시작하기 전에: 실제 발생한_capacity危机
저는。去年、ある电子商务平台的。AI功能上线时、发生了严重的容量问题。那时候、开发团队在测试环境中一切正常、但。生产环境一旦面对真实用户流量、问题就爆发了。
具体的症状包括:
- 429 Too Many Requests - API调用频率超出限制
- ConnectionError: timeout after 30s - 请求超时、无法获取响应
- RateLimitError - 模型供应商直接返回限流错误
- 服务完全不可用、用户体验极度下降
那次教训让我深刻认识到:在。AI应用开发中、容量规划不是可选的、而是必须的。本文将分享我在多次项目中积累的容量规划经验和方法论、帮助开发者避免类似的坑。
AI API容量规划的核心概念
1. 理解Rate Limit机制
每个AI API供应商都有。Rate Limit限制、这包括:
- Requests Per Minute (RPM) - 每分钟请求数限制
- Tokens Per Minute (TPM) - 每分钟令牌数限制
- Concurrent Requests - 同时处理的请求数上限
使用HolySheep AI时、这些限制可以通过统一的控制台进行监控和管理、大大简化了多模型环境下的容量规划工作。
2. Token消耗的计算方法
# Token消耗计算示例
import tiktoken
def calculate_tokens(text: str, model: str = "gpt-4") -> int:
"""计算文本的token数量"""
encoding = tiktoken.encoding_for_model(model)
tokens = encoding.encode(text)
return len(tokens)
def estimate_request_cost(
system_prompt: str,
user_prompt: str,
response: str,
model: str = "gpt-4"
) -> dict:
"""估算单次请求的成本"""
# HolySheep AI定价 (美元/百万token)
pricing = {
"gpt-4": 8.00, # GPT-4.1
"claude-sonnet": 15.00, # Claude Sonnet 4.5
"gemini-flash": 2.50, # Gemini 2.5 Flash
"deepseek-v3": 0.42, # DeepSeek V3.2
}
input_tokens = calculate_tokens(system_prompt + user_prompt, model)
output_tokens = calculate_tokens(response, model)
cost_per_token = pricing.get(model, 8.00) / 1_000_000
total_cost = (input_tokens + output_tokens) * cost_per_token
return {
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"total_tokens": input_tokens + output_tokens,
"estimated_cost_usd": round(total_cost, 6)
}
实际使用示例
result = estimate_request_cost(
system_prompt="당신은 전문가입니다.",
user_prompt="한국어 AI API 용량规划的優化方法を教えてください。",
response="용량规划은 매우 중요합니다. 먼저...",
model="gpt-4"
)
print(f"Input Tokens: {result['input_tokens']}")
print(f"Output Tokens: {result['output_tokens']}")
print(f"Total Cost: ${result['estimated_cost_usd']}")HolySheep AI环境下_的容量规划实战
基于预估负载的计算公式
import time
from dataclasses import dataclass
from typing import Optional
import httpx
@dataclass
class CapacityConfig:
"""容量配置数据类"""
rpm_limit: int # 每分钟请求数限制
tpm_limit: int # 每分钟token数限制
avg_request_tokens: int # 平均每次请求的token数
target_latency_ms: int # 目标延迟(毫秒)
def calculate_throughput(self) -> dict:
"""计算理论吞吐量"""
# 基于RPM的计算
max_rpm_throughput = self.rpm_limit
# 基于TPM的计算
max_tpm_throughput = self.tpm_limit // self.avg_request_tokens
# 取两者中的较小值
effective_rpm = min(max_rpm_throughput, max_tpm_throughput)
return {
"max_requests_per_minute": effective_rpm,
"max_requests_per_second": effective_rpm / 60,
"estimated_monthly_requests": effective_rpm * 60 * 24 * 30,
"estimated_monthly_cost_usd": self.estimate_monthly_cost()
}
def estimate_monthly_cost(self, avg_output_tokens: int = 500) -> float:
"""估算月度成本(假设使用GPT-4.1)"""
total_tokens_per_request = self.avg_request_tokens + avg_output_tokens
tokens_per_month = total_tokens_per_request * self.calculate_throughput()["estimated_monthly_requests"]
# $8/MTok for GPT-4.1 on HolySheep AI
return (tokens_per_month / 1_000_000) * 8.00
class HolySheepAPIClient:
"""HolySheep AI API客户端 - 包含容量规划功能"""
def __init__(self, api_key: str):
self.base_url = "https://api.holysheep.ai/v1"
self.api_key = api_key
self.client = httpx.Client(
base_url=self.base_url,
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
},
timeout=60.0
)
self._request_count = 0
self._token_count = 0
self._window_start = time.time()
def _check_rate_limit(self):
"""检查并处理速率限制"""
current_time = time.time()
elapsed = current_time - self._window_start
# 每分钟重置计数器
if elapsed >= 60:
self._request_count = 0
self._token_count = 0
self._window_start = current_time
def _wait_if_needed(self, required_tokens: int, config: CapacityConfig):
"""如果接近限制则等待"""
self._check_rate_limit()
# 检查RPM限制
if self._request_count >= config.rpm_limit * 0.9:
wait_time = 60 - (time.time() - self._window_start)
if wait_time > 0:
print(f"⚠️ RPM限制接近,等待 {wait_time:.1f}秒...")
time.sleep(wait_time)
# 检查TPM限制
if self._token_count + required_tokens >= config.tpm_limit:
wait_time = 60 - (time.time() - self._window_start)
if wait_time > 0:
print(f"⚠️ TPM限制接近,等待 {wait_time:.1f}秒...")
time.sleep(wait_time)
self._request_count += 1
self._token_count += required_tokens
def chat_completion(
self,
messages: list,
model: str = "gpt-4",
config: Optional[CapacityConfig] = None
) -> dict:
"""发送聊天完成请求 - 自动容量管理"""
# 计算输入token
input_text = str(messages)
input_tokens = len(input_text) // 4 # 粗略估算
if config:
self._wait_if_needed(input_tokens, config)
response = self.client.post(
"/chat/completions",
json={
"model": model,
"messages": messages,
"max_tokens": 1000
}
)
if response.status_code == 429:
raise Exception("⚠️ Rate LimitExceeded - 需要扩容或等待")
elif response.status_code == 401:
raise Exception("🔑 AuthenticationFailed - 检查API密钥")
elif response.status_code != 200:
raise Exception(f"❌ API请求失败: {response.status_code} - {response.text}")
return response.json()
使用示例
config = CapacityConfig(
rpm_limit=500,
tpm_limit=150000,
avg_request_tokens=500,
target_latency_ms=2000
)
throughput = config.calculate_throughput()
print(f"最大吞吐量: {throughput['max_requests_per_second']:.2f} 请求/秒")
print(f"月度预估成本: ${throughput['estimated_monthly_cost_usd']:.2f}")容量规划的五大策略
策略一: 金字塔式模型选择
저는 여러 프로젝트에서 검증한 효율적인 모델 선택 전략을 소개합니다. 복잡한 쿼리와 단순한 쿼리에 다른 모델을 사용하면 비용을 크게 절감할 수 있습니다.
import asyncio
from enum import Enum
from typing import List, Dict, Any
from pydantic import BaseModel
class QueryComplexity(Enum):
"""查询复杂度级别"""
SIMPLE = "simple" # 简单问答、翻译
MODERATE = "moderate" # 一般分析、内容生成
COMPLEX = "complex" # 复杂推理、多步骤任务
class ModelConfig(BaseModel):
"""模型配置"""
name: str
provider: str
cost_per_mtok: float
max_tokens: int
avg_latency_ms: int
capabilities: List[str]
HolySheep AI支持的模型配置
MODEL_CATALOG = {
QueryComplexity.SIMPLE: ModelConfig(
name="deepseek-v3",
provider="deepseek",
cost_per_mtok=0.42, # $0.42/MTok - 最便宜
max_tokens=64000,
avg_latency_ms=800,
capabilities=["translation", "summarization", "simple_qa"]
),
QueryComplexity.MODERATE: ModelConfig(
name="gemini-2.5-flash",
provider="google",
cost_per_mtok=2.50, # $2.50/MTok
max_tokens=100000,
avg_latency_ms=1200,
capabilities=["analysis", "code_generation", "content_creation"]
),
QueryComplexity.COMPLEX: ModelConfig(
name="claude-sonnet-4.5",
provider="anthropic",
cost_per_mtok=15.00, # $15/MTok - 最贵
max_tokens=200000,
avg_latency_ms=2500,
capabilities=["advanced_reasoning", "long_context", "complex_analysis"]
)
}
class IntelligentRouter:
"""智能路由 - 根据查询复杂度选择合适的模型"""
def __init__(self, client):
self.client = client
self.cost_savings = 0
self.request_counts = {k: 0 for k in QueryComplexity}
def estimate_complexity(self, query: str) -> QueryComplexity:
"""评估查询复杂度"""
# 简单启发式评估
complexity_indicators = {
QueryComplexity.COMPLEX: [
"분석", "비교", "평가", "추론", "문제 해결",
"explain", "analyze", "compare", "evaluate"
],
QueryComplexity.MODERATE: [
"생성", "작성", "번역", "요약",
"generate", "write", "translate", "summarize"
]
}
query_lower = query.lower()
for keyword in complexity_indicators[QueryComplexity.COMPLEX]:
if keyword in query_lower:
return QueryComplexity.COMPLEX
for keyword in complexity_indicators[QueryComplexity.MODERATE]:
if keyword in query_lower:
return QueryComplexity.MODERATE
return QueryComplexity.SIMPLE
async def route_request(
self,
query: str,
context: str = ""
) -> Dict[str, Any]:
"""路由请求到合适的模型"""
complexity = self.estimate_complexity(query)
model_config = MODEL_CATALOG[complexity]
self.request_counts[complexity] += 1
# 计算成本节省(相对于总是使用最高级模型)
baseline_cost = 15.00 # Claude Sonnet定价
actual_cost = model_config.cost_per_mtok
savings = baseline_cost - actual_cost
self.cost_savings += savings
print(f"📊 路由到 {model_config.name} (复杂度: {complexity.value})")
print(f"💰 成本节省: ${savings:.2f}/MTok")
# 实际API调用
messages = [
{"role": "system", "content": f"你是一个专业的{complexity.value}任务助手。"},
{"role": "user", "content": query}
]
if context:
messages.insert(1, {"role": "system", "content": f"上下文: {context}"})
try:
response = self.client.chat_completion(
messages=messages,
model=model_config.name
)
return {
"response": response["choices"][0]["message"]["content"],
"model_used": model_config.name,
"complexity": complexity.value,
"estimated_cost": actual_cost
}
except Exception as e:
print(f"❌ 请求失败: {str(e)}")
# 降级策略: 尝试使用更简单的模型
if complexity != QueryComplexity.SIMPLE:
print("🔄 尝试降级到简单模型...")
return await self._fallback(query, context)
raise
async def _fallback(self, query: str, context: str) -> Dict[str, Any]:
"""降级处理"""
fallback_config = MODEL_CATALOG[QueryComplexity.SIMPLE]
messages = [
{"role": "system", "content": "请简洁回答以下问题。"},
{"role": "user", "content": query}
]
response = self.client.chat_completion(
messages=messages,
model=fallback_config.name
)
return {
"response": response["choices"][0]["message"]["content"],
"model_used": fallback_config.name,
"complexity": "simple (fallback)",
"estimated_cost": fallback_config.cost_per_mtok,
"fallback_used": True
}
def get_cost_report(self) -> Dict[str, Any]:
"""获取成本报告"""
return {
"request_distribution": {k.value: v for k, v in self.request_counts.items()},
"total_savings_per_mtok": round(self.cost_savings, 2),
"savings_percentage": round(
(self.cost_savings / 15.00) * 100, 2
) if self.cost_savings > 0 else 0
}
使用示例
router = IntelligentRouter(client)
result = await router.route_request("이 문서를 요약해주세요.")
print(router.get_cost_report())
策略二: 批量处理与请求合并
from typing import List, Dict, Any, Callable
import asyncio
from dataclasses import dataclass
import time
@dataclass
class BatchRequest:
"""批量请求配置"""
items: List[Any]
batch_size: int = 10
max_wait_seconds: float = 5.0
priority: int = 0
class BatchProcessor:
"""批量处理器 - 减少API调用次数"""
def __init__(self, client, default_batch_size: int = 10):
self.client = client
self.default_batch_size = default_batch_size
self.pending_items: List[Any] = []
self.last_flush_time = time.time()
async def add_item(
self,
item: Any,
processor: Callable
) -> Any:
"""添加单个项目、自动批量处理"""
self.pending_items.append(item)
# 检查是否应该立即处理
should_flush = (
len(self.pending_items) >= self.default_batch_size or
time.time() - self.last_flush_time >= 5.0
)
if should_flush:
return await self.flush(processor)
return None
async def flush(self, processor: Callable) -> List[Any]:
"""处理所有待处理项目"""
if not self.pending_items:
return []
items_to_process = self.pending_items.copy()
self.pending_items = []
self.last_flush_time = time.time()
print(f"📦 批量处理 {len(items_to_process)} 个项目")
# 分批处理
results = []
for i in range(0, len(items_to_process), self.default_batch_size):
batch = items_to_process[i:i + self.default_batch_size]
batch_results = await self._process_batch(batch, processor)
results.extend(batch_results)
return results
async def _process_batch(
self,
batch: List[Any],
processor: Callable
) -> List[Any]:
"""处理单个批次"""
# 合并提示词以减少token消耗
combined_prompt = self._combine_items(batch)
try:
response = self.client.chat_completion(
messages=[
{"role": "system", "content": "你是一个批量处理助手。请依次处理以下任务。"},
{"role": "user", "content": combined_prompt}
],
model="deepseek-v3" # 使用便宜模型进行批量处理
)
# 分割响应
return self._split_response(
response["choices"][0]["message"]["content"],
len(batch)
)
except Exception as e:
print(f"❌ 批量处理失败: {e}")
# 逐个处理作为降级
return [await processor(item) for item in batch]
def _combine_items(self, items: List[Any]) -> str:
"""合并多个项目为一个提示词"""
combined = []
for i, item in enumerate(items, 1):
combined.append(f"[任务 {i}]\n{item}\n")
return "\n".join(combined)
def _split_response(self, response: str, expected_count: int) -> List[Any]:
"""分割响应为多个结果"""
# 简单的分割逻辑
parts = response.split("[任务")
results = []
for i in range(1, min(len(parts), expected_count + 1)):
results.append(parts[i].split("]")[1] if "]" in parts[i] else parts[i])
# 如果分割失败、返回相同的结果
while len(results) < expected_count:
results.append(response)
return results[:expected_count]
class TokenBucketRateLimiter:
"""令牌桶限流器 - 更精细的流量控制"""
def __init__(
self,
rpm: int = 500,
tpm: int = 150000,
burst_size: int = 50
):
self.rpm = rpm
self.tpm = tpm
self.burst_size = burst_size
self.tokens = burst_size
self.last_update = time.time()
self.token_refill_rate_rpm = rpm / 60 # 每秒补充的令牌数
self.token_refill_rate_tpm = tpm / 60
def _refill(self):
"""补充令牌"""
now = time.time()
elapsed = now - self.last_update
# 补充令牌
self.tokens = min(
self.burst_size,
self.tokens + elapsed * self.token_refill_rate_rpm
)
self.last_update = now
def acquire(self, required_tokens: int = 1, timeout: float = 60) -> bool:
"""获取令牌、阻塞直到成功或超时"""
start_time = time.time()
while True:
self._refill()
if self.tokens >= required_tokens:
self.tokens -= required_tokens
return True
# 检查超时
if time.time() - start_time >= timeout:
return False
# 等待下一个令牌
wait_time = (required_tokens - self.tokens) / self.token_refill_rate_rpm
time.sleep(min(wait_time, 1.0))
def get_status(self) -> Dict[str, Any]:
"""获取当前限流器状态"""
self._refill()
return {
"available_tokens": round(self.tokens, 2),
"tokens_per_second": round(self.token_refill_rate_rpm, 2),
"utilization_percent": round(
(1 - self.tokens / self.burst_size) * 100, 2
)
}
使用示例
limiter = TokenBucketRateLimiter(rpm=500, tpm=150000, burst_size=100)
在API调用前检查
if limiter.acquire(required_tokens=1):
# 执行API调用
response = client.chat_completion(messages, model="gpt-4")
else:
print("⚠️ 限流器超时、请稍后重试")容量监控与告警系统
import psutil
import os
from datetime import datetime, timedelta
from collections import deque
import json
class CapacityMonitor:
"""容量监控器 - 实时跟踪API使用情况"""
def __init__(self, warning_threshold: float = 0.7, critical_threshold: float = 0.9):
self.warning_threshold = warning_threshold
self.critical_threshold = critical_threshold
# 存储最近1小时的数据
self.request_history = deque(maxlen=3600)
self.token_history = deque(maxlen=3600)
self.error_history = deque(maxlen=100)
self.start_time = datetime.now()
def record_request(
self,
tokens: int,
latency_ms: int,
status_code: int,
model: str
):
"""记录一次API请求"""
timestamp = datetime.now()
record = {
"timestamp": timestamp,
"tokens": tokens,
"latency_ms": latency_ms,
"status_code": status_code,
"model": model,
"success": status_code < 400
}
self.request_history.append(record)
self.token_history.append(tokens)
if status_code >= 400:
self.error_history.append({
"timestamp": timestamp,
"status_code": status_code,
"tokens": tokens
})
def get_current_utilization(self, window_seconds: int = 60) -> dict:
"""计算当前时间窗口的利用率"""
now = datetime.now()
cutoff = now - timedelta(seconds=window_seconds)
recent_requests = [
r for r in self.request_history
if r["timestamp"] > cutoff
]
total_tokens = sum(r["tokens"] for r in recent_requests)
request_count = len(recent_requests)
error_count = sum(1 for r in recent_requests if not r["success"])
# 假设的限制值
rpm_limit = 500
tpm_limit = 150000
return {
"time_window_seconds": window_seconds,
"request_count": request_count,
"total_tokens": total_tokens,
"rpm_utilization": round(request_count / rpm_limit, 3),
"tpm_utilization": round(total_tokens / tpm_limit, 3),
"error_rate": round(error_count / max(request_count, 1), 3),
"avg_latency_ms": round(
sum(r["latency_ms"] for r in recent_requests) / max(request_count, 1),
2
) if recent_requests else 0
}
def check_thresholds(self) -> list:
"""检查是否超过阈值、返回告警列表"""
utilization = self.get_current_utilization()
alerts = []
# 检查各指标的阈值
checks = [
("rpm_utilization", "RPM", 500),
("tpm_utilization", "TPM", 150000),
("error_rate", "错误率", None)
]
for key, name, limit in checks:
value = utilization.get(key, 0)
if value >= self.critical_threshold:
alerts.append({
"level": "CRITICAL",
"message": f"🚨 {name}利用率达到 {value*100:.1f}% - 立即扩容!",
"value": value
})
elif value >= self.warning_threshold:
alerts.append({
"level": "WARNING",
"message": f"⚠️ {name}利用率达到 {value*100:.1f}% - 考虑扩容",
"value": value
})
return alerts
def generate_report(self) -> dict:
"""生成完整的容量报告"""
utilization_1min = self.get_current_utilization(60)
utilization_5min = self.get_current_utilization(300)
utilization_15min = self.get_current_utilization(900)
uptime = datetime.now() - self.start_time
return {
"report_time": datetime.now().isoformat(),
"uptime_seconds": uptime.total_seconds(),
"utilization": {
"1min": utilization_1min,
"5min": utilization_5min,
"15min": utilization_15min
},
"alerts": self.check_thresholds(),
"recent_errors": list(self.error_history)[-10:],
"recommendations": self._generate_recommendations(utilization_1min)
}
def _generate_recommendations(self, current: dict) -> list:
"""生成优化建议"""
recommendations = []
if current["rpm_utilization"] > 0.8:
recommendations.append({
"priority": "HIGH",
"suggestion": "RPM利用率过高、考虑: 1) 实现请求队列 2) 使用批量处理 3) 增加缓存"
})
if current["tpm_utilization"] > 0.8:
recommendations.append({
"priority": "HIGH",
"suggestion": "TPM利用率过高、考虑: 1) 优化提示词长度 2) 使用更小的模型处理简单任务 3) 实现响应缓存"
})
if current["error_rate"] > 0.05:
recommendations.append({
"priority": "HIGH",
"suggestion": "错误率较高、检查: 1) API密钥是否有效 2) 请求格式是否正确 3) 网络连接是否稳定"
})
if current["avg_latency_ms"] > 5000:
recommendations.append({
"priority": "MEDIUM",
"suggestion": "延迟较高、考虑: 1) 使用更快的模型(如Gemini Flash) 2) 减少上下文长度 3) 检查网络延迟"
})
return recommendations
def export_metrics(self, filepath: str = "capacity_metrics.json"):
"""导出指标到文件"""
report = self.generate_report()
# 转换datetime为字符串
report_str = json.dumps(report, default=str, indent=2)
with open(filepath, "w", encoding="utf-8") as f:
f.write(report_str)
print(f"📊 指标已导出到 {filepath}")
return filepath
使用示例
monitor = CapacityMonitor(warning_threshold=0.7, critical_threshold=0.9)
模拟记录请求
monitor.record_request(
tokens=1500,
latency_ms=1200,
status_code=200,
model="gpt-4"
)
获取当前状态
current = monitor.get_current_utilization()
print(f"当前RPM利用率: {current['rpm_utilization']*100:.1f}%")
print(f"当前TPM利用率: {current['tpm_utilization']*100:.1f}%")
检查告警
alerts = monitor.check_thresholds()
for alert in alerts:
print(alert["message"])
生成完整报告
report = monitor.generate_report()
print(json.dumps(report["recommendations"], ensure_ascii=False, indent=2))常见错误场景与解决方案
错误一: 429 Too Many Requests
# ❌ 错误代码示例
import requests
def bad_example():
"""这个实现会导致429错误"""
api_key = "YOUR_HOLYSHEEP_API_KEY"
base_url = "https://api.holysheep.ai/v1"
# 快速连续发送100个请求
for i in range(100):
response = requests.post(
f"{base_url}/chat/completions",
headers={"Authorization": f"Bearer {api_key}"},
json={
"model": "gpt-4",
"messages": [{"role": "user", "content": f"요청 {i}"}]
}
)
# 没有处理429错误、直接崩溃
response.raise_for_status()
✅ 正确实现
import time
import random
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry
def good_example():
"""带有重试和退避策略的正确实现"""
api_key = "YOUR_HOLYSHEEP_API_KEY"
base_url = "https://api.holysheep.ai/v1"
session = requests.Session()
# 配置重试策略
retry_strategy = Retry(
total=5,
backoff_factor=2, # 指数退避: 1s, 2s, 4s, 8s, 16s
status_forcelist=[429, 500, 502, 503, 504],
allowed_methods=["POST"]
)
adapter = HTTPAdapter(max_retries=retry_strategy)
session.mount("https://", adapter)
results = []
for i in range(100):
try:
response = session.post(
f"{base_url}/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
},
json={
"model": "gpt-4",
"messages": [{"role": "user", "content": f"요청 {i}"}]
},
timeout=60
)
if response.status_code == 429:
# 解析Retry-After头
retry_after = int(response.headers.get("Retry-After", 60))
print(f"⏳ Rate limit触发、等待 {retry_after} 秒...")
time.sleep(retry_after)
# 重试当前请求
continue
response.raise_for_status()
results.append(response.json())
except requests.exceptions.RequestException as e:
print(f"❌ 请求 {i} 失败: {e}")
# 添加随机抖动避免雷鸣羊群效应
time.sleep(random.uniform(1, 3))
return results错误二: 401 Unauthorized
# ❌ 常见错误配置
WRONG_CONFIG = {
"api_key": "sk-xxxx", # 错误: 使用了OpenAI格式的密钥
"base_url": "api.holysheep.ai/v1", # 错误: 缺少https://
}
✅ 正确配置
CORRECT_CONFIG = {
"api_key": "YOUR_HOLYSHEEP_API_KEY", # HolySheep AI提供的密钥
"base_url": "https://api.holysheep.ai/v1", # 完整URL
}
def verify_credentials():
"""验证API凭证是否正确"""
import httpx
api_key = "YOUR_HOLYSHEEP_API_KEY"
base_url = "https://api.holysheep.ai/v1"
try:
response = httpx.get(
f"{base_url}/models",
headers={"Authorization": f"Bearer {api_key}"},
timeout=10
)
if response.status_code == 401:
print("🔑 认证失败! 请检查:")
print("1. API密钥是否正确")
print("2. 密钥是否已激活")
print("3. 密钥是否有足够的配额")
return False
elif response.status_code == 200:
print("✅ 认证成功!")
print(f"可用模型: {len(response.json().get('data', []))} 个")
return True
else:
print(f"⚠️ 未知错误: {response.status_code}")
return False
except httpx.ConnectError:
print("🌐 连接错误! 请检查:")
print("1. 网络连接是否正常")
print("2. base_url是否正确配置")
print("3. 是否可以访问 https://api.holysheep.ai")
return False
使用OpenAI SDK的正确方式
def correct_openai_usage():
"""使用OpenAI SDK调用HolySheep AI的正确方式"""
from openai import OpenAI
client = OpenAI(
api_key="YOUR_HOLYSHEEP_API_KEY",
base_url="https://api.holysheep.ai/v1" # 关键配置!
)
try:
response = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "당신은 도움이 되는 어시스턴트입니다."},
{"role": "user", "content": "안녕하세요!"}
],
max_tokens=100
)
return response.choices[0].message.content
except Exception as e:
if "401" in str(e):
print("🔑 请确认您的API密钥是否正确")
raise
print(correct_openai_usage())错误三: Connection timeout
# ❌ 不安全的超时配置
def unsafe_request():
"""没有超时限制的危险实现"""
response = requests.post(
"https://api.holysheep.ai/v1/chat/completions",
json={"model": "gpt-4", "messages": [...]}
# 危险! 没有timeout参数
)
✅ 正确的超时配置
import httpx
from httpx._types import TimeoutDict
def safe_request_with_proper_timeout():
"""正确配置超时的安全实现"""
# 配置不同类型的超时
timeout_config = {
"connect": 10.0, # 连接超时: 10秒
"read": 60.0, # 读取超时: 60秒
"write": 30.0, # 写入超时: 30秒
"pool": 10.0 # 连接池超时: 10秒
}
client = httpx.Client(
base_url="https://api.holysheep.ai/v1",
headers={"Authorization": f"Bearer YOUR_HOLYSHEEP_API_KEY"},
timeout=httpx.Timeout(**timeout_config),
limits=httpx.Limits(
max_keepalive_connections=20,
max_connections=100
)
)
try:
response = client.post(
"/chat/completions",
json={
"model": "gpt-4",
"messages": [
{"role": "system", "content": "你是一个有用的助手。"},
{"role": "user", "content": "请回答以下问题..."}
],
"max_tokens": 500
}
)
response.raise_for_status()
return response.json()
except httpx.TimeoutException as e:
print("⏱️ 请求超时