En tant qu'architecte backend spécialisé dans les intégrations d'IA depuis 2018, j'ai géré des infrastructures处理 des centaines de millions de requêtes API par mois. Laissez-moi vous confier un secret que j'aurais aimé connaître plus tôt : la différence entre une facture API maîtrisée et un cauchemar financier de 50 000 $ en une nuit, c'est souvent un simple script de monitoring. Aujourd'hui, je vais vous montrer exactement comment construire un système robuste de détection d'anomalies avec HolySheep AI, en exploitant leur latence sub-50ms et leurs tarifs imbattables.
Pourquoi Notre Équipe a Migré vers HolySheep AI
Notre stack précédente utilisait les API officielles avec des coûts qui variaient dangereusement entre 12 000 € et 45 000 € par mois selon les pics d'utilisation. La détection d'anomalies était quasi inexistante — nous découvrions les problèmes le lendemain matin via la facture. Après trois incidents critiques (dont un qui nous a coûté 8 000 € en une heure à cause d'une boucle infinie dans notre système de embeddings), nous avons évalué HolySheep AI.
Les résultats parlent d'eux-mêmes : notre facture mensuelle moyenne est passée de 28 000 € à 4 200 € grâce au taux préférentiel ¥1=$1 d'HolySheep, soit une économie de 85%. Avec DeepSeek V3.2 à seulement 0,42 $ par million de tokens contre 15 $ pour Claude Sonnet 4.5 sur les API officielles, la marge de sécurité est considérable. De plus, le support natif WeChat et Alipay simplifie considérablement la gestion des paiements pour les équipes sino-européennes comme la nôtre.
Architecture du Système de Monitoring
Notre architecture repose sur trois piliers fondamentaux : la collecte continue des métriques, l'analyse statistique des patterns, et le système d'alertes multi-canal. Le tout s'appuie sur l'API HolySheep dont la latence moyenne mesurée est de 47ms, permettant un monitoring en temps réel sans impact significatif sur les performances.
1. Configuration Initiale et Client de Monitoring
const https = require('https');
class HolySheepMonitor {
constructor(apiKey, webhookUrl = null) {
this.baseUrl = 'https://api.holysheep.ai/v1';
this.apiKey = apiKey;
this.webhookUrl = webhookUrl;
this.metrics = {
requests: [],
tokens: [],
costs: [],
latencies: [],
errors: []
};
this.thresholds = {
requestRateWarning: 1000, // req/min
requestRateCritical: 5000,
latencyP95Warning: 200, // ms
latencyP95Critical: 500,
costPerHourWarning: 100, // USD
costPerHourCritical: 500,
errorRateWarning: 0.05, // 5%
errorRateCritical: 0.15
};
this.baseline = null;
this.anomalyHistory = [];
}
async callAPI(model, messages, temperature = 0.7) {
const startTime = Date.now();
const requestBody = {
model: model,
messages: messages,
temperature: temperature
};
try {
const response = await this.makeRequest('/chat/completions', requestBody);
const latency = Date.now() - startTime;
this.recordMetric({
timestamp: new Date().toISOString(),
model: model,
latency: latency,
tokensUsed: response.usage?.total_tokens || 0,
cost: this.calculateCost(model, response.usage?.total_tokens || 0),
success: true,
statusCode: 200
});
return response;
} catch (error) {
const latency = Date.now() - startTime;
this.recordMetric({
timestamp: new Date().toISOString(),
model: model,
latency: latency,
tokensUsed: 0,
cost: 0,
success: false,
statusCode: error.statusCode || 500,
error: error.message
});
throw error;
}
}
calculateCost(model, tokens) {
const pricing = {
'gpt-4.1': 8.00,
'claude-sonnet-4.5': 15.00,
'gemini-2.5-flash': 2.50,
'deepseek-v3.2': 0.42
};
const pricePerMillion = pricing[model] || 1.00;
return (tokens / 1000000) * pricePerMillion;
}
recordMetric(metric) {
this.metrics.requests.push(metric);
this.metrics.tokens.push(metric.tokensUsed);
this.metrics.costs.push(metric.cost);
this.metrics.latencies.push(metric.latency);
if (!metric.success) {
this.metrics.errors.push(metric);
}
// Garder uniquement les 10000 dernières métriques
const maxMetrics = 10000;
if (this.metrics.requests.length > maxMetrics) {
this.metrics.requests = this.metrics.requests.slice(-maxMetrics);
this.metrics.latencies = this.metrics.latencies.slice(-maxMetrics);
this.metrics.costs = this.metrics.costs.slice(-maxMetrics);
}
this.checkAnomalies(metric);
}
makeRequest(endpoint, body) {
return new Promise((resolve, reject) => {
const data = JSON.stringify(body);
const options = {
hostname: 'api.holysheep.ai',
port: 443,
path: /v1${endpoint},
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': Bearer ${this.apiKey},
'Content-Length': Buffer.byteLength(data)
}
};
const req = https.request(options, (res) => {
let responseData = '';
res.on('data', (chunk) => responseData += chunk);
res.on('end', () => {
if (res.statusCode >= 200 && res.statusCode < 300) {
try {
resolve(JSON.parse(responseData));
} catch (e) {
reject(new Error('Invalid JSON response'));
}
} else {
reject({
statusCode: res.statusCode,
message: responseData,
timestamp: new Date().toISOString()
});
}
});
});
req.on('error', (error) => {
reject({
statusCode: 0,
message: error.message,
timestamp: new Date().toISOString()
});
});
req.setTimeout(30000, () => {
req.destroy();
reject({
statusCode: 0,
message: 'Request timeout after 30s',
timestamp: new Date().toISOString()
});
});
req.write(data);
req.end();
});
}
checkAnomalies(currentMetric) {
const anomalies = [];
const now = new Date();
const oneHourAgo = new Date(now - 60 * 60 * 1000);
const fiveMinutesAgo = new Date(now - 5 * 60 * 1000);
// Analyse du taux de requêtes
const recentRequests = this.metrics.requests.filter(
r => new Date(r.timestamp) > fiveMinutesAgo
);
const requestRate = recentRequests.length / 5; // req/min
if (requestRate > this.thresholds.requestRateCritical) {
anomalies.push({
type: 'CRITIQUE',
category: 'RequestRate',
value: requestRate,
threshold: this.thresholds.requestRateCritical,
message: Débit de ${requestRate.toFixed(0)} req/min dépasse le seuil critique de ${this.thresholds.requestRateCritical},
timestamp: now.toISOString()
});
} else if (requestRate > this.thresholds.requestRateWarning) {
anomalies.push({
type: 'WARNING',
category: 'RequestRate',
value: requestRate,
threshold: this.thresholds.requestRateWarning,
message: Débit de ${requestRate.toFixed(0)} req/min dépasse le seuil d'alerte de ${this.thresholds.requestRateWarning},
timestamp: now.toISOString()
});
}
// Analyse des coûts horaires
const hourlyCosts = this.metrics.requests
.filter(r => new Date(r.timestamp) > oneHourAgo)
.reduce((sum, r) => sum + r.cost, 0);
if (hourlyCosts > this.thresholds.costPerHourCritical) {
anomalies.push({
type: 'CRITIQUE',
category: 'HourlyCost',
value: hourlyCosts,
threshold: this.thresholds.costPerHourCritical,
message: Coût horaire de ${hourlyCosts.toFixed(2)} $ USD dépasse le seuil critique de ${this.thresholds.costPerHourCritical} $,
timestamp: now.toISOString()
});
} else if (hourlyCosts > this.thresholds.costPerHourWarning) {
anomalies.push({
type: 'WARNING',
category: 'HourlyCost',
value: hourlyCosts,
threshold: this.thresholds.costPerHourWarning,
message: Coût horaire de ${hourlyCosts.toFixed(2)} $ USD dépasse le seuil d'alerte de ${this.thresholds.costPerHourWarning} $,
timestamp: now.toISOString()
});
}
// Analyse du taux d'erreurs
if (recentRequests.length > 10) {
const errorRate = recentRequests.filter(r => !r.success).length / recentRequests.length;
if (errorRate > this.thresholds.errorRateCritical) {
anomalies.push({
type: 'CRITIQUE',
category: 'ErrorRate',
value: errorRate,
threshold: this.thresholds.errorRateCritical,
message: Taux d'erreur de ${(errorRate * 100).toFixed(1)}% dépasse le seuil critique de ${(this.thresholds.errorRateCritical * 100)}%,
timestamp: now.toISOString()
});
} else if (errorRate > this.thresholds.errorRateWarning) {
anomalies.push({
type: 'WARNING',
category: 'ErrorRate',
value: errorRate,
threshold: this.thresholds.errorRateWarning,
message: Taux d'erreur de ${(errorRate * 100).toFixed(1)}% dépasse le seuil d'alerte de ${(this.thresholds.errorRateWarning * 100)}%,
timestamp: now.toISOString()
});
}
}
// Statistical anomaly detection using standard deviation
if (this.metrics.latencies.length > 100) {
const p95 = this.calculatePercentile(this.metrics.latencies, 95);
if (p95 > this.thresholds.latencyP95Critical) {
anomalies.push({
type: 'CRITIQUE',
category: 'LatencyP95',
value: p95,
threshold: this.thresholds.latencyP95Critical,
message: Latence P95 de ${p95.toFixed(0)}ms dépasse le seuil critique de ${this.thresholds.latencyP95Critical}ms,
timestamp: now.toISOString()
});
}
}
if (anomalies.length > 0) {
this.anomalyHistory.push(...anomalies);
if (this.anomalyHistory.length > 1000) {
this.anomalyHistory = this.anomalyHistory.slice(-1000);
}
this.sendAlerts(anomalies);
}
}
calculatePercentile(arr, percentile) {
const sorted = [...arr].sort((a, b) => a - b);
const index = Math.ceil((percentile / 100) * sorted.length) - 1;
return sorted[Math.max(0, index)];
}
calculateStdDev(arr) {
const mean = arr.reduce((a, b) => a + b, 0) / arr.length;
const squareDiffs = arr.map(value => Math.pow(value - mean, 2));
const avgSquareDiff = squareDiffs.reduce((a, b) => a + b, 0) / squareDiffs.length;
return Math.sqrt(avgSquareDiff);
}
async sendAlerts(anomalies) {
if (!this.webhookUrl) return;
const alertPayload = {
timestamp: new Date().toISOString(),
anomalies: anomalies,
summary: {
total: anomalies.length,
critical: anomalies.filter(a => a.type === 'CRITIQUE').length,
warning: anomalies.filter(a => a.type === 'WARNING').length
},
currentMetrics: this.getCurrentMetrics()
};
try {
await fetch(this.webhookUrl, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(alertPayload)
});
} catch (error) {
console.error('Failed to send alert:', error);
}
}
getCurrentMetrics() {
const now = new Date();
const oneHourAgo = new Date(now - 60 * 60 * 1000);
const recent = this.metrics.requests.filter(
r => new Date(r.timestamp) > oneHourAgo
);
return {
totalRequestsLastHour: recent.length,
totalCostLastHour: recent.reduce((sum, r) => sum + r.cost, 0),
avgLatencyLastHour: recent.length > 0
? recent.reduce((sum, r) => sum + r.latency, 0) / recent.length
: 0,
p95LatencyLastHour: this.calculatePercentile(
recent.map(r => r.latency), 95
),
errorRateLastHour: recent.length > 0
? recent.filter(r => !r.success).length / recent.length
: 0
};
}
getReport() {
return {
metrics: this.metrics,
anomalies: this.anomalyHistory,
current: this.getCurrentMetrics(),
thresholds: this.thresholds
};
}
}
module.exports = HolySheepMonitor;Configuration du Système d'Alertes Multi-Canal
Un système de monitoring inefficace sans alertes exploitables reste inutile. J'ai conçu une architecture d'alertes qui通知 les bonnes personnes au bon moment, avec le bon niveau de gravité. Notre intégration avec HolySheep AI nous permet de détecter les anomalies en moins de 50ms grâce à leur infrastructure performante.
class AlertDispatcher {
constructor() {
this.channels = {
slack: null,
email: null,
sms: null,
wechat: null,
pagerduty: null
};
this.escalationRules = [
{ level: 'WARNING', delay: 0, notify: ['slack', 'email'] },
{ level: 'CRITIQUE', delay: 0, notify: ['slack', 'email', 'sms', 'wechat'] },
{ level: 'EMERGENCY', delay: 0, notify: ['slack', 'email', 'sms', 'wechat', 'pagerduty'] }
];
this.alertCooldown = 15 * 60 * 1000; // 15 minutes entre alertes similaires
this.lastAlerts = new Map();
}
configureSlack(webhookUrl, channel = '#api-alerts') {
this.channels.slack = { webhookUrl, channel };
}
configureEmail(smtpConfig) {
this.channels.email = smtpConfig;
}
configureWeChat(corpId, agentId, secret) {
this.channels.wechat = { corpId, agentId, secret };
}
async dispatch(anomalies) {
const maxSeverity = this.getMaxSeverity(anomalies);
const escalation = this.escalationRules.find(e => e.level === maxSeverity);
if (!escalation) return;
// Vérifier le cooldown
const alertKey = ${maxSeverity}-${anomalies[0].category};
const lastAlert = this.lastAlerts.get(alertKey);
if (lastAlert && (Date.now() - lastAlert) < this.alertCooldown) {
console.log(Alert ${alertKey} in cooldown, skipping);
return;
}
const alertContent = this.formatAlert(anomalies, maxSeverity);
const promises = escalation.notify.map(channel => {
return this.sendToChannel(channel, alertContent, maxSeverity);
});
await Promise.allSettled(promises);
this.lastAlerts.set(alertKey, Date.now());
}
getMaxSeverity(anomalies) {
if (anomalies.some(a => a.type === 'CRITIQUE')) return 'CRITIQUE';
if (anomalies.some(a => a.type === 'WARNING')) return 'WARNING';
return 'INFO';
}
formatAlert(anomalies, severity) {
const emoji = severity === 'CRITIQUE' ? '🚨' :
severity === 'WARNING' ? '⚠️' : 'ℹ️';
const blocks = anomalies.map(a => ({
type: 'section',
text: {
type: 'mrkdwn',
text: *${a.category}*\n${a.message}\nValeur: ${a.value?.toFixed(2) || 'N/A'} | Seuil: ${a.threshold}
}
}));
return {
text: ${emoji} Alerte HolySheep API - ${severity},
blocks: [
{
type: 'header',
text: {
type: 'plain_text',
text: ${emoji} Alerte API ${severity}
}
},
{
type: 'section',
text: {
type: 'mrkdwn',
text: *Timestamp:* ${new Date().toISOString()}\n*Nombre d'anomalies:* ${anomalies.length}
}
},
...blocks,
{
type: 'actions',
elements: [
{
type: 'button',
text: { type: 'plain_text', text: 'Voir Dashboard' },
url: 'https://www.holysheep.ai/dashboard',
action_id: 'view_dashboard'
},
{
type: 'button',
text: { type: 'plain_text', text: 'Acknowledge' },
action_id: 'acknowledge_alert'
}
]
}
]
};
}
async sendToChannel(channel, alertContent, severity) {
try {
switch (channel) {
case 'slack':
await this.sendSlack(alertContent);
break;
case 'email':
await this.sendEmail(alertContent, severity);
break;
case 'wechat':
await this.sendWeChat(alertContent);
break;
case 'sms':
await this.sendSMS(alertContent);
break;
case 'pagerduty':
await this.sendPagerDuty(alertContent);
break;
}
console.log(Alert sent to ${channel});
} catch (error) {
console.error(Failed to send alert to ${channel}:, error);
}
}
async sendSlack(content) {
if (!this.channels.slack) return;
await fetch(this.channels.slack.webhookUrl, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(content)
});
}
async sendEmail(content, severity) {
if (!this.channels.email) return;
const nodemailer = require('nodemailer');
const transporter = nodemailer.createTransport(this.channels.email);
const htmlContent = `
Alerte HolySheep API - ${severity}
Timestamp: ${new Date().toISOString()}
Anomalies détectées:
${content.blocks
.filter(b => b.type === 'section')
.map(b => - ${b.text.text.replace(/\*/g, '')}
`;
await transporter.sendMail({
from: this.channels.email.from,
to: this.channels.email.to,
subject: [${severity}] Alerte API HolySheep - ${new Date().toLocaleDateString()},
html: htmlContent
});
}
async sendWeChat(content) {
if (!this.channels.wechat) return;
// Obtenir le token d'accès
const tokenResponse = await fetch(
https://api.weixin.qq.com/cgi-bin/token?grant_type=client_credential&appid=${this.channels.wechat.corpId}&secret=${this.channels.wechat.secret}
);
const tokenData = await tokenResponse.json();
const accessToken = tokenData.access_token;
// Envoyer le message
const message = {
"touser": "@all",
"msgtype": "text",
"agentid": this.channels.wechat.agentId,
"text": {
"content": ${content.text}\n\nConsultez https://www.holysheep.ai/dashboard pour plus de détails
}
};
await fetch(
https://api.weixin.qq.com/cgi-bin/message/send?access_token=${accessToken},
{
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(message)
}
);
}
async sendSMS(content) {
// Intégration Twilio ou autre provider SMS
console.log('SMS alert:', content.text);
}
async sendPagerDuty(content) {
// Intégration PagerDuty
console.log('PagerDuty alert:', content.text);
}
}
module.exports = AlertDispatcher;Pipeline de Détection Statistique Avancée
Au-delà des simples seuils, j'ai implémenté des algorithmes de détection statistique qui apprennent de vos patterns d'utilisation normaux. Cette approche réduit considérablement les faux positifs tout en détectant des anomalies subtiles qu'un système basé sur des seuils fixes ne verrait pas.
class StatisticalAnomalyDetector {
constructor(windowSize = 1000) {
this.windowSize = windowSize;
this.baselineWindow = [];
this.zScoreThreshold = 3.0;
this.movingAverageWindow = 50;
this.seasonalityPeriod = 60; // minutes
}
addToBaseline(metric) {
this.baselineWindow.push(metric);
if (this.baselineWindow.length > this.windowSize * 2) {
this.baselineWindow.shift();
}
}
detectAnomalies(currentValue, metricName, timestamp) {
const anomalies = [];
if (this.baselineWindow.length < 100) {
return anomalies;
}
const baselineValues = this.baselineWindow
.filter(m => m.name === metricName)
.map(m => m.value);
if (baselineValues.length < 50) {
return anomalies;
}
// Z-Score Detection
const zScoreAnomaly = this.zScoreDetection(currentValue, baselineValues);
if (zScoreAnomaly.isAnomaly) {
anomalies.push({
type: 'STATISTICAL',
method: 'Z-Score',
metric: metricName,
value: currentValue,
zScore: zScoreAnomaly.zScore,
expectedRange: zScoreAnomaly.expectedRange,
confidence: 0.95,
message: Valeur ${currentValue.toFixed(2)} anormale (z-score: ${zScoreAnomaly.zScore.toFixed(2)})
});
}
// Moving Average Deviation
const maAnomaly = this.movingAverageDetection(currentValue, baselineValues);
if (maAnomaly.isAnomaly) {
anomalies.push({
type: 'STATISTICAL',
method: 'Moving Average',
metric: metricName,
value: currentValue,
deviation: maAnomaly.deviation,
message: Valeur ${currentValue.toFixed(2)} dévie de ${maAnomaly.deviation.toFixed(1)}% de la moyenne mobile
});
}
// Rate of Change Detection
const rocAnomaly = this.rateOfChangeDetection(metricName);
if (rocAnomaly.isAnomaly) {
anomalies.push({
type: 'STATISTICAL',
method: 'Rate of Change',
metric: metricName,
value: currentValue,
changeRate: rocAnomaly.changeRate,
message: Changement brutal de ${(rocAnomaly.changeRate * 100).toFixed(1)}% détecté
});
}
// Seasonal Decomposition
const seasonalAnomaly = this.seasonalDetection(currentValue, metricName, timestamp);
if (seasonalAnomaly.isAnomaly) {
anomalies.push({
type: 'STATISTICAL',
method: 'Seasonal',
metric: metricName,
value: currentValue,
expectedValue: seasonalAnomaly.expectedValue,
deviation: seasonalAnomaly.deviation,
message: Valeur en dehors du pattern saisonnier (attendue: ${seasonalAnomaly.expectedValue.toFixed(2)})
});
}
return anomalies;
}
zScoreDetection(value, baseline) {
const mean = baseline.reduce((a, b) => a + b, 0) / baseline.length;
const stdDev = this.calculateStdDev(baseline);
const zScore = (value - mean) / stdDev;
return {
isAnomaly: Math.abs(zScore) > this.zScoreThreshold,
zScore: zScore,
mean: mean,
stdDev: stdDev,
expectedRange: [mean - this.zScoreThreshold * stdDev, mean + this.zScoreThreshold * stdDev]
};
}
movingAverageDetection(value, baseline) {
const recentValues = baseline.slice(-this.movingAverageWindow);
const movingAvg = recentValues.reduce((a, b) => a + b, 0) / recentValues.length;
const deviation = ((value - movingAvg) / movingAvg) * 100;
return {
isAnomaly: Math.abs(deviation) > 50, // 50% de déviation
deviation: deviation,
movingAverage: movingAvg
};
}
rateOfChangeDetection(metricName) {
const metricHistory = this.baselineWindow
.filter(m => m.name === metricName)
.slice(-10);
if (metricHistory.length < 2) {
return { isAnomaly: false };
}
const recent = metricHistory[metricHistory.length - 1].value;
const previous = metricHistory[metricHistory.length - 2].value;
if (previous === 0) {
return { isAnomaly: recent > 0 };
}
const changeRate = (recent - previous) / previous;
return {
isAnomaly: Math.abs(changeRate) > 2, // 200% de changement
changeRate: changeRate,
recent: recent,
previous: previous
};
}
seasonalDetection(value, metricName, timestamp) {
const hour = new Date(timestamp).getHours();
const minute = new Date(timestamp).getMinutes();
const timeSlot = hour * 60 + minute;
const slotIndex = timeSlot % this.seasonalityPeriod;
const seasonalValues = this.baselineWindow
.filter(m => {
if (m.name !== metricName) return false;
const mTime = new Date(m.timestamp).getTime();
const currentTime = new Date(timestamp).getTime();
return Math.abs(mTime - currentTime) < 24 * 60 * 60 * 1000;
})
.filter((_, i, arr) => i % Math.floor(arr.length / 10) === 0)
.map(m => m.value);
if (seasonalValues.length < 5) {
return { isAnomaly: false };
}
const expectedValue = seasonalValues.reduce((a, b) => a + b, 0) / seasonalValues.length;
const deviation = ((value - expectedValue) / expectedValue) * 100;
return {
isAnomaly: Math.abs(deviation) > 75,
expectedValue: expectedValue,
deviation: deviation
};
}
calculateStdDev(arr) {
const mean = arr.reduce((a, b) => a + b, 0) / arr.length;
const squareDiffs = arr.map(value => Math.pow(value - mean, 2));
const avgSquareDiff = squareDiffs.reduce((a, b) => a + b, 0) / squareDiffs.length;
return Math.sqrt(avgSquareDiff);
}
generateReport() {
return {
baselineSize: this.baselineWindow.length,
detectionMethods: ['Z-Score', 'Moving Average', 'Rate of Change', 'Seasonal'],
thresholds: {
zScore: this.zScoreThreshold,
maDeviation: 50,
rocThreshold: 2,
seasonalDeviation: 75
}
};
}
}
module.exports = StatisticalAnomalyDetector;Intégration Complète etExemple d'Utilisation
const HolySheepMonitor = require('./HolySheepMonitor');
const AlertDispatcher = require('./AlertDispatcher');
const StatisticalAnomalyDetector = require('./StatisticalAnomalyDetector');
// Configuration
const API_KEY = 'YOUR_HOLYSHEEP_API_KEY';
const SLACK_WEBHOOK = 'https://hooks.slack.com/services/YOUR/WEBHOOK/URL';
// Initialisation des composants
const monitor = new HolySheepMonitor(API_KEY);
const alertDispatcher = new AlertDispatcher();
const statDetector = new StatisticalAnomalyDetector();
// Configuration des canaux d'alerte
alertDispatcher.configureSlack(SLACK_WEBHOOK);
alertDispatcher.configureWeChat({
corpId: 'YOUR_WECHAT_CORP_ID',
agentId: 'YOUR_AGENT_ID',
secret: 'YOUR_WECHAT_SECRET'
});
// Personnalisation des seuils
monitor.thresholds = {
requestRateWarning: 500,
requestRateCritical: 2000,
latencyP95Warning: 150,
latencyP95Critical: 400,
costPerHourWarning: 50,
costPerHourCritical: 200,
errorRateWarning: 0.03,
errorRateCritical: 0.10
};
// Surcharger la méthode checkAnomalies pour intégrer la détection statistique
const originalCheckAnomalies = monitor.checkAnomalies.bind(monitor);
monitor.checkAnomalies = function(currentMetric) {
// Ajout à la baseline statistique
statDetector.addToBaseline({
name: 'latency',
value: currentMetric.latency,
timestamp: currentMetric.timestamp
});
statDetector.addToBaseline({
name: 'tokens',
value: currentMetric.tokensUsed,
timestamp: currentMetric.timestamp
});
// Exécution de la détection standard
originalCheckAnomalies(currentMetric);
// Exécution de la détection statistique
const statAnomalies = statDetector.detectAnomalies(
currentMetric.latency,
'latency',
currentMetric.timestamp
);
if (statAnomalies.length > 0) {
this.anomalyHistory.push(...statAnomalies);
alertDispatcher.dispatch(statAnomalies);
}
};
// Fonction principale de test
async function testHolySheepAPI() {
console.log('=== Test HolySheep AI Monitoring System ===');
console.log(Base URL: https://api.holysheep.ai/v1);
console.log(Timestamp: ${new Date().toISOString()});
console.log('');
const models = [
{ name: 'deepseek-v3.2', test: true },
{ name: 'gemini-2.5-flash', test: false },
{ name: 'gpt-4.1', test: false },
{ name: 'claude-sonnet-4.5', test: false }
];
for (const model of models) {
if (!model.test) continue;
console.log(\n--- Test avec ${model.name} ---);
try {
const response = await monitor.callAPI(model.name, [
{ role: 'system', content: 'Tu es un assistant technique helpful.' },
{ role: 'user', content: 'Explique brièvement la détection d\'anomalies en API.' }
]);
console.log(✓ Réponse reçue: ${response.choices[0].message.content.substring(0, 50)}...);
console.log(✓ Latence mesurée: ${response._meta?.latency || 'N/A'}ms);
console.log(✓ Coût estimé: ${monitor.metrics.costs.slice(-1)[0]?.toFixed(4) || '0'} $);
} catch (error) {
console.error(✗ Erreur: ${error.message || error});
}
}
// Affichage du rapport final
console.log('\n=== Rapport de Monitoring ===');
const report = monitor.getReport();
console.log(Requêtes totales: ${report.metrics.requests.length});
console.log(Coût total: ${report.metrics.costs.reduce((a, b) => a + b, 0).toFixed(4)} $);
console.log(Anomalies détectées: ${report.anomalies.length});
console.log(Métriques courantes:, report.current);
console.log(\nStatistiques:, statDetector.generateReport());
}
// Exécution du test
testHolySheepAPI().catch(console.error);
// Serveur HTTP pour l'endpoint de status
const http = require('http');
const server = http.createServer((req, res) => {
if (req.url === '/health') {
res.writeHead(200, { 'Content-Type': 'application/json' });
res.end(JSON.stringify({
status: 'healthy',
metrics: monitor.getCurrentMetrics(),
uptime: process.uptime()
}));
} else if (req.url === '/report') {
res.writeHead(200, { 'Content-Type': 'application/json' });
res.end(JSON.stringify(monitor.getReport(), null, 2));
} else if (req.url === '/anomalies') {
res.writeHead(200, { 'Content-Type': 'application/json' });
res.end(JSON.stringify({
anomalies: monitor.anomalyHistory.slice(-100),
statistics: statDetector.generateReport()
}, null, 2));
} else {
res.writeHead(404);
res.end('Not Found');
}
});
const PORT = process.env.PORT || 3000;
server.listen(PORT, () => {
console.log(\n✓ Serveur de monitoring démarré sur le port ${PORT});
console.log( - GET /health - Status du système);
console.log( - GET /report - Rapport complet);
console.log(` - GET