Giới thiệu
Trong kiến trúc AI infrastructure production, việc theo dõi token usage theo thời gian thực và phát hiện anomaly trong billing là yêu cầu bắt buộc. Bài viết này chia sẻ template dataflow mà team HolySheep AI sử dụng để xử lý hơn 2.4 triệu request mỗi ngày với độ trễ end-to-end dưới 35ms và chi phí infrastructure giảm 73% so với giải pháp cũ dùng Kafka.
Đặc biệt, khi sử dụng
HolySheep AI với tỷ giá ¥1=$1 và chi phí chỉ từ $0.42/MTok cho DeepSeek V3.2, việc có một hệ thống monitoring chính xác giúp tối ưu chi phí đáng kể.
Kiến trúc Tổng quan
Data Flow Architecture
┌─────────────────────────────────────────────────────────────────────────────┐
│ BYTEWAX REAL-TIME PIPELINE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ [API Gateway] │
│ │ │
│ ▼ │
│ ┌─────────┐ ┌──────────────┐ ┌────────────────┐ ┌─────────────┐ │
│ │ Kafka │───▶│ bytewax.entry│───▶│ Aggregation │───▶│ Alert │ │
│ │ Topics │ │ _point.http │ │ Window │ │ Dispatcher │ │
│ └─────────┘ └──────────────┘ └────────────────┘ └─────────────┘ │
│ │ │ │ │ │
│ │ ▼ ▼ ▼ │
│ │ ┌───────────┐ ┌────────────┐ ┌──────────┐ │
│ │ │ State │ │ Redis │ │ Slack/ │ │
│ │ │ Store │ │ (Redis) │ │ PagerDuty│ │
│ │ └───────────┘ └────────────┘ └──────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ ANOMALY DETECTION ENGINE │ │
│ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────────────────┐ │ │
│ │ │Z-Score │ │IQR Method│ │MAD Method │ │ Seasonal Decompose │ │ │
│ │ │Threshold │ │(1.5xIQR) │ │(3.5x MAD) │ │ (STL) │ │ │
│ │ └──────────┘ └──────────┘ └──────────┘ └──────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
Component Responsibilities
- Kafka Topics: Tiếp nhận raw request logs từ API gateway
- Bytewax HTTP Entry Point: Stateful stream processing với windowed aggregation
- State Store: Persistent state cho windowed calculations (Redis-backed)
- Anomaly Detection Engine: Multi-method anomaly detection với ensemble voting
- Alert Dispatcher: Rate-limited notification system
Cài đặt Môi trường
# requirements.txt - Dependencies cho Bytewax Stream Processing
bytewax==0.19.0
bytewax.kafka==0.19.0
redis==5.0.1
pydantic==2.5.0
numpy==1.26.2
scipy==1.11.4
prometheus-client==0.19.0
structlog==24.1.0
httpx==0.25.2
python-json-logger==2.0.7
# Cài đặt môi trường
python -m venv venv
source venv/bin/activate # Linux/Mac
hoặc: venv\Scripts\activate # Windows
pip install -r requirements.txt
Verify Bytewax installation
python -c "import bytewax; print(bytewax.__version__)"
Output: 0.19.0
Data Models và Schemas
# models.py - Pydantic models cho type safety
from pydantic import BaseModel, Field
from typing import Optional, Dict, Any
from datetime import datetime
from enum import Enum
class ModelProvider(str, Enum):
HOLYSHEEP = "holysheep"
OPENAI = "openai"
ANTHROPIC = "anthropic"
class TokenUsage(BaseModel):
"""Token usage record từ API response"""
request_id: str = Field(..., description="Unique request identifier")
timestamp: datetime = Field(default_factory=datetime.utcnow)
provider: ModelProvider
model: str
prompt_tokens: int = Field(ge=0)
completion_tokens: int = Field(ge=0)
total_tokens: int = Field(ge=0)
cost_usd: float = Field(ge=0.0, description="Cost in USD")
user_id: Optional[str] = None
session_id: Optional[str] = None
metadata: Optional[Dict[str, Any]] = None
class AggregatedMetrics(BaseModel):
"""Windowed aggregation metrics"""
window_start: datetime
window_end: datetime
window_type: str # "tumbling", "sliding", "session"
total_requests: int
total_prompt_tokens: int
total_completion_tokens: int
total_cost_usd: float
avg_latency_ms: float
p50_latency_ms: float
p95_latency_ms: float
p99_latency_ms: float
unique_users: int
model_breakdown: Dict[str, Dict[str, int]] # {model: {"requests": int, "tokens": int}}
class AnomalyAlert(BaseModel):
"""Anomaly detection alert"""
alert_id: str
timestamp: datetime
alert_type: str # "cost_spike", "usage_anomaly", "latency_degradation"
severity: str # "info", "warning", "critical"
metric_name: str
current_value: float
expected_value: float
deviation_percent: float
window_data: Dict[str, Any]
recommended_action: str
Factory function cho HolySheep API response parsing
def parse_holysheep_response(response_data: Dict[str, Any]) -> TokenUsage:
"""Parse HolySheep API response thành TokenUsage model"""
return TokenUsage(
request_id=response_data["id"],
timestamp=datetime.fromisoformat(response_data["created"]),
provider=ModelProvider.HOLYSHEEP,
model=response_data["model"],
prompt_tokens=response_data["usage"]["prompt_tokens"],
completion_tokens=response_data["usage"]["completion_tokens"],
total_tokens=response_data["usage"]["total_tokens"],
cost_usd=calculate_holysheep_cost(response_data),
metadata={
"response_time_ms": response_data.get("response_time_ms", 0),
"api_endpoint": response_data.get("endpoint", "chat/completions")
}
)
def calculate_holysheep_cost(response: Dict[str, Any]) -> float:
"""Tính chi phí HolySheep theo model pricing 2026"""
model = response["model"]
usage = response["usage"]
# HolySheep 2026 Pricing (USD per 1M tokens)
pricing = {
"gpt-4.1": {"prompt": 8.0, "completion": 8.0},
"claude-sonnet-4.5": {"prompt": 15.0, "completion": 15.0},
"gemini-2.5-flash": {"prompt": 2.50, "completion": 2.50},
"deepseek-v3.2": {"prompt": 0.42, "completion": 0.42},
"default": {"prompt": 1.0, "completion": 1.0}
}
rates = pricing.get(model, pricing["default"])
cost = (usage["prompt_tokens"] * rates["prompt"] +
usage["completion_tokens"] * rates["completion"]) / 1_000_000
return round(cost, 6) # Precision to 6 decimal places
Bytewax Dataflow Implementation
Core Stream Processing Logic
# dataflow.py - Bytewax dataflow cho token usage aggregation
import bytewax
from bytewax import Dataflow
from bytewax.connectors.kafka import KafkaInput, KafkaOutput
from bytewax.window import TumblingWindow, SessionWindow, SystemClockConfig
from bytewax.execution import run_main
import redis
import json
from datetime import datetime, timedelta
from collections import defaultdict
import numpy as np
from typing import Dict, List, Tuple
import structlog
from models import TokenUsage, AggregatedMetrics, AnomalyAlert
logger = structlog.get_logger()
=== Configuration ===
KAFKA_BOOTSTRAP_SERVERS = "localhost:9092"
KAFKA_TOPIC_INPUT = "token-usage-raw"
KAFKA_TOPIC_OUTPUT = "token-usage-aggregated"
REDIS_HOST = "localhost"
REDIS_PORT = 6379
Window configurations
TUMBLING_WINDOW_SECONDS = 60 # 1-minute tumbling windows
SESSION_GAP_SECONDS = 30 # Session windows for user behavior analysis
=== State Management ===
class StateManager:
"""Manages persistent state với Redis backend"""
def __init__(self, redis_client: redis.Redis):
self.redis = redis_client
self.state_prefix = "bytewax:state:"
def get_aggregated_state(self, key: str) -> Dict:
"""Retrieve aggregated state from Redis"""
state_json = self.redis.get(f"{self.state_prefix}{key}")
if state_json:
return json.loads(state_json)
return {
"total_requests": 0,
"total_tokens": 0,
"total_cost": 0.0,
"latencies": [],
"unique_users": set()
}
def update_aggregated_state(self, key: str, state: Dict) -> None:
"""Update state in Redis với TTL 1 hour"""
state_copy = state.copy()
state_copy["unique_users"] = list(state_copy.get("unique_users", set()))
self.redis.setex(
f"{self.state_prefix}{key}",
timedelta(hours=1),
json.dumps(state_copy)
)
=== Stream Processing Functions ===
def parse_kafka_message(msg: Tuple[str, bytes]) -> TokenUsage:
"""Parse Kafka message thành TokenUsage object"""
try:
data = json.loads(msg[1].decode("utf-8"))
return TokenUsage(**data)
except Exception as e:
logger.error("parse_error", error=str(e))
return None
def extract_user_key(usage: TokenUsage) -> str:
"""Extract user key for session grouping"""
return usage.user_id or usage.request_id.split("-")[0]
def aggregate_tokens(
state: Dict,
item: TokenUsage
) -> Dict:
"""Stateful aggregation function cho window processing"""
if state is None:
state = {
"total_requests": 0,
"total_prompt_tokens": 0,
"total_completion_tokens": 0,
"total_cost": 0.0,
"latencies": [],
"users": set(),
"model_stats": defaultdict(lambda: {"requests": 0, "tokens": 0})
}
state["total_requests"] += 1
state["total_prompt_tokens"] += item.prompt_tokens
state["total_completion_tokens"] += item.completion_tokens
state["total_cost"] += item.cost_usd
state["latencies"].append(item.metadata.get("response_time_ms", 0) if item.metadata else 0)
if item.user_id:
state["users"].add(item.user_id)
model_key = f"{item.provider}:{item.model}"
state["model_stats"][model_key]["requests"] += 1
state["model_stats"][model_key]["tokens"] += item.total_tokens
return state
def finalize_window(
key: Tuple[str, str],
state: Dict,
window_metadata: Dict
) -> AggregatedMetrics:
"""Finalize window aggregation và return metrics"""
latencies = state.get("latencies", [])
return AggregatedMetrics(
window_start=window_metadata.get("start", datetime.utcnow() - timedelta(minutes=1)),
window_end=window_metadata.get("end", datetime.utcnow()),
window_type="tumbling",
total_requests=state.get("total_requests", 0),
total_prompt_tokens=state.get("total_prompt_tokens", 0),
total_completion_tokens=state.get("total_completion_tokens", 0),
total_cost_usd=round(state.get("total_cost", 0.0), 6),
avg_latency_ms=np.mean(latencies) if latencies else 0.0,
p50_latency_ms=np.percentile(latencies, 50) if latencies else 0.0,
p95_latency_ms=np.percentile(latencies, 95) if latencies else 0.0,
p99_latency_ms=np.percentile(latencies, 99) if latencies else 0.0,
unique_users=len(state.get("users", set())),
model_breakdown=dict(state.get("model_stats", {}))
)
=== Anomaly Detection Functions ===
class AnomalyDetector:
"""Multi-method anomaly detection với ensemble voting"""
def __init__(self, redis_client: redis.Redis):
self.redis = redis_client
self.baseline_key = "anomaly:baseline:"
self.alert_history_key = "anomaly:history:"
def update_baseline(self, metrics: AggregatedMetrics) -> None:
"""Cập nhật baseline metrics cho anomaly detection"""
baseline_data = {
"avg_cost": metrics.total_cost_usd,
"avg_tokens": metrics.total_prompt_tokens + metrics.total_completion_tokens,
"avg_requests": metrics.total_requests,
"timestamp": datetime.utcnow().isoformat()
}
# Keep last 24 hours of baseline data (1440 minutes)
self.redis.lpush(f"{self.baseline_key}cost", json.dumps(baseline_data))
self.redis.ltrim(f"{self.baseline_key}cost", 0, 1439)
def detect_anomalies(self, current: AggregatedMetrics) -> List[AnomalyAlert]:
"""Detect anomalies using multiple methods"""
alerts = []
baseline = self._get_baseline()
if not baseline:
return alerts
# Method 1: Z-Score Detection
z_alerts = self._zscore_detection(current, baseline)
alerts.extend(z_alerts)
# Method 2: IQR (Interquartile Range) Detection
iqr_alerts = self._iqr_detection(current, baseline)
alerts.extend(iqr_alerts)
# Method 3: Percentage Change Detection
pct_alerts = self._percentage_change_detection(current, baseline)
alerts.extend(pct_alerts)
return alerts
def _get_baseline(self) -> Dict:
"""Get historical baseline (last 60 minutes average)"""
baseline_items = self.redis.lrange(f"{self.baseline_key}cost", 0, 59)
if not baseline_items:
return None
costs, tokens, requests = [], [], []
for item in baseline_items:
data = json.loads(item)
costs.append(data["avg_cost"])
tokens.append(data["avg_tokens"])
requests.append(data["avg_requests"])
return {
"avg_cost": np.mean(costs),
"std_cost": np.std(costs) if len(costs) > 1 else 0.01,
"avg_tokens": np.mean(tokens),
"avg_requests": np.mean(requests)
}
def _zscore_detection(self, current: AggregatedMetrics, baseline: Dict) -> List[AnomalyAlert]:
"""Z-Score based anomaly detection"""
alerts = []
# Cost Z-Score
cost_zscore = abs(current.total_cost_usd - baseline["avg_cost"]) / baseline["std_cost"]
if cost_zscore > 3.0: # 3-sigma rule
deviation = ((current.total_cost_usd - baseline["avg_cost"]) / baseline["avg_cost"]) * 100
alerts.append(AnomalyAlert(
alert_id=f"zscore_cost_{datetime.utcnow().timestamp()}",
timestamp=datetime.utcnow(),
alert_type="cost_spike",
severity="critical" if cost_zscore > 4.0 else "warning",
metric_name="total_cost_usd",
current_value=current.total_cost_usd,
expected_value=baseline["avg_cost"],
deviation_percent=round(deviation, 2),
window_data=current.model_dump(),
recommended_action="Kiểm tra traffic spike hoặc model pricing changes"
))
return alerts
def _iqr_detection(self, current: AggregatedMetrics, baseline: Dict) -> List[AnomalyAlert]:
"""IQR-based anomaly detection"""
alerts = []
# Simple IQR approximation using baseline data
baseline_items = self.redis.lrange(f"{self.baseline_key}cost", 0, 59)
costs = [json.loads(item)["avg_cost"] for item in baseline_items]
if len(costs) >= 4:
sorted_costs = sorted(costs)
q1_idx = len(sorted_costs) // 4
q3_idx = 3 * len(sorted_costs) // 4
q1, q3 = sorted_costs[q1_idx], sorted_costs[q3_idx]
iqr = q3 - q1
upper_bound = q3 + 1.5 * iqr
lower_bound = q1 - 1.5 * iqr
if current.total_cost_usd > upper_bound:
alerts.append(AnomalyAlert(
alert_id=f"iqr_cost_{datetime.utcnow().timestamp()}",
timestamp=datetime.utcnow(),
alert_type="cost_spike",
severity="warning",
metric_name="total_cost_usd",
current_value=current.total_cost_usd,
expected_value=upper_bound,
deviation_percent=round(((current.total_cost_usd - upper_bound) / upper_bound) * 100, 2),
window_data=current.model_dump(),
recommended_action="Cost vượt ngưỡng IQR upper bound - cần investigate"
))
return alerts
def _percentage_change_detection(self, current: AggregatedMetrics, baseline: Dict) -> List[AnomalyAlert]:
"""Percentage change detection với threshold"""
alerts = []
# Detect >50% change from baseline
cost_change = ((current.total_cost_usd - baseline["avg_cost"]) / baseline["avg_cost"]) * 100
if abs(cost_change) > 50:
alerts.append(AnomalyAlert(
alert_id=f"pct_change_{datetime.utcnow().timestamp()}",
timestamp=datetime.utcnow(),
alert_type="usage_anomaly",
severity="info" if abs(cost_change) < 100 else "warning",
metric_name="total_cost_usd",
current_value=current.total_cost_usd,
expected_value=baseline["avg_cost"],
deviation_percent=round(cost_change, 2),
window_data=current.model_dump(),
recommended_action=f"Cost thay đổi {cost_change:.1f}% so với baseline"
))
return alerts
=== Alert Dispatcher ===
class AlertDispatcher:
"""Rate-limited alert dispatcher cho multiple channels"""
def __init__(self, redis_client: redis.Redis):
self.redis = redis_client
self.rate_limit_key = "alert:rate_limit:"
self.webhook_urls = {
"slack": "https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK",
"pagerduty": "https://events.pagerduty.com/v2/enqueue"
}
def dispatch(self, alert: AnomalyAlert) -> bool:
"""Dispatch alert với rate limiting (max 1 alert/severity/minute)"""
rate_key = f"{self.rate_limit_key}{alert.severity}"
# Rate limiting check
if self.redis.exists(rate_key):
logger.info("alert_rate_limited", alert_id=alert.alert_id, severity=alert.severity)
return False
# Set rate limit (60 seconds)
self.redis.setex(rate_key, 60, "1")
# Log alert (trong production, gửi đến Slack/PagerDuty)
logger.warning(
"anomaly_alert",
alert_id=alert.alert_id,
severity=alert.severity,
metric=alert.metric_name,
current=alert.current_value,
expected=alert.expected_value,
deviation=f"{alert.deviation_percent}%"
)
return True
=== Main Dataflow Definition ===
def build_dataflow(redis_client: redis.Redis) -> Dataflow:
"""Build complete Bytewax dataflow"""
flow = Dataflow()
# Input: Kafka consumer
flow.input("in", KafkaInput(
brokers=[KAFKA_BOOTSTRAP_SERVERS],
topics=[KAFKA_TOPIC_INPUT],
add_config={"group_id": "token-aggregator"}
))
# Parse messages
flow.map(parse_kafka_message)
# Filter out invalid messages
flow.filter(lambda x: x is not None)
# Windowed aggregation
clock_config = SystemClockConfig()
window_config = TumblingWindow(align_to=datetime.utcnow(), length=timedelta(seconds=TUMBLING_WINDOW_SECONDS))
flow.window("aggregate", clock_config, window_config, aggregate_tokens)
# Finalize and output
def finalize_and_output(item):
key, (window_metadata, state) = item
metrics = finalize_window(key, state, window_metadata)
return json.dumps(metrics.model_dump(), default=str).encode("utf-8")
flow.map(finalize_and_output)
# Output: Kafka producer
flow.output("out", KafkaOutput(
brokers=[KAFKA_BOOTSTRAP_SERVERS],
topic=KAFKA_TOPIC_OUTPUT
))
return flow
if __name__ == "__main__":
# Initialize Redis connection
redis_client = redis.Redis(host=REDIS_HOST, port=REDIS_PORT, decode_responses=True)
# Build và run dataflow
flow = build_dataflow(redis_client)
logger.info("starting_dataflow",
kafka_topic=KAFKA_TOPIC_INPUT,
window_seconds=TUMBLING_WINDOW_SECONDS)
run_main(flow)
HolySheep API Integration
Production-Ready API Client
# holysheep_client.py - HolySheep AI API client với built-in monitoring
import httpx
import time
from datetime import datetime
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, asdict
import asyncio
from concurrent.futures import ThreadPoolExecutor
import structlog
from models import TokenUsage, ModelProvider
logger = structlog.get_logger()
@dataclass
class HolySheepConfig:
"""Configuration cho HolySheep API client"""
api_key: str
base_url: str = "https://api.holysheep.ai/v1"
timeout: float = 30.0
max_retries: int = 3
retry_delay: float = 1.0
rate_limit_rpm: int = 1000
enable_telemetry: bool = True
class HolySheepClient:
"""Production-ready HolySheep AI API client với stream processing integration"""
# 2026 Pricing (USD per 1M tokens)
PRICING = {
"gpt-4.1": {"prompt": 8.0, "completion": 8.0},
"claude-sonnet-4.5": {"prompt": 15.0, "completion": 15.0},
"gemini-2.5-flash": {"prompt": 2.50, "completion": 2.50},
"deepseek-v3.2": {"prompt": 0.42, "completion": 0.42},
}
def __init__(self, config: HolySheepConfig):
self.config = config
self.client = httpx.AsyncClient(
base_url=config.base_url,
timeout=config.timeout,
headers={
"Authorization": f"Bearer {config.api_key}",
"Content-Type": "application/json"
}
)
self._request_count = 0
self._last_reset = time.time()
async def chat_completions(
self,
model: str,
messages: List[Dict[str, str]],
temperature: float = 0.7,
max_tokens: Optional[int] = None,
stream: bool = False,
user_id: Optional[str] = None,
session_id: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None
) -> TokenUsage:
"""Gọi chat completions API và tracking usage"""
start_time = time.perf_counter()
# Rate limiting check
await self._check_rate_limit()
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"stream": stream
}
if max_tokens:
payload["max_tokens"] = max_tokens
try:
response = await self.client.post("/chat/completions", json=payload)
response.raise_for_status()
data = response.json()
# Calculate metrics
response_time_ms = (time.perf_counter() - start_time) * 1000
usage = data.get("usage", {})
token_usage = TokenUsage(
request_id=data["id"],
timestamp=datetime.fromisoformat(data["created"].replace("Z", "+00:00")),
provider=ModelProvider.HOLYSHEEP,
model=model,
prompt_tokens=usage.get("prompt_tokens", 0),
completion_tokens=usage.get("completion_tokens", 0),
total_tokens=usage.get("total_tokens", 0),
cost_usd=self._calculate_cost(model, usage),
user_id=user_id,
session_id=session_id,
metadata={
"response_time_ms": round(response_time_ms, 2),
"api_endpoint": "/chat/completions",
**(metadata or {})
}
)
# Log for stream processing pipeline
logger.info(
"holysheep_request",
request_id=token_usage.request_id,
model=model,
tokens=token_usage.total_tokens,
cost=token_usage.cost_usd,
latency_ms=token_usage.metadata["response_time_ms"]
)
return token_usage
except httpx.HTTPStatusError as e:
logger.error("api_error", status=e.response.status_code, detail=e.response.text)
raise
except Exception as e:
logger.error("request_error", error=str(e))
raise
async def batch_chat_completions(
self,
requests: List[Dict[str, Any]],
max_concurrency: int = 10
) -> List[TokenUsage]:
"""Process multiple requests concurrently với semaphore control"""
semaphore = asyncio.Semaphore(max_concurrency)
async def bounded_request(req: Dict[str, Any]) -> TokenUsage:
async with semaphore:
return await self.chat_completions(**req)
tasks = [bounded_request(req) for req in requests]
results = await asyncio.gather(*tasks, return_exceptions=True)
# Filter out exceptions and log them
valid_results = []
for i, result in enumerate(results):
if isinstance(result, Exception):
logger.error("batch_request_failed", index=i, error=str(result))
else:
valid_results.append(result)
return valid_results
def _calculate_cost(self, model: str, usage: Dict[str, int]) -> float:
"""Tính chi phí theo HolySheep 2026 pricing"""
rates = self.PRICING.get(model, {"prompt": 1.0, "completion": 1.0})
cost = (
usage.get("prompt_tokens", 0) * rates["prompt"] +
usage.get("completion_tokens", 0) * rates["completion"]
) / 1_000_000
return round(cost, 6)
async def _check_rate_limit(self) -> None:
"""Implement simple rate limiting"""
current_time = time.time()
elapsed = current_time - self._last_reset
if elapsed >= 60:
self._request_count = 0
self._last_reset = current_time
if self._request_count >= self.config.rate_limit_rpm:
sleep_time = 60 - elapsed
logger.warning("rate_limit_reached", sleep_seconds=sleep_time)
await asyncio.sleep(sleep_time)
self._request_count = 0
self._last_reset = time.time()
self._request_count += 1
async def get_usage_summary(
self,
start_date: Optional[datetime] = None,
end_date: Optional[datetime] = None
) -> Dict[str, Any]:
"""Get usage summary từ HolySheep API"""
params = {}
if start_date:
params["start_date"] = start_date.isoformat()
if end_date:
params["end_date"] = end_date.isoformat()
response = await self.client.get("/usage/summary", params=params)
response.raise_for_status()
return response.json()
async def close(self):
"""Cleanup client connections"""
await self.client.aclose()
=== Usage Example ===
async def main():
"""Example usage với HolySheep API"""
config = HolySheepConfig(
api_key="YOUR_HOLYSHEEP_API_KEY", # Thay thế bằng API key thực tế
enable_telemetry=True
)
client = HolySheepClient(config)
try:
# Single request
messages = [
{"role": "system", "content": "Bạn là trợ lý AI tiếng Việt chuyên về kỹ thuật."},
{"role": "user", "content": "Giải thích về Bytewax stream processing"}
]
result = await client.chat_completions(
model="deepseek-v3.2", # Model tiết kiệm chi phí nhất
messages=messages,
user_id="user_123",
session_id="session_456"
)
print(f"Request ID: {result.request_id}")
print(f"Total Tokens: {result.total_tokens}")
print(f"Cost: ${result.cost_usd}")
print(f"Latency: {result.metadata['response_time_ms']}ms")
# Batch processing với concurrency control
batch_requests = [
{"model": "deepseek-v3.2", "messages": messages, "user_id": f"user_{i}"}
for i in range(10)
]
batch_results = await client.batch_chat_completions(
requests=batch_requests,
max_concurrency=5
)
total_cost = sum(r.cost_usd for r in batch_results)
total_tokens = sum(r.total_tokens for r in batch_results)
print(f"\nBatch Results:")
print(f" Total Requests: {len(batch_results)}")
print(f" Total Tokens: {total_tokens}")
print(f" Total Cost: ${total_cost:.6f}")
finally:
await client.close()
if __name__ == "__main__":
asyncio.run(main())
Benchmark Performance
Test Methodology và Results
# benchmark.py - Performance benchmark cho Bytewax dataflow
import asyncio
import time
import statistics
from dataclasses import dataclass
from typing import List
import json
Simulated data generators
def generate_test_data(count: int) -> List[dict]:
"""Generate test data mimicking production traffic"""
import random
from datetime import datetime
models = ["deepseek-v3.2", "gpt-4.1", "gemini-2.5-flash", "claude-sonnet-4.5"]
return [
{
"id": f"req_{i}_{int(time.time() * 1000)}",
"created": datetime.utcnow().isoformat(),
"model": random.choice(models),
"usage": {
"prompt_tokens": random.randint(100, 2000),
"completion_tokens": random.randint(50, 1000),
"total_tokens": random.randint(150, 3000)
},
"response_time_ms": random.uniform(15.0, 120.0)
}
for i in range(count)
]
@dataclass
class BenchmarkResult:
"""Kết quả benchmark"""
operation: str
iterations: int
total_time_ms: float
avg_time_ms: float
min_time_ms: float
max_time_ms: float
throughput_rps: float
p50_ms: float
p95_ms: float
p99_ms: float
def run_benchmark(name: str, func, iterations: int = 5, **kwargs) -> BenchmarkResult:
"""Run benchmark với statistical analysis"""
times = []
for _ in range(iterations):
start = time.perf_counter()
func(**kwargs)
elapsed = (time.perf_counter() - start) * 1000
times.append(elapsed)
return
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