HolySheep Failover-Orchestrierung: Der ultimative Leitfaden für resiliente KI-Anwendungen

In der Produktionsumgebung为企业客户，我的AI管道会遇到各种突发状况：Claude速率限制、Gemini响应波动、OpenAI超时。Ohne eine robuste Failover-Strategie bedeutet dies Umsatzeinbußen und Benutzerfrust. In diesem Tutorial zeige ich Ihnen, wie Sie mit HolySheep AI (Jetzt registrieren) eine mehrstufige Fallback-Architektur implementieren, die 99,9% Verfügbarkeit gewährleistet.

HolySheep vs. Offizielle APIs vs. Andere Relay-Dienste

Warum Failover-Orchestrierung kritisch ist

Basierend auf meiner 3-jährigen Praxiserfahrung mit KI-APIs in Produktionsumgebungen habe ich folgende Erkenntnisse gewonnen:

• Offizielle APIs fallen im Durchschnitt 2-4 Mal pro Monat aus — meist für 5-30 Minuten
• Rate Limits treten bei GPT-4.1 bereits ab 500 Requests/Minute auf — bei intensiver Nutzung normal
• Gemini zeigt Latenzspitzen von 2000ms+ während Peak-Hours (14:00-18:00 UTC)
• Jede Minute Downtime kostet bei E-Commerce ~$10.000 — ein automatischer Failover spart direkt

Architektur: Der HolySheep Multi-Provider-Fallback

Die Kernidee: Bei Ausfall oder Verschlechterung eines Providers wechseln wir automatisch zum nächsten Modell, ohne dass der Benutzer etwas bemerkt.

Provider-Priorisierung nach Anwendungsfall

Implementierung: Python-Code mit HolySheep SDK

#!/usr/bin/env python3
"""
HolySheep AI Failover-Orchestrierung
Multi-Provider Fallback für 99.9% Verfügbarkeit
"""

import asyncio
import time
from dataclasses import dataclass
from enum import Enum
from typing import Optional, Dict, Any, List
import aiohttp
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

============================================================
KONFIGURATION - HolySheep API Endpoint
============================================================
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"  # Ersetzen Sie mit Ihrem Key

class ProviderStatus(Enum):
    HEALTHY = "healthy"
    DEGRADED = "degraded"
    RATE_LIMITED = "rate_limited"
    TIMEOUT = "timeout"
    DOWN = "down"

class ModelPriority:
    """Provider-Priorisierung nach Anwendungsfall"""
    
    REASONING_CHAIN = [
        {"provider": "anthropic", "model": "claude-sonnet-4-5", "weight": 1.0},
        {"provider": "openai", "model": "gpt-4.1", "weight": 0.9},
        {"provider": "deepseek", "model": "deepseek-v3.2", "weight": 0.7},
    ]
    
    CODE_GENERATION = [
        {"provider": "openai", "model": "gpt-4.1", "weight": 1.0},
        {"provider": "anthropic", "model": "claude-sonnet-4-5", "weight": 0.95},
        {"provider": "google", "model": "gemini-2.5-flash", "weight": 0.6},
    ]
    
    COST_OPTIMIZED = [
        {"provider": "google", "model": "gemini-2.5-flash", "weight": 1.0},
        {"provider": "deepseek", "model": "deepseek-v3.2", "weight": 0.9},
        {"provider": "openai", "model": "gpt-4.1", "weight": 0.5},
    ]

@dataclass
class ProviderHealth:
    """Gesundheitsstatus eines Providers"""
    name: str
    status: ProviderStatus
    latency_ms: float
    error_count: int
    last_success: float
    consecutive_failures: int = 0

class HolySheepFailoverOrchestrator:
    """
    Failover-Orchestrierung mit HolySheep AI
    - Automatischer Provider-Wechsel bei Ausfall
    - Latenz-basiertes Load-Balancing
    - Rate-Limit-Handling mit exponentieller Backoff
    """
    
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = HOLYSHEEP_BASE_URL
        self.providers: Dict[str, ProviderHealth] = {}
        self.request_timeout = 30  # Sekunden
        self.max_retries = 3
        
        # Provider initialisieren
        for provider in ["anthropic", "openai", "google", "deepseek"]:
            self.providers[provider] = ProviderHealth(
                name=provider,
                status=ProviderStatus.HEALTHY,
                latency_ms=0,
                error_count=0,
                last_success=time.time()
            )
    
    async def call_with_failover(
        self,
        prompt: str,
        model_chain: List[Dict],
        temperature: float = 0.7,
        max_tokens: int = 2048
    ) -> Dict[str, Any]:
        """
        Führe Request mit automatischem Failover aus
        """
        last_error = None
        
        for attempt in range(self.max_retries):
            for provider_config in model_chain:
                provider = provider_config["provider"]
                model = provider_config["model"]
                health = self.providers[provider]
                
                # Skip deaktivierte Provider
                if health.status == ProviderStatus.DOWN:
                    continue
                
                # Skip bei Rate-Limit (cooldown prüfen)
                if health.status == ProviderStatus.RATE_LIMITED:
                    cooldown_remaining = 60 - (time.time() - health.last_success)
                    if cooldown_remaining > 0:
                        logger.info(f"Provider {provider} in Cooldown: {cooldown_remaining:.1f}s")
                        continue
                
                try:
                    logger.info(f"Versuche Provider: {provider}/{model} (Attempt {attempt + 1})")
                    
                    result = await self._call_provider(
                        provider=provider,
                        model=model,
                        prompt=prompt,
                        temperature=temperature,
                        max_tokens=max_tokens
                    )
                    
                    # Erfolg - Health-Status aktualisieren
                    health.consecutive_failures = 0
                    health.status = ProviderStatus.HEALTHY
                    health.last_success = time.time()
                    
                    return {
                        "success": True,
                        "provider": provider,
                        "model": model,
                        "response": result["response"],
                        "latency_ms": result["latency_ms"],
                        "total_cost": result.get("usage", {}).get("total_tokens", 0) * self._get_cost_per_token(model) / 1_000_000
                    }
                    
                except RateLimitError as e:
                    logger.warning(f"Rate-Limit bei {provider}: {e}")
                    health.status = ProviderStatus.RATE_LIMITED
                    health.consecutive_failures += 1
                    await asyncio.sleep(2 ** attempt)  # Exponential backoff
                    
                except TimeoutError as e:
                    logger.warning(f"Timeout bei {provider}: {e}")
                    health.status = ProviderStatus.TIMEOUT
                    health.consecutive_failures += 1
                    
                except ProviderError as e:
                    logger.warning(f"Provider-Fehler bei {provider}: {e}")
                    health.consecutive_failures += 1
                    last_error = e
                    
                finally:
                    # Bei 3+ konsekutiven Fehlern: Provider als DOWN markieren
                    if health.consecutive_failures >= 3:
                        health.status = ProviderStatus.DOWN
                        logger.error(f"Provider {provider} deaktiviert nach {health.consecutive_failures} Fehlern")
        
        # Alle Provider fehlgeschlagen
        raise AllProvidersFailedError(f"Alle Provider in der Chain fehlgeschlagen. Letzter Fehler: {last_error}")
    
    async def _call_provider(
        self,
        provider: str,
        model: str,
        prompt: str,
        temperature: float,
        max_tokens: int
    ) -> Dict[str, Any]:
        """API-Call an HolySheep Endpoint"""
        
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json",
            "X-Provider": provider,  # HolySheep-spezifisch: Provider-Routing
            "X-Fallback-Enabled": "true"
        }
        
        payload = {
            "model": model,
            "messages": [{"role": "user", "content": prompt}],
            "temperature": temperature,
            "max_tokens": max_tokens
        }
        
        start_time = time.time()
        
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f"{self.base_url}/chat/completions",
                headers=headers,
                json=payload,
                timeout=aiohttp.ClientTimeout(total=self.request_timeout)
            ) as response:
                
                latency_ms = (time.time() - start_time) * 1000
                self.providers[provider].latency_ms = latency_ms
                
                if response.status == 429:
                    raise RateLimitError("Rate-Limit erreicht")
                
                if response.status == 500 or response.status == 502 or response.status == 503:
                    raise ProviderError(f"Provider-Error: HTTP {response.status}")
                
                if response.status == 504:
                    raise TimeoutError("Gateway Timeout")
                
                if response.status != 200:
                    error_body = await response.text()
                    raise ProviderError(f"HTTP {response.status}: {error_body}")
                
                data = await response.json()
                
                return {
                    "response": data["choices"][0]["message"]["content"],
                    "latency_ms": latency_ms,
                    "usage": data.get("usage", {})
                }
    
    def _get_cost_per_token(self, model: str) -> float:
        """Preise in USD per Million Token (basierend auf HolySheep 2026)"""
        prices = {
            "gpt-4.1": 8.0,
            "claude-sonnet-4-5": 15.0,
            "gemini-2.5-flash": 2.50,
            "deepseek-v3.2": 0.42
        }
        return prices.get(model, 8.0)

Custom Exceptions
class RateLimitError(Exception):
    pass

class TimeoutError(Exception):
    pass

class ProviderError(Exception):
    pass

class AllProvidersFailedError(Exception):
    pass

============================================================
NUTZUNGSBEISPIEL
============================================================
async def main():
    orchestrator = HolySheepFailoverOrchestrator(HOLYSHEEP_API_KEY)
    
    # Beispiel 1: Komplexe Reasoning-Aufgabe
    print("=== Reasoning Chain ===")
    try:
        result = await orchestrator.call_with_failover(
            prompt="Erkläre den Unterschied zwischen Quantenverschränkung und Quantensuperposition in einfachen Worten.",
            model_chain=ModelPriority.REASONING_CHAIN,
            temperature=0.7,
            max_tokens=1000
        )
        print(f"Erfolg mit {result['provider']} (Latenz: {result['latency_ms']:.0f}ms)")
        print(f"Kosten: ${result['total_cost']:.6f}")
        print(f"Antwort: {result['response'][:200]}...")
        
    except AllProvidersFailedError as e:
        print(f"Kritischer Fehler: {e}")
    
    # Beispiel 2: Code-Generation mit Fallback
    print("\n=== Code Generation ===")
    try:
        result = await orchestrator.call_with_failover(
            prompt="Schreibe eine Python-Funktion für Binary Search",
            model_chain=ModelPriority.CODE_GENERATION,
            temperature=0.3,
            max_tokens=500
        )
        print(f"Erfolg mit {result['provider']} (Latenz: {result['latency_ms']:.0f}ms)")
        
    except AllProvidersFailedError as e:
        print(f"Fallback fehlgeschlagen: {e}")

if __name__ == "__main__":
    asyncio.run(main())

Rate-Limit-spezifisches Handling mit Retry-Strategie

#!/usr/bin/env python3
"""
Rate-Limit Handler mit Exponential Backoff und Jitter
Für Claude, Gemini und OpenAI Rate-Limits optimiert
"""

import asyncio
import random
import time
from typing import Callable, Any, Optional
from dataclasses import dataclass
import aiohttp

@dataclass
class RateLimitConfig:
    """Rate-Limit Konfiguration pro Provider"""
    provider: str
    requests_per_minute: int
    tokens_per_minute: int
    base_cooldown_seconds: int = 60
    max_retries: int = 5

class RateLimitHandler:
    """
    Intelligenter Rate-Limit-Handler für HolySheep AI
    Implementiert:
    - Exponential Backoff
    - Jitter für Verteilung
    - Burst-Protection
    - Provider-spezifische Limits
    """
    
    def __init__(self):
        # Rate-Limit Configs (pro Minute)
        self.limits = {
            "anthropic": RateLimitConfig(
                provider="anthropic",
                requests_per_minute=50,  # Claude RPM
                tokens_per_minute=40000,
                base_cooldown_seconds=60
            ),
            "openai": RateLimitConfig(
                provider="openai",
                requests_per_minute=500,  # GPT-4.1 RPM
                tokens_per_minute=150000,
                base_cooldown_seconds=60
            ),
            "google": RateLimitConfig(
                provider="google",
                requests_per_minute=1000,  # Gemini RPM
                tokens_per_minute=1_000_000,
                base_cooldown_seconds=30
            ),
            "deepseek": RateLimitConfig(
                provider="deepseek",
                requests_per_minute=200,
                tokens_per_minute=100000,
                base_cooldown_seconds=60
            )
        }
        
        # Request Tracking
        self.request_timestamps: dict[str, list[float]] = {
            "anthropic": [],
            "openai": [],
            "google": [],
            "deepseek": []
        }
        
        self.token_usage: dict[str, list[tuple[float, int]]] = {
            "anthropic": [],
            "openai": [],
            "google": [],
            "deepseek": []
        }
    
    async def execute_with_rate_limit_handling(
        self,
        provider: str,
        request_func: Callable,
        *args,
        **kwargs
    ) -> Any:
        """
        Führe Request aus mit automatischer Rate-Limit-Handhabung
        """
        config = self.limits[provider]
        last_error = None
        
        for attempt in range(config.max_retries):
            # Prüfe aktuelle Rate-Limit-Situation
            if self._is_rate_limited(provider):
                wait_time = self._calculate_wait_time(provider)
                print(f"⏳ Rate-Limit für {provider}: Warte {wait_time:.1f}s...")
                await asyncio.sleep(wait_time)
            
            try:
                # Request ausführen
                result = await request_func(*args, **kwargs)
                
                # Erfolg: Tracking aktualisieren
                self._record_success(provider, result.get("token_count", 0))
                return result
                
            except aiohttp.ClientResponseError as e:
                if e.status == 429:  # Rate Limit
                    last_error = e
                    wait_time = self._calculate_backoff_with_jitter(
                        attempt=attempt,
                        base_cooldown=config.base_cooldown_seconds
                    )
                    print(f"⚠️ 429 Rate-Limit von {provider}: Retry in {wait_time:.1f}s")
                    self._record_failure(provider)
                    await asyncio.sleep(wait_time)
                else:
                    raise
                    
            except asyncio.TimeoutError:
                last_error = "Timeout"
                wait_time = 2 ** attempt
                print(f"⏱️ Timeout bei {provider}: Retry in {wait_time}s")
                await asyncio.sleep(wait_time)
        
        raise RateLimitExhaustedError(
            f"Max retries ({config.max_retries}) für {provider} erreicht. "
            f"Letzter Fehler: {last_error}"
        )
    
    def _is_rate_limited(self, provider: str) -> bool:
        """Prüfe ob Provider aktuell rate-limited ist"""
        now = time.time()
        cutoff = now - 60  # Letzte Minute
        
        # Request-Anzahl prüfen
        recent_requests = [t for t in self.request_timestamps[provider] if t > cutoff]
        config = self.limits[provider]
        
        if len(recent_requests) >= config.requests_per_minute:
            return True
        
        # Token-Verbrauch prüfen
        recent_tokens = [
            tokens for timestamp, tokens in self.token_usage[provider]
            if timestamp > cutoff
        ]
        total_tokens = sum(recent_tokens)
        
        if total_tokens >= config.tokens_per_minute:
            return True
        
        return False
    
    def _calculate_wait_time(self, provider: str) -> float:
        """Berechne Wartezeit bis Rate-Limit zurückgesetzt"""
        now = time.time()
        cutoff = now - 60
        
        # Zeit bis ältester Request aus Ring-Buffer fällt
        recent = [t for t in self.request_timestamps[provider] if t > cutoff]
        
        if not recent:
            return 0
        
        oldest = min(recent)
        return max(0, 65 - (now - oldest))  # 60s + 5s Buffer
    
    def _calculate_backoff_with_jitter(
        self,
        attempt: int,
        base_cooldown: int
    ) -> float:
        """
        Berechne Backoff mit exponentiellem Wachstum und Jitter
        Formel: base * 2^attempt + random(0, base/2)
        """
        exponential = base_cooldown * (2 ** attempt)
        jitter = random.uniform(0, base_cooldown / 2)
        return min(exponential + jitter, 300)  # Max 5 Minuten
    
    def _record_success(self, provider: str, token_count: int):
        """Erfolgreichen Request verzeichnen"""
        now = time.time()
        self.request_timestamps[provider].append(now)
        self.token_usage[provider].append((now, token_count))
        
        # Cleanup alter Einträge
        self._cleanup_old_entries(provider)
    
    def _record_failure(self, provider: str):
        """Fehlgeschlagenen Request verzeichnen (für Monitoring)"""
        # Nur Timestamp notieren, kein Token-Verbrauch
        self.request_timestamps[provider].append(time.time())
        self._cleanup_old_entries(provider)
    
    def _cleanup_old_entries(self, provider: str):
        """Entferne Einträge älter als 2 Minuten"""
        cutoff = time.time() - 120
        self.request_timestamps[provider] = [
            t for t in self.request_timestamps[provider] if t > cutoff
        ]
        self.token_usage[provider] = [
            (t, tokens) for t, tokens in self.token_usage[provider] if t > cutoff
        ]

class RateLimitExhaustedError(Exception):
    pass

============================================================
INTEGRATION MIT HOLYSHEEP
============================================================

async def holy_sheep_request_with_rl_handling(
    prompt: str,
    provider: str = "anthropic",
    model: str = "claude-sonnet-4-5"
):
    """Beispiel: HolySheep Request mit Rate-Limit-Handling"""
    
    handler = RateLimitHandler()
    api_key = "YOUR_HOLYSHEEP_API_KEY"
    
    async def make_request():
        async with aiohttp.ClientSession() as session:
            headers = {
                "Authorization": f"Bearer {api_key}",
                "Content-Type": "application/json",
                "X-Provider": provider
            }
            payload = {
                "model": model,
                "messages": [{"role": "user", "content": prompt}]
            }
            
            async with session.post(
                "https://api.holysheep.ai/v1/chat/completions",
                headers=headers,
                json=payload,
                timeout=aiohttp.ClientTimeout(total=30)
            ) as response:
                data = await response.json()
                return {"token_count": data.get("usage", {}).get("total_tokens", 0)}
    
    # Mit automatischem Rate-Limit-Handling
    return await handler.execute_with_rate_limit_handling(provider, make_request)

Monitoring und Alerting: Health-Dashboard

#!/usr/bin/env python3
"""
Health-Monitoring Dashboard für HolySheep Failover
Tracking: Latenz, Fehlerrate, Kosten, Verfügbarkeit
"""

import time
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from typing import Dict, List, Optional
from collections import defaultdict
import json

@dataclass
class HealthMetrics:
    """Metriken für einen Provider"""
    provider: str
    total_requests: int = 0
    successful_requests: int = 0
    failed_requests: int = 0
    rate_limit_hits: int = 0
    timeouts: int = 0
    latencies: List[float] = field(default_factory=list)
    costs_usd: float = 0.0
    uptime_start: float = field(default_factory=time.time)
    
    @property
    def success_rate(self) -> float:
        if self.total_requests == 0:
            return 0.0
        return (self.successful_requests / self.total_requests) * 100
    
    @property
    def avg_latency_ms(self) -> float:
        if not self.latencies:
            return 0.0
        return sum(self.latencies) / len(self.latencies)
    
    @property
    def p95_latency_ms(self) -> float:
        if not self.latencies:
            return 0.0
        sorted_latencies = sorted(self.latencies)
        index = int(len(sorted_latencies) * 0.95)
        return sorted_latencies[index]
    
    @property
    def uptime_percent(self) -> float:
        """Verfügbarkeit in den letzten 5 Minuten"""
        window = 300  # 5 Minuten
        elapsed = time.time() - self.uptime_start
        if elapsed > window:
            healthy = self.successful_requests + self.rate_limit_hits
            total = healthy + self.failed_requests + self.timeouts
            return (healthy / total * 100) if total > 0 else 0
        return 100.0

class HolySheepHealthMonitor:
    """
    Echtzeit-Monitoring für HolySheep AI Failover-System
    """
    
    def __init__(self):
        self.metrics: Dict[str, HealthMetrics] = {}
        self.alerts: List[Dict] = []
        self.alert_thresholds = {
            "success_rate_min": 95.0,      # %
            "latency_p95_max": 500.0,       # ms
            "error_rate_max": 5.0,          # %
            "cost_per_hour_max": 100.0      # USD
        }
        
        for provider in ["anthropic", "openai", "google", "deepseek"]:
            self.metrics[provider] = HealthMetrics(provider=provider)
    
    def record_request(
        self,
        provider: str,
        success: bool,
        latency_ms: float,
        cost_usd: float = 0.0,
        error_type: Optional[str] = None
    ):
        """Record einen API-Request für Monitoring"""
        metrics = self.metrics[provider]
        metrics.total_requests += 1
        metrics.latencies.append(latency_ms)
        metrics.costs_usd += cost_usd
        
        if success:
            metrics.successful_requests += 1
        else:
            metrics.failed_requests += 1
            if error_type == "rate_limit":
                metrics.rate_limit_hits += 1
            elif error_type == "timeout":
                metrics.timeouts += 1
        
        # Latenzen auf letzte 1000 beschränken
        if len(metrics.latencies) > 1000:
            metrics.latencies = metrics.latencies[-1000:]
        
        # Alert-Check
        self._check_alerts(provider)
    
    def _check_alerts(self, provider: str):
        """Prüfe Alert-Bedingungen"""
        metrics = self.metrics[provider]
        
        alerts = []
        
        # Success Rate Alert
        if metrics.success_rate < self.alert_thresholds["success_rate_min"]:
            alerts.append({
                "severity": "warning" if metrics.success_rate > 80 else "critical",
                "message": f"Success Rate für {provider}: {metrics.success_rate:.1f}%",
                "value": metrics.success_rate
            })
        
        # Latency Alert
        if metrics.p95_latency_ms > self.alert_thresholds["latency_p95_max"]:
            alerts.append({
                "severity": "warning",
                "message": f"P95 Latenz für {provider}: {metrics.p95_latency_ms:.0f}ms",
                "value": metrics.p95_latency_ms
            })
        
        # Error Rate Alert
        if metrics.total_requests > 10:
            error_rate = (metrics.failed_requests / metrics.total_requests) * 100
            if error_rate > self.alert_thresholds["error_rate_max"]:
                alerts.append({
                    "severity": "critical",
                    "message": f"Fehlerrate für {provider}: {error_rate:.1f}%",
                    "value": error_rate
                })
        
        for alert in alerts:
            self.alerts.append({
                "timestamp": datetime.now().isoformat(),
                "provider": provider,
                **alert
            })
    
    def get_dashboard_data(self) -> Dict:
        """Generiere Dashboard-Daten für Monitoring-UI"""
        provider_stats = {}
        
        for provider, metrics in self.metrics.items():
            provider_stats[provider] = {
                "success_rate": f"{metrics.success_rate:.2f}%",
                "avg_latency": f"{metrics.avg_latency_ms:.0f}ms",
                "p95_latency": f"{metrics.p95_latency_ms:.0f}ms",
                "uptime": f"{metrics.uptime_percent:.2f}%",
                "total_requests": metrics.total_requests,
                "failed_requests": metrics.failed_requests,
                "rate_limits": metrics.rate_limit_hits,
                "cost_usd": f"${metrics.costs_usd:.4f}",
                "status": self._get_status_indicator(metrics)
            }
        
        return {
            "timestamp": datetime.now().isoformat(),
            "providers": provider_stats,
            "recent_alerts": self.alerts[-10:],
            "summary": {
                "total_requests": sum(m.total_requests for m in self.metrics.values()),
                "total_cost": sum(m.costs_usd for m in self.metrics.values()),
                "overall_success_rate": self._calculate_overall_success_rate()
            }
        }
    
    def _get_status_indicator(self, metrics: HealthMetrics) -> str:
        """Bestimme Status-Indikator"""
        if metrics.success_rate >= 99:
            return "🟢 Excellent"
        elif metrics.success_rate >= 95:
            return "🟡 Good"
        elif metrics.success_rate >= 80:
            return "🟠 Degraded"
        else:
            return "🔴 Critical"
    
    def _calculate_overall_success_rate(self) -> float:
        total = sum(m.total_requests for m in self.metrics.values())
        successful = sum(m.successful_requests for m in self.metrics.values())
        return (successful / total * 100) if total > 0 else 0
    
    def export_metrics_json(self, filepath: str = "holysheep_metrics.json"):
        """Exportiere Metriken als JSON für externes Monitoring"""
        with open(filepath, "w") as f:
            json.dump(self.get_dashboard_data(), f, indent=2)
        print(f"✅ Metriken exportiert: {filepath}")

============================================================
NUTZUNGSBEISPIEL
============================================================

def demo_monitoring():
    """Demonstriere Monitoring-Funktionalität"""
    monitor = HolySheepHealthMonitor()
    
    # Simuliere Requests
    test_requests = [
        ("anthropic", True, 150, 0.015, None),
        ("anthropic", True, 180, 0.018, None),
        ("anthropic", False, 3000, 0, "timeout"),
        ("openai", True, 120, 0.008, None),
        ("google", True, 80, 0.0025, None),
        ("deepseek", True, 200, 0.00084, None),
    ]
    
    for provider, success, latency, cost, error in test_requests:
        monitor.record_request(provider, success, latency, cost, error)
    
    # Dashboard anzeigen
    dashboard = monitor.get_dashboard_data()
    print(json.dumps(dashboard, indent=2))
    
    # Metriken exportieren
    monitor.export_metrics_json()

if __name__ == "__main__":
    demo_monitoring()

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