wifi-densepose/npm/packages/sona/examples/llm-integration.js

/**
 * LLM Integration Example
 * Demonstrates how to integrate SONA with an LLM inference pipeline
 */

const { SonaEngine } = require('../index.js');

class AdaptiveLLM {
  constructor(hiddenDim = 4096) {
    // Create SONA engine with LLM-appropriate configuration
    this.sona = SonaEngine.withConfig({
      hiddenDim: hiddenDim,
      embeddingDim: hiddenDim,
      microLoraRank: 2,
      baseLoraRank: 16,
      microLoraLr: 0.002,
      baseLoraLr: 0.0001,
      qualityThreshold: 0.7,
      backgroundIntervalMs: 1800000, // 30 minutes
    });

    this.layers = 32; // Simulated layer count
    console.log(`🤖 Initialized Adaptive LLM with SONA (hidden_dim=${hiddenDim})`);
  }

  /**
   * Simulate LLM inference with SONA enhancement
   */
  async generate(prompt) {
    console.log(`\n📝 Generating response for: "${prompt}"`);

    // 1. Embed the prompt (simulated)
    const embedding = this.embedPrompt(prompt);

    // 2. Start SONA trajectory
    const builder = this.sona.beginTrajectory(embedding);

    // 3. Run inference through layers
    let output = embedding;
    for (let layer = 0; layer < this.layers; layer++) {
      // Simulate layer forward pass
      const activations = this.forwardLayer(layer, output);

      // Apply SONA micro-LoRA enhancement
      const enhanced = this.sona.applyMicroLora(activations);

      // Record trajectory step
      const attention = this.getAttention(layer);
      const reward = this.calculateReward(enhanced, layer);
      builder.addStep(activations, attention, reward);

      output = enhanced;

      // Progress indicator
      if ((layer + 1) % 8 === 0) {
        console.log(`  Layer ${layer + 1}/${this.layers} processed`);
      }
    }

    // 4. Decode output (simulated)
    const generatedText = this.decode(output);

    // 5. Calculate quality score
    const quality = this.assessQuality(generatedText, prompt);

    // 6. Complete trajectory
    builder.setRoute('main_model');
    builder.addContext(prompt);
    this.sona.endTrajectory(builder, quality);

    console.log(`✓ Generated (quality: ${quality.toFixed(3)}): "${generatedText}"`);

    // 7. Run periodic background learning
    const status = this.sona.tick();
    if (status) {
      console.log(`🔄 Background learning: ${status}`);
    }

    return generatedText;
  }

  /**
   * Simulate prompt embedding
   */
  embedPrompt(prompt) {
    const dim = 4096;
    // Simple hash-based embedding (in real use, use actual embeddings)
    const seed = prompt.split('').reduce((acc, char) => acc + char.charCodeAt(0), 0);
    const embedding = Array(dim).fill(0).map((_, i) => {
      return Math.sin(seed * (i + 1) * 0.001) * Math.cos(i * 0.1);
    });
    return embedding;
  }

  /**
   * Simulate layer forward pass
   */
  forwardLayer(layer, input) {
    // Simple transformation (in real use, actual neural network layer)
    return input.map((x, i) => {
      return Math.tanh(x + Math.sin(layer * i * 0.01));
    });
  }

  /**
   * Simulate attention weights
   */
  getAttention(layer) {
    const seqLen = 64;
    const weights = Array(seqLen).fill(0).map(() => Math.random());
    const sum = weights.reduce((a, b) => a + b, 0);
    return weights.map(w => w / sum); // Normalize
  }

  /**
   * Calculate reward for a layer
   */
  calculateReward(activations, layer) {
    // Higher reward for middle layers, lower for early/late
    const midLayer = this.layers / 2;
    const distance = Math.abs(layer - midLayer) / midLayer;
    const base = 0.7 + Math.random() * 0.2;
    return base * (1 - distance * 0.3);
  }

  /**
   * Decode activations to text (simulated)
   */
  decode(activations) {
    // Simple simulation - in real use, actual decoder
    const templates = [
      'This is a thoughtful response.',
      'Here is the information you requested.',
      'Based on the context, the answer is...',
      'Let me explain this concept.',
      'The solution involves several steps.',
    ];
    const hash = activations.slice(0, 10).reduce((a, b) => a + b, 0);
    const index = Math.floor(Math.abs(hash) * 100) % templates.length;
    return templates[index];
  }

  /**
   * Assess output quality
   */
  assessQuality(output, prompt) {
    // Simple quality metric (in real use, actual quality assessment)
    const lengthScore = Math.min(output.length / 50, 1.0);
    const randomness = Math.random() * 0.2;
    return 0.6 + lengthScore * 0.2 + randomness;
  }

  /**
   * Find similar patterns for routing
   */
  findSimilarPatterns(prompt, k = 5) {
    const embedding = this.embedPrompt(prompt);
    const patterns = this.sona.findPatterns(embedding, k);

    console.log(`\n🔍 Found ${patterns.length} similar patterns:`);
    patterns.forEach((pattern, i) => {
      console.log(`  ${i + 1}. Quality: ${pattern.avgQuality.toFixed(3)}, ` +
                  `Type: ${pattern.patternType}, Size: ${pattern.clusterSize}`);
    });

    return patterns;
  }

  /**
   * Get engine statistics
   */
  getStats() {
    const stats = this.sona.getStats();
    console.log('\n📊 SONA Engine Statistics:');
    console.log(stats);
    return stats;
  }

  /**
   * Force background learning
   */
  forceLearn() {
    console.log('\n🎓 Forcing background learning...');
    const result = this.sona.forceLearn();
    console.log(result);
    return result;
  }
}

// Example usage
async function main() {
  console.log('🚀 SONA LLM Integration Example\n');

  const llm = new AdaptiveLLM(4096);

  // Generate responses for different prompts
  const prompts = [
    'What is machine learning?',
    'Explain neural networks',
    'How does gradient descent work?',
    'What are transformers?',
  ];

  for (const prompt of prompts) {
    await llm.generate(prompt);
    // Small delay to simulate async processing
    await new Promise(resolve => setTimeout(resolve, 100));
  }

  // Pattern analysis
  llm.findSimilarPatterns('Tell me about AI');

  // Statistics
  llm.getStats();

  // Force learning
  llm.forceLearn();

  console.log('\n✓ LLM integration example completed!');
}

main().catch(console.error);