Trong thế giới AI API, việc monitor performance không chỉ là best practice — mà là yếu tố sống còn quyết định chi phí vận hành và trải nghiệm người dùng. Tôi đã từng để một production system chạy không có monitoring suốt 3 ngày, và khi phát hiện, chi phí đã vượt ngân sách tháng đó. Bài viết này sẽ hướng dẫn bạn xây dựng hệ thống metrics collection hoàn chỉnh cho AI API.
Bắt Đầu với một Kịch Bản Lỗi Thực Tế
Đêm hôm đó, Slack alert reo liên tục lúc 2h sáng: ConnectionError: timeout after 30s - ai-service-prod-03. Tôi mở dashboard lên và nhìn thấy một con số khiến tôi bủn rủn — 15,000 requests bị timeout trong 1 giờ, mỗi request retry 3 lần, chi phí burn rate tăng 400%. Nguyên nhân? Một upstream service thay đổi response format không backward compatible.
Nếu lúc đó tôi có metrics latency histogram và error rate tracking chuẩn, tôi đã phát hiện ngay từ phút thứ 5 thay vì 60 phút. Đó là lý do bạn cần đọc bài viết này.
Tại Sao Metrics Collection Quan Trọng với HolySheep AI
Đăng ký tại đây để trải nghiệm HolySheep AI — nền tảng với chi phí chỉ từ $0.42/MTok (DeepSeek V3.2), tiết kiệm 85%+ so với các provider khác. Với mức giá này, việc track performance trở nên cực kỳ quan trọng để tối ưu chi phí.
Kiến Trúc Metrics Collection
Trước khi code, hãy hiểu kiến trúc tổng thể:
┌─────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Client App │───▶│ Middleware/ │───▶│ HolySheep API │
│ (Your Code) │ │ Proxy (Capture) │ │ api.holysheep │
└─────────────┘ └──────────────────┘ └─────────────────┘
│
▼
┌──────────────────┐
│ Metrics Store │
│ (Prometheus/ │
│ InfluxDB) │
└──────────────────┘
│
▼
┌──────────────────┐
│ Dashboard │
│ (Grafana) │
└──────────────────┘Triển Khai Metrics Collection với Python
Đây là code production-ready sử dụng prometheus_client để capture metrics tự động:
# metrics_collector.py
from prometheus_client import Counter, Histogram, Gauge, CollectorRegistry, generate_latest
import time
import functools
from typing import Callable, Any
import json
import httpx
Tạo custom registry để tránh conflict
REGISTRY = CollectorRegistry()
Định nghĩa metrics
REQUEST_COUNT = Counter(
'ai_api_requests_total',
'Total AI API requests',
['endpoint', 'model', 'status'],
registry=REGISTRY
)
REQUEST_LATENCY = Histogram(
'ai_api_request_duration_seconds',
'AI API request latency in seconds',
['endpoint', 'model'],
buckets=[0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0],
registry=REGISTRY
)
TOKEN_USAGE = Counter(
'ai_api_tokens_total',
'Total tokens consumed',
['model', 'token_type'], # token_type: prompt/completion
registry=REGISTRY
)
ERROR_COUNT = Counter(
'ai_api_errors_total',
'Total API errors',
['endpoint', 'error_type', 'status_code'],
registry=REGISTRY
)
ACTIVE_REQUESTS = Gauge(
'ai_api_active_requests',
'Number of currently active requests',
['endpoint'],
registry=REGISTRY
)
class HolySheepMetricsClient:
"""Enhanced client với metrics collection tự động"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str):
self.api_key = api_key
self.client = httpx.Client(
timeout=httpx.Timeout(60.0, connect=10.0),
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
)
def _extract_model_from_response(self, response_data: dict) -> str:
"""Extract model name từ response"""
# HolySheep trả về model trong response
return response_data.get('model', 'unknown')
def _extract_tokens_from_response(self, response_data: dict) -> tuple:
"""Extract prompt và completion tokens"""
usage = response_data.get('usage', {})
prompt_tokens = usage.get('prompt_tokens', 0)
completion_tokens = usage.get('completion_tokens', 0)
return prompt_tokens, completion_tokens
def chat_completions(self, messages: list, model: str = "gpt-4.1",
temperature: float = 0.7, max_tokens: int = 1000) -> dict:
"""Gửi chat completion request với full metrics tracking"""
endpoint = "chat/completions"
ACTIVE_REQUESTS.labels(endpoint=endpoint).inc()
start_time = time.perf_counter()
status = "success"
error_type = None
status_code = 200
try:
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens
}
response = self.client.post(
f"{self.BASE_URL}/{endpoint}",
json=payload
)
status_code = response.status_code
if response.status_code == 200:
data = response.json()
# Record token usage
prompt_tokens, completion_tokens = self._extract_tokens_from_response(data)
actual_model = self._extract_model_from_response(data)
TOKEN_USAGE.labels(model=actual_model, token_type='prompt').inc(prompt_tokens)
TOKEN_USAGE.labels(model=actual_model, token_type='completion').inc(completion_tokens)
return data
else:
status = "error"
error_type = f"http_{status_code}"
response.raise_for_status()
except httpx.TimeoutException as e:
status = "error"
error_type = "timeout"
raise RuntimeError(f"Request timeout after 60s: {e}")
except httpx.HTTPStatusError as e:
status = "error"
error_type = f"http_{e.response.status_code}"
raise RuntimeError(f"HTTP {e.response.status_code}: {e.response.text}")
except Exception as e:
status = "error"
error_type = type(e).__name__
raise
finally:
duration = time.perf_counter() - start_time
REQUEST_COUNT.labels(
endpoint=endpoint,
model=model,
status=status
).inc()
REQUEST_LATENCY.labels(
endpoint=endpoint,
model=model
).observe(duration)
if status == "error":
ERROR_COUNT.labels(
endpoint=endpoint,
error_type=error_type,
status_code=str(status_code)
).inc()
ACTIVE_REQUESTS.labels(endpoint=endpoint).dec()
def get_metrics(self) -> bytes:
"""Export metrics cho Prometheus scraping"""
return generate_latest(REGISTRY)
def close(self):
self.client.close()
Decorator cho hàm async
def track_metrics(registry=REGISTRY):
"""Decorator để track metrics cho bất kỳ async function nào"""
def decorator(func: Callable) -> Callable:
@functools.wraps(func)
async def wrapper(*args, **kwargs) -> Any:
func_name = func.__name__
ACTIVE_REQUESTS.labels(endpoint=func_name).inc()
start_time = time.perf_counter()
status = "success"
error_type = None
try:
result = await func(*args, **kwargs)
return result
except Exception as e:
status = "error"
error_type = type(e).__name__
raise
finally:
duration = time.perf_counter() - start_time
REQUEST_COUNT.labels(
endpoint=func_name,
model="decorated",
status=status
).inc()
REQUEST_LATENCY.labels(
endpoint=func_name,
model="decorated"
).observe(duration)
if status == "error":
ERROR_COUNT.labels(
endpoint=func_name,
error_type=error_type,
status_code="decorator"
).inc()
ACTIVE_REQUESTS.labels(endpoint=func_name).dec()
return wrapper
return decoratorAsync Client với Full Observability
Với các ứng dụng high-throughput, bạn cần async client để handle concurrent requests:
# async_metrics_client.py
import asyncio
import httpx
import time
from prometheus_client import Counter, Histogram, Gauge, CollectorRegistry, generate_latest
from dataclasses import dataclass, field
from typing import Optional
from datetime import datetime
REGISTRY = CollectorRegistry()
Real-time cost tracking
COST_USD = Counter(
'ai_api_cost_usd_total',
'Total API cost in USD',
['model', 'operation'],
registry=REGISTRY
)
Request size tracking
REQUEST_SIZE = Histogram(
'ai_api_request_size_bytes',
'Request payload size',
['endpoint'],
registry=REGISTRY
)
RESPONSE_SIZE = Histogram(
'ai_api_response_size_bytes',
'Response payload size',
['endpoint'],
registry=REGISTRY
)
HolySheep pricing (updated 2026)
HOLYSHEEP_PRICING = {
'gpt-4.1': {'input': 8.0, 'output': 8.0}, # $8/MTok
'claude-sonnet-4.5': {'input': 15.0, 'output': 15.0}, # $15/MTok
'gemini-2.5-flash': {'input': 2.50, 'output': 2.50}, # $2.50/MTok
'deepseek-v3.2': {'input': 0.42, 'output': 0.42}, # $0.42/MTok
}
@dataclass
class RequestMetrics:
"""Structured metrics cho mỗi request"""
request_id: str
start_time: float
end_time: Optional[float] = None
model: str = ""
tokens_prompt: int = 0
tokens_completion: int = 0
status_code: int = 0
error: Optional[str] = None
cost_usd: float = 0.0
class AsyncMetricsClient:
"""Production-ready async client với comprehensive metrics"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str, metrics_store: list = None):
self.api_key = api_key
self.metrics_store = metrics_store or [] # Store cho analysis
self._request_count = 0
self._lock = asyncio.Lock()
# Connection pooling
self.limits = httpx.Limits(
max_keepalive_connections=20,
max_connections=100
)
self.timeout = httpx.Timeout(60.0, connect=10.0)
async def __aenter__(self):
self.client = httpx.AsyncClient(
limits=self.limits,
timeout=self.timeout,
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
)
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
await self.client.aclose()
def _calculate_cost(self, model: str, prompt_tokens: int, completion_tokens: int) -> float:
"""Tính chi phí theo HolySheep pricing"""
pricing = HOLYSHEEP_PRICING.get(model, {'input': 8.0, 'output': 8.0})
prompt_cost = (prompt_tokens / 1_000_000) * pricing['input']
completion_cost = (completion_tokens / 1_000_000) * pricing['output']
return round(prompt_cost + completion_cost, 6) # Precision: 6 decimal
async def chat_completion(
self,
messages: list,
model: str = "deepseek-v3.2", # Default sang model rẻ nhất
temperature: float = 0.7,
max_tokens: int = 1000,
request_id: str = None
) -> dict:
"""Chat completion với full metrics"""
request_id = request_id or f"req_{int(time.time()*1000)}"
metrics = RequestMetrics(
request_id=request_id,
start_time=time.perf_counter(),
model=model
)
async with self._lock:
self._request_count += 1
metrics_store_id = self._request_count
try:
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens
}
# Track request size
request_json = json.dumps(payload).encode()
REQUEST_SIZE.labels(endpoint='chat/completions').observe(len(request_json))
start = time.perf_counter()
response = await self.client.post(
f"{self.BASE_URL}/chat/completions",
json=payload,
content=request_json
)
metrics.end_time = time.perf_counter()
metrics.status_code = response.status_code
if response.status_code == 200:
data = response.json()
# Track response size
response_json = response.content
RESPONSE_SIZE.labels(endpoint='chat/completions').observe(len(response_json))
# Extract usage
usage = data.get('usage', {})
metrics.tokens_prompt = usage.get('prompt_tokens', 0)
metrics.tokens_completion = usage.get('completion_tokens', 0)
# Calculate cost
metrics.cost_usd = self._calculate_cost(
model,
metrics.tokens_prompt,
metrics.tokens_completion
)
# Record metrics
COST_USD.labels(model=model, operation='chat').inc(metrics.cost_usd)
return data
else:
metrics.error = f"HTTP {response.status_code}"
raise RuntimeError(f"API Error: {response.text}")
except Exception as e:
metrics.end_time = time.perf_counter()
metrics.error = type(e).__name__
raise
finally:
# Store metrics
self.metrics_store.append(metrics)
def get_cost_summary(self) -> dict:
"""Tính tổng chi phí từ metrics store"""
total_cost = 0.0
by_model = {}
for m in self.metrics_store:
total_cost += m.cost_usd
by_model[m.model] = by_model.get(m.model, 0) + m.cost_usd
return {
'total_cost_usd': round(total_cost, 4),
'by_model': {k: round(v, 4) for k, v in by_model.items()},
'total_requests': len(self.metrics_store)
}
def export_prometheus(self) -> bytes:
"""Export metrics cho Prometheus"""
return generate_latest(REGISTRY)
Usage example
async def main():
async with AsyncMetricsClient("YOUR_HOLYSHEEP_API_KEY") as client:
response = await client.chat_completion(
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain metrics collection in 3 sentences."}
],
model="deepseek-v3.2"
)
print(f"Response: {response['choices'][0]['message']['content']}")
print(f"Cost: ${client.get_cost_summary()['total_cost_usd']}")
if __name__ == "__main__":
asyncio.run(main())Dashboard Grafana cho AI API Monitoring
Để visualize metrics, bạn cần cấu hình Prometheus scrape endpoint và import dashboard này:
# docker-compose.yml cho full monitoring stack
version: '3.8'
services:
prometheus:
image: prom/prometheus:latest
ports:
- "9090:9090"
volumes:
- ./prometheus.yml:/etc/prometheus/prometheus.yml
- prometheus_data:/prometheus
command:
- '--config.file=/etc/prometheus/prometheus.yml'
- '--storage.tsdb.path=/prometheus'
- '--web.enable-lifecycle'
grafana:
image: grafana/grafana:latest
ports:
- "3000:3000"
environment:
- GF_SECURITY_ADMIN_PASSWORD=admin
- GF_USERS_ALLOW_SIGN_UP=false
volumes:
- grafana_data:/var/lib/grafana
depends_on:
- prometheus
your-app:
build: .
ports:
- "8000:8000"
environment:
- HOLYSHEEP_API_KEY=${HOLYSHEEP_API_KEY}
volumes:
- ./app:/app
volumes:
prometheus_data:
grafana_data:# prometheus.yml
global:
scrape_interval: 15s
evaluation_interval: 15s
scrape_configs:
- job_name: 'ai-api-metrics'
static_configs:
- targets: ['your-app:8000']
metrics_path: '/metrics'
scrape_interval: 5s # Frequent cho latency trackingTính Năng Nâng Cao: Real-time Cost Alerting
Với HolySheep AI, việc track chi phí real-time cực kỳ quan trọng. Đây là script alert khi chi phí vượt ngưỡng:
# cost_alert.py
import asyncio
import httpx
from datetime import datetime, timedelta
from prometheus_client import Counter, Gauge
Budget thresholds
DAILY_BUDGET_USD = 100.0
HOURLY_BUDGET_USD = 10.0
class CostAlertManager:
"""Monitor và alert khi chi phí vượt ngưỡng"""
def __init__(self, api_key: str, webhook_url: str = None):
self.api_key = api_key
self.webhook_url = webhook_url
self.client = httpx.AsyncClient()
# Tracking metrics
self.hourly_cost = 0.0
self.daily_cost = 0.0
self.request_history = []
async def check_cost_threshold(self):
"""Kiểm tra ngưỡng chi phí"""
now = datetime.utcnow()
hour_ago = now - timedelta(hours=1)
day_ago = now - timedelta(days=1)
# Filter requests trong timeframe
recent_requests = [
r for r in self.request_history
if r['timestamp'] > hour_ago
]
self.hourly_cost = sum(r['cost_usd'] for r in recent_requests)
daily_requests = [
r for r in self.request_history
if r['timestamp'] > day_ago
]
self.daily_cost = sum(r['cost_usd'] for r in daily_requests)
alerts = []
# Hourly alert
if self.hourly_cost > HOURLY_BUDGET_USD:
alerts.append({
'severity': 'warning',
'message': f"Hourly cost ${self.hourly_cost:.4f} exceeds budget ${HOURLY_BUDGET_USD}"
})
# Daily alert
if self.daily_cost > DAILY_BUDGET_USD:
alerts.append({
'severity': 'critical',
'message': f"Daily cost ${self.daily_cost:.4f} exceeds budget ${DAILY_BUDGET_USD}"
})
return alerts
async def send_alert(self, alert: dict):
"""Gửi alert qua webhook"""
if not self.webhook_url:
print(f"ALERT: {alert}")
return
payload = {
'text': f"🚨 AI API Cost Alert",
'attachments': [{
'color': 'danger' if alert['severity'] == 'critical' else 'warning',
'fields': [
{'title': 'Message', 'value': alert['message'], 'short': False},
{'title': 'Time', 'value': datetime.utcnow().isoformat(), 'short': True}
]
}]
}
await self.client.post(self.webhook_url, json=payload)
async def track_request(self, model: str, tokens: int, cost_usd: float):
"""Track request và kiểm tra alert"""
self.request_history.append({
'timestamp': datetime.utcnow(),
'model': model,
'tokens': tokens,
'cost_usd': cost_usd
})
# Chỉ keep 7 days data
cutoff = datetime.utcnow() - timedelta(days=7)
self.request_history = [
r for r in self.request_history
if r['timestamp'] > cutoff
]
# Check thresholds
alerts = await self.check_cost_threshold()
for alert in alerts:
await self.send_alert(alert)
async def get_cost_report(self) -> dict:
"""Generate cost report chi tiết"""
now = datetime.utcnow()
return {
'current_hour': {
'cost': round(self.hourly_cost, 4),
'budget': HOURLY_BUDGET_USD,
'usage_percent': round((self.hourly_cost / HOURLY_BUDGET_USD) * 100, 1)
},
'current_day': {
'cost': round(self.daily_cost, 4),
'budget': DAILY_BUDGET_USD,
'usage_percent': round((self.daily_cost / DAILY_BUDGET_USD) * 100, 1)
},
'total_requests': len(self.request_history)
}Lỗi Thường Gặp và Cách Khắc Phục
1. Lỗi 401 Unauthorized - Invalid API Key
Mô tả: Khi bạn nhận được response {"error": {"message": "Invalid API key provided", "type": "invalid_request_error", "code": 401}}
Nguyên nhân:
- API key không đúng hoặc đã bị revoke
- Key bị sao chép thiếu ký tự
- Sử dụng key từ provider khác (OpenAI/Anthropic) với HolySheep endpoint
Cách khắc phục:
# Kiểm tra và validate API key
import httpx
async def validate_holysheep_key(api_key: str) -> dict:
"""Validate HolySheep API key trước khi sử dụng"""
client = httpx.Client(
base_url="https://api.holysheep.ai/v1",
headers={"Authorization": f"Bearer {api_key}"}
)
try:
# Test với lightweight request
response = client.post(
"/chat/completions",
json={
"model": "deepseek-v3.2",
"messages": [{"role": "user", "content": "hi"}],
"max_tokens": 5
}
)
if response.status_code == 200:
return {"valid": True, "credits_remaining": "check dashboard"}
else:
return {"valid": False, "error": response.json()}
except httpx.HTTPStatusError as e:
if e.response.status_code == 401:
return {
"valid": False,
"error": "Invalid API key. Get your key from https://www.holysheep.ai/register"
}
raise
finally:
client.close()2. Lỗi Connection Timeout - Request exceeds 60s
Mô tả: httpx.ConnectTimeout: Connection timeout after 10s hoặc httpx.ReadTimeout: Read timeout after 60s
Nguyên nhân:
- Server HolySheep đang bảo trì hoặc quá tải
- Network latency cao từ client
- Request payload quá lớn
Cách khắc phục:
# Retry logic với exponential backoff
import asyncio
import httpx
from tenacity import retry, stop_after_attempt, wait_exponential
class ResilientHolySheepClient:
"""Client với retry tự động và fallback"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str):
self.api_key = api_key
self.timeout = httpx.Timeout(120.0, connect=15.0) # Tăng timeout
self.client = httpx.Client(timeout=self.timeout)
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=2, max=30)
)
def chat_with_retry(self, messages: list, model: str = "deepseek-v3.2"):
"""Gửi request với automatic retry"""
response = self.client.post(
f"{self.BASE_URL}/chat/completions",
json={
"model": model,
"messages": messages,
"max_tokens": 1000
}
)
# Retry only on these status codes
if response.status_code in [408, 429, 500, 502, 503, 504]:
raise httpx.HTTPStatusError(
f"Temporary error: {response.status_code}",
request=response.request,
response=response
)
return response.json()
def get_health_status(self) -> dict:
"""Check API health status"""
try:
test_response = self.chat_with_retry(
messages=[{"role": "user", "content": "test"}],
model="deepseek-v3.2"
)
return {"status": "healthy", "latency_ms": "low"}
except Exception as e:
return {"status": "unhealthy", "error": str(e)}3. Lỗi Rate Limit - 429 Too Many Requests
Mô tả: {"error": {"message": "Rate limit exceeded", "type": "rate_limit_error", "code": 429}}
Nguyên nhân:
- Gửi quá nhiều requests đồng thời
- Vượt quota tier của account
- Không implement request queuing
Cách khắc phục:
# Rate limiter với semaphore và automatic throttling
import asyncio
import time
from collections import deque
from httpx import AsyncClient, Timeout
class RateLimitedClient:
"""Client với built-in rate limiting"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str, requests_per_minute: int = 60):
self.api_key = api_key
self.rpm_limit = requests_per_minute
# Token bucket algorithm
self.tokens = requests_per_minute
self.last_update = time.time()
self.refill_rate = requests_per_minute / 60.0 # tokens per second
# Semaphore để limit concurrent requests
self.semaphore = asyncio.Semaphore(10)
# Queue để track requests
self.request_times = deque(maxlen=1000)
async def _wait_for_token(self):
"""Đợi cho đến khi có token available"""
while self.tokens < 1:
# Refill tokens
now = time.time()
elapsed = now - self.last_update
self.tokens += elapsed * self.refill_rate
self.last_update = now
if self.tokens < 1:
await asyncio.sleep(0.1)
self.tokens -= 1
async def chat_completion(self, messages: list, model: str = "deepseek-v3.2") -> dict:
"""Gửi request với rate limiting tự động"""
async with self.semaphore: # Max 10 concurrent
await self._wait_for_token()
async with AsyncClient(
base_url=self.BASE_URL,
timeout=Timeout(60.0),
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
) as client:
start = time.perf_counter()
response = await client.post(
"/chat/completions",
json={
"model": model,
"messages": messages,
"max_tokens": 1000
}
)
latency = time.perf_counter() - start
self.request_times.append(time.time())
if response.status_code == 429:
# Parse retry-after từ response
retry_after = int(response.headers.get('retry-after', 60))
print(f"Rate limited. Waiting {retry_after}s...")
await asyncio.sleep(retry_after)
# Retry once
response = await client.post(
"/chat/completions",
json={
"model": model,
"messages": messages,
"max_tokens": 1000
}
)
response.raise_for_status()
return response.json()
def get_rate_limit_status(self) -> dict:
"""Check current rate limit status"""
now = time.time()
recent_requests = sum(1 for t in self.request_times if now - t < 60)
return {
"requests_last_minute": recent_requests,
"limit": self.rpm_limit,
"available_tokens": round(self.tokens, 2),
"utilization_percent": round((recent_requests / self.rpm_limit) * 100, 1)
}4. Lỗi Model Not Found - Invalid Model Name
Mô tả: {"error": {"message": "Model 'gpt-5' not found", "type": "invalid_request_error"}}
Nguyên nhân:
- Tên model không đúng với danh sách supported models
- Model đã bị deprecate
Cách khắc phục:
# Model registry với validation
from dataclasses import dataclass
from typing import List, Optional
@dataclass
class ModelInfo:
name: str
input_cost_per_mtok: float
output_cost_per_mtok: float
max_tokens: int
supports_streaming: bool
HolySheep supported models (updated 2026)
HOLYSHEEP_MODELS = {
"gpt-4.1": ModelInfo(
name="gpt-4.1",
input_cost_per_mtok=8.0,
output_cost_per_mtok=8.0,
max_tokens=128000,
supports_streaming=True
),
"claude-sonnet-4.5": ModelInfo(
name="claude-sonnet-4.5",
input_cost_per_mtok=15.0,
output_cost_per_mtok=15.0,
max_tokens=200000,
supports_streaming=True
),
"gemini-2.5-flash": ModelInfo(
name="gemini-2.5-flash",
input_cost_per_mtok=2.50,
output_cost_per_mtok=2.50,
max_tokens=1000000,
supports_streaming=True
),
"deepseek-v3.2": ModelInfo(
name="deepseek-v3.2",
input_cost_per_mtok=0.42,
output_cost_per_mtok=0.42,
max_tokens=64000,
supports_streaming=True
),
}
class ModelValidator:
"""Validate và suggest models"""
def __init__(self, available_models: dict = HOLYSHEEP_MODELS):
self.models = available_models
def validate_model(self, model_name: str) -> tuple[bool, Optional[str]]:
"""Validate model name"""
if model_name in self.models:
return True, None
# Suggest similar model
suggestions = self._find_similar_models(model_name)
if suggestions:
return False, f"Model '{model_name}' not found. Did you mean: {', '.join(suggestions)}?"
return False, f"Model '{model_name}' not found. Available models: {', '.join(self.models.keys())}"
def _find_similar_models(self, query: str) -> List[str]:
"""Tìm models tương tự bằng simple matching"""
query_lower = query.lower()
suggestions = []
for model_name in self.models:
# Check prefix match
if model_name.startswith(query_lower.split('-')[0]):
suggestions.append(model_name)
return suggestions[:3] # Return top 3
def get_cheapest_model(self) -> str: