凌晨两点，运营突然拉群：「AI 客服全部超时，用户在骂，研发快看」。这是我上个月亲历的真实场景——某电商 618 大促预热，QPS 从日常 200 瞬间飙到 12000，结果自建的限流中间件直接 OOM，后端 AI 服务被打到熔断。事后复盘，光那 20 分钟的异常流量，浪费的 API 调用费用就超过 2 万块。
今天这篇文章，我用实战经验拆解一套完整的网关层抗刷 + WAF 防护方案，覆盖 CC 防护、token 粒度限速、IP/设备指纹黑名单动态更新，以及基于时序特征的异常流量画像。这套方案同样适用于企业 RAG 系统、独立开发者个人项目等场景。
为什么网关层防护比应用层限流更重要
很多开发者的第一反应是「在代码里加个计数器」，但真正扛过双十一的人都知道——流量进到应用层就已经晚了。网关层是第一道防线，它的作用是：
- 在流量到达业务逻辑之前完成清洗，节省后端 AI 推理成本
- 防止恶意爬虫、CC 攻击绕过简单 User-Agent 检测
- 为不同的 API Key 配置独立的限速策略
- 实时识别并封禁异常 IP，减少无效调用
如果你正在使用 HolySheep AI 的中转服务，配合网关层防护，API 费用可以再降 30%~50%，因为大量无效请求根本不会发到上游。
一、CC 防护：从滑动窗口到令牌桶的实战选型
1.1 滑动窗口限速实现
滑动窗口是抗刷的基础算法，相比固定窗口（Redis INCR）更平滑，能有效防止窗口边界的突发流量。下面是 Python 实现：
# sliding_window_rate_limiter.py
import time
import hashlib
from collections import defaultdict
from threading import Lock
class SlidingWindowRateLimiter:
"""
滑动窗口限速器 - 适用于 API 网关层
window_size: 窗口大小（秒）
max_requests: 窗口内最大请求数
"""
def __init__(self, window_size: int = 60, max_requests: int = 60):
self.window_size = window_size
self.max_requests = max_requests
self.requests = defaultdict(list) # {key: [timestamp1, timestamp2, ...]}
self.lock = Lock()
def is_allowed(self, key: str) -> bool:
"""返回 True 表示允许通过，False 表示被限流"""
current_time = time.time()
window_start = current_time - self.window_size
with self.lock:
# 清理过期记录
self.requests[key] = [
ts for ts in self.requests[key]
if ts > window_start
]
# 检查是否超限
if len(self.requests[key]) >= self.max_requests:
return False
# 记录本次请求
self.requests[key].append(current_time)
return True
def get_remaining(self, key: str) -> int:
"""获取剩余可用配额"""
current_time = time.time()
window_start = current_time - self.window_size
with self.lock:
valid_requests = [
ts for ts in self.requests[key]
if ts > window_start
]
return max(0, self.max_requests - len(valid_requests))
使用示例
limiter = SlidingWindowRateLimiter(window_size=60, max_requests=60)
def check_request(api_key: str, client_ip: str) -> dict:
"""检查请求是否允许通过"""
# 组合限速 key：支持按 API Key / IP / 设备指纹 多维度
rate_key = hashlib.sha256(
f"{api_key}:{client_ip}".encode()
).hexdigest()[:16]
if limiter.is_allowed(rate_key):
return {
"allowed": True,
"remaining": limiter.get_remaining(rate_key),
"reset_at": int(time.time()) + 60
}
else:
return {
"allowed": False,
"remaining": 0,
"retry_after": 60,
"error": "Rate limit exceeded. 限速: 60次/分钟"
}1.2 令牌桶算法：支持突发流量
滑动窗口适合「匀速限制」，但电商促销场景需要支持突发流量（比如用户集中点击）。令牌桶算法更适合：
# token_bucket.py
import time
import threading
from dataclasses import dataclass
@dataclass
class TokenBucket:
"""令牌桶实现"""
capacity: float # 桶容量
refill_rate: float # 每秒补充令牌数
tokens: float
last_refill: float
@classmethod
def create(cls, capacity: int, rpm: int) -> 'TokenBucket':
"""创建令牌桶：capacity=突发容量，rpm=每分钟补充量"""
return cls(
capacity=capacity,
refill_rate=rpm / 60.0,
tokens=float(capacity),
last_refill=time.time()
)
def refill(self):
"""补充令牌"""
now = time.time()
elapsed = now - self.last_refill
self.tokens = min(
self.capacity,
self.tokens + elapsed * self.refill_rate
)
self.last_refill = now
def consume(self, tokens: int = 1) -> bool:
"""尝试消费令牌，返回是否成功"""
self.refill()
if self.tokens >= tokens:
self.tokens -= tokens
return True
return False
class TokenBucketManager:
"""支持按 API Key 的独立令牌桶"""
def __init__(self):
self.buckets: dict[str, TokenBucket] = {}
self.lock = threading.Lock()
def get_or_create(self, api_key: str, capacity: int = 10, rpm: int = 60) -> TokenBucket:
with self.lock:
if api_key not in self.buckets:
self.buckets[api_key] = TokenBucket.create(capacity, rpm)
return self.buckets[api_key]
def check(self, api_key: str, tokens: int = 1) -> tuple[bool, float]:
"""
检查是否允许通过
返回: (是否允许, 剩余令牌数)
"""
bucket = self.get_or_create(api_key)
allowed = bucket.consume(tokens)
return allowed, bucket.tokens
HolySheep API 调用示例
def call_holysheep_with_limit(api_key: str, prompt: str) -> dict:
manager = TokenBucketManager()
allowed, remaining = manager.check(api_key)
if not allowed:
return {
"error": "rate_limit_exceeded",
"message": "令牌桶已空，请稍后重试",
"retry_after_ms": 1000
}
# 调用 HolySheep API
import requests
response = requests.post(
"https://api.holysheep.ai/v1/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
},
json={
"model": "gpt-4.1",
"messages": [{"role": "user", "content": prompt}],
"max_tokens": 1000
},
timeout=30
)
return response.json()二、动态黑名单：IP + 设备指纹双维度防护
2.1 基于 Redis 的黑名单实现
静态黑名单不够用，我们需要动态黑名单——根据实时行为自动封禁可疑 IP。我用 Redis Sorted Set 实现了一个带 TTL 的动态封禁机制：
# dynamic_blacklist.py
import redis
import time
import json
from typing import Optional
from dataclasses import dataclass, asdict
@dataclass
class BanRecord:
"""封禁记录"""
ip: str
reason: str # 'cc_attack' | 'suspicious_behavior' | 'manual_ban'
score: float # 违规积分，越高越可疑
banned_at: float
expires_at: float
metadata: dict # 额外信息：User-Agent、设备指纹等
class DynamicBlacklist:
"""
动态黑名单管理器
使用 Redis Sorted Set 存储：score=封禁到期时间，member=IP/指纹
"""
def __init__(self, redis_client: redis.Redis):
self.redis = redis_client
self.ip_key = "waf:blacklist:ip"
self.fingerprint_key = "waf:blacklist:fingerprint"
self.violation_key = "waf:violation:{}" # 违规积分，30分钟窗口
def add_violation(self, ip: str, fingerprint: str, violation_type: str, weight: int = 1):
"""
记录违规行为，累计积分
violation_type: 'rapid_request'(3分) | 'error_rate_high'(5分) | 'pattern_suspicious'(10分)
"""
violation_weights = {
'rapid_request': 3,
'error_rate_high': 5,
'pattern_suspicious': 10,
'bot_detected': 15
}
weight = violation_weights.get(violation_type, 1)
pipe = self.redis.pipeline()
# 30分钟窗口内的违规积分
pipe.zincrby(self.violation_key.format(ip), weight, str(time.time()))
pipe.expire(self.violation_key.format(ip), 1800)
# 如果有设备指纹，也记录
if fingerprint:
pipe.zincrby(self.violation_key.format(fingerprint), weight, str(time.time()))
pipe.expire(self.violation_key.format(fingerprint), 1800)
pipe.execute()
def check_ban(self, ip: str, fingerprint: str = None) -> tuple[bool, Optional[str]]:
"""
检查是否被封禁
返回: (是否被封禁, 封禁原因)
"""
now = time.time()
# 检查 IP 黑名单
if self.redis.zscore(self.ip_key, ip):
return True, "ip_banned"
# 检查指纹黑名单
if fingerprint and self.redis.zscore(self.fingerprint_key, fingerprint):
return True, "fingerprint_banned"
# 检查违规积分，超过 50 分自动封禁 1 小时
ip_score = self._get_violation_score(ip)
if ip_score >= 50:
self.ban_ip(ip, reason="violation_threshold", duration=3600)
return True, "violation_ban"
return False, None
def _get_violation_score(self, key: str) -> float:
"""计算 30 分钟内的违规积分"""
cutoff = time.time() - 1800
members = self.redis.zrangebyscore(
self.violation_key.format(key),
cutoff, '+inf'
)
if not members:
return 0
scores = self.redis.zmscore(
self.violation_key.format(key),
members
)
return sum(float(s) for s in scores if s)
def ban_ip(self, ip: str, reason: str, duration: int = 3600, metadata: dict = None):
"""封禁 IP"""
expires_at = time.time() + duration
record = BanRecord(
ip=ip,
reason=reason,
score=100,
banned_at=time.time(),
expires_at=expires_at,
metadata=metadata or {}
)
pipe = self.redis.pipeline()
pipe.zadd(self.ip_key, {ip: expires_at})
pipe.set(f"ban:detail:{ip}", json.dumps(asdict(record)), ex=duration)
pipe.execute()
def unban_ip(self, ip: str):
"""解封 IP"""
self.redis.zrem(self.ip_key, ip)
self.redis.delete(f"ban:detail:{ip}")
集成到 HolySheep 请求流程
def waf_check_request(
api_key: str,
ip: str,
user_agent: str,
request_path: str
) -> dict:
"""
WAF 检查入口
返回允许/拒绝及原因
"""
blacklist = DynamicBlacklist(redis.Redis(host='localhost', port=6379))
# 生成设备指纹
fingerprint = hashlib.sha256(
f"{ip}:{user_agent}:{request_path}".encode()
).hexdigest()[:32]
# 1. 检查黑名单
is_banned, ban_reason = blacklist.check_ban(ip, fingerprint)
if is_banned:
return {
"action": "deny",
"status": 403,
"error": "Access denied",
"reason": ban_reason,
"retry_after": 3600
}
# 2. 检查限速（token 桶）
bucket_manager = TokenBucketManager()
allowed, remaining = bucket_manager.check(api_key)
if not allowed:
blacklist.add_violation(ip, fingerprint, "rapid_request")
return {
"action": "rate_limit",
"status": 429,
"error": "Too many requests",
"remaining": remaining,
"retry_after": 1
}
return {"action": "allow"}2.2 设备指纹识别与关联分析
仅靠 IP 封禁容易被代理绕过，我们需要设备指纹多维度识别：
# device_fingerprint.py
import hashlib
import re
from typing import Dict, List
from collections import defaultdict
class DeviceFingerprinter:
"""
设备指纹生成器
组合多个信号生成唯一指纹
"""
def __init__(self):
self.ip_request_count = defaultdict(int) # IP 请求计数
self.fingerprint_ip_map = defaultdict(set) # 指纹 -> IP 集合
def generate(self,
ip: str,
user_agent: str,
accept_language: str = None,
headers: dict = None) -> str:
"""生成设备指纹"""
# 标准化 User-Agent
ua = self._normalize_ua(user_agent)
# 提取关键特征
features = [
ua,
self._extract_ua_version(ua),
accept_language or "",
self._extract_platform(ua),
headers.get('Accept-Encoding', '') if headers else '',
headers.get('Accept', '') if headers else '',
]
fingerprint = hashlib.sha256(
"|".join(features).encode('utf-8')
).hexdigest()[:24]
# 维护 IP -> 指纹映射
self.ip_request_count[ip] += 1
self.fingerprint_ip_map[fingerprint].add(ip)
return fingerprint
def _normalize_ua(self, ua: str) -> str:
"""标准化 User-Agent"""
if not ua:
return "unknown"
# 移除随机部分，保留核心特征
ua = re.sub(r'Chrome/\d+\.\d+\.\d+\.\d+', 'Chrome/X', ua)
ua = re.sub(r'Safari/\d+\.\d+', 'Safari/X', ua)
return ua[:200] # 限制长度
def _extract_ua_version(self, ua: str) -> str:
"""提取浏览器版本"""
match = re.search(r'(Chrome|Firefox|Safari|Edge)/(\d+)', ua)
return match.group(2) if match else "unknown"
def _extract_platform(self, ua: str) -> str:
"""提取平台信息"""
if 'Windows' in ua:
return 'windows'
elif 'Mac OS' in ua:
return 'macos'
elif 'Linux' in ua:
return 'linux'
elif 'Android' in ua:
return 'android'
elif 'iOS' in ua or 'iPhone' in ua:
return 'ios'
return 'unknown'
def detect_multi_ip_abuse(self, fingerprint: str) -> List[str]:
"""
检测同一指纹是否关联多个 IP
正常用户通常不会在短时间内切换大量 IP
"""
return list(self.fingerprint_ip_map.get(fingerprint, set()))
def detect_ip_multi_fingerprint(self, ip: str) -> bool:
"""
检测同一 IP 是否使用多个指纹
可能是代理池或爬虫
"""
# 如果一个 IP 关联超过 5 个不同指纹，标记为可疑
suspicious_ips = set()
for fp, ips in self.fingerprint_ip_map.items():
if ip in ips and len(ips) > 5:
suspicious_ips.add(ip)
return ip in suspicious_ips
异常流量画像分析
class TrafficProfiler:
"""
异常流量画像
基于时序特征识别恶意流量
"""
def __init__(self, window_size: int = 300):
self.window_size = window_size
self.ip_timestamps: Dict[str, List[float]] = defaultdict(list)
self.ip_request_sizes: Dict[str, List[int]] = defaultdict(list)
def record_request(self, ip: str, request_size: int):
"""记录请求"""
now = time.time()
self.ip_timestamps[ip].append(now)
self.ip_request_sizes[ip].append(request_size)
# 清理过期数据
cutoff = now - self.window_size
self.ip_timestamps[ip] = [t for t in self.ip_timestamps[ip] if t > cutoff]
self.ip_request_sizes[ip] = [
(t, s) for t, s in zip(self.ip_timestamps[ip], self.ip_request_sizes[ip])
if t > cutoff
]
def analyze_pattern(self, ip: str) -> dict:
"""
分析流量模式，返回异常指标
"""
timestamps = self.ip_timestamps[ip]
if not timestamps:
return {"risk_level": "low", "anomalies": []}
anomalies = []
now = time.time()
# 1. 请求频率检测（正常用户 < 10 RPM）
if len(timestamps) > 10:
anomalies.append({
"type": "high_frequency",
"score": 10,
"detail": f"{len(timestamps)} requests in {self.window_size}s"
})
# 2. 周期性检测（爬虫通常有固定间隔）
intervals = [timestamps[i+1] - timestamps[i] for i in range(len(timestamps)-1)]
if intervals:
avg_interval = sum(intervals) / len(intervals)
variance = sum((i - avg_interval)**2 for i in intervals) / len(intervals)
if variance < 0.1 and avg_interval < 1: # 间隔过于规律
anomalies.append({
"type": "periodic_pattern",
"score": 15,
"detail": f"Too regular: avg={avg_interval:.3f}s, var={variance:.6f}"
})
# 3. 请求大小异常检测
sizes = [s for _, s in self.ip_request_sizes[ip]]
if sizes:
avg_size = sum(sizes) / len(sizes)
if avg_size < 100: # 极小的请求体可能是探测
anomalies.append({
"type": "suspicious_size",
"score": 5,
"detail": f"Avg request size: {avg_size} bytes"
})
# 计算风险等级
total_score = sum(a["score"] for a in anomalies)
if total_score >= 20:
risk_level = "critical"
elif total_score >= 10:
risk_level = "high"
elif total_score >= 5:
risk_level = "medium"
else:
risk_level = "low"
return {
"ip": ip,
"risk_level": risk_level,
"total_score": total_score,
"anomalies": anomalies,
"request_count": len(timestamps),
"time_window": self.window_size
}三、HolySheep 网关层集成实战
把上述组件组合起来，配合 HolySheep AI 的高性价比 API（GPT-4.1 $8/MTok、DeepSeek V3.2 $0.42/MTok），实现完整的防护架构：
# holyheep_waf_gateway.py
import os
import time
import json
import redis
import hashlib
from flask import Flask, request, jsonify, g
from functools import wraps
app = Flask(__name__)
初始化组件
redis_client = redis.Redis(host=os.getenv('REDIS_HOST', 'localhost'), port=6379, db=0)
blacklist = DynamicBlacklist(redis_client)
token_bucket = TokenBucketManager()
fingerprinter = DeviceFingerprinter()
profiler = TrafficProfiler(window_size=300)
HolySheep API 配置
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = os.getenv('HOLYSHEEP_API_KEY') # 从环境变量读取
def waf_protect(f):
"""WAF 防护装饰器"""
@wraps(f)
def decorated_function(*args, **kwargs):
# 获取请求信息
client_ip = request.headers.get('X-Forwarded-For', request.remote_addr).split(',')[0]
api_key = request.headers.get('Authorization', '').replace('Bearer ', '')
user_agent = request.headers.get('User-Agent', 'unknown')
# 1. 生成设备指纹
fingerprint = fingerprinter.generate(
ip=client_ip,
user_agent=user_agent,
accept_language=request.headers.get('Accept-Language'),
headers=dict(request.headers)
)
# 2. 黑名单检查
is_banned, ban_reason = blacklist.check_ban(client_ip, fingerprint)
if is_banned:
return jsonify({
"error": "access_denied",
"message": "Your IP has been temporarily blocked",
"reason": ban_reason
}), 403
# 3. 流量画像分析
profiler.record_request(client_ip, len(request.data))
profile = profiler.analyze_pattern(client_ip)
if profile["risk_level"] in ["high", "critical"]:
# 记录违规，动态加入黑名单
blacklist.add_violation(
client_ip, fingerprint,
profile["anomalies"][0]["type"] if profile["anomalies"] else "suspicious_behavior"
)
if profile["risk_level"] == "critical":
blacklist.ban_ip(client_ip, "critical_risk", duration=300)
return jsonify({
"error": "rate_limit_exceeded",
"message": "Abnormal traffic detected",
"risk_level": profile["risk_level"]
}), 429
# 4. Token 桶限速
allowed, remaining = token_bucket.check(api_key)
if not allowed:
return jsonify({
"error": "rate_limit_exceeded",
"message": "API rate limit exceeded",
"retry_after": 1
}), 429
# 存储上下文供后续使用
g.client_ip = client_ip
g.fingerprint = fingerprint
g.api_key = api_key
g.remaining_quota = remaining
return f(*args, **kwargs)
return decorated_function
@app.route('/v1/chat/completions', methods=['POST'])
@waf_protect
def chat_completions():
"""AI 对话接口 - 集成 WAF + HolySheep"""
data = request.get_json()
# 调用 HolySheep API
import requests
try:
response = requests.post(
f"{HOLYSHEEP_BASE_URL}/chat/completions",
headers={
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json"
},
json={
"model": data.get("model", "gpt-4.1"),
"messages": data.get("messages", []),
"max_tokens": data.get("max_tokens", 1000),
"temperature": data.get("temperature", 0.7)
},
timeout=30
)
return jsonify(response.json()), response.status_code
except requests.exceptions.Timeout:
return jsonify({
"error": "upstream_timeout",
"message": "AI service temporarily unavailable"
}), 504
except requests.exceptions.RequestException as e:
return jsonify({
"error": "upstream_error",
"message": str(e)
}), 502
@app.route('/admin/blacklist', methods=['GET', 'POST'])
def admin_blacklist():
"""管理接口 - 查看/添加黑名单"""
if request.method == 'GET':
# 获取当前黑名单
ips = redis_client.zrangebyscore(blacklist.ip_key, 0, '+inf', withscores=True)
return jsonify({
"banned_ips": [
{"ip": ip.decode(), "expires_at": score}
for ip, score in ips
]
})
else:
# 添加黑名单
data = request.get_json()
ip = data.get('ip')
duration = data.get('duration', 3600)
reason = data.get('reason', 'manual_ban')
blacklist.ban_ip(ip, reason, duration)
return jsonify({"success": True, "message": f"IP {ip} banned for {duration}s"})
if __name__ == '__main__':
app.run(host='0.0.0.0', port=8080, debug=False)四、实战效果与成本对比
上线这套方案后，我们在大促期间的实际数据：
- 拦截无效请求：峰值期间约 35% 的请求被网关层拦截，不进入 AI 推理
- 响应延迟：网关层增加约 3~5ms 开销，在可接受范围内
- API 成本节省：按 HolySheep 的 DeepSeek V3.2 价格（$0.42/MTok）计算，节省约 40% 的 API 调用费用
- 误杀率：通过动态画像，单 IP 封禁误杀率 < 0.1%
五、适合谁与不适合谁
| 场景 | 推荐程度 | 原因 |
|---|---|---|
| 电商大促 / 限时活动 AI 客服 | ⭐⭐⭐⭐⭐ 强烈推荐 | 突发流量大，需要精细化限速防止 API 费用暴增 |
| 企业 RAG 知识库系统 | ⭐⭐⭐⭐ 推荐 | 有多用户并发场景，需要按 Key 限速和流量隔离 |
| 独立开发者个人项目 | ⭐⭐⭐⭐ 推荐 | 低成本实现企业级防护，配合 HolySheep 性价比极高 |
| 日均请求 < 1000 的低频场景 | ⭐⭐ 可选 | 流量小，自带免费额度可能够用，防护方案略显复杂 |
| 对延迟极度敏感的核心业务 | ⭐⭐ 需要评估 | 网关层会增加 3~5ms，可能需要优化或旁路检测 |
六、价格与回本测算
以月均 API 消费 $500 的中型项目为例，对比自建中间件 vs HolySheep 方案：
| 费用项 | 无防护方案 | HolySheep + WAF 方案 |
|---|---|---|
| API 费用（GPT-4.1） | $500（汇率 7.3，约 ¥3650） | $500 × 0.6 = $300（汇率 1:1，约 ¥300） |
| 无效请求损耗 | ~25%，额外 $125 | ~5%，额外 $15 |
| 网关服务器成本 | $0（可复用现有机器） | $0（单机部署，无需额外资源） |
| Redis 成本 | $0（单机足够） | $0（同上） |
| 月度总成本 | ¥3773 | ¥315（约 $315） |
| 节省 | - | >90% |
七、为什么选 HolySheep
在实际测试了市面上多家中转服务后，HolySheep AI 的核心优势在于：
| 对比维度 | 官方 API（OpenAI/Anthropic） | 部分中转商 | HolySheep |
|---|---|---|---|
| 汇率 | ¥7.3 = $1 | ¥6.5~7.0 = $1 | ¥1 = $1（无损） |
| 国内延迟 | >200ms，跨境抖动 | 50~150ms | <50ms，直连优化 |
| 充值方式 | 海外信用卡 | 部分支持支付宝 | 微信/支付宝，秒到账 |
| DeepSeek V3.2 | 暂不支持 | 部分支持 | $0.42/MTok（性价比最高） |
| 注册赠送 | $5~18 | 无或极少 | 注册送免费额度 |
| 模型覆盖 | 仅官方模型 | 部分主流 | GPT-4.1、Claude Sonnet 4.5、Gemini 2.5 Flash 等 |
八、常见报错排查
错误 1：Redis 连接失败 "ConnectionRefusedError"
# 错误日志
redis.exceptions.ConnectionRefusedError: Error 111 connecting to localhost:6379.
解决代码
import redis
from redis.exceptions import ConnectionError, TimeoutError
def create_redis_client():
"""创建带重试的 Redis 客户端"""
try:
client = redis.Redis(
host=os.getenv('REDIS_HOST', 'localhost'),
port=int(os.getenv('REDIS_PORT', 6379)),
db=int(os.getenv('REDIS_DB', 0)),
password=os.getenv('REDIS_PASSWORD', None),
socket_timeout=5,
socket_connect_timeout=5,
retry_on_timeout=True,
decode_responses=True
)
# 测试连接
client.ping()
return client
except (ConnectionError, TimeoutError) as e:
print(f"Redis 连接失败: {e}")
# 降级方案：使用本地内存缓存
return None错误 2：令牌桶限速失效 "请求通过但 API 返回 429"
# 错误日志
HTTP 429: {"error":{"message":"Too many requests","type":"rate_limit_error"}}
原因：本地限速和上游限速不同步
解决代码：获取上游实际限速信息
import requests
def sync_rate_limit_info(api_key: str):
"""
同步 HolySheep API 的实际限速信息
返回: {"limit": 500, "remaining": 450, "reset": 1699999999}
"""
try:
response = requests.get(
"https://api.holysheep.ai/v1 Usage",
headers={"Authorization": f"Bearer {api_key}"},
timeout=10
)
# 解析响应头中的限速信息
return {
"limit": int(response.headers.get("X-RateLimit-Limit", 0)),
"remaining": int(response.headers.get("X-RateLimit-Remaining", 0)),
"reset": int(response.headers.get("X-RateLimit-Reset", 0))
}
except Exception as e:
return None
智能限速实现
class SmartRateLimiter:
def __init__(self, api_key: str):
self.api_key = api_key
self.local_bucket = TokenBucketManager()
self.last_sync = 0
self.sync_interval = 60 # 每 60 秒同步一次
def check(self, tokens: int = 1) -> bool:
# 先检查本地
allowed, _ = self.local