HolySheep Agent 工程落地指南：限流重试、SLA 监控、故障切换一体化配置实操

Khi triển khai AI Agent production-ready, có ba vấn đề kỹ thuật khiến developer đau đầu nhất: rate limiting, retry strategy và failover khi provider gặp sự cố. Bài viết này sẽ hướng dẫn bạn cấu hình một hệ thống HolySheep Agent với độ trễ thực tế dưới 50ms, chi phí tiết kiệm 85% so với API chính thức, và quan trọng nhất — zero downtime khi upstream provider có vấn đề.

Kết luận: Với mức giá DeepSeek V3.2 chỉ $0.42/MTok, hỗ trợ thanh toán WeChat/Alipay, và free credits khi đăng ký tại HolySheep AI, đây là giải pháp tối ưu cho team production muốn deploy AI agent với chi phí thấp nhưng uptime cao.

So sánh HolySheep với API chính thức và đối thủ

Tiêu chí	HolySheep AI	OpenAI API	Anthropic API	Google Gemini
GPT-4.1	$8/MTok	$8/MTok	-	-
Claude Sonnet 4.5	$15/MTok	-	$15/MTok	-
Gemini 2.5 Flash	$2.50/MTok	-	-	$2.50/MTok
DeepSeek V3.2	$0.42/MTok ⭐	-	-	-
Độ trễ trung bình	<50ms	200-500ms	300-800ms	150-400ms
Thanh toán	WeChat/Alipay/USD	Card quốc tế	Card quốc tế	Card quốc tế
Tín dụng miễn phí	✅ Có	❌ Không	❌ Không	✅ $300
Rate limit	Tùy gói	500 RPM	1000 RPM	60 RPM

Phù hợp / không phù hợp với ai

✅ Nên dùng HolySheep nếu bạn thuộc nhóm:

• Team startup/SaaS cần giảm chi phí AI xuống mức tối thiểu (tiết kiệm 85%+ với DeepSeek V3.2)
• Developer Trung Quốc/Đông Nam Á — thanh toán qua WeChat/Alipay thuận tiện
• Production system cần failover tự động và retry logic phức tạp
• Batch processing với volume lớn (DeepSeek V3.2 chỉ $0.42/MTok)
• Multi-agent system cần latency thấp (<50ms) để các agent giao tiếp nhanh

❌ Không nên dùng HolySheep nếu:

• Cần model độc quyền không có trên HolySheep
• Yêu cầu compliance HIPAA/GDPR chưa được hỗ trợ
• Hệ thống chỉ dùng được thẻ tín dụng quốc tế (không hỗ trợ WeChat/Alipay)

Giá và ROI

Dưới đây là bảng tính ROI thực tế khi migrate từ OpenAI sang HolySheep:

Use case	Volume/tháng	OpenAI chi phí	HolySheep chi phí	Tiết kiệm
Chatbot trung bình	10M tokens	$80	$42 (DeepSeek V3.2)	47.5%
AI agent workflow	50M tokens	$400	$21	94.75%
Enterprise batch	500M tokens	$4000	$210	94.75%

Vì sao chọn HolySheep

1. Chi phí thấp nhất thị trường — DeepSeek V3.2 chỉ $0.42/MTok, rẻ hơn 95% so với GPT-4
2. Latency <50ms — Nhanh hơn 4-10x so với API chính thức
3. Tín dụng miễn phí khi đăng ký — Không rủi ro khi test
4. Thanh toán linh hoạt — WeChat/Alipay/USD phù hợp developer châu Á
5. API tương thích OpenAI — Migrate dễ dàng, không cần thay đổi code nhiều

Cấu hình HolySheep Agent — Thực chiến

Tôi đã deploy hệ thống AI agent cho 3 startup và mỗi lần gặp cùng một vấn đề: rate limit 429 khi traffic tăng đột biến, timeout khi upstream slow, và failover không smooth giữa các provider. Phần này sẽ hướng dẫn bạn config một hệ thống hoàn chỉnh.

1. Cài đặt SDK và Client Base

# Cài đặt dependencies
pip install httpx aiohttp tenacity asyncio-rate-limiter

Hoặc sử dụng package manager
uv add httpx aiohttp tenacity

Cấu trúc project
"""
holy_sheep_agent/
├── config.py           # Cấu hình rate limit, retry, SLA
├── client.py           # HolySheep HTTP client với retry logic
├── failover.py         # Circuit breaker và failover manager
├── sla_monitor.py      # SLA monitoring và alerting
├── main.py             # Entry point
└── requirements.txt
"""

Cấu hình .env
echo "HOLYSHEEP_API_KEY=YOUR_HOLYSHEEP_API_KEY" > .env
echo "HOLYSHEEP_BASE_URL=https://api.holysheep.ai/v1" >> .env

2. Client với Retry Logic và Rate Limiting

import os
import time
import httpx
import asyncio
from tenacity import (
    retry,
    stop_after_attempt,
    wait_exponential,
    retry_if_exception_type
)
from typing import Optional, Dict, Any

Cấu hình từ environment
HOLYSHEEP_API_KEY = os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"

class HolySheepClient:
    """
    HolySheep AI Client với:
    - Automatic retry với exponential backoff
    - Rate limiting (requests per minute)
    - Timeout handling
    - Circuit breaker pattern
    """
    
    def __init__(
        self,
        api_key: str = HOLYSHEEP_API_KEY,
        base_url: str = HOLYSHEEP_BASE_URL,
        rpm_limit: int = 100,  # Requests per minute
        timeout: float = 30.0,
        max_retries: int = 5
    ):
        self.api_key = api_key
        self.base_url = base_url
        self.rpm_limit = rpm_limit
        self.timeout = timeout
        self.max_retries = max_retries
        
        # Rate limiter state
        self._request_timestamps: list = []
        self._lock = asyncio.Lock()
        
        # HTTP client
        self._client = httpx.AsyncClient(
            timeout=httpx.Timeout(timeout),
            headers={
                "Authorization": f"Bearer {api_key}",
                "Content-Type": "application/json"
            }
        )
    
    async def _check_rate_limit(self):
        """Kiểm tra và enforce rate limit"""
        async with self._lock:
            now = time.time()
            # Remove requests older than 60 seconds
            self._request_timestamps = [
                ts for ts in self._request_timestamps 
                if now - ts < 60
            ]
            
            if len(self._request_timestamps) >= self.rpm_limit:
                # Calculate wait time
                oldest = self._request_timestamps[0]
                wait_seconds = 60 - (now - oldest) + 1
                await asyncio.sleep(wait_seconds)
                self._request_timestamps = []
            
            self._request_timestamps.append(time.time())
    
    @retry(
        retry=retry_if_exception_type((httpx.HTTPStatusError, httpx.TimeoutException)),
        stop=stop_after_attempt(5),
        wait=wait_exponential(multiplier=1, min=1, max=30)
    )
    async def chat_completions(
        self,
        model: str = "deepseek-v3.2",
        messages: list[dict],
        temperature: float = 0.7,
        max_tokens: int = 2048,
        **kwargs
    ) -> Dict[str, Any]:
        """
        Gửi request đến HolySheep Chat Completions API
        
        Args:
            model: Model name (deepseek-v3.2, gpt-4.1, claude-sonnet-4.5, etc.)
            messages: List of message objects
            temperature: Sampling temperature (0-2)
            max_tokens: Maximum tokens to generate
        
        Returns:
            API response as dictionary
        """
        await self._check_rate_limit()
        
        endpoint = f"{self.base_url}/chat/completions"
        payload = {
            "model": model,
            "messages": messages,
            "temperature": temperature,
            "max_tokens": max_tokens,
            **kwargs
        }
        
        async with self._client.stream("POST", endpoint, json=payload) as response:
            if response.status_code == 429:
                # Rate limited - let tenacity handle retry
                raise httpx.HTTPStatusError(
                    "Rate limit exceeded",
                    request=response.request,
                    response=response
                )
            response.raise_for_status()
            return await response.json()
    
    async def embeddings(
        self,
        model: str = "text-embedding-3-large",
        input_text: str | list[str],
        **kwargs
    ) -> Dict[str, Any]:
        """Tạo embeddings qua HolySheep"""
        await self._check_rate_limit()
        
        endpoint = f"{self.base_url}/embeddings"
        payload = {
            "model": model,
            "input": input_text,
            **kwargs
        }
        
        response = await self._client.post(endpoint, json=payload)
        response.raise_for_status()
        return response.json()
    
    async def close(self):
        """Cleanup HTTP client"""
        await self._client.aclose()

3. Circuit Breaker và Failover Manager

import asyncio
import time
from enum import Enum
from typing import Callable, Any, Optional
from dataclasses import dataclass, field

class CircuitState(Enum):
    CLOSED = "closed"      # Normal operation
    OPEN = "open"          # Failing, reject requests
    HALF_OPEN = "half_open"  # Testing recovery

@dataclass
class CircuitBreakerConfig:
    failure_threshold: int = 5      # Open after N failures
    success_threshold: int = 3      # Close after N successes (half-open)
    timeout: float = 30.0          # Seconds before half-open
    half_open_max_calls: int = 3   # Max calls in half-open state

@dataclass
class CircuitBreakerMetrics:
    total_calls: int = 0
    successful_calls: int = 0
    failed_calls: int = 0
    rejected_calls: int = 0
    last_failure_time: Optional[float] = None
    state_history: list = field(default_factory=list)

class CircuitBreaker:
    """
    Circuit Breaker Pattern cho HolySheep API
    
    States:
    - CLOSED: Normal operation, all requests pass through
    - OPEN: Circuit is broken, reject requests immediately
    - HALF_OPEN: Testing if service recovered
    """
    
    def __init__(self, config: CircuitBreakerConfig = None):
        self.config = config or CircuitBreakerConfig()
        self.state = CircuitState.CLOSED
        self.failure_count = 0
        self.success_count = 0
        self.last_failure_time: Optional[float] = None
        self.half_open_calls = 0
        self.metrics = CircuitBreakerMetrics()
    
    async def call(self, func: Callable, *args, **kwargs) -> Any:
        """Execute function with circuit breaker protection"""
        self.metrics.total_calls += 1
        
        # Check if circuit should transition
        if self.state == CircuitState.OPEN:
            if self._should_attempt_reset():
                self.state = CircuitState.HALF_OPEN
                self.half_open_calls = 0
            else:
                self.metrics.rejected_calls += 1
                raise CircuitBreakerOpenError(
                    f"Circuit breaker is OPEN. Last failure: {self.last_failure_time}"
                )
        
        # Execute call
        try:
            result = await func(*args, **kwargs)
            self._on_success()
            return result
        except Exception as e:
            self._on_failure()
            raise
    
    def _should_attempt_reset(self) -> bool:
        """Check if timeout has passed for half-open transition"""
        if self.last_failure_time is None:
            return True
        return (time.time() - self.last_failure_time) >= self.config.timeout
    
    def _on_success(self):
        """Handle successful call"""
        self.metrics.successful_calls += 1
        self.failure_count = 0
        
        if self.state == CircuitState.HALF_OPEN:
            self.success_count += 1
            self.half_open_calls += 1
            
            if self.success_count >= self.config.success_threshold:
                self.state = CircuitState.CLOSED
                self.success_count = 0
        elif self.state == CircuitState.CLOSED:
            # Keep track of recent successes
            pass
    
    def _on_failure(self):
        """Handle failed call"""
        self.metrics.failed_calls += 1
        self.failure_count += 1
        self.last_failure_time = time.time()
        
        if self.state == CircuitState.HALF_OPEN:
            # Immediate open on failure in half-open
            self.state = CircuitState.OPEN
        elif self.failure_count >= self.config.failure_threshold:
            self.state = CircuitState.OPEN
    
    def get_status(self) -> dict:
        return {
            "state": self.state.value,
            "failure_count": self.failure_count,
            "success_count": self.success_count,
            "metrics": self.metrics.__dict__
        }

class CircuitBreakerOpenError(Exception):
    """Raised when circuit breaker is open"""
    pass

class FailoverManager:
    """
    Quản lý failover giữa multiple API providers
    
    Priority:
    1. HolySheep DeepSeek V3.2 (rẻ nhất, nhanh nhất)
    2. HolySheep GPT-4.1 (fallback model)
    3. External provider (emergency fallback)
    """
    
    def __init__(self, client: HolySheepClient):
        self.client = client
        
        # Circuit breakers for each model
        self.circuit_breakers = {
            "deepseek-v3.2": CircuitBreaker(CircuitBreakerConfig(
                failure_threshold=3,
                timeout=30
            )),
            "gpt-4.1": CircuitBreaker(CircuitBreakerConfig(
                failure_threshold=5,
                timeout=60
            ))
        }
        
        # Fallback order
        self.model_priority = ["deepseek-v3.2", "gpt-4.1"]
    
    async def chat_with_failover(
        self,
        messages: list[dict],
        **kwargs
    ) -> dict:
        """Execute chat with automatic failover"""
        errors = []
        
        for model in self.model_priority:
            cb = self.circuit_breakers[model]
            
            try:
                # Check circuit breaker
                if cb.state == CircuitState.OPEN:
                    continue
                
                result = await cb.call(
                    self.client.chat_completions,
                    model=model,
                    messages=messages,
                    **kwargs
                )
                
                # Add metadata about which model was used
                result["_meta"] = {
                    "model_used": model,
                    "circuit_state": cb.state.value,
                    "latency_ms": result.get("response_ms", 0)
                }
                
                return result
                
            except CircuitBreakerOpenError:
                errors.append(f"{model}: Circuit breaker open")
                continue
            except Exception as e:
                errors.append(f"{model}: {str(e)}")
                continue
        
        # All models failed
        raise AllProvidersFailedError(
            f"All models failed. Errors: {errors}"
        )
    
    def get_health_status(self) -> dict:
        """Get health status of all circuit breakers"""
        return {
            model: cb.get_status()
            for model, cb in self.circuit_breakers.items()
        }

class AllProvidersFailedError(Exception):
    """All upstream providers have failed"""
    pass

4. SLA Monitor và Alerting

import asyncio
import time
from dataclasses import dataclass, field
from typing import Callable, Optional
from collections import deque

@dataclass
class SLAMetrics:
    """SLA metrics tracking"""
    total_requests: int = 0
    successful_requests: int = 0
    failed_requests: int = 0
    timeout_requests: int = 0
    total_latency_ms: float = 0.0
    min_latency_ms: float = float('inf')
    max_latency_ms: float = 0.0
    latencies: deque = field(default_factory=lambda: deque(maxlen=1000))
    error_log: deque = field(default_factory=lambda: deque(maxlen=100))

@dataclass
class SLAConfig:
    """SLA thresholds"""
    latency_p99_threshold_ms: float = 2000  # P99 latency must be under 2s
    latency_p95_threshold_ms: float = 1000   # P95 latency must be under 1s
    availability_target: float = 0.999       # 99.9% uptime
    error_rate_threshold: float = 0.01       # Max 1% error rate
    consecutive_failures_alert: int = 5      # Alert after 5 consecutive failures

class SLAMonitor:
    """
    SLA Monitoring cho HolySheep Agent
    
    Tracks:
    - Request success/failure rates
    - Latency percentiles (P50, P95, P99)
    - Error types distribution
    - Alert on SLA breach
    """
    
    def __init__(self, config: SLAConfig = None):
        self.config = config or SLAConfig()
        self.metrics = SLAMetrics()
        self._lock = asyncio.Lock()
        
        # Alert callbacks
        self._alert_callbacks: list[Callable] = []
        
        # Tracking
        self._consecutive_failures = 0
        self._start_time = time.time()
    
    def add_alert_callback(self, callback: Callable):
        """Add callback for SLA alerts"""
        self._alert_callbacks.append(callback)
    
    async def track_request(
        self,
        latency_ms: float,
        success: bool,
        error_type: Optional[str] = None,
        error_message: Optional[str] = None
    ):
        """Track a single request"""
        async with self._lock:
            self.metrics.total_requests += 1
            
            if success:
                self.metrics.successful_requests += 1
                self._consecutive_failures = 0
            else:
                self.metrics.failed_requests += 1
                self._consecutive_failures += 1
                
                # Track error
                self.metrics.error_log.append({
                    "timestamp": time.time(),
                    "type": error_type,
                    "message": error_message
                })
                
                # Check consecutive failures alert
                if self._consecutive_failures >= self.config.consecutive_failures_alert:
                    await self._trigger_alert(
                        "consecutive_failures",
                        f"{self._consecutive_failures} consecutive failures detected"
                    )
                
                if error_type == "TimeoutError":
                    self.metrics.timeout_requests += 1
            
            # Update latency metrics
            self.metrics.latencies.append(latency_ms)
            self.metrics.total_latency_ms += latency_ms
            self.metrics.min_latency_ms = min(
                self.metrics.min_latency_ms, 
                latency_ms
            )
            self.metrics.max_latency_ms = max(
                self.metrics.max_latency_ms, 
                latency_ms
            )
    
    async def _trigger_alert(self, alert_type: str, message: str):
        """Trigger alert via callbacks"""
        alert = {
            "type": alert_type,
            "message": message,
            "timestamp": time.time(),
            "metrics": self.get_current_metrics()
        }
        
        for callback in self._alert_callbacks:
            try:
                await callback(alert)
            except Exception as e:
                print(f"Alert callback error: {e}")
    
    def _calculate_percentile(self, percentile: float) -> float:
        """Calculate latency percentile"""
        if not self.metrics.latencies:
            return 0
        
        sorted_latencies = sorted(self.metrics.latencies)
        index = int(len(sorted_latencies) * percentile / 100)
        return sorted_latencies[min(index, len(sorted_latencies) - 1)]
    
    def get_current_metrics(self) -> dict:
        """Get current SLA metrics"""
        uptime_seconds = time.time() - self._start_time
        
        # Calculate derived metrics
        avg_latency = (
            self.metrics.total_latency_ms / self.metrics.total_requests 
            if self.metrics.total_requests > 0 
            else 0
        )
        
        error_rate = (
            self.metrics.failed_requests / self.metrics.total_requests 
            if self.metrics.total_requests > 0 
            else 0
        )
        
        availability = (
            self.metrics.successful_requests / self.metrics.total_requests 
            if self.metrics.total_requests > 0 
            else 1.0
        )
        
        return {
            "uptime_seconds": uptime_seconds,
            "total_requests": self.metrics.total_requests,
            "successful_requests": self.metrics.successful_requests,
            "failed_requests": self.metrics.failed_requests,
            "error_rate": error_rate,
            "availability": availability,
            "latency": {
                "min_ms": self.metrics.min_latency_ms,
                "max_ms": self.metrics.max_latency_ms,
                "avg_ms": avg_latency,
                "p50_ms": self._calculate_percentile(50),
                "p95_ms": self._calculate_percentile(95),
                "p99_ms": self._calculate_percentile(99)
            },
            "consecutive_failures": self._consecutive_failures,
            "sla_compliance": {
                "p99_compliant": self._calculate_percentile(99) <= self.config.latency_p99_threshold_ms,
                "p95_compliant": self._calculate_percentile(95) <= self.config.latency_p95_threshold_ms,
                "availability_compliant": availability >= self.config.availability_target,
                "error_rate_compliant": error_rate <= self.config.error_rate_threshold
            }
        }
    
    def print_status(self):
        """Print formatted SLA status"""
        metrics = self.get_current_metrics()
        
        print("=" * 60)
        print("HOLYSHEEP AGENT SLA STATUS")
        print("=" * 60)
        print(f"Uptime: {metrics['uptime_seconds']:.2f}s")
        print(f"Total Requests: {metrics['total_requests']}")
        print(f"Success Rate: {metrics['availability']*100:.3f}%")
        print(f"Error Rate: {metrics['error_rate']*100:.3f}%")
        print(f"Consecutive Failures: {metrics['consecutive_failures']}")
        print()
        print("LATENCY:")
        print(f"  Min: {metrics['latency']['min_ms']:.2f}ms")
        print(f"  Avg: {metrics['latency']['avg_ms']:.2f}ms")
        print(f"  P95: {metrics['latency']['p95_ms']:.2f}ms")
        print(f"  P99: {metrics['latency']['p99_ms']:.2f}ms")
        print(f"  Max: {metrics['latency']['max_ms']:.2f}ms")
        print()
        print("SLA COMPLIANCE:")
        for check, passed in metrics['sla_compliance'].items():
            status = "✅" if passed else "❌"
            print(f"  {status} {check}")
        print("=" * 60)

5. Integration — Chạy Agent Production

import asyncio
from main import HolySheepClient, FailoverManager, SLAMonitor, CircuitState

async def alert_callback(alert: dict):
    """Xử lý alert khi SLA breach"""
    print(f"🚨 ALERT [{alert['type']}]: {alert['message']}")
    # Gửi notification: Slack, PagerDuty, email, etc.
    # await send_slack_notification(alert)

async def main():
    # Khởi tạo components
    client = HolySheepClient(
        api_key="YOUR_HOLYSHEEP_API_KEY",
        rpm_limit=100,
        timeout=30.0
    )
    
    failover_manager = FailoverManager(client)
    
    sla_monitor = SLAMonitor()
    sla_monitor.add_alert_callback(alert_callback)
    
    print("HolySheep Agent Started - Production Mode")
    print("Base URL: https://api.holysheep.ai/v1")
    
    # Ví dụ: Xử lý user request
    test_messages = [
        {"role": "system", "content": "Bạn là AI assistant hữu ích"},
        {"role": "user", "content": "Giải thích về circuit breaker pattern"}
    ]
    
    try:
        start_time = asyncio.get_event_loop().time()
        
        # Execute với failover tự động
        response = await failover_manager.chat_with_failover(
            messages=test_messages,
            temperature=0.7,
            max_tokens=1500
        )
        
        latency_ms = (asyncio.get_event_loop().time() - start_time) * 1000
        
        # Track SLA
        await sla_monitor.track_request(
            latency_ms=latency_ms,
            success=True
        )
        
        print(f"\n✅ Response from {response['_meta']['model_used']}:")
        print(f"Latency: {latency_ms:.2f}ms")
        print(f"Circuit State: {response['_meta']['circuit_state']}")
        print(f"\nContent:\n{response['choices'][0]['message']['content']}")
        
    except Exception as e:
        # Track failure
        await sla_monitor.track_request(
            latency_ms=0,
            success=False,
            error_type=type(e).__name__,
            error_message=str(e)
        )
        print(f"❌ Error: {e}")
    
    finally:
        # Cleanup
        await client.close()
        
        # Print final SLA status
        sla_monitor.print_status()

Chạy với multiple concurrent requests
async def load_test(num_requests: int = 50):
    """Load test để verify rate limiting và failover"""
    client = HolySheepClient(rpm_limit=100)
    failover_manager = FailoverManager(client)
    sla_monitor = SLAMonitor()
    
    tasks = []
    for i in range(num_requests):
        task = asyncio.create_task(
            process_request(failover_manager, sla_monitor, i)
        )
        tasks.append(task)
    
    results = await asyncio.gather(*tasks, return_exceptions=True)
    
    # Summary
    success_count = sum(1 for r in results if not isinstance(r, Exception))
    print(f"\nLoad Test Summary: {success_count}/{num_requests} successful")
    print(f"Success Rate: {success_count/num_requests*100:.1f}%")
    
    sla_monitor.print_status()

async def process_request(failover, sla, request_id):
    """Xử lý một request"""
    messages = [{"role": "user", "content": f"Request {request_id}"}]
    
    start = asyncio.get_event_loop().time()
    try:
        response = await failover.chat_with_failover(messages)
        latency = (asyncio.get_event_loop().time() - start) * 1000
        await sla.track_request(latency, True)
        return response
    except Exception as e:
        await sla.track_request(0, False, type(e).__name__, str(e))
        raise

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

Lỗi thường gặp và cách khắc phục

Lỗi 1: HTTP 429 - Rate Limit Exceeded

Mô tả: Khi gửi quá nhiều request trong thời gian ngắn, HolySheep trả về lỗi 429.

Nguyên nhân:

• Vượt quá RPM limit (requests per minute) của gói subscription
• Không implement client-side rate limiting
• Traffic spike không được dự đoán

Giải pháp:

# Cách 1: Implement local rate limiter (đã có trong code ở trên)
async def _check_rate_limit(self):
    async with self._lock:
        now = time.time()
        # Remove requests older than 60 seconds
        self._request_timestamps = [
            ts for ts in self._request_timestamps 
            if now - ts < 60
        ]
        
        if len(self._request_timestamps) >= self.rpm_limit:
            # Wait until we can make another request
            oldest = self._request_timestamps[0]
            wait_seconds = 60 - (now - oldest) + 1
            await asyncio.sleep(wait_seconds)
            self._request_timestamps = []
        
        self._request_timestamps.append(time.time())

Cách 2: Sử dụng token bucket algorithm
import asyncio
import time

class TokenBucketRateLimiter:
    """Token bucket algorithm cho smooth rate limiting"""
    
    def __init__(self, rate: float, capacity: int):
        """
        Args:
            rate: Tokens per second
            capacity: Maximum tokens in bucket
        """
        self.rate = rate
        self.capacity = capacity
        self.tokens = capacity
        self.last_update = time.time()
        self._lock = asyncio.Lock()
    
    async def acquire(self, tokens
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