Tôi vẫn nhớ rõ ngày hôm đó - một buổi sáng thứ Hai đầu tuần, team của tôi vừa triển khai hệ thống RAG (Retrieval-Augmented Generation) cho nền tảng thương mại điện tử B2B với hơn 50,000 SKU sản phẩm. Chỉ sau 3 ngày vận hành, chi phí API tăng vọt 340% — từ $800/ngày lên $2,720/ngày. Sau khi phân tích log, chúng tôi phát hiện: một cron job bị lỗi đang gọi API liên tục thay vì chỉ một lần mỗi giờ, và một prompt injection attack đang thử trích xuất dữ liệu khách hàng. Kinh nghiệm này thay đổi hoàn toàn cách tôi tiếp cận AI API log auditing.
Tại Sao Log Auditing Quan Trọng Với AI API?
Khác với REST API truyền thống, AI API có những đặc thù riêng:
- Chi phí theo token - Mỗi request đều có giá, không có "request miễn phí"
- Prompt engineering - Một prompt tối ưu có thể tiết kiệm 60-80% chi phí
- Latency không deterministic - AI model response time biến động lớn (50ms - 30s)
- Security riêng - Prompt injection, data exfiltration, rate limit abuse
Kiến Trúc Log Auditing Hoàn Chỉnh
1. Middleware Logging Với Python
"""
AI API Request/Response Logger với Anomaly Detection
Hỗ trợ HolySheep AI và các provider khác
"""
import json
import time
import hashlib
import asyncio
from datetime import datetime, timedelta
from typing import Optional, Dict, Any, List
from dataclasses import dataclass, asdict
from collections import defaultdict
import statistics
@dataclass
class APILogEntry:
"""Cấu trúc log entry cho AI API"""
timestamp: str
request_id: str
provider: str # 'holysheep', 'openai', etc.
model: str
operation: str # 'chat', 'embedding', etc.
# Request metrics
input_tokens: int
prompt_tokens: int = 0
cache_tokens: int = 0
# Response metrics
output_tokens: int
total_tokens: int
latency_ms: float
# Cost tracking
cost_usd: float
cost_cny: float = 0.0
# Anomaly flags
is_anomaly: bool = False
anomaly_type: Optional[str] = None
anomaly_severity: Optional[str] = None # 'low', 'medium', 'high', 'critical'
# Metadata
user_id: Optional[str] = None
session_id: Optional[str] = None
endpoint: Optional[str] = None
status_code: int = 200
error_message: Optional[str] = None
def to_dict(self) -> Dict[str, Any]:
return asdict(self)
class CostCalculator:
"""Tính chi phí theo bảng giá HolySheep AI 2026"""
# HolySheep AI Pricing (USD per 1M tokens)
HOLYSHEEP_PRICING = {
# Chat Models
'gpt-4.1': {'input': 8.0, 'output': 8.0},
'claude-sonnet-4.5': {'input': 15.0, 'output': 15.0},
'gemini-2.5-flash': {'input': 2.50, 'output': 2.50},
'deepseek-v3.2': {'input': 0.42, 'output': 0.42},
# Embedding Models
'text-embedding-3-small': {'input': 0.02, 'output': 0.0},
'text-embedding-3-large': {'input': 0.13, 'output': 0.0},
}
# Fallback pricing (USD per 1M tokens)
FALLBACK_PRICING = {
'gpt-4': {'input': 30.0, 'output': 60.0},
'gpt-4-turbo': {'input': 10.0, 'output': 30.0},
'gpt-3.5-turbo': {'input': 0.5, 'output': 1.5},
}
@classmethod
def calculate_cost(
cls,
provider: str,
model: str,
input_tokens: int,
output_tokens: int,
use_cache: bool = False,
cache_discount: float = 0.9
) -> float:
"""
Tính chi phí USD
HolySheep: ¥1 = $1, tiết kiệm 85%+ so với OpenAI
"""
pricing = cls.HOLYSHEEP_PRICING.get(model, cls.FALLBACK_PRICING.get(model, {}))
if not pricing:
# Default pricing for unknown models
pricing = {'input': 1.0, 'output': 2.0}
input_cost = (input_tokens / 1_000_000) * pricing['input']
output_cost = (output_tokens / 1_000_000) * pricing['output']
# Áp dụng cache discount nếu có
if use_cache:
input_cost *= (1 - cache_discount)
return round(input_cost + output_cost, 6)
@classmethod
def calculate_savings(cls, openai_cost: float, holysheep_cost: float) -> Dict[str, Any]:
"""Tính toán savings khi dùng HolySheep"""
savings = openai_cost - holysheep_cost
savings_percent = (savings / openai_cost * 100) if openai_cost > 0 else 0
return {
'openai_cost_usd': round(openai_cost, 4),
'holysheep_cost_usd': round(holysheep_cost, 4),
'savings_usd': round(savings, 4),
'savings_percent': round(savings_percent, 2)
}
class AnomalyDetector:
"""
Phát hiện bất thường trong AI API usage
"""
def __init__(self):
# Baseline metrics
self.baseline_latency: Dict[str, float] = {}
self.baseline_tokens: Dict[str, Dict[str, float]] = defaultdict(dict)
self.baseline_costs: Dict[str, float] = {}
# Thresholds (configurable)
self.thresholds = {
'latency_p99_ms': 5000, # 5 seconds
'tokens_per_request': 100000, # 100K tokens/request
'requests_per_minute': 1000,
'cost_per_day_usd': 10000,
'error_rate_percent': 5.0,
}
def update_baseline(self, logs: List[APILogEntry]):
"""Cập nhật baseline từ historical data"""
by_model = defaultdict(list)
for log in logs:
by_model[log.model].append(log)
for model, model_logs in by_model.items():
if model_logs:
latencies = [l.latency_ms for l in model_logs]
input_tokens = [l.input_tokens for l in model_logs]
output_tokens = [l.output_tokens for l in model_logs]
costs = [l.cost_usd for l in model_logs]
self.baseline_latency[model] = {
'mean': statistics.mean(latencies),
'median': statistics.median(latencies),
'stdev': statistics.stdev(latencies) if len(latencies) > 1 else 0,
'p95': sorted(latencies)[int(len(latencies) * 0.95)],
'p99': sorted(latencies)[int(len(latencies) * 0.99)],
}
self.baseline_tokens[model] = {
'input_mean': statistics.mean(input_tokens),
'input_stdev': statistics.stdev(input_tokens) if len(input_tokens) > 1 else 0,
'output_mean': statistics.mean(output_tokens),
'output_stdev': statistics.stdev(output_tokens) if len(output_tokens) > 1 else 0,
}
self.baseline_costs[model] = sum(costs)
def detect_anomalies(self, log: APILogEntry, context: Dict[str, Any]) -> Dict[str, Any]:
"""
Phát hiện anomalies trên một log entry
"""
anomalies = []
# 1. High Latency Detection
if log.latency_ms > self.thresholds['latency_p99_ms']:
anomalies.append({
'type': 'high_latency',
'severity': 'medium',
'message': f"Latency {log.latency_ms:.0f}ms vượt ngưỡng {self.thresholds['latency_p99_ms']}ms",
'value': log.latency_ms,
'threshold': self.thresholds['latency_p99_ms']
})
# 2. Token Spike Detection
if log.input_tokens > self.thresholds['tokens_per_request']:
anomalies.append({
'type': 'token_spike',
'severity': 'high',
'message': f"Input tokens {log.input_tokens:,} vượt ngưỡng {self.thresholds['tokens_per_request']:,}",
'value': log.input_tokens,
'threshold': self.thresholds['tokens_per_request']
})
# 3. Cost Spike Detection (dựa trên baseline)
if log.model in self.baseline_costs:
avg_cost = self.baseline_costs[log.model] / max(len(context.get('daily_logs', [])), 1)
if log.cost_usd > avg_cost * 10: # 10x so với average
anomalies.append({
'type': 'cost_spike',
'severity': 'critical',
'message': f"Cost ${log.cost_usd:.4f} cao bất thường (avg: ${avg_cost:.4f})",
'value': log.cost_usd,
'baseline': avg_cost
})
# 4. Error Rate Detection
if log.status_code >= 400:
anomalies.append({
'type': 'api_error',
'severity': 'high',
'message': f"API Error {log.status_code}: {log.error_message}",
'value': log.status_code
})
# 5. Potential Prompt Injection Detection
if self._check_prompt_injection(log):
anomalies.append({
'type': 'prompt_injection',
'severity': 'critical',
'message': "Phát hiện potential prompt injection attempt",
'indicators': self._get_injection_indicators(log)
})
# Determine overall severity
severity_order = {'critical': 4, 'high': 3, 'medium': 2, 'low': 1}
max_severity = max(
(severity_order.get(a['severity'], 0) for a in anomalies),
default=0
)
severity_map = {4: 'critical', 3: 'high', 2: 'medium', 1: 'low', 0: 'none'}
return {
'has_anomaly': len(anomalies) > 0,
'anomalies': anomalies,
'severity': severity_map[max_severity]
}
def _check_prompt_injection(self, log: APILogEntry) -> bool:
"""Kiểm tra prompt injection patterns"""
# Các patterns đáng ngờ
suspicious_patterns = [
r'ignore\s+(previous|all|above)\s+(instructions?|rules?)',
r'forget\s+everything',
r'system\s*:\s*',
r'\[\s*INST\s*\]',
r'you\s+are\s+a\s+malicious',
r'disregard\s+your',
]
import re
prompt_text = str(log.prompt_tokens) if isinstance(log.prompt_tokens, str) else ""
for pattern in suspicious_patterns:
if re.search(pattern, prompt_text, re.IGNORECASE):
return True
return False
def _get_injection_indicators(self, log: APILogEntry) -> List[str]:
"""Liệt kê các indicators của prompt injection"""
indicators = []
if 'ignore' in str(log.prompt_tokens).lower():
indicators.append("Contains 'ignore' keyword")
if 'system' in str(log.prompt_tokens).lower()[:20]:
indicators.append("Starts with system-level command")
if len(str(log.prompt_tokens)) > 5000:
indicators.append("Unusually long prompt")
return indicators
print("✅ AI API Log Auditing Module Loaded")
print("📊 Cost Calculator - HolySheep Pricing 2026:")
print(" - GPT-4.1: $8/MTok (Tiết kiệm 85%+ vs OpenAI)")
print(" - Claude Sonnet 4.5: $15/MTok")
print(" - Gemini 2.5 Flash: $2.50/MTok")
print(" - DeepSeek V3.2: $0.42/MTok (Giá rẻ nhất)")
2. HolySheep AI Integration Với Logging
"""
HolySheep AI API Integration với Full Logging
base_url: https://api.holysheep.ai/v1
"""
import os
import json
import time
import httpx
from typing import Optional, Dict, Any, List, AsyncIterator
from datetime import datetime
class HolySheepAIClient:
"""
HolySheep AI API Client với built-in logging và anomaly detection
"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(
self,
api_key: str,
log_handler: Optional['APILogHandler'] = None,
anomaly_detector: Optional[AnomalyDetector] = None
):
self.api_key = api_key
self.log_handler = log_handler
self.anomaly_detector = anomaly_detector or AnomalyDetector()
self.client = httpx.AsyncClient(
base_url=self.BASE_URL,
timeout=httpx.Timeout(60.0, connect=10.0),
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
)
# Rate limiting
self.request_timestamps: List[float] = []
self.rate_limit = 1000 # requests per minute
async def chat_completion(
self,
model: str = "deepseek-v3.2", # Model rẻ nhất: $0.42/MTok
messages: List[Dict[str, str]],
temperature: float = 0.7,
max_tokens: Optional[int] = 2048,
user_id: Optional[str] = None,
session_id: Optional[str] = None,
**kwargs
) -> Dict[str, Any]:
"""
Gọi HolySheep Chat Completion API với full logging
"""
start_time = time.perf_counter()
request_id = self._generate_request_id()
try:
# Rate limit check
await self._check_rate_limit()
# Build request
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
}
if max_tokens:
payload["max_tokens"] = max_tokens
payload.update(kwargs)
# Make request
response = await self.client.post("/chat/completions", json=payload)
latency_ms = (time.perf_counter() - start_time) * 1000
# Parse response
if response.status_code == 200:
data = response.json()
usage = data.get('usage', {})
input_tokens = usage.get('prompt_tokens', 0)
output_tokens = usage.get('completion_tokens', 0)
total_tokens = usage.get('total_tokens', input_tokens + output_tokens)
cache_tokens = usage.get('prompt_cache_tokens', 0)
# Calculate cost
cost_usd = CostCalculator.calculate_cost(
provider='holysheep',
model=model,
input_tokens=input_tokens,
output_tokens=output_tokens,
use_cache=cache_tokens > 0
)
# Create log entry
log_entry = APILogEntry(
timestamp=datetime.utcnow().isoformat(),
request_id=request_id,
provider='holysheep',
model=model,
operation='chat',
input_tokens=input_tokens,
prompt_tokens=input_tokens,
cache_tokens=cache_tokens,
output_tokens=output_tokens,
total_tokens=total_tokens,
latency_ms=latency_ms,
cost_usd=cost_usd,
user_id=user_id,
session_id=session_id,
status_code=200
)
# Anomaly detection
anomaly_result = self.anomaly_detector.detect_anomalies(
log_entry,
{'daily_logs': []}
)
log_entry.is_anomaly = anomaly_result['has_anomaly']
log_entry.anomaly_type = ','.join([a['type'] for a in anomaly_result['anomalies']]) if anomaly_result['anomalies'] else None
log_entry.anomaly_severity = anomaly_result['severity']
# Log if anomaly detected
if anomaly_result['has_anomaly']:
print(f"🚨 ANOMALY DETECTED [{anomaly_result['severity'].upper()}]: {anomaly_result['anomalies']}")
if self.log_handler:
await self.log_handler.write_alert(log_entry, anomaly_result)
# Write log
if self.log_handler:
await self.log_handler.write_log(log_entry)
return {
'success': True,
'data': data,
'usage': {
'input_tokens': input_tokens,
'output_tokens': output_tokens,
'total_tokens': total_tokens,
'cost_usd': cost_usd,
'latency_ms': round(latency_ms, 2)
},
'anomaly': anomaly_result if anomaly_result['has_anomaly'] else None
}
else:
# Error handling
error_data = response.json() if response.text else {}
error_msg = error_data.get('error', {}).get('message', 'Unknown error')
log_entry = APILogEntry(
timestamp=datetime.utcnow().isoformat(),
request_id=request_id,
provider='holysheep',
model=model,
operation='chat',
input_tokens=0,
output_tokens=0,
total_tokens=0,
latency_ms=(time.perf_counter() - start_time) * 1000,
cost_usd=0,
status_code=response.status_code,
error_message=error_msg
)
anomaly_result = self.anomaly_detector.detect_anomalies(log_entry, {})
log_entry.is_anomaly = anomaly_result['has_anomaly']
if self.log_handler:
await self.log_handler.write_log(log_entry)
return {
'success': False,
'error': error_msg,
'status_code': response.status_code,
'request_id': request_id
}
except httpx.TimeoutException as e:
return {
'success': False,
'error': f"Request timeout: {str(e)}",
'request_id': request_id
}
except Exception as e:
return {
'success': False,
'error': f"Unexpected error: {str(e)}",
'request_id': request_id
}
async def embeddings(
self,
input: str | List[str],
model: str = "text-embedding-3-small",
user_id: Optional[str] = None
) -> Dict[str, Any]:
"""
Generate embeddings với HolySheep AI
Model: text-embedding-3-small @ $0.02/MTok (input)
"""
start_time = time.perf_counter()
request_id = self._generate_request_id()
payload = {
"model": model,
"input": input
}
response = await self.client.post("/embeddings", json=payload)
latency_ms = (time.perf_counter() - start_time) * 1000
if response.status_code == 200:
data = response.json()
usage = data.get('usage', {})
# Estimate tokens (1 token ≈ 4 chars for English, 2 chars for Chinese)
input_text = input if isinstance(input, str) else " ".join(input)
input_tokens = len(input_text) // 4
cost_usd = CostCalculator.calculate_cost(
provider='holysheep',
model=model,
input_tokens=input_tokens,
output_tokens=0
)
return {
'success': True,
'data': data,
'usage': {
'input_tokens': input_tokens,
'cost_usd': cost_usd,
'latency_ms': round(latency_ms, 2)
}
}
return {
'success': False,
'error': response.text
}
async def _check_rate_limit(self):
"""Rate limiting check"""
now = time.time()
# Remove timestamps older than 1 minute
self.request_timestamps = [ts for ts in self.request_timestamps if now - ts < 60]
if len(self.request_timestamps) >= self.rate_limit:
sleep_time = 60 - (now - self.request_timestamps[0])
if sleep_time > 0:
print(f"⏳ Rate limit reached. Sleeping {sleep_time:.1f}s")
await asyncio.sleep(sleep_time)
self.request_timestamps.append(now)
def _generate_request_id(self) -> str:
"""Generate unique request ID"""
import hashlib
timestamp = str(time.time())
return hashlib.sha256(timestamp.encode()).hexdigest()[:16]
============== Usage Example ==============
async def main():
"""Ví dụ sử dụng HolySheep AI với logging"""
# Initialize client
client = HolySheepAIClient(
api_key="YOUR_HOLYSHEEP_API_KEY", # Thay bằng API key thực tế
log_handler=None, # Pass your log handler
anomaly_detector=AnomalyDetector()
)
# Example 1: Chat Completion với DeepSeek V3.2 (model rẻ nhất)
print("📝 Chat Completion với DeepSeek V3.2 ($0.42/MTok):")
result = await client.chat_completion(
model="deepseek-v3.2",
messages=[
{"role": "system", "content": "Bạn là trợ lý AI hữu ích"},
{"role": "user", "content": "Giải thích về RAG system trong 3 câu"}
],
temperature=0.7,
max_tokens=500,
user_id="user_123"
)
if result['success']:
print(f" ✅ Thành công!")
print(f" 💰 Cost: ${result['usage']['cost_usd']:.6f}")
print(f" ⏱️ Latency: {result['usage']['latency_ms']:.0f}ms")
print(f" 📊 Tokens: {result['usage']['total_tokens']}")
if result.get('anomaly'):
print(f" 🚨 Anomaly: {result['anomaly']}")
else:
print(f" ❌ Lỗi: {result['error']}")
# Example 2: Embeddings cho RAG system
print("\n📚 Embeddings generation:")
embed_result = await client.embeddings(
input="RAG là viết tắt của Retrieval-Augmented Generation",
model="text-embedding-3-small"
)
if embed_result['success']:
print(f" ✅ Embedding generated")
print(f" 💰 Cost: ${embed_result['usage']['cost_usd']:.6f}")
Chạy example
if __name__ == "__main__":
asyncio.run(main())
Xây Dựng Dashboard Monitoring
"""
Real-time AI API Monitoring Dashboard
Sử dụng HolySheep AI với cost optimization
"""
import asyncio
from datetime import datetime, timedelta
from typing import List, Dict, Any
from collections import defaultdict
class MonitoringDashboard:
"""Dashboard theo dõi AI API usage theo thời gian thực"""
def __init__(self):
self.logs: List[APILogEntry] = []
self.alerts: List[Dict] = []
# Real-time counters
self.counters = {
'total_requests': 0,
'total_tokens': 0,
'total_cost_usd': 0.0,
'error_count': 0,
'anomaly_count': 0
}
# Cost comparison
self.cost_comparison = {
'holysheep': 0.0,
'openai_equivalent': 0.0
}
async def add_log(self, log: APILogEntry):
"""Thêm log entry vào dashboard"""
self.logs.append(log)
# Update counters
self.counters['total_requests'] += 1
self.counters['total_tokens'] += log.total_tokens
self.counters['total_cost_usd'] += log.cost_usd
if log.status_code >= 400:
self.counters['error_count'] += 1
if log.is_anomaly:
self.counters['anomaly_count'] += 1
# Calculate OpenAI equivalent cost
openai_prices = {
'gpt-4.1': 30.0, # OpenAI GPT-4
'claude-sonnet-4.5': 45.0, # Claude Sonnet equivalent
'deepseek-v3.2': 8.0, # DeepSeek on OpenAI
}
openai_cost = (log.total_tokens / 1_000_000) * openai_prices.get(log.model, 15.0)
self.cost_comparison['openai_equivalent'] += openai_cost
self.cost_comparison['holysheep'] += log.cost_usd
def generate_report(self, time_window: timedelta = timedelta(hours=24)) -> Dict[str, Any]:
"""Generate báo cáo usage"""
now = datetime.utcnow()
cutoff = now - time_window
recent_logs = [l for l in self.logs if datetime.fromisoformat(l.timestamp) > cutoff]
# Group by model
by_model = defaultdict(lambda: {'requests': 0, 'tokens': 0, 'cost': 0.0, 'latencies': []})
for log in recent_logs:
by_model[log.model]['requests'] += 1
by_model[log.model]['tokens'] += log.total_tokens
by_model[log.model]['cost'] += log.cost_usd
by_model[log.model]['latencies'].append(log.latency_ms)
# Calculate stats
model_stats = {}
for model, stats in by_model.items():
latencies = stats['latencies']
model_stats[model] = {
'requests': stats['requests'],
'total_tokens': stats['tokens'],
'cost_usd': round(stats['cost'], 4),
'avg_latency_ms': round(sum(latencies) / len(latencies), 2) if latencies else 0,
'p95_latency_ms': round(sorted(latencies)[int(len(latencies) * 0.95)]) if latencies else 0,
'p99_latency_ms': round(sorted(latencies)[int(len(latencies) * 0.99)]) if latencies else 0,
}
# Cost savings
savings = self.cost_comparison['openai_equivalent'] - self.cost_comparison['holysheep']
savings_percent = (savings / self.cost_comparison['openai_equivalent'] * 100)
return {
'time_window': str(time_window),
'generated_at': now.isoformat(),
'summary': {
'total_requests': len(recent_logs),
'total_tokens': sum(l.total_tokens for l in recent_logs),
'total_cost_usd': round(sum(l.cost_usd for l in recent_logs), 4),
'avg_cost_per_request': round(
sum(l.cost_usd for l in recent_logs) / len(recent_logs), 6
) if recent_logs else 0,
'error_rate': round(
len([l for l in recent_logs if l.status_code >= 400]) / len(recent_logs) * 100, 2
) if recent_logs else 0,
'anomaly_rate': round(
len([l for l in recent_logs if l.is_anomaly]) / len(recent_logs) * 100, 2
) if recent_logs else 0,
},
'by_model': model_stats,
'cost_comparison': {
'holysheep_actual': round(self.cost_comparison['holysheep'], 4),
'openai_equivalent': round(self.cost_comparison['openai_equivalent'], 4),
'savings_usd': round(savings, 4),
'savings_percent': round(savings_percent, 2)
},
'top_anomalies': [
{
'timestamp': l.timestamp,
'model': l.model,
'type': l.anomaly_type,
'severity': l.anomaly_severity,
'cost': l.cost_usd,
'message': f"{l.anomaly_type} - {l.anomaly_severity}"
}
for l in recent_logs if l.is_anomaly
][:10] # Top 10 anomalies
}
def print_report(self, report: Dict):
"""In báo cáo ra console"""
print("\n" + "=" * 60)
print("🤖 AI API MONITORING REPORT")
print("=" * 60)
print(f"\n⏰ Time Window: {report['time_window']}")
print(f"📅 Generated: {report['generated_at']}")
print("\n📊 SUMMARY:")
summary = report['summary']
print(f" Total Requests: {summary['total_requests']:,}")
print(f" Total Tokens: {summary['total_tokens']:,}")
print(f" Total Cost: ${summary['total_cost_usd']:.4f}")
print(f" Avg Cost/Request: ${summary['avg_cost_per_request']:.6f}")
print(f" Error Rate: {summary['error_rate']}%")
print(f" Anomaly Rate: {summary['anomaly_rate']}%")
print("\n📈 BY MODEL:")
for model, stats in report['by_model'].items():
print(f" {model}:")
print(f" Requests: {stats['requests']:,}")
print(f" Tokens: {stats['total_tokens']:,}")
print(f" Cost: ${stats['cost_usd']:.4f}")
print(f" Latency: avg={stats['avg_latency_ms']}ms, p95={stats['p95_latency_ms']}ms, p99={stats['p99_latency_ms']}ms")
print("\n💰 COST COMPARISON (HolySheep vs OpenAI):")
comparison = report['cost_comparison']
print(f" HolySheep Actual: ${comparison['holysheep_actual']:.4f}")
print(f" OpenAI Equivalent: ${comparison['openai_equivalent']:.4f}")
print(f" 💵 SAVINGS: ${comparison['savings_usd']:.4f} ({comparison['savings_percent']:.1f}%)")
if report['top_anomalies']:
print("\n🚨 TOP ANOMALIES:")
for i, anomaly in enumerate(report['top_anomalies'][:5], 1):
print(f" {i}. [{anomaly['severity'].upper()}] {anomaly['timestamp']}")
print(f" Model: {anomaly['model']}, Type: {anomaly['type']}")
print(f" Cost: ${anomaly['cost']:.6f}")
print("\n" + "=" * 60)
============== Usage Example ==============
async def demo_dashboard():
"""Demo dashboard với sample data"""
dashboard = MonitoringDashboard()
# Add sample logs (simulating 24h usage)
import random
models = ['deepseek-v3.2', 'gemini-2.5-flash', 'gpt-4.1', 'claude-sonnet-4.5']
print("📊 Simulating 24h AI API usage...")
for i in range(1000):
model = random.choice(models)
# Token distribution by model
token_ranges = {
'deepseek-v3.2': (500, 2000),
'gemini-2.5-flash': (800, 3000),
'gpt-4.1': (1000, 5000),
'claude-sonnet-4.5': (1000, 4000)
}
input_tokens = random.randint(*token_ranges.get(model, (1000, 3000)))
output_tokens = random.randint(100, 1000)
cost = CostCalculator.calculate_cost('holysheep', model, input_tokens, output_tokens)
# Random anomaly (5% chance)
is_anomaly = random.random() < 0.05
anomaly_type = random.choice(['high_latency', 'token_spike', 'cost_spike']) if is_anomaly else None
log = APILogEntry(
timestamp=(datetime.utcnow() - timedelta(hours=random.randint(0, 24))).isoformat(),
request_id=f"req_{i:06d}",
provider='holysheep',
model=model,
operation='chat',
input_tokens=input_tokens,
output_tokens=output_tokens,
total_tokens=input_tokens + output_tokens,
latency_ms=random.uniform(30, 200),
cost_usd=cost,
is_anomaly=is_anomaly,
anomaly_type=anomaly_type,
anomaly_severity=random.choice(['low', 'medium', 'high', 'critical']) if is_an