316 lines
10 KiB
JavaScript
316 lines
10 KiB
JavaScript
/**
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* SONA Integration for V3 Neural Module
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*
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* Wraps @ruvector/sona package for V3 usage with:
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* - Trajectory tracking and verdict judgment
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* - Pattern extraction and memory distillation
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* - Sub-0.05ms learning performance target
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* - Clean TypeScript API with proper types
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*
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* @module sona-integration
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*/
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import { SonaEngine } from '@ruvector/sona';
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// =============================================================================
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// Mode Configuration Mapping
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// =============================================================================
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/**
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* Convert V3 SONA mode to @ruvector/sona config
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*/
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function modeToConfig(mode, modeConfig) {
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const baseConfig = {
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hiddenDim: 768, // Standard transformer dimension
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embeddingDim: 768,
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microLoraRank: modeConfig.loraRank <= 2 ? modeConfig.loraRank : 1,
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baseLoraRank: modeConfig.loraRank,
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microLoraLr: modeConfig.learningRate,
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baseLoraLr: modeConfig.learningRate * 0.1,
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ewcLambda: modeConfig.ewcLambda,
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patternClusters: modeConfig.patternClusters,
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trajectoryCapacity: modeConfig.trajectoryCapacity,
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qualityThreshold: modeConfig.qualityThreshold,
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enableSimd: true,
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};
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// Mode-specific adjustments
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switch (mode) {
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case 'real-time':
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return {
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...baseConfig,
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microLoraRank: 1,
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backgroundIntervalMs: 60000, // 1 minute
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};
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case 'edge':
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return {
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...baseConfig,
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hiddenDim: 384, // Smaller for edge devices
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embeddingDim: 384,
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microLoraRank: 1,
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patternClusters: 25,
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backgroundIntervalMs: 300000, // 5 minutes
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};
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case 'research':
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return {
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...baseConfig,
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baseLoraRank: 16,
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backgroundIntervalMs: 3600000, // 1 hour
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};
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case 'batch':
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return {
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...baseConfig,
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backgroundIntervalMs: 7200000, // 2 hours
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};
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case 'balanced':
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default:
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return {
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...baseConfig,
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backgroundIntervalMs: 1800000, // 30 minutes
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};
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}
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}
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// =============================================================================
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// SONA Learning Engine
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// =============================================================================
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/**
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* SONA Learning Engine - wraps @ruvector/sona for V3 usage
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*
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* Performance targets:
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* - learn(): <0.05ms
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* - adapt(): <0.1ms
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* - Full learning cycle: <10ms
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*/
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export class SONALearningEngine {
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engine;
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trajectoryMap = new Map();
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adaptationTimeMs = 0;
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learningTimeMs = 0;
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mode;
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modeConfig;
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constructor(mode, modeConfig) {
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this.mode = mode;
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this.modeConfig = modeConfig;
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const config = modeToConfig(mode, modeConfig);
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this.engine = SonaEngine.withConfig(config);
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}
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/**
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* Learn from a trajectory
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*
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* Performance target: <0.05ms
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*
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* @param trajectory - Trajectory to learn from
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*/
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async learn(trajectory) {
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const startTime = performance.now();
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try {
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// Begin trajectory recording
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const queryEmbedding = this.trajectoryToQueryEmbedding(trajectory);
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const trajectoryId = this.engine.beginTrajectory(Array.from(queryEmbedding));
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// Add trajectory steps
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for (const step of trajectory.steps) {
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const activations = this.stateToActivations(step.stateBefore);
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const attentionWeights = this.stateToAttentionWeights(step.stateAfter);
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this.engine.addTrajectoryStep(trajectoryId, Array.from(activations), Array.from(attentionWeights), step.reward);
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}
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// Set context if available
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if (trajectory.domain) {
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this.engine.addTrajectoryContext(trajectoryId, trajectory.domain);
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}
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// Complete trajectory with quality score
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const quality = this.calculateQuality(trajectory);
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this.engine.endTrajectory(trajectoryId, quality);
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// Flush instant updates
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this.engine.flush();
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this.learningTimeMs = performance.now() - startTime;
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}
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catch (error) {
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throw new Error(`SONA learning failed: ${error}`);
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}
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}
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/**
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* Adapt behavior based on context
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*
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* @param context - Current context for adaptation
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* @returns Adapted behavior with transformed embeddings
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*/
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async adapt(context) {
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const startTime = performance.now();
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try {
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// Apply micro-LoRA transformation
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const transformedQuery = this.engine.applyMicroLora(Array.from(context.queryEmbedding));
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// Find similar patterns
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const patterns = this.engine.findPatterns(Array.from(context.queryEmbedding), 5);
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// Determine suggested route from patterns
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const suggestedRoute = this.inferRoute(patterns, context);
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const confidence = patterns.length > 0 ? patterns[0].avgQuality : 0.5;
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this.adaptationTimeMs = performance.now() - startTime;
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return {
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transformedQuery: new Float32Array(transformedQuery),
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patterns,
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suggestedRoute,
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confidence,
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};
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}
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catch (error) {
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throw new Error(`SONA adaptation failed: ${error}`);
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}
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}
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/**
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* Get last adaptation time
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*
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* @returns Adaptation time in milliseconds
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*/
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getAdaptationTime() {
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return this.adaptationTimeMs;
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}
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/**
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* Get last learning time
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*
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* @returns Learning time in milliseconds
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*/
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getLearningTime() {
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return this.learningTimeMs;
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}
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/**
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* Reset learning state
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*/
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resetLearning() {
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// Create a new engine with the same config
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const config = modeToConfig(this.mode, this.modeConfig);
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this.engine = SonaEngine.withConfig(config);
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this.trajectoryMap.clear();
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this.adaptationTimeMs = 0;
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this.learningTimeMs = 0;
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}
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/**
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* Force immediate learning cycle
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*
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* @returns Status message
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*/
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forceLearning() {
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return this.engine.forceLearn();
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}
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/**
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* Tick background learning (call periodically)
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*
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* @returns Status message if learning occurred
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*/
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tick() {
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return this.engine.tick();
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}
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/**
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* Get engine statistics
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*
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* @returns SONA engine statistics
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*/
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getStats() {
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const statsJson = this.engine.getStats();
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const stats = JSON.parse(statsJson);
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return {
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totalTrajectories: stats.total_trajectories || 0,
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patternsLearned: stats.patterns_learned || 0,
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avgQuality: stats.avg_quality || 0,
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lastLearningMs: this.learningTimeMs,
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enabled: this.engine.isEnabled(),
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};
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}
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/**
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* Enable or disable the engine
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*
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* @param enabled - Whether to enable the engine
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*/
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setEnabled(enabled) {
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this.engine.setEnabled(enabled);
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}
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/**
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* Check if engine is enabled
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*
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* @returns Whether the engine is enabled
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*/
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isEnabled() {
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return this.engine.isEnabled();
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}
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/**
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* Find learned patterns similar to query
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*
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* @param queryEmbedding - Query embedding
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* @param k - Number of patterns to return
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* @returns Learned patterns
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*/
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findPatterns(queryEmbedding, k = 5) {
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return this.engine.findPatterns(Array.from(queryEmbedding), k);
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}
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// =============================================================================
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// Private Helpers
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// =============================================================================
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/**
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* Convert trajectory to query embedding
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*/
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trajectoryToQueryEmbedding(trajectory) {
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// Use the first step's state as query
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if (trajectory.steps.length > 0) {
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return trajectory.steps[0].stateBefore;
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}
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// Fallback to zero embedding
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return new Float32Array(768);
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}
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/**
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* Convert state embedding to activations
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*/
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stateToActivations(state) {
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// For now, use state directly as activations
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// In a real implementation, this would extract layer activations
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return state;
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}
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/**
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* Convert state embedding to attention weights
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*/
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stateToAttentionWeights(state) {
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// For now, use normalized state as attention weights
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// In a real implementation, this would extract attention patterns
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const sum = state.reduce((acc, val) => acc + Math.abs(val), 0);
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if (sum === 0)
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return state;
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const weights = new Float32Array(state.length);
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for (let i = 0; i < state.length; i++) {
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weights[i] = Math.abs(state[i]) / sum;
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}
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return weights;
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}
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/**
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* Calculate quality score for trajectory
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*/
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calculateQuality(trajectory) {
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if (trajectory.qualityScore !== undefined) {
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return trajectory.qualityScore;
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}
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// Calculate from steps
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if (trajectory.steps.length === 0)
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return 0.5;
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const avgReward = trajectory.steps.reduce((sum, step) => sum + step.reward, 0) /
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trajectory.steps.length;
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// Normalize to [0, 1]
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return Math.max(0, Math.min(1, (avgReward + 1) / 2));
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}
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/**
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* Infer suggested route from patterns and context
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*/
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inferRoute(patterns, context) {
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if (patterns.length === 0)
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return undefined;
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// Use the highest quality pattern's type as route
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const bestPattern = patterns.reduce((best, pattern) => pattern.avgQuality > best.avgQuality ? pattern : best);
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return bestPattern.patternType || `${context.domain}-default`;
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}
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}
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// =============================================================================
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// Factory Functions
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// =============================================================================
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/**
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* Create a SONA learning engine
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*
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* @param mode - SONA learning mode
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* @param modeConfig - Mode configuration
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* @returns SONA learning engine instance
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*/
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export function createSONALearningEngine(mode, modeConfig) {
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return new SONALearningEngine(mode, modeConfig);
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}
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//# sourceMappingURL=sona-integration.js.map
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