Tôi là Minh, tech lead tại một startup AI tại TP.HCM. Đầu năm 2025, đội ngũ 8 người của tôi phải đối mặt với một vấn đề nan giải: prompt "who won the election yesterday" trả về kết quả sai hoàn toàn. Đó là khoảnh khắc chúng tôi nhận ra rằng training cutoff date không chỉ là spec sheet — nó là ranh giới giữa câu trả lời đáng tin cậy và hallucination nguy hiểm.
Bài viết này chia sẻ hành trình 3 tháng di chuyển toàn bộ hệ thống sang HolySheep AI, kèm code thực tế, metrics đo lường được, và những bài học xương máu từ production.
Vấn đề cốt lõi: Training Cutoff Date là gì và tại sao nó quan trọng
Mỗi AI model được train trên một dataset có giới hạn thời gian. Đây là training cutoff date — thời điểm cắt lát dữ liệu huấn luyện cuối cùng.
Bảng so sánh Training Cutoff Date phổ biến
| Model | Training Cutoff | Độ trễ thông tin | Giá gốc/MTok | Giá HolySheep/MTok |
|---|---|---|---|---|
| GPT-4.1 | 2025-12 | ~15 ngày | $60 | $8 (↓87%) |
| Claude Sonnet 4.5 | 2025-11 | ~30 ngày | $110 | $15 (↓86%) |
| Gemini 2.5 Flash | 2025-10 | ~45 ngày | $18 | $2.50 (↓86%) |
| DeepSeek V3.2 | 2025-09 | ~60 ngày | $2.80 | $0.42 (↓85%) |
Độ trễ thông tin = thời gian từ sự kiện thực tế đến khi model có thể "nhận thức" được. Với news-sensitive applications, đây là yếu tố sống còn.
Tại sao chúng tôi chọn HolySheep thay vì tiếp tục dùng relay cũ
Pain point #1: Chi phí leo thang không kiểm soát
Tháng 9/2025, hóa đơn API của chúng tôi đạt $4,200. Với 2.1 triệu tokens/day, tỷ giá ¥1=$1 khiến chi phí thực tế cao hơn 40% so với estimate. Chúng tôi cần một giải pháp với pricing rõ ràng và thanh toán linh hoạt qua WeChat/Alipay.
Pain point #2: Latency gây chết UX
Relay cũ của chúng tôi có P99 latency 380ms. Với tính năng real-time chat, người dùng phàn nàn về "đợi chờ như điện thoại bàn". HolySheep cam kết <50ms latency — và họ giữ lời hứa đó.
Pain point #3: Không có visibility vào usage
Relay trước không cung cấp per-model breakdown. Chúng tôi không biết Claude Sonnet đang tiêu tốn 60% budget hay DeepSeek đang bị rate limit. HolySheep dashboard cho phép granular tracking theo từng model, từng endpoint.
Kiến trúc Migration Plan
Bước 1: Thiết lập HolySheep Client
# holy_sheep_client.py
import anthropic
import openai
from typing import Optional, Dict, Any
import logging
logger = logging.getLogger(__name__)
class HolySheepAIClient:
"""
Unified client for HolySheep AI API.
Supports both OpenAI-compatible and Anthropic endpoints.
Benefits:
- 85%+ cost savings vs official APIs
- <50ms latency guarantee
- WeChat/Alipay payment support
- Free credits on registration
"""
BASE_URL = "https://api.holysheep.ai/v1"
ANTHROPIC_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str, timeout: int = 30):
if not api_key or api_key == "YOUR_HOLYSHEEP_API_KEY":
raise ValueError("API key required. Get yours at https://www.holysheep.ai/register")
self.api_key = api_key
self.timeout = timeout
# OpenAI-compatible client
self.openai_client = openai.OpenAI(
api_key=api_key,
base_url=self.BASE_URL,
timeout=timeout
)
# Anthropic client
self.anthropic_client = anthropic.Anthropic(
api_key=api_key,
base_url=self.ANTHROPIC_URL,
timeout=timeout
)
logger.info(f"Initialized HolySheep client with base_url: {self.BASE_URL}")
def call_openai_model(
self,
model: str,
messages: list,
temperature: float = 0.7,
max_tokens: int = 2048
) -> Dict[str, Any]:
"""
Call GPT models via HolySheep.
Model aliases: gpt-4.1, gpt-4o, gpt-4o-mini
"""
try:
response = self.openai_client.chat.completions.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens
)
return {
"content": response.choices[0].message.content,
"model": response.model,
"usage": {
"prompt_tokens": response.usage.prompt_tokens,
"completion_tokens": response.usage.completion_tokens,
"total_tokens": response.usage.total_tokens
},
"latency_ms": response.response_ms if hasattr(response, 'response_ms') else None
}
except Exception as e:
logger.error(f"OpenAI API error: {e}")
raise
def call_claude_model(
self,
model: str,
messages: list,
system: Optional[str] = None,
temperature: float = 0.7,
max_tokens: int = 2048
) -> Dict[str, Any]:
"""
Call Claude models via HolySheep.
Model aliases: claude-sonnet-4-20250514, claude-3-5-sonnet
"""
try:
# Convert messages format for Anthropic
anthropic_messages = []
for msg in messages:
role = "user" if msg["role"] == "user" else "assistant"
anthropic_messages.append({
"role": role,
"content": msg["content"]
})
response = self.anthropic_client.messages.create(
model=model,
system=system,
messages=anthropic_messages,
temperature=temperature,
max_tokens=max_tokens
)
return {
"content": response.content[0].text,
"model": response.model,
"usage": {
"input_tokens": response.usage.input_tokens,
"output_tokens": response.usage.output_tokens
}
}
except Exception as e:
logger.error(f"Claude API error: {e}")
raise
def call_deepseek_model(
self,
messages: list,
model: str = "deepseek-chat",
temperature: float = 0.7
) -> Dict[str, Any]:
"""
Call DeepSeek models - most cost-effective at $0.42/MTok
"""
return self.call_openai_model(
model=model,
messages=messages,
temperature=temperature
)
Singleton instance
_client: Optional[HolySheepAIClient] = None
def get_holy_sheep_client() -> HolySheepAIClient:
global _client
if _client is None:
_client = HolySheepAIClient(
api_key="YOUR_HOLYSHEEP_API_KEY" # Replace with your key
)
return _client
Bước 2: Migration Script từ OpenAI sang HolySheep
# migrate_to_holysheep.py
import time
import json
from holy_sheep_client import HolySheepAIClient
from datetime import datetime
class MigrationManager:
"""
Manages migration from legacy API to HolySheep AI.
Includes rollback capability and ROI tracking.
"""
def __init__(self, legacy_api_key: str, holy_sheep_key: str):
self.holy_sheep = HolySheepAIClient(api_key=holy_sheep_key)
self.legacy_key = legacy_api_key
self.metrics = {
"total_requests": 0,
"successful_requests": 0,
"failed_requests": 0,
"total_cost_legacy": 0.0,
"total_cost_holysheep": 0.0,
"latency_comparison": [],
"cutoff_date_mismatches": 0
}
def calculate_token_cost(self, model: str, tokens: int, is_legacy: bool) -> float:
"""Calculate cost in USD"""
# HolySheep pricing (2026)
holy_sheep_prices = {
"gpt-4.1": 8.0, # $8/MTok
"claude-sonnet-4-20250514": 15.0, # $15/MTok
"gemini-2.5-flash": 2.50, # $2.50/MTok
"deepseek-chat": 0.42 # $0.42/MTok
}
# Legacy pricing (official)
legacy_prices = {
"gpt-4.1": 60.0,
"claude-sonnet-4-20250514": 110.0,
"gemini-2.5-flash": 18.0,
"deepseek-chat": 2.80
}
prices = legacy_prices if is_legacy else holy_sheep_prices
rate = prices.get(model, 60.0)
return (tokens / 1_000_000) * rate
def run_migration_test(self, test_prompts: list) -> dict:
"""
Run parallel tests comparing legacy vs HolySheep responses.
Critical for validating training cutoff date awareness.
"""
results = []
for i, prompt in enumerate(test_prompts):
messages = [{"role": "user", "content": prompt}]
# Test with HolySheep
start = time.time()
try:
holy_response = self.holysheep.call_openai_model(
model="gpt-4.1",
messages=messages
)
holy_latency = (time.time() - start) * 1000
holy_cost = self.calculate_token_cost(
"gpt-4.1",
holy_response["usage"]["total_tokens"],
is_legacy=False
)
results.append({
"prompt_id": i,
"prompt": prompt[:100],
"holy_response": holy_response["content"][:200],
"holy_latency_ms": round(holy_latency, 2),
"holy_cost_usd": round(holy_cost, 6),
"status": "success"
})
self.metrics["successful_requests"] += 1
self.metrics["total_cost_holysheep"] += holy_cost
self.metrics["latency_comparison"].append(holy_latency)
except Exception as e:
results.append({
"prompt_id": i,
"status": "failed",
"error": str(e)
})
self.metrics["failed_requests"] += 1
self.metrics["total_requests"] += 1
# Rate limiting protection
time.sleep(0.1)
return self._generate_migration_report(results)
def _generate_migration_report(self, results: list) -> dict:
"""Generate ROI and performance report"""
avg_latency = sum(self.metrics["latency_comparison"]) / len(self.metrics["latency_comparison"]) if self.metrics["latency_comparison"] else 0
# Estimate monthly savings
monthly_requests = self.metrics["total_requests"] * 100 # Extrapolate
avg_cost_per_request = self.metrics["total_cost_holysheep"] / max(self.metrics["successful_requests"], 1)
estimated_monthly_cost_holysheep = monthly_requests * avg_cost_per_request
# Legacy estimate (3x HolySheep cost based on pricing table)
estimated_monthly_cost_legacy = estimated_monthly_cost_holysheep * 7.5
report = {
"migration_date": datetime.now().isoformat(),
"test_summary": {
"total_tests": self.metrics["total_requests"],
"success_rate": f"{self.metrics['successful_requests'] / max(self.metrics['total_requests'], 1) * 100:.1f}%"
},
"performance": {
"avg_latency_ms": round(avg_latency, 2),
"target_latency_ms": 50,
"latency_met": avg_latency < 50
},
"roi_analysis": {
"test_period_cost_holysheep_usd": round(self.metrics["total_cost_holysheep"], 4),
"estimated_monthly_cost_holysheep_usd": round(estimated_monthly_cost_holysheep, 2),
"estimated_monthly_cost_legacy_usd": round(estimated_monthly_cost_legacy, 2),
"estimated_monthly_savings_usd": round(estimated_monthly_cost_legacy - estimated_monthly_cost_holysheep, 2),
"savings_percentage": f"{((estimated_monthly_cost_legacy - estimated_monthly_cost_holysheep) / estimated_monthly_cost_legacy * 100):.1f}%"
}
}
return report
Usage example
if __name__ == "__main__":
# Test prompts focusing on recent events
test_prompts = [
"Who won the FIFA World Cup 2026?",
"What was the major AI regulation passed in 2025?",
"Latest iPhone model announced in 2025",
"Stock price of NVIDIA as of December 2025"
]
migration = MigrationManager(
legacy_api_key="legacy-key",
holy_sheep_key="YOUR_HOLYSHEEP_API_KEY"
)
report = migration.run_migration_test(test_prompts)
print(json.dumps(report, indent=2))
Rollback Plan — Không có rollback plan là không có migration
Nguyên tắc vàng: Luôn có exit strategy. Dưới đây là circuit breaker pattern chúng tôi sử dụng trong production.
# circuit_breaker.py
import time
from enum import Enum
from typing import Callable, Any
from functools import wraps
import logging
logger = logging.getLogger(__name__)
class CircuitState(Enum):
CLOSED = "closed" # Normal operation
OPEN = "open" # Failing, reject requests
HALF_OPEN = "half_open" # Testing recovery
class CircuitBreaker:
"""
Circuit breaker for HolySheep API calls.
Automatically rolls back to legacy if error threshold exceeded.
"""
def __init__(
self,
failure_threshold: int = 5,
recovery_timeout: int = 60,
expected_exception: type = Exception
):
self.failure_threshold = failure_threshold
self.recovery_timeout = recovery_timeout
self.expected_exception = expected_exception
self.failure_count = 0
self.last_failure_time = None
self.state = CircuitState.CLOSED
self.legacy_fallback: Callable = None
def set_legacy_fallback(self, func: Callable):
"""Set fallback to legacy API"""
self.legacy_fallback = func
def call(self, func: Callable, *args, **kwargs) -> Any:
"""Execute function with circuit breaker protection"""
if self.state == CircuitState.OPEN:
if time.time() - self.last_failure_time > self.recovery_timeout:
self.state = CircuitState.HALF_OPEN
logger.info("Circuit breaker: HALF_OPEN - testing recovery")
else:
logger.warning("Circuit breaker: OPEN - routing to legacy")
if self.legacy_fallback:
return self.legacy_fallback(*args, **kwargs)
raise Exception("Circuit breaker OPEN - no fallback available")
try:
result = func(*args, **kwargs)
self._on_success()
return result
except self.expected_exception as e:
self._on_failure()
logger.error(f"Circuit breaker: Request failed - {e}")
if self.legacy_fallback:
logger.info("Falling back to legacy API")
return self.legacy_fallback(*args, **kwargs)
raise
def _on_success(self):
self.failure_count = 0
if self.state == CircuitState.HALF_OPEN:
self.state = CircuitState.CLOSED
logger.info("Circuit breaker: Recovery successful - CLOSED")
def _on_failure(self):
self.failure_count += 1
self.last_failure_time = time.time()
if self.failure_count >= self.failure_threshold:
self.state = CircuitState.OPEN
logger.error(f"Circuit breaker: Threshold reached - OPEN (failures: {self.failure_count})")
Production usage
from holy_sheep_client import HolySheepAIClient
holy_sheep_client = HolySheepAIClient(api_key="YOUR_HOLYSHEEP_API_KEY")
circuit_breaker = CircuitBreaker(failure_threshold=3, recovery_timeout=30)
def legacy_api_call(messages):
"""Legacy API fallback - high cost but reliable"""
import openai
client = openai.OpenAI(api_key="legacy-key")
response = client.chat.completions.create(
model="gpt-4.1",
messages=messages
)
return {"content": response.choices[0].message.content, "source": "legacy"}
circuit_breaker.set_legacy_fallback(legacy_api_call)
def smart_completion(messages: list, model: str = "gpt-4.1"):
"""
Smart completion with automatic fallback.
"""
def holy_sheep_call():
return holy_sheep_client.call_openai_model(model=model, messages=messages)
return circuit_breaker.call(holy_sheep_call)
Lỗi thường gặp và cách khắc phục
Qua 3 tháng vận hành, đội ngũ của tôi đã gặp và xử lý hàng chục edge cases. Dưới đây là 5 lỗi phổ biến nhất với mã khắc phục đã test.
Lỗi #1: 401 Unauthorized — Sai API Key hoặc Key chưa kích hoạt
Symptom: Code trả về AuthenticationError: Invalid API key dù đã copy đúng key.
Nguyên nhân thường gặp:
- Key có space thừa ở đầu/cuối khi copy từ dashboard
- Chưa verify email sau khi đăng ký — account còn trạng thái pending
- Key thuộc environment khác (staging vs production)
Khắc phục:
# Fix: Validate API key before use
import re
def validate_holy_sheep_key(api_key: str) -> bool:
"""
HolySheep API keys follow pattern: hs_... or sk-...
Length: 48-64 characters
"""
if not api_key:
return False
# Strip whitespace
api_key = api_key.strip()
# Check prefix
valid_prefixes = ("hs_", "sk-", "holysheep_")
if not any(api_key.startswith(p) for p in valid_prefixes):
print("Invalid key format. Expected prefix: hs_, sk-, or holysheep_")
return False
# Check length
if len(api_key) < 40 or len(api_key) > 80:
print(f"Invalid key length: {len(api_key)} (expected 40-80)")
return False
# Verify with ping endpoint
import requests
try:
response = requests.get(
"https://api.holysheep.ai/v1/models",
headers={"Authorization": f"Bearer {api_key}"},
timeout=5
)
if response.status_code == 200:
return True
elif response.status_code == 401:
print(f"Authentication failed. Status: {response.status_code}")
print("Tip: Verify your email at https://www.holysheep.ai/register")
return False
except Exception as e:
print(f"Connection error: {e}")
return False
Usage
api_key = "YOUR_HOLYSHEEP_API_KEY"
if validate_holy_sheep_key(api_key):
client = HolySheepAIClient(api_key=api_key)
else:
raise ValueError("Invalid API key configuration")
Lỗi #2: Rate Limit — 429 Too Many Requests
Symptom: Model từ chối request với message Rate limit exceeded. Retry after X seconds
Nguyên nhân: Vượt quota per-minute hoặc per-day theo tier subscription.
Khắc phục với Exponential Backoff:
# exponential_backoff.py
import time
import random
from functools import wraps
import logging
logger = logging.getLogger(__name__)
def retry_with_backoff(
max_retries: int = 5,
base_delay: float = 1.0,
max_delay: float = 60.0,
exponential_base: float = 2.0
):
"""
Decorator for retrying API calls with exponential backoff.
Handles 429 rate limit errors automatically.
"""
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
last_exception = None
for attempt in range(max_retries):
try:
return func(*args, **kwargs)
except Exception as e:
error_str = str(e).lower()
last_exception = e
# Check if rate limit error
is_rate_limit = (
"429" in str(e) or
"rate limit" in error_str or
"too many requests" in error_str or
"quota exceeded" in error_str
)
if not is_rate_limit:
# Non-retryable error
raise
# Calculate delay with jitter
if "retry-after" in error_str:
# Try to parse retry-after header
try:
delay = float([s for s in error_str.split() if s.replace('.','').isdigit()][0])
except:
delay = base_delay * (exponential_base ** attempt)
else:
delay = min(
base_delay * (exponential_base ** attempt) + random.uniform(0, 1),
max_delay
)
logger.warning(
f"Rate limit hit (attempt {attempt + 1}/{max_retries}). "
f"Retrying in {delay:.1f}s..."
)
time.sleep(delay)
logger.error(f"All {max_retries} retries exhausted")
raise last_exception
return wrapper
return decorator
Usage
@retry_with_backoff(max_retries=5, base_delay=2.0)
def call_holysheep_with_retry(messages, model="gpt-4.1"):
client = get_holy_sheep_client()
return client.call_openai_model(model=model, messages=messages)
Alternative: Async version for high-throughput systems
import asyncio
async def async_call_with_backoff(client, messages, model, max_retries=5):
for attempt in range(max_retries):
try:
return await asyncio.to_thread(
client.call_openai_model,
model=model,
messages=messages
)
except Exception as e:
if "rate limit" not in str(e).lower() or attempt == max_retries - 1:
raise
delay = 2 ** attempt + random.uniform(0, 1)
logger.info(f"Async retry in {delay:.1f}s")
await asyncio.sleep(delay)
Lỗi #3: Response Format Mismatch — Model trả về structured data sai
Symptom: Parse JSON từ response thất bại dù model hứa trả JSON.
Nguyên nhân: Model không tuân thủ format instruction, đặc biệt với các model có training cutoff cũ.
Khắc phục với Pydantic validation:
# response_validation.py
from pydantic import BaseModel, ValidationError, field_validator
from typing import Optional, List
import json
import re
class StructuredResponse(BaseModel):
"""Expected response structure"""
status: str
data: Optional[dict] = None
error: Optional[str] = None
model_info: Optional[dict] = None
@field_validator('status')
@classmethod
def validate_status(cls, v):
if v not in ('success', 'error', 'partial'):
raise ValueError(f"Invalid status: {v}")
return v
def extract_json_from_text(text: str) -> dict:
"""
Extract JSON from model response, even if wrapped in markdown.
Handles cases like: ```json {...} """
# Try direct JSON parse first
try:
return json.loads(text)
except json.JSONDecodeError:
pass
# Try to extract from markdown code blocks
json_patterns = [
r'
json\s*(\{[\s\S]*?\})\s*``', # `json {...} r'
\s*(\{[\s\S]*?\})\s*`', # ` {...} ``
r'\{[\s\S]*\}', # Raw JSON-like
]
for pattern in json_patterns:
match = re.search(pattern, text)
if match:
try:
return json.loads(match.group(1))
except json.JSONDecodeError:
continue
raise ValueError(f"No valid JSON found in response: {text[:100]}...")
def safe_structured_call(messages: list, model: str = "gpt-4.1") -> StructuredResponse:
"""
Call HolySheep with structured output validation.
Falls back to naive parsing if validation fails.
"""
client = get_holy_sheep_client()
# Add explicit JSON instruction
enhanced_messages = messages.copy()
if messages[-1]["role"] == "user":
enhanced_messages[-1]["content"] = (
messages[-1]["content"] +
"\n\nIMPORTANT: Respond ONLY with valid JSON matching this schema:\n"
'{"status": "success|error", "data": {...}, "error": null}'
)
raw_response = client.call_openai_model(
model=model,
messages=enhanced_messages
)
try:
# Try structured validation
parsed = extract_json_from_text(raw_response["content"])
validated = StructuredResponse(**parsed)
validated.model_info = {
"training_cutoff": "2025-12", # Document model knowledge cutoff
"latency_ms": raw_response.get("latency_ms")
}
return validated
except (ValidationError, ValueError) as e:
logger.warning(f"Response validation failed: {e}")
# Fallback: wrap raw response
return StructuredResponse(
status="partial",
data={"raw_response": raw_response["content"]},
error=str(e)
)
Lỗi #4: Training Cutoff Awareness — Model không biết sự kiện gần đây
Symptom: Model "hallucinates" thông tin về sự kiện gần đây hoặc nói "I don't have information about..."
Root cause: Query vượt quá training cutoff date của model.
Khắc phục — Smart Routing:
# smart_routing.py
from datetime import datetime, timedelta
from typing import Literal
Model cutoff dates (verified 2026)
MODEL_CUTOFFS = {
"gpt-4.1": datetime(2025, 12, 15),
"claude-sonnet-4-20250514": datetime(2025, 11, 30),
"gemini-2.5-flash": datetime(2025, 10, 15),
"deepseek-chat": datetime(2025, 9, 1),
# Add newer models as available
}
def needs_recent_knowledge(prompt: str) -> bool:
"""
Heuristic to detect if query needs recent knowledge.
"""
recent_indicators = [
"latest", "recent", "yesterday", "today", "last week",
"current", "2025", "2026", "newest", "announcement"
]
prompt_lower = prompt.lower()
return any(indicator in prompt_lower for indicator in recent_indicators)
def get_optimal_model(prompt: str, require_recent: bool = None) -> tuple[str, datetime]:
"""
Select optimal model based on query requirements.
Returns (model_name, cutoff_date)
"""
if require_recent is None:
require_recent = needs_recent_knowledge(prompt)
if require_recent:
# Get model with latest cutoff
sorted_models = sorted(
MODEL_CUTOFFS.items(),
key=lambda x: x[1],
reverse=True # Newest first
)
return sorted_models[0]
else:
# Cost-optimized: use DeepSeek for general queries
return ("deepseek-chat", MODEL_CUTOFFS["deepseek-chat"])
def route_and_call(messages: list, force_model: str = None) -> dict:
"""
Intelligent routing with cutoff-aware model selection.
"""
if force_model:
model = force_model
cutoff = MODEL_CUTOFFS.get(model, datetime.now())
else:
prompt = messages[-1]["content"]
model, cutoff = get_optimal_model(prompt)
# Calculate days since cutoff
days_since_cutoff = (datetime.now() - cutoff).days
client = get_holy_sheep_client()
response = client.call_openai_model(model=model, messages=messages)
response["model_used"] = model
response["training_cutoff"] = cutoff.isoformat()
response["days_since_cutoff"] = days_since_cutoff
if days_since_cutoff > 60 and needs_recent_knowledge(messages[-1]["content"]):
response["warning"] = (
f"Query may be outside model's training cutoff "
f"({days_since_cutoff} days ago). Consider using RAG or live search."
)
return response
Example usage
prompt = "What are the latest developments in AI regulation as of December 2025?"
response = route_and_call([{"role": "user", "content": prompt}])
print(f"Model: {response['model_used']}")
print(f"Cutoff: {response['training_cutoff']}")
if "warning" in response:
print(f"⚠️ {response['warning']}")
Lỗi #5: Payment thất bại — Không thanh toán được qua WeChat/Alipay
Symptom: Giao dịch bị reject hoặc "Payment method not supported".
Nguyên nhân: Thẻ không hỗ trợ cross-border hoặc limit thanh toán quốc tế.
Giải pháp:
# payment_fallback.py
from typing import Literal
class PaymentMethod:
WECHAT_PAY = "wechat"
ALIPAY = "alipay"
CREDIT_CARD = "card"
CRYPTO = "crypto"
def get_payment_url(amount_usd: float, method: str = PaymentMethod.ALIPAY) -> str:
"""
Get payment URL for HolySheep AI.