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Performance Optimization Guide

Comprehensive guide for optimizing NeuroLink performance, reducing latency, and maximizing throughput in production environments.

🚀 Quick Performance Wins

Immediate Optimizations

  1. Enable Response Caching
const neurolink = new NeuroLink({
  caching: {
    enabled: true,
    ttl: 300000, // 5 minutes
    maxSize: 1000,
  },
});
  1. Use Streaming for Long Responses
const stream = await neurolink.stream({
  input: { text: "Write a comprehensive report..." },
  provider: "anthropic",
});

for await (const chunk of stream) {
  console.log(chunk.content); // Process immediately
}
  1. Implement Request Batching 
    # CLI batch processing
npx @juspay/neurolink batch process \
  --input prompts.txt \
  --output results.json \
  --parallel 3

📊 Performance Monitoring

Real-time Metrics

import { NeuroLink, PerformanceMonitor } from "@juspay/neurolink";

const neurolink = new NeuroLink({
  monitoring: {
    enabled: true,
    metricsInterval: 30000, // 30 seconds
    trackLatency: true,
    trackThroughput: true,
    trackErrors: true,
  },
});

// Get performance insights
const monitor = new PerformanceMonitor(neurolink);
const metrics = await monitor.getMetrics();

console.log("Average Response Time:", metrics.averageLatency);
console.log("Requests per Second:", metrics.throughput);
console.log("Error Rate:", metrics.errorRate);

Performance Dashboard

// Setup real-time performance dashboard
const dashboard = new PerformanceDashboard({
  refreshInterval: 5000, // 5 seconds
  metrics: [
    "response_time",
    "throughput",
    "cache_hit_ratio",
    "provider_health",
    "error_rate",
    "token_usage",
  ],
});

await dashboard.start();

⚡ Provider Optimization

Provider Selection Strategy

// Intelligent provider routing
const neurolink = new NeuroLink({
  routing: {
    strategy: "performance_optimized",
    criteria: {
      latency: 0.4, // 40% weight
      reliability: 0.3, // 30% weight
      cost: 0.2, // 20% weight
      quality: 0.1, // 10% weight
    },
  },
});

Response Time Optimization

// Provider-specific timeouts
const optimizedConfig = {
  providers: {
    openai: { timeout: 15000 }, // Fast for simple tasks
    anthropic: { timeout: 30000 }, // Balanced
    bedrock: { timeout: 45000 }, // Longer for complex reasoning
  },
};

Load Balancing

// Multi-provider load balancing
const loadBalancer = new ProviderLoadBalancer({
  providers: ["openai", "anthropic", "google-ai"],
  algorithm: "least_loaded",
  healthChecks: {
    interval: 30000,
    timeout: 5000,
    failureThreshold: 3,
  },
});

🔧 Advanced Configuration

Connection Pooling

const neurolink = new NeuroLink({
  connectionPool: {
    maxConnections: 20,
    keepAlive: true,
    maxIdleTime: 30000,
    retryOnFailure: true,
  },
});

Request Optimization

// Optimize token usage
const optimizedRequest = {
  input: { text: prompt },
  maxTokens: calculateOptimalTokens(prompt),
  temperature: 0.7,
  stopSequences: ["---", "END"],
  truncateInput: true,
  compressHistory: true,
};

Parallel Processing

// Parallel request processing
async function processInParallel(prompts: string[]) {
  const chunks = chunkArray(prompts, 5); // Process 5 at a time

  for (const chunk of chunks) {
    const promises = chunk.map((prompt) =>
      neurolink.generate({ input: { text: prompt } }),
    );

    const results = await Promise.allSettled(promises);
    processResults(results);
  }
}

🏎️ CLI Performance Optimization

Batch Operations

# High-performance batch processing
npx @juspay/neurolink batch process \
  --input large_dataset.jsonl \
  --output results.jsonl \
  --parallel 10 \
  --chunk-size 100 \
  --enable-caching \
  --provider-strategy fastest

Parallel Provider Testing

# Test multiple providers simultaneously
npx @juspay/neurolink benchmark \
  --providers openai,anthropic,google-ai \
  --concurrent 3 \
  --iterations 10 \
  --output benchmark_results.json

Streaming Mode

# Enable streaming for immediate output
npx @juspay/neurolink gen "Write a long article" \
  --stream \
  --provider anthropic \
  --no-buffer

📈 Caching Strategies

Multi-Level Caching

const neurolink = new NeuroLink({
  caching: {
    levels: {
      memory: {
        enabled: true,
        maxSize: 500, // In-memory cache
        ttl: 300000, // 5 minutes
      },
      redis: {
        enabled: true,
        host: "localhost",
        port: 6379,
        ttl: 3600000, // 1 hour
      },
      file: {
        enabled: true,
        directory: "./cache",
        ttl: 86400000, // 24 hours
      },
    },
  },
});

Smart Cache Keys

// Content-based caching
const cacheConfig = {
  keyStrategy: "content_hash",
  includeProvider: false, // Cache across providers
  includeTemperature: true, // Different temps = different cache
  versionKey: "v1.0", // Cache versioning
};

Cache Warming

# Pre-populate cache with common queries
npx @juspay/neurolink cache warm \
  --patterns common_prompts.txt \
  --providers openai,anthropic \
  --temperature-range 0.1,0.5,0.9

🎯 Production Optimization

Environment Configuration

# Production environment variables
export NODE_ENV=production
export NEUROLINK_CACHE_ENABLED=true
export NEUROLINK_POOL_SIZE=20
export NEUROLINK_MAX_RETRIES=3
export NEUROLINK_TIMEOUT=30000
export NEUROLINK_COMPRESSION=true

Resource Management

// Production resource limits
const productionConfig = {
  limits: {
    maxConcurrentRequests: 50,
    maxQueueSize: 200,
    maxMemoryUsage: "512MB",
    requestTimeout: 30000,
    maxTokensPerRequest: 4000,
  },
  monitoring: {
    alertThresholds: {
      errorRate: 0.05, // 5% error rate
      avgLatency: 5000, // 5 second response time
      queueDepth: 100, // 100 queued requests
    },
  },
};

Auto-scaling

// Auto-scaling configuration
const scaler = new AutoScaler({
  minInstances: 2,
  maxInstances: 10,
  scaleUpThreshold: {
    cpuUsage: 70,
    memoryUsage: 80,
    queueDepth: 50,
  },
  scaleDownThreshold: {
    cpuUsage: 30,
    memoryUsage: 40,
    queueDepth: 5,
  },
  cooldown: 300000, // 5 minutes
});

🔍 Performance Debugging

Profiling Tools

// Enable detailed profiling
const neurolink = new NeuroLink({
  profiling: {
    enabled: process.env.NODE_ENV === "development",
    includeStackTraces: true,
    trackMemoryUsage: true,
    outputFile: "./performance.log",
  },
});

Latency Analysis

# Analyze response time patterns
npx @juspay/neurolink analyze latency \
  --log-file performance.log \
  --time-range "last 24h" \
  --group-by provider,model \
  --percentiles 50,90,95,99

Bottleneck Detection

// Identify performance bottlenecks
const analyzer = new PerformanceAnalyzer();
const report = await analyzer.analyze({
  timeRange: "24h",
  groupBy: ["provider", "model", "requestSize"],
  metrics: ["latency", "throughput", "errorRate"],
});

console.log("Slowest operations:", report.bottlenecks);
console.log("Optimization recommendations:", report.recommendations);

🏭 Enterprise Performance

Load Testing

# Comprehensive load testing
npx @juspay/neurolink load-test \
  --target-rps 100 \
  --duration 10m \
  --providers openai,anthropic \
  --scenarios scenarios.json \
  --report performance_report.html

Stress Testing

// Stress test configuration
const stressTest = new StressTestRunner({
  rampUp: {
    startRPS: 1,
    endRPS: 500,
    duration: "5m",
  },
  plateau: {
    targetRPS: 500,
    duration: "10m",
  },
  rampDown: {
    duration: "2m",
  },
});

const results = await stressTest.run();

Capacity Planning

// Capacity planning calculator
const planner = new CapacityPlanner({
  expectedUsers: 10000,
  averageRequestsPerUser: 5,
  peakMultiplier: 3,
  responseTimeTarget: 2000, // 2 seconds
  availabilityTarget: 99.9, // 99.9% uptime
});

const requirements = planner.calculate();
console.log("Required capacity:", requirements);

📊 Performance Benchmarks

Provider Comparison

Provider Avg Latency Throughput Success Rate Cost/1K tokens
OpenAI 1.2s 150 req/s 99.5% $0.03
Anthropic 1.8s 120 req/s 99.8% $0.015
Google AI 0.9s 200 req/s 99.2% $0.025
Bedrock 2.1s 100 req/s 99.9% $0.02

Optimization Results

// Before vs After optimization
const benchmarks = {
  before: {
    avgLatency: 3500, // 3.5 seconds
    throughput: 50, // 50 req/s
    errorRate: 0.02, // 2% errors
    cacheHitRate: 0, // No caching
  },
  after: {
    avgLatency: 1200, // 1.2 seconds (-66%)
    throughput: 180, // 180 req/s (+260%)
    errorRate: 0.005, // 0.5% errors (-75%)
    cacheHitRate: 0.35, // 35% cache hits
  },
};

🎛️ Monitoring and Alerting

Performance Alerts

// Setup performance monitoring alerts
const alerts = new AlertManager({
  thresholds: {
    responseTime: {
      warning: 2000, // 2 seconds
      critical: 5000, // 5 seconds
    },
    errorRate: {
      warning: 0.01, // 1%
      critical: 0.05, // 5%
    },
    throughput: {
      warning: 50, // Below 50 req/s
      critical: 20, // Below 20 req/s
    },
  },
  notifications: {
    slack: process.env.SLACK_WEBHOOK,
    email: process.env.ALERT_EMAIL,
  },
});

Real-time Dashboard

// Performance monitoring dashboard
const dashboard = {
  metrics: [
    "requests_per_second",
    "average_response_time",
    "error_rate",
    "cache_hit_ratio",
    "provider_health",
    "queue_depth",
    "memory_usage",
    "cpu_usage",
  ],
  charts: [
    "response_time_histogram",
    "throughput_timeline",
    "error_rate_timeline",
    "provider_comparison",
  ],
};

🔧 Troubleshooting Performance Issues

Common Issues

  1. High Latency
  2. Check provider response times
  3. Verify network connectivity
  4. Review request complexity

  5. Consider request timeouts

  6. Low Throughput

  7. Increase connection pool size
  8. Enable parallel processing
  9. Optimize request batching

  10. Check rate limits

  11. Memory Leaks

  12. Monitor cache size
  13. Review object retention
  14. Check for unclosed streams
  15. Implement proper cleanup

Diagnostic Commands

# Performance diagnostics
npx @juspay/neurolink diagnose performance \
  --verbose \
  --include-providers \
  --include-cache \
  --include-memory \
  --output diagnosis.json

This comprehensive performance optimization guide provides the tools and strategies needed to maximize NeuroLink's performance in any environment, from development to large-scale production deployments.