HolySheep AI 监控看板设计：API成功率・延迟・错误桶・模型占比・团队用量日报 完全ガイド

AI API 利用の可視化と最適化は、チーム全体のコスト管理与效率提升に直結します。本稿では、HolySheep AI の API を活用した実践的な监控看板アーキテクチャ 设计 从入门到生产环境まで涵盖します。

向いている人・向いていない人

HolySheep AI vs 競合サービスの比較

価格とROI分析

2026年5月時点の HolySheep AI 出力価格（$ / 1M Tokens）：

ROI計算例：月間に1億トークンを処理するチームの場合、DeepSeek V3.2 利用时可节省约$358（HolySheep ¥1=$1 レート对比官方）。

HolySheep を選ぶ理由

• 85%コスト節約：¥1=$1の為替レートで、公式価格の約15%�
• 超低遅延：P99 <50ms（OpenAI直比50%高速）
• 日本語対応：ドキュメント・サポートが完全日本語化
• 柔軟な決済：WeChat Pay / Alipay対応で中国企业も安心
• 無料クレジット：登録時に体験クレジット付与
• 統一エンドポイント：複数モデルを1つのbase_urlで管理

监控看板アーキテクチャ概要

监控看板 系统架构
├── Data Collection Layer (Prometheus Exporter)
├── Storage Layer (InfluxDB / TimescaleDB)
├── Visualization Layer (Grafana / Custom Dashboard)
└── Alerting Layer (AlertManager / Slack / PagerDuty)

import requests
import json
from datetime import datetime, timedelta
from collections import defaultdict

class HolySheepMetricsCollector:
    """
    HolySheep AI API 监控数据采集器
    采集成功率、延迟分布、错误分类、模型占比
    """
    
    BASE_URL = "https://api.holysheep.ai/v1"
    
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
    
    def log_request(self, model: str, latency_ms: float, status_code: int, 
                    tokens_used: int, error_type: str = None):
        """单次请求日志记录"""
        metrics = {
            "timestamp": datetime.utcnow().isoformat(),
            "model": model,
            "latency_ms": latency_ms,
            "status_code": status_code,
            "tokens_used": tokens_used,
            "success": status_code == 200,
            "error_type": error_type
        }
        # 发送到时序数据库
        self._write_to_timeseriesdb(metrics)
        return metrics
    
    def calculate_success_rate(self, start_time: datetime, 
                               end_time: datetime) -> float:
        """计算时间范围内的成功率"""
        query = f"""
        SELECT 
            COUNT(*) as total,
            SUM(CASE WHEN status_code = 200 THEN 1 ELSE 0 END) as success
        FROM api_requests
        WHERE time >= '{start_time.isoformat()}'
          AND time <= '{end_time.isoformat()}'
        """
        result = self._query_timeseriesdb(query)
        if result['total'] > 0:
            return (result['success'] / result['total']) * 100
        return 0.0
    
    def get_latency_percentiles(self, start_time: datetime,
                                end_time: datetime) -> dict:
        """获取延迟百分位数"""
        query = f"""
        SELECT 
            PERCENTILE(latency_ms, 50) as p50,
            PERCENTILE(latency_ms, 90) as p90,
            PERCENTILE(latency_ms, 95) as p95,
            PERCENTILE(latency_ms, 99) as p99
        FROM api_requests
        WHERE time >= '{start_time.isoformat()}'
          AND time <= '{end_time.isoformat()}'
        """
        return self._query_timeseriesdb(query)
    
    def get_error_bucket_distribution(self, start_time: datetime,
                                      end_time: datetime) -> dict:
        """错误桶分布统计"""
        query = f"""
        SELECT 
            error_type,
            COUNT(*) as count,
            COUNT(*) * 100.0 / (SELECT COUNT(*) FROM api_requests) as percentage
        FROM api_requests
        WHERE time >= '{start_time.isoformat()}'
          AND time <= '{end_time.isoformat()}'
          AND status_code != 200
        GROUP BY error_type
        ORDER BY count DESC
        """
        return self._query_timeseriesdb(query)
    
    def get_model_usage_ratio(self, start_time: datetime,
                             end_time: datetime) -> dict:
        """模型使用占比统计"""
        query = f"""
        SELECT 
            model,
            SUM(tokens_used) as total_tokens,
            COUNT(*) as request_count,
            AVG(latency_ms) as avg_latency
        FROM api_requests
        WHERE time >= '{start_time.isoformat()}'
          AND time <= '{end_time.isoformat()}'
        GROUP BY model
        ORDER BY total_tokens DESC
        """
        return self._query_timeseriesdb(query)
    
    def _write_to_timeseriesdb(self, metrics: dict):
        """写入时序数据库（InfluxDB示例）"""
        # TODO: 实现实际的数据库写入逻辑
        pass
    
    def _query_timeseriesdb(self, query: str) -> dict:
        """查询时序数据库"""
        # TODO: 实现实际的数据库查询逻辑
        pass

API成功率ダッシュボード実装

// HolySheep API 成功率监控面板
// Prometheus + Grafana 集成

// prometheus.yml 配置
/*
global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'holysheep-api'
    static_configs:
      - targets: ['your-exporter:9090']
    metrics_path: '/metrics'
    params:
      api_key: ['YOUR_HOLYSHEEP_API_KEY']
*/

// Grafana Dashboard JSON Query 示例
const successRateQuery = {
  expr: `
    (
      sum(rate(holysheep_api_requests_total{status=~"2.."}[5m]))
      /
      sum(rate(holysheep_api_requests_total[5m]))
    ) * 100
  `,
  legendFormat: 'Success Rate %',
  refId: 'A'
};

const latencyQuery = {
  expr: `
    histogram_quantile(0.99, 
      sum(rate(holysheep_api_request_duration_seconds_bucket[5m])) by (le)
    ) * 1000
  `,
  legendFormat: 'P99 Latency (ms)',
  refId: 'B'
};

const errorRateByCodeQuery = {
  expr: `
    sum by (status_code) (
      rate(holysheep_api_requests_total{status!~"2.."}[5m])
    )
  `,
  legendFormat: 'Error {{status_code}}',
  refId: 'C'
};

// Team Usage Daily Report Generator
class TeamUsageReport {
  constructor(holysheepKey) {
    this.baseUrl = 'https://api.holysheep.ai/v1';
    this.apiKey = holysheepKey;
  }

  async generateDailyReport(teamId, date = new Date()) {
    const startTime = new Date(date);
    startTime.setHours(0, 0, 0, 0);
    const endTime = new Date(date);
    endTime.setHours(23, 59, 59, 999);

    const report = {
      report_date: date.toISOString().split('T')[0],
      team_id: teamId,
      summary: await this.getTeamSummary(teamId, startTime, endTime),
      model_breakdown: await this.getModelBreakdown(teamId, startTime, endTime),
      error_analysis: await this.getErrorAnalysis(teamId, startTime, endTime),
      cost_estimation: await this.estimateCost(teamId, startTime, endTime),
      recommendations: []
    };

    // 生成优化建议
    if (report.summary.avg_latency_p99 > 200) {
      report.recommendations.push({
        priority: 'HIGH',
        message: 'P99延迟超过200ms，考虑切换到Gemini 2.5 Flash',
        potential_saving: '约30%成本'
      });
    }

    if (report.error_analysis.rate > 1) {
      report.recommendations.push({
        priority: 'MEDIUM',
        message: '错误率超过1%，请检查错误桶分布',
        action: '查看详细错误日志'
      });
    }

    return report;
  }

  async getTeamSummary(teamId, startTime, endTime) {
    // 查询团队总览数据
    return {
      total_requests: 0,
      total_tokens: 0,
      success_rate: 0,
      avg_latency_p50: 0,
      avg_latency_p99: 0
    };
  }

  async getModelBreakdown(teamId, startTime, endTime) {
    // 查询各模型使用情况
    return [
      { model: 'gpt-4.1', tokens: 0, requests: 0, percentage: 0 },
      { model: 'claude-sonnet-4.5', tokens: 0, requests: 0, percentage: 0 },
      { model: 'gemini-2.5-flash', tokens: 0, requests: 0, percentage: 0 },
      { model: 'deepseek-v3.2', tokens: 0, requests: 0, percentage: 0 }
    ];
  }

  async getErrorAnalysis(teamId, startTime, endTime) {
    // 查询错误分布
    return {
      rate: 0,
      errors_by_type: {},
      top_errors: []
    };
  }

  async estimateCost(teamId, startTime, endTime) {
    // 基于HolySheep ¥1=$1汇率计算成本
    const modelPrices = {
      'gpt-4.1': { per_mtok: 8.00, currency: 'USD' },
      'claude-sonnet-4.5': { per_mtok: 15.00, currency: 'USD' },
      'gemini-2.5-flash': { per_mtok: 2.50, currency: 'USD' },
      'deepseek-v3.2': { per_mtok: 0.42, currency: 'USD' }
    };
    // TODO: 实现成本估算逻辑
    return { total_usd: 0, total_jpy: 0 };
  }
}

// 使用示例
const reportGenerator = new TeamUsageReport('YOUR_HOLYSHEEP_API_KEY');
reportGenerator.generateDailyReport('team-123')
  .then(report => {
    console.log('Daily Report:', JSON.stringify(report, null, 2));
  });

错误桶（Error Bucket）设计

错误桶分类与监控
HolySheep API 错误码映射

from enum import Enum
from typing import Dict, List
from dataclasses import dataclass
from datetime import datetime

class HolySheepErrorCode(Enum):
    """HolySheep API 错误码定义"""
    # 认证错误 (4xx)
    AUTH_001 = "INVALID_API_KEY"           # API密钥无效
    AUTH_002 = "EXPIRED_API_KEY"            # API密钥过期
    AUTH_003 = "RATE_LIMIT_EXCEEDED"        # 速率限制超出
    
    # 请求错误 (4xx)
    REQ_001 = "INVALID_MODEL"               # 无效的模型名称
    REQ_002 = "CONTEXT_LENGTH_EXCEEDED"     # 上下文长度超出
    REQ_003 = "INVALID_JSON_FORMAT"         # JSON格式错误
    REQ_004 = "MISSING_REQUIRED_FIELD"      # 缺少必需字段
    
    # 服务器错误 (5xx)
    SRV_001 = "INTERNAL_SERVER_ERROR"       # 内部服务器错误
    SRV_002 = "MODEL_OVERLOADED"            # 模型过载
    SRV_003 = "SERVICE_MAINTENANCE"         # 服务维护中
    SRV_004 = "UPSTREAM_TIMEOUT"            # 上游超时

@dataclass
class ErrorBucket:
    """错误桶定义"""
    code: str
    category: str
    severity: str  # CRITICAL, HIGH, MEDIUM, LOW
    retry_recommended: bool
    alert_threshold: float  # 百分比，超过则告警

class ErrorBucketMonitor:
    """
    错误桶监控器
    自动分类错误并生成告警
    """
    
    # 预定义错误桶配置
    ERROR_BUCKETS = {
        "AUTH_ERRORS": ErrorBucket(
            code="AUTH_ERRORS",
            category="Authentication",
            severity="CRITICAL",
            retry_recommended=False,
            alert_threshold=0.1  # 0.1%以上告警
        ),
        "RATE_LIMIT": ErrorBucket(
            code="RATE_LIMIT",
            category="Rate Limiting",
            severity="HIGH",
            retry_recommended=True,
            alert_threshold=1.0
        ),
        "VALIDATION_ERRORS": ErrorBucket(
            code="VALIDATION_ERRORS",
            category="Request Validation",
            severity="MEDIUM",
            retry_recommended=False,
            alert_threshold=5.0
        ),
        "SERVER_ERRORS": ErrorBucket(
            code="SERVER_ERRORS",
            category="Server Errors",
            severity="CRITICAL",
            retry_recommended=True,
            alert_threshold=0.5
        ),
        "TIMEOUT_ERRORS": ErrorBucket(
            code="TIMEOUT_ERRORS",
            category="Timeout",
            severity="HIGH",
            retry_recommended=True,
            alert_threshold=1.0
        ),
        "UNKNOWN_ERRORS": ErrorBucket(
            code="UNKNOWN_ERRORS",
            category="Unknown",
            severity="MEDIUM",
            retry_recommended=False,
            alert_threshold=2.0
        )
    }
    
    def classify_error(self, status_code: int, error_message: str) -> str:
        """根据HTTP状态码和错误消息分类错误"""
        if status_code == 401:
            return "AUTH_ERRORS"
        elif status_code == 429:
            return "RATE_LIMIT"
        elif status_code == 400:
            if "context" in error_message.lower():
                return "VALIDATION_ERRORS"
            return "VALIDATION_ERRORS"
        elif status_code == 500:
            return "SERVER_ERRORS"
        elif status_code == 504 or "timeout" in error_message.lower():
            return "TIMEOUT_ERRORS"
        elif status_code >= 400:
            return "VALIDATION_ERRORS"
        else:
            return "UNKNOWN_ERRORS"
    
    def process_error(self, error_data: dict) -> dict:
        """处理单个错误并返回桶分类"""
        bucket_key = self.classify_error(
            error_data['status_code'],
            error_data.get('error_message', '')
        )
        bucket = self.ERROR_BUCKETS[bucket_key]
        
        return {
            "bucket": bucket_key,
            "severity": bucket.severity,
            "retry_recommended": bucket.retry_recommended,
            "timestamp": datetime.utcnow().isoformat(),
            "error_details": error_data
        }
    
    def check_alert_conditions(self, error_distribution: Dict[str, float]) -> List[dict]:
        """检查告警条件"""
        alerts = []
        for bucket_key, percentage in error_distribution.items():
            bucket = self.ERROR_BUCKETS.get(bucket_key)
            if bucket and percentage > bucket.alert_threshold:
                alerts.append({
                    "bucket": bucket_key,
                    "severity": bucket.severity,
                    "current_percentage": percentage,
                    "threshold": bucket.alert_threshold,
                    "message": f"{bucket_key}错误率 {percentage:.2f}% 超过阈值 {bucket.alert_threshold}%"
                })
        return alerts
    
    def generate_error_report(self, errors: List[dict]) -> dict:
        """生成错误报告"""
        bucket_counts = {}
        for error in errors:
            bucket_key = error.get('bucket', 'UNKNOWN_ERRORS')
            bucket_counts[bucket_key] = bucket_counts.get(bucket_key, 0) + 1
        
        total_errors = sum(bucket_counts.values())
        distribution = {
            k: (v / total_errors * 100) if total_errors > 0 else 0
            for k, v in bucket_counts.items()
        }
        
        return {
            "total_errors": total_errors,
            "bucket_distribution": distribution,
            "alerts": self.check_alert_conditions(distribution),
            "generated_at": datetime.utcnow().isoformat()
        }

使用示例
monitor = ErrorBucketMonitor()
errors = [
    {"status_code": 429, "error_message": "Rate limit exceeded"},
    {"status_code": 500, "error_message": "Internal server error"},
    {"status_code": 400, "error_message": "Invalid request"}
]

classified_errors = [monitor.process_error(e) for e in errors]
report = monitor.generate_error_report(classified_errors)
print(report)

チーム用量日报自动化

"""
HolySheep API 团队用量日报自动化
每日定时生成并发送报告
"""

import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from datetime import datetime, timedelta
import asyncio

class TeamDailyReport:
    """团队每日用量报告生成器"""
    
    def __init__(self, holysheep_api_key: str, notification_config: dict):
        self.api_key = holysheep_api_key
        self.base_url = "https://api.holysheep.ai/v1"
        self.notification_config = notification_config
    
    async def generate_report(self, team_id: str, date: datetime) -> dict:
        """生成指定日期的报告"""
        report = {
            "report_date": date.strftime("%Y-%m-%d"),
            "team_id": team_id,
            "generated_at": datetime.utcnow().isoformat(),
            "sections": {}
        }
        
        # 并行获取所有指标
        tasks = [
            self._get_request_stats(team_id, date),
            self._get_token_usage(team_id, date),
            self._get_model_breakdown(team_id, date),
            self._get_error_summary(team_id, date),
            self._get_cost_summary(team_id, date)
        ]
        
        results = await asyncio.gather(*tasks, return_exceptions=True)
        
        report["sections"]["request_stats"] = results[0] if not isinstance(results[0], Exception) else {}
        report["sections"]["token_usage"] = results[1] if not isinstance(results[1], Exception) else {}
        report["sections"]["model_breakdown"] = results[2] if not isinstance(results[2], Exception) else {}
        report["sections"]["error_summary"] = results[3] if not isinstance(results[3], Exception) else {}
        report["sections"]["cost_summary"] = results[4] if not isinstance(results[4], Exception) else {}
        
        return report
    
    async def _get_request_stats(self, team_id: str, date: datetime) -> dict:
        """获取请求统计"""
        # 实际实现中查询数据库
        return {
            "total_requests": 0,
            "successful_requests": 0,
            "failed_requests": 0,
            "success_rate": 0.0
        }
    
    async def _get_token_usage(self, team_id: str, date: datetime) -> dict:
        """获取Token使用量"""
        return {
            "input_tokens": 0,
            "output_tokens": 0,
            "total_tokens": 0
        }
    
    async def _get_model_breakdown(self, team_id: str, date: datetime) -> dict:
        """获取模型使用分布"""
        return {
            "gpt-4.1": {"requests": 0, "tokens": 0, "cost_jpy": 0},
            "claude-sonnet-4.5": {"requests": 0, "tokens": 0, "cost_jpy": 0},
            "gemini-2.5-flash": {"requests": 0, "tokens": 0, "cost_jpy": 0},
            "deepseek-v3.2": {"requests": 0, "tokens": 0, "cost_jpy": 0}
        }
    
    async def _get_error_summary(self, team_id: str, date: datetime) -> dict:
        """获取错误摘要"""
        return {
            "total_errors": 0,
            "error_rate": 0.0,
            "top_errors": []
        }
    
    async def _get_cost_summary(self, team_id: str, date: datetime) -> dict:
        """获取成本汇总（基于¥1=$1汇率）"""
        return {
            "total_cost_usd": 0.0,
            "total_cost_jpy": 0.0,
            "cost_vs_yesterday": 0.0,
            "cost_vs_last_week": 0.0
        }
    
    def format_html_report(self, report: dict) -> str:
        """格式化HTML报告"""
        html = f"""
        
        
            
📊 HolySheep AI 团队日报
            
日期: {report['report_date']} | 团队: {report['team_id']}
            
            

                
概览
                
成功率: {report['sections']['request_stats'].get('success_rate', 0):.2f}%
                
总请求数: {report['sections']['request_stats'].get('total_requests', 0):,}
                
Token使用量: {report['sections']['token_usage'].get('total_tokens', 0):,} MTok
                
今日成本: ¥{report['sections']['cost_summary'].get('total_cost_jpy', 0):,.2f}
            
            
            
模型使用分布
            
        """
        
        for model, data in report['sections']['model_breakdown'].items():
            html += f"""
                
            """
        
        html += """
            

                

                    
模型
                    
请求数
                    
Token数
                    
成本(¥)
                

                    
{model}
                    
{data.get('requests', 0):,}
                    
{data.get('tokens', 0):,}
                    
¥{data.get('cost_jpy', 0):,.2f}
                
            

                本报告由 HolySheep AI 监控看板自动生成
            
        
        
        """
        return html
    
    async def send_report(self, report: dict):
        """发送报告通知"""
        if self.notification_config['type'] == 'email':
            await self._send_email(report)
        elif self.notification_config['type'] == 'slack':
            await self._send_slack(report)
    
    async def _send_email(self, report: dict):
        """发送邮件"""
        # 实现邮件发送逻辑
        pass
    
    async def _send_slack(self, report: dict):
        """发送Slack通知"""
        # 实现Slack发送逻辑
        pass

使用示例
async def main():
    report_generator = TeamDailyReport(
        holysheep_api_key='YOUR_HOLYSHEEP_API_KEY',
        notification_config={'type': 'email', 'recipients': ['[email protected]']}
    )
    
    today = datetime.utcnow()
    report = await report_generator.generate_report('team-123', today)
    html_report = report_generator.format_html_report(report)
    
    await report_generator.send_report(report)
    print(f"Report generated: {report['report_date']}")

asyncio.run(main())

Prometheus Exporter 完整实现

// HolySheep API Prometheus Exporter
// Go语言实现生产级监控导出器

package main

import (
    "encoding/json"
    "fmt"
    "net/http"
    "sync"
    "time"
    
    "github.com/prometheus/client_golang/prometheus"
    "github.com/prometheus/client_golang/prometheus/promhttp"
)

type HolySheepExporter struct {
    apiKey string
    mutex  sync.RWMutex
    
    // Prometheus指标定义
    requestsTotal      *prometheus.CounterVec
    requestDuration    *prometheus.HistogramVec
    tokensUsed         *prometheus.CounterVec
    errorsTotal        *prometheus.CounterVec
    modelUsageTokens   *prometheus.GaugeVec
    currentRateLimit   *prometheus.GaugeVec
}

func NewHolySheepExporter(apiKey string) *HolySheepExporter {
    e := &HolySheepExporter{
        apiKey: apiKey,
        requestsTotal: prometheus.NewCounterVec(
            prometheus.CounterOpts{
                Name: "holysheep_api_requests_total",
                Help: "Total number of HolySheep API requests",
            },
            []string{"model", "status"},
        ),
        requestDuration: prometheus.NewHistogramVec(
            prometheus.HistogramOpts{
                Name:    "holysheep_api_request_duration_seconds",
                Help:    "HolySheep API request duration in seconds",
                Buckets: []float64{0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0},
            },
            []string{"model"},
        ),
        tokensUsed: prometheus.NewCounterVec(
            prometheus.CounterOpts{
                Name: "holysheep_api_tokens_used_total",
                Help: "Total number of tokens used",
            },
            []string{"model", "type"}, // type: input or output
        ),
        errorsTotal: prometheus.NewCounterVec(
            prometheus.CounterOpts{
                Name: "holysheep_api_errors_total",
                Help: "Total number of HolySheep API errors",
            },
            []string{"error_type", "model"},
        ),
        modelUsageTokens: prometheus.NewGaugeVec(
            prometheus.GaugeOpts{
                Name: "holysheep_api_model_usage_tokens",
                Help: "Current model usage in tokens",
            },
            []string{"model", "team_id"},
        ),
        currentRateLimit: prometheus.NewGaugeVec(
            prometheus.GaugeOpts{
                Name: "holysheep_api_rate_limit_remaining",
                Help: "Remaining rate limit",
            },
            []string{"model"},
        ),
    }
    
    prometheus.MustRegister(e.requestsTotal)
    prometheus.MustRegister(e.requestDuration)
    prometheus.MustRegister(e.tokensUsed)
    prometheus.MustRegister(e.errorsTotal)
    prometheus.MustRegister(e.modelUsageTokens)
    prometheus.MustRegister(e.currentRateLimit)
    
    return e
}

func (e *HolySheepExporter) RecordRequest(req RequestMetrics) {
    e.mutex.Lock()
    defer e.mutex.Unlock()
    
    statusStr := fmt.Sprintf("%d", req.StatusCode)
    e.requestsTotal.WithLabelValues(req.Model, statusStr).Inc()
    
    duration := time.Duration(req.LatencyMs) * time.Millisecond
    e.requestDuration.WithLabelValues(req.Model).Observe(duration.Seconds())
    
    if req.InputTokens > 0 {
        e.tokensUsed.WithLabelValues(req.Model, "input").Add(float64(req.InputTokens))
    }
    if req.OutputTokens > 0 {
        e.tokensUsed.WithLabelValues(req.Model, "output").Add(float64(req.OutputTokens))
    }
    
    if req.ErrorType != "" {
        e.errorsTotal.WithLabelValues(req.ErrorType, req.Model).Inc()
    }
    
    e.modelUsageTokens.WithLabelValues(req.Model, req.TeamID).Add(
        float64(req.InputTokens + req.OutputTokens),
    )
}

type RequestMetrics struct {
    Model        string
    TeamID       string
    StatusCode   int
    LatencyMs    int64
    InputTokens  int64
    OutputTokens int64
    ErrorType    string
}

func (e *HolySheepExporter) StartCollection() {
    go func() {
        ticker := time.NewTicker(15 * time.Second)
        for range ticker.C {
            e.collectFromHolySheepAPI()
        }
    }()
}

func (e *HolySheepExporter) collectFromHolySheepAPI() {
    // 从HolySheep API获取使用统计
    // base_url: https://api.holysheep.ai/v1
    client := &http.Client{Timeout: 10 * time.Second}
    
    req, err := http.NewRequest("GET", 
        "https://api.holysheep.ai/v1/usage", nil)
    if err != nil {
        return
    }
    
    req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", e.apiKey))
    
    resp, err := client.Do(req)
    if err != nil {
        return
    }
    defer resp.Body.Close()
    
    var usageResp UsageResponse
    if err := json.NewDecoder(resp.Body).Decode(&usageResp); err != nil {
        return
    }
    
    for model, usage := range usageResp.Models {
        e.currentRateLimit.WithLabelValues(model).Set(float64(usage.RateLimitRemaining))
    }
}

type UsageResponse struct {
    Models map[string]struct {
        RateLimitRemaining int64 json:"rate_limit_remaining"
        UsageToday         int64 json:"usage_today"
    } json:"models"
}

func main() {
    exporter := NewHolySheepExporter("YOUR_HOLYSHEEP_API_KEY")
    exporter.StartCollection()
    
    http.Handle("/metrics", promhttp.Handler())
    http.HandleFunc("/health", func(w http.ResponseWriter, r *http.Request) {
        w.WriteHeader(http.Status
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