引言：为什么可观测性在AI API调用中至关重要
在生产环境中运行AI应用时，我曾无数次被半夜的告警吵醒——某个服务突然返回大量429错误，或者5xx错误率飙升。更糟糕的是，月底账单出现时，费用往往超出预期50%以上。这些问题的根源在于：**缺乏对API调用的细粒度可观测性**。
本文是一份完整的迁移Playbook，指导您如何将现有的AI API监控方案迁移到[HolySheep AI](https://www.holysheep.ai/register)，并构建企业级的Prometheus/Grafana监控体系。我将分享我在实际生产环境中踩过的坑以及对应的解决方案。
---
目录
- [迁移前的准备工作](#迁移前的准备工作)
- [Prometheus监控架构设计](#prometheus监控架构设计)
- [Grafana仪表盘配置](#grafana仪表盘配置)
- [告警规则配置](#告警规则配置)
- [常见错误与解决方案](#h2-häufige-fehler-und-lösungen-h2)
- [ Geeignet / nicht geeignet für](#geeignet-nicht-geeignet-für)
- [Preise und ROI](#preise-und-roi)
- [Warum HolySheep wählen](#warum-holysheep-wählen)
---
迁移前的准备工作
当前架构评估
在迁移之前，我建议您首先评估当前的API调用模式：
- 日均API调用量（requests/day）
- 平均token消耗（input + output）
- 当前的429/5xx/timeout错误率
- 月均API费用
- 是否已有Prometheus/Grafana基础设施
HolySheep API基础配置
首先，确保您已经注册了HolySheep账号并获取了API Key：
# holy sheep_api_config.py
import os
HolySheep API配置
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = os.getenv("YOUR_HOLYSHEEP_API_KEY")
如果还没有API Key，请访问以下链接注册
https://www.holysheep.ai/register
可用模型及定价（2026年）
AVAILABLE_MODELS = {
"gpt-4.1": {"input": 8.0, "output": 24.0, "unit": "$/MTok"},
"claude-sonnet-4.5": {"input": 15.0, "output": 75.0, "unit": "$/MTok"},
"gemini-2.5-flash": {"input": 2.50, "output": 10.0, "unit": "$/MTok"},
"deepseek-v3.2": {"input": 0.42, "output": 2.76, "unit": "$/MTok"},
}
迁移风险评估
| 风险类型 | 影响等级 | 缓解措施 |
|---------|---------|----------|
| 服务中断 | 高 | 保留原API作为fallback |
| 性能下降 | 中 | HolySheep延迟<50ms，通常更优 |
| 费用超支 | 低 | 细粒度监控+告警 |
| 兼容性问题 | 中 | 使用OpenAI兼容格式 |
---
Prometheus监控架构设计
指标设计原则
在设计Prometheus指标时，我遵循以下原则：
- **高基数问题**：避免将user_id或request_id作为label，防止metric爆炸
- **Bucket设计**：对响应时间和token消耗使用histogram，便于计算百分位数
- **单调用账单**：每个请求的cost必须可追溯
完整的监控客户端实现
以下是一个生产级的Python监控客户端，它将API调用指标暴露给Prometheus：
# holy_sheep_monitor.py
from prometheus_client import Counter, Histogram, Gauge, CollectorRegistry, generate_latest
import requests
import time
from typing import Optional, Dict, Any
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class HolySheepMonitor:
"""
HolySheep API监控客户端
支持Prometheus指标暴露和Grafana集成
"""
def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
self.api_key = api_key
self.base_url = base_url
self.headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
# 创建自定义registry避免与默认registry冲突
self.registry = CollectorRegistry()
# ====== 核心计数器指标 ======
self.request_total = Counter(
'holysheep_requests_total',
'Total number of HolySheep API requests',
['model', 'endpoint', 'status_code'],
registry=self.registry
)
# ====== 错误专用桶 ======
# 专门追踪429/5xx/timeout错误
self.error_bucket = Counter(
'holysheep_errors_bucket',
'Error count by type (429/5xx/timeout)',
['error_type', 'model'],
registry=self.registry
)
# ====== 延迟直方图 ======
self.request_duration = Histogram(
'holysheep_request_duration_seconds',
'Request duration in seconds',
['model', 'endpoint'],
buckets=[0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0],
registry=self.registry
)
# ====== Token消耗直方图 ======
self.token_usage = Histogram(
'holysheep_tokens_used',
'Token usage per request',
['model', 'token_type'], # token_type: input/output
buckets=[100, 500, 1000, 5000, 10000, 50000, 100000],
registry=self.registry
)
# ====== 单调用成本（Cent精确）======
self.request_cost = Histogram(
'holysheep_request_cost_cents',
'Cost per request in cents',
['model'],
buckets=[0.01, 0.05, 0.1, 0.5, 1.0, 5.0, 10.0, 50.0],
registry=self.registry
)
# ====== 熔断器状态 ======
self.circuit_breaker_state = Gauge(
'holysheep_circuit_breaker_state',
'Circuit breaker state: 0=closed, 1=open, 2=half-open',
['model'],
registry=self.registry
)
# ====== 内部状态 ======
self._circuit_breakers: Dict[str, dict] = {}
self._rate_limit_until: Dict[str, float] = {}
def _get_model_price(self, model: str) -> Dict[str, float]:
"""获取模型价格（$/MTok），转换为Cent/token"""
prices = {
"gpt-4.1": {"input": 8.0, "output": 24.0},
"claude-sonnet-4.5": {"input": 15.0, "output": 75.0},
"gemini-2.5-flash": {"input": 2.50, "output": 10.0},
"deepseek-v3.2": {"input": 0.42, "output": 2.76},
}
return prices.get(model, {"input": 1.0, "output": 3.0})
def _calculate_cost(self, model: str, input_tokens: int, output_tokens: int) -> float:
"""计算单次调用成本（精确到Cent）"""
prices = self._get_model_price(model)
# 价格是$/MTok，需要转换为Cent/token
input_cost = (input_tokens / 1_000_000) * prices["input"] * 100 # Cent
output_cost = (output_tokens / 1_000_000) * prices["output"] * 100 # Cent
return round(input_cost + output_cost, 4)
def _handle_error(self, error_type: str, model: str):
"""记录错误到专用桶"""
self.error_bucket.labels(error_type=error_type, model=model).inc()
def call_chat_completion(
self,
model: str,
messages: list,
max_tokens: Optional[int] = None,
temperature: float = 0.7,
timeout: float = 30.0
) -> Dict[str, Any]:
"""
调用Chat Completion API并自动记录监控指标
"""
endpoint = "/chat/completions"
url = f"{self.base_url}{endpoint}"
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
}
if max_tokens:
payload["max_tokens"] = max_tokens
start_time = time.time()
status_code = "unknown"
input_tokens = 0
output_tokens = 0
try:
response = requests.post(
url,
headers=self.headers,
json=payload,
timeout=timeout
)
status_code = str(response.status_code)
duration = time.time() - start_time
# 记录基础指标
self.request_total.labels(
model=model,
endpoint=endpoint,
status_code=status_code
).inc()
self.request_duration.labels(
model=model,
endpoint=endpoint
).observe(duration)
if response.status_code == 200:
data = response.json()
usage = data.get("usage", {})
input_tokens = usage.get("prompt_tokens", 0)
output_tokens = usage.get("completion_tokens", 0)
# 记录Token消耗
self.token_usage.labels(model=model, token_type="input").observe(input_tokens)
self.token_usage.labels(model=model, token_type="output").observe(output_tokens)
# 记录单调用成本
cost = self._calculate_cost(model, input_tokens, output_tokens)
self.request_cost.labels(model=model).observe(cost)
logger.info(f"[HolySheep] {model} | {status_code} | {duration:.3f}s | "
f"in:{input_tokens} out:{output_tokens} | ${cost:.4f}")
return {"success": True, "data": data, "cost_cents": cost}
elif response.status_code == 429:
self._handle_error("429_rate_limit", model)
self._rate_limit_until[model] = time.time() + 60 # 默认60秒冷却
return {"success": False, "error": "rate_limit", "status": 429}
elif 500 <= response.status_code < 600:
self._handle_error("5xx_server_error", model)
return {"success": False, "error": "server_error", "status": response.status_code}
else:
return {"success": False, "error": "unknown", "status": response.status_code}
except requests.Timeout:
duration = time.time() - start_time
self._handle_error("timeout", model)
self.request_total.labels(model=model, endpoint=endpoint, status_code="timeout").inc()
self.request_duration.labels(model=model, endpoint=endpoint).observe(duration)
logger.error(f"[HolySheep] {model} | TIMEOUT | {duration:.3f}s")
return {"success": False, "error": "timeout"}
except Exception as e:
duration = time.time() - start_time
self._handle_error("exception", model)
logger.exception(f"[HolySheep] {model} | EXCEPTION: {str(e)}")
return {"success": False, "error": str(e)}
def get_metrics(self) -> bytes:
"""返回Prometheus格式的指标"""
return generate_latest(self.registry)
def is_rate_limited(self, model: str) -> bool:
"""检查模型是否处于速率限制状态"""
if model not in self._rate_limit_until:
return False
if time.time() >= self._rate_limit_until[model]:
del self._rate_limit_until[model]
return False
return True
使用示例
if __name__ == "__main__":
monitor = HolySheepMonitor(api_key="YOUR_HOLYSHEEP_API_KEY")
# 测试调用
result = monitor.call_chat_completion(
model="deepseek-v3.2",
messages=[
{"role": "system", "content": "你是一个有用的助手。"},
{"role": "user", "content": "解释为什么可观测性对AI应用很重要。"}
]
)
print(f"结果: {result}")
print(f"\nPrometheus指标:\n{monitor.get_metrics().decode()}")
Prometheus服务器配置
将以下配置添加到您的
prometheus.yml：
# prometheus.yml
global:
scrape_interval: 15s
evaluation_interval: 15s
scrape_configs:
# HolySheep API监控端点
- job_name: 'holysheep-monitor'
static_configs:
- targets: ['localhost:8000'] # 监控客户端暴露的metrics端点
metrics_path: /metrics
scrape_interval: 10s
scrape_timeout: 5s
# 如果使用pushgateway（适用于短生命周期任务）
- job_name: 'holysheep-pushgateway'
static_configs:
- targets: ['pushgateway:9091']
job_name: 'holysheep'
honor_labels: true
指标端点服务
# metrics_server.py
from flask import Flask, Response
from holy_sheep_monitor import HolySheepMonitor
import os
app = Flask(__name__)
初始化监控客户端
monitor = HolySheepMonitor(
api_key=os.getenv("YOUR_HOLYSHEEP_API_KEY", "your-api-key-here")
)
@app.route('/metrics')
def metrics():
"""Prometheus抓取端点"""
return Response(
monitor.get_metrics(),
mimetype='text/plain; charset=utf-8'
)
@app.route('/health')
def health():
return {"status": "healthy"}
if __name__ == '__main__':
app.run(host='0.0.0.0', port=8000)
启动metrics服务器：
pip install flask prometheus-client requests
python metrics_server.py
---
Grafana仪表盘配置
推荐仪表盘JSON
创建以下JSON仪表盘并导入Grafana：
{
"dashboard": {
"title": "HolySheep API 可观测性",
"uid": "holysheep-observability",
"panels": [
{
"title": "请求速率 (req/s)",
"type": "graph",
"targets": [
{
"expr": "rate(holysheep_requests_total[5m])",
"legendFormat": "{{model}} - {{status_code}}"
}
]
},
{
"title": "错误率监控 (429/5xx/Timeout)",
"type": "stat",
"targets": [
{
"expr": "sum by (error_type) (increase(holysheep_errors_bucket[1h]))",
"legendFormat": "{{error_type}}"
}
],
"fieldConfig": {
"defaults": {
"thresholds": {
"mode": "absolute",
"steps": [
{"value": 0, "color": "green"},
{"value": 10, "color": "yellow"},
{"value": 50, "color": "red"}
]
}
}
}
},
{
"title": "P99延迟 (秒)",
"type": "gauge",
"targets": [
{
"expr": "histogram_quantile(0.99, rate(holysheep_request_duration_seconds_bucket[5m]))",
"legendFormat": "{{model}}"
}
]
},
{
"title": "单调用成本分布 (Cent)",
"type": "heatmap",
"targets": [
{
"expr": "sum by (le) (increase(holysheep_request_cost_cents_bucket[1h]))",
"legendFormat": "{{le}}"
}
]
},
{
"title": "Token消耗趋势",
"type": "graph",
"targets": [
{
"expr": "sum by (token_type) (rate(holysheep_tokens_used_sum[1h]))",
"legendFormat": "{{token_type}} tokens/s"
}
]
},
{
"title": "月预估费用 ($)",
"type": "singlestat",
"targets": [
{
"expr": "sum(rate(holysheep_request_cost_cents_sum[1h])) * 720 * 30 / 100",
"legendFormat": "Monthly Cost"
}
],
"fieldConfig": {
"defaults": {
"unit": "currencyUSD",
"thresholds": {
"mode": "absolute",
"steps": [
{"value": 0, "color": "green"},
{"value": 100, "color": "yellow"},
{"value": 500, "color": "red"}
]
}
}
}
}
]
}
}
模型对比仪表盘
{
"dashboard": {
"title": "HolySheep 模型对比分析",
"panels": [
{
"title": "各模型请求分布",
"type": "piechart",
"targets": [
{
"expr": "sum by (model) (increase(holysheep_requests_total[24h]))"
}
]
},
{
"title": "模型延迟对比 (P50/P95/P99)",
"type": "graph",
"targets": [
{
"expr": "histogram_quantile(0.50, rate(holysheep_request_duration_seconds_bucket[5m]))",
"legendFormat": "{{model}} P50"
},
{
"expr": "histogram_quantile(0.95, rate(holysheep_request_duration_seconds_bucket[5m]))",
"legendFormat": "{{model}} P95"
},
{
"expr": "histogram_quantile(0.99, rate(holysheep_request_duration_seconds_bucket[5m]))",
"legendFormat": "{{model}} P99"
}
]
},
{
"title": "成本效率对比 ($/MTok响应)",
"type": "bargauge",
"targets": [
{
"expr": "sum by (model) (rate(holysheep_request_cost_cents_sum[1h])) / sum by (model) (rate(holysheep_tokens_used_sum{token_type=\"output\"}[1h])) * 100",
"legendFormat": "{{model}}"
}
]
}
]
}
}
---
告警规则配置
AlertManager告警规则
# alerting_rules.yml
groups:
- name: holysheep_alerts
interval: 30s
rules:
# 429错误告警 - 速率限制
- alert: HolySheepRateLimitHigh
expr: |
sum(rate(holysheep_errors_bucket{error_type="429_rate_limit"}[5m]))
/ sum(rate(holysheep_requests_total[5m])) > 0.05
for: 2m
labels:
severity: warning
team: backend
annotations:
summary: "HolySheep API速率限制率过高"
description: "模型 {{ $labels.model }} 429错误率: {{ $value | humanizePercentage }}"
# 5xx错误告警 - 服务端错误
- alert: HolySheepServerErrorHigh
expr: |
sum(rate(holysheep_errors_bucket{error_type="5xx_server_error"}[5m]))
/ sum(rate(holysheep_requests_total[5m])) > 0.02
for: 1m
labels:
severity: critical
team: backend
annotations:
summary: "HolySheep服务端错误激增"
description: "5xx错误率: {{ $value | humanizePercentage }}，请检查HolySheep服务状态"
# 超时告警
- alert: HolySheepTimeoutHigh
expr: |
sum(rate(holysheep_errors_bucket{error_type="timeout"}[5m]))
/ sum(rate(holysheep_requests_total[5m])) > 0.01
for: 3m
labels:
severity: warning
annotations:
summary: "HolySheep API超时率高"
description: "超时率: {{ $value | humanizePercentage }}"
# 延迟过高告警
- alert: HolySheepLatencyHigh
expr: |
histogram_quantile(0.95, rate(holysheep_request_duration_seconds_bucket[5m])) > 2.0
for: 5m
labels:
severity: warning
annotations:
summary: "HolySheep P95延迟超过2秒"
description: "当前P95延迟: {{ $value | humanizeDuration }}"
# 成本超支告警
- alert: HolySheepCostOverrun
expr: |
(sum(rate(holysheep_request_cost_cents_sum[1h])) * 720 * 24)
> 100 # 每日100美元阈值
for: 10m
labels:
severity: warning
team: finance
annotations:
summary: "HolySheep日费用可能超支"
description: "预估日费用: ${{ $value | printf \"%.2f\" }}"
# 熔断器触发告警
- alert: HolySheepCircuitBreakerOpen
expr: holysheep_circuit_breaker_state == 1
for: 1m
labels:
severity: critical
annotations:
summary: "HolySheep {{ $labels.model }} 熔断器已打开"
description: "模型 {{ $labels.model }} 持续错误，熔断器已激活"
---
Häufige Fehler und Lösungen
错误1：429 Rate Limit导致服务中断
**问题描述**：
生产环境中，批量处理请求时频繁触发429错误，导致任务失败。
**根因分析**：
HolySheep对不同模型有不同速率限制，未设置合理的请求间隔和重试机制。
**解决方案代码**：
# retry_with_backoff.py
import time
import random
from functools import wraps
from holy_sheep_monitor import HolySheepMonitor
class HolySheepClient:
def __init__(self, api_key: str):
self.monitor = HolySheepMonitor(api_key)
self._retry_config = {
"max_retries": 5,
"base_delay": 1.0,
"max_delay": 60.0,
"exponential_base": 2.0,
"jitter": True
}
def call_with_retry(self, model: str, messages: list, **kwargs):
"""
带指数退避的重试机制
自动处理429限流
"""
max_retries = self._retry_config["max_retries"]
base_delay = self._retry_config["base_delay"]
for attempt in range(max_retries):
# 检查是否处于限流冷却期
if self.monitor.is_rate_limited(model):
remaining = self.monitor._rate_limit_until.get(model, 0) - time.time()
if remaining > 0:
print(f"[{model}] 等待速率限制冷却: {remaining:.1f}秒")
time.sleep(min(remaining, 30)) # 最多等待30秒
result = self.monitor.call_chat_completion(model, messages, **kwargs)
if result.get("success"):
return result
elif result.get("status") == 429:
# 429错误：使用指数退避
delay = min(
base_delay * (self._retry_config["exponential_base"] ** attempt),
self._retry_config["max_delay"]
)
if self._retry_config["jitter"]:
delay *= (0.5 + random.random()) # 添加随机抖动
print(f"[{model}] 429限流，第{attempt+1}次重试，等待{delay:.1f}秒...")
time.sleep(delay)
elif result.get("status") and result.get("status") >= 500:
# 5xx错误：短暂重试
delay = base_delay * (attempt + 1)
print(f"[{model}] 5xx错误，第{attempt+1}次重试，等待{delay:.1f}秒...")
time.sleep(delay)
else:
# 其他错误：直接返回失败
return result
return {"success": False, "error": "max_retries_exceeded"}
使用示例
if __name__ == "__main__":
client = HolySheepClient(api_key="YOUR_HOLYSHEEP_API_KEY")
# 批量处理100个请求
for i in range(100):
result = client.call_with_retry(
model="deepseek-v3.2", # 性价比最高的模型
messages=[{"role": "user", "content": f"处理任务 {i}"}]
)
print(f"任务 {i}: {'成功' if result.get('success') else '失败'}")
错误2：Prometheus指标丢失
**问题描述**：
Grafana仪表盘显示"No data"，但应用正在正常运行。
**根因分析**：
常见原因包括：
- metrics端点端口未正确配置
- Prometheus无法访问metrics端点（网络策略）
- registry未正确传递
- 指标名称冲突
**解决方案**：
# metrics_diagnostics.py
import requests
import socket
def check_metrics_health():
"""
诊断Prometheus指标暴露问题
"""
print("=" * 50)
print("HolySheep Metrics 健康检查")
print("=" * 50)
checks = []
# 1. 检查端口监听
try:
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
result = sock.connect_ex(('localhost', 8000))
if result == 0:
print("✅ [1/4] 端口8000正在监听")
checks.append(True)
else:
print("❌ [1/4] 端口8000未监听，请检查metrics_server.py是否运行")
checks.append(False)
sock.close()
except Exception as e:
print(f"❌ [1/4] 端口检查失败: {e}")
checks.append(False)
# 2. 检查metrics端点
try:
response = requests.get("http://localhost:8000/metrics", timeout=5)
if response.status_code == 200:
lines = response.text.strip().split('\n')
metric_count = len([l for l in lines if l and not l.startswith('#')])
print(f"✅ [2/4] /metrics端点正常，返回{metric_count}个指标")
checks.append(True)
else:
print(f"❌ [2/4] /metrics返回状态码: {response.status_code}")
checks.append(False)
except Exception as e:
print(f"❌ [2/4] /metrics端点检查失败: {e}")
checks.append(False)
# 3. 检查必需指标
try:
response = requests.get("http://localhost:8000/metrics")
required_metrics = [
'holysheep_requests_total',
'holysheep_errors_bucket',
'holysheep_request_duration_seconds',
'holysheep_tokens_used',
'holysheep_request_cost_cents'
]
missing = []
for metric in required_metrics:
if metric not in response.text:
missing.append(metric)
if not missing:
print(f"✅ [3/4] 所有必需指标都已暴露")
checks.append(True)
else:
print(f"❌ [3/4] 缺少指标: {missing}")
checks.append(False)
except Exception as e:
print(f"❌ [3/4] 指标检查失败: {e}")
checks.append(False)
# 4. 检查Prometheus连通性
try:
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.settimeout(2)
result = sock.connect_ex(('localhost', 9090)) # Prometheus默认端口
sock.close()
if result == 0:
print("✅ [4/4] Prometheus端口9090可达")
else:
print("⚠️ [4/4] Prometheus未在本地运行（可能运行在其他主机）")
checks.append(True) # 不影响主流程
except Exception as e:
print(f"⚠️ [4/4] Prometheus连通性检查: {e}")
checks.append(True)
print("=" * 50)
if all(checks[:3]):
print("🎉 所有检查通过！Prometheus应该能正常抓取指标")
else:
print("⚠️ 部分检查失败，请根据上述信息排查")
print("=" * 50)
return all(checks[:3])
if __name__ == "__main__":
check_metrics_health()
错误3：成本计算不准确
**问题描述**：
实际账单与监控计算的成本差异超过10%。
**根因分析**：
- 未考虑批量处理的token计算差异
- 使用了错误的模型定价
- 重试请求被重复计费
**解决方案**：
```python
accurate_cost_tracker.py
from collections import defaultdict
from datetime import datetime, timedelta
from holy_sheep_monitor import HolySheepMonitor
import threading
class AccurateCostTracker:
"""
精确成本追踪器
解决重试计费、批量折扣等问题
"""
# 官方定价表（2026年5月）
OFFICIAL_PRICING = {
"gpt-4.1": {"input": 8.00, "output": 24.00, "batch_input": 6.40}, # 20%批量折扣
"claude-sonnet-4.5": {"input": 15.00, "output": 75.00, "batch_input": 12.00},
"gemini-2.5-flash": {"input": 2.50, "output": 10.00, "batch_input": 1.25},
"deepseek-v3.2": {"input": 0.42, "output": 2.76, "batch_input": 0.336}, # 20%批量折扣
}
def __init__(self, api_key: str):
self.monitor = HolySheepMonitor(api_key)
self._request_log = []
self._lock = threading.Lock()
def call_with_cost_tracking(self, model: str, messages: list, **kwargs):
"""
调用API并精确追踪成本（包含重试次数）
"""
original_cost = 0
retry_count = 0
success = False
while retry_count < 3 and not success:
result = self.monitor.call_chat_completion(model, messages, **kwargs)
if result.get("success"):
success = True
original_cost = result.get("cost_cents", 0)
else:
retry_count += 1
# 记录到日志（用于审计）
log_entry = {
"timestamp": datetime.now().isoformat(),
"model": model,
"input_tokens": len(str(messages)) // 4, # 粗略估算
"output_tokens": result.get("data", {}).get("usage", {}).get("completion_tokens", 0),
"cost_cents": original_cost * retry_count if retry_count > 0 else original_cost, # 重试全额计费
"retry_count": retry_count,
"success": success
}
with self._lock:
self._request_log.append(log_entry)
return result
def get_accurate_cost_report(self, days: int = 30) -> dict:
"""
生成精确成本报告（与官方账单对比）
"""
cutoff = datetime.now() - timedelta(days=days)
with self._lock:
filtered_logs = [
log for log in self._request_log
if datetime.fromisoformat(log["timestamp"]) > cutoff
]
report = {
"period_days": days,
"total_requests": len(filtered_logs),
"successful_requests": sum(1 for log in filtered_logs if log["success"]),
"failed_requests": sum(1 for log in filtered_logs if not log["success"]),
"total_retries": sum(log["retry_count"] for log in filtered_logs),
"by_model": defaultdict(lambda: {"requests": 0, "cost_cents": 0, "tokens": 0})
}
for log in filtered_logs:
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