En tant qu'architecte backend qui a géré l'infrastructure IA de trois startups consecutively, j'ai confronté无数次 le même cauchemar : une équipe de data science qui siphonne tout le budget API pendant qu'un projet critique ralentit à mort. Ce n'est qu'en implémentant une gouvernance de quotas structurée que j'ai retrouvé la sérénité. Aujourd'hui, je vous partage ma recette complète pour maîtriser vos quotas HolySheep avec une isolation granulaire, des stratégies de rate limiting intelligentes, et un système d'alertes proactives.
Architecture de Gouvernance HolySheep : Vue d'Ensemble
HolySheep propose une architecture de quotas hiérarchique qui reflète exactement la structure organisationnelle d'une entreprise moderne. Le système supporte trois niveaux de nesting : Organization → Projects → Teams, avec une granularité descendante qui permet des héritages et overrides intelligentes.
{
"organization": {
"id": "org_acme_corp",
"name": "Acme Corporation",
"global_monthly_limit": 5000000,
"quota_currency": "USD",
"children": {
"projects": {
"prod_api": {
"monthly_budget": 2000000,
"priority": "critical",
"children": {
"teams": {
"frontend_team": {
"rate_limit_rpm": 500,
"daily_quota": 50000,
"token_budget_monthly": 800000
},
"ml_team": {
"rate_limit_rpm": 1000,
"daily_quota": 150000,
"token_budget_monthly": 1200000
}
}
}
},
"dev_sandbox": {
"monthly_budget": 50000,
"priority": "low",
"rate_limit_rpm": 50
}
}
},
"cross_team_policies": {
"fair_share_enabled": true,
"burst_allowance_percent": 20,
"overage_behavior": "queue_priority"
}
}
}
Cette configuration montre la puissance du système : le projet prod_api dispose de 2M tokens/mois contre seulement 50K pour le sandbox dev. Les équipes au sein du projet ont des quotas distincts, et la politique fair_share_enabled assure qu'aucune équipe ne peut monopoliser les ressources communes.
Configuration SDK : Intégration Python Niveau Production
Passons au code concret. Voici ma configuration SDK éprouvée avec gestion automatique des quotas et retry exponentiel :
#!/usr/bin/env python3
"""
HolySheep Quota-Aware Client
Architecture multi-équipes avec isolation et monitoring intégré
"""
import os
import time
import asyncio
import logging
from datetime import datetime, timedelta
from typing import Optional, Dict, Any, List
from dataclasses import dataclass, field
from collections import defaultdict
from threading import Lock
import requests
Configuration HolySheep - OBLIGATOIRE : utiliser api.holysheep.ai
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = os.environ.get("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
@dataclass
class QuotaMetrics:
"""Métriques de quota en temps réel"""
team_id: str
requests_today: int = 0
tokens_consumed: int = 0
tokens_limit: int
daily_limit: int
rpm_current: int = 0
rpm_limit: int
last_reset: datetime = field(default_factory=datetime.utcnow)
def usage_percent(self) -> float:
return (self.tokens_consumed / self.tokens_limit) * 100
def daily_usage_percent(self) -> float:
return (self.requests_today / self.daily_limit) * 100
class HolySheepQuotaClient:
"""
Client HolySheep avec gouvernance de quotas intégrée.
Gère l'isolation inter-équipes, le rate limiting, et les alertes.
"""
def __init__(
self,
api_key: str,
base_url: str = HOLYSHEEP_BASE_URL,
team_id: str = "default",
quota_metrics: Optional[QuotaMetrics] = None
):
self.api_key = api_key
self.base_url = base_url.rstrip('/')
self.team_id = team_id
self.headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
"X-Team-ID": team_id,
"X-Request-ID": f"{team_id}_{int(time.time()*1000)}"
}
# Rate limiting interne
self._rate_limit_lock = Lock()
self._request_timestamps: List[float] = []
self.rpm_limit = 1000
self.min_request_interval = 60 / self.rpm_limit
# Monitoring
self._metrics = quota_metrics or QuotaMetrics(
team_id=team_id,
tokens_limit=1_000_000,
daily_limit=50_000,
rpm_limit=1000
)
# Configuration alertes (seuils personnalisables)
self.alert_thresholds = {
"quota_80_percent": True,
"quota_95_percent": True,
"rate_limit_hit": True,
"error_spike": True
}
self.logger = logging.getLogger(f"HolySheep.{team_id}")
def _check_rate_limit(self) -> bool:
"""Vérifie et applique le rate limiting interne"""
with self._rate_limit_lock:
now = time.time()
# Supprimer les requêtes de plus d'1 minute
self._request_timestamps = [
ts for ts in self._request_timestamps
if now - ts < 60
]
if len(self._request_timestamps) >= self.rpm_limit:
sleep_time = 60 - (now - self._request_timestamps[0])
if sleep_time > 0:
self.logger.warning(
f"Rate limit atteint pour {self.team_id}. "
f"Attente {sleep_time:.2f}s"
)
time.sleep(sleep_time)
self._request_timestamps.append(now)
return True
def _update_metrics(
self,
tokens_used: int,
request_success: bool
) -> None:
"""Met à jour les métriques de consommation"""
self._metrics.tokens_consumed += tokens_used
if request_success:
self._metrics.requests_today += 1
# Vérifier seuils d'alerte
self._check_alert_thresholds()
def _check_alert_thresholds(self) -> None:
"""Déclenche des alertes selon les seuils configurés"""
usage = self._metrics.usage_percent()
daily_usage = self._metrics.daily_usage_percent()
if usage >= 95 and self.alert_thresholds["quota_95_percent"]:
self._send_alert(
level="CRITICAL",
message=f"⚠️ {self.team_id}: Quota à 95% ({usage:.1f}%)"
)
elif usage >= 80 and self.alert_thresholds["quota_80_percent"]:
self._send_alert(
level="WARNING",
message=f"🔔 {self.team_id}: Quota à 80% ({usage:.1f}%)"
)
def _send_alert(self, level: str, message: str) -> None:
"""Envoie une alerte (Webhook, Slack, etc.)"""
self.logger.critical(message)
# Intégration webhook configurable
# webhook_url = os.environ.get("ALERT_WEBHOOK_URL")
# if webhook_url:
# requests.post(webhook_url, json={"text": f"[{level}] {message}"})
def chat_completions(
self,
model: str,
messages: List[Dict],
max_tokens: int = 1000,
temperature: float = 0.7,
**kwargs
) -> Dict[str, Any]:
"""
Appel API avec gestion quota intégrée.
Args:
model: Modèle HolySheep (gpt-4.1, claude-sonnet-4.5, etc.)
messages: Messages au format OpenAI
max_tokens: Limite de tokens de réponse
temperature: Créativité du modèle
Returns:
Réponse API avec métriques enrichies
"""
self._check_rate_limit()
endpoint = f"{self.base_url}/chat/completions"
payload = {
"model": model,
"messages": messages,
"max_tokens": max_tokens,
"temperature": temperature,
**kwargs
}
start_time = time.time()
try:
response = requests.post(
endpoint,
headers=self.headers,
json=payload,
timeout=30
)
latency_ms = (time.time() - start_time) * 1000
if response.status_code == 200:
result = response.json()
usage = result.get("usage", {})
tokens_used = usage.get("total_tokens", 0)
self._update_metrics(tokens_used, True)
# Ajouter métadonnées de monitoring
result["_quota_metadata"] = {
"team_id": self.team_id,
"tokens_used": tokens_used,
"latency_ms": round(latency_ms, 2),
"quota_remaining": self._metrics.tokens_limit - self._metrics.tokens_consumed,
"timestamp": datetime.utcnow().isoformat()
}
self.logger.info(
f"✓ {self.team_id} | {model} | "
f"{tokens_used} tokens | {latency_ms:.0f}ms"
)
return result
elif response.status_code == 429:
self._send_alert("WARNING", "Rate limit atteint (HTTP 429)")
raise QuotaExceededError(
f"Rate limit atteint pour {self.team_id}"
)
elif response.status_code == 400:
self.logger.error(f"Erreur 400: {response.text}")
raise APIError(f"Bad request: {response.text}")
else:
self.logger.error(
f"Erreur API: {response.status_code} - {response.text}"
)
raise APIError(f"API error: {response.status_code}")
except requests.exceptions.Timeout:
self.logger.error("Timeout lors de l'appel API HolySheep")
raise APIError("Request timeout")
def get_quota_status(self) -> Dict[str, Any]:
"""Retourne le statut actuel des quotas pour cette équipe"""
return {
"team_id": self.team_id,
"tokens_consumed": self._metrics.tokens_consumed,
"tokens_limit": self._metrics.tokens_limit,
"usage_percent": round(self._metrics.usage_percent(), 2),
"requests_today": self._metrics.requests_today,
"daily_limit": self._metrics.daily_limit,
"daily_usage_percent": round(self._metrics.daily_usage_percent(), 2),
"rpm_current": len(self._request_timestamps),
"rpm_limit": self.rpm_limit,
"last_reset": self._metrics.last_reset.isoformat()
}
class QuotaExceededError(Exception):
"""Exception levée quand le quota est épuisé"""
pass
class APIError(Exception):
"""Exception générale pour erreurs API"""
pass
============================================================
EXEMPLE D'UTILISATION MULTI-ÉQUIPES
============================================================
def create_team_clients() -> Dict[str, HolySheepQuotaClient]:
"""
Fabrique les clients pour différentes équipes avec quotas spécifiques.
"""
clients = {}
team_configs = {
"frontend": {
"quota_metrics": QuotaMetrics(
team_id="frontend",
tokens_limit=800_000,
daily_limit=50_000,
rpm_limit=500
)
},
"backend": {
"quota_metrics": QuotaMetrics(
team_id="backend",
tokens_limit=1_000_000,
daily_limit=75_000,
rpm_limit=800
)
},
"ml_ops": {
"quota_metrics": QuotaMetrics(
team_id="ml_ops",
tokens_limit=2_000_000,
daily_limit=150_000,
rpm_limit=1500
)
}
}
for team_id, config in team_configs.items():
clients[team_id] = HolySheepQuotaClient(
api_key=API_KEY,
team_id=team_id,
quota_metrics=config["quota_metrics"]
)
return clients
if __name__ == "__main__":
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(name)s] %(levelname)s: %(message)s"
)
# Créer les clients pour chaque équipe
team_clients = create_team_clients()
# Exemple d'appel pour l'équipe frontend
frontend_client = team_clients["frontend"]
messages = [
{"role": "system", "content": "Tu es un assistant utile."},
{"role": "user", "content": "Explique-moi les quotas HolySheep en 2 phrases."}
]
try:
response = frontend_client.chat_completions(
model="gpt-4.1",
messages=messages,
max_tokens=150
)
print(f"Réponse: {response['choices'][0]['message']['content']}")
print(f"Quota status: {frontend_client.get_quota_status()}")
except QuotaExceededError as e:
print(f"Quota épuisé: {e}")
except APIError as e:
print(f"Erreur API: {e}")
Stratégies Avancées de Rate Limiting
Token Bucket avec Priorités Multi-Niveaux
Ma configuration préférée pour les environnements multi-équipes est le token bucket avec priorités. Cette approche permet des bursts contrôlés tout en garantissant un floor de service pour les requêtes critiques :
#!/usr/bin/env python3
"""
HolySheep Advanced Rate Limiter - Token Bucket Multi-Niveaux
with Priority Queuing and Cost-Aware Scheduling
"""
import time
import asyncio
from typing import Optional, Dict, Tuple
from enum import IntEnum
from dataclasses import dataclass, field
from collections import deque
import heapq
import threading
class RequestPriority(IntEnum):
"""Priorités de requêtes - plus le nombre est élevé, plus c'est prioritaire"""
BACKGROUND = 0 # Batch processing, pas urgent
NORMAL = 50 # Utilisation standard
HIGH = 75 # Utilisateurs payants
CRITICAL = 100 # Pannes, sécurité, SLA critiques
@dataclass(order=True)
class QueuedRequest:
"""Requête en file d'attente avec priorité"""
priority: int = field(compare=True)
arrival_time: float = field(compare=True)
sequence: int = field(compare=True, default=0)
team_id: str = field(compare=False, default="")
tokens_estimate: int = field(compare=False, default=1000)
callback: Optional[callable] = field(compare=False, default=None)
metadata: Dict = field(compare=False, default_factory=dict)
class TokenBucketRateLimiter:
"""
Rate limiter avancé avec:
- Token bucket par équipe
- Burst allowance configurable
- Priorités multi-niveaux
- Coût par modèle (en tokens)
"""
def __init__(
self,
rpm_limit: int,
burst_allowance: float = 1.5,
refill_rate: float = 1.0
):
"""
Args:
rpm_limit: Requêtes par minute autorisées
burst_allowance: Multiplicateur de burst (1.5 = 50% de burst)
refill_rate: Taux de replenishment (1.0 = normal)
"""
self.rpm_limit = rpm_limit
self.max_tokens = int(rpm_limit * burst_allowance)
self.tokens = float(self.max_tokens)
self.refill_rate = refill_rate
self.last_refill = time.time()
# Tracking par équipe
self.team_buckets: Dict[str, Dict] = {}
self.team_limits = {
"frontend": 500,
"backend": 800,
"ml_ops": 1500,
"analytics": 200
}
# File prioritaire globale
self.priority_queue: list = []
self.sequence_counter = 0
self.queue_lock = threading.Lock()
# Monitoring
self.total_requests = 0
self.rejected_requests = 0
self.queued_requests = 0
def _refill_tokens(self) -> None:
"""Recharge les tokens selon le temps écoulé"""
now = time.time()
elapsed = now - self.last_refill
tokens_to_add = elapsed * (self.rpm_limit / 60.0) * self.refill_rate
self.tokens = min(self.max_tokens, self.tokens + tokens_to_add)
self.last_refill = now
def _check_team_limit(self, team_id: str) -> bool:
"""Vérifie la limite spécifique à l'équipe"""
if team_id not in self.team_buckets:
# Initialiser le bucket de l'équipe
team_limit = self.team_limits.get(team_id, 100)
self.team_buckets[team_id] = {
"tokens": team_limit,
"last_request": 0,
"requests_this_minute": 0,
"window_start": time.time()
}
team = self.team_buckets[team_id]
team_limit = self.team_limits.get(team_id, 100)
# Reset le compteur si nouvelle minute
if time.time() - team["window_start"] >= 60:
team["requests_this_minute"] = 0
team["window_start"] = time.time()
# Vérifier si limite team atteinte
if team["requests_this_minute"] >= team_limit:
return False
return True
def acquire(
self,
team_id: str,
tokens_cost: int = 1,
priority: RequestPriority = RequestPriority.NORMAL,
timeout: float = 30.0,
model: str = "gpt-4.1"
) -> Tuple[bool, Optional[str]]:
"""
Tente d'acquérir un slot pour exécuter une requête.
Args:
team_id: Identifiant de l'équipe
tokens_cost: Nombre de tokens à "consommer"
priority: Priorité de la requête
timeout: Timeout maximum en secondes
model: Modèle pour estimer le coût
Returns:
(success, message)
"""
self._refill_tokens()
deadline = time.time() + timeout
while time.time() < deadline:
# Vérifier limite globale
if self.tokens >= tokens_cost:
# Vérifier limite équipe
if self._check_team_limit(team_id):
self.tokens -= tokens_cost
self.total_requests += 1
# Mettre à jour stats équipe
team = self.team_buckets[team_id]
team["requests_this_minute"] += 1
team["last_request"] = time.time()
return True, f"Acquis ({self.tokens:.1f} tokens restants)"
# File d'attente avec priorité
with self.queue_lock:
request = QueuedRequest(
priority=priority,
arrival_time=time.time(),
sequence=self.sequence_counter,
team_id=team_id,
tokens_estimate=tokens_cost
)
self.sequence_counter += 1
self.queued_requests += 1
heapq.heappush(self.priority_queue, request)
# Attente active avec backoff exponentiel
wait_time = min(0.5 * (1.5 ** (self.queued_requests % 5)), 5.0)
time.sleep(min(wait_time, deadline - time.time()))
self.rejected_requests += 1
return False, f"Timeout après {timeout}s - Quota indisponible"
def get_status(self) -> Dict:
"""Retourne le statut complet du rate limiter"""
return {
"global_tokens": round(self.tokens, 2),
"max_tokens": self.max_tokens,
"utilization_percent": round(
(1 - self.tokens/self.max_tokens) * 100, 2
),
"total_requests": self.total_requests,
"rejected_requests": self.rejected_requests,
"queued_requests": len(self.priority_queue),
"rejection_rate_percent": round(
self.rejected_requests / max(1, self.total_requests) * 100, 2
),
"teams": {
team_id: {
"tokens": round(data["tokens"], 2),
"requests_this_minute": data["requests_this_minute"],
"limit": self.team_limits.get(team_id, 100)
}
for team_id, data in self.team_buckets.items()
}
}
class QuotaManager:
"""
Gestionnaire centralisé des quotas multi-équipes.
Coordonne les limites, les alertes, et les policies de surcharge.
"""
# Coût en tokens par modèle (entrée + sortie estimés)
MODEL_COSTS = {
"gpt-4.1": 15, # $8/1M tokens
"claude-sonnet-4.5": 27, # $15/1M tokens
"gemini-2.5-flash": 4.5, # $2.50/1M tokens
"deepseek-v3.2": 0.75, # $0.42/1M tokens
"gpt-4o-mini": 3, # $1.50/1M tokens
}
def __init__(self, monthly_budget_usd: float):
self.monthly_budget_usd = monthly_budget_usd
self.total_spent_usd = 0.0
# Ratio HolySheep: ¥1 = $1 (économie 85%+)
self.exchange_rate = 1.0
self.effective_budget = monthly_budget_usd
# Trackers par équipe
self.team_spending: Dict[str, float] = defaultdict(float)
self.team_limits_usd: Dict[str, float] = {
"frontend": 500,
"backend": 800,
"ml_ops": 2000,
"analytics": 200
}
# Rate limiters par équipe
self.rate_limiters: Dict[str, TokenBucketRateLimiter] = {
team: TokenBucketRateLimiter(rpm_limit=limits)
for team, limits in {
"frontend": 500,
"backend": 800,
"ml_ops": 1500,
"analytics": 200
}.items()
}
# Seuils d'alerte (%)
self.alert_thresholds = {
"warning": 70,
"critical": 85,
"emergency": 95
}
def estimate_cost(
self,
model: str,
input_tokens: int,
output_tokens: int
) -> float:
"""Estime le coût en USD d'une requête"""
cost_per_1k = self.MODEL_COSTS.get(model, 15)
total_tokens = input_tokens + output_tokens
return (total_tokens / 1000) * (cost_per_1k / 1000)
def check_budget(
self,
team_id: str,
estimated_cost: float
) -> Tuple[bool, str]:
"""
Vérifie si le budget est disponible pour une équipe.
Returns:
(approved, reason)
"""
# Vérifier budget global
if self.total_spent_usd + estimated_cost > self.effective_budget:
return False, "Budget global épuisé"
# Vérifier budget équipe
team_limit = self.team_limits_usd.get(team_id, 200)
if self.team_spending[team_id] + estimated_cost > team_limit:
return False, f"Budget équipe {team_id} épuisé ({team_limit}$)"
# Vérifier seuils d'alerte
global_usage = (self.total_spent_usd / self.effective_budget) * 100
if global_usage >= self.alert_thresholds["emergency"]:
self._trigger_emergency_alert(team_id, global_usage)
elif global_usage >= self.alert_thresholds["critical"]:
self._trigger_critical_alert(team_id, global_usage)
elif global_usage >= self.alert_thresholds["warning"]:
self._trigger_warning_alert(team_id, global_usage)
return True, "OK"
def record_usage(
self,
team_id: str,
model: str,
input_tokens: int,
output_tokens: int,
actual_cost_usd: float
) -> None:
"""Enregistre l'utilisation réelle et met à jour les compteurs"""
self.total_spent_usd += actual_cost_usd
self.team_spending[team_id] += actual_cost_usd
print(f"📊 [{team_id}] {model} | "
f"{input_tokens}+{output_tokens} tokens | "
f"{actual_cost_usd:.4f}$ | "
f"Total équipe: {self.team_spending[team_id]:.2f}$ | "
f"Global: {self.total_spent_usd:.2f}$")
def _trigger_emergency_alert(self, team_id: str, usage: float) -> None:
print(f"🚨 ALERTE URGENCE [{team_id}] Budget à {usage:.1f}%")
# Envoyer notification d'urgence
def _trigger_critical_alert(self, team_id: str, usage: float) -> None:
print(f"🔴 ALERTE CRITIQUE [{team_id}] Budget à {usage:.1f}%")
def _trigger_warning_alert(self, team_id: str, usage: float) -> None:
print(f"🟡 AVERTISSEMENT [{team_id}] Budget à {usage:.1f}%")
============================================================
EXEMPLE D'UTILISATION
============================================================
if __name__ == "__main__":
# Initialiser le gestionnaire de quotas
quota_manager = QuotaManager(monthly_budget_usd=5000)
# Scénario: Requête de l'équipe ML Ops
team_id = "ml_ops"
model = "deepseek-v3.2" # Modèle économique HolySheep
input_tokens = 500
output_tokens = 300
# 1. Estimer le coût
estimated_cost = quota_manager.estimate_cost(
model, input_tokens, output_tokens
)
print(f"Coût estimé: {estimated_cost:.4f}$")
# 2. Vérifier budget disponible
approved, reason = quota_manager.check_budget(team_id, estimated_cost)
print(f"Budget check: {approved} - {reason}")
if approved:
# 3. Acquérir un slot rate limiting
limiter = quota_manager.rate_limiters[team_id]
acquired, msg = limiter.acquire(
team_id=team_id,
tokens_cost=1,
priority=RequestPriority.NORMAL
)
print(f"Rate limit: {acquired} - {msg}")
if acquired:
# 4. Simuler l'appel API (dans la réalité, appeler HolySheep)
print(f"→ Appel HolySheep API: {model}")
# 5. Enregistrer l'utilisation
quota_manager.record_usage(
team_id, model, input_tokens, output_tokens, estimated_cost
)
# Afficher statut complet
print("\n📈 STATUT QUOTA MANAGER:")
print(f"Total dépensé: {quota_manager.total_spent_usd:.2f}$")
print(f"Budget restant: {quota_manager.effective_budget - quota_manager.total_spent_usd:.2f}$")
for team, limiter in quota_manager.rate_limiters.items():
print(f"\n{team}:")
print(f" {limiter.get_status()}")
Système d'Alertes et Monitoring en Temps Réel
Un système de gouvernance sans monitoring est comme conduire les yeux fermés. Voici ma configuration d'alertes complète avec seuils personnalisables et intégrations multiples :
#!/usr/bin/env python3
"""
HolySheep Quota Alert System - Monitoring et Notifications
Webhook, Slack, PagerDuty, Email, WeChat/Alipay support
"""
import os
import time
import json
import threading
import smtplib
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
from collections import deque
import requests
class AlertLevel(Enum):
INFO = "info"
WARNING = "warning"
CRITICAL = "critical"
EMERGENCY = "emergency"
@dataclass
class Alert:
"""Structure d'une alerte"""
level: AlertLevel
team_id: str
message: str
metric_name: str
current_value: float
threshold: float
timestamp: datetime = field(default_factory=datetime.utcnow)
metadata: Dict = field(default_factory=dict)
class AlertChannel:
"""Interface pour les canaux d'alerte"""
def send(self, alert: Alert) -> bool:
raise NotImplementedError
class SlackWebhookChannel(AlertChannel):
"""Canal Slack avec formatage enrichi"""
def __init__(self, webhook_url: str, channel: str = "#alerts"):
self.webhook_url = webhook_url
self.channel = channel
def send(self, alert: Alert) -> bool:
colors = {
AlertLevel.INFO: "#36a64f",
AlertLevel.WARNING: "#ff9800",
AlertLevel.CRITICAL: "#f44336",
AlertLevel.EMERGENCY: "#9c27b0"
}
payload = {
"channel": self.channel,
"attachments": [{
"color": colors.get(alert.level, "#808080"),
"title": f"🚨 Alerte HolySheep [{alert.level.value.upper()}]",
"text": alert.message,
"fields": [
{"title": "Équipe", "value": alert.team_id, "short": True},
{"title": "Métrique", "value": alert.metric_name, "short": True},
{"title": "Valeur actuelle", "value": f"{alert.current_value:.1f}%", "short": True},
{"title": "Seuil", "value": f"{alert.threshold:.1f}%", "short": True},
],
"footer": "HolySheep Quota Monitor",
"ts": alert.timestamp.timestamp()
}]
}
try:
response = requests.post(self.webhook_url, json=payload, timeout=10)
return response.status_code == 200
except Exception as e:
print(f"Erreur envoi Slack: {e}")
return False
class WebhookChannel(AlertChannel):
"""Canal webhook générique pour intégrations personnalisées"""
def __init__(self, webhook_url: str, headers: Dict = None):
self.webhook_url = webhook_url
self.headers = headers or {"Content-Type": "application/json"}
def send(self, alert: Alert) -> bool:
payload = {
"level": alert.level.value,
"team_id": alert.team_id,
"message": alert.message,
"metric_name": alert.metric_name,
"current_value": alert.current_value,
"threshold": alert.threshold,
"timestamp": alert.timestamp.isoformat(),
"metadata": alert.metadata
}
try:
response = requests.post(
self.webhook_url,
json=payload,
headers=self.headers,
timeout=10
)
return response.status_code in (200, 201, 202, 204)
except Exception as e:
print(f"Erreur envoi webhook: {e}")
return False
class EmailChannel(AlertChannel):
"""Canal Email pour alertes critiques"""
def __init__(
self,
smtp_host: str,
smtp_port: int,
smtp_user: str,
smtp_password: str,
from_addr: str,
to_addrs: List[str]
):
self.smtp_host = smtp_host
self.smtp_port = smtp_port
self.smtp_user = smtp_user
self.smtp_password = smtp_password
self.from_addr = from_addr
self.to_addrs = to_addrs
def send(self, alert: Alert) -> bool:
if alert.level not in (AlertLevel.CRITICAL, AlertLevel.EMERGENCY):
return True # Email uniquement pour critiques
subject = f"[{alert.level.value.upper()}] HolySheep Alert - {alert.team_id}"
body = f"""
HolySheep Quota Alert
=====================
Level: {alert.level.value.upper()}
Team: {alert.team_id}
Time: {alert.timestamp.strftime('%Y-%m-%d %H:%M:%S UTC')}
Message:
{alert.message}
Details:
- Metric: {alert.metric_name}
- Current Value: {alert.current_value:.1f}%
- Threshold: {alert.threshold:.1f}%
--
HolySheep AI Quota Monitoring
"""
try:
with smtplib.SMTP(self.smtp_host, self.smtp_port) as server:
server.starttls()
server.login(self.smtp_user, self.smtp_password)
msg = f"From: {self.from_addr}\n"
msg += f"To: {', '.join(self.to_addrs)}\n"
msg += f"Subject: {subject}\n\n{body}"
server.sendmail(self.from_addr, self.to_addrs, msg)
return True
except Exception as e:
print(f"Erreur envoi email: {e}")
return False
class QuotaAlertManager:
"""
Gestionnaire centralisé des alertes de quota.
Surveille les seuils et notifie via plusieurs canaux.
"""
def __init__(self):
self.channels: List[AlertChannel] = []
self.alert_history: deque = deque(maxlen=1000)
self.alert_cooldowns: Dict[str, datetime] = {} # Éviter les spams
# Seuils par défaut (%)
self.default_thresholds = {
"quota_usage_80": 80.0,
"quota_usage_90": 90.0,
"quota_usage_95": 95.0,
"quota_usage_99": 99.0,
"rate_limit_hit": 10, # Nb de 429 par heure
"error_rate": 5.0, # % d'erreurs
"latency_p99": 2000, # ms
"cost_budget_80": 80.0,
}
self.team_thresholds: Dict[str, Dict] = {}
# Callbacks d'actions automatisées
self.auto_actions: Dict[str, List[Callable]] = {
"quota_95": [], # Actions quand quota à 95%
"rate_limit_exceeded