TIQOLOGY_INTEGRATION_EXAMPLES.md

TiQology Integration Guide

🔌 Integration Patterns

This guide explains how to integrate TiQology's modular systems into your application.

1. Rendering Integration

Basic 3D Scene

// app/3d-demo/page.tsx
'use client';

import { useEffect, useRef } from 'react';
import { initializeThreeRenderer } from '@/lib/rendering/three-renderer';

export default function ThreeDemoPage() {
  const containerRef = useRef<HTMLDivElement>(null);

  useEffect(() => {
    async function init() {
      if (!containerRef.current) return;

      const canvas = document.createElement('canvas');
      containerRef.current.appendChild(canvas);

      const renderer = await initializeThreeRenderer({
        canvas,
        antialias: true,
        width: window.innerWidth,
        height: window.innerHeight,
      });

      const scene = await renderer.createScene('main');
      
      // Add objects to scene
      // renderer.addObject('main', mesh);

      renderer.startAnimationLoop('main', () => {
        // Animation logic
      });

      return () => renderer.dispose();
    }

    init();
  }, []);

  return <div ref={containerRef} className="w-full h-screen" />;
}

WebGPU with Fallback

import { getBestRenderer, initializeWebGPU, initializeThreeRenderer } from '@/lib/rendering';

async function initRenderer() {
  const bestBackend = await getBestRenderer();
  
  if (bestBackend === 'webgpu') {
    const webgpu = await initializeWebGPU({ powerPreference: 'high-performance' });
    if (webgpu) return webgpu;
  }
  
  // Fallback to Three.js
  return initializeThreeRenderer();
}

2. Holographic UI Integration

VR Chat Interface

// app/vr-chat/page.tsx
'use client';

import { HolographicUI, HolographicPanel, HolographicButton } from '@/lib/xr/holographic-ui';
import { Chat } from '@/components/chat';

export default function VRChatPage() {
  return (
    <HolographicUI enableVR enableHandTracking>
      <HolographicPanel 
        position={[0, 1.6, -2]}
        title="AI Chat Assistant"
      >
        <Chat />
      </HolographicPanel>

      <HolographicPanel 
        position={[1.5, 1.6, -2]}
        title="Controls"
      >
        <HolographicButton onClick={() => console.log('Action')}>
          Action
        </HolographicButton>
      </HolographicPanel>
    </HolographicUI>
  );
}

AR Overlay

import { HolographicUI } from '@/lib/xr/holographic-ui';

<HolographicUI 
  enableAR 
  sessionMode="immersive-ar"
>
  {/* AR content */}
</HolographicUI>

3. Quantum Computing Integration

Simple Quantum Circuit

// app/api/quantum/route.ts
import { initializeQuantumEngine } from '@/lib/quantum/compute-engine';
import { NextResponse } from 'next/server';

export async function POST(request: Request) {
  const { qubits, gates } = await request.json();

  const engine = await initializeQuantumEngine('mock');
  if (!engine) {
    return NextResponse.json({ error: 'Quantum engine unavailable' }, { status: 500 });
  }

  const circuit = engine.createCircuit(qubits);
  
  for (const gate of gates) {
    engine.addGate(circuit.id, gate);
  }

  engine.measure(circuit.id, Array.from({ length: qubits }, (_, i) => i));
  
  const result = await engine.execute(circuit.id, { shots: 1024 });

  return NextResponse.json(result);
}

Grover's Search

import { getQuantumEngine } from '@/lib/quantum/compute-engine';

async function findInDatabase(searchSpace: number, targetIndex: number) {
  const engine = getQuantumEngine();
  await engine.initialize();
  
  const result = await engine.groverSearch(searchSpace, targetIndex);
  
  return result?.counts;
}

4. AI Inference Integration

Basic Inference

// app/api/chat/route.ts
import { getInferencePipeline } from '@/lib/ai/inference-pipeline';
import { NextResponse } from 'next/server';

export async function POST(request: Request) {
  const { prompt, model = 'gpt-4' } = await request.json();

  const pipeline = getInferencePipeline();
  const result = await pipeline.infer(prompt, {
    model,
    temperature: 0.7,
    maxTokens: 2048,
    cache: true,
  });

  return NextResponse.json(result);
}

Streaming Inference

import { getInferencePipeline } from '@/lib/ai/inference-pipeline';
import { StreamingTextResponse } from 'ai';

export async function POST(request: Request) {
  const { prompt } = await request.json();

  const pipeline = getInferencePipeline();
  const result = await pipeline.infer(prompt, {
    model: 'gpt-4',
    stream: true,
  });

  return new StreamingTextResponse(result.text);
}

Chain of Thought

const pipeline = getInferencePipeline();

const steps = await pipeline.chainOfThought(
  'Explain quantum entanglement',
  3,
  { model: 'gpt-4', temperature: 0.7 }
);

steps.forEach((step, i) => {
  console.log(`Step ${i + 1}:`, step.text);
});

Multi-Model Consensus

const pipeline = getInferencePipeline();

const { consensus, results } = await pipeline.multiModelConsensus(
  'What is the meaning of life?',
  ['gpt-4', 'gpt-4-turbo', 'claude-3-opus'],
  { temperature: 0.7 }
);

console.log('Consensus:', consensus);

5. GPU Acceleration Integration

Matrix Operations

import { initializeGPUAccelerator } from '@/lib/ai/gpu-acceleration';

async function multiplyMatrices(a: number[][], b: number[][]) {
  const gpu = await initializeGPUAccelerator({ mode: 'webgpu' });
  if (!gpu) return null;

  const tensorA = gpu.createTensor([a.length, a[0].length], new Float32Array(a.flat()));
  const tensorB = gpu.createTensor([b.length, b[0].length], new Float32Array(b.flat()));

  const result = await gpu.matrixMultiply(tensorA, tensorB);
  
  return result.data;
}

Neural Network Layer

import { getGPUAccelerator } from '@/lib/ai/gpu-acceleration';

async function runDenseLayer(input: Float32Array, weights: Float32Array, bias: Float32Array) {
  const gpu = getGPUAccelerator();
  await gpu.initialize();

  const inputTensor = gpu.createTensor([1, input.length], input);
  const weightTensor = gpu.createTensor([input.length, bias.length], weights);
  const biasTensor = gpu.createTensor([bias.length], bias);

  const output = await gpu.denseLayer(inputTensor, weightTensor, biasTensor, 'relu');

  return output.data;
}

6. Cloud Orchestration Integration

Deploy to Multiple Clouds

import { getCloudOrchestrator } from '@/lib/cloud/orchestration';

async function deployApplication() {
  const orchestrator = getCloudOrchestrator();

  const result = await orchestrator.deployFullStack({
    environment: 'production',
    branch: 'main',
    buildCommand: 'pnpm build',
    envVars: {
      NODE_ENV: 'production',
      ENABLE_ANALYTICS: 'true',
    },
  });

  console.log('Deployment:', result);
  
  // Wait for deployment to complete
  if (result.vercel) {
    await orchestrator.waitForDeployment(result.vercel.id);
  }

  return result;
}

Database Migration

import { getCloudOrchestrator } from '@/lib/cloud/orchestration';

const orchestrator = getCloudOrchestrator();

const migrations = [
  'CREATE TABLE new_table (...)',
  'ALTER TABLE users ADD COLUMN ...',
];

await orchestrator.migrateDatabase(migrations);

Health Monitoring

import { checkHealth } from '@/lib/cloud/orchestration';

async function monitorServices() {
  const health = await checkHealth();

  console.log('Vercel:', health.vercel ? '✅' : '❌');
  console.log('Supabase:', health.supabase ? '✅' : '❌');
  console.log('AWS:', health.aws ? '✅' : '❌');
  console.log('Cloudflare:', health.cloudflare ? '✅' : '❌');
}

7. Database Scalability Integration

Apply Optimizations

import { applyDatabaseOptimizations } from '@/lib/db/scalability';
import { db } from '@/lib/db';

async function optimizeDatabase() {
  const result = await applyDatabaseOptimizations(db);

  console.log('✅ Applied optimizations:', result.applied.length);
  console.log('❌ Errors:', result.errors.length);

  return result;
}

Database Monitoring

import { DatabaseMonitor } from '@/lib/db/scalability';
import { db } from '@/lib/db';

const monitor = new DatabaseMonitor(db);

// Collect metrics
const metrics = await monitor.collectMetrics();
console.log('Connection count:', metrics.connectionCount);
console.log('Database size:', metrics.databaseSize);

// Run maintenance
await monitor.performMaintenance();

8. Component Integration Examples

AI-Powered 3D Scene

'use client';

import { useState } from 'react';
import { ThreeFiberScene, Model3D } from '@/lib/xr/three-fiber-scene';
import { quickInfer } from '@/lib/ai/inference-pipeline';

export default function AI3DScene() {
  const [modelUrl, setModelUrl] = useState('/models/default.glb');

  async function generateModel(prompt: string) {
    const response = await quickInfer(
      `Generate 3D model description for: ${prompt}`,
      'gpt-4'
    );
    
    // Process response and update model
    setModelUrl(`/models/${response}.glb`);
  }

  return (
    <div>
      <input 
        type="text" 
        placeholder="Describe a 3D object..."
        onKeyPress={(e) => {
          if (e.key === 'Enter') {
            generateModel(e.currentTarget.value);
          }
        }}
      />
      
      <ThreeFiberScene shadows>
        <Model3D url={modelUrl} position={[0, 0, 0]} />
      </ThreeFiberScene>
    </div>
  );
}

Quantum-Enhanced Search

import { getQuantumEngine } from '@/lib/quantum/compute-engine';
import { getInferencePipeline } from '@/lib/ai/inference-pipeline';

async function quantumSearch(query: string, database: string[]) {
  // Use quantum for search space reduction
  const quantum = getQuantumEngine();
  await quantum.initialize();
  
  const searchSpace = database.length;
  const result = await quantum.groverSearch(searchSpace, 0);
  
  // Use AI to refine results
  const pipeline = getInferencePipeline();
  const refined = await pipeline.infer(
    `Filter these results for: ${query}\nResults: ${JSON.stringify(result?.counts)}`,
    { model: 'gpt-4' }
  );
  
  return refined.text;
}

9. Best Practices

Error Handling

try {
  const engine = await initializeQuantumEngine('aws-braket');
  // ... use engine
} catch (error) {
  console.error('Quantum engine failed:', error);
  // Fallback to classical algorithm
}

Resource Cleanup

useEffect(() => {
  const engine = getQuantumEngine();
  engine.initialize();

  return () => {
    engine.dispose();
  };
}, []);

Performance Monitoring

import { getInferencePipeline } from '@/lib/ai/inference-pipeline';

const pipeline = getInferencePipeline();
const stats = pipeline.getStats();

console.log('Cache size:', stats.cacheSize);
console.log('Queue size:', stats.queueSize);
console.log('Hit rate:', stats.cacheHitRate);

10. Testing Integration

// __tests__/quantum.test.ts
import { QuantumComputeEngine } from '@/lib/quantum/compute-engine';

describe('Quantum Engine', () => {
  let engine: QuantumComputeEngine;

  beforeAll(async () => {
    engine = new QuantumComputeEngine('mock');
    await engine.initialize();
  });

  test('should create circuit', () => {
    const circuit = engine.createCircuit(3);
    expect(circuit.qubits).toBe(3);
  });

  test('should execute Grover search', async () => {
    const result = await engine.groverSearch(16, 7);
    expect(result?.shots).toBe(1024);
  });

  afterAll(() => {
    engine.dispose();
  });
});

---

📚 Additional Resources

• WebGPU Specification
• WebXR Device API
• Three.js Documentation
• AWS Braket Documentation
• Supabase Documentation

🤝 Need Help?

• Open an issue on GitHub
• Join our Discord community
• Check the main architecture guide: TIQOLOGY_INFRASTRUCTURE_GUIDE.md