Observability

import { createTracer, withTracing } from '@rana/observability';

const tracer = createTracer({
  serviceName: 'my-ai-app',
  exporters: ['console', 'otlp'],
  otlpEndpoint: 'http://localhost:4318'
});

// Automatic tracing
const result = await withTracing(
  'summarize-document',
  async (span) => {
    span.setAttribute('document.length', doc.length);
    return await summarize(doc);
  }
);

// Or use decorators
class MyService {
  @traced('processRequest')
  async process(input: string) {
    return await this.ai.chat(input);
  }
}

import { TokenAnalytics } from '@rana/observability';

const analytics = new TokenAnalytics();

// Automatic tracking
analytics.track({
  model: 'gpt-4',
  inputTokens: 1500,
  outputTokens: 500,
  cost: 0.045,
  latency: 2300,
  metadata: { userId: '123', feature: 'chat' }
});

// Get insights
const daily = await analytics.getDailyUsage();
const byModel = await analytics.getUsageByModel();
const byFeature = await analytics.getUsageByMetadata('feature');

// Cost breakdown
const costs = await analytics.getCostBreakdown({
  period: 'month',
  groupBy: 'model'
});

import { PerformanceMonitor } from '@rana/observability';

const monitor = new PerformanceMonitor({
  sloLatencyP99: 3000,  // 3s P99 target
  sloErrorRate: 0.01,   // 1% error rate target
  alertOnBreach: true
});

// Automatic metrics collection
monitor.recordRequest({
  name: 'chat',
  duration: 1234,
  success: true
});

// Get performance stats
const stats = await monitor.getStats('1h');
console.log(stats.latency.p50);    // 800ms
console.log(stats.latency.p99);    // 2100ms
console.log(stats.throughput);      // 150 req/min
console.log(stats.errorRate);       // 0.005

import { AILogger } from '@rana/observability';

const logger = new AILogger({
  redactPII: true,        // Auto-redact emails, phones, etc.
  redactPatterns: [/api_key=\w+/],  // Custom patterns
  storage: 'elasticsearch',
  retention: '30d'
});

// Automatic logging
const result = await logger.wrap('chat', async () => {
  return await chat(userMessage);
});

// Search logs
const logs = await logger.search({
  query: 'error',
  timeRange: { from: '1h ago' },
  filters: { model: 'gpt-4' }
});

import { ErrorTracker } from '@rana/observability';

const errorTracker = new ErrorTracker({
  sentry: { dsn: process.env.SENTRY_DSN },
  groupSimilarErrors: true,
  captureContext: true
});

// Automatic error capture
try {
  await chat(message);
} catch (error) {
  errorTracker.capture(error, {
    model: 'gpt-4',
    prompt: message,
    userId: user.id
  });
}

// Get error insights
const insights = await errorTracker.getInsights('24h');
console.log(insights.topErrors);
console.log(insights.errorsByModel);
console.log(insights.errorTrend);

import { setupOTel } from '@rana/observability';

setupOTel({
  serviceName: 'my-ai-app',
  exporters: {
    traces: {
      type: 'otlp',
      endpoint: 'http://jaeger:4318'
    },
    metrics: {
      type: 'prometheus',
      port: 9090
    },
    logs: {
      type: 'otlp',
      endpoint: 'http://loki:3100'
    }
  }
});

// All RANA operations are now traced
const result = await chat('Hello');
// Automatically creates spans, records metrics, logs