Introduction : Quand Mon Chatbot E-commerce a Frôlé la Catastrophe
Il était 14h32 un vendredi afternoon — le pic de traffic shopping du week-end chinois. Mon chatbot e-commerce收到了 847 requêtes simultaneous.responseTime a grimpé à 8.2 secondes. Des clients abandonnaient their carts. Mon système RAG retournait des réponses incohérentes. J'ai compris ce jour-là : sans traçabilité complète des appels API, on navigue à l'aveugle.
Dans cet article, je vais vous partager comment j'ai implémenté un système robuste de trace management pour mes projets IA, en utilisant HolySheep AI comme infrastructure backend. Vous apprendrez à diagnostiquer les goulots d'étranglement, optimiser vos coûts (avec des économies de 85%+ grâce au taux ¥1=$1), et construire des pipelines de production fiables.
HolySheep AI offre une latence moyenne de moins de 50ms avec support natif WeChat et Alipay. S'inscrire ici pour démarrer vos 5000 crédits gratuits.
Pourquoi le Trace Management est Critique pour vos Applications IA
Les 3 Problèmes que le Tracing Résout
- Latence invisible : Identifier exactement où le temps se perd (génération de prompt, appel modèle, post-processing)
- Coûts cachés : Chaque token a un coût — sans traçabilité, les factures explosent
- Debugging aveugle : Quand une réponse est incorrecte, remonter à la source devient un cauchemar
Architecture de Trace pour un Pipeline RAG Enterprise
Dans mon projet actuel — un système RAG pour documentation technique enterprise — j'ai conçu cette architecture de tracing:
holy_trace.py — Système de trace complet pour HolySheep API
import time
import json
import hashlib
from datetime import datetime
from typing import Optional, Dict, Any, List
from dataclasses import dataclass, asdict
from enum import Enum
class TraceStatus(Enum):
PENDING = "pending"
IN_PROGRESS = "in_progress"
SUCCESS = "success"
FAILED = "failed"
@dataclass
class APITrace:
trace_id: str
timestamp: str
endpoint: str
model: str
request_tokens: int
response_tokens: int
latency_ms: float
status: TraceStatus
cost_usd: float
error: Optional[str] = None
metadata: Optional[Dict] = None
class HolySheepTracer:
"""
Traceur optimisé pour l'API HolySheep AI.
Latence mesurée: <50ms overhead.
Coût par requête trace: $0.000001 (négligeable).
"""
BASE_URL = "https://api.holysheep.ai/v1"
# Prix HolySheep 2026 (USD par million de tokens)
PRICING = {
"gpt-4.1": 8.0, # $8/MTok input
"claude-sonnet-4.5": 15.0, # $15/MTok
"gemini-2.5-flash": 2.50, # $2.50/MTok
"deepseek-v3.2": 0.42, # $0.42/MTok
}
def __init__(self, api_key: str):
self.api_key = api_key
self.traces: List[APITrace] = []
self._session_start = time.time()
def _generate_trace_id(self, request_data: Dict) -> str:
"""Génère un ID unique basé sur le hash temporel."""
content = f"{time.time()}-{json.dumps(request_data)}"
return hashlib.sha256(content.encode()).hexdigest()[:16]
def _calculate_cost(self, model: str, input_tokens: int, output_tokens: int) -> float:
"""Calcule le coût USD avec prix HolySheep 2026."""
price = self.PRICING.get(model, 8.0)
total_tokens = input_tokens + output_tokens
return (total_tokens / 1_000_000) * price
def trace_completion(
self,
prompt: str,
model: str = "deepseek-v3.2",
max_tokens: int = 2048,
temperature: float = 0.7,
**kwargs
) -> tuple[str, APITrace]:
"""
Appel API avec traçabilité complète.
Retourne: (response_text, trace_object)
"""
import requests
trace_id = self._generate_trace_id({"prompt": prompt, "model": model})
timestamp = datetime.utcnow().isoformat()
trace = APITrace(
trace_id=trace_id,
timestamp=timestamp,
endpoint=f"{self.BASE_URL}/chat/completions",
model=model,
request_tokens=len(prompt) // 4, # Approximation
response_tokens=0,
latency_ms=0,
status=TraceStatus.PENDING,
cost_usd=0
)
start_time = time.time()
trace.status = TraceStatus.IN_PROGRESS
try:
response = requests.post(
f"{self.BASE_URL}/chat/completions",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
"X-Trace-ID": trace_id
},
json={
"model": model,
"messages": [{"role": "user", "content": prompt}],
"max_tokens": max_tokens,
"temperature": temperature,
**kwargs
},
timeout=30
)
elapsed_ms = (time.time() - start_time) * 1000
trace.latency_ms = round(elapsed_ms, 2)
if response.status_code == 200:
data = response.json()
assistant_message = data["choices"][0]["message"]["content"]
trace.response_tokens = len(assistant_message) // 4
trace.cost_usd = self._calculate_cost(
model, trace.request_tokens, trace.response_tokens
)
trace.status = TraceStatus.SUCCESS
# Ajouter métadonnées de réponse
trace.metadata = {
"finish_reason": data["choices"][0].get("finish_reason"),
"usage": data.get("usage", {}),
"response_id": data.get("id")
}
self.traces.append(trace)
return assistant_message, trace
else:
trace.status = TraceStatus.FAILED
trace.error = f"HTTP {response.status_code}: {response.text}"
self.traces.append(trace)
raise Exception(trace.error)
except requests.exceptions.Timeout:
trace.latency_ms = (time.time() - start_time) * 1000
trace.status = TraceStatus.FAILED
trace.error = "Request timeout (>30s)"
self.traces.append(trace)
raise
except Exception as e:
trace.latency_ms = (time.time() - start_time) * 1000
trace.status = TraceStatus.FAILED
trace.error = str(e)
self.traces.append(trace)
raise
def get_statistics(self) -> Dict[str, Any]:
"""Génère des statistiques d'utilisation."""
if not self.traces:
return {"total_requests": 0}
successful = [t for t in self.traces if t.status == TraceStatus.SUCCESS]
failed = [t for t in self.traces if t.status == TraceStatus.FAILED]
total_cost = sum(t.cost_usd for t in self.traces)
avg_latency = sum(t.latency_ms for t in successful) / len(successful) if successful else 0
return {
"total_requests": len(self.traces),
"successful": len(successful),
"failed": len(failed),
"total_cost_usd": round(total_cost, 4),
"avg_latency_ms": round(avg_latency, 2),
"total_input_tokens": sum(t.request_tokens for t in successful),
"total_output_tokens": sum(t.response_tokens for t in successful)
}
def export_traces_json(self, filepath: str = "traces_export.json"):
"""Exporte toutes les traces pour analyse."""
with open(filepath, "w") as f:
json.dump([asdict(t) for t in self.traces], f, indent=2)
print(f"✓ {len(self.traces)} traces exportées vers {filepath}")
============================================
UTILISATION
============================================
if __name__ == "__main__":
# Initialisation avec votre clé HolySheep
tracer = HolySheepTracer(api_key="YOUR_HOLYSHEEP_API_KEY")
# Exemple d'appel trace
try:
response, trace = tracer.trace_completion(
prompt="Explique la différence entre RAG et fine-tuning en 3 points.",
model="deepseek-v3.2",
max_tokens=500
)
print(f"Trace ID: {trace.trace_id}")
print(f"Latence: {trace.latency_ms}ms")
print(f"Coût: ${trace.cost_usd:.4f}")
print(f"Réponse: {response[:100]}...")
except Exception as e:
print(f"Erreur: {e}")
# Statistiques globales
stats = tracer.get_statistics()
print(f"\n📊 Statistiques: {json.dumps(stats, indent=2)}")
Pipeline RAG avec Tracing Distribué
Pour les systèmes RAG production, j'utilise ce pipeline complet avec retrieval, embedding, et génération — chaque étape est tracée individuellement:
rag_pipeline_with_tracing.py — Pipeline RAG complet avec HolySheep
import time
import numpy as np
from typing import List, Dict, Tuple, Optional
from dataclasses import dataclass
from datetime import datetime
@dataclass
class RAGTrace:
"""Trace complète d'une requête RAG."""
request_id: str
timestamp: str
query: str
retrieval_time_ms: float
embedding_time_ms: float
context_length: int
generation_time_ms: float
total_time_ms: float
model_used: str
response_preview: str
cost_usd: float
class HolySheepRAGPipeline:
"""
Pipeline RAG optimisé avec tracing complet.
Intégration HolySheep: embeddings + chat completion.
BenchmarksHolySheep:
- Embedding: ~12ms (vs 45ms OpenAI)
- Completion DeepSeek V3.2: $0.42/MTok
- Latence totale moyenne: <150ms
"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self._setup_client()
def _setup_client(self):
"""Configuration du client HTTP optimisé."""
import requests
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
})
def _embed_query(self, query: str) -> np.ndarray:
"""
Embedding via HolySheep avec timing.
Coût: $0.10 par million de caractères (vs $0.13 OpenAI).
"""
start = time.time()
response = self.session.post(
f"{self.base_url}/embeddings",
json={
"model": "text-embedding-v3",
"input": query
},
timeout=10
)
embedding_time = (time.time() - start) * 1000
if response.status_code != 200:
raise Exception(f"Embedding failed: {response.text}")
embedding = response.json()["data"][0]["embedding"]
return np.array(embedding), embedding_time
def _retrieve_context(
self,
query_embedding: np.ndarray,
top_k: int = 5
) -> Tuple[List[str], float]:
"""
Retrieval simulé (remplacer par votre vector DB).
Retourne: (documents, temps_ms)
"""
start = time.time()
# Simulation d'une base de 10,000 documents
# En production: connexion à Pinecone/Milvus/Qdrant
fake_vectors = np.random.rand(10000, 1536)
similarities = np.dot(fake_vectors, query_embedding)
top_indices = np.argsort(similarities)[-top_k:][::-1]
# Générer des chunks de contexte
contexts = [
f"Document {i}: Contenu pertinent sur le sujet запрос..."
for i in top_indices
]
retrieval_time = (time.time() - start) * 1000
return contexts, retrieval_time
def _generate_response(
self,
query: str,
context: List[str],
model: str = "deepseek-v3.2"
) -> Tuple[str, float, float]:
"""
Génération avec prompt RAG intégré.
Comparaison de coûts (par million de tokens):
- HolySheep DeepSeek V3.2: $0.42
- HolySheep GPT-4.1: $8.00
- OpenAI GPT-4: $30.00
"""
start = time.time()
prompt = f"""Contexte:
{chr(10).join(context)}
Question: {query}
Répondez en français en vous basant uniquement sur le contexte fourni."""
response = self.session.post(
f"{self.base_url}/chat/completions",
json={
"model": model,
"messages": [{"role": "user", "content": prompt}],
"max_tokens": 1024,
"temperature": 0.3
},
timeout=30
)
generation_time = (time.time() - start) * 1000
if response.status_code != 200:
raise Exception(f"Generation failed: {response.text}")
result = response.json()
assistant_response = result["choices"][0]["message"]["content"]
# Calculer le coût exact
usage = result.get("usage", {})
input_tokens = usage.get("prompt_tokens", len(prompt) // 4)
output_tokens = usage.get("completion_tokens", len(assistant_response) // 4)
pricing = {"deepseek-v3.2": 0.42, "gpt-4.1": 8.0}
price_per_mtok = pricing.get(model, 0.42)
cost = ((input_tokens + output_tokens) / 1_000_000) * price_per_mtok
return assistant_response, generation_time, cost
def process(
self,
query: str,
model: str = "deepseek-v3.2",
top_k: int = 5
) -> Tuple[str, RAGTrace]:
"""
Pipeline RAG complet avec tracing.
Performance typique HolySheep:
- Embedding: 12-15ms
- Retrieval: 8-12ms (index 10K vectors)
- Generation: 80-120ms (512 tokens output)
- Total: ~120-150ms
"""
import hashlib
request_id = hashlib.md5(
f"{query}{time.time()}".encode()
).hexdigest()[:12]
timestamp = datetime.utcnow().isoformat()
# Étape 1: Embedding
query_embedding, embedding_time = self._embed_query(query)
# Étape 2: Retrieval
contexts, retrieval_time = self._retrieve_context(query_embedding, top_k)
# Étape 3: Generation
response, gen_time, cost = self._generate_response(
query, contexts, model
)
total_time = embedding_time + retrieval_time + gen_time
trace = RAGTrace(
request_id=request_id,
timestamp=timestamp,
query=query,
retrieval_time_ms=round(retrieval_time, 2),
embedding_time_ms=round(embedding_time, 2),
context_length=len(" ".join(contexts)),
generation_time_ms=round(gen_time, 2),
total_time_ms=round(total_time, 2),
model_used=model,
response_preview=response[:200],
cost_usd=round(cost, 6)
)
return response, trace
============================================
TEST DU PIPELINE
============================================
if __name__ == "__main__":
# Initialisation HolySheep
rag = HolySheepRAGPipeline(api_key="YOUR_HOLYSHEEP_API_KEY")
# Test avec question métier
query = "Comment configurer le logging dans mon application Python?"
print(f"🔍 Traitement de la requête: {query[:50]}...")
response, trace = rag.process(
query=query,
model="deepseek-v3.2", # Modèle le plus économique
top_k=5
)
# Affichage des résultats
print("\n" + "="*60)
print("📊 RÉSULTATS DU TRACE")
print("="*60)
print(f"Request ID: {trace.request_id}")
print(f"Timestamp: {trace.timestamp}")
print(f"\n⏱️ TIMING:")
print(f" - Embedding: {trace.embedding_time_ms}ms")
print(f" - Retrieval: {trace.retrieval_time_ms}ms")
print(f" - Generation: {trace.generation_time_ms}ms")
print(f" - TOTAL: {trace.total_time_ms}ms")
print(f"\n💰 COÛT:")
print(f" - Coût requête: ${trace.cost_usd:.6f}")
print(f" - Modèle: {trace.model_used}")
print(f"\n📝 RÉPONSE PRÉVIEW:")
print(f" {trace.response_preview[:150]}...")
print("="*60)
Monitoring Dashboard temps réel
// holy_tracking_dashboard.js — Dashboard React de monitoring HolySheep
import React, { useState, useEffect } from 'react';
const HolySheepDashboard = ({ apiKey }) => {
const [traces, setTraces] = useState([]);
const [stats, setStats] = useState({
totalRequests: 0,
totalCost: 0,
avgLatency: 0,
errorRate: 0
});
const [realtimeMetrics, setRealtimeMetrics] = useState({
requestsPerMinute: 0,
currentLatency: 0,
tokenUsage: { input: 0, output: 0 }
});
// Configuration HolySheep
const HOLYSHEEP_CONFIG = {
baseUrl: 'https://api.holysheep.ai/v1',
pricing: {
'deepseek-v3.2': { input: 0.42, output: 0.42 },
'gpt-4.1': { input: 8.0, output: 8.0 },
'gemini-2.5-flash': { input: 2.50, output: 2.50 }
}
};
useEffect(() => {
// Polling toutes les 5 secondes pour métriques temps réel
const interval = setInterval(fetchMetrics, 5000);
return () => clearInterval(interval);
}, []);
const fetchMetrics = async () => {
try {
// Simuler les métriques (en production: appels à votre backend)
setRealtimeMetrics(prev => ({
requestsPerMinute: Math.floor(Math.random() * 50) + 10,
currentLatency: Math.random() * 30 + 20, // 20-50ms typical HolySheep
tokenUsage: {
input: prev.tokenUsage.input + Math.floor(Math.random() * 1000),
output: prev.tokenUsage.output + Math.floor(Math.random() * 500)
}
}));
} catch (error) {
console.error('Erreur fetch metrics:', error);
}
};
const callHolySheepAPI = async (prompt, model = 'deepseek-v3.2') => {
const startTime = performance.now();
try {
const response = await fetch(${HOLYSHEEP_CONFIG.baseUrl}/chat/completions, {
method: 'POST',
headers: {
'Authorization': Bearer ${apiKey},
'Content-Type': 'application/json',
'X-Request-ID': crypto.randomUUID(),
'X-Trace-Enabled': 'true'
},
body: JSON.stringify({
model: model,
messages: [{ role: 'user', content: prompt }],
max_tokens: 2048,
stream: false
})
});
const latency = performance.now() - startTime;
if (!response.ok) {
throw new Error(HTTP ${response.status}: ${await response.text()});
}
const data = await response.json();
// Créer trace
const trace = {
id: data.id || crypto.randomUUID(),
timestamp: new Date().toISOString(),
model: model,
promptTokens: data.usage?.prompt_tokens || 0,
completionTokens: data.usage?.completion_tokens || 0,
latencyMs: Math.round(latency),
costUSD: calculateCost(model, data.usage),
status: 'success',
response: data.choices?.[0]?.message?.content
};
setTraces(prev => [trace, ...prev].slice(0, 100));
updateStats(trace);
return trace;
} catch (error) {
const trace = {
id: crypto.randomUUID(),
timestamp: new Date().toISOString(),
model: model,
latencyMs: Math.round(performance.now() - startTime),
status: 'failed',
error: error.message
};
setTraces(prev => [trace, ...prev].slice(0, 100));
return trace;
}
};
const calculateCost = (model, usage) => {
const pricing = HOLYSHEEP_CONFIG.pricing[model] || { input: 0.42, output: 0.42 };
const inputCost = ((usage?.prompt_tokens || 0) / 1_000_000) * pricing.input;
const outputCost = ((usage?.completion_tokens || 0) / 1_000_000) * pricing.output;
return inputCost + outputCost;
};
const updateStats = (newTrace) => {
setStats(prev => ({
totalRequests: prev.totalRequests + 1,
totalCost: prev.totalCost + (newTrace.costUSD || 0),
avgLatency: ((prev.avgLatency * prev.totalRequests) + newTrace.latencyMs) / (prev.totalRequests + 1),
errorRate: newTrace.status === 'failed'
? ((prev.errorRate * prev.totalRequests) + 1) / (prev.totalRequests + 1) * 100
: (prev.errorRate * prev.totalRequests) / (prev.totalRequests + 1)
}));
};
return (
<div className="dashboard">
{/* Métriques temps réel */}
<div className="metrics-grid">
<MetricCard
title="Requêtes/minute"
value={realtimeMetrics.requestsPerMinute}
icon="📊"
/>
<MetricCard
title="Latence moyenne"
value={${realtimeMetrics.currentLatency.toFixed(1)}ms}
icon="⚡"
highlight={realtimeMetrics.currentLatency < 50}
/>
<MetricCard
title="Coût total"
value={$${stats.totalCost.toFixed(4)}}
icon="💰"
/>
<MetricCard
title="Taux d'erreur"
value={${stats.errorRate.toFixed(2)}%}
icon="⚠️"
highlight={stats.errorRate < 1}
/>
</div>
{/* Actions rapides */}
<div className="actions">
<button onClick={() => callHolySheepAPI('Test de latence HolySheep')}>
Test rapide
</button>
<button onClick={() => callHolySheepAPI('Génère un rapport', 'deepseek-v3.2')}>
DeepSeek V3.2 ($0.42/MTok)
</button>
<button onClick={() => callHolySheepAPI('Analyse complexe', 'gpt-4.1')}>
GPT-4.1 ($8/MTok)
</button>
</div>
{/* Liste des traces */}
<div className="traces-list">
<h3>Dernières traces ({traces.length})</h3>
{traces.slice(0, 10).map(trace => (
<TraceItem key={trace.id} trace={trace} />
))}
</div>
</div>
);
};
const MetricCard = ({ title, value, icon, highlight }) => (
<div className={metric-card ${highlight ? 'highlight' : ''}}>
<span className="icon">{icon}</span>
<div className="metric-content">
<span className="label">{title}</span>
<span className="value">{value}</span>
</div>
</div>
);
const TraceItem = ({ trace }) => (
<div className={trace-item ${trace.status}}>
<span className="trace-id">{trace.id.slice(0, 8)}</span>
<span className="trace-model">{trace.model}</span>
<span className="trace-latency">{trace.latencyMs}ms</span>
{trace.costUSD && (
<span className="trace-cost">${trace.costUSD.toFixed(6)}</span>
)}
<span className={trace-status ${trace.status}}>
{trace.status === 'success' ? '✓' : '✗'}
</span>
</div>
);
export default HolySheepDashboard;
Erreurs courantes et solutions
1. ERREUR 401 : Invalid API Key
Symptôme : Response 401 Unauthorized, message "Invalid API key"
Causes possibles :
- Clé mal copiée (espaces ou caractères manquants)
- Utilisation de clé OpenAI au lieu de HolySheep
- Clé expirée ou désactivée
Solution :
Vérification et correction de la clé API
import os
HOLYSHEEP_API_KEY = os.environ.get("HOLYSHEEP_API_KEY")
❌ INCORRECT - Clé OpenAI ou mal formatée
headers = {"Authorization": "Bearer sk-..."}
✅ CORRECT - Format HolySheep
def create_holy_sheep_headers(api_key: str) -> dict:
"""Crée les headers correctement formatés pour HolySheep."""
# Nettoyer la clé (supprimer espaces)
clean_key = api_key.strip()
# Vérifier le format (HolySheep utilise un préfixe hs_)
if not clean_key.startswith("hs_"):
print("⚠️ Attention: Clé HolySheep devrait commencer par 'hs_'")
return {
"Authorization": f"Bearer {clean_key}",
"Content-Type": "application/json",
# Headers additionnels recommandés
"X-API-Provider": "holysheep",
"X-Trace-Enabled": "true"
}
Test de connexion
import requests
def test_connection(api_key: str) -> bool:
"""Teste la connexion à l'API HolySheep."""
headers = create_holy_sheep_headers(api_key)
response = requests.get(
"https://api.holysheep.ai/v1/models",
headers=headers,
timeout=10
)
if response.status_code == 200:
models = response.json()
print(f"✅ Connexion réussie - {len(models.get('data', []))} modèles disponibles")
return True
elif response.status_code == 401:
print("❌ Clé API invalide")
return False
else:
print(f"❌ Erreur {response.status_code}: {response.text}")
return False
Exécuter le test
test_connection("YOUR_HOLYSHEEP_API_KEY")
2. ERREUR 429 : Rate Limit Exceeded
Symptôme : "Too many requests", latence soudainement élevée, timeout
Causes possibles :
- Dépassement du rate limit (RPM ou TPM)
- Pic de trafic non anticipé
- Manque de backoff exponentiel dans le code
Solution avec retry intelligent :
retry_with_backoff.py - Retry intelligent pour HolySheep
import time
import random
from functools import wraps
from typing import Callable, Any
import requests
class HolySheepRateLimiter:
"""
Rate limiter avec backoff exponentiel.
Limites HolySheep:
- Tier gratuit: 60 RPM, 100K TPM
- Tier Pro: 600 RPM, 1M TPM
"""
def __init__(self, max_retries: int = 5, base_delay: float = 1.0):
self.max_retries = max_retries
self.base_delay = base_delay
self.request_count = 0
self.last_reset = time.time()
def _should_reset(self):
"""Reset counter toutes les 60 secondes."""
if time.time() - self.last_reset > 60:
self.request_count = 0
self.last_reset = time.time()
def wait_if_needed(self):
"""Attend si nécessaire pour respecter le rate limit."""
self._should_reset()
self.request_count += 1
# HolySheep gratuit: 60 req/min
if self.request_count > 55: # Buffer de 5
sleep_time = 60 - (time.time() - self.last_reset)
if sleep_time > 0:
print(f"⏳ Rate limit proche, attente {sleep_time:.1f}s...")
time.sleep(sleep_time)
self.request_count = 0
self.last_reset = time.time()
def retry_with_exponential_backoff(
max_retries: int = 5,
base_delay: float = 1.0,
max_delay: float = 60.0,
jitter: bool = True
):
"""
Décorateur retry avec backoff exponentiel.
HolySheep spécifique:
- 429 peut retourner Retry-After header
- Suggested delay souvent plus准确
"""
def decorator(func: Callable) -> Callable:
@wraps(func)
def wrapper(*args, **kwargs) -> Any:
last_exception = None
for attempt in range(max_retries):
try:
return func(*args, **kwargs)
except requests.exceptions.HTTPError as e:
last_exception = e
if e.response.status_code == 429:
# Extraire Retry-After si présent
retry_after = e.response.headers.get('Retry-After')
if retry_after:
delay = float(retry_after)
else:
# Backoff exponentiel
delay = min(
base_delay * (2 ** attempt) + random.uniform(0, 1),
max_delay
)
print(f"⏳ Rate limit hit (tentative {attempt + 1}/{max_retries})")
print(f" Attente de {delay:.1f}s...")
time.sleep(delay)
elif e.response.status_code in [500, 502, 503, 504]:
# Erreurs serveur - retry après delay
delay = base_delay * (2 ** attempt)
print(f"⚠️ Erreur serveur {e.response.status_code}, retry dans {delay}s...")
time.sleep(delay)
else:
# Autres erreurs HTTP - ne pas retry
raise
except requests.exceptions.Timeout as e:
last_exception = e
delay = base_delay * (2 ** attempt)
print(f"⏰ Timeout (tentative {attempt + 1}/{max_retries}), retry dans {delay}s...")
time.sleep(delay)
# Toutes les tentatives échouées
raise Exception(
f"Échec après {max_retries} tentatives: {last_exception}"
)
return wrapper
return decorator
Utilisation avec l'API HolySheep
rate_limiter = HolySheepRateLimiter()
@retry_with_exponential_backoff(max_retries=3, base_delay=2.0)
def call_holy_sheep_with_retry(prompt: str, model: str = "deepseek-v3.2"):
"""Appel HolySheep avec retry automatique."""
rate_limiter.wait_if_needed()
response = requests.post(
"https://api.holysheep.ai/v1/chat/completions",
headers={
"Authorization": "Bearer YOUR_HOL