Fazit vorab: HolySheep AI bietet für die Kimi K2.6 Long-Context-API eine Kostenreduktion von über 85% gegenüber der offiziellen API, mit sub-50ms Latenz und flexiblen Zahlungsmethoden via WeChat/Alipay. Die Integration erfordert intelligente Caching-Strategien und Sharding-Mechanismen, die wir in diesem Tutorial vollständig implementieren.
Vergleich: HolySheep vs. Offizielle API vs. Wettbewerber
| Anbieter | Preis pro Mio. Token | Latenz (P50) | Max. Kontext | Zahlungsmethoden | Geeignet für |
|---|---|---|---|---|---|
| HolySheep AI | $0.42 (DeepSeek V3.2) $2.50 (Gemini 2.5 Flash) |
<50ms | 2.6M Token | WeChat, Alipay, Kreditkarte, PayPal | Enterprise-Teams, Kostensparer |
| Offizielle Kimi API | $15-45 | 120-200ms | 2.6M Token | Nur internationale Kreditkarten | Große Unternehmen ohne China-Fokus |
| OpenAI GPT-4.1 | $8 | 80-150ms | 128K Token | Kreditkarte, PayPal | Breite Modellunterstützung |
| Anthropic Claude 4.5 | $15 | 100-180ms | 200K Token | Kreditkarte, PayPal | Hochqualitative Textarbeit |
| Google Gemini 2.5 Flash | $2.50 | 60-100ms | 1M Token | Kreditkarte, PayPal | Schnelle Verarbeitung |
Geeignet / Nicht geeignet für
✅ Ideal für:
- Entwicklerteams, die 2.6M Token-Kontexte für Dokumentenanalyse benötigen
- Unternehmen mit China-Marktfokus (WeChat/Alipay-Integration)
- Kostensensitive Projekte mit hohem Volumen
- Langzeit-Conversation-Memory-Implementierungen
- Legal-Tech und Research-Anwendungen mit umfangreichen Dokumenten
❌ Weniger geeignet für:
- Projekte, die ausschließlich auf Claude/GPT-Modelle setzen müssen
- Anwendungen mit <1.000 Token pro Anfrage (Overhead nicht lohnend)
- Strict GDPR-Compliance-Szenarien ohne Datenverarbeitungsvereinbarung
Preise und ROI
Basierend auf einem monatlichen Volumen von 100 Millionen Token:
| Szenario | Offizielle API | HolySheep AI | Ersparnis |
|---|---|---|---|
| 100M Token/Monat | $4.500 | $630 | $3.870 (86%) |
| 1B Token/Monat | $45.000 | $6.300 | $38.700 (86%) |
| 500M Token/Monat | $22.500 | $3.150 | $19.350 (86%) |
ROI-Analyse: Bei einem typischen Entwicklerteam (3 Personen, 50M Token/Monat) sparen Sie ca. $1.935 monatlich – genug für einen zusätzlichen Entwickler oder Infrastructure-Upgrades.
Warum HolySheep wählen?
- 85%+ Kostenersparnis durch optimierte Infrastruktur und Yuan-Dollar-Parität (¥1≈$1)
- <50ms Latenz durch Edge-Caching in Asien-Pazifik
- Flexible Zahlung via WeChat Pay, Alipay, Kreditkarte, PayPal
- Kostenlose Credits bei Registrierung für erste Tests
- Multi-Modell-Support: DeepSeek V3.2 ($0.42), Gemini 2.5 Flash ($2.50), GPT-4.1 ($8), Claude Sonnet 4.5 ($15)
- 2.6M Token Kontext für Kimi K2 –branchenführend
👉 Jetzt bei HolySheep AI registrieren – Startguthaben inklusive
Tutorial: Kimi K2.6 API mit HolySheep integrieren
Voraussetzungen
- HolySheep API Key (erhalten Sie hier kostenlos)
- Python 3.8+ oder Node.js 18+
- Grundlegendes Verständnis von Async/Await
Schritt 1: Grundlegende API-Integration
"""
Kimi K2.6 Long-Context API Integration mit HolySheep
=====================================================
Base URL: https://api.holysheep.ai/v1
Modell: kimi-k2.6-context
Max. Token: 2.6 Millionen
"""
import httpx
import asyncio
from typing import Optional, List, Dict, Any
class HolySheepKimiClient:
"""High-Level Client für Kimi K2.6 API mit Caching und Retry-Logik"""
def __init__(
self,
api_key: str,
base_url: str = "https://api.holysheep.ai/v1",
max_retries: int = 3,
timeout: float = 300.0 # 5 Minuten für Long-Context
):
self.api_key = api_key
self.base_url = base_url.rstrip("/")
self.max_retries = max_retries
self.timeout = timeout
# Connection Pooling
self.client = httpx.AsyncClient(
timeout=httpx.Timeout(timeout),
limits=httpx.Limits(max_keepalive_connections=20, max_connections=100),
follow_redirects=True
)
# Simple Token-Cache (LRU, 1000 Einträge)
self._cache: Dict[str, str] = {}
self._cache_hits = 0
self._cache_misses = 0
async def chat_completion(
self,
messages: List[Dict[str, str]],
model: str = "kimi-k2.6-context",
temperature: float = 0.7,
max_tokens: Optional[int] = 4096,
use_cache: bool = True
) -> Dict[str, Any]:
"""
Sende Chat-Completion an Kimi K2.6 mit automatischer Retry-Logik
"""
# Cache-Key aus Messages generieren
cache_key = self._generate_cache_key(messages, temperature, max_tokens)
# Cache prüfen
if use_cache and cache_key in self._cache:
self._cache_hits += 1
print(f"Cache-Hit! ({self._cache_hits}/{self._cache_hits + self._cache_misses})")
return {"cached": True, "content": self._cache[cache_key]}
self._cache_misses += 1
# Request aufbauen
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens
}
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
# Retry-Loop mit exponentiellem Backoff
for attempt in range(self.max_retries):
try:
response = await self.client.post(
f"{self.base_url}/chat/completions",
json=payload,
headers=headers
)
response.raise_for_status()
result = response.json()
# Ergebnis cachen
if use_cache:
self._cache[cache_key] = result["choices"][0]["message"]["content"]
# LRU: Max 1000 Einträge
if len(self._cache) > 1000:
oldest_key = next(iter(self._cache))
del self._cache[oldest_key]
return {"cached": False, "content": result}
except httpx.HTTPStatusError as e:
if e.response.status_code == 429:
# Rate Limit: Warte und retry
wait_time = 2 ** attempt * 5
print(f"Rate Limited. Warte {wait_time}s...")
await asyncio.sleep(wait_time)
elif e.response.status_code >= 500:
# Server Error: Retry
wait_time = 2 ** attempt * 2
print(f"Server Error ({e.response.status_code}). Retry in {wait_time}s...")
await asyncio.sleep(wait_time)
else:
raise
except httpx.TimeoutException:
if attempt < self.max_retries - 1:
wait_time = 2 ** attempt * 10
print(f"Timeout. Retry in {wait_time}s...")
await asyncio.sleep(wait_time)
else:
raise Exception(f"Timeout nach {self.max_retries} Versuchen")
raise Exception("Max retries exceeded")
def _generate_cache_key(
self,
messages: List[Dict],
temperature: float,
max_tokens: Optional[int]
) -> str:
"""Deterministischer Cache-Key"""
import hashlib
import json
content = json.dumps({
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens
}, sort_keys=True)
return hashlib.sha256(content.encode()).hexdigest()
async def close(self):
await self.client.aclose()
def get_cache_stats(self) -> Dict[str, Any]:
total = self._cache_hits + self._cache_misses
hit_rate = (self._cache_hits / total * 100) if total > 0 else 0
return {
"hits": self._cache_hits,
"misses": self._cache_misses,
"hit_rate_percent": round(hit_rate, 2),
"cache_size": len(self._cache)
}
============== VERWENDUNGSBEISPIEL ==============
async def main():
# API Key aus Umgebung oder direkt
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
client = HolySheepKimiClient(api_key=API_KEY)
try:
# Beispiel: Analyse eines langen Vertrags (100K+ Token)
messages = [
{
"role": "system",
"content": "Du bist ein juristischer Assistent. Analysiere Verträge präzise."
},
{
"role": "user",
"content": """
Analysiere den folgenden Vertrag und identifiziere:
1. Alle Haftungsklauseln
2. Kündigungsfristen
3. Salvatorische Klauseln
4. Gerichtsstandsklauseln
[HIER WÜRDE DER VERTRAGSTEXT STEHEN - BIS ZU 2.6M TOKEN MÖGLICH]
"""
}
]
result = await client.chat_completion(
messages=messages,
model="kimi-k2.6-context",
temperature=0.3,
max_tokens=2048,
use_cache=True
)
print(f"Result: {result}")
print(f"Cache-Stats: {client.get_cache_stats()}")
finally:
await client.close()
if __name__ == "__main__":
asyncio.run(main())
Schritt 2: Context Sharding für ultralange Dokumente
"""
Context Sharding für 2.6M+ Token Dokumente
===========================================
Teilt große Dokumente automatisch in shards auf
und recombiniert die Ergebnisse
"""
import asyncio
import tiktoken
from typing import List, Dict, Tuple, Optional
from dataclasses import dataclass
from concurrent.futures import ThreadPoolExecutor
@dataclass
class Shard:
"""Ein Fragment des Dokuments mit Metadaten"""
index: int
content: str
token_count: int
start_char: int
end_char: int
@dataclass
class ShardResult:
"""Ergebnis eines Shard-Processing"""
shard_index: int
response: str
processing_time_ms: float
success: bool
error: Optional[str] = None
class ContextSharder:
"""
Intelligent Context Sharding für Long-Context APIs
===================================================
- Token-basiertes Splitting (nicht Character-basiert)
- Overlap zwischen Shards für Kontextkontinuität
- Parallele Verarbeitung mit Semaphore-Limit
- Automatische Rekombination der Ergebnisse
"""
def __init__(
self,
client: 'HolySheepKimiClient', # Forward reference
model: str = "cl100k_base", # tiktoken encoding
max_tokens_per_shard: int = 128_000, # Sicherer Puffer unter 2.6M
overlap_tokens: int = 2000, # Overlap für Kontextkontinuität
max_concurrent: int = 3 # Rate Limit Protection
):
self.client = client
self.encoding = tiktoken.get_encoding(model)
self.max_tokens = max_tokens_per_shard
self.overlap = overlap_tokens
self.semaphore = asyncio.Semaphore(max_concurrent)
def split_into_shards(
self,
text: str,
document_id: Optional[str] = None
) -> List[Shard]:
"""
Teilt Text intelligent in token-basierte Shards
"""
total_tokens = len(self.encoding.encode(text))
print(f"Gesamt-Tokens: {total_tokens}")
if total_tokens <= self.max_tokens:
return [Shard(
index=0,
content=text,
token_count=total_tokens,
start_char=0,
end_char=len(text)
)]
shards = []
tokens = self.encoding.encode(text)
chunk_size = self.max_tokens - self.overlap
start_token = 0
shard_index = 0
while start_token < len(tokens):
end_token = min(start_token + self.max_tokens, len(tokens))
# Shard-Content extrahieren
shard_tokens = tokens[start_token:end_token]
shard_content = self.encoding.decode(shard_tokens)
# Character-Positionen berechnen
start_char = len(self.encoding.decode(tokens[:start_token]))
end_char = len(self.encoding.decode(tokens[:end_token]))
shards.append(Shard(
index=shard_index,
content=shard_content,
token_count=len(shard_tokens),
start_char=start_char,
end_char=end_char
))
print(f"Shard {shard_index}: {len(shard_tokens)} Tokens "
f"(Pos {start_char}-{end_char})")
start_token += chunk_size
shard_index += 1
return shards
async def process_shard(
self,
shard: Shard,
system_prompt: str,
shard_prompt_template: str,
timeout: float = 120.0
) -> ShardResult:
"""
Verarbeitet einen einzelnen Shard mit Timeout-Schutz
"""
import time
async with self.semaphore: # Concurrent-Limit
start_time = time.time()
try:
# Timeout-Wrapper
messages = [
{"role": "system", "content": system_prompt},
{
"role": "user",
"content": shard_prompt_template.format(
shard_index=shard.index + 1,
total_shards="?", # Werden später aktualisiert
shard_content=shard.content
)
}
]
result = await asyncio.wait_for(
self.client.chat_completion(
messages=messages,
use_cache=True
),
timeout=timeout
)
processing_time = (time.time() - start_time) * 1000
return ShardResult(
shard_index=shard.index,
response=result["content"]["choices"][0]["message"]["content"],
processing_time_ms=processing_time,
success=True
)
except asyncio.TimeoutError:
processing_time = (time.time() - start_time) * 1000
return ShardResult(
shard_index=shard.index,
response="",
processing_time_ms=processing_time,
success=False,
error=f"Timeout nach {timeout}s"
)
except Exception as e:
processing_time = (time.time() - start_time) * 1000
return ShardResult(
shard_index=shard.index,
response="",
processing_time_ms=processing_time,
success=False,
error=str(e)
)
async def process_document(
self,
text: str,
system_prompt: str,
user_prompt: str,
progress_callback: Optional[callable] = None
) -> Dict:
"""
Hauptmethode: Verarbeitet ein vollständiges Dokument
mit Sharding und Rekombination
"""
# 1. Sharding
shards = self.split_into_shards(text)
total_shards = len(shards)
print(f"Verarbeite {total_shards} Shards...")
# 2. Parallele Verarbeitung mit Progress
tasks = []
for shard in shards:
# Prompt mit Shard-Info aktualisieren
task = self.process_shard(
shard=shard,
system_prompt=system_prompt,
shard_prompt_template=f"""
Du verarbeitest Shard {{{{shard_index}}}} von {{{{total_shards}}}}.
{user_prompt}
=== SHARD CONTENT ===
{{shard_content}}
=== END SHARD ===
Antworte MITTLSCHWIERIGkeit (KISS-Prinzip) und verweise auf
Shard-Nummern wenn du spezifische Informationen zitierst.
"""
)
tasks.append(task)
# 3. Ergebnisse sammeln mit Progress-Update
results = []
for i, coro in enumerate(asyncio.as_completed(tasks)):
result = await coro
results.append(result)
if progress_callback:
progress_callback(i + 1, total_shards, result)
print(f"Shard {result.shard_index}: "
f"{'✓' if result.success else '✗'} "
f"({result.processing_time_ms:.0f}ms)")
# 4. Sortieren und Rekombination
results.sort(key=lambda x: x.shard_index)
successful = [r for r in results if r.success]
failed = [r for r in results if not r.success]
# 5. Finales Zusammenfassung-Request
if len(successful) > 1:
combined_analysis = "\n\n---\n\n".join([
f"[Shard {r.shard_index}]\n{r.response}"
for r in successful
])
final_summary = await self.client.chat_completion(
messages=[
{"role": "system", "content": "Du bist ein Synthese-Experte."},
{"role": "user", "content": f"""
Fasse die folgenden Shard-Analysen zu einer kohärenten
Gesamtübersicht zusammen:
{combined_analysis}
Entferne Redundanzen und erstelle eine strukturierte Zusammenfassung.
"""}
],
temperature=0.3
)
final_response = final_summary["content"]["choices"][0]["message"]["content"]
else:
final_response = successful[0].response if successful else ""
return {
"success": len(successful) == len(shards),
"shard_count": total_shards,
"successful_shards": len(successful),
"failed_shards": len(failed),
"final_response": final_response,
"shard_results": results,
"total_processing_time_ms": sum(r.processing_time_ms for r in results)
}
============== VERWENDUNGSBEISPIEL ==============
async def main_document_analysis():
"""Beispiel: Analyse eines 500-Seiten-Dokuments"""
from holysheep_client import HolySheepKimiClient
# Client initialisieren
client = HolySheepKimiClient(api_key="YOUR_HOLYSHEEP_API_KEY")
# Sharder konfigurieren
sharder = ContextSharder(
client=client,
max_tokens_per_shard=128_000, # 128K pro Shard
overlap_tokens=2000, # 2K Overlap
max_concurrent=3 # Max 3 parallele Requests
)
# Langes Dokument laden (Beispiel)
with open("vertrag_500_seiten.txt", "r") as f:
document = f.read()
# Progress-Callback
def progress(current, total, result):
status = "✓" if result.success else "✗"
print(f" [{current}/{total}] Shard {result.shard_index}: {status}")
# Dokument verarbeiten
result = await sharder.process_document(
text=document,
system_prompt="Du bist ein juristischer Assistent.",
user_prompt="""
Analysiere diesen Vertragabschnitt und identifiziere:
1. Haftungsklauseln
2. Kündigungsfristen
3. Besondere Bedingungen
Sei präzise und zitiere relevante Textstellen.
""",
progress_callback=progress
)
print(f"\n=== ERGEBNIS ===")
print(f"Shards: {result['successful_shards']}/{result['shard_count']}")
print(f"Gesamtzeit: {result['total_processing_time_ms']/1000:.1f}s")
print(f"\n{result['final_response']}")
await client.close()
if __name__ == "__main__":
asyncio.run(main_document_analysis())
Schritt 3: Timeout-Schutz und Resilienz-Pattern
"""
Timeout-Schutz und Resilienz-Pattern für Kimi K2.6 API
======================================================
- Circuit Breaker Pattern
- Exponential Backoff
- Graceful Degradation
- Health Checks
"""
import asyncio
import time
from typing import Optional, Callable, Any
from dataclasses import dataclass, field
from enum import Enum
from collections import deque
import random
class CircuitState(Enum):
CLOSED = "closed" # Normal, Requests durchlassen
OPEN = "open" # Blockiert, keine Requests
HALF_OPEN = "half_open" # Test-Phase nach Timeout
@dataclass
class CircuitBreaker:
"""
Circuit Breaker für API-Resilienz
=================================
Schützt vor Kaskadenfehlern bei API-Ausfällen
"""
failure_threshold: int = 5 # Fehler bis OPEN
success_threshold: int = 3 # Erfolge bis CLOSED (von HALF_OPEN)
timeout_seconds: float = 30.0 # Zeit bis HALF_OPEN
_state: CircuitState = field(default=CircuitState.CLOSED, init=False)
_failure_count: int = field(default=0, init=False)
_success_count: int = field(default=0, init=False)
_last_failure_time: float = field(default=0.0, init=False)
_last_success_time: float = field(default=0.0, init=False)
def record_success(self):
"""Erfolgreichen Request verzeichnen"""
self._last_success_time = time.time()
if self._state == CircuitState.HALF_OPEN:
self._success_count += 1
if self._success_count >= self.success_threshold:
self._state = CircuitState.CLOSED
self._failure_count = 0
self._success_count = 0
print("🔄 Circuit Breaker: CLOSED → Normalbetrieb")
else:
self._failure_count = max(0, self._failure_count - 1)
def record_failure(self):
"""Fehlgeschlagenen Request verzeichnen"""
self._last_failure_time = time.time()
self._failure_count += 1
self._success_count = 0
if self._state == CircuitState.CLOSED:
if self._failure_count >= self.failure_threshold:
self._state = CircuitState.OPEN
print(f"⚠️ Circuit Breaker: CLOSED → OPEN (nach {self._failure_count} Fehlern)")
elif self._state == CircuitState.HALF_OPEN:
self._state = CircuitState.OPEN
self._failure_count = self.failure_threshold
print("⚠️ Circuit Breaker: HALF_OPEN → OPEN")
def can_attempt(self) -> bool:
"""Prüft ob Request erlaubt ist"""
if self._state == CircuitState.CLOSED:
return True
if self._state == CircuitState.OPEN:
elapsed = time.time() - self._last_failure_time
if elapsed >= self.timeout_seconds:
self._state = CircuitState.HALF_OPEN
self._success_count = 0
print("🔄 Circuit Breaker: OPEN → HALF_OPEN (Test-Phase)")
return True
return False
# HALF_OPEN: 1 Request erlaubt
return True
@property
def state(self) -> CircuitState:
return self._state
class TimeoutProtection:
"""
Timeout-Management mit verschiedenen Strategien
===============================================
"""
# Timeout-Konfiguration nach Operationstyp
TIMEOUTS = {
"chat": 120.0, # 2 min für Standard-Chat
"long_context": 300.0, # 5 min für 2.6M Token
"embedding": 30.0, # 30s für Embeddings
"health_check": 10.0 # 10s für Health Check
}
@classmethod
def get_timeout(cls, operation: str) -> float:
return cls.TIMEOUTS.get(operation, 60.0)
@classmethod
async def with_timeout(
cls,
operation: str,
coro,
fallback: Optional[Callable] = None,
on_timeout: Optional[Callable] = None
) -> Any:
"""
Führt Coroutine mit Timeout aus
Args:
operation: Operationstyp für Timeout-Selection
coro: Coroutine
fallback: Fallback-Funktion bei Timeout
on_timeout: Callback bei Timeout
"""
timeout = cls.get_timeout(operation)
try:
result = await asyncio.wait_for(coro, timeout=timeout)
return {"success": True, "result": result, "timed_out": False}
except asyncio.TimeoutError:
print(f"⏱️ Timeout nach {timeout}s für Operation: {operation}")
if on_timeout:
await on_timeout()
if fallback:
print("→ Führe Fallback aus...")
result = await fallback()
return {"success": True, "result": result, "timed_out": True, "used_fallback": True}
return {"success": False, "error": "Timeout", "timed_out": True}
class ResilientKimiClient:
"""
Resilienter Client mit allen Protection-Mechanismen
====================================================
"""
def __init__(
self,
api_key: str,
base_url: str = "https://api.holysheep.ai/v1",
max_retries: int = 3,
enable_circuit_breaker: bool = True
):
self.api_key = api_key
self.base_url = base_url
self.max_retries = max_retries
self.circuit_breaker = CircuitBreaker() if enable_circuit_breaker else None
# Retry-Tracking
self._retry_log = deque(maxlen=100)
# Metrics
self._metrics = {
"total_requests": 0,
"successful_requests": 0,
"failed_requests": 0,
"timeouts": 0,
"circuit_open_count": 0
}
async def request_with_protection(
self,
payload: dict,
operation: str = "chat"
) -> dict:
"""
Request mit vollem Schutz (Circuit Breaker + Timeout + Retry)
"""
self._metrics["total_requests"] += 1
# Circuit Breaker Check
if self.circuit_breaker and not self.circuit_breaker.can_attempt():
self._metrics["circuit_open_count"] += 1
return {
"success": False,
"error": "Circuit Breaker OPEN",
"circuit_state": self.circuit_breaker.state.value
}
# Retry-Loop
last_error = None
for attempt in range(self.max_retries):
try:
result = await TimeoutProtection.with_timeout(
operation=operation,
coro=self._make_request(payload),
fallback=lambda: self._fallback_response(payload)
)
if result["success"]:
if self.circuit_breaker:
self.circuit_breaker.record_success()
self._metrics["successful_requests"] += 1
return result["result"]
else:
if result.get("timed_out"):
self._metrics["timeouts"] += 1
raise Exception(result.get("error", "Unknown"))
except Exception as e:
last_error = e
self._retry_log.append({
"attempt": attempt + 1,
"error": str(e),
"timestamp": time.time()
})
if attempt < self.max_retries - 1:
wait_time = self._exponential_backoff(attempt)
print(f"Retry {attempt + 1}/{self.max_retries} nach {wait_time}s: {e}")
await asyncio.sleep(wait_time)
# Final Failure
if self.circuit_breaker:
self.circuit_breaker.record_failure()
self._metrics["failed_requests"] += 1
return {
"success": False,
"error": f"Failed after {self.max_retries} retries: {last_error}"
}
async def _make_request(self, payload: dict) -> dict:
"""Interner Request (implementieren mit httpx)"""
import httpx
async with httpx.AsyncClient() as client:
response = await client.post(
f"{self.base_url}/chat/completions",
json=payload,
headers={"Authorization": f"Bearer {self.api_key}"},
timeout=TimeoutProtection.get_timeout(payload.get("operation", "chat"))
)
response.raise_for_status()
return response.json()
async def _fallback_response(self, payload: dict) -> dict:
"""Fallback bei Timeout: Gibt gecachte oder reduzierte Antwort"""
return {
"fallback": True,
"message": "Anfrage timeout - Bitte erneut versuchen oder kürzeren Kontext verwenden",
"suggestion": "Consider splitting the document into smaller shards"
}
def _exponential_backoff(self, attempt: int, base: float = 1.0, max_delay: float = 30.0) -> float:
"""Exponential Backoff mit Jitter"""
delay = min(base * (2 ** attempt), max_delay)
jitter = random.uniform(0, delay * 0.1)
return delay + jitter
def get_metrics(self) -> dict:
"""Gibt aktuelle Metriken zurück"""
m = self._metrics.copy()
total = m["total_requests"]
if total > 0:
m["success_rate"] = f"{m['successful_requests'] / total * 100:.1f}%"
return m
async def health_check(self) -> dict:
"""Health Check für Monitoring"""
try:
result = await TimeoutProtection.with_timeout(
operation="health_check",
coro=self._make_request({"model": "test", "messages": []})
)
return {
"healthy": result["success"],
"latency_ms": result.get("latency", 0),
"circuit_state": self.circuit_breaker.state.value if self.circuit_breaker else "disabled"
}
except Exception as e:
return {
"healthy": False,
"error": str(e),
"circuit_state": self.circuit_breaker.state.value if self.circuit_breaker else "unknown"
}
============== VERWENDUNGSBEISPIEL ==============
async def main_resilient():
"""Beispiel: Resilienter Client mit Monitoring"""
client = ResilientKimiClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
enable_circuit_breaker=True
)
# Health Check
health = await client.health_check()
print(f"Health Check: {health}")
# Request mit vollem Schutz
payload = {
"model": "kimi-k2.6-context",
"messages": [{"role": "user", "content": "Test-Anfrage"}],
"operation": "long_context"
}
result = await client.request_with_protection(payload, operation="long_context")
print(f"Result: {result}")
# Metriken
print(f"Metrics: {client.get_metrics()}")
if
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