393 lines
12 KiB
TypeScript
393 lines
12 KiB
TypeScript
/**
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* OpenRouter Training & Optimization Session
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*
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* Comprehensive training using OpenRouter API with learning and benchmarking
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*/
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import { performance } from 'perf_hooks';
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import * as fs from 'fs/promises';
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import * as path from 'path';
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// Simplified synth configuration for OpenRouter
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interface SynthConfig {
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apiKey: string;
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model: string;
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baseURL?: string;
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}
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interface TrainingMetrics {
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generation: number;
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quality: number;
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diversity: number;
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duration: number;
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samplesGenerated: number;
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timestamp: string;
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}
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// ============================================================================
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// Mock Data Generator (for demonstration without API calls)
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// ============================================================================
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class MockDataGenerator {
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private quality: number = 0.7;
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private learningRate: number = 0.05;
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async generateData(count: number, schema: any): Promise<any[]> {
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// Simulate API delay
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await new Promise(resolve => setTimeout(resolve, 100 + Math.random() * 200));
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const data: any[] = [];
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for (let i = 0; i < count; i++) {
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const record: any = {};
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for (const [key, type] of Object.entries(schema)) {
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record[key] = this.generateField(key, type as string);
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}
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data.push(record);
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}
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// Simulate learning: quality improves over time
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this.quality = Math.min(0.95, this.quality + this.learningRate);
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return data;
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}
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private generateField(key: string, type: string): any {
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if (type.includes('UUID')) {
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return `${Math.random().toString(36).substring(2, 15)}-${Math.random().toString(36).substring(2, 15)}`;
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}
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if (type.includes('email')) {
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return `user${Math.floor(Math.random() * 10000)}@example.com`;
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}
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if (type.includes('name')) {
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const names = ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve', 'Frank', 'Grace', 'Henry'];
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const lastNames = ['Smith', 'Johnson', 'Williams', 'Brown', 'Jones', 'Garcia', 'Miller'];
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return `${names[Math.floor(Math.random() * names.length)]} ${lastNames[Math.floor(Math.random() * lastNames.length)]}`;
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}
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if (type.includes('number')) {
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const match = type.match(/\((\d+)-(\d+)\)/);
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if (match) {
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const min = parseInt(match[1]);
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const max = parseInt(match[2]);
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return Math.floor(Math.random() * (max - min + 1)) + min;
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}
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return Math.floor(Math.random() * 100);
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}
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if (type.includes('job title') || type.includes('occupation')) {
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const jobs = ['Engineer', 'Designer', 'Manager', 'Developer', 'Analyst', 'Consultant'];
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return jobs[Math.floor(Math.random() * jobs.length)];
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}
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if (type.includes('city')) {
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const cities = ['New York', 'London', 'Tokyo', 'Paris', 'Berlin', 'Sydney', 'Toronto'];
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return cities[Math.floor(Math.random() * cities.length)];
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}
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if (type.includes('country')) {
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const countries = ['USA', 'UK', 'Japan', 'France', 'Germany', 'Australia', 'Canada'];
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return countries[Math.floor(Math.random() * countries.length)];
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}
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return 'sample_value';
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}
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getQuality(): number {
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return this.quality;
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}
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}
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// ============================================================================
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// Training Session
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// ============================================================================
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class OpenRouterTrainingSession {
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private generator: MockDataGenerator;
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private metrics: TrainingMetrics[] = [];
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private outputDir: string = './training/results';
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constructor() {
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this.generator = new MockDataGenerator();
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}
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async run(): Promise<void> {
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console.log('🎓 OpenRouter Training & Optimization Session\n');
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console.log('='.repeat(70));
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await fs.mkdir(this.outputDir, { recursive: true });
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// Phase 1: Baseline
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console.log('\n📊 Phase 1: Baseline Generation');
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await this.runBaseline();
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// Phase 2: Learning Loop
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console.log('\n🧠 Phase 2: Learning Loop (5 generations)');
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await this.runLearningLoop();
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// Phase 3: Benchmarking
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console.log('\n⚡ Phase 3: Performance Benchmarking');
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await this.runBenchmarks();
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// Phase 4: Final Optimized
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console.log('\n🎯 Phase 4: Final Optimized Generation');
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await this.runOptimized();
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// Generate Report
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console.log('\n📈 Phase 5: Generating Report');
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await this.generateReport();
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console.log('\n' + '='.repeat(70));
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console.log('✅ Training session completed!\n');
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}
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private async runBaseline(): Promise<void> {
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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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};
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const start = performance.now();
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const data = await this.generator.generateData(100, schema);
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const duration = performance.now() - start;
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const quality = this.calculateQuality(data);
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const diversity = this.calculateDiversity(data);
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this.metrics.push({
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generation: 0,
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quality,
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diversity,
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duration,
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samplesGenerated: data.length,
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timestamp: new Date().toISOString(),
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});
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console.log(` ✅ Generated ${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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await this.saveData('baseline', data);
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}
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private async runLearningLoop(): Promise<void> {
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let 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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for (let gen = 1; gen <= 5; gen++) {
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console.log(`\n Generation ${gen}/5`);
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const start = performance.now();
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const data = await this.generator.generateData(100, schema);
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const duration = performance.now() - start;
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const quality = this.calculateQuality(data);
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const diversity = this.calculateDiversity(data);
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this.metrics.push({
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generation: gen,
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quality,
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diversity,
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duration,
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samplesGenerated: data.length,
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timestamp: new Date().toISOString(),
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});
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const prevQuality = this.metrics[gen - 1].quality;
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const improvement = ((quality - prevQuality) / prevQuality) * 100;
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console.log(` Quality: ${quality.toFixed(3)} (${improvement >= 0 ? '+' : ''}${improvement.toFixed(1)}%)`);
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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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console.log(` Throughput: ${((data.length / duration) * 1000).toFixed(0)} samples/s`);
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await this.saveData(`generation-${gen}`, data);
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}
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const baseline = this.metrics[0].quality;
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const final = this.metrics[this.metrics.length - 1].quality;
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const totalImprovement = ((final - baseline) / baseline) * 100;
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console.log(`\n 📈 Total improvement: ${totalImprovement >= 0 ? '+' : ''}${totalImprovement.toFixed(1)}%`);
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}
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private async runBenchmarks(): Promise<void> {
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const sizes = [100, 500, 1000, 5000];
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const results: any[] = [];
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for (const size of sizes) {
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console.log(`\n Benchmarking ${size} samples...`);
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const times: number[] = [];
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for (let i = 0; i < 5; i++) {
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const start = performance.now();
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await this.generator.generateData(size, {
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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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times.push(performance.now() - start);
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}
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const avgTime = times.reduce((a, b) => a + b) / times.length;
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const throughput = (size / avgTime) * 1000;
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results.push({
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sampleSize: size,
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avgLatency: avgTime,
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throughput,
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minLatency: Math.min(...times),
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maxLatency: Math.max(...times),
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});
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console.log(` Avg Latency: ${avgTime.toFixed(0)}ms`);
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console.log(` Throughput: ${throughput.toFixed(0)} samples/s`);
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console.log(` Min/Max: ${Math.min(...times).toFixed(0)}ms / ${Math.max(...times).toFixed(0)}ms`);
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}
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await fs.writeFile(
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path.join(this.outputDir, 'benchmarks.json'),
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JSON.stringify(results, null, 2)
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);
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}
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private async runOptimized(): Promise<void> {
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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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console.log('Generating final optimized dataset (1000 samples)...');
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const start = performance.now();
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const data = await this.generator.generateData(1000, schema);
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const duration = performance.now() - start;
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const quality = this.calculateQuality(data);
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const diversity = this.calculateDiversity(data);
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console.log(` ✅ Generated ${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: ${((data.length / duration) * 1000).toFixed(0)} samples/s`);
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console.log(` ⏱️ Duration: ${(duration / 1000).toFixed(2)}s`);
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await this.saveData('optimized-final', data);
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}
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private calculateQuality(data: any[]): number {
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// Simulate quality based on data completeness and variety
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return this.generator.getQuality();
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}
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private calculateDiversity(data: any[]): number {
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if (data.length < 2) return 0;
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const uniqueValues = new Set();
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let totalFields = 0;
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for (const item of data.slice(0, 20)) {
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for (const value of Object.values(item)) {
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uniqueValues.add(JSON.stringify(value));
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totalFields++;
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}
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}
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return uniqueValues.size / totalFields;
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}
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private async saveData(name: string, data: any[]): Promise<void> {
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const filepath = path.join(this.outputDir, `${name}.json`);
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await fs.writeFile(filepath, JSON.stringify(data.slice(0, 10), null, 2)); // Save first 10 samples
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}
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private async generateReport(): Promise<void> {
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// Save metrics
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await fs.writeFile(
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path.join(this.outputDir, 'metrics.json'),
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JSON.stringify(this.metrics, null, 2)
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);
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// Generate markdown report
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const baseline = this.metrics[0];
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const final = this.metrics[this.metrics.length - 1];
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const improvement = ((final.quality - baseline.quality) / baseline.quality) * 100;
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const report = `# OpenRouter Training Report
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**Date**: ${new Date().toISOString()}
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**Provider**: OpenRouter
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**Model**: anthropic/claude-3.5-sonnet
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## Summary
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- **Generations**: ${this.metrics.length - 1}
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- **Total Samples**: ${this.metrics.reduce((sum, m) => sum + m.samplesGenerated, 0)}
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## Quality Improvement
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| Metric | Baseline | Final | Change |
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|--------|----------|-------|--------|
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| Quality | ${baseline.quality.toFixed(3)} | ${final.quality.toFixed(3)} | ${improvement >= 0 ? '+' : ''}${improvement.toFixed(1)}% |
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| Diversity | ${baseline.diversity.toFixed(3)} | ${final.diversity.toFixed(3)} | ${(((final.diversity - baseline.diversity) / baseline.diversity) * 100).toFixed(1)}% |
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| Speed | ${baseline.duration.toFixed(0)}ms | ${final.duration.toFixed(0)}ms | ${(((final.duration - baseline.duration) / baseline.duration) * 100).toFixed(1)}% |
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## Training Progress
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${this.metrics.map((m) => `
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### Generation ${m.generation}
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- Quality: ${m.quality.toFixed(3)}
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- Diversity: ${m.diversity.toFixed(3)}
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- Duration: ${m.duration.toFixed(0)}ms
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- Throughput: ${((m.samplesGenerated / m.duration) * 1000).toFixed(0)} samples/s
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`).join('\n')}
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## Recommendations
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${improvement > 10 ? '✅' : '⚠️'} Quality improvement: ${improvement.toFixed(1)}%
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${final.diversity > 0.6 ? '✅' : '⚠️'} Diversity score: ${final.diversity.toFixed(3)}
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${final.duration < 1000 ? '✅' : '⚠️'} Generation speed: ${final.duration.toFixed(0)}ms
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---
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Generated by agentic-synth training session
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`;
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await fs.writeFile(
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path.join(this.outputDir, 'TRAINING_REPORT.md'),
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report
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);
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console.log(` ✅ Reports saved to ${this.outputDir}/`);
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console.log(` - metrics.json`);
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console.log(` - benchmarks.json`);
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console.log(` - TRAINING_REPORT.md`);
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console.log(` - Data files (baseline, generations, optimized)`);
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}
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}
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// Run
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async function main() {
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const session = new OpenRouterTrainingSession();
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await session.run();
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}
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main().catch(console.error);
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