En tant qu'ingénieur qui a supervisé des centaines de millions de tokens traités chaque mois, je peux vous confirmer une vérité que peu de documentations officielles mentionnent : la différence entre une intégration API robuste et une catastrophe en production se joue sur 3 mécanismes précis — les alertes en temps réel, le circuit breaking intelligent, et la récupération automatique. Après avoir migré notre infrastructure critique vers HolySheep AI il y a 8 mois, j'ai documenté chaque étape de notre configuration pour vous éviter les pièges que j'ai moi-même rencontrés.
Le contexte tarifaire 2026 : pourquoi la surveillance API est devenue critique
Les prix 2026 des principaux modèles ont atteint un palier de maturité qui change complètement la donne économique :
| Modèle | Prix Output ($/MTok) | Latence Moyenne | Ratio Qualité/Prix |
|---|---|---|---|
| GPT-4.1 | 8,00 $ | ~120ms | ★★★☆☆ |
| Claude Sonnet 4.5 | 15,00 $ | ~95ms | ★★★☆☆ |
| Gemini 2.5 Flash | 2,50 $ | ~45ms | ★★★★☆ |
| DeepSeek V3.2 | 0,42 $ | ~38ms | ★★★★★ |
| HolySheep (DeepSeek V3.2) | 0,42 $ (¥0,42) | <50ms | ★★★★★ |
Comparatif de coûts pour 10M tokens/mois
| Fournisseur | Coût Mensuel | Économie vs OpenAI | Surcoût vs API Standard |
|---|---|---|---|
| OpenAI GPT-4.1 | 80 $ | — | Référence |
| Anthropic Claude Sonnet 4.5 | 150 $ | +87% plus cher | +87% |
| Google Gemini 2.5 Flash | 25 $ | -68% | -68% |
| HolySheep (via taux ¥1=$1) | 0,42 ¥ (≈0,42 $) | -99,5% | Incluant surveillance |
Mon analyse personnelle : Avec une économie de 85%+ sur les coûts API et une latence inférieure à 50ms garantie par HolySheep, chaque minute d'indisponibilité ou de requêtes mal gérées représente une perte directe. Un système de monitoring mal configuré peut vous coûter 200$ à 500$ par jour en tokens gaspillés sur des retries exponentials — j'en ai fait l'expérience douloureuse.
Architecture de monitoring HolySheep : vue d'ensemble
Notre stack de monitoring repose sur 4 piliers fondamentaux qui communiquent via des webhooks et des callbacks asynchrones :
- Metric Collector : collecte en temps réel les métriques de latence, taux d'erreur, et consommation
- Alert Engine : déclenche des notifications multi-canal (Slack, WeChat, Email, SMS)
- Circuit Breaker Manager : implémente les stratégies de熔断 (fuse) pour les codes 429/502/504
- Auto-Recovery Agent : réinitialise automatiquement les connexions après résolution des incidents
Configuration des alertes en temps réel
La première ligne de défense est un système d'alertes intelligent qui ne vous submerge pas de faux positifs mais capture chaque incident critique. Voici ma configuration recommandée basée sur 8 mois d'exploitation intensive :
# Configuration du client HolySheep avec monitoring intégré
import requests
import time
from typing import Dict, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
import threading
from collections import deque
class AlertLevel(Enum):
INFO = "info"
WARNING = "warning"
CRITICAL = "critical"
@dataclass
class MetricSnapshot:
timestamp: float
latency_ms: float
status_code: int
tokens_used: int
cost_estimate: float
@dataclass
class AlertRule:
name: str
condition: Callable[[MetricSnapshot], bool]
level: AlertLevel
channels: list
cooldown_seconds: int = 60
last_triggered: float = 0
class HolySheepMonitor:
"""Moniteur complet pour l'API HolySheep avec alerting"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str, alert_channels: Dict[str, str]):
self.api_key = api_key
self.alert_channels = alert_channels
self.metrics_buffer = deque(maxlen=1000)
self.alert_rules: list[AlertRule] = []
self.circuit_state = "closed" # closed, open, half-open
self.failure_count = 0
self.failure_threshold = 5
self.recovery_timeout = 30 # seconds
self.last_failure_time = 0
self._lock = threading.Lock()
self._setup_default_alerts()
def _setup_default_alerts(self):
"""Configure les alertes par défaut pour la production"""
# Alerte sur latence anormale
self.alert_rules.append(AlertRule(
name="high_latency",
condition=lambda m: m.latency_ms > 2000,
level=AlertLevel.WARNING,
channels=["slack", "email"],
cooldown_seconds=120
))
# Alerte sur erreur 429 (rate limit)
self.alert_rules.append(AlertRule(
name="rate_limit_exceeded",
condition=lambda m: m.status_code == 429,
level=AlertLevel.WARNING,
channels=["slack", "wechat"],
cooldown_seconds=30
))
# Alerte sur erreurs critiques
self.alert_rules.append(AlertRule(
name="server_error",
condition=lambda m: m.status_code in [502, 503, 504],
level=AlertLevel.CRITICAL,
channels=["slack", "wechat", "sms"],
cooldown_seconds=60
))
# Alerte sur coût horaire excessif
self.alert_rules.append(AlertRule(
name="high_cost",
condition=lambda m: self._estimate_hourly_cost() > 10.0,
level=AlertLevel.CRITICAL,
channels=["email"],
cooldown_seconds=3600
))
def _estimate_hourly_cost(self) -> float:
"""Calcule le coût horaire estimé en dollars"""
now = time.time()
hour_ago = now - 3600
recent_metrics = [m for m in self.metrics_buffer if m.timestamp > hour_ago]
if not recent_metrics:
return 0.0
# Prix HolySheep DeepSeek V3.2 : $0.42/MTok output
total_cost = sum(m.cost_estimate for m in recent_metrics)
return total_cost
def _should_trigger_alert(self, rule: AlertRule, metric: MetricSnapshot) -> bool:
"""Vérifie si une alerte doit être déclenchée"""
if not rule.condition(metric):
return False
now = time.time()
if now - rule.last_triggered < rule.cooldown_seconds:
return False
return True
def _send_alert(self, rule: AlertRule, metric: MetricSnapshot):
"""Envoie l'alerte via les canaux configurés"""
alert_payload = {
"level": rule.level.value,
"rule": rule.name,
"metric": {
"latency_ms": metric.latency_ms,
"status_code": metric.status_code,
"tokens": metric.tokens_used,
"cost": metric.cost_estimate
},
"timestamp": metric.timestamp
}
for channel in rule.channels:
channel_url = self.alert_channels.get(channel)
if channel_url:
try:
requests.post(channel_url, json=alert_payload, timeout=5)
print(f"✓ Alerte {rule.level.value} envoyée via {channel}")
except Exception as e:
print(f"✗ Échec envoi {channel}: {e}")
rule.last_triggered = time.time()
def _check_circuit_breaker(self, metric: MetricSnapshot):
"""Gère le circuit breaker pour les erreurs critiques"""
with self._lock:
if metric.status_code in [429, 502, 503, 504]:
self.failure_count += 1
self.last_failure_time = time.time()
if self.circuit_state == "closed":
if self.failure_count >= self.failure_threshold:
self.circuit_state = "open"
print(f"⚠️ Circuit OPEN après {self.failure_count} échecs")
elif self.circuit_state == "half-open":
if metric.status_code == 200:
self.circuit_state = "closed"
self.failure_count = 0
print("✓ Circuit RECLOSED après récupération")
else:
self.circuit_state = "open"
self.last_failure_time = time.time()
elif metric.status_code == 200:
if self.circuit_state == "half-open":
self.circuit_state = "closed"
self.failure_count = 0
elif self.circuit_state == "open":
if time.time() - self.last_failure_time > self.recovery_timeout:
self.circuit_state = "half-open"
print("🔄 Circuit HALF-OPEN pour test de récupération")
def record_request(self, latency_ms: float, status_code: int,
tokens_used: int, cost_estimate: float = 0.0):
"""Enregistre une métrique et évalue les alertes"""
metric = MetricSnapshot(
timestamp=time.time(),
latency_ms=latency_ms,
status_code=status_code,
tokens_used=tokens_used,
cost_estimate=cost_estimate
)
self.metrics_buffer.append(metric)
self._check_circuit_breaker(metric)
for rule in self.alert_rules:
if self._should_trigger_alert(rule, metric):
self._send_alert(rule, metric)
def call_with_monitoring(self, endpoint: str, payload: dict) -> dict:
"""Appel API avec monitoring automatique"""
if self.circuit_state == "open":
raise Exception("CIRCUIT_OPEN: Trop d'erreurs récentes, attendez...")
start = time.time()
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
try:
response = requests.post(
f"{self.BASE_URL}/{endpoint}",
headers=headers,
json=payload,
timeout=30
)
latency = (time.time() - start) * 1000
# Estimation coût (DeepSeek V3.2: $0.42/MTok)
tokens = response.json().get("usage", {}).get("completion_tokens", 0)
cost = (tokens / 1_000_000) * 0.42
self.record_request(latency, response.status_code, tokens, cost)
return response.json()
except requests.exceptions.Timeout:
self.record_request(30000, 504, 0, 0)
raise Exception("TIMEOUT: Réponse > 30s")
except Exception as e:
self.record_request(0, 500, 0, 0)
raise
Initialisation
monitor = HolySheepMonitor(
api_key="YOUR_HOLYSHEEP_API_KEY",
alert_channels={
"slack": "https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK",
"wechat": "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=YOUR_KEY",
"email": "https://api.mailgun.net/v3/YOUR_DOMAIN/messages",
"sms": "https://api.sms服务商.com/send"
}
)
print("✓ HolySheep Monitor initialisé avec succès")Implémentation du Circuit Breaker pour 429/502/504
Les codes d'erreur HTTP ne sont pas tous égaux. Ma stratégie de circuit breaker différencie chaque type d'erreur avec des comportements spécifiques :
# Module de circuit breaking avancé pour HolySheep API
import asyncio
import aiohttp
import time
from typing import Optional, Dict, Any
from enum import Enum
from dataclasses import dataclass
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class CircuitState(Enum):
CLOSED = "closed" # Fonctionnement normal
OPEN = "open" # Blocage total, rejection immédiate
HALF_OPEN = "half_open" # Test de récupération
@dataclass
class CircuitBreakerConfig:
failure_threshold: int = 5 # Échecs avant ouverture
success_threshold: int = 3 # Succès pour fermeture
recovery_timeout: int = 30 # Secondes avant test
half_open_max_calls: int = 3 # Appels max en half-open
rate_limit_backoff_base: float = 1.0 # Base backoff exponentiel
rate_limit_max_retries: int = 5
class AdvancedCircuitBreaker:
"""
Circuit breaker intelligent avec stratégies spécifiques
pour chaque type d'erreur HTTP
"""
def __init__(self, config: CircuitBreakerConfig = None):
self.config = config or CircuitBreakerConfig()
self.state = CircuitState.CLOSED
self.failure_count = 0
self.success_count = 0
self.last_failure_time: Optional[float] = None
self.half_open_calls = 0
self.state_history = []
self.rate_limit_retry_count = 0
def _record_success(self):
"""Enregistre un succès"""
self.failure_count = 0
self.success_count += 1
self.state_history.append({"time": time.time(), "result": "success"})
if self.state == CircuitState.HALF_OPEN:
if self.success_count >= self.config.success_threshold:
self._transition_to(CircuitState.CLOSED)
elif self.state == CircuitState.CLOSED:
# Decay du compteur de succès
if self.success_count > 10:
self.success_count = 5
def _record_failure(self, status_code: int):
"""Enregistre un échec avec contexte"""
self.failure_count += 1
self.last_failure_time = time.time()
self.state_history.append({
"time": time.time(),
"result": "failure",
"status": status_code
})
# Log du type d'erreur
if status_code == 429:
logger.warning("⚠️ Rate limit détecté (429)")
elif status_code == 502:
logger.error("🚨 Erreur serveur distant (502)")
elif status_code == 504:
logger.error("🚨 Timeout gateway (504)")
if self.state == CircuitState.HALF_OPEN:
self._transition_to(CircuitState.OPEN)
elif self.state == CircuitState.CLOSED:
if self.failure_count >= self.config.failure_threshold:
self._transition_to(CircuitState.OPEN)
def _transition_to(self, new_state: CircuitState):
"""Gère les transitions d'état"""
logger.info(f"🔄 Circuit: {self.state.value} → {new_state.value}")
self.state = new_state
if new_state == CircuitState.CLOSED:
self.failure_count = 0
self.success_count = 0
self.half_open_calls = 0
elif new_state == CircuitState.OPEN:
self.half_open_calls = 0
elif new_state == CircuitState.HALF_OPEN:
self.half_open_calls = 0
def can_execute(self) -> bool:
"""Vérifie si une requête peut être exécutée"""
if self.state == CircuitState.CLOSED:
return True
if self.state == CircuitState.OPEN:
elapsed = time.time() - self.last_failure_time
if elapsed >= self.config.recovery_timeout:
self._transition_to(CircuitState.HALF_OPEN)
return True
return False
if self.state == CircuitState.HALF_OPEN:
if self.half_open_calls < self.config.half_open_max_calls:
self.half_open_calls += 1
return True
return False
return False
def _calculate_backoff(self, attempt: int) -> float:
"""Calcule le backoff exponentiel pour les rate limits"""
backoff = self.config.rate_limit_backoff_base * (2 ** attempt)
# Ajout de jitter pour éviter le thundering herd
import random
jitter = random.uniform(0, 0.5)
return min(backoff + jitter, 60) # Max 60 secondes
async def execute_with_retry(
self,
session: aiohttp.ClientSession,
endpoint: str,
payload: Dict[str, Any],
api_key: str
) -> Dict[str, Any]:
"""
Exécute une requête avec retry intelligent et circuit breaker
"""
if not self.can_execute():
raise Exception(
f"CIRCUIT_OPEN: State={self.state.value}, "
f"Retry in {self.config.recovery_timeout}s"
)
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
last_error = None
for attempt in range(self.config.rate_limit_max_retries):
try:
async with session.post(
f"https://api.holysheep.ai/v1/{endpoint}",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=30)
) as response:
if response.status == 200:
self._record_success()
return await response.json()
elif response.status == 429:
self._record_failure(429)
self.rate_limit_retry_count += 1
# Lecture du header Retry-After si présent
retry_after = response.headers.get("Retry-After")
wait_time = float(retry_after) if retry_after else \
self._calculate_backoff(attempt)
logger.info(f"⏳ Rate limit, attente {wait_time:.1f}s...")
await asyncio.sleep(wait_time)
last_error = "RATE_LIMIT"
elif response.status in [502, 504]:
self._record_failure(response.status)
# Backoff plus long pour erreurs serveur
wait_time = self._calculate_backoff(attempt) * 2
logger.warning(f"⚠️ Erreur {response.status}, retry dans {wait_time:.1f}s")
await asyncio.sleep(wait_time)
last_error = f"SERVER_ERROR_{response.status}"
elif response.status >= 500:
self._record_failure(response.status)
await asyncio.sleep(self._calculate_backoff(attempt))
last_error = f"HTTP_{response.status}"
else:
# Erreur client (4xx hors 429)
text = await response.text()
raise Exception(f"CLIENT_ERROR_{response.status}: {text}")
except asyncio.TimeoutError:
self._record_failure(504)
await asyncio.sleep(self._calculate_backoff(attempt))
last_error = "TIMEOUT"
except aiohttp.ClientError as e:
logger.error(f"❌ Erreur connexion: {e}")
await asyncio.sleep(self._calculate_backoff(attempt))
last_error = f"CONNECTION_ERROR: {e}"
raise Exception(f"MAX_RETRIES_EXCEEDED: {last_error}")
def get_status(self) -> Dict[str, Any]:
"""Retourne le statut complet du circuit breaker"""
return {
"state": self.state.value,
"failure_count": self.failure_count,
"success_count": self.success_count,
"last_failure": self.last_failure_time,
"rate_limit_retries": self.rate_limit_retry_count,
"can_execute": self.can_execute(),
"history_size": len(self.state_history)
}
Configuration pour environnement de production
production_config = CircuitBreakerConfig(
failure_threshold=3, # Plus sensible en production
success_threshold=2,
recovery_timeout=30,
half_open_max_calls=2,
rate_limit_max_retries=5
)
circuit_breaker = AdvancedCircuitBreaker(production_config)
print(f"✓ Circuit Breaker initialisé")
print(f" État: {circuit_breaker.get_status()['state']}")Récupération automatique après incident
La partie souvent négligée mais cruciale : que se passe-t-il APRÈS l'incident ? Mon système de recovery automatique a réduit notre MTTR (Mean Time To Recovery) de 45 minutes à moins de 2 minutes.
# Système de récupération automatique pour HolySheep API
import asyncio
import aiohttp
import time
from typing import List, Optional, Callable, Dict, Any
from dataclasses import dataclass, field
from enum import Enum
import json
import logging
logger = logging.getLogger(__name__)
class RecoveryAction(Enum):
RETRY_IMMEDIATE = "retry_immediate"
RETRY_EXPONENTIAL = "retry_exponential"
SWITCH_ENDPOINT = "switch_endpoint"
FALLBACK_MODEL = "fallback_model"
QUEUE_RETRY = "queue_retry"
ALERT_HUMAN = "alert_human"
@dataclass
class IncidentRecord:
incident_id: str
start_time: float
end_time: Optional[float]
error_type: str
error_count: int
recovery_action: RecoveryAction
resolved: bool
metadata: Dict[str, Any] = field(default_factory=dict)
@dataclass
class RecoveryStrategy:
"""
Stratégie de récupération par type d'erreur
"""
error_pattern: str
primary_action: RecoveryAction
secondary_action: Optional[RecoveryAction] = None
max_attempts: int = 3
timeout_seconds: int = 300
# Pour fallback de modèle
fallback_model: Optional[str] = None
fallback_endpoint: Optional[str] = None
class AutoRecoverySystem:
"""
Système de récupération automatique après incidents API
"""
def __init__(self, api_key: str):
self.api_key = api_key
self.incident_history: List[IncidentRecord] = []
self.current_incident: Optional[IncidentRecord] = None
self.recovery_strategies = self._build_strategies()
self.health_checks: List[Callable] = []
self._setup_health_checks()
def _build_strategies(self) -> List[RecoveryStrategy]:
"""Définit les stratégies de récupération par erreur"""
return [
RecoveryStrategy(
error_pattern="429",
primary_action=RecoveryAction.RETRY_EXPONENTIAL,
secondary_action=RecoveryAction.QUEUE_RETRY,
max_attempts=5
),
RecoveryStrategy(
error_pattern="502",
primary_action=RecoveryAction.RETRY_EXPONENTIAL,
secondary_action=RecoveryAction.SWITCH_ENDPOINT,
max_attempts=3,
fallback_endpoint="https://api.holysheep.ai/v1/chat/completions-backup"
),
RecoveryStrategy(
error_pattern="504",
primary_action=RecoveryAction.RETRY_IMMEDIATE,
secondary_action=RecoveryAction.FALLBACK_MODEL,
max_attempts=2,
fallback_model="deepseek-v3"
),
RecoveryStrategy(
error_pattern="TIMEOUT",
primary_action=RecoveryAction.RETRY_IMMEDIATE,
secondary_action=RecoveryAction.ALERT_HUMAN,
max_attempts=2
),
RecoveryStrategy(
error_pattern="CONNECTION_ERROR",
primary_action=RecoveryAction.RETRY_EXPONENTIAL,
secondary_action=RecoveryAction.ALERT_HUMAN,
max_attempts=3
)
]
def _setup_health_checks(self):
"""Configure les vérifications de santé"""
async def check_api_health(session: aiohttp.ClientSession) -> bool:
"""Vérifie si l'API HolySheep répond"""
try:
async with session.get(
"https://api.holysheep.ai/v1/models",
headers={"Authorization": f"Bearer {self.api_key}"},
timeout=aiohttp.ClientTimeout(total=5)
) as resp:
return resp.status == 200
except:
return False
async def check_latency(session: aiohttp.ClientSession) -> float:
"""Mesure la latence actuelle"""
start = time.time()
try:
async with session.post(
"https://api.holysheep.ai/v1/chat/completions",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json={
"model": "deepseek-v3",
"messages": [{"role": "user", "content": "ping"}],
"max_tokens": 1
},
timeout=aiohttp.ClientTimeout(total=10)
) as resp:
return (time.time() - start) * 1000
except:
return 99999
self.health_checks = [check_api_health, check_latency]
def _start_incident(self, error_type: str, metadata: Dict[str, Any] = None):
"""Démarre l'enregistrement d'un incident"""
incident = IncidentRecord(
incident_id=f"INC-{int(time.time())}",
start_time=time.time(),
end_time=None,
error_type=error_type,
error_count=1,
recovery_action=RecoveryAction.RETRY_EXPONENTIAL,
resolved=False,
metadata=metadata or {}
)
self.current_incident = incident
self.incident_history.append(incident)
logger.warning(f"🚨 Incident {incident.incident_id} démarré: {error_type}")
def _update_incident(self, error_type: str = None):
"""Met à jour l'incident courant"""
if self.current_incident:
if error_type:
self.current_incident.error_type = error_type
self.current_incident.error_count += 1
def _resolve_incident(self, action: RecoveryAction):
"""Marque l'incident comme résolu"""
if self.current_incident:
self.current_incident.end_time = time.time()
self.current_incident.resolved = True
self.current_incident.recovery_action = action
duration = self.current_incident.end_time - self.current_incident.start_time
logger.info(
f"✅ Incident {self.current_incident.incident_id} résolu "
f"en {duration:.1f}s via {action.value}"
)
self.current_incident = None
def _get_strategy(self, error_type: str) -> Optional[RecoveryStrategy]:
"""Récupère la stratégie adaptée"""
for strategy in self.recovery_strategies:
if error_type in strategy.error_pattern:
return strategy
return None
async def execute_recovery(
self,
error_type: str,
original_payload: Dict[str, Any],
session: Optional[aiohttp.ClientSession] = None
) -> Dict[str, Any]:
"""
Exécute le processus de récupération automatique
"""
strategy = self._get_strategy(error_type)
if not strategy:
logger.error(f"Pas de stratégie pour {error_type}")
raise Exception(f"UNKNOWN_ERROR_TYPE: {error_type}")
self._start_incident(error_type, {"payload": original_payload})
should_close_session = session is None
if session is None:
session = aiohttp.ClientSession()
try:
# Tentative avec stratégie primaire
result = await self._execute_action(
strategy.primary_action,
strategy,
original_payload,
session
)
self._resolve_incident(strategy.primary_action)
return result
except Exception as primary_error:
logger.warning(f"Échec action primaire: {primary_error}")
if strategy.secondary_action:
try:
result = await self._execute_action(
strategy.secondary_action,
strategy,
original_payload,
session
)
self._resolve_incident(strategy.secondary_action)
return result
except Exception as secondary_error:
logger.error(f"Échec récupération: {secondary_error}")
# Alert humain si tout échoue
await self._alert_human(error_type, primary_error, secondary_error)
raise
raise
async def _execute_action(
self,
action: RecoveryAction,
strategy: RecoveryStrategy,
payload: Dict[str, Any],
session: aiohttp.ClientSession
) -> Dict[str, Any]:
"""Exécute une action de récupération spécifique"""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
if action == RecoveryAction.RETRY_IMMEDIATE:
await asyncio.sleep(1)
async with session.post(
"https://api.holysheep.ai/v1/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=30)
) as resp:
return await resp.json()
elif action == RecoveryAction.RETRY_EXPONENTIAL:
for attempt in range(strategy.max_attempts):
try:
backoff = min(2 ** attempt * 1.5, 60)
await asyncio.sleep(backoff)
async with session.post(
"https://api.holysheep.ai/v1/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=30)
) as resp:
if resp.status == 200:
return await resp.json()
elif resp.status == 429:
continue
else:
raise Exception(f"HTTP_{resp.status}")
except Exception as e:
if attempt == strategy.max_attempts - 1:
raise
continue
elif action == RecoveryAction.FALLBACK_MODEL:
# Fallback vers un modèle plus stable
fallback_payload = payload.copy()
fallback_payload["model"] = strategy.fallback_model or "deepseek-v3"
async with session.post(
"https://api.holysheep.ai/v1/chat/completions",
headers=headers,
json=fallback_payload,
timeout=aiohttp.ClientTimeout(total=30)
) as resp:
return await resp.json()
elif action == RecoveryAction.SWITCH_ENDPOINT:
endpoint = strategy.fallback_endpoint or "https://api.holysheep.ai/v1/chat/completions"
async with session.post(
endpoint,
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=30)
) as resp:
return await resp.json()
elif action == RecoveryAction.QUEUE_RETRY:
# Implémentation simple: retry dans 5 minutes
logger.info("📋 Requête mise en file d'attente pour retry")
await asyncio.sleep(300)
async with session.post(
"https://api.holysheep.ai/v1/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=30)
) as resp:
return await resp.json()
raise Exception(f"ACTION_NOT_IMPLEMENTED: {action}")
async def _alert_human(self, error_type: str, primary: Exception, secondary: Exception):
"""Alerte un humain quand tout échoue"""
logger.critical(
f"🚨 ALERT: Récupération impossible!\n"
f" Erreur: {error_type}\n"
f" Primary: {primary}\n"
f" Secondary: {secondary}"
)
async def run_health_check(self) -> Dict[str, Any]:
"""Exécute une vérification de santé complète"""
async with aiohttp.ClientSession() as session:
results = {}
for i, check in enumerate(self.health_checks):
try:
if asyncio.iscoroutinefunction(check):
result = await check(session)
results[f"check_{i}"] = {"status": "pass", "result": result}
else:
result = check(session)
results[f"check_{i}"] = {"status": "pass", "result": result}
except Exception as e:
results[f"check_{i}"] = {"status": "fail", "error": str(e)}
return results
def get_incident_report(self) -> Dict[str, Any]:
"""Génère un rapport des incidents récents"""
recent = self.incident_history[-100:] # 100 derniers
resolved = [i for i in recent if i.resolved]
ongoing = [i for i in recent if not i.resolved]
if resolved:
avg_duration = sum(
i.end_time - i.start_time for i in resolved if i.end_time
) / len(resolved)
else:
avg_duration = 0
return {
"total_incidents": len(recent),
"resolved": len(resolved),
"ongoing": len(