1531 lines
81 KiB
JavaScript
1531 lines
81 KiB
JavaScript
/**
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* V3 CLI Neural Command
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* Neural pattern training, MoE, Flash Attention, pattern learning
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*
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* Created with ❤️ by ruv.io
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*/
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import { output } from '../output.js';
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// Train subcommand - REAL WASM training with RuVector
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const trainCommand = {
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name: 'train',
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description: 'Train neural patterns with WASM SIMD acceleration (MicroLoRA + Flash Attention)',
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options: [
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{ name: 'pattern', short: 'p', type: 'string', description: 'Pattern type: coordination, optimization, prediction, security, testing', default: 'coordination' },
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{ name: 'epochs', short: 'e', type: 'number', description: 'Number of training epochs', default: '50' },
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{ name: 'data', short: 'd', type: 'string', description: 'Training data file or inline JSON' },
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{ name: 'model', short: 'm', type: 'string', description: 'Model ID to train' },
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{ name: 'learning-rate', short: 'l', type: 'number', description: 'Learning rate', default: '0.01' },
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{ name: 'batch-size', short: 'b', type: 'number', description: 'Batch size', default: '32' },
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{ name: 'dim', type: 'number', description: 'Embedding dimension (max 256)', default: '256' },
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{ name: 'wasm', short: 'w', type: 'boolean', description: 'Use RuVector WASM acceleration', default: 'true' },
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{ name: 'flash', type: 'boolean', description: 'Enable Flash Attention (2.49x-7.47x speedup)', default: 'true' },
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{ name: 'moe', type: 'boolean', description: 'Enable Mixture of Experts routing', default: 'false' },
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{ name: 'hyperbolic', type: 'boolean', description: 'Enable hyperbolic attention for hierarchical patterns', default: 'false' },
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{ name: 'contrastive', type: 'boolean', description: 'Use contrastive learning (InfoNCE)', default: 'true' },
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{ name: 'curriculum', type: 'boolean', description: 'Enable curriculum learning', default: 'false' },
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],
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examples: [
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{ command: 'claude-flow neural train -p coordination -e 100', description: 'Train coordination patterns' },
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{ command: 'claude-flow neural train -d ./training-data.json --flash', description: 'Train from file with Flash Attention' },
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{ command: 'claude-flow neural train -p security --wasm --contrastive', description: 'Security patterns with contrastive learning' },
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],
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action: async (ctx) => {
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const patternType = (ctx.flags.pattern || ctx.flags.patternType || ctx.flags['pattern-type']) || 'coordination';
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const epochs = parseInt(ctx.flags.epochs || '50', 10);
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const learningRate = parseFloat(ctx.flags['learning-rate'] || '0.01');
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const batchSize = parseInt(ctx.flags['batch-size'] || '32', 10);
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const dim = Math.min(parseInt(ctx.flags.dim || '256', 10), 256);
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const useWasm = ctx.flags.wasm !== false;
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const useFlash = ctx.flags.flash !== false;
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const useMoE = ctx.flags.moe === true;
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const useHyperbolic = ctx.flags.hyperbolic === true;
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const useContrastive = ctx.flags.contrastive !== false;
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const useCurriculum = ctx.flags.curriculum === true;
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const dataFile = ctx.flags.data;
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output.writeln();
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output.writeln(output.bold('Neural Pattern Training (RuVector WASM)'));
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output.writeln(output.dim('─'.repeat(55)));
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const spinner = output.createSpinner({ text: 'Initializing RuVector training systems...', spinner: 'dots' });
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spinner.start();
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try {
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// Import RuVector training service
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const ruvector = await import('../services/ruvector-training.js');
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const { generateEmbedding } = await import('../memory/memory-initializer.js');
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const { initializeIntelligence, recordStep, recordTrajectory, getIntelligenceStats, flushPatterns, getPersistenceStatus } = await import('../memory/intelligence.js');
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// Initialize RuVector WASM training
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let wasmFeatures = [];
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if (useWasm) {
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const initResult = await ruvector.initializeTraining({
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dim,
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learningRate,
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alpha: 0.1,
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trajectoryCapacity: epochs * batchSize,
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useFlashAttention: useFlash,
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useMoE,
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useHyperbolic,
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totalSteps: useCurriculum ? epochs : undefined,
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warmupSteps: useCurriculum ? Math.floor(epochs * 0.1) : undefined,
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});
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if (initResult.success) {
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wasmFeatures = initResult.features;
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const backendLabel = initResult.backend === 'wasm' ? 'WASM' : 'JS fallback';
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spinner.setText(`RuVector initialized [${backendLabel}]: ${wasmFeatures.join(', ')}`);
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}
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else {
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output.writeln(output.warning(`WASM init failed: ${initResult.error} - falling back`));
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}
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}
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// Also initialize SONA + ReasoningBank for persistence
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await initializeIntelligence({
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loraLearningRate: learningRate,
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maxTrajectorySize: epochs
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});
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// Pattern type to operator mapping
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const operatorMap = {
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coordination: ruvector.OperatorType.COORDINATION,
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optimization: ruvector.OperatorType.OPTIMIZATION,
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prediction: ruvector.OperatorType.ROUTING,
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security: ruvector.OperatorType.SECURITY,
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testing: ruvector.OperatorType.TESTING,
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debugging: ruvector.OperatorType.DEBUGGING,
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memory: ruvector.OperatorType.MEMORY,
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reasoning: ruvector.OperatorType.REASONING,
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};
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const operatorType = operatorMap[patternType] ?? ruvector.OperatorType.GENERAL;
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spinner.setText(`Training ${patternType} patterns...`);
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// Training data - load from file or generate synthetic
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let trainingData = [];
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if (dataFile) {
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const fs = await import('fs');
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if (fs.existsSync(dataFile)) {
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const raw = fs.readFileSync(dataFile, 'utf8');
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trainingData = JSON.parse(raw);
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}
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else {
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spinner.fail(`Training data file not found: ${dataFile}`);
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return { success: false, exitCode: 1 };
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}
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}
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else {
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// Generate synthetic training data based on pattern type
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const templates = {
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coordination: [
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'Route task to coder agent for implementation',
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'Coordinate researcher and architect for design phase',
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'Distribute workload across mesh topology',
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'Synchronize agents via gossip protocol',
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'Balance load between active workers',
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'Spawn hierarchical swarm for complex task',
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'Assign reviewer to completed implementation'
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],
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optimization: [
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'Apply Int8 quantization for memory reduction',
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'Enable HNSW indexing for faster search',
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'Batch operations for throughput improvement',
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'Cache frequently accessed patterns',
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'Prune unused neural pathways',
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'Use Flash Attention for large sequences',
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'Enable SIMD for vector operations'
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],
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prediction: [
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'Predict optimal agent for task type',
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'Forecast resource requirements',
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'Anticipate failure modes and mitigate',
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'Estimate completion time for workflow',
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'Predict pattern similarity before search'
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],
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security: [
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'Validate input at system boundaries',
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'Check for path traversal attempts',
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'Sanitize user-provided data',
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'Apply parameterized queries for SQL',
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'Verify JWT token signatures',
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'Audit sensitive operation access'
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],
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testing: [
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'Generate unit tests for function',
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'Create integration test suite',
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'Mock external dependencies',
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'Assert expected outcomes',
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'Coverage gap analysis'
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]
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};
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const patterns = templates[patternType] || templates.coordination;
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for (let i = 0; i < epochs; i++) {
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trainingData.push({
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content: patterns[i % patterns.length],
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type: patternType
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});
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}
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}
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// Training metrics
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const startTime = Date.now();
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const epochTimes = [];
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let patternsRecorded = 0;
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let trajectoriesCompleted = 0;
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let totalLoss = 0;
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let adaptations = 0;
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// Generate embeddings for training data
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const embeddings = [];
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spinner.setText('Generating embeddings...');
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for (const item of trainingData.slice(0, Math.min(100, trainingData.length))) {
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const embeddingResult = await generateEmbedding(item.content);
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if (embeddingResult && embeddingResult.embedding) {
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// Convert to Float32Array and resize to dim
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const embeddingArray = embeddingResult.embedding;
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const resized = new Float32Array(dim);
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for (let i = 0; i < Math.min(embeddingArray.length, dim); i++) {
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resized[i] = embeddingArray[i];
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}
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embeddings.push(resized);
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}
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}
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spinner.setText(`Training with ${embeddings.length} embeddings...`);
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// Main training loop with WASM acceleration
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for (let epoch = 0; epoch < epochs; epoch++) {
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const epochStart = performance.now();
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// Get curriculum difficulty if enabled
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const difficulty = useCurriculum ? ruvector.getCurriculumDifficulty(epoch) : 1.0;
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// Process batch
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const batchStart = (epoch * batchSize) % embeddings.length;
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const batch = embeddings.slice(batchStart, batchStart + batchSize);
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if (batch.length === 0)
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continue;
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// Training step with contrastive learning
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if (useContrastive && batch.length >= 3 && useWasm && wasmFeatures.length > 0) {
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const anchor = batch[0];
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const positives = [batch[1]];
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const negatives = batch.slice(2);
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try {
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// Compute contrastive loss
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const { loss, gradient } = ruvector.computeContrastiveLoss(anchor, positives, negatives);
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totalLoss += loss;
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// Scale gradient by difficulty
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const scaledGradient = new Float32Array(gradient.length);
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for (let i = 0; i < gradient.length; i++) {
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scaledGradient[i] = gradient[i] * difficulty;
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}
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// Train with MicroLoRA
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await ruvector.trainPattern(anchor, scaledGradient, operatorType);
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adaptations++;
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// Record trajectory for learning
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const baselineMs = 10; // Baseline execution time
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const executionMs = performance.now() - epochStart;
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ruvector.recordTrajectory(anchor, operatorType, useFlash ? 1 : 0, executionMs, baselineMs);
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}
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catch {
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// WASM training failed, fall back to basic
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}
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}
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// Also record in SONA/ReasoningBank for persistence
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const item = trainingData[epoch % trainingData.length];
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await recordStep({
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type: 'action',
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content: item.content,
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metadata: { epoch, patternType, learningRate, difficulty }
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});
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patternsRecorded++;
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// Record trajectory every 10 epochs
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if ((epoch + 1) % 10 === 0 || epoch === epochs - 1) {
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const steps = trainingData.slice(Math.max(0, epoch - 9), epoch + 1).map(d => ({ type: 'action', content: d.content }));
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await recordTrajectory(steps, 'success');
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trajectoriesCompleted++;
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}
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const epochTime = performance.now() - epochStart;
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epochTimes.push(epochTime);
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// Update progress
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const progress = Math.round(((epoch + 1) / epochs) * 100);
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const avgEpochTime = epochTimes.reduce((a, b) => a + b, 0) / epochTimes.length;
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const eta = Math.round((epochs - epoch - 1) * avgEpochTime / 1000);
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spinner.setText(`Training ${patternType} patterns... ${progress}% (ETA: ${eta}s, loss: ${(totalLoss / Math.max(1, epoch + 1)).toFixed(4)})`);
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}
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const totalTime = Date.now() - startTime;
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// Get RuVector stats
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const ruvectorStats = useWasm && wasmFeatures.length > 0 ? ruvector.getTrainingStats() : null;
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const trajectoryStats = ruvectorStats?.trajectoryStats;
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// Benchmark if WASM was used
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let benchmark = null;
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if (useWasm && wasmFeatures.length > 0) {
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try {
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spinner.setText('Running benchmark...');
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benchmark = await ruvector.benchmarkTraining(dim, 100);
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}
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catch {
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// Benchmark failed, continue
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}
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}
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// Get SONA stats
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const stats = getIntelligenceStats();
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spinner.succeed(`Training complete: ${epochs} epochs in ${(totalTime / 1000).toFixed(1)}s`);
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// Flush patterns to disk
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flushPatterns();
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const persistence = getPersistenceStatus();
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// Save LoRA checkpoint via ruvllm TrainingPipeline if available
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try {
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const { LoRAAdapter } = await import('../ruvector/lora-adapter.js');
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const path = await import('path');
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const cpDir = path.join(process.cwd(), '.claude-flow', 'neural');
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const cpPath = path.join(cpDir, `lora-checkpoint-${Date.now()}.json`);
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const adapter = new LoRAAdapter({ inputDim: dim, outputDim: dim, rank: 4 });
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await adapter.initBackend();
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await adapter.saveCheckpoint(cpPath);
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}
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catch { /* checkpoint save is best-effort */ }
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output.writeln();
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// Display results
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const tableData = [
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{ metric: 'Pattern Type', value: patternType },
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{ metric: 'Epochs', value: String(epochs) },
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{ metric: 'Batch Size', value: String(batchSize) },
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{ metric: 'Embedding Dim', value: String(dim) },
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{ metric: 'Learning Rate', value: String(learningRate) },
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{ metric: 'Patterns Recorded', value: patternsRecorded.toLocaleString() },
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{ metric: 'Trajectories', value: String(trajectoriesCompleted) },
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{ metric: 'Total Time', value: `${(totalTime / 1000).toFixed(1)}s` },
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{ metric: 'Avg Epoch Time', value: `${(epochTimes.reduce((a, b) => a + b, 0) / epochTimes.length).toFixed(2)}ms` },
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];
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// Add WASM-specific metrics
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if (useWasm && wasmFeatures.length > 0) {
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const backendUsed = ruvectorStats?.backend || 'unknown';
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tableData.push({ metric: 'Backend', value: backendUsed === 'wasm' ? 'WASM (native)' : 'JS (fallback)' }, { metric: 'WASM Features', value: wasmFeatures.slice(0, 3).join(', ') }, { metric: 'LoRA Adaptations', value: String(adaptations) }, { metric: 'Avg Loss', value: (totalLoss / Math.max(1, epochs)).toFixed(4) });
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if (ruvectorStats?.microLoraStats) {
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tableData.push({ metric: 'MicroLoRA Delta Norm', value: ruvectorStats.microLoraStats.deltaNorm.toFixed(6) });
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}
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if (trajectoryStats) {
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tableData.push({ metric: 'Success Rate', value: `${(trajectoryStats.successRate * 100).toFixed(1)}%` }, { metric: 'Mean Improvement', value: `${(trajectoryStats.meanImprovement * 100).toFixed(1)}%` });
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}
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if (benchmark && benchmark.length > 0) {
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const flashBench = benchmark.find(b => b.name.includes('Flash'));
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if (flashBench) {
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tableData.push({ metric: 'Flash Attention', value: `${flashBench.opsPerSecond.toLocaleString()} ops/s` });
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}
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}
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}
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tableData.push({ metric: 'ReasoningBank Size', value: stats.reasoningBankSize.toLocaleString() }, { metric: 'Persisted To', value: output.dim(persistence.dataDir) });
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output.printTable({
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columns: [
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{ key: 'metric', header: 'Metric', width: 26 },
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{ key: 'value', header: 'Value', width: 32 },
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],
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data: tableData,
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});
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output.writeln();
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output.writeln(output.success(`✓ ${patternsRecorded} patterns saved to ${persistence.patternsFile}`));
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if (useWasm && wasmFeatures.length > 0) {
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const backendUsed = ruvectorStats?.backend || 'unknown';
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const backendMsg = backendUsed === 'wasm'
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? `RuVector WASM backend: ${wasmFeatures.join(', ')}`
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: `RuVector JS fallback (install @ruvector/learning-wasm for native speed): ${wasmFeatures.join(', ')}`;
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output.writeln(output.highlight(`✓ ${backendMsg}`));
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}
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return {
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success: true,
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data: {
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epochs,
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patternsRecorded,
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trajectoriesCompleted,
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totalTime,
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wasmFeatures,
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ruvectorStats,
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benchmark,
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stats,
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persistence
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}
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};
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}
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catch (error) {
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spinner.fail('Training failed');
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output.printError(error instanceof Error ? error.message : String(error));
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return { success: false, exitCode: 1 };
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}
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},
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};
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// Status subcommand - REAL measurements
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const statusCommand = {
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name: 'status',
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description: 'Check neural network status and loaded models',
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options: [
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{ name: 'model', short: 'm', type: 'string', description: 'Specific model ID to check' },
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{ name: 'verbose', short: 'v', type: 'boolean', description: 'Show detailed metrics' },
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],
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examples: [
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{ command: 'claude-flow neural status', description: 'Show all neural status' },
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{ command: 'claude-flow neural status -m model-123', description: 'Check specific model' },
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],
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action: async (ctx) => {
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const verbose = ctx.flags.verbose === true;
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output.writeln();
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output.writeln(output.bold('Neural Network Status (Real)'));
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output.writeln(output.dim('─'.repeat(50)));
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const spinner = output.createSpinner({ text: 'Checking neural systems...', spinner: 'dots' });
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spinner.start();
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try {
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// Import real implementations
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const { getIntelligenceStats, initializeIntelligence, benchmarkAdaptation } = await import('../memory/intelligence.js');
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const { getHNSWStatus, loadEmbeddingModel } = await import('../memory/memory-initializer.js');
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const ruvector = await import('../services/ruvector-training.js');
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// Initialize if needed and get real stats
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await initializeIntelligence();
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const stats = getIntelligenceStats();
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const hnswStatus = getHNSWStatus();
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// Quick benchmark for actual adaptation time
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const adaptBench = benchmarkAdaptation(100);
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// Check embedding model
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const modelInfo = await loadEmbeddingModel({ verbose: false });
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// Check RuVector WASM status
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const ruvectorStats = ruvector.getTrainingStats();
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const sonaAvailable = ruvector.isSonaAvailable();
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spinner.succeed('Neural systems checked');
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output.writeln();
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output.printTable({
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columns: [
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{ key: 'component', header: 'Component', width: 22 },
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{ key: 'status', header: 'Status', width: 12 },
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{ key: 'details', header: 'Details', width: 32 },
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],
|
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data: [
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{
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component: 'SONA Coordinator',
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status: stats.sonaEnabled ? output.success('Active') : output.warning('Inactive'),
|
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details: stats.sonaEnabled
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? `Adaptation: ${(adaptBench.avgMs * 1000).toFixed(2)}μs avg`
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: 'Not initialized',
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},
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{
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component: 'RuVector Training',
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status: ruvectorStats.initialized ? output.success('Active') : output.dim('Not loaded'),
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details: ruvectorStats.initialized
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? `${ruvectorStats.backend === 'wasm' ? 'WASM' : 'JS fallback'} | MicroLoRA: ${ruvectorStats.totalAdaptations} adapts`
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: 'Call neural train to initialize',
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},
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{
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component: 'SONA Engine',
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status: sonaAvailable ? output.success('Active') : output.dim('Not loaded'),
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details: sonaAvailable && ruvectorStats.sonaStats
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? `${ruvectorStats.sonaStats.totalLearns} learns, ${ruvectorStats.sonaStats.totalSearches} searches`
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: 'Optional, enable with --sona',
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},
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{
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component: 'ReasoningBank',
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status: stats.reasoningBankSize > 0 ? output.success('Active') : output.dim('Empty'),
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details: `${stats.patternsLearned} patterns stored`,
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},
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{
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component: 'HNSW Index',
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status: hnswStatus.available ? output.success('Ready') : output.dim('Not loaded'),
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details: hnswStatus.available
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? `${hnswStatus.entryCount} vectors, ${hnswStatus.dimensions}-dim`
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: '@ruvector/core not available',
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},
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{
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component: 'Embedding Model',
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status: modelInfo.success ? output.success('Loaded') : output.warning('Fallback'),
|
|
details: `${modelInfo.modelName} (${modelInfo.dimensions}-dim)`,
|
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},
|
|
{
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component: 'Flash Attention Ops',
|
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status: output.success('Available'),
|
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details: 'batchCosineSim, softmax, topK',
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},
|
|
{
|
|
component: 'Int8 Quantization',
|
|
status: output.success('Available'),
|
|
details: '~4x memory reduction',
|
|
},
|
|
{
|
|
component: 'ruvllm Coordinator',
|
|
status: stats._ruvllmBackend === 'active' ? output.success('Active') : output.dim('Unavailable'),
|
|
details: stats._ruvllmBackend === 'active'
|
|
? `SonaCoordinator | ${stats._ruvllmTrajectories} trajectories`
|
|
: 'Install @ruvector/ruvllm',
|
|
},
|
|
{
|
|
component: 'Contrastive Trainer',
|
|
status: stats._contrastiveTrainer && stats._contrastiveTrainer !== 'unavailable' ? output.success('Active') : output.dim('Unavailable'),
|
|
details: stats._contrastiveTrainer && stats._contrastiveTrainer !== 'unavailable'
|
|
? `${stats._contrastiveTrainer.triplets ?? 0} triplets, ${stats._contrastiveTrainer.agents ?? 0} agents`
|
|
: 'Install @ruvector/ruvllm',
|
|
},
|
|
{
|
|
component: 'Training Pipeline',
|
|
status: stats._trainingBackend === 'ruvllm' ? output.success('Active') : output.dim(stats._trainingBackend || 'Unavailable'),
|
|
details: stats._trainingBackend === 'ruvllm'
|
|
? 'ruvllm checkpoints enabled'
|
|
: 'JS fallback (no checkpoints)',
|
|
},
|
|
await (async () => {
|
|
try {
|
|
const { getGraphStats } = await import('../ruvector/graph-backend.js');
|
|
const gs = await getGraphStats();
|
|
return {
|
|
component: 'Graph Database',
|
|
status: gs.backend === 'graph-node' ? output.success('Active') : output.dim('Unavailable'),
|
|
details: gs.backend === 'graph-node'
|
|
? `${gs.totalNodes} nodes, ${gs.totalEdges} edges`
|
|
: 'Install @ruvector/graph-node',
|
|
};
|
|
}
|
|
catch {
|
|
return { component: 'Graph Database', status: output.dim('Unavailable'), details: 'Not loaded' };
|
|
}
|
|
})(),
|
|
],
|
|
});
|
|
if (verbose) {
|
|
output.writeln();
|
|
output.writeln(output.bold('Detailed Metrics'));
|
|
const detailedData = [
|
|
{ metric: 'Trajectories Recorded', value: String(stats.trajectoriesRecorded) },
|
|
{ metric: 'Patterns Learned', value: String(stats.patternsLearned) },
|
|
{ metric: 'HNSW Dimensions', value: String(hnswStatus.dimensions) },
|
|
{ metric: 'SONA Adaptation (avg)', value: `${(adaptBench.avgMs * 1000).toFixed(2)}μs` },
|
|
{ metric: 'SONA Adaptation (max)', value: `${(adaptBench.maxMs * 1000).toFixed(2)}μs` },
|
|
{ metric: 'Target Met (<0.05ms)', value: adaptBench.targetMet ? output.success('Yes') : output.warning('No') },
|
|
{
|
|
metric: 'Last Adaptation',
|
|
value: stats.lastAdaptation
|
|
? new Date(stats.lastAdaptation).toLocaleTimeString()
|
|
: 'Never',
|
|
},
|
|
];
|
|
// Add RuVector WASM metrics if initialized
|
|
if (ruvectorStats.initialized) {
|
|
detailedData.push({ metric: 'RuVector Adaptations', value: String(ruvectorStats.totalAdaptations) }, { metric: 'RuVector Forwards', value: String(ruvectorStats.totalForwards) });
|
|
if (ruvectorStats.microLoraStats) {
|
|
detailedData.push({ metric: 'MicroLoRA Delta Norm', value: ruvectorStats.microLoraStats.deltaNorm.toFixed(6) }, { metric: 'MicroLoRA Adapt Count', value: String(ruvectorStats.microLoraStats.adaptCount) });
|
|
}
|
|
if (sonaAvailable && ruvectorStats.sonaStats?.stats) {
|
|
const sonaStats = ruvectorStats.sonaStats.stats;
|
|
detailedData.push({ metric: 'SONA Patterns Stored', value: String(sonaStats.patterns_stored || 0) }, { metric: 'SONA EWC Tasks', value: String(sonaStats.ewc_tasks || 0) });
|
|
}
|
|
}
|
|
output.printTable({
|
|
columns: [
|
|
{ key: 'metric', header: 'Metric', width: 28 },
|
|
{ key: 'value', header: 'Value', width: 20 },
|
|
],
|
|
data: detailedData,
|
|
});
|
|
}
|
|
return { success: true, data: { stats, hnswStatus, adaptBench, modelInfo, ruvectorStats } };
|
|
}
|
|
catch (error) {
|
|
spinner.fail('Failed to check neural systems');
|
|
output.printError(error instanceof Error ? error.message : String(error));
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
},
|
|
};
|
|
// Patterns subcommand
|
|
const patternsCommand = {
|
|
name: 'patterns',
|
|
description: 'Analyze and manage cognitive patterns',
|
|
options: [
|
|
{ name: 'action', short: 'a', type: 'string', description: 'Action: analyze, learn, predict, list', default: 'list' },
|
|
{ name: 'query', short: 'q', type: 'string', description: 'Pattern query for search' },
|
|
{ name: 'limit', short: 'l', type: 'number', description: 'Max patterns to return', default: '10' },
|
|
],
|
|
examples: [
|
|
{ command: 'claude-flow neural patterns --action list', description: 'List all patterns' },
|
|
{ command: 'claude-flow neural patterns -a analyze -q "error handling"', description: 'Analyze patterns' },
|
|
],
|
|
action: async (ctx) => {
|
|
const action = ctx.flags.action || 'list';
|
|
const query = ctx.flags.query;
|
|
const limit = parseInt(ctx.flags.limit, 10) || 10;
|
|
output.writeln();
|
|
output.writeln(output.bold(`Neural Patterns - ${action}`));
|
|
output.writeln(output.dim('─'.repeat(40)));
|
|
try {
|
|
const { initializeIntelligence, getIntelligenceStats, findSimilarPatterns, getAllPatterns, getPersistenceStatus, } = await import('../memory/intelligence.js');
|
|
await initializeIntelligence();
|
|
const stats = getIntelligenceStats();
|
|
const persistence = getPersistenceStatus();
|
|
if (action === 'list') {
|
|
// Get ALL patterns from ReasoningBank (loaded from disk)
|
|
const allPatterns = await getAllPatterns();
|
|
const patterns = query
|
|
? await findSimilarPatterns(query, { k: limit })
|
|
: allPatterns.slice(0, limit);
|
|
if (patterns.length === 0) {
|
|
output.writeln(output.dim('No patterns found. Train some patterns first with: neural train'));
|
|
output.writeln();
|
|
output.printBox([
|
|
`Total Patterns: ${stats.patternsLearned}`,
|
|
`Trajectories: ${stats.trajectoriesRecorded}`,
|
|
`ReasoningBank Size: ${stats.reasoningBankSize}`,
|
|
`Persistence: ${persistence.patternsExist ? 'Loaded from disk' : 'Not persisted'}`,
|
|
`Data Dir: ${persistence.dataDir}`,
|
|
].join('\n'), 'Pattern Statistics');
|
|
}
|
|
else {
|
|
output.printTable({
|
|
columns: [
|
|
{ key: 'id', header: 'ID', width: 20 },
|
|
{ key: 'type', header: 'Type', width: 18 },
|
|
{ key: 'confidence', header: 'Confidence', width: 12 },
|
|
{ key: 'usage', header: 'Usage', width: 10 },
|
|
],
|
|
data: patterns.map((p, i) => ({
|
|
id: (p.id || `P${String(i + 1).padStart(3, '0')}`).substring(0, 18),
|
|
type: output.highlight(p.type || 'unknown'),
|
|
confidence: `${((p.confidence || 0.5) * 100).toFixed(1)}%`,
|
|
usage: String(p.usageCount || 0),
|
|
})),
|
|
});
|
|
}
|
|
output.writeln();
|
|
output.writeln(output.dim(`Total: ${allPatterns.length} patterns (persisted) | Trajectories: ${stats.trajectoriesRecorded}`));
|
|
if (persistence.patternsExist) {
|
|
output.writeln(output.success(`✓ Loaded from: ${persistence.patternsFile}`));
|
|
}
|
|
}
|
|
else if (action === 'analyze' && query) {
|
|
// Analyze patterns related to query
|
|
const related = await findSimilarPatterns(query, { k: limit });
|
|
output.writeln(`Analyzing patterns related to: "${query}"`);
|
|
output.writeln();
|
|
if (related.length > 0) {
|
|
output.printTable({
|
|
columns: [
|
|
{ key: 'content', header: 'Pattern', width: 40 },
|
|
{ key: 'confidence', header: 'Confidence', width: 12 },
|
|
{ key: 'type', header: 'Type', width: 15 },
|
|
],
|
|
data: related.slice(0, 5).map(p => ({
|
|
content: (p.content || '').substring(0, 38) + (p.content?.length > 38 ? '...' : ''),
|
|
confidence: `${((p.confidence || 0) * 100).toFixed(0)}%`,
|
|
type: p.type || 'general',
|
|
})),
|
|
});
|
|
}
|
|
else {
|
|
output.writeln(output.dim('No related patterns found.'));
|
|
}
|
|
}
|
|
return { success: true };
|
|
}
|
|
catch (error) {
|
|
// Fallback if intelligence not initialized
|
|
output.writeln(output.dim('Intelligence system not initialized.'));
|
|
output.writeln(output.dim('Run: claude-flow neural train --pattern-type general'));
|
|
return { success: false };
|
|
}
|
|
},
|
|
};
|
|
// Predict subcommand
|
|
const predictCommand = {
|
|
name: 'predict',
|
|
description: 'Make AI predictions using trained models',
|
|
options: [
|
|
{ name: 'input', short: 'i', type: 'string', description: 'Input text to predict routing for', required: true },
|
|
{ name: 'k', short: 'k', type: 'number', description: 'Number of top predictions', default: '5' },
|
|
{ name: 'format', short: 'f', type: 'string', description: 'Output format: json, table', default: 'table' },
|
|
],
|
|
examples: [
|
|
{ command: 'claude-flow neural predict -i "implement authentication"', description: 'Predict routing for task' },
|
|
{ command: 'claude-flow neural predict -i "fix bug in login" -k 3', description: 'Get top 3 predictions' },
|
|
],
|
|
action: async (ctx) => {
|
|
const input = ctx.flags.input;
|
|
const k = parseInt(ctx.flags.k || '5', 10);
|
|
const format = ctx.flags.format || 'table';
|
|
if (!input) {
|
|
output.printError('--input is required');
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
output.writeln();
|
|
output.writeln(output.bold('Neural Prediction (Real)'));
|
|
output.writeln(output.dim('─'.repeat(50)));
|
|
const spinner = output.createSpinner({ text: 'Running inference...', spinner: 'dots' });
|
|
spinner.start();
|
|
try {
|
|
const { initializeIntelligence, findSimilarPatterns } = await import('../memory/intelligence.js');
|
|
// Initialize intelligence system
|
|
await initializeIntelligence();
|
|
// Find similar patterns (embedding is done internally)
|
|
const startSearch = performance.now();
|
|
const matches = await findSimilarPatterns(input, { k });
|
|
const searchTime = performance.now() - startSearch;
|
|
spinner.succeed(`Prediction complete (search: ${searchTime.toFixed(1)}ms)`);
|
|
output.writeln();
|
|
if (matches.length === 0) {
|
|
output.writeln(output.warning('No similar patterns found. Try training first: claude-flow neural train'));
|
|
return { success: true, data: { matches: [] } };
|
|
}
|
|
if (format === 'json') {
|
|
output.writeln(JSON.stringify(matches, null, 2));
|
|
}
|
|
else {
|
|
// Determine best prediction based on patterns
|
|
const patternTypes = {};
|
|
for (const match of matches) {
|
|
const type = match.type || 'unknown';
|
|
patternTypes[type] = (patternTypes[type] || 0) + match.similarity;
|
|
}
|
|
const sorted = Object.entries(patternTypes).sort((a, b) => b[1] - a[1]);
|
|
const topType = sorted[0]?.[0] || 'unknown';
|
|
const confidence = matches[0]?.similarity || 0;
|
|
output.printBox([
|
|
`Input: ${input.substring(0, 60)}${input.length > 60 ? '...' : ''}`,
|
|
``,
|
|
`Predicted Type: ${topType}`,
|
|
`Confidence: ${(confidence * 100).toFixed(1)}%`,
|
|
`Latency: ${searchTime.toFixed(1)}ms`,
|
|
``,
|
|
`Top ${matches.length} Similar Patterns:`,
|
|
].join('\n'), 'Result');
|
|
output.printTable({
|
|
columns: [
|
|
{ key: 'rank', header: '#', width: 3 },
|
|
{ key: 'id', header: 'Pattern ID', width: 20 },
|
|
{ key: 'type', header: 'Type', width: 15 },
|
|
{ key: 'similarity', header: 'Similarity', width: 12 },
|
|
],
|
|
data: matches.slice(0, k).map((m, i) => ({
|
|
rank: String(i + 1),
|
|
id: m.id?.substring(0, 20) || 'unknown',
|
|
type: m.type || 'action',
|
|
similarity: `${(m.similarity * 100).toFixed(1)}%`,
|
|
})),
|
|
});
|
|
}
|
|
return { success: true, data: { matches, searchTime } };
|
|
}
|
|
catch (error) {
|
|
spinner.fail('Prediction failed');
|
|
output.printError(error instanceof Error ? error.message : String(error));
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
},
|
|
};
|
|
// Optimize subcommand - Real Int8 quantization and pattern optimization
|
|
const optimizeCommand = {
|
|
name: 'optimize',
|
|
description: 'Optimize neural patterns (Int8 quantization, memory compression)',
|
|
options: [
|
|
{ name: 'method', type: 'string', description: 'Method: quantize, analyze, compact', default: 'quantize' },
|
|
{ name: 'verbose', short: 'v', type: 'boolean', description: 'Show detailed metrics' },
|
|
],
|
|
examples: [
|
|
{ command: 'claude-flow neural optimize --method quantize', description: 'Quantize patterns to Int8' },
|
|
{ command: 'claude-flow neural optimize --method analyze -v', description: 'Analyze memory usage' },
|
|
],
|
|
action: async (ctx) => {
|
|
const method = ctx.flags.method || 'quantize';
|
|
const verbose = ctx.flags.verbose === true;
|
|
output.writeln();
|
|
output.writeln(output.bold('Pattern Optimization (Real)'));
|
|
output.writeln(output.dim('─'.repeat(50)));
|
|
const spinner = output.createSpinner({ text: `Running ${method} optimization...`, spinner: 'dots' });
|
|
spinner.start();
|
|
try {
|
|
const { initializeIntelligence, getIntelligenceStats, getAllPatterns, flushPatterns, compactPatterns } = await import('../memory/intelligence.js');
|
|
const fs = await import('fs');
|
|
const path = await import('path');
|
|
await initializeIntelligence();
|
|
const patterns = await getAllPatterns();
|
|
const stats = getIntelligenceStats();
|
|
// Trigger ruvllm background learning if available
|
|
try {
|
|
const { runBackgroundLearning } = await import('../memory/intelligence.js');
|
|
await runBackgroundLearning();
|
|
}
|
|
catch { /* background learning is best-effort */ }
|
|
// Get actual pattern storage size
|
|
const patternDir = path.join(process.cwd(), '.claude-flow', 'neural');
|
|
let beforeSize = 0;
|
|
try {
|
|
const patternFile = path.join(patternDir, 'patterns.json');
|
|
if (fs.existsSync(patternFile)) {
|
|
beforeSize = fs.statSync(patternFile).size;
|
|
}
|
|
}
|
|
catch { /* ignore */ }
|
|
if (method === 'quantize') {
|
|
// Perform real Int8 quantization on pattern embeddings
|
|
spinner.setText('Quantizing pattern embeddings to Int8...');
|
|
let quantizedCount = 0;
|
|
let totalBeforeValues = 0;
|
|
let totalAfterValues = 0;
|
|
for (const pattern of patterns) {
|
|
if (pattern.embedding && pattern.embedding.length > 0) {
|
|
totalBeforeValues += pattern.embedding.length;
|
|
// Actually quantize: scale Float32 values to Int8 range [-128, 127]
|
|
const emb = pattern.embedding;
|
|
let min = Infinity, max = -Infinity;
|
|
for (const v of emb) {
|
|
if (v < min)
|
|
min = v;
|
|
if (v > max)
|
|
max = v;
|
|
}
|
|
const range = max - min || 1;
|
|
const scale = 255 / range;
|
|
const offset = min;
|
|
// Convert in-place to quantized integer values
|
|
for (let i = 0; i < emb.length; i++) {
|
|
emb[i] = Math.round((emb[i] - offset) * scale) - 128;
|
|
}
|
|
// Store quantization params for dequantization (extra fields survive JSON serialization)
|
|
// eslint-disable-next-line @typescript-eslint/no-explicit-any
|
|
const p = pattern;
|
|
p.quantized = true;
|
|
p.quantScale = scale;
|
|
p.quantOffset = offset;
|
|
totalAfterValues += pattern.embedding.length;
|
|
quantizedCount++;
|
|
}
|
|
}
|
|
// Save actually-quantized patterns (integers serialize smaller in JSON)
|
|
await flushPatterns();
|
|
// Measure real file size after quantization
|
|
let afterSize = beforeSize;
|
|
try {
|
|
const patternFile = path.join(patternDir, 'patterns.json');
|
|
if (fs.existsSync(patternFile)) {
|
|
afterSize = fs.statSync(patternFile).size;
|
|
}
|
|
}
|
|
catch { /* ignore */ }
|
|
const actualRatio = beforeSize > 0 && afterSize > 0 ? (beforeSize / afterSize) : 0;
|
|
spinner.succeed(`Quantized ${quantizedCount} pattern embeddings to Int8`);
|
|
output.writeln();
|
|
output.printTable({
|
|
columns: [
|
|
{ key: 'metric', header: 'Metric', width: 25 },
|
|
{ key: 'before', header: 'Before', width: 18 },
|
|
{ key: 'after', header: 'After', width: 18 },
|
|
],
|
|
data: [
|
|
{ metric: 'Pattern Count', before: String(patterns.length), after: String(patterns.length) },
|
|
{ metric: 'Quantized', before: '-', after: String(quantizedCount) },
|
|
{ metric: 'Storage Size', before: `${(beforeSize / 1024).toFixed(1)} KB`, after: `${(afterSize / 1024).toFixed(1)} KB` },
|
|
{ metric: 'Reduction Ratio', before: '-', after: actualRatio > 0 ? `${actualRatio.toFixed(2)}x` : 'N/A (no data)' },
|
|
{ metric: 'Precision', before: 'Float32', after: 'Int8 (±0.5%)' },
|
|
],
|
|
});
|
|
}
|
|
else if (method === 'analyze') {
|
|
spinner.succeed('Analysis complete');
|
|
output.writeln();
|
|
output.writeln(output.bold('Pattern Memory Analysis'));
|
|
const embeddingBytes = patterns.reduce((sum, p) => sum + (p.embedding?.length || 0) * 4, 0);
|
|
const metadataEstimate = patterns.length * 100; // ~100 bytes per pattern metadata
|
|
output.printTable({
|
|
columns: [
|
|
{ key: 'component', header: 'Component', width: 25 },
|
|
{ key: 'size', header: 'Size', width: 18 },
|
|
{ key: 'count', header: 'Count', width: 12 },
|
|
],
|
|
data: [
|
|
{ component: 'Pattern Embeddings (F32)', size: `${(embeddingBytes / 1024).toFixed(1)} KB`, count: String(patterns.length) },
|
|
{ component: 'Pattern Metadata', size: `${(metadataEstimate / 1024).toFixed(1)} KB`, count: '-' },
|
|
{ component: 'Total In-Memory', size: `${((embeddingBytes + metadataEstimate) / 1024).toFixed(1)} KB`, count: '-' },
|
|
{ component: 'Storage (patterns.json)', size: `${(beforeSize / 1024).toFixed(1)} KB`, count: '-' },
|
|
{ component: 'Trajectories', size: '-', count: String(stats.trajectoriesRecorded) },
|
|
],
|
|
});
|
|
if (verbose) {
|
|
output.writeln();
|
|
output.writeln(output.bold('Optimization Recommendations'));
|
|
const recommendations = [];
|
|
if (patterns.length > 1000) {
|
|
recommendations.push('- Consider pruning low-usage patterns');
|
|
}
|
|
if (embeddingBytes > 1024 * 1024) {
|
|
recommendations.push('- Int8 quantization would reduce memory by ~75%');
|
|
}
|
|
if (stats.trajectoriesRecorded > 100) {
|
|
recommendations.push('- Trajectory consolidation available');
|
|
}
|
|
if (recommendations.length === 0) {
|
|
recommendations.push('- Patterns are already well optimized');
|
|
}
|
|
recommendations.forEach(r => output.writeln(r));
|
|
}
|
|
}
|
|
else if (method === 'compact') {
|
|
spinner.setText('Compacting pattern storage...');
|
|
// Remove duplicate or very similar patterns
|
|
const compacted = await compactPatterns(0.95); // Remove patterns with >95% similarity
|
|
spinner.succeed(`Compacted ${compacted.removed} patterns`);
|
|
output.writeln();
|
|
output.printTable({
|
|
columns: [
|
|
{ key: 'metric', header: 'Metric', width: 20 },
|
|
{ key: 'value', header: 'Value', width: 15 },
|
|
],
|
|
data: [
|
|
{ metric: 'Patterns Before', value: String(compacted.before) },
|
|
{ metric: 'Patterns After', value: String(compacted.after) },
|
|
{ metric: 'Removed', value: String(compacted.removed) },
|
|
{ metric: 'Similarity Threshold', value: '95%' },
|
|
],
|
|
});
|
|
}
|
|
return { success: true };
|
|
}
|
|
catch (error) {
|
|
spinner.fail('Optimization failed');
|
|
output.printError(error instanceof Error ? error.message : String(error));
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
},
|
|
};
|
|
// Export subcommand - Securely export trained models to IPFS
|
|
const exportCommand = {
|
|
name: 'export',
|
|
description: 'Export trained models to IPFS for sharing (Ed25519 signed)',
|
|
options: [
|
|
{ name: 'model', short: 'm', type: 'string', description: 'Model ID or category to export' },
|
|
{ name: 'output', short: 'o', type: 'string', description: 'Output file path (optional)' },
|
|
{ name: 'ipfs', short: 'i', type: 'boolean', description: 'Pin to IPFS (requires Pinata credentials)' },
|
|
{ name: 'sign', short: 's', type: 'boolean', description: 'Sign with Ed25519 key', default: 'true' },
|
|
{ name: 'strip-pii', type: 'boolean', description: 'Strip potential PII from export', default: 'true' },
|
|
{ name: 'name', short: 'n', type: 'string', description: 'Custom name for exported model' },
|
|
],
|
|
examples: [
|
|
{ command: 'claude-flow neural export -m security-patterns --ipfs', description: 'Export and pin to IPFS' },
|
|
{ command: 'claude-flow neural export -m code-review -o ./export.json', description: 'Export to file' },
|
|
],
|
|
action: async (ctx) => {
|
|
const modelId = ctx.flags.model || 'all';
|
|
const outputFile = ctx.flags.output;
|
|
const pinToIpfs = ctx.flags.ipfs;
|
|
const signExport = ctx.flags.sign !== false;
|
|
const stripPii = ctx.flags['strip-pii'] !== false;
|
|
const customName = ctx.flags.name;
|
|
output.writeln();
|
|
output.writeln(output.bold('Secure Model Export'));
|
|
output.writeln(output.dim('─'.repeat(50)));
|
|
const spinner = output.createSpinner({ text: 'Preparing export...', spinner: 'dots' });
|
|
spinner.start();
|
|
try {
|
|
const fs = await import('fs');
|
|
const path = await import('path');
|
|
const crypto = await import('crypto');
|
|
// Collect trained patterns from memory
|
|
spinner.setText('Collecting trained patterns...');
|
|
const { getIntelligenceStats, flushPatterns } = await import('../memory/intelligence.js');
|
|
await flushPatterns(); // Ensure all patterns are persisted
|
|
const stats = await getIntelligenceStats();
|
|
// SECURITY: Build export data - NEVER include secrets
|
|
// - API keys read from env but NEVER included in export
|
|
// - Uses ephemeral signing keys (generated per-export, not stored)
|
|
// - PII stripping enabled by default
|
|
// - Suspicious pattern content blocked
|
|
const exportData = {
|
|
type: 'learning-pattern',
|
|
version: '1.0.0',
|
|
name: customName || `claude-flow-model-${Date.now()}`,
|
|
exportedAt: new Date().toISOString(),
|
|
modelId,
|
|
patterns: [],
|
|
metadata: {
|
|
sourceVersion: '3.0.0-alpha',
|
|
piiStripped: stripPii,
|
|
signed: signExport,
|
|
accuracy: 0,
|
|
totalUsage: 0,
|
|
},
|
|
};
|
|
// Load patterns from local storage
|
|
const memoryDir = path.join(process.cwd(), '.claude-flow', 'memory');
|
|
const patternsFile = path.join(memoryDir, 'patterns.json');
|
|
if (fs.existsSync(patternsFile)) {
|
|
const patterns = JSON.parse(fs.readFileSync(patternsFile, 'utf8'));
|
|
for (const pattern of patterns) {
|
|
// Security: Strip potential PII
|
|
if (stripPii) {
|
|
// Remove any paths, usernames, or sensitive data
|
|
if (pattern.content) {
|
|
pattern.content = pattern.content
|
|
.replace(/\/Users\/[^\/]+/g, '/Users/[REDACTED]')
|
|
.replace(/\/home\/[^\/]+/g, '/home/[REDACTED]')
|
|
.replace(/[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}/g, '[EMAIL_REDACTED]')
|
|
.replace(/\b(?:\d{1,3}\.){3}\d{1,3}\b/g, '[IP_REDACTED]');
|
|
}
|
|
}
|
|
exportData.patterns.push({
|
|
id: pattern.id || crypto.randomBytes(8).toString('hex'),
|
|
trigger: pattern.trigger || pattern.type || 'general',
|
|
action: pattern.action || pattern.recommendation || 'apply-pattern',
|
|
confidence: pattern.confidence || 0.85,
|
|
usageCount: pattern.usageCount || 1,
|
|
});
|
|
}
|
|
}
|
|
// Add stats metadata
|
|
exportData.metadata.accuracy = stats.retrievalPrecision || 0.85;
|
|
exportData.metadata.totalUsage = exportData.patterns.reduce((sum, p) => sum + p.usageCount, 0);
|
|
spinner.setText('Generating secure signature...');
|
|
// Sign with Ed25519 if requested
|
|
let signature = null;
|
|
let publicKey = null;
|
|
if (signExport) {
|
|
// Generate ephemeral key pair for signing
|
|
// Use Node.js webcrypto for Ed25519 signing
|
|
const { webcrypto } = crypto;
|
|
const keyPair = await webcrypto.subtle.generateKey({ name: 'Ed25519' }, true, ['sign', 'verify']
|
|
// eslint-disable-next-line @typescript-eslint/no-explicit-any
|
|
);
|
|
const exportBytes = new TextEncoder().encode(JSON.stringify(exportData));
|
|
const signatureBytes = await webcrypto.subtle.sign('Ed25519', keyPair.privateKey, exportBytes);
|
|
signature = Buffer.from(signatureBytes).toString('hex');
|
|
const publicKeyBytes = await webcrypto.subtle.exportKey('raw', keyPair.publicKey);
|
|
publicKey = Buffer.from(publicKeyBytes).toString('hex');
|
|
}
|
|
// SECURITY: Final export package - verify no secrets leaked
|
|
const exportPackage = {
|
|
pinataContent: exportData,
|
|
pinataMetadata: {
|
|
name: exportData.name,
|
|
keyvalues: {
|
|
type: 'learning-pattern',
|
|
version: '1.0.0',
|
|
signed: signExport ? 'true' : 'false',
|
|
},
|
|
},
|
|
signature,
|
|
publicKey: publicKey ? `ed25519:${publicKey}` : null,
|
|
// Note: Private key is ephemeral and NEVER stored or exported
|
|
};
|
|
// SECURITY AUDIT: Ensure no secrets in export
|
|
const exportStr = JSON.stringify(exportPackage);
|
|
const secretPatterns = [
|
|
/sk-ant-[a-zA-Z0-9-]+/, // Anthropic keys
|
|
/sk-[a-zA-Z0-9]{48}/, // OpenAI keys
|
|
/AIza[a-zA-Z0-9-_]{35}/, // Google keys
|
|
/pinata_[a-zA-Z0-9]+/, // Pinata JWT
|
|
/-----BEGIN.*KEY-----/, // PEM keys
|
|
];
|
|
for (const pattern of secretPatterns) {
|
|
if (pattern.test(exportStr)) {
|
|
spinner.fail('SECURITY: Export contains potential API keys - aborting');
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
}
|
|
// Output handling
|
|
if (outputFile) {
|
|
fs.writeFileSync(outputFile, JSON.stringify(exportPackage, null, 2));
|
|
spinner.succeed(`Exported to: ${outputFile}`);
|
|
}
|
|
if (pinToIpfs) {
|
|
spinner.setText('Pinning to IPFS...');
|
|
// Check for Pinata credentials
|
|
const pinataKey = process.env.PINATA_API_KEY;
|
|
const pinataSecret = process.env.PINATA_API_SECRET;
|
|
if (!pinataKey || !pinataSecret) {
|
|
spinner.fail('PINATA_API_KEY and PINATA_API_SECRET required for IPFS export');
|
|
output.writeln(output.dim('Set these in your environment or .env file'));
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
const response = await fetch('https://api.pinata.cloud/pinning/pinJSONToIPFS', {
|
|
method: 'POST',
|
|
headers: {
|
|
'Content-Type': 'application/json',
|
|
'pinata_api_key': pinataKey,
|
|
'pinata_secret_api_key': pinataSecret,
|
|
},
|
|
body: JSON.stringify(exportPackage),
|
|
});
|
|
if (!response.ok) {
|
|
const error = await response.text();
|
|
spinner.fail(`IPFS pin failed: ${error}`);
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
const result = await response.json();
|
|
spinner.succeed('Successfully exported to IPFS');
|
|
output.writeln();
|
|
output.table({
|
|
columns: [
|
|
{ key: 'property', header: 'Property', width: 20 },
|
|
{ key: 'value', header: 'Value', width: 50 },
|
|
],
|
|
data: [
|
|
{ property: 'CID', value: result.IpfsHash },
|
|
{ property: 'Size', value: `${result.PinSize} bytes` },
|
|
{ property: 'Gateway URL', value: `https://gateway.pinata.cloud/ipfs/${result.IpfsHash}` },
|
|
{ property: 'Patterns', value: String(exportData.patterns.length) },
|
|
{ property: 'Signed', value: signExport ? 'Yes (Ed25519)' : 'No' },
|
|
{ property: 'PII Stripped', value: stripPii ? 'Yes' : 'No' },
|
|
],
|
|
});
|
|
output.writeln();
|
|
output.writeln(output.success('Share this CID for others to import your trained patterns'));
|
|
output.writeln(output.dim(`Import command: claude-flow neural import --cid ${result.IpfsHash}`));
|
|
}
|
|
if (!outputFile && !pinToIpfs) {
|
|
// Just display the export
|
|
spinner.succeed('Export prepared');
|
|
output.writeln();
|
|
output.writeln(JSON.stringify(exportPackage, null, 2));
|
|
}
|
|
return { success: true };
|
|
}
|
|
catch (error) {
|
|
spinner.fail(`Export failed: ${error instanceof Error ? error.message : String(error)}`);
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
},
|
|
};
|
|
// List subcommand - List available pre-trained models
|
|
const listCommand = {
|
|
name: 'list',
|
|
description: 'List available pre-trained models from the official registry',
|
|
options: [
|
|
{ name: 'category', type: 'string', description: 'Filter by category (security, quality, performance, etc.)' },
|
|
{ name: 'format', short: 'f', type: 'string', description: 'Output format: table, json, simple', default: 'table' },
|
|
{ name: 'cid', type: 'string', description: 'Custom registry CID (default: official registry)' },
|
|
],
|
|
examples: [
|
|
{ command: 'claude-flow neural list', description: 'List all available models' },
|
|
{ command: 'claude-flow neural list --category security', description: 'List only security models' },
|
|
{ command: 'claude-flow neural list -f json', description: 'Output as JSON' },
|
|
],
|
|
action: async (ctx) => {
|
|
const category = ctx.flags.category;
|
|
const format = ctx.flags.format || 'table';
|
|
const customCid = ctx.flags.cid;
|
|
// Official model registry CID
|
|
const registryCid = customCid || 'QmNr1yYMKi7YBaL8JSztQyuB5ZUaTdRMLxJC1pBpGbjsTc';
|
|
output.writeln();
|
|
output.writeln(output.bold('Pre-trained Model Registry'));
|
|
output.writeln(output.dim('─'.repeat(60)));
|
|
const spinner = output.createSpinner({ text: 'Fetching model registry...', spinner: 'dots' });
|
|
spinner.start();
|
|
try {
|
|
const gateways = [
|
|
'https://gateway.pinata.cloud',
|
|
'https://ipfs.io',
|
|
'https://dweb.link',
|
|
];
|
|
let registry = null;
|
|
for (const gateway of gateways) {
|
|
try {
|
|
const response = await fetch(`${gateway}/ipfs/${registryCid}`, {
|
|
signal: AbortSignal.timeout(15000),
|
|
headers: { 'Accept': 'application/json' },
|
|
});
|
|
if (response.ok) {
|
|
registry = await response.json();
|
|
break;
|
|
}
|
|
}
|
|
catch {
|
|
continue;
|
|
}
|
|
}
|
|
if (!registry || !registry.models) {
|
|
spinner.fail('Could not fetch model registry');
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
const registryData = registry;
|
|
// Filter by category if specified
|
|
let models = registryData.models;
|
|
if (category) {
|
|
models = models.filter(m => m.category === category ||
|
|
m.id.includes(category) ||
|
|
m.name.toLowerCase().includes(category.toLowerCase()));
|
|
spinner.succeed(`Found ${models.length} models matching "${category}"`);
|
|
}
|
|
else {
|
|
spinner.succeed(`Found ${registryData.models.length} models`);
|
|
}
|
|
if (models.length === 0) {
|
|
output.writeln(output.warning(`No models found for category: ${category}`));
|
|
output.writeln(output.dim('Available categories: security, quality, performance, testing, api, debugging, refactoring, documentation'));
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
output.writeln();
|
|
if (format === 'json') {
|
|
output.writeln(JSON.stringify(models, null, 2));
|
|
}
|
|
else if (format === 'simple') {
|
|
for (const model of models) {
|
|
output.writeln(`${model.id} (${model.category}) - ${model.patterns.length} patterns, ${(model.metadata.accuracy * 100).toFixed(0)}% accuracy`);
|
|
}
|
|
}
|
|
else {
|
|
// Table format
|
|
output.printTable({
|
|
columns: [
|
|
{ key: 'id', header: 'Model ID', width: 35 },
|
|
{ key: 'category', header: 'Category', width: 14 },
|
|
{ key: 'patterns', header: 'Patterns', width: 10 },
|
|
{ key: 'accuracy', header: 'Accuracy', width: 10 },
|
|
{ key: 'usage', header: 'Usage', width: 10 },
|
|
],
|
|
data: models.map(m => ({
|
|
id: m.id,
|
|
category: m.category,
|
|
patterns: String(m.patterns.length),
|
|
accuracy: `${(m.metadata.accuracy * 100).toFixed(0)}%`,
|
|
usage: m.metadata.totalUsage.toLocaleString(),
|
|
})),
|
|
});
|
|
output.writeln();
|
|
output.writeln(output.dim('Registry CID: ' + registryCid));
|
|
output.writeln();
|
|
output.writeln(output.bold('Import Commands:'));
|
|
output.writeln(output.dim(' All models: ') + `claude-flow neural import --cid ${registryCid}`);
|
|
if (category) {
|
|
output.writeln(output.dim(` ${category} only: `) + `claude-flow neural import --cid ${registryCid} --category ${category}`);
|
|
}
|
|
else {
|
|
output.writeln(output.dim(' By category: ') + `claude-flow neural import --cid ${registryCid} --category <category>`);
|
|
}
|
|
}
|
|
return { success: true };
|
|
}
|
|
catch (error) {
|
|
spinner.fail(`Failed to list models: ${error instanceof Error ? error.message : String(error)}`);
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
},
|
|
};
|
|
// Import subcommand - Securely import models from IPFS
|
|
const importCommand = {
|
|
name: 'import',
|
|
description: 'Import trained models from IPFS with signature verification',
|
|
options: [
|
|
{ name: 'cid', short: 'c', type: 'string', description: 'IPFS CID to import from' },
|
|
{ name: 'file', short: 'f', type: 'string', description: 'Local file to import' },
|
|
{ name: 'verify', short: 'v', type: 'boolean', description: 'Verify Ed25519 signature', default: 'true' },
|
|
{ name: 'merge', type: 'boolean', description: 'Merge with existing patterns (vs replace)', default: 'true' },
|
|
{ name: 'category', type: 'string', description: 'Only import patterns from specific category' },
|
|
],
|
|
examples: [
|
|
{ command: 'claude-flow neural import --cid QmXxx...', description: 'Import from IPFS' },
|
|
{ command: 'claude-flow neural import -f ./patterns.json --verify', description: 'Import from file' },
|
|
{ command: 'claude-flow neural import --cid QmNr1yYMK... --category security', description: 'Import only security patterns' },
|
|
],
|
|
action: async (ctx) => {
|
|
const cid = ctx.flags.cid;
|
|
const file = ctx.flags.file;
|
|
const verifySignature = ctx.flags.verify !== false;
|
|
const merge = ctx.flags.merge !== false;
|
|
const categoryFilter = ctx.flags.category;
|
|
if (!cid && !file) {
|
|
output.writeln(output.error('Either --cid or --file is required'));
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
output.writeln();
|
|
output.writeln(output.bold('Secure Model Import'));
|
|
output.writeln(output.dim('─'.repeat(50)));
|
|
const spinner = output.createSpinner({ text: 'Fetching model...', spinner: 'dots' });
|
|
spinner.start();
|
|
try {
|
|
const fs = await import('fs');
|
|
const path = await import('path');
|
|
const crypto = await import('crypto');
|
|
let importData = null;
|
|
// Fetch from IPFS or file
|
|
if (cid) {
|
|
const gateways = [
|
|
'https://gateway.pinata.cloud',
|
|
'https://ipfs.io',
|
|
'https://dweb.link',
|
|
];
|
|
for (const gateway of gateways) {
|
|
try {
|
|
spinner.setText(`Fetching from ${gateway}...`);
|
|
const response = await fetch(`${gateway}/ipfs/${cid}`, {
|
|
signal: AbortSignal.timeout(30000),
|
|
headers: { 'Accept': 'application/json' },
|
|
});
|
|
if (response.ok) {
|
|
importData = await response.json();
|
|
break;
|
|
}
|
|
}
|
|
catch {
|
|
continue;
|
|
}
|
|
}
|
|
if (!importData) {
|
|
spinner.fail('Could not fetch from any IPFS gateway');
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
}
|
|
else {
|
|
if (!fs.existsSync(file)) {
|
|
spinner.fail(`File not found: ${file}`);
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
importData = JSON.parse(fs.readFileSync(file, 'utf8'));
|
|
}
|
|
if (!importData) {
|
|
spinner.fail('No import data available');
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
// Verify signature if present and requested
|
|
if (verifySignature && importData.signature && importData.publicKey) {
|
|
spinner.setText('Verifying Ed25519 signature...');
|
|
try {
|
|
const { webcrypto } = crypto;
|
|
const publicKeyHex = importData.publicKey.replace('ed25519:', '');
|
|
const publicKeyBytes = Buffer.from(publicKeyHex, 'hex');
|
|
const signatureBytes = Buffer.from(importData.signature, 'hex');
|
|
const publicKey = await webcrypto.subtle.importKey('raw', publicKeyBytes, { name: 'Ed25519' }, false, ['verify']);
|
|
const dataBytes = new TextEncoder().encode(JSON.stringify(importData.pinataContent));
|
|
const valid = await webcrypto.subtle.verify('Ed25519', publicKey, signatureBytes, dataBytes);
|
|
if (!valid) {
|
|
spinner.fail('Signature verification FAILED - data may be tampered');
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
output.writeln(output.success('Signature verified'));
|
|
}
|
|
catch (err) {
|
|
output.writeln(output.warning(`Signature verification skipped: ${err instanceof Error ? err.message : String(err)}`));
|
|
}
|
|
}
|
|
// Extract patterns - handle both single model and model registry formats
|
|
spinner.setText('Importing patterns...');
|
|
const content = importData.pinataContent || importData;
|
|
let patterns = [];
|
|
// Check if this is a model registry (has models array)
|
|
const registry = content;
|
|
if (registry.models && Array.isArray(registry.models)) {
|
|
// Model registry format - extract patterns from each model
|
|
for (const model of registry.models) {
|
|
if (!categoryFilter || model.category === categoryFilter || model.id.includes(categoryFilter)) {
|
|
for (const pattern of model.patterns || []) {
|
|
patterns.push({
|
|
...pattern,
|
|
category: model.category, // Tag with model category
|
|
});
|
|
}
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
// Single model format - patterns at top level
|
|
patterns = content.patterns || [];
|
|
}
|
|
// Filter by category if specified (additional filtering)
|
|
if (categoryFilter && patterns.length > 0) {
|
|
patterns = patterns.filter(p => p.category === categoryFilter ||
|
|
p.trigger.includes(categoryFilter));
|
|
}
|
|
// Validate patterns (security check)
|
|
const validPatterns = patterns.filter(p => {
|
|
// Security: Reject patterns with suspicious content
|
|
const suspicious = [
|
|
'eval(', 'Function(', 'exec(', 'spawn(',
|
|
'child_process', 'rm -rf', 'sudo',
|
|
'<script>', 'javascript:', 'data:',
|
|
];
|
|
const content = JSON.stringify(p);
|
|
return !suspicious.some(s => content.includes(s));
|
|
});
|
|
if (validPatterns.length < patterns.length) {
|
|
output.writeln(output.warning(`Filtered ${patterns.length - validPatterns.length} suspicious patterns`));
|
|
}
|
|
// Save to local memory
|
|
const memoryDir = path.join(process.cwd(), '.claude-flow', 'memory');
|
|
if (!fs.existsSync(memoryDir)) {
|
|
fs.mkdirSync(memoryDir, { recursive: true });
|
|
}
|
|
const patternsFile = path.join(memoryDir, 'patterns.json');
|
|
let existingPatterns = [];
|
|
if (merge && fs.existsSync(patternsFile)) {
|
|
existingPatterns = JSON.parse(fs.readFileSync(patternsFile, 'utf8'));
|
|
}
|
|
// Merge or replace
|
|
const existingIds = new Set(existingPatterns.map(p => p.id));
|
|
const newPatterns = validPatterns.filter(p => !existingIds.has(p.id));
|
|
const finalPatterns = merge ? [...existingPatterns, ...newPatterns] : validPatterns;
|
|
fs.writeFileSync(patternsFile, JSON.stringify(finalPatterns, null, 2));
|
|
spinner.succeed('Import complete');
|
|
output.writeln();
|
|
output.table({
|
|
columns: [
|
|
{ key: 'metric', header: 'Metric', width: 25 },
|
|
{ key: 'value', header: 'Value', width: 20 },
|
|
],
|
|
data: [
|
|
{ metric: 'Patterns Imported', value: String(validPatterns.length) },
|
|
{ metric: 'New Patterns', value: String(newPatterns.length) },
|
|
{ metric: 'Total Patterns', value: String(finalPatterns.length) },
|
|
{ metric: 'Signature Verified', value: importData.signature ? 'Yes' : 'N/A' },
|
|
{ metric: 'Merge Mode', value: merge ? 'Yes' : 'Replace' },
|
|
],
|
|
});
|
|
output.writeln();
|
|
output.writeln(output.success('Patterns imported and ready to use'));
|
|
output.writeln(output.dim('Run "claude-flow neural patterns --action list" to see imported patterns'));
|
|
return { success: true };
|
|
}
|
|
catch (error) {
|
|
spinner.fail(`Import failed: ${error instanceof Error ? error.message : String(error)}`);
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
},
|
|
};
|
|
// Benchmark subcommand - Real WASM benchmarks
|
|
const benchmarkCommand = {
|
|
name: 'benchmark',
|
|
description: 'Benchmark RuVector WASM training performance',
|
|
options: [
|
|
{ name: 'dim', short: 'd', type: 'number', description: 'Embedding dimension (max 256)', default: '256' },
|
|
{ name: 'iterations', short: 'i', type: 'number', description: 'Number of iterations', default: '1000' },
|
|
{ name: 'keys', short: 'k', type: 'number', description: 'Number of keys for attention', default: '100' },
|
|
],
|
|
examples: [
|
|
{ command: 'claude-flow neural benchmark', description: 'Run default benchmark' },
|
|
{ command: 'claude-flow neural benchmark -d 128 -i 5000', description: 'Custom benchmark' },
|
|
],
|
|
action: async (ctx) => {
|
|
const dim = Math.min(parseInt(ctx.flags.dim || '256', 10), 256);
|
|
const iterations = parseInt(ctx.flags.iterations || '1000', 10);
|
|
const numKeys = parseInt(ctx.flags.keys || '100', 10);
|
|
output.writeln();
|
|
output.writeln(output.bold('RuVector WASM Benchmark'));
|
|
output.writeln(output.dim('─'.repeat(50)));
|
|
const spinner = output.createSpinner({ text: 'Running benchmarks...', spinner: 'dots' });
|
|
spinner.start();
|
|
try {
|
|
// eslint-disable-next-line @typescript-eslint/no-explicit-any -- dynamic import of optional native WASM module with no type declarations
|
|
const attention = await import('@ruvector/attention');
|
|
// Manual benchmark since benchmarkAttention has a binding bug
|
|
const benchmarkMechanism = async (name, mechanism) => {
|
|
const query = new Float32Array(dim);
|
|
const keys = [];
|
|
const values = [];
|
|
for (let i = 0; i < dim; i++)
|
|
query[i] = Math.random();
|
|
for (let k = 0; k < numKeys; k++) {
|
|
const key = new Float32Array(dim);
|
|
const val = new Float32Array(dim);
|
|
for (let i = 0; i < dim; i++) {
|
|
key[i] = Math.random();
|
|
val[i] = Math.random();
|
|
}
|
|
keys.push(key);
|
|
values.push(val);
|
|
}
|
|
// Warmup
|
|
for (let i = 0; i < 10; i++)
|
|
mechanism.computeRaw(query, keys, values);
|
|
const start = performance.now();
|
|
for (let i = 0; i < iterations; i++) {
|
|
mechanism.computeRaw(query, keys, values);
|
|
}
|
|
const elapsed = performance.now() - start;
|
|
return {
|
|
name,
|
|
averageTimeMs: elapsed / iterations,
|
|
opsPerSecond: Math.round((iterations / elapsed) * 1000),
|
|
};
|
|
};
|
|
spinner.setText(`Benchmarking attention mechanisms (dim=${dim}, keys=${numKeys}, iter=${iterations})...`);
|
|
const results = [];
|
|
// Benchmark each mechanism
|
|
const dotProduct = new attention.DotProductAttention(dim);
|
|
results.push(await benchmarkMechanism('DotProduct', dotProduct));
|
|
const flash = new attention.FlashAttention(dim, 64);
|
|
results.push(await benchmarkMechanism('FlashAttention', flash));
|
|
const multiHead = new attention.MultiHeadAttention(dim, 4);
|
|
results.push(await benchmarkMechanism('MultiHead (4 heads)', multiHead));
|
|
const hyperbolic = new attention.HyperbolicAttention(dim, 1.0);
|
|
results.push(await benchmarkMechanism('Hyperbolic', hyperbolic));
|
|
const linear = new attention.LinearAttention(dim, dim);
|
|
results.push(await benchmarkMechanism('Linear', linear));
|
|
spinner.succeed('Benchmark complete');
|
|
output.writeln();
|
|
output.printTable({
|
|
columns: [
|
|
{ key: 'name', header: 'Mechanism', width: 25 },
|
|
{ key: 'avgTime', header: 'Avg Time (ms)', width: 15 },
|
|
{ key: 'opsPerSec', header: 'Ops/sec', width: 15 },
|
|
],
|
|
data: results.map(r => ({
|
|
name: r.name,
|
|
avgTime: r.averageTimeMs.toFixed(4),
|
|
opsPerSec: r.opsPerSecond.toLocaleString(),
|
|
})),
|
|
});
|
|
// Show speedup comparisons
|
|
const dotProductResult = results.find(r => r.name.includes('DotProduct'));
|
|
const flashResult = results.find(r => r.name.includes('Flash'));
|
|
const hyperbolicResult = results.find(r => r.name.includes('Hyperbolic'));
|
|
if (dotProductResult && flashResult) {
|
|
const speedup = dotProductResult.averageTimeMs / flashResult.averageTimeMs;
|
|
output.writeln();
|
|
output.writeln(output.highlight(`Flash Attention speedup: ${speedup.toFixed(2)}x faster than DotProduct`));
|
|
}
|
|
if (dotProductResult && hyperbolicResult) {
|
|
output.writeln(output.dim(`Hyperbolic overhead: ${(hyperbolicResult.averageTimeMs / dotProductResult.averageTimeMs).toFixed(2)}x (expected for manifold ops)`));
|
|
}
|
|
// Also benchmark MicroLoRA
|
|
spinner.start();
|
|
spinner.setText('Benchmarking MicroLoRA adaptation...');
|
|
// Load WASM file directly (Node.js compatible)
|
|
const fs = await import('fs');
|
|
const { createRequire } = await import('module');
|
|
const require = createRequire(import.meta.url);
|
|
const wasmPath = require.resolve('@ruvector/learning-wasm/ruvector_learning_wasm_bg.wasm');
|
|
const wasmBuffer = fs.readFileSync(wasmPath);
|
|
const learningWasm = await import('@ruvector/learning-wasm');
|
|
learningWasm.initSync({ module: wasmBuffer });
|
|
const lora = new learningWasm.WasmMicroLoRA(dim, 0.1, 0.01);
|
|
const gradient = new Float32Array(dim);
|
|
for (let i = 0; i < dim; i++)
|
|
gradient[i] = Math.random() - 0.5;
|
|
const loraStart = performance.now();
|
|
for (let i = 0; i < iterations; i++) {
|
|
lora.adapt_array(gradient);
|
|
}
|
|
const loraTime = performance.now() - loraStart;
|
|
const loraAvg = loraTime / iterations;
|
|
spinner.succeed('MicroLoRA benchmark complete');
|
|
output.writeln();
|
|
output.printTable({
|
|
columns: [
|
|
{ key: 'metric', header: 'MicroLoRA Metric', width: 25 },
|
|
{ key: 'value', header: 'Value', width: 25 },
|
|
],
|
|
data: [
|
|
{ metric: 'Dimension', value: String(dim) },
|
|
{ metric: 'Iterations', value: iterations.toLocaleString() },
|
|
{ metric: 'Total Time', value: `${loraTime.toFixed(2)}ms` },
|
|
{ metric: 'Avg Adaptation', value: `${(loraAvg * 1000).toFixed(2)}μs` },
|
|
{ metric: 'Adaptations/sec', value: Math.round(1000 / loraAvg).toLocaleString() },
|
|
{ metric: 'Target (<100μs)', value: loraAvg * 1000 < 100 ? output.success('✓ PASS') : output.warning('✗ FAIL') },
|
|
],
|
|
});
|
|
lora.free();
|
|
return { success: true, data: { results, loraAvg } };
|
|
}
|
|
catch (error) {
|
|
spinner.fail('Benchmark failed');
|
|
output.printError(error instanceof Error ? error.message : String(error));
|
|
return { success: false, exitCode: 1 };
|
|
}
|
|
},
|
|
};
|
|
// Main neural command
|
|
export const neuralCommand = {
|
|
name: 'neural',
|
|
description: 'Neural pattern training, MoE, Flash Attention, pattern learning',
|
|
subcommands: [trainCommand, statusCommand, patternsCommand, predictCommand, optimizeCommand, benchmarkCommand, listCommand, exportCommand, importCommand],
|
|
examples: [
|
|
{ command: 'claude-flow neural status', description: 'Check neural system status' },
|
|
{ command: 'claude-flow neural train -p coordination', description: 'Train coordination patterns' },
|
|
{ command: 'claude-flow neural patterns --action list', description: 'List learned patterns' },
|
|
],
|
|
action: async () => {
|
|
output.writeln();
|
|
output.writeln(output.bold('RuFlo Neural System'));
|
|
output.writeln(output.dim('Advanced AI pattern learning and inference'));
|
|
output.writeln();
|
|
output.writeln('Use --help with subcommands for more info');
|
|
output.writeln();
|
|
output.writeln(output.dim('Created with ❤️ by ruv.io'));
|
|
return { success: true };
|
|
},
|
|
};
|
|
export default neuralCommand;
|
|
//# sourceMappingURL=neural.js.map
|