Jeason
26737747d9
- bcrypt 密码哈希升级（兼容旧 SHA-256，登录时自动升级） - auth_manager.secret_key 从环境变量读取 - 前端事件总线（on/off/emit） - ConfigService 统一配置服务 - AI 监控错误详情修复（Conversation 没有 category/source） - README.md 重写
517 lines
20 KiB
Python
517 lines
20 KiB
Python
# -*- coding: utf-8 -*-
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"""
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监控管理蓝图
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处理监控相关的API路由
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"""
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from datetime import datetime, timedelta
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from flask import Blueprint, request, jsonify
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from sqlalchemy import func
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from src.main import TSPAssistant
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from src.core.database import db_manager
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from src.core.models import Conversation, WorkOrder, Alert, KnowledgeEntry, VehicleData
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monitoring_bp = Blueprint('monitoring', __name__, url_prefix='/api')
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def estimate_tokens(text):
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"""估算文本的Token数量"""
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if not text:
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return 0
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# 中文字符约1.5字符=1token，英文字符约4字符=1token
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chinese_chars = len([c for c in text if '\u4e00' <= c <= '\u9fff'])
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english_chars = len(text) - chinese_chars
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return int(chinese_chars / 1.5 + english_chars / 4)
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def calculate_conversation_tokens(conversations):
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"""计算对话记录的Token使用量"""
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total_tokens = 0
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for conv in conversations:
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user_message = conv.user_message or ""
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assistant_response = conv.assistant_response or ""
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total_tokens += estimate_tokens(user_message) + estimate_tokens(assistant_response)
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return total_tokens
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def get_assistant():
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"""获取TSP助手实例（懒加载）"""
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global _assistant
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if '_assistant' not in globals():
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_assistant = TSPAssistant()
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return _assistant
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# Token监控相关API
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@monitoring_bp.route('/token-monitor/stats')
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def get_token_monitor_stats():
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"""获取Token监控统计"""
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try:
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from datetime import datetime, timedelta
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import calendar
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now = datetime.now()
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today_start = now.replace(hour=0, minute=0, second=0, microsecond=0)
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month_start = now.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
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with db_manager.get_session() as session:
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# 优化：使用单个查询获取所有需要的数据
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conversations_query = session.query(Conversation).filter(
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Conversation.timestamp >= month_start
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).all()
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# 分离今日和本月数据
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today_conversations = [c for c in conversations_query if c.timestamp >= today_start]
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month_conversations = conversations_query
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# 计算真实的Token使用量
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today_tokens = calculate_conversation_tokens(today_conversations)
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month_tokens = calculate_conversation_tokens(month_conversations)
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# 根据真实Token使用量计算成本
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total_cost = month_tokens * 0.0008 / 1000 # qwen-turbo输入价格
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budget_limit = 1000 # 预算限制
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return jsonify({
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'success': True,
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'today_tokens': today_tokens,
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'month_tokens': month_tokens,
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'total_cost': round(total_cost, 2),
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'budget_limit': budget_limit
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})
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@monitoring_bp.route('/token-monitor/chart')
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def get_token_monitor_chart():
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"""获取Token使用趋势图表数据"""
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try:
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period = request.args.get('period', 'day')
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from datetime import datetime, timedelta
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now = datetime.now()
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labels = []
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tokens = []
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costs = []
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if period == 'hour':
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# 最近24小时
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for i in range(24):
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hour_start = now - timedelta(hours=i+1)
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hour_end = now - timedelta(hours=i)
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labels.insert(0, hour_start.strftime('%H:00'))
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with db_manager.get_session() as session:
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hour_conversations = session.query(Conversation).filter(
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Conversation.timestamp >= hour_start,
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Conversation.timestamp < hour_end
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).all()
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# 计算真实Token使用量
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hour_tokens = calculate_conversation_tokens(hour_conversations)
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tokens.insert(0, hour_tokens)
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costs.insert(0, hour_tokens * 0.0008 / 1000)
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elif period == 'day':
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# 最近7天
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for i in range(7):
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day_start = now - timedelta(days=i+1)
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day_end = now - timedelta(days=i)
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labels.insert(0, day_start.strftime('%m-%d'))
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with db_manager.get_session() as session:
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day_conversations = session.query(Conversation).filter(
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Conversation.timestamp >= day_start,
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Conversation.timestamp < day_end
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).all()
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# 计算真实Token使用量
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day_tokens = calculate_conversation_tokens(day_conversations)
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tokens.insert(0, day_tokens)
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costs.insert(0, day_tokens * 0.0008 / 1000)
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elif period == 'week':
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# 最近4周
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for i in range(4):
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week_start = now - timedelta(weeks=i+1)
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week_end = now - timedelta(weeks=i)
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labels.insert(0, f"第{i+1}周")
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with db_manager.get_session() as session:
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week_conversations = session.query(Conversation).filter(
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Conversation.timestamp >= week_start,
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Conversation.timestamp < week_end
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).all()
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# 计算真实Token使用量
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week_tokens = calculate_conversation_tokens(week_conversations)
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tokens.insert(0, week_tokens)
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costs.insert(0, week_tokens * 0.0008 / 1000)
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return jsonify({
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'success': True,
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'labels': labels,
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'tokens': tokens,
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'costs': costs
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})
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@monitoring_bp.route('/token-monitor/records')
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def get_token_monitor_records():
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"""获取Token使用详细记录"""
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try:
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limit = request.args.get('limit', 50, type=int)
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with db_manager.get_session() as session:
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conversations = session.query(Conversation).order_by(
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Conversation.timestamp.desc()
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).limit(limit).all()
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records = []
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for conv in conversations:
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# 从对话内容估算真实的Token使用量
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user_message = conv.user_message or ""
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assistant_response = conv.assistant_response or ""
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input_tokens = estimate_tokens(user_message)
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output_tokens = estimate_tokens(assistant_response)
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total_tokens = input_tokens + output_tokens
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# 根据qwen-turbo价格计算成本
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cost = (input_tokens * 0.0008 + output_tokens * 0.002) / 1000
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records.append({
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'timestamp': conv.timestamp.isoformat() if conv.timestamp else None,
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'user_id': f"user_{conv.id}",
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'model': 'qwen-turbo',
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'input_tokens': input_tokens,
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'output_tokens': output_tokens,
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'total_tokens': total_tokens,
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'cost': round(cost, 6),
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'response_time': conv.response_time or 0
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})
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return jsonify({
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'success': True,
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'records': records
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})
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@monitoring_bp.route('/token-monitor/settings', methods=['POST'])
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def save_token_monitor_settings():
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"""保存Token监控设置"""
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try:
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data = request.get_json()
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# 这里可以将设置保存到数据库或配置文件
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# 暂时返回成功
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return jsonify({
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'success': True,
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'message': 'Token设置已保存'
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})
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@monitoring_bp.route('/token-monitor/export')
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def export_token_monitor_data():
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"""导出Token使用数据"""
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try:
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from openpyxl import Workbook
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from openpyxl.styles import Font
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wb = Workbook()
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ws = wb.active
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ws.title = "Token使用数据"
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# 添加标题
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ws['A1'] = 'Token使用数据导出'
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ws['A1'].font = Font(size=16, bold=True)
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# 添加表头
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headers = ['时间', '用户', '模型', '输入Token', '输出Token', '总Token', '成本', '响应时间']
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for col, header in enumerate(headers, 1):
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ws.cell(row=3, column=col, value=header)
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# 添加数据
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with db_manager.get_session() as session:
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conversations = session.query(Conversation).order_by(
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Conversation.timestamp.desc()
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).limit(1000).all()
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for row, conv in enumerate(conversations, 4):
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ws.cell(row=row, column=1, value=conv.timestamp.isoformat() if conv.timestamp else '')
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ws.cell(row=row, column=2, value=conv.user_id or '')
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ws.cell(row=row, column=3, value='qwen-turbo')
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ws.cell(row=row, column=4, value=conv.response_time or 0)
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ws.cell(row=row, column=5, value=(conv.response_time or 0) * 0.5)
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ws.cell(row=row, column=6, value=(conv.response_time or 0) * 1.5)
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ws.cell(row=row, column=7, value=(conv.response_time or 0) * 0.0001)
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ws.cell(row=row, column=8, value=conv.response_time or 0)
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# 保存文件
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import tempfile
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import os
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temp_path = os.path.join(tempfile.gettempdir(), 'token_usage_data.xlsx')
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wb.save(temp_path)
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from flask import send_file
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return send_file(temp_path, as_attachment=True, download_name='token_usage_data.xlsx')
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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# AI监控相关API
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@monitoring_bp.route('/ai-monitor/stats')
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def get_ai_monitor_stats():
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"""获取AI监控统计"""
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try:
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with db_manager.get_session() as session:
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# 优化：限制查询数量，只获取最近的数据
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conversations = session.query(Conversation).order_by(
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Conversation.timestamp.desc()
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).limit(1000).all() # 限制查询数量
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total_calls = len(conversations)
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if total_calls == 0:
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return jsonify({
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'success': True,
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'total_calls': 0,
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'success_rate': 0,
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'error_rate': 0,
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'avg_response_time': 0
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})
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# 基于实际对话质量计算成功率
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successful_calls = 0
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total_response_time = 0
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response_times = []
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for conv in conversations:
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# 判断对话是否成功
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is_success = True
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# 检查响应时间
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if conv.response_time:
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response_times.append(conv.response_time)
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total_response_time += conv.response_time
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if conv.response_time > 10000: # 超过10秒认为失败
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is_success = False
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# 检查置信度
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if conv.confidence_score and conv.confidence_score < 0.3:
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is_success = False
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# 检查回复内容
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if not conv.assistant_response or len(conv.assistant_response.strip()) < 5:
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is_success = False
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if is_success:
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successful_calls += 1
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success_rate = (successful_calls / total_calls * 100) if total_calls > 0 else 0
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error_rate = 100 - success_rate
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avg_response_time = (total_response_time / len(response_times)) if response_times else 0
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return jsonify({
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'success': True,
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'total_calls': total_calls,
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'success_rate': round(success_rate, 1),
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'error_rate': round(error_rate, 1),
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'avg_response_time': round(avg_response_time, 0)
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})
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@monitoring_bp.route('/ai-monitor/model-comparison')
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def get_model_comparison():
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"""获取模型性能对比数据"""
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try:
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with db_manager.get_session() as session:
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conversations = session.query(Conversation).all()
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# 分析实际使用的模型（目前只有qwen-turbo）
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model_stats = {}
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for conv in conversations:
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model = 'qwen-turbo' # 实际使用的模型
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if model not in model_stats:
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model_stats[model] = {
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'total_calls': 0,
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'successful_calls': 0,
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'total_response_time': 0,
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'response_times': []
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}
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model_stats[model]['total_calls'] += 1
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# 判断是否成功
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is_success = True
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if conv.response_time and conv.response_time > 10000:
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is_success = False
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if conv.confidence_score and conv.confidence_score < 0.3:
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is_success = False
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if not conv.assistant_response or len(conv.assistant_response.strip()) < 5:
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is_success = False
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if is_success:
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model_stats[model]['successful_calls'] += 1
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if conv.response_time:
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model_stats[model]['response_times'].append(conv.response_time)
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model_stats[model]['total_response_time'] += conv.response_time
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# 计算性能指标
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models = []
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success_rates = []
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response_times = []
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for model, stats in model_stats.items():
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models.append(model)
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success_rate = (stats['successful_calls'] / stats['total_calls'] * 100) if stats['total_calls'] > 0 else 0
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success_rates.append(round(success_rate, 1))
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avg_response_time = (stats['total_response_time'] / len(stats['response_times'])) if stats['response_times'] else 0
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response_times.append(round(avg_response_time, 0))
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# 如果没有数据，显示默认值
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if not models:
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models = ['qwen-turbo']
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success_rates = [100.0]
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response_times = [0]
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return jsonify({
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'success': True,
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'models': models,
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'success_rates': success_rates,
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'response_times': response_times
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})
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@monitoring_bp.route('/ai-monitor/error-distribution')
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def get_error_distribution():
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"""获取错误类型分布"""
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try:
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with db_manager.get_session() as session:
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# 基于实际对话记录分析错误类型
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conversations = session.query(Conversation).all()
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# 分析对话记录中的错误模式
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error_types = ['成功', '响应超时', '内容异常', '格式错误', '其他错误']
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counts = [0, 0, 0, 0, 0]
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for conv in conversations:
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# 基于响应时间和内容质量判断错误类型
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if conv.response_time and conv.response_time > 10000: # 超过10秒
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counts[1] += 1 # 响应超时
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elif conv.confidence_score and conv.confidence_score < 0.3: # 低置信度
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counts[2] += 1 # 内容异常
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elif not conv.assistant_response or len(conv.assistant_response.strip()) < 5:
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counts[3] += 1 # 格式错误
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elif conv.assistant_response and len(conv.assistant_response.strip()) >= 5:
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counts[0] += 1 # 成功
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else:
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counts[4] += 1 # 其他错误
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return jsonify({
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'success': True,
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'error_types': error_types,
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'counts': counts
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})
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@monitoring_bp.route('/ai-monitor/error-log')
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def get_error_log():
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"""获取错误日志"""
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try:
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with db_manager.get_session() as session:
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# 获取有问题的对话记录作为错误日志
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conversations = session.query(Conversation).order_by(
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Conversation.timestamp.desc()
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).limit(50).all()
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errors = []
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for conv in conversations:
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error_type = None
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error_message = None
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# 判断错误类型
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if conv.response_time and conv.response_time > 10000: # 超过10秒
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error_type = '响应超时'
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error_message = f'响应时间过长: {conv.response_time}ms'
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elif conv.confidence_score and conv.confidence_score < 0.3: # 低置信度
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error_type = '内容异常'
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error_message = f'置信度过低: {conv.confidence_score}'
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elif not conv.assistant_response or len(conv.assistant_response.strip()) < 5:
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error_type = '格式错误'
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error_message = '助手回复内容过短或为空'
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elif conv.assistant_response and 'error' in conv.assistant_response.lower():
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error_type = 'API错误'
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error_message = '回复中包含错误信息'
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# 只记录有错误的对话
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if error_type:
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errors.append({
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'id': conv.id,
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'timestamp': conv.timestamp.isoformat() if conv.timestamp else None,
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'error_type': error_type,
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'error_message': error_message,
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'model': 'qwen-turbo',
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'user_id': f'user_{conv.id}',
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'user_message': (conv.user_message or '')[:100],
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'assistant_response': (conv.assistant_response or '')[:200],
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'confidence_score': conv.confidence_score,
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'response_time': conv.response_time,
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})
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return jsonify({
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'success': True,
|
||
'errors': errors
|
||
})
|
||
except Exception as e:
|
||
return jsonify({"error": str(e)}), 500
|
||
|
||
|
||
@monitoring_bp.route('/ai-monitor/error-log/<int:conv_id>')
|
||
def get_error_detail(conv_id):
|
||
"""获取单条错误详情"""
|
||
try:
|
||
with db_manager.get_session() as session:
|
||
conv = session.query(Conversation).filter_by(id=conv_id).first()
|
||
if not conv:
|
||
return jsonify({"error": "记录不存在"}), 404
|
||
return jsonify({
|
||
'success': True,
|
||
'detail': {
|
||
'id': conv.id,
|
||
'timestamp': conv.timestamp.isoformat() if conv.timestamp else None,
|
||
'user_message': conv.user_message,
|
||
'assistant_response': conv.assistant_response,
|
||
'confidence_score': conv.confidence_score,
|
||
'response_time': conv.response_time,
|
||
'session_id': conv.session_id,
|
||
'invocation_method': conv.invocation_method,
|
||
}
|
||
})
|
||
except Exception as e:
|
||
return jsonify({"error": str(e)}), 500
|
||
|
||
@monitoring_bp.route('/ai-monitor/error-log', methods=['DELETE'])
|
||
def clear_error_log():
|
||
"""清空错误日志"""
|
||
try:
|
||
# 这里应该清空实际的错误日志表
|
||
return jsonify({
|
||
'success': True,
|
||
'message': '错误日志已清空'
|
||
})
|
||
except Exception as e:
|
||
return jsonify({"error": str(e)}), 500
|