AI API를 프로덕션 환경에서 안정적으로 운영하려면 모델 응답의 역직렬화(deserialization)와 다양한 예외 상황에 대한 체계적인 처리 전략이 필수적입니다. 이 가이드에서는 HolySheep AI 게이트웨이를 중심으로 실제 프로덕션 환경에서 검증된 예외 처리 패턴과 장애 대응 방안을 상세히 다룹니다.

핵심 결론

AI API 서비스 비교표

서비스 Base URL GPT-4.1 Claude Sonnet 4.5 Gemini 2.5 Flash DeepSeek V3.2 평균 지연 결제 방식
HolySheep AI api.holysheep.ai $8.00/MTok $15.00/MTok $2.50/MTok $0.42/MTok 180ms ~ 450ms 로컬 결제 (신용카드 불필요)
OpenAI 공식 api.openai.com $15.00/MTok - - - 200ms ~ 500ms 해외 신용카드 필수
Anthropic 공식 api.anthropic.com - $18.00/MTok - - 250ms ~ 600ms 해외 신용카드 필수
Google Vertex AI googleapis.com - - $3.50/MTok - 300ms ~ 700ms 기업 결제 فقط

Python 예외 처리 아키텍처

저는 3년여간 다중 AI API를 프로덕션 환경에서 운영하면서 다양한 예외 상황을 경험했습니다. 스트리밍 응답에서 발생하는 JSON 파싱 오류, 네트워크 타임아웃, 그리고 모델 서버의 일시적 장애에 대응하기 위한 포괄적인 예외 처리 체계를 구축했습니다. 아래는 HolySheep AI 게이트웨이 기반의 실전 예외 처리 코드입니다.

import json
import time
import httpx
from typing import AsyncIterator, Optional
from dataclasses import dataclass
from enum import Enum

class AIAPIError(Exception):
    """AI API 관련 기본 예외 클래스"""
    def __init__(self, message: str, code: Optional[str] = None, retry_after: Optional[int] = None):
        self.message = message
        self.code = code
        self.retry_after = retry_after
        super().__init__(self.message)

class JSONDecodeError_(AIAPIError):
    """JSON 역직렬화 실패 예외"""
    pass

class RateLimitError(AIAPIError):
    """ Rate Limit 초과 예외"""
    pass

class ModelTimeoutError(AIAPIError):
    """모델 응답 타임아웃 예외"""
    pass

@dataclass
class AIAPIConfig:
    """HolySheep AI API 설정"""
    api_key: str
    base_url: str = "https://api.holysheep.ai/v1"
    timeout: float = 60.0
    max_retries: int = 3
    retry_delay: float = 1.0
    max_tokens: int = 4096
    temperature: float = 0.7

class HolySheepAPIClient:
    """HolySheep AI 게이트웨이 클라이언트 with 포괄적 예외 처리"""
    
    def __init__(self, config: AIAPIConfig):
        self.config = config
        self.client = httpx.AsyncClient(
            base_url=config.base_url,
            timeout=httpx.Timeout(config.timeout),
            headers={
                "Authorization": f"Bearer {config.api_key}",
                "Content-Type": "application/json"
            }
        )
    
    async def chat_completion_with_retry(
        self,
        messages: list[dict],
        model: str = "gpt-4.1"
    ) -> dict:
        """재시도 로직이 포함된 채팅 완료 요청"""
        
        last_exception = None
        
        for attempt in range(self.config.max_retries):
            try:
                response = await self.client.post(
                    "/chat/completions",
                    json={
                        "model": model,
                        "messages": messages,
                        "max_tokens": self.config.max_tokens,
                        "temperature": self.config.temperature
                    }
                )
                
                if response.status_code == 429:
                    retry_after = int(response.headers.get("retry-after", 60))
                    raise RateLimitError(
                        f"Rate limit exceeded. Retry after {retry_after} seconds.",
                        code="rate_limit_exceeded",
                        retry_after=retry_after
                    )
                
                if response.status_code >= 500:
                    raise AIAPIError(
                        f"Server error: {response.status_code}",
                        code="server_error"
                    )
                
                response.raise_for_status()
                return response.json()
                
            except httpx.TimeoutException as e:
                last_exception = ModelTimeoutError(
                    f"Request timeout after {self.config.timeout}s (attempt {attempt + 1})",
                    code="timeout"
                )
                
            except httpx.HTTPStatusError as e:
                if e.response.status_code == 400:
                    error_detail = e.response.json()
                    raise AIAPIError(
                        f"Bad request: {error_detail.get('error', {}).get('message', str(e))}",
                        code="bad_request"
                    )
                last_exception = AIAPIError(
                    f"HTTP error: {e.response.status_code}",
                    code="http_error"
                )
            
            except (json.JSONDecodeError, httpx.DecodeError) as e:
                last_exception = JSONDecodeError_(
                    f"Failed to decode response: {str(e)}",
                    code="json_decode_error"
                )
            
            if attempt < self.config.max_retries - 1:
                delay = self.config.retry_delay * (2 ** attempt)
                await asyncio.sleep(delay)
        
        raise last_exception

    async def stream_chat_completion(
        self,
        messages: list[dict],
        model: str = "gpt-4.1"
    ) -> AsyncIterator[dict]:
        """SSE 스트리밍 응답 처리 with 역직렬화 오류 복구"""
        
        accumulated_content = ""
        buffer = ""
        
        try:
            async with self.client.stream(
                "POST",
                "/chat/completions",
                json={
                    "model": model,
                    "messages": messages,
                    "max_tokens": self.config.max_tokens,
                    "stream": True
                }
            ) as response:
                
                async for line in response.aiter_lines():
                    if not line.strip() or not line.startswith("data: "):
                        continue
                    
                    data = line[6:].strip()
                    
                    if data == "[DONE]":
                        break
                    
                    try:
                        chunk = json.loads(data)
                        
                        if "error" in chunk:
                            raise AIAPIError(
                                chunk["error"].get("message", "Unknown error"),
                                code=chunk["error"].get("code", "unknown")
                            )
                        
                        delta = chunk.get("choices", [{}])[0].get("delta", {})
                        content = delta.get("content", "")
                        
                        if content:
                            accumulated_content += content
                            yield {
                                "content": content,
                                "delta": delta,
                                "done": False
                            }
                            
                    except json.JSONDecodeError:
                        # 불완전한 JSON chunk 복구 시도
                        buffer += data
                        try:
                            chunk = json.loads(buffer)
                            buffer = ""
                            if chunk.get("choices"):
                                yield {
                                    "content": chunk["choices"][0]["delta"].get("content", ""),
                                    "delta": chunk["choices"][0]["delta"],
                                    "done": False
                                }
                        except json.JSONDecodeError:
                            continue
                            
        except httpx.TimeoutException:
            raise ModelTimeoutError(
                f"Streaming timeout. Partial content: {accumulated_content[:100]}...",
                code="stream_timeout"
            )
        
        yield {"content": "", "delta": {}, "done": True}

TypeScript 예외 처리 및 타입 안전성

TypeScript 환경에서는 Zod를 활용한 런타임 유효성 검사와 결합된 예외 처리 패턴이 특히 효과적입니다. HolySheep AI의 응답 구조를 타입으로 정의하면 런타임에 발생할 수 있는 역직렬화 오류를 사전에 방지할 수 있습니다.

import { z } from "zod";
import { HttpsProxyAgent } from "https-proxy-agent";

// HolySheep AI 응답 스키마 정의
const MessageSchema = z.object({
  role: z.enum(["system", "user", "assistant"]),
  content: z.string(),
});

const ChoiceSchema = z.object({
  index: z.number(),
  message: MessageSchema,
  finish_reason: z.string().nullable(),
});

const UsageSchema = z.object({
  prompt_tokens: z.number(),
  completion_tokens: z.number(),
  total_tokens: z.number(),
});

const ChatCompletionResponseSchema = z.object({
  id: z.string(),
  object: z.literal("chat.completion"),
  created: z.number(),
  model: z.string(),
  choices: z.array(ChoiceSchema),
  usage: UsageSchema,
});

type ChatCompletionResponse = z.infer;

// 스트리밍 chunk 스키마
const StreamChunkSchema = z.object({
  id: z.string(),
  object: z.literal("chat.completion.chunk"),
  created: z.number(),
  model: z.string(),
  choices: z.array(z.object({
    index: z.number(),
    delta: z.object({
      content: z.string().optional(),
      role: z.string().optional(),
    }),
    finish_reason: z.string().nullable().optional(),
  })),
});

class HolySheepError extends Error {
  constructor(
    message: string,
    public code: string,
    public statusCode?: number,
    public retryAfter?: number
  ) {
    super(message);
    this.name = "HolySheepError";
  }
}

class JSONDecodeException extends HolySheepError {
  constructor(message: string, public rawData: unknown) {
    super(message, "JSON_DECODE_ERROR", 200);
    this.name = "JSONDecodeException";
  }
}

interface HolySheepConfig {
  apiKey: string;
  baseUrl?: string;
  timeout?: number;
  maxRetries?: number;
}

class HolySheepAIClient {
  private readonly baseUrl: string;
  private readonly timeout: number;
  private readonly maxRetries: number;

  constructor(private readonly config: HolySheepConfig) {
    this.baseUrl = config.baseUrl || "https://api.holysheep.ai/v1";
    this.timeout = config.timeout || 60000;
    this.maxRetries = config.maxRetries || 3;
  }

  private async fetchWithRetry(
    endpoint: string,
    body: object,
    attempt: number = 0
  ): Promise {
    const controller = new AbortController();
    const timeoutId = setTimeout(() => controller.abort(), this.timeout);

    try {
      const response = await fetch(${this.baseUrl}${endpoint}, {
        method: "POST",
        headers: {
          "Authorization": Bearer ${this.config.apiKey},
          "Content-Type": "application/json",
        },
        body: JSON.stringify(body),
        signal: controller.signal,
      });

      clearTimeout(timeoutId);

      if (response.status === 429) {
        const retryAfter = parseInt(
          response.headers.get("retry-after") || "60",
          10
        );
        throw new HolySheepError(
          Rate limit exceeded. Retry after ${retryAfter}s.,
          "RATE_LIMIT_EXCEEDED",
          429,
          retryAfter
        );
      }

      if (response.status >= 500) {
        throw new HolySheepError(
          Server error: ${response.status},
          "SERVER_ERROR",
          response.status
        );
      }

      if (!response.ok) {
        const errorBody = await response.json().catch(() => ({}));
        throw new HolySheepError(
          errorBody.error?.message || Request failed: ${response.status},
          errorBody.error?.code || "REQUEST_FAILED",
          response.status
        );
      }

      const text = await response.text();
      
      try {
        const jsonData = JSON.parse(text);
        return ChatCompletionResponseSchema.parse(jsonData);
      } catch (parseError) {
        throw new JSONDecodeException(
          Failed to parse response: ${(parseError as Error).message},
          text
        );
      }

    } catch (error) {
      clearTimeout(timeoutId);

      if (error instanceof HolySheepError) {
        throw error;
      }

      if (error instanceof DOMException && error.name === "AbortError") {
        throw new HolySheepError(
          Request timeout after ${this.timeout}ms,
          "TIMEOUT",
          408
        );
      }

      if (attempt < this.maxRetries - 1) {
        const delay = 1000 * Math.pow(2, attempt);
        await new Promise(resolve => setTimeout(resolve, delay));
        return this.fetchWithRetry(endpoint, body, attempt + 1);
      }

      throw new HolySheepError(
        Request failed after ${this.maxRetries} attempts: ${(error as Error).message},
        "MAX_RETRIES_EXCEEDED"
      );
    }
  }

  async *streamChatCompletion(
    messages: Array<{ role: string; content: string }>,
    model: string = "gpt-4.1"
  ): AsyncGenerator {
    const controller = new AbortController();
    const timeoutId = setTimeout(() => controller.abort(), this.timeout);

    try {
      const response = await fetch(${this.baseUrl}/chat/completions, {
        method: "POST",
        headers: {
          "Authorization": Bearer ${this.config.apiKey},
          "Content-Type": "application/json",
        },
        body: JSON.stringify({
          model,
          messages,
          stream: true,
          max_tokens: 4096,
          temperature: 0.7,
        }),
        signal: controller.signal,
      });

      clearTimeout(timeoutId);

      if (!response.ok) {
        throw new HolySheepError(
          Stream request failed: ${response.status},
          "STREAM_ERROR",
          response.status
        );
      }

      if (!response.body) {
        throw new HolySheepError(
          "Response body is null",
          "NULL_BODY"
        );
      }

      const reader = response.body.getReader();
      const decoder = new TextDecoder();
      let buffer = "";

      while (true) {
        const { done, value } = await reader.read();
        
        if (done) break;

        buffer += decoder.decode(value, { stream: true });
        const lines = buffer.split("\n");
        buffer = lines.pop() || "";

        for (const line of lines) {
          if (!line.startsWith("data: ")) continue;
          
          const data = line.slice(6).trim();
          if (data === "[DONE]") return;

          try {
            const chunk = StreamChunkSchema.parse(JSON.parse(data));
            const content = chunk.choices[0]?.delta?.content;
            if (content) {
              yield content;
            }
          } catch {
            // 불완전한 chunk 스킵
            continue;
          }
        }
      }

    } finally {
      clearTimeout(timeoutId);
    }
  }
}

// 사용 예시
async function main() {
  const client = new HolySheepAIClient({
    apiKey: process.env.HOLYSHEEP_API_KEY || "YOUR_HOLYSHEEP_API_KEY",
    timeout: 90000,
    maxRetries: 3,
  });

  try {
    const response = await client.fetchWithRetry("/chat/completions", {
      model: "claude-sonnet-4.5",
      messages: [
        { role: "system", content: "You are a helpful assistant." },
        { role: "user", content: "Explain exception handling in AI APIs." }
      ],
    });

    console.log("Response:", response.choices[0].message.content);

  } catch (error) {
    if (error instanceof HolySheepError) {
      console.error([${error.code}] ${error.message});
      if (error.retryAfter) {
        console.log(Retry after: ${error.retryAfter}s);
      }
    } else {
      console.error("Unexpected error:", error);
    }
  }

  // 스트리밍 예시
  console.log("\nStreaming response:");
  for await (const chunk of client.streamChatCompletion([
    { role: "user", content: "Count to 5" }
  ], "gpt-4.1")) {
    process.stdout.write(chunk);
  }
}

main();

모니터링 및 경보 설정

프로덕션 환경에서 AI API의 상태를 실시간으로 모니터링하고 장애를 조기에 감지하는 체계적인 시스템이 중요합니다. HolySheep AI는 통합 대시보드를 통해 모든 모델의 지연 시간, 에러율, 사용량을 한눈에 확인할 수 있습니다.

import asyncio
import logging
from datetime import datetime, timedelta
from dataclasses import dataclass, field
from typing import Optional
import httpx

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class HealthMetrics:
    """API 헬스 메트릭"""
    timestamp: datetime
    latency_ms: float
    success_count: int
    error_count: int
    timeout_count: int
    rate_limit_count: int
    error_rate: float = 0.0
    p99_latency_ms: float = 0.0

class AIMonitoringService:
    """AI API 모니터링 및 경보 서비스"""
    
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.holysheep.ai/v1"
        self.metrics_history: list[HealthMetrics] = []
        self.alert_thresholds = {
            "error_rate_percent": 5.0,
            "latency_p99_ms": 2000,
            "timeout_rate_percent": 2.0,
        }
    
    async def health_check(self) -> HealthMetrics:
        """단일 API 헬스 체크"""
        start_time = asyncio.get_event_loop().time()
        
        async with httpx.AsyncClient(timeout=30.0) as client:
            try:
                response = await client.post(
                    f"{self.base_url}/chat/completions",
                    headers={"Authorization": f"Bearer {self.api_key}"},
                    json={
                        "model": "gpt-4.1",
                        "messages": [{"role": "user", "content": "ping"}],
                        "max_tokens": 10
                    }
                )
                
                latency = (asyncio.get_event_loop().time() - start_time) * 1000
                
                if response.status_code == 200:
                    return HealthMetrics(
                        timestamp=datetime.now(),
                        latency_ms=latency,
                        success_count=1,
                        error_count=0,
                        timeout_count=0,
                        rate_limit_count=0
                    )
                elif response.status_code == 429:
                    return HealthMetrics(
                        timestamp=datetime.now(),
                        latency_ms=latency,
                        success_count=0,
                        error_count=0,
                        timeout_count=0,
                        rate_limit_count=1
                    )
                else:
                    return HealthMetrics(
                        timestamp=datetime.now(),
                        latency_ms=latency,
                        success_count=0,
                        error_count=1,
                        timeout_count=0,
                        rate_limit_count=0
                    )
                    
            except httpx.TimeoutException:
                return HealthMetrics(
                    timestamp=datetime.now(),
                    latency_ms=self.alert_thresholds["latency_p99_ms"] + 1,
                    success_count=0,
                    error_count=0,
                    timeout_count=1,
                    rate_limit_count=0
                )
    
    def check_alerts(self, metrics: HealthMetrics) -> list[str]:
        """경보 조건 검사"""
        alerts = []
        
        if metrics.error_rate > self.alert_thresholds["error_rate_percent"]:
            alerts.append(
                f"[CRITICAL] Error rate {metrics.error_rate:.2f}% exceeds "
                f"threshold {self.alert_thresholds['error_rate_percent']}%"
            )
        
        if metrics.p99_latency_ms > self.alert_thresholds["latency_p99_ms"]:
            alerts.append(
                f"[WARNING] P99 latency {metrics.p99_latency_ms:.0f}ms exceeds "
                f"threshold {self.alert_thresholds['latency_p99_ms']}ms"
            )
        
        if metrics.timeout_count > 0:
            alerts.append(
                f"[ALERT] Timeout detected: {metrics.timeout_count} timeouts in last check"
            )
        
        return alerts
    
    async def continuous_monitoring(
        self,
        interval_seconds: int = 60,
        duration_minutes: Optional[int] = None
    ):
        """지속적 모니터링 실행"""
        start_time = datetime.now()
        check_count = 0
        total_success = 0
        total_errors = 0
        total_timeouts = 0
        total_rate_limits = 0
        latencies = []
        
        while True:
            if duration_minutes and (datetime.now() - start_time).seconds >= duration_minutes * 60:
                break
            
            metrics = await self.health_check()
            
            check_count += 1
            total_success += metrics.success_count
            total_errors += metrics.error_count
            total_timeouts += metrics.timeout_count
            total_rate_limits += metrics.rate_limit_count
            latencies.append(metrics.latency_ms)
            
            total_requests = total_success + total_errors + total_timeouts
            overall_error_rate = ((total_errors + total_timeouts) / total_requests * 100) if total_requests > 0 else 0
            
            metrics.error_rate = overall_error_rate
            metrics.p99_latency_ms = sorted(latencies)[int(len(latencies) * 0.99)] if latencies else 0
            
            self.metrics_history.append(metrics)
            if len(self.metrics_history) > 1000:
                self.metrics_history = self.metrics_history[-1000:]
            
            logger.info(
                f"[Check #{check_count}] Latency: {metrics.latency_ms:.0f}ms | "
                f"Errors: {total_errors} | Timeouts: {total_timeouts} | "
                f"Rate Limits: {total_rate_limits} | Error Rate: {overall_error_rate:.2f}%"
            )
            
            alerts = self.check_alerts(metrics)
            for alert in alerts:
                logger.warning(alert)
            
            await asyncio.sleep(interval_seconds)

async def main():
    monitor = AIMonitoringService("YOUR_HOLYSHEEP_API_KEY")
    await monitor.continuous_monitoring(interval_seconds=30)

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

비용 최적화 및 토큰 관리

AI API 운영에서 비용 제어는 핵심 과제입니다. HolySheep AI의 게이트웨이 구조를 활용하면 모델 간 비용 차이를 자동으로 exploited하여 월간 비용을 40% 이상 절감할 수 있습니다. 저는 실무에서 모델 선택 로직과 캐싱 전략을 결합하여 비용을 최적화했습니다.

from dataclasses import dataclass
from typing import Optional, Callable
from enum import Enum
import hashlib
import time

class ModelTier(Enum):
    """AI 모델 티어 분류"""
    FAST_CHEAP = "fast_cheap"      # Gemini 2.5 Flash, DeepSeek V3.2
    BALANCED = "balanced"           # GPT-4.1, Claude Sonnet 4.5
    PREMIUM = "premium"             # GPT-4o, Claude Opus

@dataclass
class ModelInfo:
    """모델 정보"""
    name: str
    tier: ModelTier
    cost_per_mtok: float
    avg_latency_ms: float
    max_tokens: int
    supports_streaming: bool = True
    supports_function_call: bool = False


HolySheep AI 지원 모델 카탈로그

MODEL_CATALOG: dict[str, ModelInfo] = {
    "gpt-4.1": ModelInfo("gpt-4.1", ModelTier.BALANCED, 8.00, 350, 128000),
    "claude-sonnet-4.5": ModelInfo("claude-sonnet-4.5", ModelTier.BALANCED, 15.00, 400, 200000),
    "gemini-2.5-flash": ModelInfo("gemini-2.5-flash", ModelTier.FAST_CHEAP, 2.50, 180, 1000000),
    "deepseek-v3.2": ModelInfo("deepseek-v3.2", ModelTier.FAST_CHEAP, 0.42, 200, 64000),
    "gpt-4o": ModelInfo("gpt-4o", ModelTier.PREMIUM, 15.00, 300, 128000, supports_function_call=True),
}

class CostOptimizer:
    """AI API 비용 최적화 로직"""
    
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.holysheep.ai/v1"
        self.cache: dict[str, tuple[str, float]] = {}
        self.cache_ttl_seconds = 3600
        self.usage_stats: dict[str, dict] = {}
    
    def estimate_cost(
        self,
        model: str,
        input_tokens: int,
        output_tokens: int
    ) -> float:
        """비용 추정 (USD)"""
        model_info = MODEL_CATALOG.get(model)
        if not model_info:
            return 0.0
        
        input_cost = (input_tokens / 1_000_000) * model_info.cost_per_mtok
        output_cost = (output_tokens / 1_000_000) * model_info.cost_per_mtok
        return input_cost + output_cost
    
    def select_optimal_model(
        self,
        task_complexity: str,
        required_capabilities: list[str],
        priority: str = "cost"
    ) -> str:
        """작업 특성에 따른 최적 모델 선택
        
        Args:
            task_complexity: "simple" | "moderate" | "complex"
            required_capabilities: ["streaming", "function_call", "vision"]
            priority: "cost" | "latency" | "quality"
        """
        
        def meets_requirements(model: ModelInfo) -> bool:
            if "function_call" in required_capabilities and not model.supports_function_call:
                return False
            if "streaming" in required_capabilities and not model.supports_streaming:
                return False
            return True
        
        candidates = [
            m for m in MODEL_CATALOG.values()
            if meets_requirements(m)
        ]
        
        if task_complexity == "simple":
            candidates = [m for m in candidates if m.tier == ModelTier.FAST_CHEAP]
        elif task_complexity == "moderate":
            candidates = [m for m in candidates if m.tier in [ModelTier.FAST_CHEAP, ModelTier.BALANCED]]
        elif task_complexity == "complex":
            candidates = [m for m in candidates if m.tier in [ModelTier.BALANCED, ModelTier.PREMIUM]]
        
        if not candidates:
            return "gpt-4.1"
        
        if priority == "cost":
            return min(candidates, key=lambda m: m.cost_per_mtok).name
        elif priority == "latency":
            return min(candidates, key=lambda m: m.avg_latency_ms).name
        else:
            return candidates[-1].name
    
    def get_cache_key(self, messages: list[dict], model: str) -> str:
        """캐시 키 생성"""
        content = f"{model}:{''.join(m.get('content', '') for m in messages)}"
        return hashlib.sha256(content.encode()).hexdigest()
    
    def get_cached_response(self, cache_key: str) -> Optional[str]:
        """캐시된 응답 조회"""
        if cache_key in self.cache:
            response, timestamp = self.cache[cache_key]
            if time.time() - timestamp < self.cache_ttl_seconds:
                return response
            del self.cache[cache_key]
        return None
    
    def cache_response(self, cache_key: str, response: str):
        """응답 캐싱"""
        self.cache[cache_key] = (response, time.time())
        if len(self.cache) > 1000:
            oldest = min(self.cache.items(), key=lambda x: x[1][1])
            del self.cache[oldest[0]]
    
    def track_usage(self, model: str, input_tokens: int, output_tokens: int, cost: float):
        """사용량 추적"""
        if model not in self.usage_stats:
            self.usage_stats[model] = {
                "requests": 0,
                "input_tokens": 0,
                "output_tokens": 0,
                "total_cost": 0.0
            }
        
        self.usage_stats[model]["requests"] += 1
        self.usage_stats[model]["input_tokens"] += input_tokens
        self.usage_stats[model]["output_tokens"] += output_tokens
        self.usage_stats[model]["total_cost"] += cost
    
    def get_cost_report(self) -> dict:
        """비용 보고서 생성"""
        total_cost = sum(s["total_cost"] for s in self.usage_stats.values())
        total_tokens = sum(
            s["input_tokens"] + s["output_tokens"]
            for s in self.usage_stats.values()
        )
        
        return {
            "total_cost_usd": round(total_cost, 4),
            "total_tokens": total_tokens,
            "by_model": {
                model: {
                    "requests": stats["requests"],
                    "cost": round(stats["total_cost"], 4),
                    "percentage": round(stats["total_cost"] / total_cost * 100, 2) if total_cost > 0 else 0
                }
                for model, stats in self.usage_stats.items()
            }
        }

async def main():
    optimizer = CostOptimizer("YOUR_HOLYSHEEP_API_KEY")
    
    # 단순 작업: 비용 최적화
    model = optimizer.select_optimal_model(
        task_complexity="simple",
        required_capabilities=["streaming"],
        priority="cost"
    )
    print(f"Simple task optimal model (cost priority): {model}")
    
    # 함수 호출 필요: GPT-4o 선택
    model = optimizer.select_optimal_model(
        task_complexity="complex",
        required_capabilities=["function_call"],
        priority="quality"
    )
    print(f"Complex task optimal model (quality priority): {model}")
    
    # 비용 추정
    cost = optimizer.estimate_cost("gemini-2.5-flash", 1000, 500)
    print(f"Estimated cost for 1K input + 500 output tokens: ${cost:.4f}")
    
    # DeepSeek V3.2 vs Gemini 2.5 Flash 비교
    deepseek_cost = optimizer.estimate_cost("deepseek-v3.2", 1000, 500)
    gemini_cost = optimizer.estimate_cost("gemini-2.5-flash", 1000, 500)
    gpt_cost = optimizer.estimate_cost("gpt-4.1", 1000, 500)
    
    print(f"\nCost comparison for 1K/500 tokens:")
    print(f"  DeepSeek V3.2: ${deepseek_cost:.4f}")
    print(f"  Gemini 2.5 Flash: ${gemini_cost:.4f}")
    print(f"  GPT-4.1: ${gpt_cost:.4f}")
    print(f"  Potential savings with DeepSeek: ${((gpt_cost - deepseek_cost) / gpt_cost * 100):.1f}%")

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

자주 발생하는 오류와 해결책

1. JSONDecodeError: Unexpected end of JSON input

원인: 스트리밍 응답에서 SSE chunk가 불완전하게 수신되거나, 빈 응답이 반환된 경우

# 문제 코드
data = response.json()  # 빈 응답에서 JSONDecodeError 발생


해결 코드

async def safe_json_parse(text: str) -> Optional[dict]:
    """안전한 JSON 파싱 with 폴백"""
    if not text or not text.strip():
        return None
    
    try:
        return json.loads(text)
    except json.JSONDecodeError:
        # 불완전한 JSON 복구 시도
        if text.startswith("{"):
            # 닫는 괄호 누락된 경우
            for i in range(len(text), 0, -1):
                try:
                    return json.loads(text[:i] + "}")
                except json.JSONDecodeError:
                    continue
        return None

2. RateLimitError: Rate limit exceeded

원인: HolySheep AI의 요청 제한 초과. 기본的限制은 분당 요청 수와 토큰 사용량에 따라 적용됩니다.

# 문제 코드 - 재시도 없이 즉시 실패
response = await client.post("/chat/completions", json=data)
response.raise_for_status()


해결 코드 - 지수 백오프 재시도

async def request_with_backoff(
    client: httpx.AsyncClient,
    url: str,
    json_data: dict,
    max_retries: int = 5,
    base_delay: float = 1.0
) -> httpx.Response:
    
    for attempt in range(max_retries):
        try:
            response = await client.post(url, json=json_data)
            
            if response.status_code == 429:
                retry_after = int(response.headers.get("retry-after", base_delay * (2 ** attempt)))
                print(f"Rate limited. Waiting {retry_after}s before retry...")
                await asyncio.sleep(retry_after)
                continue
            
            response.raise

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