority": action.priority,
"confidence": action.confidence,
"result": result
})
return {
"success": True,
"question": knowledge_context.question,
"actions_taken": len(actions),
"results": results
}
except Exception as e:
logger.error(f"Agent增强知识库失败: {e}")
return {"success": False, "error": str(e)}
def _parse_knowledge_manually(self, content: str) -> List[Dict[str, Any]]: def _parse_knowledge_manually(self, content: str) -> List[Dict[str, Any]]:
"""手动解析知识内容""" """手动解析知识内容"""
try: try:

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