Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
This commit is contained in:
ruv
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"use strict";
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/**
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* Comprehensive Agentic-Synth Training & Learning Session
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*
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* This script demonstrates a complete training workflow using OpenRouter API:
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* 1. Baseline generation and measurement
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* 2. Learning from successful patterns
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* 3. Adaptive optimization
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* 4. Comprehensive benchmarking
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* 5. Final optimized generation
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*
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* Usage:
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* export OPENROUTER_API_KEY=your-key-here
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* npx tsx training/openrouter-learning-session.ts
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*/
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var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {
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if (k2 === undefined) k2 = k;
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var desc = Object.getOwnPropertyDescriptor(m, k);
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if (!desc || ("get" in desc ? !m.__esModule : desc.writable || desc.configurable)) {
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||||
desc = { enumerable: true, get: function() { return m[k]; } };
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}
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Object.defineProperty(o, k2, desc);
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}) : (function(o, m, k, k2) {
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if (k2 === undefined) k2 = k;
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o[k2] = m[k];
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}));
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var __setModuleDefault = (this && this.__setModuleDefault) || (Object.create ? (function(o, v) {
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||||
Object.defineProperty(o, "default", { enumerable: true, value: v });
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}) : function(o, v) {
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o["default"] = v;
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});
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var __importStar = (this && this.__importStar) || (function () {
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var ownKeys = function(o) {
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ownKeys = Object.getOwnPropertyNames || function (o) {
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var ar = [];
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||||
for (var k in o) if (Object.prototype.hasOwnProperty.call(o, k)) ar[ar.length] = k;
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return ar;
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||||
};
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return ownKeys(o);
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};
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return function (mod) {
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if (mod && mod.__esModule) return mod;
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var result = {};
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if (mod != null) for (var k = ownKeys(mod), i = 0; i < k.length; i++) if (k[i] !== "default") __createBinding(result, mod, k[i]);
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__setModuleDefault(result, mod);
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return result;
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};
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})();
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.TrainingSession = void 0;
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const index_js_1 = require("../dist/index.js");
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const perf_hooks_1 = require("perf_hooks");
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const fs = __importStar(require("fs/promises"));
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const path = __importStar(require("path"));
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// ============================================================================
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// Configuration
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// ============================================================================
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const CONFIG = {
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provider: 'openrouter',
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apiKey: process.env.OPENROUTER_API_KEY || '',
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models: [
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'anthropic/claude-3.5-sonnet', // High quality
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'openai/gpt-4-turbo', // Balanced
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'meta-llama/llama-3.1-70b-instruct' // Fast
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],
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outputDir: './training/results',
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// Training parameters
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generations: 5,
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samplesPerGeneration: 100,
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learningRate: 0.1,
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qualityThreshold: 0.85,
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// Benchmark parameters
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benchmarkIterations: 10,
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benchmarkSizes: [100, 500, 1000, 5000],
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};
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// ============================================================================
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// Training Session Class
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// ============================================================================
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class TrainingSession {
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constructor() {
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this.metrics = [];
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this.patterns = new Map();
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this.bestSchema = null;
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this.bestQuality = 0;
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if (!CONFIG.apiKey) {
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throw new Error('OPENROUTER_API_KEY environment variable is required');
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}
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this.synth = new index_js_1.AgenticSynth({
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provider: CONFIG.provider,
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apiKey: CONFIG.apiKey,
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model: CONFIG.models[0], // Start with highest quality
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cacheStrategy: 'memory',
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cacheTTL: 3600,
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maxCacheSize: 10000,
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});
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}
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/**
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* Run complete training session
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*/
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async run() {
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console.log('🎓 Starting Agentic-Synth Training & Learning Session\n');
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console.log('='.repeat(70));
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// Ensure output directory exists
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await fs.mkdir(CONFIG.outputDir, { recursive: true });
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try {
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// Phase 1: Baseline Generation
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console.log('\n📊 Phase 1: Baseline Generation');
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await this.runBaselineGeneration();
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// Phase 2: Learning Loop
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console.log('\n🧠 Phase 2: Learning & Optimization Loop');
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await this.runLearningLoop();
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// Phase 3: Model Comparison
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console.log('\n🔬 Phase 3: Multi-Model Comparison');
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await this.runModelComparison();
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// Phase 4: Comprehensive Benchmarking
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console.log('\n⚡ Phase 4: Comprehensive Benchmarking');
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await this.runComprehensiveBenchmarks();
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// Phase 5: Final Optimized Generation
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console.log('\n🎯 Phase 5: Final Optimized Generation');
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await this.runOptimizedGeneration();
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// Generate Reports
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console.log('\n📈 Phase 6: Generating Reports');
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await this.generateReports();
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console.log('\n' + '='.repeat(70));
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console.log('✅ Training session completed successfully!\n');
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}
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catch (error) {
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console.error('\n❌ Training session failed:', error.message);
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throw error;
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}
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}
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/**
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* Phase 1: Baseline Generation
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*/
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async runBaselineGeneration() {
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console.log('Generating baseline dataset...');
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const schema = {
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id: 'UUID',
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name: 'full name',
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email: 'valid email',
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age: 'number (18-80)',
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occupation: 'job title',
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salary: 'number (30000-200000)',
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city: 'city name',
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country: 'country name',
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};
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const start = perf_hooks_1.performance.now();
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const result = await this.synth.generateStructured({
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count: CONFIG.samplesPerGeneration,
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schema,
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});
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const duration = perf_hooks_1.performance.now() - start;
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// Calculate quality metrics
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const quality = this.calculateQuality(result.data);
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const diversity = this.calculateDiversity(result.data);
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// Record metrics
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this.recordMetrics({
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generation: 0,
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quality,
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diversity,
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speed: duration,
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cacheHitRate: 0,
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memoryUsage: process.memoryUsage().heapUsed / 1024 / 1024,
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timestamp: new Date().toISOString(),
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});
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console.log(` ✅ Generated ${result.data.length} samples`);
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console.log(` 📊 Quality: ${quality.toFixed(3)}`);
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console.log(` 🎨 Diversity: ${diversity.toFixed(3)}`);
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console.log(` ⏱️ Duration: ${duration.toFixed(0)}ms`);
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// Save baseline data
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await this.saveData('baseline', result.data);
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}
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/**
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* Phase 2: Learning Loop
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*/
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async runLearningLoop() {
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let currentSchema = {
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id: 'UUID',
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name: 'full name',
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email: 'valid email',
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age: 'number (18-80)',
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occupation: 'job title',
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salary: 'number (30000-200000)',
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city: 'city name',
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country: 'country name',
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};
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for (let gen = 1; gen <= CONFIG.generations; gen++) {
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console.log(`\n Generation ${gen}/${CONFIG.generations}`);
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const start = perf_hooks_1.performance.now();
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const result = await this.synth.generateStructured({
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count: CONFIG.samplesPerGeneration,
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schema: currentSchema,
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});
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const duration = perf_hooks_1.performance.now() - start;
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// Measure quality
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const quality = this.calculateQuality(result.data);
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const diversity = this.calculateDiversity(result.data);
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// Get cache stats
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const cacheStats = this.synth.cache.getStats();
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// Record metrics
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this.recordMetrics({
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generation: gen,
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quality,
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diversity,
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speed: duration,
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cacheHitRate: cacheStats.hitRate,
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memoryUsage: process.memoryUsage().heapUsed / 1024 / 1024,
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timestamp: new Date().toISOString(),
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});
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console.log(` Quality: ${quality.toFixed(3)} (${quality > this.bestQuality ? '↑' : '↓'})`);
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console.log(` Diversity: ${diversity.toFixed(3)}`);
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console.log(` Cache Hit: ${(cacheStats.hitRate * 100).toFixed(1)}%`);
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console.log(` Duration: ${duration.toFixed(0)}ms`);
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// Learn from this generation
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if (quality > CONFIG.qualityThreshold) {
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await this.learnFromSuccess(result.data, currentSchema, quality);
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console.log(` 🧠 Learned new pattern (quality: ${quality.toFixed(3)})`);
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}
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// Track best schema
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if (quality > this.bestQuality) {
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this.bestQuality = quality;
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this.bestSchema = { ...currentSchema };
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console.log(` ⭐ New best quality: ${quality.toFixed(3)}`);
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}
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// Evolve schema based on learning
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currentSchema = await this.evolveSchema(currentSchema, quality);
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// Save generation data
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await this.saveData(`generation-${gen}`, result.data);
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}
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console.log(`\n 📚 Learned ${this.patterns.size} successful patterns`);
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console.log(` 🎯 Best quality achieved: ${this.bestQuality.toFixed(3)}`);
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}
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/**
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* Phase 3: Model Comparison
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*/
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async runModelComparison() {
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const results = [];
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for (const model of CONFIG.models) {
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console.log(`\n Testing model: ${model}`);
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// Create synth instance with this model
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const synth = new index_js_1.AgenticSynth({
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provider: CONFIG.provider,
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apiKey: CONFIG.apiKey,
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model,
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cacheStrategy: 'memory',
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cacheTTL: 3600,
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});
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const start = perf_hooks_1.performance.now();
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const result = await synth.generateStructured({
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count: CONFIG.samplesPerGeneration,
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schema: this.bestSchema || {
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id: 'UUID',
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name: 'full name',
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email: 'valid email',
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},
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});
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const duration = perf_hooks_1.performance.now() - start;
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const quality = this.calculateQuality(result.data);
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const cacheStats = synth.cache.getStats();
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results.push({
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model,
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quality,
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duration,
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cacheHitRate: cacheStats.hitRate,
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throughput: (CONFIG.samplesPerGeneration / duration) * 1000,
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});
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console.log(` Quality: ${quality.toFixed(3)}`);
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console.log(` Duration: ${duration.toFixed(0)}ms`);
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console.log(` Throughput: ${((CONFIG.samplesPerGeneration / duration) * 1000).toFixed(0)} samples/s`);
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}
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// Save comparison results
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await fs.writeFile(path.join(CONFIG.outputDir, 'model-comparison.json'), JSON.stringify(results, null, 2));
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// Determine best model
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const bestModel = results.reduce((best, current) => current.quality > best.quality ? current : best);
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console.log(`\n 🏆 Best model: ${bestModel.model}`);
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console.log(` Quality: ${bestModel.quality.toFixed(3)}`);
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console.log(` Speed: ${bestModel.duration.toFixed(0)}ms`);
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}
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/**
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* Phase 4: Comprehensive Benchmarking
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*/
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async runComprehensiveBenchmarks() {
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const benchmarks = [];
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for (const size of CONFIG.benchmarkSizes) {
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console.log(`\n Benchmarking ${size} samples...`);
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const times = [];
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const qualities = [];
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for (let i = 0; i < CONFIG.benchmarkIterations; i++) {
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const start = perf_hooks_1.performance.now();
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const result = await this.synth.generateStructured({
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count: size,
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schema: this.bestSchema,
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});
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const duration = perf_hooks_1.performance.now() - start;
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times.push(duration);
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qualities.push(this.calculateQuality(result.data));
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process.stdout.write(` Iteration ${i + 1}/${CONFIG.benchmarkIterations}\r`);
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}
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const avgLatency = times.reduce((a, b) => a + b) / times.length;
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const avgQuality = qualities.reduce((a, b) => a + b) / qualities.length;
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const throughput = (size / avgLatency) * 1000;
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const cacheStats = this.synth.cache.getStats();
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benchmarks.push({
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model: CONFIG.models[0],
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sampleSize: size,
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avgLatency,
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throughput,
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quality: avgQuality,
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cacheHitRate: cacheStats.hitRate,
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});
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console.log(` Avg Latency: ${avgLatency.toFixed(0)}ms`);
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console.log(` Throughput: ${throughput.toFixed(0)} samples/s`);
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console.log(` Quality: ${avgQuality.toFixed(3)}`);
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console.log(` Cache Hit: ${(cacheStats.hitRate * 100).toFixed(1)}%`);
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}
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// Save benchmark results
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await fs.writeFile(path.join(CONFIG.outputDir, 'benchmarks.json'), JSON.stringify(benchmarks, null, 2));
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}
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/**
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* Phase 5: Final Optimized Generation
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*/
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async runOptimizedGeneration() {
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console.log('Generating final optimized dataset...');
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||||
const start = perf_hooks_1.performance.now();
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const result = await this.synth.generateStructured({
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count: CONFIG.samplesPerGeneration * 10, // 10x larger
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schema: this.bestSchema,
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||||
});
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||||
const duration = perf_hooks_1.performance.now() - start;
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const quality = this.calculateQuality(result.data);
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const diversity = this.calculateDiversity(result.data);
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const cacheStats = this.synth.cache.getStats();
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console.log(` ✅ Generated ${result.data.length} samples`);
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console.log(` 📊 Quality: ${quality.toFixed(3)}`);
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||||
console.log(` 🎨 Diversity: ${diversity.toFixed(3)}`);
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||||
console.log(` ⚡ Throughput: ${((result.data.length / duration) * 1000).toFixed(0)} samples/s`);
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||||
console.log(` 💾 Cache Hit: ${(cacheStats.hitRate * 100).toFixed(1)}%`);
|
||||
console.log(` ⏱️ Duration: ${(duration / 1000).toFixed(2)}s`);
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||||
// Save optimized data
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await this.saveData('optimized-final', result.data);
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||||
// Calculate improvement
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||||
const baselineQuality = this.metrics[0].quality;
|
||||
const improvement = ((quality - baselineQuality) / baselineQuality) * 100;
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||||
console.log(`\n 📈 Improvement over baseline: ${improvement >= 0 ? '+' : ''}${improvement.toFixed(1)}%`);
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||||
}
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||||
/**
|
||||
* Phase 6: Generate Reports
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||||
*/
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||||
async generateReports() {
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||||
// Save metrics history
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||||
await fs.writeFile(path.join(CONFIG.outputDir, 'metrics-history.json'), JSON.stringify(this.metrics, null, 2));
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||||
// Save learned patterns
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||||
const patternsArray = Array.from(this.patterns.values());
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await fs.writeFile(path.join(CONFIG.outputDir, 'learned-patterns.json'), JSON.stringify(patternsArray, null, 2));
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||||
// Generate markdown report
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const report = this.generateMarkdownReport();
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||||
await fs.writeFile(path.join(CONFIG.outputDir, 'TRAINING_REPORT.md'), report);
|
||||
console.log(` ✅ Reports saved to ${CONFIG.outputDir}/`);
|
||||
console.log(` - metrics-history.json`);
|
||||
console.log(` - learned-patterns.json`);
|
||||
console.log(` - benchmarks.json`);
|
||||
console.log(` - model-comparison.json`);
|
||||
console.log(` - TRAINING_REPORT.md`);
|
||||
}
|
||||
// ============================================================================
|
||||
// Helper Methods
|
||||
// ============================================================================
|
||||
/**
|
||||
* Calculate quality score for generated data
|
||||
*/
|
||||
calculateQuality(data) {
|
||||
if (data.length === 0)
|
||||
return 0;
|
||||
let score = 0;
|
||||
let checks = 0;
|
||||
for (const item of data.slice(0, 10)) { // Sample first 10
|
||||
// Check completeness
|
||||
const fields = Object.keys(item);
|
||||
score += fields.length > 0 ? 1 : 0;
|
||||
checks++;
|
||||
// Check data types
|
||||
if (typeof item.id === 'string')
|
||||
score += 1;
|
||||
if (typeof item.name === 'string' && item.name.length > 3)
|
||||
score += 1;
|
||||
if (typeof item.email === 'string' && item.email.includes('@'))
|
||||
score += 1;
|
||||
if (typeof item.age === 'number' && item.age >= 18 && item.age <= 80)
|
||||
score += 1;
|
||||
checks += 4;
|
||||
// Check uniqueness
|
||||
if (item.id && item.id.length > 10)
|
||||
score += 1;
|
||||
checks++;
|
||||
}
|
||||
return score / checks;
|
||||
}
|
||||
/**
|
||||
* Calculate diversity score
|
||||
*/
|
||||
calculateDiversity(data) {
|
||||
if (data.length < 2)
|
||||
return 0;
|
||||
const uniqueValues = new Set();
|
||||
let totalFields = 0;
|
||||
for (const item of data.slice(0, 20)) {
|
||||
for (const value of Object.values(item)) {
|
||||
uniqueValues.add(JSON.stringify(value));
|
||||
totalFields++;
|
||||
}
|
||||
}
|
||||
return uniqueValues.size / totalFields;
|
||||
}
|
||||
/**
|
||||
* Record training metrics
|
||||
*/
|
||||
recordMetrics(metrics) {
|
||||
this.metrics.push(metrics);
|
||||
}
|
||||
/**
|
||||
* Learn from successful generation
|
||||
*/
|
||||
async learnFromSuccess(data, schema, quality) {
|
||||
const patternKey = JSON.stringify(schema);
|
||||
if (this.patterns.has(patternKey)) {
|
||||
const pattern = this.patterns.get(patternKey);
|
||||
pattern.successRate += 1;
|
||||
pattern.avgQuality = (pattern.avgQuality + quality) / 2;
|
||||
pattern.examples.push(...data.slice(0, 3));
|
||||
}
|
||||
else {
|
||||
this.patterns.set(patternKey, {
|
||||
pattern: patternKey,
|
||||
successRate: 1,
|
||||
avgQuality: quality,
|
||||
examples: data.slice(0, 3),
|
||||
});
|
||||
}
|
||||
}
|
||||
/**
|
||||
* Evolve schema based on learning
|
||||
*/
|
||||
async evolveSchema(currentSchema, quality) {
|
||||
// If quality is high, keep schema
|
||||
if (quality >= CONFIG.qualityThreshold) {
|
||||
return currentSchema;
|
||||
}
|
||||
// Otherwise, try adding a field
|
||||
const newSchema = { ...currentSchema };
|
||||
// Randomly add a new field
|
||||
const possibleFields = [
|
||||
{ phone: 'phone number' },
|
||||
{ address: 'street address' },
|
||||
{ company: 'company name' },
|
||||
{ skills: 'array of 3-5 skills' },
|
||||
{ bio: 'short bio (1-2 sentences)' },
|
||||
];
|
||||
const randomField = possibleFields[Math.floor(Math.random() * possibleFields.length)];
|
||||
Object.assign(newSchema, randomField);
|
||||
return newSchema;
|
||||
}
|
||||
/**
|
||||
* Save data to file
|
||||
*/
|
||||
async saveData(name, data) {
|
||||
const filepath = path.join(CONFIG.outputDir, `${name}.json`);
|
||||
await fs.writeFile(filepath, JSON.stringify(data, null, 2));
|
||||
}
|
||||
/**
|
||||
* Generate markdown report
|
||||
*/
|
||||
generateMarkdownReport() {
|
||||
const baseline = this.metrics[0];
|
||||
const final = this.metrics[this.metrics.length - 1];
|
||||
const improvement = ((final.quality - baseline.quality) / baseline.quality) * 100;
|
||||
return `# Agentic-Synth Training Report
|
||||
|
||||
**Date**: ${new Date().toISOString()}
|
||||
**Provider**: ${CONFIG.provider}
|
||||
**Model**: ${CONFIG.models[0]}
|
||||
|
||||
## Summary
|
||||
|
||||
- **Generations**: ${CONFIG.generations}
|
||||
- **Samples per Generation**: ${CONFIG.samplesPerGeneration}
|
||||
- **Total Samples Generated**: ${CONFIG.samplesPerGeneration * (CONFIG.generations + 1)}
|
||||
- **Patterns Learned**: ${this.patterns.size}
|
||||
|
||||
## Quality Improvement
|
||||
|
||||
| Metric | Baseline | Final | Change |
|
||||
|--------|----------|-------|--------|
|
||||
| Quality | ${baseline.quality.toFixed(3)} | ${final.quality.toFixed(3)} | ${improvement >= 0 ? '+' : ''}${improvement.toFixed(1)}% |
|
||||
| Diversity | ${baseline.diversity.toFixed(3)} | ${final.diversity.toFixed(3)} | ${(((final.diversity - baseline.diversity) / baseline.diversity) * 100).toFixed(1)}% |
|
||||
| Speed | ${baseline.speed.toFixed(0)}ms | ${final.speed.toFixed(0)}ms | ${(((final.speed - baseline.speed) / baseline.speed) * 100).toFixed(1)}% |
|
||||
| Cache Hit | ${(baseline.cacheHitRate * 100).toFixed(1)}% | ${(final.cacheHitRate * 100).toFixed(1)}% | +${((final.cacheHitRate - baseline.cacheHitRate) * 100).toFixed(1)}% |
|
||||
|
||||
## Training Progress
|
||||
|
||||
${this.metrics.map((m, i) => `
|
||||
### Generation ${i}
|
||||
|
||||
- Quality: ${m.quality.toFixed(3)}
|
||||
- Diversity: ${m.diversity.toFixed(3)}
|
||||
- Speed: ${m.speed.toFixed(0)}ms
|
||||
- Cache Hit: ${(m.cacheHitRate * 100).toFixed(1)}%
|
||||
- Memory: ${m.memoryUsage.toFixed(0)}MB
|
||||
`).join('\n')}
|
||||
|
||||
## Learned Patterns
|
||||
|
||||
Total patterns learned: ${this.patterns.size}
|
||||
|
||||
${Array.from(this.patterns.values()).map(p => `
|
||||
- Success Rate: ${p.successRate}
|
||||
- Avg Quality: ${p.avgQuality.toFixed(3)}
|
||||
`).join('\n')}
|
||||
|
||||
## Best Configuration
|
||||
|
||||
\`\`\`json
|
||||
${JSON.stringify(this.bestSchema, null, 2)}
|
||||
\`\`\`
|
||||
|
||||
**Best Quality Achieved**: ${this.bestQuality.toFixed(3)}
|
||||
|
||||
## Recommendations
|
||||
|
||||
${improvement > 10 ? '✅' : '⚠️'} Quality improvement: ${improvement.toFixed(1)}%
|
||||
${final.cacheHitRate > 0.7 ? '✅' : '⚠️'} Cache hit rate: ${(final.cacheHitRate * 100).toFixed(1)}%
|
||||
${this.patterns.size >= 3 ? '✅' : '⚠️'} Patterns learned: ${this.patterns.size}
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. ${improvement < 10 ? 'Increase learning rate or generation count' : 'Continue with current parameters'}
|
||||
2. ${final.cacheHitRate < 0.7 ? 'Optimize caching strategy' : 'Cache performance is good'}
|
||||
3. ${this.patterns.size < 3 ? 'Generate more diverse schemas' : 'Explore schema variations'}
|
||||
|
||||
---
|
||||
|
||||
Generated by agentic-synth v0.1.0
|
||||
`;
|
||||
}
|
||||
}
|
||||
exports.TrainingSession = TrainingSession;
|
||||
// ============================================================================
|
||||
// Main Execution
|
||||
// ============================================================================
|
||||
async function main() {
|
||||
try {
|
||||
const session = new TrainingSession();
|
||||
await session.run();
|
||||
}
|
||||
catch (error) {
|
||||
console.error('Fatal error:', error.message);
|
||||
process.exit(1);
|
||||
}
|
||||
}
|
||||
// Run if executed directly
|
||||
if (import.meta.url === `file://${process.argv[1]}`) {
|
||||
main();
|
||||
}
|
||||
//# sourceMappingURL=openrouter-learning-session.js.map
|
||||
Reference in New Issue
Block a user