Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/npm/packages/sona/examples/custom-config.js

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+/**
+ * Custom Configuration Example
+ * Demonstrates advanced configuration options
+ */
+
+const { SonaEngine } = require('../index.js');
+
+function main() {
+  console.log('🔧 SONA - Custom Configuration Example\n');
+
+  // Create engine with custom configuration
+  const config = {
+    hiddenDim: 512,
+    embeddingDim: 512,
+    microLoraRank: 2,
+    baseLoraRank: 16,
+    microLoraLr: 0.002,
+    baseLoraLr: 0.0002,
+    ewcLambda: 500.0,
+    patternClusters: 100,
+    trajectoryCapacity: 5000,
+    backgroundIntervalMs: 1800000, // 30 minutes
+    qualityThreshold: 0.7,
+    enableSimd: true,
+  };
+
+  console.log('Configuration:', JSON.stringify(config, null, 2));
+  const engine = SonaEngine.withConfig(config);
+  console.log('✓ Engine created with custom config\n');
+
+  // Record high-quality trajectories
+  console.log('Recording high-quality trajectories...');
+  for (let i = 0; i < 20; i++) {
+    const queryEmbedding = Array(512).fill(0).map(() => Math.random());
+    const builder = engine.beginTrajectory(queryEmbedding);
+
+    // Multiple inference steps
+    for (let step = 0; step < 5; step++) {
+      const activations = Array(512).fill(0).map(() => Math.random());
+      const attentionWeights = Array(128).fill(0).map(() => Math.random());
+      const reward = 0.8 + Math.random() * 0.2;
+
+      builder.addStep(activations, attentionWeights, reward);
+    }
+
+    builder.setRoute(`high_quality_model_${i % 4}`);
+    const quality = 0.85 + Math.random() * 0.15;
+    engine.endTrajectory(builder, quality);
+  }
+  console.log('✓ Recorded 20 high-quality trajectories\n');
+
+  // Apply both micro and base LoRA
+  console.log('Applying LoRA transformations...');
+  const input = Array(512).fill(1.0);
+
+  const microOutput = engine.applyMicroLora(input);
+  console.log(`✓ Micro-LoRA: ${input.length} -> ${microOutput.length}`);
+
+  const baseOutput = engine.applyBaseLora(0, input);
+  console.log(`✓ Base-LoRA (layer 0): ${input.length} -> ${baseOutput.length}\n`);
+
+  // Pattern analysis
+  console.log('Pattern analysis...');
+  const testQuery = Array(512).fill(0).map(() => Math.random());
+  const topPatterns = engine.findPatterns(testQuery, 10);
+
+  console.log(`Found ${topPatterns.length} patterns:`);
+  topPatterns.slice(0, 3).forEach((pattern, i) => {
+    console.log(`  ${i + 1}. ID: ${pattern.id}`);
+    console.log(`     Quality: ${pattern.avgQuality.toFixed(3)}`);
+    console.log(`     Cluster size: ${pattern.clusterSize}`);
+    console.log(`     Type: ${pattern.patternType}`);
+  });
+  console.log();
+
+  // Enable/disable engine
+  console.log('Testing enable/disable...');
+  console.log(`Engine enabled: ${engine.isEnabled()}`);
+  engine.setEnabled(false);
+  console.log(`Engine enabled: ${engine.isEnabled()}`);
+  engine.setEnabled(true);
+  console.log(`Engine enabled: ${engine.isEnabled()}\n`);
+
+  console.log('✓ Custom configuration example completed!');
+}
+
+main();