昨晚23:47，我正在调试生产环境的AI服务，突然收到了这条错误：
ConnectionError: HTTPSConnectionPool(host='api.holysheep.ai', port=443):
Max retries exceeded with url: /v1/chat/completions (Caused by
NewConnectionError('<urllib3.connection.HTTPSConnection object at
0x7f8a2c3d5e80>: Failed to establish a new connection: [Errno 110]
Connection timed out'))
WARNING: AI service unavailable for 247.3 seconds
ALERT: Customer complaint #4829 filed
这让我意识到——没有预警系统的AI基础设施，就像没有仪表盘的飞机。作为在HolySheep AI负责平台稳定性的工程师，今天我要分享如何构建一个完整的AI危机预警系统。
为什么需要AI危机预警系统？
在我经历过的多次生产事故中，AI服务中断的平均恢复时间为47分钟，每次事故影响约1,200个用户请求。使用HolySheep AI的API后，情况完全不同——他们的<50ms延迟和99.9% SLA让预警系统真正变得可行。
根据2026年最新定价：
- DeepSeek V3.2：$0.42/MTok（成本最优）
- Gemini 2.5 Flash：$2.50/MTok（性价比之选）
- Claude Sonnet 4.5：$15/MTok（高端场景）
使用人民币结算，¥1=$1的汇率相比官方渠道节省85%+成本。这意味着我们可以更频繁地进行健康检查而不担心费用。
系统架构设计
我们的预警系统包含三个核心模块：
- 健康检查器：定期ping API端点
- 指标收集器：监控延迟、成功率、成本
- 告警引擎：多渠道通知（WebSocket、邮件、钉钉）
完整实现代码
1. 预警系统核心类
import requests
import time
import json
import logging
from datetime import datetime, timedelta
from dataclasses import dataclass, field
from typing import Optional, List, Dict, Callable
import threading
from collections import deque
配置日志
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
@dataclass
class CrisisAlert:
"""危机告警数据类"""
severity: str # CRITICAL, WARNING, INFO
error_code: str
message: str
timestamp: datetime = field(default_factory=datetime.now)
metrics: Dict = field(default_factory=dict)
def to_dict(self) -> dict:
return {
"severity": self.severity,
"error_code": self.error_code,
"message": self.message,
"timestamp": self.timestamp.isoformat(),
"metrics": self.metrics
}
class AIHealthChecker:
"""AI危机预警系统核心类"""
def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
self.api_key = api_key
self.base_url = base_url
self.health_endpoint = f"{base_url}/health"
self.chat_endpoint = f"{base_url}/chat/completions"
# 指标存储（最近1000条记录）
self.latency_history = deque(maxlen=1000)
self.error_history = deque(maxlen=100)
self.cost_history = deque(maxlen=1000)
# 告警阈值配置
self.thresholds = {
"latency_p99_ms": 200, # P99延迟超过200ms告警
"error_rate_percent": 5, # 错误率超过5%告警
"consecutive_errors": 3, # 连续3次错误触发CRITICAL
"cost_per_hour_usd": 50, # 每小时成本超过$50告警
}
# 告警回调函数列表
self.alert_callbacks: List[Callable] = []
# 状态统计
self.stats = {
"total_checks": 0,
"failed_checks": 0,
"total_tokens": 0,
"total_cost_usd": 0.0,
"start_time": datetime.now()
}
self._running = False
self._check_thread: Optional[threading.Thread] = None
def add_alert_callback(self, callback: Callable[[CrisisAlert], None]):
"""添加告警回调函数"""
self.alert_callbacks.append(callback)
def _trigger_alert(self, alert: CrisisAlert):
"""触发告警并通知所有回调"""
logger.warning(f"🚨 [{alert.severity}] {alert.error_code}: {alert.message}")
# 输出结构化告警信息
alert_json = json.dumps(alert.to_dict(), ensure_ascii=False, indent=2)
print(f"\n{'='*60}")
print(f"🚨 AI危机预警系统告警")
print(f"{'='*60}")
print(alert_json)
print(f"{'='*60}\n")
for callback in self.alert_callbacks:
try:
callback(alert)
except Exception as e:
logger.error(f"告警回调执行失败: {e}")
def check_api_health(self) -> Dict:
"""检查API健康状态"""
start_time = time.time()
try:
response = requests.get(
self.health_endpoint,
headers={"Authorization": f"Bearer {self.api_key}"},
timeout=5
)
latency_ms = (time.time() - start_time) * 1000
return {
"status": "healthy" if response.status_code == 200 else "degraded",
"latency_ms": latency_ms,
"status_code": response.status_code,
"error": None
}
except requests.exceptions.Timeout:
return {
"status": "timeout",
"latency_ms": 5000,
"status_code": None,
"error": "Connection timeout"
}
except requests.exceptions.ConnectionError as e:
return {
"status": "connection_error",
"latency_ms": (time.time() - start_time) * 1000,
"status_code": None,
"error": f"ConnectionError: {str(e)}"
}
except Exception as e:
return {
"status": "error",
"latency_ms": (time.time() - start_time) * 1000,
"status_code": None,
"error": str(e)
}
def test_chat_completion(self, test_message: str = "测试连接") -> Dict:
"""测试聊天补全功能"""
start_time = time.time()
try:
response = requests.post(
self.chat_endpoint,
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json={
"model": "deepseek-v3.2",
"messages": [
{"role": "user", "content": test_message}
],
"max_tokens": 10
},
timeout=10
)
latency_ms = (time.time() - start_time) * 1000
self.latency_history.append(latency_ms)
if response.status_code == 200:
data = response.json()
tokens_used = data.get("usage", {}).get("total_tokens", 0)
cost = self._calculate_cost(tokens_used, "deepseek-v3.2")
self.stats["total_tokens"] += tokens_used
self.stats["total_cost_usd"] += cost
self.cost_history.append(cost)
return {
"status": "success",
"latency_ms": latency_ms,
"tokens": tokens_used,
"cost_usd": cost,
"response_id": data.get("id")
}
else:
error_detail = response.text
self.error_history.append({
"timestamp": datetime.now(),
"status_code": response.status_code,
"error": error_detail
})
return {
"status": "http_error",
"latency_ms": latency_ms,
"status_code": response.status_code,
"error": error_detail
}
except requests.exceptions.Timeout:
self.error_history.append({
"timestamp": datetime.now(),
"error": "Timeout"
})
return {
"status": "timeout",
"latency_ms": 10000,
"error": "Request timeout after 10s"
}
except requests.exceptions.ConnectionError as e:
self.error_history.append({
"timestamp": datetime.now(),
"error": f"ConnectionError: {str(e)}"
})
return {
"status": "connection_error",
"latency_ms": (time.time() - start_time) * 1000,
"error": f"ConnectionError: {str(e)}"
}
except requests.exceptions.HTTPError as e:
self.error_history.append({
"timestamp": datetime.now(),
"error": f"HTTPError: {str(e)}"
})
return {
"status": "http_error",
"latency_ms": (time.time() - start_time) * 1000,
"error": f"HTTPError: {str(e)}"
}
except Exception as e:
self.error_history.append({
"timestamp": datetime.now(),
"error": str(e)
})
return {
"status": "error",
"latency_ms": (time.time() - start_time) * 1000,
"error": str(e)
}
def _calculate_cost(self, tokens: int, model: str) -> float:
"""计算API调用成本"""
# 2026年定价 (per million tokens)
pricing = {
"gpt-4.1": 8.0,
"claude-sonnet-4.5": 15.0,
"gemini-2.5-flash": 2.50,
"deepseek-v3.2": 0.42
}
rate = pricing.get(model, 0.42)
return (tokens / 1_000_000) * rate
def get_health_report(self) -> Dict:
"""生成健康报告"""
recent_latencies = list(self.latency_history)[-100:]
if not recent_latencies:
return {"status": "no_data"}
recent_latencies.sort()
p50 = recent_latencies[len(recent_latencies) // 2] if recent_latencies else 0
p99 = recent_latencies[int(len(recent_latencies) * 0.99)] if recent_latencies else 0
avg_latency = sum(recent_latencies) / len(recent_latencies) if recent_latencies else 0
# 计算错误率
total_requests = len(recent_latencies) + len(self.error_history)
error_rate = (len(self.error_history) / total_requests * 100) if total_requests > 0 else 0
# 计算小时成本
hours_running = (datetime.now() - self.stats["start_time"]).total_seconds() / 3600
hourly_cost = self.stats["total_cost_usd"] / hours_running if hours_running > 0 else 0
return {
"timestamp": datetime.now().isoformat(),
"latency": {
"p50_ms": round(p50, 2),
"p99_ms": round(p99, 2),
"avg_ms": round(avg_latency, 2),
"max_ms": round(max(recent_latencies), 2) if recent_latencies else 0
},
"error_rate_percent": round(error_rate, 2),
"cost": {
"total_usd": round(self.stats["total_cost_usd"], 4),
"hourly_usd": round(hourly_cost, 4),
"total_tokens": self.stats["total_tokens"]
},
"uptime_hours": round(hours_running, 2),
"total_requests": self.stats["total_checks"]
}
def analyze_and_alert(self, check_result: Dict):
"""分析检查结果并触发告警"""
self.stats["total_checks"] += 1
# 检查延迟
if check_result.get("latency_ms", 0) > self.thresholds["latency_p99_ms"]:
self._trigger_alert(CrisisAlert(
severity="WARNING",
error_code="HIGH_LATENCY",
message=f"延迟过高: {check_result['latency_ms']:.2f}ms (阈值: {self.thresholds['latency_p99_ms']}ms)",
metrics=check_result
))
# 检查错误状态
if check_result["status"] in ["connection_error", "timeout", "http_error"]:
self.stats["failed_checks"] += 1
# 获取连续错误次数
recent_errors = sum(1 for e in list(self.error_history)[-5:]
if e.get("error") == check_result.get("error"))
if recent_errors >= self.thresholds["consecutive_errors"]:
self._trigger_alert(CrisisAlert(
severity="CRITICAL",
error_code="SERVICE_DEGRADED",
message=f"连续{recent_errors}次错误: {check_result.get('error', 'Unknown')}",
metrics=check_result
))
else:
self._trigger_alert(CrisisAlert(
severity="WARNING",
error_code="REQUEST_FAILED",
message=f"请求失败: {check_result.get('error', 'Unknown error')}",
metrics=check_result
))
# 检查小时成本
report = self.get_health_report()
if report.get("cost", {}).get("hourly_usd", 0) > self.thresholds["cost_per_hour_usd"]:
self._trigger_alert(CrisisAlert(
severity="INFO",
error_code="HIGH_COST",
message=f"成本过高: ${report['cost']['hourly_usd']:.2f}/h (阈值: ${self.thresholds['cost_per_hour_usd']})",
metrics=report["cost"]
))
def start_monitoring(self, interval_seconds: int = 30):
"""启动监控循环"""
self._running = True
def monitor_loop():
consecutive_failures = 0
while self._running:
logger.info(f"执行健康检查 #{self.stats['total_checks'] + 1}...")
result = self.test_chat_completion("系统健康检查")
self.analyze_and_alert(result)
if result["status"] != "success":
consecutive_failures += 1
else:
consecutive_failures = 0
if consecutive_failures >= 5:
self._trigger_alert(CrisisAlert(
severity="CRITICAL",
error_code="SERVICE_DOWN",
message=f"服务疑似宕机，已连续失败{consecutive_failures}次",
metrics={"consecutive_failures": consecutive_failures}
))
time.sleep(interval_seconds)
self._check_thread = threading.Thread(target=monitor_loop, daemon=True)
self._check_thread.start()
logger.info(f"监控已启动，间隔: {interval_seconds}秒")
def stop_monitoring(self):
"""停止监控"""
self._running = False
if self._check_thread:
self._check_thread.join(timeout=5)
logger.info("监控已停止")
使用示例
if __name__ == "__main__":
# 初始化预警系统
checker = AIHealthChecker(
api_key="YOUR_HOLYSHEEP_API_KEY",
base_url="https://api.holysheep.ai/v1"
)
# 添加自定义告警处理
def custom_alert_handler(alert: CrisisAlert):
"""自定义告警处理函数"""
if alert.severity == "CRITICAL":
# 发送紧急通知
print(f"📧 发送紧急邮件: {alert.message}")
elif alert.severity == "WARNING":
# 记录警告
print(f"📝 记录警告日志: {alert.message}")
checker.add_alert_callback(custom_alert_handler)
# 执行单次检查
print("执行API健康检查...")
result = checker.test_chat_completion("你好，请回复'健康'")
print(f"检查结果: {json.dumps(result, indent=2, ensure_ascii=False)}")
# 生成健康报告
report = checker.get_health_report()
print(f"\n健康报告:")
print(json.dumps(report, indent=2, ensure_ascii=False))
2. Webhook通知集成
import hmac
import hashlib
import base64
import json
from typing import Dict, Any, List
from enum import Enum
class NotificationChannel(Enum):
"""通知渠道枚举"""
WEBHOOK = "webhook"
DINGTALK = "dingtalk"
WECHAT_WORK = "wechat_work"
EMAIL = "email"
SMS = "sms"
class AlertNotifier:
"""多渠道告警通知器"""
def __init__(self, api_key: str):
self.api_key = api_key
self.channels: Dict[NotificationChannel, Dict] = {}
def add_webhook(self, url: str, secret: str = None, headers: Dict = None):
"""添加Webhook通知渠道"""
self.channels[NotificationChannel.WEBHOOK] = {
"url": url,
"secret": secret,
"headers": headers or {"Content-Type": "application/json"}
}
def add_dingtalk(self, webhook_url: str, secret: str = None):
"""添加钉钉群通知"""
self.channels[NotificationChannel.DINGTALK] = {
"webhook_url": webhook_url,
"secret": secret
}
def add_wechat_work(self, webhook_url: str):
"""添加企业微信通知"""
self.channels[NotificationChannel.WECHAT_WORK] = {
"webhook_url": webhook_url
}
def _generate_dingtalk_sign(self, secret: str) -> str:
"""生成钉钉签名"""
timestamp = str(round(time.time() * 1000))
secret_enc = secret.encode('utf-8')
string_to_sign = f'{timestamp}\n{secret}'
string_to_sign_enc = string_to_sign.encode('utf-8')
hmac_code = hmac.new(secret_enc, string_to_sign_enc, digestmod=hashlib.sha256).digest()
sign = base64.b64encode(hmac_code).decode('utf-8')
return timestamp, sign
def _send_webhook(self, channel_config: Dict, message: Dict) -> bool:
"""发送Webhook请求"""
try:
import requests
url = channel_config["url"]
headers = channel_config["headers"]
# 如果配置了密钥，添加签名
if channel_config.get("secret"):
sign_data = self._generate_webhook_sign(message, channel_config["secret"])
message["sign"] = sign_data
response = requests.post(
url,
headers=headers,
json=message,
timeout=10
)
return response.status_code in [200, 201, 204]
except Exception as e:
print(f"Webhook发送失败: {e}")
return False
def _send_dingtalk(self, channel_config: Dict, alert: 'CrisisAlert') -> bool:
"""发送钉钉通知"""
try:
import requests
webhook_url = channel_config["webhook_url"]
# 添加签名
if channel_config.get("secret"):
timestamp, sign = self._generate_dingtalk_sign(channel_config["secret"])
webhook_url = f"{webhook_url}×tamp={timestamp}&sign={sign}"
# 构建消息
color_map = {
"CRITICAL": "red",
"WARNING": "orange",
"INFO": "green"
}
content = f"""🤖 **AI危机预警系统**
**严重级别**: {alert.severity}
**错误代码**: {alert.error_code}
**消息**: {alert.message}
**时间**: {alert.timestamp.strftime('%Y-%m-%d %H:%M:%S')}
**指标**: {json.dumps(alert.metrics, ensure_ascii=False)}"""
payload = {
"msgtype": "markdown",
"markdown": {
"title": f"[{alert.severity}] AI预警",
"text": content
}
}
response = requests.post(webhook_url, json=payload, timeout=10)
return response.json().get("errcode") == 0
except Exception as e:
print(f"钉钉通知发送失败: {e}")
return False
def _send_wechat_work(self, channel_config: Dict, alert: 'CrisisAlert') -> bool:
"""发送企业微信通知"""
try:
import requests
content = f"""🤖 AI危机预警
严重级别: {alert.severity}
错误代码: {alert.error_code}
消息: {alert.message}
时间: {alert.timestamp.strftime('%Y-%m-%d %H:%M:%S')}"""
payload = {
"msgtype": "text",
"text": {
"content": content
}
}
response = requests.post(
channel_config["webhook_url"],
json=payload,
timeout=10
)
return response.json().get("errcode") == 0
except Exception as e:
print(f"企业微信通知发送失败: {e}")
return False
def send_alert(self, alert: 'CrisisAlert') -> Dict[str, bool]:
"""向所有配置的通知渠道发送告警"""
results = {}
for channel, config in self.channels.items():
if channel == NotificationChannel.WEBHOOK:
results["webhook"] = self._send_webhook(config, alert.to_dict())
elif channel == NotificationChannel.DINGTALK:
results["dingtalk"] = self._send_dingtalk(config, alert)
elif channel == NotificationChannel.WECHAT_WORK:
results["wechat_work"] = self._send_wechat_work(config, alert)
return results
集成示例
if __name__ == "__main__":
notifier = AlertNotifier(api_key="YOUR_HOLYSHEEP_API_KEY")
# 配置钉钉通知
notifier.add_dingtalk(
webhook_url="https://oapi.dingtalk.com/robot/send?access_token=YOUR_TOKEN",
secret="SECRET"
)
# 配置企业微信
notifier.add_wechat_work(
webhook_url="https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=YOUR_KEY"
)
# 创建测试告警
test_alert = CrisisAlert(
severity="CRITICAL",
error_code="CONNECTION_TIMEOUT",
message="API连接超时超过30秒",
metrics={"timeout_duration": 30000, "retry_count": 3}
)
# 发送通知
results = notifier.send_alert(test_alert)
print(f"通知发送结果: {results}")
3. 自动故障转移系统
import asyncio
import aiohttp
from typing import List, Optional, Dict
from dataclasses import dataclass
import time
@dataclass
class ModelEndpoint:
"""模型端点配置"""
name: str
model_id: str
base_url: str
priority: int # 1-10, 数字越小优先级越高
is_available: bool = True
latency_history: List[float] = None
def __post_init__(self):
if self.latency_history is None:
self.latency_history = []
class FailoverManager:
"""AI服务故障转移管理器"""
# 2026年模型定价参考
MODEL_PRICING = {
"deepseek-v3.2": {"input": 0.42, "output": 0.42},
"gemini-2.5-flash": {"input": 2.50, "output": 2.50},
"claude-sonnet-4.5": {"input": 15.0, "output": 15.0},
"gpt-4.1": {"input": 8.0, "output": 8.0}
}
def __init__(self, api_key: str):
self.api_key = api_key
self.primary_url = "https://api.holysheep.ai/v1"
# 备用端点配置（使用不同的模型作为备份）
self.endpoints: List[ModelEndpoint] = [
ModelEndpoint("DeepSeek-V3.2", "deepseek-v3.2",
f"{self.primary_url}/chat/completions", priority=1),
ModelEndpoint("Gemini-2.5-Flash", "gemini-2.5-flash",
f"{self.primary_url}/chat/completions", priority=2),
ModelEndpoint("Claude-Sonnet-4.5", "claude-sonnet-4.5",
f"{self.primary_url}/chat/completions", priority=3),
]
self.current_endpoint: Optional[ModelEndpoint] = None
self.failure_count: Dict[str, int] = {}
self.circuit_breaker_threshold = 5
self.circuit_open_until: float = 0
def _update_endpoint_latency(self, endpoint_name: str, latency_ms: float):
"""更新端点延迟历史"""
for endpoint in self.endpoints:
if endpoint.name == endpoint_name:
endpoint.latency_history.append(latency_ms)
# 保持最近100条记录
if len(endpoint.latency_history) > 100:
endpoint.latency_history.pop(0)
break
def get_best_endpoint(self) -> Optional[ModelEndpoint]:
"""选择最优端点（基于延迟和可用性）"""
current_time = time.time()
# 检查熔断状态
if self.circuit_open_until > current_time:
remaining = int(self.circuit_open_until - current_time)
print(f"⚠️ 熔断器开启，剩余 {remaining} 秒")
return None
available_endpoints = [e for e in self.endpoints if e.is_available]
if not available_endpoints:
print("❌ 所有端点都不可用")
return None
# 按平均延迟排序
def avg_latency(endpoint: ModelEndpoint) -> float:
if not endpoint.latency_history:
return 0
return sum(endpoint.latency_history) / len(endpoint.latency_history)
available_endpoints.sort(key=lambda e: (e.priority, avg_latency(e)))
return available_endpoints[0]
def record_failure(self, endpoint_name: str):
"""记录失败并可能触发熔断"""
self.failure_count[endpoint_name] = self.failure_count.get(endpoint_name, 0) + 1
if self.failure_count[endpoint_name] >= self.circuit_breaker_threshold:
print(f"🚨 熔断器触发: {endpoint_name}")
# 设置熔断时间为30秒
self.circuit_open_until = time.time() + 30
# 标记端点为不可用
for endpoint in self.endpoints:
if endpoint.name == endpoint_name:
endpoint.is_available = False
# 30秒后恢复
asyncio.create_task(self._restore_endpoint(endpoint_name, delay=30))
async def _restore_endpoint(self, endpoint_name: str, delay: float):
"""恢复端点可用性"""
await asyncio.sleep(delay)
for endpoint in self.endpoints:
if endpoint.name == endpoint_name:
endpoint.is_available = True
self.failure_count[endpoint_name] = 0
print(f"✅ 端点恢复: {endpoint_name}")
break
def record_success(self, endpoint_name: str):
"""记录成功调用"""
self.failure_count[endpoint_name] = 0
async def call_with_failover(self, prompt: str, model: str = None) -> Dict:
"""带故障转移的API调用"""
start_time = time.time()
# 选择端点
endpoint = self.get_best_endpoint()
if not endpoint:
return {
"status": "circuit_breaker_open",
"error": "所有端点不可用或熔断器开启",
"retry_after": int(self.circuit_open_until - time.time()) if self.circuit_open_until > time.time() else 0
}
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": model or endpoint.model_id,
"messages": [{"role": "user", "content": prompt}],
"max_tokens": 500,
"temperature": 0.7
}
try:
async with aiohttp.ClientSession() as session:
async with session.post(
endpoint.name,
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=30)
) as response:
latency_ms = (time.time() - start_time) * 1000
self._update_endpoint_latency(endpoint.name, latency_ms)
if response.status == 200:
self.record_success(endpoint.name)
data = await response.json()
# 计算成本
tokens = data.get("usage", {}).get("total_tokens", 0)
cost = (tokens / 1_000_000) * self.MODEL_PRICING.get(
payload["model"], {"input": 0.42, "output": 0.42}
)["output"]
return {
"status": "success",
"data": data,
"endpoint_used": endpoint.name,
"latency_ms": round(latency_ms, 2),
"cost_usd": round(cost, 6),
"tokens": tokens
}
else:
error_text = await response.text()
self.record_failure(endpoint.name)
return {
"status": "http_error",
"status_code": response.status,
"error": error_text,
"endpoint": endpoint.name
}
except asyncio.TimeoutError:
latency_ms = (time.time() - start_time) * 1000
self.record_failure(endpoint.name)
return {
"status": "timeout",
"latency_ms": latency_ms,
"error": "Request timeout"
}
except aiohttp.ClientConnectorError as e:
self.record_failure(endpoint.name)
return {
"status": "connection_error",
"error": f"ConnectionError: {str(e)}"
}
except Exception as e:
self.record_failure(endpoint.name)
return {
"status": "error",
"error": str(e)
}
def get_system_status(self) -> Dict:
"""获取系统状态"""
return {
"endpoints": [
{
"name": e.name,
"model": e.model_id,
"available": e.is_available,
"avg_latency_ms": round(
sum(e.latency_history) / len(e.latency_history), 2
) if e.latency_history else None,
"failure_count": self.failure_count.get(e.name, 0)
}
for e in self.endpoints
],
"circuit_breaker": {
"open": self.circuit_open_until > time.time(),
"opens_at": self.circuit_open_until if self.circuit_open_until > time.time() else None
}
}
使用示例
async def main():
manager = FailoverManager(api_key="YOUR_HOLYSHEEP_API_KEY")
# 测试故障转移
print("测试带故障转移的API调用...")
response = await manager.call_with_failover(
"解释一下量子计算的基本原理",
model="deepseek-v3.2"
)
print(f"响应状态: {response['status']}")
if response['status'] == 'success':
print(f"使用端点: {response['endpoint_used']}")
print(f"延迟: {response['latency_ms']}ms")
print(f"成本: ${response['cost_usd']}")
# 获取系统状态
status = manager.get_system_status()
print(f"\n系统状态: {status}")
if __name__ == "__main__":
asyncio.run(main())
Erreurs courantes et solutions
在我搭建这套系统的过程中，遇到了很多典型的错误。下面是三个最常见的问题及其解决方案：
Erreur 1 : 401 Unauthorized - Clé API invalide
# ❌ Erreur typique
{
"error": {
"message": "Incorrect API key provided",
"type": "invalid_request_error",
"code": "401"
}
}
✅ Solution - Vérifiez votre clé API
import os
Méthode 1: Variable d'environnement (recommandé)
API_KEY = os.environ.get("HOLYSHEEP_API_KEY")
Méthode 2: Fichier de configuration
Créez un fichier .env à la racine du projet:
HOLYSHEEP_API_KEY=votre_cle_api_ici
Méthode 3: Chargement sécurisé
def load_api_key():
from dotenv import load_dotenv
load_dotenv()
key = os.getenv("HOLYSHEEP_API_KEY")
if not key:
raise ValueError("HOLYSHEEP_API_KEY non configurée")
if key == "YOUR_HOLYSHEEP_API_KEY":
raise ValueError(
"⚠️ Veuillez remplacer YOUR_HOLYSHEEP_API_KEY par votre vraie clé!\n"
"👉 https://www.holysheep.ai/register"
)
return key
Vérification de la clé
api_key = load_api_key()
print(f"✅ Clé API chargée: {api_key[:8]}...{api_key[-4:]}")
Erreur 2 : ConnectionError - Timeout récurrent
# ❌ Er