Khi làm việc với các API AI trong môi trường production, chi phí có thể leo thang nhanh chóng nếu không có chiến lược testing hiệu quả. Bài viết này sẽ hướng dẫn bạn xây dựng hệ thống mock testing hoàn chỉnh, từ kiến trúc cơ bản đến tối ưu hóa hiệu suất và kiểm soát chi phí. Tôi đã triển khai giải pháp này cho nhiều dự án và tiết kiệm được hơn 85% chi phí API trong giai đoạn development.
Tại Sao Mock Testing Quan Trọng Với AI API?
Khác với REST API truyền thống, AI API có đặc thù riêng:
- Chi phí theo token - Mỗi request đều tốn tiền thật
- Độ trễ không đồng nhất - Response time biến đổi từ 200ms đến 30 giây
- Non-deterministic output - Cùng prompt có thể cho kết quả khác nhau
- Tiered pricing - Giá thay đổi theo model và độ dài context
Với HolyShehe AI, bạn được hưởng tỷ giá ưu đãi ¥1=$1, hỗ trợ WeChat/Alipay thanh toán, và độ trễ dưới 50ms. Tuy nhiên, ngay cả với mức giá này, việc mock testing vẫn là best practice cần thiết.
Kiến Trúc Mock Server Tập Trung
Tôi khuyến nghị kiến trúc proxy-based mock, cho phép switch giữa mock và real API một cách linh hoạt:
# mock_ai_proxy.py
import asyncio
import hashlib
import json
import time
from typing import Optional, Dict, Any
from dataclasses import dataclass, field
from datetime import datetime
import httpx
@dataclass
class MockResponse:
"""Cấu trúc response mô phỏng"""
content: str
model: str
usage: Dict[str, int] = field(default_factory=lambda: {
"prompt_tokens": 10,
"completion_tokens": 20,
"total_tokens": 30
})
latency_ms: float = 150.0
finish_reason: str = "stop"
class MockAIRouter:
"""
Proxy router với mock mode
Cache responses theo request hash
"""
def __init__(self, base_url: str = "https://api.holysheep.ai/v1"):
self.base_url = base_url
self.mock_mode = True
self.cache: Dict[str, MockResponse] = {}
self.request_log: list = []
self._mock_scenarios = self._load_mock_scenarios()
def _load_mock_scenarios(self) -> Dict[str, MockResponse]:
"""Load các mock scenario từ config"""
return {
"success_simple": MockResponse(
content="Đây là response mô phỏng thành công.",
model="gpt-4.1",
latency_ms=120.0
),
"success_long": MockResponse(
content="*" * 500, # Response dài 500 chars
model="gpt-4.1",
usage={"prompt_tokens": 50, "completion_tokens": 500, "total_tokens": 550},
latency_ms=450.0
),
"rate_limit": None, # Special case
"timeout": None, # Special case
}
def _generate_cache_key(self, messages: list, model: str, **params) -> str:
"""Tạo cache key từ request payload"""
payload = json.dumps({"messages": messages, "model": model, **params}, sort_keys=True)
return hashlib.sha256(payload.encode()).hexdigest()[:16]
def toggle_mock_mode(self, enabled: bool):
"""Switch giữa mock và real API"""
self.mock_mode = enabled
print(f"[MockAIRouter] Mode: {'MOCK' if enabled else 'REAL API'}")
async def chat_completions(
self,
messages: list,
model: str = "gpt-4.1",
temperature: float = 0.7,
max_tokens: int = 1000,
**kwargs
) -> Dict[str, Any]:
"""
Main entry point cho chat completions
Tự động route sang mock hoặc real API
"""
start_time = time.time()
cache_key = self._generate_cache_key(messages, model, temperature=temperature, max_tokens=max_tokens)
# Log request
log_entry = {
"timestamp": datetime.now().isoformat(),
"model": model,
"cache_hit": False,
"mode": "mock" if self.mock_mode else "real"
}
# Check mock scenarios
if self.mock_mode:
# Xử lý special cases
if model == "rate-limit-test":
log_entry["error"] = "rate_limit_exceeded"
self.request_log.append(log_entry)
return self._build_error_response(429, "Rate limit exceeded")
if model == "timeout-test":
log_entry["error"] = "request_timeout"
self.request_log.append(log_entry)
return self._build_error_response(408, "Request timeout")
# Check cache
if cache_key in self.cache:
response = self.cache[cache_key]
log_entry["cache_hit"] = True
self.request_log.append(log_entry)
return self._build_success_response(response, start_time)
# Return mock response
mock_resp = self._mock_scenarios.get("success_simple", MockResponse(
content="Mock response default",
model=model
))
# Adjust response theo params
if max_tokens > 500:
mock_resp = self._mock_scenarios["success_long"]
# Cache response
self.cache[cache_key] = mock_resp
log_entry["cache_hit"] = False
self.request_log.append(log_entry)
return self._build_success_response(mock_resp, start_time)
# Real API call
return await self._call_real_api(messages, model, temperature, max_tokens, kwargs, log_entry, start_time)
async def _call_real_api(
self,
messages: list,
model: str,
temperature: float,
max_tokens: int,
extra_params: dict,
log_entry: dict,
start_time: float
) -> Dict[str, Any]:
"""Gọi HolySheep AI API thực sự"""
headers = {
"Authorization": f"Bearer {os.getenv('HOLYSHEEP_API_KEY')}",
"Content-Type": "application/json"
}
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens,
**extra_params
}
async with httpx.AsyncClient(timeout=60.0) as client:
response = await client.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload
)
response.raise_for_status()
result = response.json()
log_entry["latency_ms"] = (time.time() - start_time) * 1000
self.request_log.append(log_entry)
return result
def _build_success_response(self, mock: MockResponse, start_time: float) -> Dict[str, Any]:
"""Build OpenAI-compatible response format"""
elapsed_ms = (time.time() - start_time) * 1000
total_latency = mock.latency_ms + elapsed_ms
return {
"id": f"mock-{hashlib.md5(str(time.time()).encode()).hexdigest()[:8]}",
"object": "chat.completion",
"created": int(datetime.now().timestamp()),
"model": mock.model,
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": mock.content
},
"finish_reason": mock.finish_reason
}],
"usage": mock.usage,
"mock_latency_ms": total_latency
}
def _build_error_response(self, status_code: int, message: str) -> Dict[str, Any]:
"""Build error response"""
raise httpx.HTTPStatusError(
message,
request=httpx.Request("POST", "mock://"),
response=httpx.Response(status_code, json={"error": {"message": message}})
)
def get_stats(self) -> Dict[str, Any]:
"""Lấy thống kê request"""
total = len(self.request_log)
cache_hits = sum(1 for log in self.request_log if log.get("cache_hit"))
errors = sum(1 for log in self.request_log if "error" in log)
return {
"total_requests": total,
"cache_hit_rate": cache_hits / total if total > 0 else 0,
"error_rate": errors / total if total > 0 else 0,
"mode": "mock" if self.mock_mode else "real",
"cache_size": len(self.cache)
}
Usage example
router = MockAIRouter()
asyncio.run(router.chat_completions(
messages=[{"role": "user", "content": "Hello!"}],
model="gpt-4.1"
))Chiến Lược Response Caching Thông Minh
Việc cache response là chìa khóa để giảm chi phí và tăng tốc testing. Dưới đây là implementation nâng cao với semantic similarity:
# semantic_cache.py
import hashlib
import json
import re
from typing import Optional, Tuple
from collections import OrderedDict
import numpy as np
class SemanticCache:
"""
Cache với approximate matching
So khớp prompt tương tự thay vì exact match
"""
def __init__(self, max_size: int = 1000, similarity_threshold: float = 0.85):
self.max_size = max_size
self.similarity_threshold = similarity_threshold
self.cache: OrderedDict[str, dict] = OrderedDict()
self.embeddings: dict = {}
self._embedding_model = None
def _normalize_text(self, text: str) -> str:
"""Chuẩn hóa text trước khi hash"""
text = text.lower().strip()
text = re.sub(r'\s+', ' ', text)
text = re.sub(r'[^\w\s\u00C0-\u024F]', '', text) # Giữ tiếng Việt
return text
def _generate_key(self, prompt: str, model: str, **params) -> str:
"""Tạo cache key với content-aware hashing"""
normalized = self._normalize_text(prompt)
payload = json.dumps({
"prompt": normalized[:500], # Limit length
"model": model,
**{k: v for k, v in sorted(params.items()) if k in ["temperature", "max_tokens"]}
}, sort_keys=True)
return hashlib.sha256(payload.encode()).hexdigest()[:32]
async def get(self, prompt: str, model: str, **params) -> Optional[dict]:
"""Lookup cache với fallback strategy"""
key = self._generate_key(prompt, model, **params)
# Exact match
if key in self.cache:
self.cache.move_to_end(key)
return self.cache[key]
# Approximate match - tìm prompt tương tự
if self.embeddings:
best_match = await self._find_similar(prompt, model)
if best_match:
return best_match
return None
async def _find_similar(self, prompt: str, model: str) -> Optional[dict]:
"""Tìm cached response gần đúng"""
try:
# Lazy load embedding model
if self._embedding_model is None:
from sentence_transformers import SentenceTransformer
self._embedding_model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
query_embedding = self._embedding_model.encode([prompt])[0]
best_similarity = 0
best_key = None
for cache_key, cached in self.cache.items():
if cached["model"] != model:
continue
if cache_key not in self.embeddings:
continue
similarity = np.dot(query_embedding, self.embeddings[cache_key])
if similarity > best_similarity:
best_similarity = similarity
best_key = cache_key
if best_similarity >= self.similarity_threshold:
cached = self.cache[best_key]
# Ghi log approximate hit
print(f"[SemanticCache] Approximate hit: {best_similarity:.2%} similarity")
return cached
except ImportError:
pass # sentence_transformers not installed
return None
async def set(self, prompt: str, model: str, response: dict, **params):
"""Lưu vào cache với eviction policy"""
key = self._generate_key(prompt, model, **params)
# Eviction if full
if len(self.cache) >= self.max_size:
evicted_key = next(iter(self.cache))
del self.cache[evicted_key]
if evicted_key in self.embeddings:
del self.embeddings[evicted_key]
# Store with metadata
self.cache[key] = {
**response,
"_cached_at": datetime.now().isoformat(),
"model": model
}
self.cache.move_to_end(key)
# Store embedding
if self._embedding_model:
self.embeddings[key] = self._embedding_model.encode([prompt])[0]
def get_hit_rate(self) -> float:
"""Tính cache hit rate"""
if not self.cache:
return 0.0
hits = sum(1 for v in self.cache.values() if "_cached_at" in v)
return hits / len(self.cache)
Integration với Mock Router
class HybridMockRouter(MockAIRouter):
"""Mock router với semantic caching"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.semantic_cache = SemanticCache(max_size=500, similarity_threshold=0.90)
async def chat_completions(self, messages: list, **kwargs):
"""Override với cache lookup trước"""
prompt = messages[-1]["content"] if messages else ""
# Try semantic cache first
cached = await self.semantic_cache.get(prompt, kwargs.get("model", "gpt-4.1"))
if cached:
print(f"[HybridRouter] Cache HIT - saved ${self._estimate_cost(cached):.4f}")
return cached
# Call parent (mock hoặc real)
response = await super().chat_completions(messages, **kwargs)
# Cache the response
await self.semantic_cache.set(prompt, kwargs.get("model", "gpt-4.1"), response)
return response
def _estimate_cost(self, response: dict) -> float:
"""Ước tính chi phí tiết kiệm được"""
pricing = {
"gpt-4.1": 8.0, # $8/MTok
"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
}
model = response.get("model", "gpt-4.1")
tokens = response.get("usage", {}).get("total_tokens", 0)
rate = pricing.get(model, 8.0)
return (tokens / 1_000_000) * rateTối Ưu Hóa Chi Phí Với HolySheep AI
Bảng giá HolySheep AI 2026 cho thấy sự chênh lệch đáng kể giữa các provider:
| Model | Giá/MTok | Độ trễ | Use Case |
|---|---|---|---|
| DeepSeek V3.2 | $0.42 | <50ms | Cost-sensitive tasks |
| Gemini 2.5 Flash | $2.50 | <50ms | High volume, fast response |
| GPT-4.1 | $8.00 | <50ms | Complex reasoning |
| Claude Sonnet 4.5 | $15.00 | <50ms | Long context, analysis |
Với tỷ giá ¥1=$1 và hỗ trợ thanh toán WeChat/Alipay, HolySheep AI là lựa chọn tối ưu cho thị trường châu Á. Đăng ký tại đây để nhận tín dụng miễn phí khi bắt đầu.
Concurrency Control Và Rate Limiting
Mock testing cần simulate đúng behavior của rate limiting để QA có thể test error handling:
# rate_limited_mock.py
import asyncio
import time
from typing import Dict
from dataclasses import dataclass, field
from collections import deque
import threading
@dataclass
class RateLimitConfig:
"""Cấu hình rate limiting cho mock"""
requests_per_minute: int = 60
requests_per_second: int = 10
tokens_per_minute: int = 100_000
burst_size: int = 20
# Simulated delays (ms)
min_latency: float = 50.0
max_latency: float = 2000.0
error_injection_rate: float = 0.02 # 2% error rate
class TokenBucket:
"""Token bucket algorithm cho rate limiting"""
def __init__(self, capacity: int, refill_rate: float):
self.capacity = capacity
self.tokens = capacity
self.refill_rate = refill_rate
self.last_refill = time.time()
self._lock = threading.Lock()
def consume(self, tokens: int = 1) -> bool:
with self._lock:
self._refill()
if self.tokens >= tokens:
self.tokens -= tokens
return True
return False
def _refill(self):
now = time.time()
elapsed = now - self.last_refill
self.tokens = min(self.capacity, self.tokens + elapsed * self.refill_rate)
self.last_refill = now
class RateLimitedMockServer:
"""
Mock server với realistic rate limiting
Simulates HolySheep AI rate limits
"""
def __init__(self, config: RateLimitConfig = None):
self.config = config or RateLimitConfig()
# Multiple buckets cho different limits
self.rpm_bucket = TokenBucket(
capacity=self.config.requests_per_minute,
refill_rate=self.config.requests_per_minute / 60
)
self.rps_bucket = TokenBucket(
capacity=self.config.requests_per_second,
refill_rate=self.config.requests_per_second
)
# Token tracking
self.token_tracker: deque = deque(maxlen=1000)
self.request_history: deque = deque(maxlen=10000)
# Simulated errors
self.error_count = 0
self.total_requests = 0
async def handle_request(
self,
prompt_tokens: int,
completion_tokens: int,
simulate_error: bool = False
) -> dict:
"""
Handle request với rate limiting
Returns response hoặc raises RateLimitError
"""
self.total_requests += 1
# Check token bucket
if not self.rpm_bucket.consume(1):
self.error_count += 1
return self._rate_limit_error("rpm")
if not self.rps_bucket.consume(1):
self.error_count += 1
return self._rate_limit_error("rps")
# Check token limit
total_tokens = prompt_tokens + completion_tokens
now = time.time()
# Clean old entries
while self.token_tracker and now - self.token_tracker[0]["time"] > 60:
self.token_tracker.popleft()
# Sum tokens in window
tokens_used = sum(entry["tokens"] for entry in self.token_tracker)
if tokens_used + total_tokens > self.config.tokens_per_minute:
self.error_count += 1
return self._rate_limit_error("tpm")
# Record usage
self.token_tracker.append({"time": now, "tokens": total_tokens})
# Inject random errors
if simulate_error or (self.config.error_injection_rate > 0 and
self.total_requests % int(1/self.config.error_injection_rate) == 0):
return self._random_error()
# Simulate latency
latency = self._calculate_latency()
await asyncio.sleep(latency / 1000)
# Track request
self.request_history.append({
"time": now,
"tokens": total_tokens,
"latency_ms": latency
})
return {
"success": True,
"latency_ms": latency,
"tokens_used": total_tokens
}
def _calculate_latency(self) -> float:
"""Tính latency dựa trên token count và randomness"""
import random
base = self.config.min_latency
token_factor = (self.token_tracker[-1]["tokens"] if self.token_tracker else 100) / 10
jitter = random.uniform(-10, 10)
return min(base + token_factor + jitter, self.config.max_latency)
def _rate_limit_error(self, limit_type: str) -> dict:
"""Tạo rate limit error response"""
headers = {
"X-RateLimit-Limit-RPM": str(self.config.requests_per_minute),
"X-RateLimit-Limit-RPS": str(self.config.requests_per_second),
"X-RateLimit-Limit-TPM": str(self.config.tokens_per_minute),
"X-RateLimit-Remaining-RPM": "0",
"X-RateLimit-Reset": str(int(time.time()) + 60)
}
messages = {
"rpm": "Rate limit exceeded. Maximum 60 requests per minute.",
"rps": "Rate limit exceeded. Maximum 10 requests per second.",
"tpm": "Token limit exceeded. Maximum 100,000 tokens per minute."
}
return {
"success": False,
"error": {
"type": "rate_limit_exceeded",
"code": 429,
"message": messages[limit_type],
"limit_type": limit_type,
"headers": headers
}
}
def _random_error(self) -> dict:
"""Random server error simulation"""
import random
errors = [
("internal_error", "Internal server error occurred"),
("service_unavailable", "Service temporarily unavailable"),
("timeout", "Request timeout after 30 seconds")
]
error_type, message = random.choice(errors)
return {
"success": False,
"error": {
"type": error_type,
"code": 500 if error_type == "internal_error" else 503,
"message": message
}
}
def get_metrics(self) -> dict:
"""Lấy metrics cho monitoring"""
now = time.time()
# Requests in last minute
recent = [r for r in self.request_history if now - r["time"] < 60]
avg_latency = sum(r["latency_ms"] for r in recent) / len(recent) if recent else 0
return {
"total_requests": self.total_requests,
"error_count": self.error_count,
"error_rate": self.error_count / self.total_requests if self.total_requests > 0 else 0,
"avg_latency_ms": avg_latency,
"requests_last_minute": len(recent),
"buckets": {
"rpm_remaining": int(self.rpm_bucket.tokens),
"rps_remaining": int(self.rps_bucket.tokens)
}
}
Usage demonstration
async def test_rate_limiting():
config = RateLimitConfig(
requests_per_minute=10, # Low limit for testing
requests_per_second=2,
error_injection_rate=0.1 # 10% error rate
)
server = RateLimitedMockServer(config)
print("Testing rate limiting...")
for i in range(15):
result = await server.handle_request(
prompt_tokens=100,
completion_tokens=50,
simulate_error=False
)
if result["success"]:
print(f"Request {i+1}: OK ({result['latency_ms']:.1f}ms)")
else:
print(f"Request {i+1}: {result['error']['type']} - {result['error']['message']}")
metrics = server.get_metrics()
print(f"\nMetrics: {metrics}")
Run test
asyncio.run(test_rate_limiting())Tích Hợp Với Pytest Cho QA Automation
Đây là fixture pytest hoàn chỉnh cho việc integrate mock testing vào CI/CD pipeline:
# conftest.py
import pytest
import asyncio
from typing import Generator
from mock_ai_proxy import MockAIRouter, HybridMockRouter
from semantic_cache import SemanticCache
from rate_limited_mock import RateLimitedMockServer, RateLimitConfig
@pytest.fixture(scope="session")
def event_loop():
"""Event loop cho async tests"""
loop = asyncio.new_event_loop()
yield loop
loop.close()
@pytest.fixture
def mock_router():
"""Basic mock router fixture"""
router = MockAIRouter(base_url="https://api.holysheep.ai/v1")
router.toggle_mock_mode(True)
yield router
stats = router.get_stats()
print(f"\nMock Stats: {stats}")
@pytest.fixture
def hybrid_router():
"""Hybrid router với caching"""
router = HybridMockRouter(base_url="https://api.holysheep.ai/v1")
router.toggle_mock_mode(True)
yield router
# Log savings
cache_stats = router.semantic_cache.get_hit_rate()
print(f"\nCache Hit Rate: {cache_stats:.1%}")
@pytest.fixture
def rate_limited_server():
"""Rate limited mock server"""
config = RateLimitConfig(
requests_per_minute=60,
requests_per_second=10,
error_injection_rate=0.05
)
server = RateLimitedMockServer(config)
yield server
metrics = server.get_metrics()
print(f"\nRate Limit Stats: {metrics}")
tests/test_ai_client.py
import pytest
from your_ai_client import AIClient # Import your actual client
class TestAIClientWithMock:
"""Test suite cho AI client với mock responses"""
@pytest.mark.asyncio
async def test_successful_completion(self, mock_router):
"""Test happy path"""
response = await mock_router.chat_completions(
messages=[{"role": "user", "content": "Hello AI"}],
model="gpt-4.1"
)
assert "choices" in response
assert response["choices"][0]["message"]["content"]
assert "usage" in response
@pytest.mark.asyncio
async def test_caching_behavior(self, hybrid_router):
"""Test response caching reduces API calls"""
prompt = "What is 2+2?"
# First call - cache miss
response1 = await hybrid_router.chat_completions(
messages=[{"role": "user", "content": prompt}],
model="gpt-4.1"
)
# Second call - should hit cache
response2 = await hybrid_router.chat_completions(
messages=[{"role": "user", "content": prompt}],
model="gpt-4.1"
)
assert response1["choices"][0]["message"]["content"] == response2["choices"][0]["message"]["content"]
@pytest.mark.asyncio
async def test_rate_limit_handling(self, rate_limited_server):
"""Test client handles rate limits gracefully"""
config = RateLimitConfig(requests_per_minute=5, requests_per_second=2)
server = RateLimitedMockServer(config)
results = []
for i in range(10):
result = await server.handle_request(prompt_tokens=100, completion_tokens=50)
results.append(result)
# Should have some rate limited responses
failures = [r for r in results if not r.get("success")]
assert len(failures) > 0, "Expected some rate limit failures"
# All failures should be rate limit errors
for failure in failures:
assert failure["error"]["type"] == "rate_limit_exceeded"
@pytest.mark.asyncio
async def test_cost_estimation(self, mock_router):
"""Test cost tracking"""
response = await mock_router.chat_completions(
messages=[{"role": "user", "content": "Tell me a story"}],
model="gpt-4.1",
max_tokens=1000
)
# Calculate estimated cost
tokens = response["usage"]["total_tokens"]
gpt41_cost_per_mtok = 8.0 # HolySheep pricing
estimated_cost = (tokens / 1_000_000) * gpt41_cost_per_mtok
assert estimated_cost < 0.01 # Should be less than 1 cent
pytest.ini configuration
"""
[pytest]
asyncio_mode = auto
testpaths = tests
python_files = test_*.py
python_functions = test_*
addopts = -v --tb=short
markers =
slow: marks tests as slow
integration: marks tests requiring real API
"""Lỗi Thường Gặp Và Cách Khắc Phục
1. Lỗi "Connection timeout" Khi Switch Sang Real API
Nguyên nhân: Default timeout của httpx quá ngắn cho AI API với long context.
# FIX: Tăng timeout cho production
from httpx import Timeout
BAD - default 5s timeout
async with httpx.AsyncClient() as client:
response = await client.post(url, json=payload) # Timeout sau 5s
GOOD - configurable timeout
timeout = Timeout(
connect=10.0, # Connection timeout
read=120.0, # Read timeout (AI responses có thể lâu)
write=10.0,
pool=30.0 # Connection pool timeout
)
async with httpx.AsyncClient(timeout=timeout) as client:
response = await client.post(url, json=payload)2. Lỗi "403 Forbidden" - Sai API Key Hoặc Quyền
Nguyên nhân: API key không đúng format hoặc chưa kích hoạt quyền truy cập model.
# FIX: Validate và rotate API key
import os
class HolySheepClient:
def __init__(self, api_key: str = None):
self.api_key = api_key or os.getenv("HOLYSHEEP_API_KEY")
if not self.api_key:
raise ValueError("HOLYSHEEP_API_KEY not set")
# Validate key format
if not self.api_key.startswith("hs_"):
raise ValueError(f"Invalid API key format: {self.api_key[:10]}...")
self.base_url = "https://api.holysheep.ai/v1"
self.session = httpx.AsyncClient(
headers={"Authorization": f"Bearer {self.api_key}"}
)
async def validate_key(self) -> bool:
"""Validate API key bằng cách gọi models endpoint"""
try:
response = await self.session.get(f"{self.base_url}/models")
return response.status_code == 200
except httpx.HTTPStatusError as e:
if e.response.status_code == 401:
raise ValueError("Invalid API key - please check at https://www.holysheep.ai/api-keys")
raise
except httpx.RequestError:
raise ConnectionError("Cannot connect to HolySheep AI - check network")3. Lỗi "504 Gateway Timeout" - Model Quá Tải
Nguyên nhân: HolySheep AI server quá tải hoặc context quá dài.
# FIX: Implement exponential backoff và fallback
import asyncio
from typing import Optional
class ResilientAIRouter:
def __init__(self):
self.providers = [
{"name": "holysheep", "url": "https://api.holysheep.ai/v1", "priority": 1},
{"name": "backup", "url":