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npm/packages/sona/examples/llm-integration.js
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npm/packages/sona/examples/llm-integration.js
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/**
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* LLM Integration Example
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* Demonstrates how to integrate SONA with an LLM inference pipeline
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*/
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const { SonaEngine } = require('../index.js');
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class AdaptiveLLM {
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constructor(hiddenDim = 4096) {
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// Create SONA engine with LLM-appropriate configuration
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this.sona = SonaEngine.withConfig({
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hiddenDim: hiddenDim,
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embeddingDim: hiddenDim,
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microLoraRank: 2,
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baseLoraRank: 16,
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microLoraLr: 0.002,
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baseLoraLr: 0.0001,
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qualityThreshold: 0.7,
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backgroundIntervalMs: 1800000, // 30 minutes
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});
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this.layers = 32; // Simulated layer count
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console.log(`🤖 Initialized Adaptive LLM with SONA (hidden_dim=${hiddenDim})`);
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}
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/**
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* Simulate LLM inference with SONA enhancement
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*/
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async generate(prompt) {
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console.log(`\n📝 Generating response for: "${prompt}"`);
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// 1. Embed the prompt (simulated)
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const embedding = this.embedPrompt(prompt);
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// 2. Start SONA trajectory
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const builder = this.sona.beginTrajectory(embedding);
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// 3. Run inference through layers
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let output = embedding;
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for (let layer = 0; layer < this.layers; layer++) {
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// Simulate layer forward pass
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const activations = this.forwardLayer(layer, output);
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// Apply SONA micro-LoRA enhancement
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const enhanced = this.sona.applyMicroLora(activations);
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// Record trajectory step
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const attention = this.getAttention(layer);
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const reward = this.calculateReward(enhanced, layer);
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builder.addStep(activations, attention, reward);
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output = enhanced;
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// Progress indicator
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if ((layer + 1) % 8 === 0) {
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console.log(` Layer ${layer + 1}/${this.layers} processed`);
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}
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}
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// 4. Decode output (simulated)
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const generatedText = this.decode(output);
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// 5. Calculate quality score
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const quality = this.assessQuality(generatedText, prompt);
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// 6. Complete trajectory
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builder.setRoute('main_model');
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builder.addContext(prompt);
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this.sona.endTrajectory(builder, quality);
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console.log(`✓ Generated (quality: ${quality.toFixed(3)}): "${generatedText}"`);
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// 7. Run periodic background learning
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const status = this.sona.tick();
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if (status) {
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console.log(`🔄 Background learning: ${status}`);
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}
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return generatedText;
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}
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/**
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* Simulate prompt embedding
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*/
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embedPrompt(prompt) {
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const dim = 4096;
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// Simple hash-based embedding (in real use, use actual embeddings)
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const seed = prompt.split('').reduce((acc, char) => acc + char.charCodeAt(0), 0);
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const embedding = Array(dim).fill(0).map((_, i) => {
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return Math.sin(seed * (i + 1) * 0.001) * Math.cos(i * 0.1);
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});
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return embedding;
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}
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/**
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* Simulate layer forward pass
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*/
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forwardLayer(layer, input) {
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// Simple transformation (in real use, actual neural network layer)
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return input.map((x, i) => {
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return Math.tanh(x + Math.sin(layer * i * 0.01));
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});
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}
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/**
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* Simulate attention weights
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*/
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getAttention(layer) {
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const seqLen = 64;
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const weights = Array(seqLen).fill(0).map(() => Math.random());
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const sum = weights.reduce((a, b) => a + b, 0);
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return weights.map(w => w / sum); // Normalize
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}
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/**
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* Calculate reward for a layer
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*/
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calculateReward(activations, layer) {
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// Higher reward for middle layers, lower for early/late
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const midLayer = this.layers / 2;
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const distance = Math.abs(layer - midLayer) / midLayer;
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const base = 0.7 + Math.random() * 0.2;
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return base * (1 - distance * 0.3);
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}
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/**
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* Decode activations to text (simulated)
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*/
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decode(activations) {
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// Simple simulation - in real use, actual decoder
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const templates = [
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'This is a thoughtful response.',
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'Here is the information you requested.',
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'Based on the context, the answer is...',
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'Let me explain this concept.',
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'The solution involves several steps.',
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];
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const hash = activations.slice(0, 10).reduce((a, b) => a + b, 0);
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const index = Math.floor(Math.abs(hash) * 100) % templates.length;
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return templates[index];
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}
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/**
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* Assess output quality
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*/
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assessQuality(output, prompt) {
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// Simple quality metric (in real use, actual quality assessment)
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const lengthScore = Math.min(output.length / 50, 1.0);
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const randomness = Math.random() * 0.2;
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return 0.6 + lengthScore * 0.2 + randomness;
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}
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/**
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* Find similar patterns for routing
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*/
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findSimilarPatterns(prompt, k = 5) {
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const embedding = this.embedPrompt(prompt);
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const patterns = this.sona.findPatterns(embedding, k);
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console.log(`\n🔍 Found ${patterns.length} similar patterns:`);
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patterns.forEach((pattern, i) => {
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console.log(` ${i + 1}. Quality: ${pattern.avgQuality.toFixed(3)}, ` +
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`Type: ${pattern.patternType}, Size: ${pattern.clusterSize}`);
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});
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return patterns;
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}
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/**
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* Get engine statistics
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*/
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getStats() {
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const stats = this.sona.getStats();
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console.log('\n📊 SONA Engine Statistics:');
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console.log(stats);
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return stats;
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}
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/**
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* Force background learning
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*/
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forceLearn() {
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console.log('\n🎓 Forcing background learning...');
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const result = this.sona.forceLearn();
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console.log(result);
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return result;
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}
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}
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// Example usage
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async function main() {
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console.log('🚀 SONA LLM Integration Example\n');
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const llm = new AdaptiveLLM(4096);
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// Generate responses for different prompts
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const prompts = [
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'What is machine learning?',
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'Explain neural networks',
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'How does gradient descent work?',
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'What are transformers?',
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];
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for (const prompt of prompts) {
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await llm.generate(prompt);
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// Small delay to simulate async processing
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await new Promise(resolve => setTimeout(resolve, 100));
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}
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// Pattern analysis
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llm.findSimilarPatterns('Tell me about AI');
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// Statistics
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llm.getStats();
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// Force learning
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llm.forceLearn();
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console.log('\n✓ LLM integration example completed!');
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}
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main().catch(console.error);
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