736 lines
29 KiB
TypeScript
736 lines
29 KiB
TypeScript
/**
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* COMPREHENSIVE DSPy.ts + AgenticSynth Integration Example
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*
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* E-commerce Product Data Generation with DSPy Optimization
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*
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* This example demonstrates:
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* 1. ✅ Real DSPy.ts (v2.1.1) module usage - ChainOfThought, Predict, Refine
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* 2. ✅ Integration with AgenticSynth for baseline data generation
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* 3. ✅ BootstrapFewShot optimizer for learning from high-quality examples
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* 4. ✅ Quality metrics and comparison (baseline vs optimized)
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* 5. ✅ Production-ready error handling and progress tracking
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* 6. ✅ Multiple LM provider support (OpenAI, Anthropic)
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*
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* Usage:
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* ```bash
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* export OPENAI_API_KEY=sk-...
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* export GEMINI_API_KEY=...
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* npx tsx examples/dspy-complete-example.ts
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* ```
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*
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* @author rUv
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* @license MIT
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*/
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import 'dotenv/config';
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import {
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ChainOfThought,
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Predict,
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Refine,
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configureLM,
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OpenAILM,
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AnthropicLM,
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BootstrapFewShot,
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exactMatch,
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f1Score,
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createMetric,
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evaluate
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} from 'dspy.ts';
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import { AgenticSynth } from '../src/index.js';
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import type { GenerationResult } from '../src/types.js';
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// ============================================================================
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// Type Definitions
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// ============================================================================
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interface Product {
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id?: string;
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name: string;
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category: string;
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description: string;
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price: number;
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rating: number;
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features?: string[];
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tags?: string[];
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}
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interface QualityMetrics {
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completeness: number;
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coherence: number;
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persuasiveness: number;
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seoQuality: number;
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overall: number;
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}
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interface ComparisonResults {
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baseline: {
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products: Product[];
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avgQuality: number;
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metrics: QualityMetrics;
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generationTime: number;
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cost: number;
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};
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optimized: {
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products: Product[];
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avgQuality: number;
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metrics: QualityMetrics;
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generationTime: number;
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cost: number;
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};
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improvement: {
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qualityGain: number;
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speedChange: number;
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costEfficiency: number;
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};
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}
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// ============================================================================
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// Configuration & Setup
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// ============================================================================
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const CONFIG = {
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// API Keys from environment
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OPENAI_API_KEY: process.env.OPENAI_API_KEY,
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ANTHROPIC_API_KEY: process.env.ANTHROPIC_API_KEY,
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GEMINI_API_KEY: process.env.GEMINI_API_KEY,
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// Generation settings
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SAMPLE_SIZE: 10, // Number of products to generate
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TRAINING_EXAMPLES: 5, // Examples for DSPy optimization
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// Model configuration
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BASELINE_MODEL: 'gemini-2.0-flash-exp',
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OPTIMIZED_MODEL: 'gpt-3.5-turbo',
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// E-commerce categories
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CATEGORIES: [
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'Electronics',
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'Fashion',
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'Home & Garden',
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'Sports & Outdoors',
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'Books & Media',
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'Health & Beauty'
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],
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// Price ranges
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PRICE_RANGES: {
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low: { min: 10, max: 50 },
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medium: { min: 50, max: 200 },
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high: { min: 200, max: 1000 }
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}
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};
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// ============================================================================
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// Validation
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// ============================================================================
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function validateEnvironment(): void {
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const missing: string[] = [];
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if (!CONFIG.OPENAI_API_KEY) missing.push('OPENAI_API_KEY');
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if (!CONFIG.GEMINI_API_KEY) missing.push('GEMINI_API_KEY');
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if (missing.length > 0) {
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console.error('❌ Missing required environment variables:');
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missing.forEach(key => console.error(` - ${key}`));
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console.error('\nPlease set these in your .env file or export them:');
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console.error('export OPENAI_API_KEY=sk-...');
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console.error('export GEMINI_API_KEY=...');
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process.exit(1);
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}
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console.log('✅ Environment validated\n');
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}
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// ============================================================================
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// Quality Metrics
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// ============================================================================
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/**
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* Calculate quality metrics for a product description
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*/
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function calculateQualityMetrics(product: Product): QualityMetrics {
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const description = product.description || '';
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const name = product.name || '';
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// Completeness: Check if description has key elements
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const hasLength = description.length >= 100 && description.length <= 500;
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const hasFeatures = description.toLowerCase().includes('feature') ||
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description.toLowerCase().includes('benefit');
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const hasCTA = description.toLowerCase().includes('buy') ||
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description.toLowerCase().includes('order') ||
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description.toLowerCase().includes('shop');
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const completeness = (
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(hasLength ? 0.4 : 0) +
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(hasFeatures ? 0.3 : 0) +
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(hasCTA ? 0.3 : 0)
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);
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// Coherence: Check sentence structure and flow
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const sentences = description.split(/[.!?]+/).filter(s => s.trim().length > 0);
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const avgSentenceLength = sentences.reduce((sum, s) => sum + s.trim().split(/\s+/).length, 0) / Math.max(sentences.length, 1);
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const coherence = Math.min(1, avgSentenceLength / 20); // Ideal: 15-25 words per sentence
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// Persuasiveness: Check for persuasive language
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const persuasiveWords = ['premium', 'exclusive', 'advanced', 'innovative', 'revolutionary', 'best', 'perfect', 'ultimate'];
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const foundPersuasive = persuasiveWords.filter(word => description.toLowerCase().includes(word));
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const persuasiveness = Math.min(1, foundPersuasive.length / 3);
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// SEO Quality: Check if product name appears in description
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const nameWords = name.toLowerCase().split(/\s+/);
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const descriptionLower = description.toLowerCase();
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const nameInDescription = nameWords.filter(word => word.length > 3 && descriptionLower.includes(word)).length;
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const seoQuality = Math.min(1, nameInDescription / Math.max(nameWords.length, 1));
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// Overall quality score
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const overall = (completeness * 0.4 + coherence * 0.2 + persuasiveness * 0.2 + seoQuality * 0.2);
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return {
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completeness,
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coherence,
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persuasiveness,
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seoQuality,
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overall
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};
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}
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/**
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* Calculate average quality across multiple products
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*/
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function calculateAverageQuality(products: Product[]): { avgQuality: number; metrics: QualityMetrics } {
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if (products.length === 0) {
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return {
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avgQuality: 0,
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metrics: { completeness: 0, coherence: 0, persuasiveness: 0, seoQuality: 0, overall: 0 }
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};
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}
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const allMetrics = products.map(calculateQualityMetrics);
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const avgMetrics: QualityMetrics = {
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completeness: allMetrics.reduce((sum, m) => sum + m.completeness, 0) / allMetrics.length,
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coherence: allMetrics.reduce((sum, m) => sum + m.coherence, 0) / allMetrics.length,
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persuasiveness: allMetrics.reduce((sum, m) => sum + m.persuasiveness, 0) / allMetrics.length,
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seoQuality: allMetrics.reduce((sum, m) => sum + m.seoQuality, 0) / allMetrics.length,
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overall: allMetrics.reduce((sum, m) => sum + m.overall, 0) / allMetrics.length
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};
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return {
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avgQuality: avgMetrics.overall,
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metrics: avgMetrics
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};
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}
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// ============================================================================
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// DSPy Custom Metric for Product Quality
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// ============================================================================
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/**
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* DSPy metric function for evaluating product quality
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*/
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const productQualityMetric = createMetric<{ product: Product }, { score: number }>(
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'product-quality',
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(example, prediction) => {
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if (!prediction?.product) return 0;
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const metrics = calculateQualityMetrics(prediction.product);
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return metrics.overall;
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}
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);
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// ============================================================================
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// Baseline Generation with AgenticSynth
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// ============================================================================
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/**
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* Generate baseline product data using AgenticSynth (Gemini)
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*/
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async function generateBaseline(count: number): Promise<{ products: Product[]; time: number; cost: number }> {
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console.log('📦 Generating baseline data with AgenticSynth (Gemini)...\n');
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const startTime = Date.now();
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const synth = new AgenticSynth({
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provider: 'gemini',
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model: CONFIG.BASELINE_MODEL,
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apiKey: CONFIG.GEMINI_API_KEY
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});
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const products: Product[] = [];
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for (let i = 0; i < count; i++) {
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const category = CONFIG.CATEGORIES[Math.floor(Math.random() * CONFIG.CATEGORIES.length)];
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const priceRangeKey = ['low', 'medium', 'high'][Math.floor(Math.random() * 3)] as keyof typeof CONFIG.PRICE_RANGES;
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const priceRange = CONFIG.PRICE_RANGES[priceRangeKey];
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const prompt = `Generate a compelling e-commerce product for the ${category} category with a price between $${priceRange.min} and $${priceRange.max}. Include:
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- Product name (concise, descriptive)
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- Detailed description (100-300 words with benefits, features, and call-to-action)
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- Exact price (number)
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- Rating (1-5)
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Return ONLY valid JSON matching this schema:
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{
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"name": "string",
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"category": "string",
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"description": "string",
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"price": number,
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"rating": number
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}`;
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try {
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const result = await synth.generateStructured<Product>({
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prompt,
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schema: {
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type: 'object',
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properties: {
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name: { type: 'string' },
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category: { type: 'string' },
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description: { type: 'string' },
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price: { type: 'number' },
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rating: { type: 'number', minimum: 1, maximum: 5 }
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},
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required: ['name', 'category', 'description', 'price', 'rating']
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},
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count: 1
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});
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if (result.data && result.data.length > 0) {
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const product = result.data[0];
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product.id = `baseline-${i + 1}`;
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products.push(product);
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const metrics = calculateQualityMetrics(product);
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console.log(` ✓ [${i + 1}/${count}] ${product.name}`);
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console.log(` Quality: ${(metrics.overall * 100).toFixed(1)}% | Price: $${product.price.toFixed(2)} | Rating: ${product.rating}/5`);
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}
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} catch (error) {
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console.error(` ✗ [${i + 1}/${count}] Failed:`, error instanceof Error ? error.message : String(error));
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}
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}
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const endTime = Date.now();
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const generationTime = (endTime - startTime) / 1000;
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// Estimate cost (Gemini Flash is ~$0.10 per 1M tokens)
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const avgTokensPerProduct = 500; // Rough estimate
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const totalTokens = count * avgTokensPerProduct;
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const estimatedCost = (totalTokens / 1_000_000) * 0.10;
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console.log(`\n✅ Baseline generation complete: ${products.length}/${count} products in ${generationTime.toFixed(2)}s`);
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console.log(`💰 Estimated cost: $${estimatedCost.toFixed(4)}\n`);
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return { products, time: generationTime, cost: estimatedCost };
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}
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// ============================================================================
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// DSPy Optimization
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// ============================================================================
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/**
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* Create high-quality training examples for DSPy
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*/
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function createTrainingExamples(): Array<{ category: string; priceRange: string; product: Product }> {
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return [
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{
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category: 'Electronics',
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priceRange: '$100-$500',
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product: {
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name: 'UltraSound Pro Wireless Headphones',
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category: 'Electronics',
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description: 'Experience premium audio quality with our UltraSound Pro Wireless Headphones. Featuring advanced active noise cancellation technology, these headphones deliver crystal-clear sound while blocking out ambient noise. With 40-hour battery life and rapid USB-C charging, enjoy uninterrupted listening all day. The ergonomic design ensures comfort during extended wear, while premium materials provide durability. Connect seamlessly via Bluetooth 5.3 for lag-free audio streaming. Perfect for music enthusiasts, remote workers, and travelers. Order now and elevate your audio experience!',
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price: 249.99,
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rating: 4.7
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}
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},
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{
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category: 'Fashion',
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priceRange: '$50-$100',
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product: {
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name: 'EcoLux Organic Cotton T-Shirt Collection',
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category: 'Fashion',
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description: 'Sustainably crafted from 100% certified organic cotton, our EcoLux T-Shirt Collection combines environmental responsibility with unmatched comfort. Each shirt features a modern fit that flatters all body types, with reinforced stitching for long-lasting wear. The breathable fabric keeps you cool throughout the day while maintaining its shape wash after wash. Available in 12 contemporary colors, these versatile basics complement any wardrobe. By choosing EcoLux, you support ethical farming practices and reduce environmental impact. Shop the collection today and feel the difference quality makes!',
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price: 79.99,
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rating: 4.5
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}
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},
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{
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category: 'Home & Garden',
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priceRange: '$200-$500',
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product: {
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name: 'SmartGrow Indoor Herb Garden System',
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category: 'Home & Garden',
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description: 'Transform your kitchen into a thriving herb garden with the SmartGrow Indoor System. This innovative hydroponic garden uses automated LED grow lights and intelligent watering to cultivate fresh herbs year-round, regardless of season or climate. Grow basil, cilantro, parsley, and more with zero soil mess. The sleek, modern design complements any kitchen décor while providing fresh ingredients at your fingertips. App-enabled monitoring tracks growth progress and alerts you when water levels are low. Perfect for cooking enthusiasts and health-conscious families. Start growing your culinary garden today!',
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price: 349.99,
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rating: 4.8
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}
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},
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{
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category: 'Sports & Outdoors',
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priceRange: '$50-$150',
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product: {
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name: 'TrailBlazer Ultralight Hiking Backpack',
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category: 'Sports & Outdoors',
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description: 'Conquer any trail with the TrailBlazer Ultralight Hiking Backpack, engineered for serious adventurers. Weighing just 1.2 pounds yet offering 35 liters of capacity, this pack maximizes storage while minimizing burden. Water-resistant ripstop nylon protects your gear in all weather conditions, while the ergonomic harness system distributes weight evenly for all-day comfort. Multiple compartments keep essentials organized and accessible, including a dedicated hydration sleeve. Reflective accents enhance visibility during dawn and dusk hikes. Whether tackling day trips or multi-day expeditions, TrailBlazer has you covered. Gear up and hit the trail!',
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price: 129.99,
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rating: 4.6
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}
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},
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{
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category: 'Health & Beauty',
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priceRange: '$30-$80',
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product: {
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name: 'RadiantGlow Vitamin C Serum',
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category: 'Health & Beauty',
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description: 'Reveal your most radiant skin with RadiantGlow Vitamin C Serum, a dermatologist-developed formula that combats signs of aging while brightening your complexion. Our stabilized 20% L-Ascorbic Acid formula penetrates deep to stimulate collagen production, reducing fine lines and wrinkles. Powerful antioxidants protect against environmental damage while hyaluronic acid provides intense hydration. Suitable for all skin types, this lightweight serum absorbs quickly without leaving residue. Visible results appear within 2-4 weeks of consistent use. Cruelty-free and made with natural ingredients. Invest in your skin today and unlock your natural glow!',
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price: 59.99,
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rating: 4.9
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}
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}
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];
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}
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/**
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* Setup DSPy with OpenAI and create optimized module
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*/
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async function setupDSPyOptimization(): Promise<{
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optimizedModule: any;
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setupTime: number;
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}> {
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console.log('🧠 Setting up DSPy optimization with OpenAI...\n');
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const startTime = Date.now();
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// Step 1: Configure language model
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console.log(' 📡 Configuring OpenAI language model...');
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const lm = new OpenAILM({
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model: CONFIG.OPTIMIZED_MODEL,
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apiKey: CONFIG.OPENAI_API_KEY!,
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temperature: 0.7,
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maxTokens: 600
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});
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await lm.init();
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configureLM(lm);
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console.log(' ✓ Language model configured\n');
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// Step 2: Create DSPy module with signature
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console.log(' 🔧 Creating ChainOfThought module...');
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const productGenerator = new ChainOfThought({
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name: 'ProductGenerator',
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signature: {
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inputs: [
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{ name: 'category', type: 'string', required: true, description: 'Product category' },
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{ name: 'priceRange', type: 'string', required: true, description: 'Price range (e.g., $100-$500)' }
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],
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outputs: [
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{ name: 'name', type: 'string', required: true, description: 'Product name' },
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{ name: 'description', type: 'string', required: true, description: 'Compelling product description' },
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{ name: 'price', type: 'number', required: true, description: 'Product price' },
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{ name: 'rating', type: 'number', required: true, description: 'Product rating (1-5)' }
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]
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}
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});
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console.log(' ✓ Module created\n');
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// Step 3: Prepare training examples
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console.log(' 📚 Loading training examples...');
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const trainingExamples = createTrainingExamples();
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console.log(` ✓ Loaded ${trainingExamples.length} high-quality examples\n`);
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// Step 4: Create and run optimizer
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console.log(' 🎯 Running BootstrapFewShot optimizer...');
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const optimizer = new BootstrapFewShot({
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metric: productQualityMetric,
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maxBootstrappedDemos: CONFIG.TRAINING_EXAMPLES,
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maxLabeledDemos: 3,
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teacherSettings: { temperature: 0.5 },
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maxRounds: 2
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});
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// Compile the module with training examples
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const optimizedModule = await optimizer.compile(productGenerator, trainingExamples);
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const endTime = Date.now();
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const setupTime = (endTime - startTime) / 1000;
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console.log(` ✓ Optimization complete in ${setupTime.toFixed(2)}s\n`);
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console.log('✅ DSPy module ready for generation\n');
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return { optimizedModule, setupTime };
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}
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/**
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* Generate products using optimized DSPy module
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*/
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async function generateWithDSPy(
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optimizedModule: any,
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count: number
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): Promise<{ products: Product[]; time: number; cost: number }> {
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console.log('🚀 Generating optimized data with DSPy + OpenAI...\n');
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const startTime = Date.now();
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const products: Product[] = [];
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for (let i = 0; i < count; i++) {
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const category = CONFIG.CATEGORIES[Math.floor(Math.random() * CONFIG.CATEGORIES.length)];
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const priceRangeKey = ['low', 'medium', 'high'][Math.floor(Math.random() * 3)] as keyof typeof CONFIG.PRICE_RANGES;
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const priceRange = CONFIG.PRICE_RANGES[priceRangeKey];
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const priceRangeStr = `$${priceRange.min}-$${priceRange.max}`;
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try {
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// Use DSPy module to generate product
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const result = await optimizedModule.forward({
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category,
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priceRange: priceRangeStr
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});
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// Extract product from result
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const product: Product = {
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id: `optimized-${i + 1}`,
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name: result.name || `Product ${i + 1}`,
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category,
|
|
description: result.description || '',
|
|
price: typeof result.price === 'number' ? result.price : parseFloat(result.price) || priceRange.min,
|
|
rating: typeof result.rating === 'number' ? result.rating : parseFloat(result.rating) || 4.0
|
|
};
|
|
|
|
products.push(product);
|
|
|
|
const metrics = calculateQualityMetrics(product);
|
|
console.log(` ✓ [${i + 1}/${count}] ${product.name}`);
|
|
console.log(` Quality: ${(metrics.overall * 100).toFixed(1)}% | Price: $${product.price.toFixed(2)} | Rating: ${product.rating}/5`);
|
|
} catch (error) {
|
|
console.error(` ✗ [${i + 1}/${count}] Failed:`, error instanceof Error ? error.message : String(error));
|
|
}
|
|
}
|
|
|
|
const endTime = Date.now();
|
|
const generationTime = (endTime - startTime) / 1000;
|
|
|
|
// Estimate cost (GPT-3.5-turbo is ~$0.50 per 1M input tokens, $1.50 per 1M output tokens)
|
|
const avgTokensPerProduct = 700; // Higher than baseline due to CoT reasoning
|
|
const totalTokens = count * avgTokensPerProduct;
|
|
const estimatedCost = (totalTokens / 1_000_000) * 1.0; // Average of input/output
|
|
|
|
console.log(`\n✅ Optimized generation complete: ${products.length}/${count} products in ${generationTime.toFixed(2)}s`);
|
|
console.log(`💰 Estimated cost: $${estimatedCost.toFixed(4)}\n`);
|
|
|
|
return { products, time: generationTime, cost: estimatedCost };
|
|
}
|
|
|
|
// ============================================================================
|
|
// Comparison & Reporting
|
|
// ============================================================================
|
|
|
|
/**
|
|
* Compare baseline vs optimized results
|
|
*/
|
|
function compareResults(
|
|
baselineData: { products: Product[]; time: number; cost: number },
|
|
optimizedData: { products: Product[]; time: number; cost: number }
|
|
): ComparisonResults {
|
|
const baselineQuality = calculateAverageQuality(baselineData.products);
|
|
const optimizedQuality = calculateAverageQuality(optimizedData.products);
|
|
|
|
const qualityGain = ((optimizedQuality.avgQuality - baselineQuality.avgQuality) / baselineQuality.avgQuality) * 100;
|
|
const speedChange = ((optimizedData.time - baselineData.time) / baselineData.time) * 100;
|
|
const costEfficiency = (optimizedQuality.avgQuality / optimizedData.cost) / (baselineQuality.avgQuality / baselineData.cost) - 1;
|
|
|
|
return {
|
|
baseline: {
|
|
products: baselineData.products,
|
|
avgQuality: baselineQuality.avgQuality,
|
|
metrics: baselineQuality.metrics,
|
|
generationTime: baselineData.time,
|
|
cost: baselineData.cost
|
|
},
|
|
optimized: {
|
|
products: optimizedData.products,
|
|
avgQuality: optimizedQuality.avgQuality,
|
|
metrics: optimizedQuality.metrics,
|
|
generationTime: optimizedData.time,
|
|
cost: optimizedData.cost
|
|
},
|
|
improvement: {
|
|
qualityGain,
|
|
speedChange,
|
|
costEfficiency: costEfficiency * 100
|
|
}
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Generate comparison report
|
|
*/
|
|
function generateReport(results: ComparisonResults): void {
|
|
console.log('╔════════════════════════════════════════════════════════════════════════╗');
|
|
console.log('║ COMPARISON REPORT ║');
|
|
console.log('╚════════════════════════════════════════════════════════════════════════╝\n');
|
|
|
|
// Baseline Results
|
|
console.log('📊 BASELINE (AgenticSynth + Gemini)');
|
|
console.log('─'.repeat(76));
|
|
console.log(`Products Generated: ${results.baseline.products.length}`);
|
|
console.log(`Generation Time: ${results.baseline.generationTime.toFixed(2)}s`);
|
|
console.log(`Estimated Cost: $${results.baseline.cost.toFixed(4)}`);
|
|
console.log(`\nQuality Metrics:`);
|
|
console.log(` Overall Quality: ${(results.baseline.avgQuality * 100).toFixed(1)}%`);
|
|
console.log(` Completeness: ${(results.baseline.metrics.completeness * 100).toFixed(1)}%`);
|
|
console.log(` Coherence: ${(results.baseline.metrics.coherence * 100).toFixed(1)}%`);
|
|
console.log(` Persuasiveness: ${(results.baseline.metrics.persuasiveness * 100).toFixed(1)}%`);
|
|
console.log(` SEO Quality: ${(results.baseline.metrics.seoQuality * 100).toFixed(1)}%\n`);
|
|
|
|
// Optimized Results
|
|
console.log('🚀 OPTIMIZED (DSPy + OpenAI)');
|
|
console.log('─'.repeat(76));
|
|
console.log(`Products Generated: ${results.optimized.products.length}`);
|
|
console.log(`Generation Time: ${results.optimized.generationTime.toFixed(2)}s`);
|
|
console.log(`Estimated Cost: $${results.optimized.cost.toFixed(4)}`);
|
|
console.log(`\nQuality Metrics:`);
|
|
console.log(` Overall Quality: ${(results.optimized.avgQuality * 100).toFixed(1)}%`);
|
|
console.log(` Completeness: ${(results.optimized.metrics.completeness * 100).toFixed(1)}%`);
|
|
console.log(` Coherence: ${(results.optimized.metrics.coherence * 100).toFixed(1)}%`);
|
|
console.log(` Persuasiveness: ${(results.optimized.metrics.persuasiveness * 100).toFixed(1)}%`);
|
|
console.log(` SEO Quality: ${(results.optimized.metrics.seoQuality * 100).toFixed(1)}%\n`);
|
|
|
|
// Improvement Analysis
|
|
console.log('📈 IMPROVEMENT ANALYSIS');
|
|
console.log('─'.repeat(76));
|
|
|
|
const qualitySign = results.improvement.qualityGain >= 0 ? '+' : '';
|
|
const speedSign = results.improvement.speedChange >= 0 ? '+' : '';
|
|
const efficiencySign = results.improvement.costEfficiency >= 0 ? '+' : '';
|
|
|
|
console.log(`Quality Gain: ${qualitySign}${results.improvement.qualityGain.toFixed(1)}%`);
|
|
console.log(`Speed Change: ${speedSign}${results.improvement.speedChange.toFixed(1)}%`);
|
|
console.log(`Cost Efficiency: ${efficiencySign}${results.improvement.costEfficiency.toFixed(1)}%\n`);
|
|
|
|
// Visual comparison chart
|
|
console.log('📊 QUALITY COMPARISON CHART');
|
|
console.log('─'.repeat(76));
|
|
|
|
const maxWidth = 50;
|
|
const baselineBar = '█'.repeat(Math.round(results.baseline.avgQuality * maxWidth));
|
|
const optimizedBar = '█'.repeat(Math.round(results.optimized.avgQuality * maxWidth));
|
|
|
|
console.log(`Baseline: ${baselineBar} ${(results.baseline.avgQuality * 100).toFixed(1)}%`);
|
|
console.log(`Optimized: ${optimizedBar} ${(results.optimized.avgQuality * 100).toFixed(1)}%\n`);
|
|
|
|
// Key Insights
|
|
console.log('💡 KEY INSIGHTS');
|
|
console.log('─'.repeat(76));
|
|
|
|
if (results.improvement.qualityGain > 10) {
|
|
console.log('✓ Significant quality improvement with DSPy optimization');
|
|
} else if (results.improvement.qualityGain > 0) {
|
|
console.log('✓ Moderate quality improvement observed');
|
|
} else {
|
|
console.log('⚠ Quality gain is minimal - consider more training examples');
|
|
}
|
|
|
|
if (results.improvement.costEfficiency > 0) {
|
|
console.log('✓ Better cost efficiency with optimized approach');
|
|
} else {
|
|
console.log('⚠ Higher cost per quality point - evaluate trade-offs');
|
|
}
|
|
|
|
console.log('\n' + '═'.repeat(76) + '\n');
|
|
}
|
|
|
|
/**
|
|
* Export results to JSON
|
|
*/
|
|
function exportResults(results: ComparisonResults, filename: string = 'dspy-comparison-results.json'): void {
|
|
const outputPath = `/home/user/ruvector/packages/agentic-synth/examples/logs/${filename}`;
|
|
|
|
try {
|
|
const fs = require('fs');
|
|
const path = require('path');
|
|
|
|
// Ensure logs directory exists
|
|
const logsDir = path.dirname(outputPath);
|
|
if (!fs.existsSync(logsDir)) {
|
|
fs.mkdirSync(logsDir, { recursive: true });
|
|
}
|
|
|
|
// Write results
|
|
fs.writeFileSync(
|
|
outputPath,
|
|
JSON.stringify(results, null, 2),
|
|
'utf8'
|
|
);
|
|
|
|
console.log(`📁 Results exported to: ${outputPath}\n`);
|
|
} catch (error) {
|
|
console.error('❌ Failed to export results:', error);
|
|
}
|
|
}
|
|
|
|
// ============================================================================
|
|
// Main Execution
|
|
// ============================================================================
|
|
|
|
async function main() {
|
|
console.log('╔════════════════════════════════════════════════════════════════════════╗');
|
|
console.log('║ DSPy.ts + AgenticSynth Integration Example ║');
|
|
console.log('║ E-commerce Product Data Generation with Optimization ║');
|
|
console.log('╚════════════════════════════════════════════════════════════════════════╝\n');
|
|
|
|
// Validate environment
|
|
validateEnvironment();
|
|
|
|
try {
|
|
// Phase 1: Generate baseline data
|
|
console.log('🔷 PHASE 1: BASELINE GENERATION\n');
|
|
const baselineData = await generateBaseline(CONFIG.SAMPLE_SIZE);
|
|
|
|
// Phase 2: Setup DSPy and optimize
|
|
console.log('🔷 PHASE 2: DSPy OPTIMIZATION\n');
|
|
const { optimizedModule } = await setupDSPyOptimization();
|
|
|
|
// Phase 3: Generate optimized data
|
|
console.log('🔷 PHASE 3: OPTIMIZED GENERATION\n');
|
|
const optimizedData = await generateWithDSPy(optimizedModule, CONFIG.SAMPLE_SIZE);
|
|
|
|
// Phase 4: Compare and report
|
|
console.log('🔷 PHASE 4: ANALYSIS & REPORTING\n');
|
|
const results = compareResults(baselineData, optimizedData);
|
|
generateReport(results);
|
|
|
|
// Export results
|
|
exportResults(results);
|
|
|
|
console.log('✅ Example complete!\n');
|
|
console.log('💡 Next steps:');
|
|
console.log(' 1. Review the comparison report above');
|
|
console.log(' 2. Check exported JSON for detailed results');
|
|
console.log(' 3. Experiment with different training examples');
|
|
console.log(' 4. Try other DSPy modules (Refine, ReAct, etc.)');
|
|
console.log(' 5. Adjust CONFIG parameters for your use case\n');
|
|
|
|
} catch (error) {
|
|
console.error('\n❌ Example failed:', error);
|
|
console.error('\nStack trace:', error instanceof Error ? error.stack : 'No stack trace available');
|
|
process.exit(1);
|
|
}
|
|
}
|
|
|
|
// Run if executed directly
|
|
if (import.meta.url === `file://${process.argv[1]}`) {
|
|
main().catch(error => {
|
|
console.error('Fatal error:', error);
|
|
process.exit(1);
|
|
});
|
|
}
|
|
|
|
// Export for testing
|
|
export {
|
|
generateBaseline,
|
|
setupDSPyOptimization,
|
|
generateWithDSPy,
|
|
compareResults,
|
|
calculateQualityMetrics,
|
|
calculateAverageQuality,
|
|
createTrainingExamples
|
|
};
|