d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
88 lines
2.8 KiB
JavaScript
88 lines
2.8 KiB
JavaScript
/**
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* Custom Configuration Example
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* Demonstrates advanced configuration options
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*/
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const { SonaEngine } = require('../index.js');
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function main() {
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console.log('🔧 SONA - Custom Configuration Example\n');
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// Create engine with custom configuration
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const config = {
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hiddenDim: 512,
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embeddingDim: 512,
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microLoraRank: 2,
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baseLoraRank: 16,
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microLoraLr: 0.002,
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baseLoraLr: 0.0002,
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ewcLambda: 500.0,
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patternClusters: 100,
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trajectoryCapacity: 5000,
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backgroundIntervalMs: 1800000, // 30 minutes
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qualityThreshold: 0.7,
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enableSimd: true,
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};
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console.log('Configuration:', JSON.stringify(config, null, 2));
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const engine = SonaEngine.withConfig(config);
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console.log('✓ Engine created with custom config\n');
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// Record high-quality trajectories
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console.log('Recording high-quality trajectories...');
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for (let i = 0; i < 20; i++) {
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const queryEmbedding = Array(512).fill(0).map(() => Math.random());
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const builder = engine.beginTrajectory(queryEmbedding);
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// Multiple inference steps
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for (let step = 0; step < 5; step++) {
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const activations = Array(512).fill(0).map(() => Math.random());
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const attentionWeights = Array(128).fill(0).map(() => Math.random());
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const reward = 0.8 + Math.random() * 0.2;
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builder.addStep(activations, attentionWeights, reward);
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}
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builder.setRoute(`high_quality_model_${i % 4}`);
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const quality = 0.85 + Math.random() * 0.15;
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engine.endTrajectory(builder, quality);
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}
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console.log('✓ Recorded 20 high-quality trajectories\n');
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// Apply both micro and base LoRA
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console.log('Applying LoRA transformations...');
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const input = Array(512).fill(1.0);
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const microOutput = engine.applyMicroLora(input);
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console.log(`✓ Micro-LoRA: ${input.length} -> ${microOutput.length}`);
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const baseOutput = engine.applyBaseLora(0, input);
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console.log(`✓ Base-LoRA (layer 0): ${input.length} -> ${baseOutput.length}\n`);
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// Pattern analysis
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console.log('Pattern analysis...');
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const testQuery = Array(512).fill(0).map(() => Math.random());
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const topPatterns = engine.findPatterns(testQuery, 10);
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console.log(`Found ${topPatterns.length} patterns:`);
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topPatterns.slice(0, 3).forEach((pattern, i) => {
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console.log(` ${i + 1}. ID: ${pattern.id}`);
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console.log(` Quality: ${pattern.avgQuality.toFixed(3)}`);
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console.log(` Cluster size: ${pattern.clusterSize}`);
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console.log(` Type: ${pattern.patternType}`);
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});
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console.log();
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// Enable/disable engine
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console.log('Testing enable/disable...');
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console.log(`Engine enabled: ${engine.isEnabled()}`);
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engine.setEnabled(false);
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console.log(`Engine enabled: ${engine.isEnabled()}`);
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engine.setEnabled(true);
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console.log(`Engine enabled: ${engine.isEnabled()}\n`);
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console.log('✓ Custom configuration example completed!');
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}
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main();
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