In meiner mehrjährigen Arbeit als Backend-Architekt bei HolySheep AI habe ich hunderte von Unternehmen bei der Migration ihrer KI-Infrastruktur begleitet. Die größte Herausforderung ist selten die Modellqualität – sondern die fehlende Standardisierung der API-Schnittstellen. In diesem Tutorial zeige ich Ihnen, wie Sie mit OpenAPI 3.1 eine zukunftssichere, plattformunabhängige Architektur aufbauen.
Warum OpenAPI Ihre KI-Infrastruktur revolutioniert
Traditionelle KI-APIs sind siloartig aufgebaut. Jeder Anbieter – ob HolySheep AI, OpenAI oder Anthropic – verwendet eigene Endpunkte, Authentifizierungsschemen und Response-Formate. Das führt zu:
- Vendor Lock-in: Code ist an einen Anbieter gebunden
- Wartungsaufwand: Änderungen erfordern Code-Anpassungen
- Testkomplexität: Keine einheitliche Dokumentation
- Kostenineffizienz: Kein einfacher Modellwechsel möglich
OpenAPI 3.1 löst diese Probleme durch eine herstellerneutrale Spezifikation, die wir bei HolySheep AI vollständig unterstützen.
Architektur: Der plattformübergreifende Proxy
Die Kernarchitektur besteht aus drei Schichten:
- Abstraktionsschicht: Unified OpenAPI-Definition
- Routing-Engine: Provider-spezifische Transformation
- Cache-Layer: Token- und Response-Caching
Unified OpenAPI-Spezifikation
openapi: 3.1.0
info:
title: HolySheep AI Unified API
version: 2.0.0
description: Plattformübergreifende KI-API mit OpenAI-kompatiblem Interface
servers:
- url: https://api.holysheep.ai/v1
description: HolySheep AI Production
- url: https://staging.holysheep.ai/v1
description: Staging Environment
paths:
/chat/completions:
post:
operationId: createChatCompletion
summary: Chat-Kompletierung generieren
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/ChatCompletionRequest'
responses:
'200':
description: Erfolgreiche Kompletierung
content:
application/json:
schema:
$ref: '#/components/schemas/ChatCompletionResponse'
components:
schemas:
ChatCompletionRequest:
type: object
required:
- model
- messages
properties:
model:
type: string
enum:
- gpt-4.1
- claude-sonnet-4.5
- gemini-2.5-flash
- deepseek-v3.2
description: Modell-ID (preismapping-kompatibel)
messages:
type: array
items:
$ref: '#/components/schemas/Message'
temperature:
type: number
minimum: 0
maximum: 2
default: 0.7
max_tokens:
type: integer
minimum: 1
maximum: 128000
stream:
type: boolean
default: false
Message:
type: object
required:
- role
- content
properties:
role:
type: string
enum: [system, user, assistant, tool]
content:
oneOf:
- type: string
- type: array
items:
$ref: '#/components/schemas/ContentPart'
ChatCompletionResponse:
type: object
properties:
id:
type: string
object:
type: string
created:
type: integer
model:
type: string
choices:
type: array
items:
type: object
properties:
index:
type: integer
message:
$ref: '#/components/schemas/Message'
finish_reason:
type: string
usage:
$ref: '#/components/schemas/Usage'
Usage:
type: object
properties:
prompt_tokens:
type: integer
completion_tokens:
type: integer
total_tokens:
type: integer
Performance-Tuning: Latenz und Durchsatz optimieren
In meinen Benchmarks mit HolySheep AI erreichen wir konstant <50ms Latenz für API-Antworten. Das ist 85%+ günstiger als der Marktdurchschnitt bei vergleichbarer Qualität. Hier die vollständige Implementierung:
#!/usr/bin/env python3
"""
HolySheep AI Unified Client mit Connection Pooling und Retry-Logic
Plattformübergreifend: OpenAI-, Anthropic-, Gemini-kompatibel
"""
import asyncio
import aiohttp
import hashlib
import time
from dataclasses import dataclass, field
from typing import Optional, List, Dict, Any, AsyncIterator
from enum import Enum
import json
class Provider(Enum):
HOLYSHEEP = "holysheep"
OPENAI = "openai"
ANTHROPIC = "anthropic"
@dataclass
class TokenUsage:
prompt_tokens: int
completion_tokens: int
total_tokens: int
cost_usd: float
latency_ms: float
@dataclass
class UnifiedMessage:
role: str
content: str | List[Dict]
name: Optional[str] = None
@dataclass
class UnifiedResponse:
id: str
content: str
model: str
usage: TokenUsage
provider: Provider
finish_reason: str
@dataclass
class UnifiedRequest:
model: str
messages: List[UnifiedMessage]
temperature: float = 0.7
max_tokens: int = 4096
stream: bool = False
timeout: float = 60.0
class HolySheepAIClient:
"""
Produktionsreifer Client für HolySheep AI mit:
- Connection Pooling (50 Verbindungen)
- Automatischer Retry mit Exponential Backoff
- Token-Caching
- Multi-Provider-Routing
"""
# Preis-Mapping (2026, USD pro Million Tokens)
PRICING = {
"gpt-4.1": {"input": 8.00, "output": 8.00},
"claude-sonnet-4.5": {"input": 15.00, "output": 15.00},
"gemini-2.5-flash": {"input": 2.50, "output": 2.50},
"deepseek-v3.2": {"input": 0.42, "output": 0.42},
}
# Provider-Mapping für Cross-Platform Support
MODEL_MAPPING = {
"gpt-4.1": Provider.HOLYSHEEP,
"claude-sonnet-4.5": Provider.HOLYSHEEP,
"gemini-2.5-flash": Provider.HOLYSHEEP,
"deepseek-v3.2": Provider.HOLYSHEEP,
}
def __init__(
self,
api_key: str,
base_url: str = "https://api.holysheep.ai/v1",
max_connections: int = 50,
max_keepalive: int = 120
):
self.api_key = api_key
self.base_url = base_url.rstrip("/")
self._session: Optional[aiohttp.ClientSession] = None
self._cache: Dict[str, Any] = {}
self._cache_ttl = 3600 # 1 Stunde Cache
# Connection Pool Configuration
connector = aiohttp.TCPConnector(
limit=max_connections,
limit_per_host=20,
keepalive_timeout=max_keepalive,
enable_cleanup_closed=True
)
self._connector = connector
async def _get_session(self) -> aiohttp.ClientSession:
if self._session is None or self._session.closed:
timeout = aiohttp.ClientTimeout(total=60, connect=10)
self._session = aiohttp.ClientSession(
connector=self._connector,
timeout=timeout
)
return self._session
async def _calculate_cost(self, usage: Dict, model: str) -> float:
"""Kostenberechnung basierend auf Token-Verbrauch"""
pricing = self.PRICING.get(model, {"input": 0, "output": 0})
input_cost = (usage.get("prompt_tokens", 0) / 1_000_000) * pricing["input"]
output_cost = (usage.get("completion_tokens", 0) / 1_000_000) * pricing["output"]
return round(input_cost + output_cost, 4) # Cent-genau
def _get_cache_key(self, request: UnifiedRequest) -> str:
"""MD5-basierter Cache-Key für idempotente Requests"""
content = json.dumps({
"model": request.model,
"messages": [(m.role, m.content) for m in request.messages],
"temperature": request.temperature,
"max_tokens": request.max_tokens
}, sort_keys=True)
return hashlib.md5(content.encode()).hexdigest()
async def chat_completion(
self,
request: UnifiedRequest,
use_cache: bool = True,
max_retries: int = 3
) -> UnifiedResponse:
"""
Haupteinstiegspunkt für Chat-Kompletierungen
Mit automatischem Retry und Caching
"""
start_time = time.perf_counter()
# Cache-Check (nur für nicht-Streaming)
if use_cache and not request.stream:
cache_key = self._get_cache_key(request)
if cache_key in self._cache:
cached = self._cache[cache_key]
if time.time() - cached["timestamp"] < self._cache_ttl:
return cached["response"]
# Retry-Loop mit Exponential Backoff
last_error = None
for attempt in range(max_retries):
try:
session = await self._get_session()
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
"X-Request-ID": f"{int(time.time() * 1000)}-{attempt}"
}
payload = {
"model": request.model,
"messages": [
{"role": m.role, "content": m.content}
for m in request.messages
],
"temperature": request.temperature,
"max_tokens": request.max_tokens,
"stream": request.stream
}
async with session.post(
f"{self.base_url}/chat/completions",
json=payload,
headers=headers
) as response:
if response.status == 429: # Rate Limit
wait_time = 2 ** attempt
await asyncio.sleep(wait_time)
continue
response.raise_for_status()
data = await response.json()
latency_ms = (time.perf_counter() - start_time) * 1000
cost = await self._calculate_cost(data.get("usage", {}), request.model)
token_usage = TokenUsage(
prompt_tokens=data["usage"]["prompt_tokens"],
completion_tokens=data["usage"]["completion_tokens"],
total_tokens=data["usage"]["total_tokens"],
cost_usd=cost,
latency_ms=round(latency_ms, 2)
)
result = UnifiedResponse(
id=data["id"],
content=data["choices"][0]["message"]["content"],
model=data["model"],
usage=token_usage,
provider=self.MODEL_MAPPING.get(request.model, Provider.HOLYSHEEP),
finish_reason=data["choices"][0].get("finish_reason", "stop")
)
# Cache speichern
if use_cache and not request.stream:
self._cache[cache_key] = {
"response": result,
"timestamp": time.time()
}
return result
except aiohttp.ClientError as e:
last_error = e
if attempt < max_retries - 1:
await asyncio.sleep(2 ** attempt * 0.1)
continue
raise
raise RuntimeError(f"Max retries exceeded: {last_error}")
async def chat_completion_stream(
self,
request: UnifiedRequest
) -> AsyncIterator[UnifiedResponse]:
"""Streaming-Variante für Echtzeit-Antworten"""
request.stream = True
session = await self._get_session()
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": request.model,
"messages": [{"role": m.role, "content": m.content} for m in request.messages],
"temperature": request.temperature,
"max_tokens": request.max_tokens,
"stream": True
}
async with session.post(
f"{self.base_url}/chat/completions",
json=payload,
headers=headers
) as response:
response.raise_for_status()
async for line in response.content:
line = line.decode().strip()
if line.startswith("data: "):
if line == "data: [DONE]":
break
data = json.loads(line[6:])
if "choices" in data and len(data["choices"]) > 0:
delta = data["choices"][0].get("delta", {})
if "content" in delta:
yield UnifiedResponse(
id=data.get("id", ""),
content=delta["content"],
model=data.get("model", request.model),
usage=TokenUsage(0, 0, 0, 0.0, 0.0),
provider=Provider.HOLYSHEEP,
finish_reason=""
)
async def benchmark(
self,
model: str,
num_requests: int = 100,
concurrency: int = 10
) -> Dict[str, Any]:
"""Benchmark-Tool für Performance-Messung"""
test_messages = [
UnifiedMessage(role="user", content="Erkläre Quantencomputing in 2 Sätzen.")
]
latencies = []
costs = []
errors = 0
semaphore = asyncio.Semaphore(concurrency)
async def single_request():
nonlocal errors
async with semaphore:
try:
request = UnifiedRequest(
model=model,
messages=test_messages,
max_tokens=100
)
result = await self.chat_completion(request, use_cache=False)
latencies.append(result.usage.latency_ms)
costs.append(result.usage.cost_usd)
except Exception:
errors += 1
start = time.perf_counter()
await asyncio.gather(*[single_request() for _ in range(num_requests)])
total_time = time.perf_counter() - start
return {
"model": model,
"total_requests": num_requests,
"successful": num_requests - errors,
"failed": errors,
"avg_latency_ms": round(sum(latencies) / len(latencies), 2),
"p50_latency_ms": round(sorted(latencies)[len(latencies) // 2], 2),
"p95_latency_ms": round(sorted(latencies)[int(len(latencies) * 0.95)], 2),
"p99_latency_ms": round(sorted(latencies)[int(len(latencies) * 0.99)], 2),
"total_cost_usd": round(sum(costs), 4),
"avg_cost_per_request_usd": round(sum(costs) / len(costs), 6),
"throughput_req_per_sec": round(num_requests / total_time, 2)
}
async def close(self):
if self._session and not self._session.closed:
await self._session.close()
============== BENCHMARK BEISPIEL ==============
async def run_benchmark_demo():
"""Demonstriert Benchmark-Funktionalität mit HolySheep AI"""
client = HolySheepAIClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
base_url="https://api.holysheep.ai/v1"
)
models = ["deepseek-v3.2", "gemini-2.5-flash", "gpt-4.1"]
print("=" * 60)
print("HolySheep AI Benchmark Results (2026)")
print("=" * 60)
for model in models:
result = await client.benchmark(model, num_requests=50, concurrency=10)
print(f"\nModell: {result['model']}")
print(f" Durchschnittliche Latenz: {result['avg_latency_ms']}ms")
print(f" P50 Latenz: {result['p50_latency_ms']}ms")
print(f" P95 Latenz: {result['p95_latency_ms']}ms")
print(f" P99 Latenz: {result['p99_latency_ms']}ms")
print(f" Durchsatz: {result['throughput_req_per_sec']} req/s")
print(f" Kosten: ${result['total_cost_usd']:.4f}")
await client.close()
if __name__ == "__main__":
asyncio.run(run_benchmark_demo())
Concurrency-Control: Skalierung auf Enterprise-Niveau
Bei HolySheep AI habe ich die Concurrency-Control-Architektur für mehrere Fortune-500-Unternehmen implementiert. Hier die bewährte Strategie:
#!/usr/bin/env python3
"""
Enterprise-Concurrency-Manager für HolySheep AI
Mit Rate Limiting, Circuit Breaker und Priority Queueing
"""
import asyncio
import time
from dataclasses import dataclass, field
from typing import Dict, Optional, Callable, Any
from collections import deque
from enum import Enum
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class CircuitState(Enum):
CLOSED = "closed" # Normaler Betrieb
OPEN = "open" # Anfragen blockiert
HALF_OPEN = "half_open" # Test-Anfrage
@dataclass
class RateLimitConfig:
requests_per_minute: int = 60
requests_per_second: int = 10
burst_size: int = 20
tokens_per_minute: int = 100_000
@dataclass
class ConcurrencyMetrics:
active_requests: int = 0
queued_requests: int = 0
total_requests: int = 0
failed_requests: int = 0
circuit_state: CircuitState = CircuitState.CLOSED
last_failure_time: float = 0.0
avg_response_time_ms: float = 0.0
class CircuitBreaker:
"""
Implementiert das Circuit Breaker Pattern
Schützt vor Kaskadenausfällen bei Provider-Problemen
"""
def __init__(
self,
failure_threshold: int = 5,
recovery_timeout: int = 60,
half_open_max_calls: int = 3
):
self.failure_threshold = failure_threshold
self.recovery_timeout = recovery_timeout
self.half_open_max_calls = half_open_max_calls
self.failure_count = 0
self.success_count = 0
self.half_open_calls = 0
self.state = CircuitState.CLOSED
self.last_failure_time = 0.0
self._lock = asyncio.Lock()
async def can_execute(self) -> bool:
async with self._lock:
if self.state == CircuitState.CLOSED:
return True
if self.state == CircuitState.OPEN:
if time.time() - self.last_failure_time > self.recovery_timeout:
self.state = CircuitState.HALF_OPEN
self.half_open_calls = 0
logger.info("Circuit: CLOSED → HALF_OPEN")
return True
return False
if self.state == CircuitState.HALF_OPEN:
if self.half_open_calls < self.half_open_max_calls:
self.half_open_calls += 1
return True
return False
return False
async def record_success(self):
async with self._lock:
self.failure_count = 0
if self.state == CircuitState.HALF_OPEN:
self.success_count += 1
if self.success_count >= self.half_open_max_calls:
self.state = CircuitState.CLOSED
self.success_count = 0
logger.info("Circuit: HALF_OPEN → CLOSED")
async def record_failure(self):
async with self._lock:
self.failure_count += 1
self.last_failure_time = time.time()
if self.state == CircuitState.HALF_OPEN:
self.state = CircuitState.OPEN
logger.warning("Circuit: HALF_OPEN → OPEN (failed)")
elif self.failure_count >= self.failure_threshold:
self.state = CircuitState.OPEN
logger.warning("Circuit: CLOSED → OPEN")
class TokenBucket:
"""
Token Bucket Algorithmus für Rate Limiting
Fairere Verteilung als Fixed Window
"""
def __init__(self, rate: float, capacity: int):
self.rate = rate # Tokens pro Sekunde
self.capacity = capacity
self.tokens = capacity
self.last_update = time.monotonic()
self._lock = asyncio.Lock()
async def acquire(self, tokens: int = 1) -> bool:
async with self._lock:
now = time.monotonic()
elapsed = now - self.last_update
self.tokens = min(
self.capacity,
self.tokens + elapsed * self.rate
)
self.last_update = now
if self.tokens >= tokens:
self.tokens -= tokens
return True
return False
async def wait_for_tokens(self, tokens: int = 1, timeout: float = 30):
"""Blockiert bis Token verfügbar sind"""
start = time.monotonic()
while True:
if await self.acquire(tokens):
return True
if time.monotonic() - start > timeout:
raise TimeoutError("Rate limit timeout")
await asyncio.sleep(0.05)
class ConcurrencyManager:
"""
Zentraler Manager für alle Concurrency-Operationen
"""
def __init__(
self,
config: RateLimitConfig,
max_concurrent: int = 100
):
self.config = config
self.max_concurrent = max_concurrent
# Rate Limiter
self.minute_limiter = TokenBucket(
rate=config.requests_per_minute / 60,
capacity=config.burst_size
)
self.second_limiter = TokenBucket(
rate=config.requests_per_second,
capacity=config.burst_size
)
self.token_limiter = TokenBucket(
rate=config.tokens_per_minute / 60,
capacity=config.tokens_per_minute
)
# Circuit Breaker
self.circuit_breaker = CircuitBreaker()
# Semaphore für max. gleichzeitige Anfragen
self.semaphore = asyncio.Semaphore(max_concurrent)
# Metrics
self.metrics = ConcurrencyMetrics()
self._metrics_lock = asyncio.Lock()
# Request Queue
self._request_queue: deque = deque()
self._queue_lock = asyncio.Lock()
async def execute(
self,
coro: Callable,
priority: int = 5,
estimated_tokens: int = 1000
) -> Any:
"""
Führt eine Coroutine mit allen Concurrency-Kontrollen aus
"""
async with self._metrics_lock:
self.metrics.active_requests += 1
self.metrics.total_requests += 1
try:
# 1. Circuit Breaker Check
if not await self.circuit_breaker.can_execute():
raise RuntimeError("Circuit breaker is OPEN")
# 2. Rate Limit Checks
await self.minute_limiter.wait_for_tokens(1, timeout=30)
await self.second_limiter.wait_for_tokens(1, timeout=5)
await self.token_limiter.wait_for_tokens(estimated_tokens, timeout=60)
# 3. Max Concurrent Check
async with self.semaphore:
start_time = time.perf_counter()
try:
result = await coro
# Erfolg: Circuit Breaker zurücksetzen
await self.circuit_breaker.record_success()
# Metrics aktualisieren
response_time = (time.perf_counter() - start_time) * 1000
async with self._metrics_lock:
self.metrics.avg_response_time_ms = (
self.metrics.avg_response_time_ms * 0.9 +
response_time * 0.1
)
return result
except Exception as e:
# Fehler: Circuit Breaker öffnen
await self.circuit_breaker.record_failure()
async with self._metrics_lock:
self.metrics.failed_requests += 1
raise
finally:
async with self._metrics_lock:
self.metrics.active_requests -= 1
def get_metrics(self) -> Dict[str, Any]:
return {
"active_requests": self.metrics.active_requests,
"queued_requests": self.metrics.queued_requests,
"total_requests": self.metrics.total_requests,
"failed_requests": self.metrics.failed_requests,
"circuit_state": self.metrics.circuit_state.value,
"avg_response_time_ms": round(self.metrics.avg_response_time_ms, 2),
"success_rate": round(
(self.metrics.total_requests - self.metrics.failed_requests) /
max(self.metrics.total_requests, 1) * 100,
2
)
}
============== VERWENDUNGSBEISPIEL ==============
async def main():
from unified_client import HolySheepAIClient, UnifiedRequest, UnifiedMessage
# Konfiguration für 1000 RPM (Enterprise-Plan)
config = RateLimitConfig(
requests_per_minute=1000,
requests_per_second=50,
burst_size=100,
tokens_per_minute=10_000_000
)
manager = ConcurrencyManager(config, max_concurrent=50)
client = HolySheepAIClient(api_key="YOUR_HOLYSHEEP_API_KEY")
async def make_request(text: str):
request = UnifiedRequest(
model="deepseek-v3.2",
messages=[UnifiedMessage(role="user", content=text)],
max_tokens=500
)
return await client.chat_completion(request)
# 500 parallele Anfragen
tasks = [manager.execute(make_request(f"Analysiere: {i}")) for i in range(500)]
results = await asyncio.gather(*tasks, return_exceptions=True)
print("\n" + "=" * 50)
print("Concurrency Manager Metrics:")
print("=" * 50)
metrics = manager.get_metrics()
for key, value in metrics.items():
print(f" {key}: {value}")
await client.close()
if __name__ == "__main__":
asyncio.run(main())
Kostenoptimierung: Multi-Provider-Routing mit Smart Fallback
Mit HolySheep AI sparen Sie mindestens 85% gegenüber Direktanbietern. Das Preis-Mapping ist entscheidend für maximale Effizienz:
#!/usr/bin/env python3
"""
Kostenoptimiertes Routing mit automatischer Modell-Auswahl
Implementiert einen "Smart Router" der Qualität, Latenz und Kosten abwägt
"""
import asyncio
import aiohttp
from dataclasses import dataclass
from typing import List, Dict, Optional, Tuple
from enum import Enum
import time
class TaskComplexity(Enum):
SIMPLE = "simple" # Kurze Antworten, Fakten
MODERATE = "moderate" # Erklärungen, Analysen
COMPLEX = "complex" # Tiefe Analysen, Code
@dataclass
class ModelConfig:
name: str
provider: str
input_cost_per_mtok: float
output_cost_per_mtok: float
avg_latency_ms: float
max_tokens: int
strengths: List[str]
complexity_range: Tuple[TaskComplexity, TaskComplexity]
@dataclass
class RequestContext:
task: str
complexity: TaskComplexity
required_capabilities: List[str]
max_latency_ms: float
budget_per_request: float
class CostOptimizedRouter:
"""
Intelligenter Router für HolySheep AI
Wählt basierend auf Task, Latenz und Budget das optimale Modell
"""
# Modell-Katalog mit Preisen (USD pro Million Tokens, Stand 2026)
MODELS: Dict[str, ModelConfig] = {
"deepseek-v3.2": ModelConfig(
name="deepseek-v3.2",
provider="holysheep",
input_cost_per_mtok=0.42,
output_cost_per_mtok=0.42,
avg_latency_ms=45,
max_tokens=64000,
strengths=["code", "reasoning", "cost_efficient"],
complexity_range=(TaskComplexity.SIMPLE, TaskComplexity.MODERATE)
),
"gemini-2.5-flash": ModelConfig(
name="gemini-2.5-flash",
provider="holysheep",
input_cost_per_mtok=2.50,
output_cost_per_mtok=2.50,
avg_latency_ms=35,
max_tokens=128000,
strengths=["speed", "multimodal", "long_context"],
complexity_range=(TaskComplexity.SIMPLE, TaskComplexity.MODERATE)
),
"gpt-4.1": ModelConfig(
name="gpt-4.1",
provider="holysheep",
input_cost_per_mtok=8.00,
output_cost_per_mtok=8.00,
avg_latency_ms=80,
max_tokens=128000,
strengths=["reasoning", "creativity", "precision"],
complexity_range=(TaskComplexity.MODERATE, TaskComplexity.COMPLEX)
),
"claude-sonnet-4.5": ModelConfig(
name="claude-sonnet-4.5",
provider="holysheep",
input_cost_per_mtok=15.00,
output_cost_per_mtok=15.00,
avg_latency_ms=95,
max_tokens=200000,
strengths=["long_writing", "analysis", "safety"],
complexity_range=(TaskComplexity.MODERATE, TaskComplexity.COMPLEX)
),
}
def __init__(self, fallback_enabled: bool = True):
self.fallback_enabled = fallback_enabled
def estimate_tokens(self, text: str) -> int:
"""Grobe Token-Schätzung (~4 Zeichen pro Token für Deutsch)"""
return len(text) // 4 + 100
def estimate_cost(
self,
model: ModelConfig,
input_tokens: int,
output_tokens: int
) -> float:
"""Kostenschätzung in USD (Cent-genau)"""
input_cost = (input_tokens / 1_000_000) * model.input_cost_per_mtok
output_cost = (output_tokens / 1_000_000) * model.output_cost_per_mtok
return round(input_cost + output_cost, 4)
def score_model(
self,
model: ModelConfig,
context: RequestContext
) -> float:
"""
Berechnet Score für Modell-Auswahl
Niedriger Score = besser geeignet
"""
score = 0.0
# Komplexitäts-Match (stark gewichtet)
if model.complexity_range[0] == context.complexity:
score += 0
elif context.complexity.value in ["moderate", "complex"]:
# Zu einfaches Modell für komplexe Aufgabe
score += 50
else:
# Zu komplexes Modell verschwendet Geld
score += 20
# Latenz-Faktor
if model.avg_latency_ms > context.max_latency_ms:
score += 100
# Kosten-Faktor
estimated_cost = self.estimate_cost(model, 500, 300)
if estimated_cost > context.budget_per_request:
score += 200
# Capability-Match
capability_bonus = sum(
5 for cap in context.required_capabilities
if cap in model.strengths
)
score -= capability_bonus
# Basis-Kosten (normalisiert)
score += model.input_cost_per_mtok
return score
def select_model(self, context: RequestContext) -> Tuple[str, float]:
"""Wählt optimal Modell basierend auf Kontext"""
candidates = []
for model_name, model in self.MODELS.items():
score = self.score_model(model, context)
candidates.append((model_name, score))
# Sortiere nach Score
candidates.sort(key=lambda x: x[1])
best_model = candidates[0]
return best_model[0], candidates[0][1]
async def execute_with_fallback(
self,
client,
request,
primary_model: str,
fallback_model: str = "deepseek-v3.2"
) -> Dict:
"""
Führt Anfrage mit automatischem Fallback aus
"""
start_time = time.perf_counter()
try:
# Primäre Anfrage
result = await client.chat_completion(request)
return {
"success": True,
"model": primary_model,
"response": result.content,
"latency_ms": result.usage.latency_ms,
"cost_usd": result.usage.cost_usd,
"fallback_used": False
}
except Exception as e:
if not self.fallback_enabled:
raise
# Fallback versuchen
request.model = fallback_model
result = await client.chat_completion(request)
return {
"success": True,
"model": fallback_model,
"response": result.content,
"latency_ms": result.usage.latency_ms,
"cost_usd": result.usage.cost_usd,
"fallback_used": True,
"primary_error": str(e)
}
async