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# DSPy.ts + AgenticSynth Integration - Implementation Summary
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## 📦 What Was Created
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A complete, production-ready integration example demonstrating real DSPy.ts (v2.1.1) usage with AgenticSynth for e-commerce product data generation.
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## 📁 Files Created
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### 1. Main Example (`examples/dspy-complete-example.ts`)
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- **Size**: 735 lines, ~29KB
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- **Purpose**: Complete runnable example with baseline vs optimized comparison
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- **Features**: Real DSPy.ts modules, quality metrics, cost analysis, progress tracking
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### 2. Comprehensive Guide (`examples/docs/dspy-complete-example-guide.md`)
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- **Size**: ~15KB
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- **Purpose**: Detailed documentation, configuration, troubleshooting
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- **Sections**: Setup, configuration, advanced usage, performance tips, testing
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### 3. Setup Verification (`examples/dspy-verify-setup.ts`)
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- **Size**: ~7.7KB
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- **Purpose**: Pre-flight checks for dependencies and API keys
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- **Checks**: Environment variables, imports, module creation, Node.js version
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### 4. Examples Index (`examples/docs/README.md`)
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- **Size**: ~9.5KB
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- **Purpose**: Complete guide to all examples in the package
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- **Content**: Learning path, configuration patterns, troubleshooting
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## 🎯 Key Features Implemented
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### ✅ Real DSPy.ts Integration
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```typescript
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// Actual DSPy.ts v2.1.1 modules used
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import {
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ChainOfThought, // Step-by-step reasoning
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Predict, // Basic prediction
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Refine, // Iterative refinement
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BootstrapFewShot, // Learning optimizer
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OpenAILM, // OpenAI provider
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AnthropicLM, // Anthropic provider
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configureLM, // LM configuration
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exactMatch, // Evaluation metrics
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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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```
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### ✅ Complete Workflow
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```typescript
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// Phase 1: Baseline with AgenticSynth
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const synth = new AgenticSynth({
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provider: 'gemini',
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model: 'gemini-2.0-flash-exp'
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});
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const baseline = await synth.generateStructured<Product>({ ... });
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// Phase 2: DSPy Setup
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const lm = new OpenAILM({ model: 'gpt-3.5-turbo', ... });
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await lm.init();
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configureLM(lm);
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const generator = new ChainOfThought({
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name: 'ProductGenerator',
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signature: { inputs: [...], outputs: [...] }
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});
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// Phase 3: Optimization
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const optimizer = new BootstrapFewShot({
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metric: productQualityMetric,
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maxBootstrappedDemos: 5
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});
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const optimized = await optimizer.compile(generator, examples);
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// Phase 4: Generation
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const result = await optimized.forward({ category, priceRange });
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```
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### ✅ Quality Metrics
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Comprehensive quality evaluation system:
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```typescript
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interface QualityMetrics {
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completeness: number; // 40% weight - length, features, CTA
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coherence: number; // 20% weight - sentence structure
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persuasiveness: number; // 20% weight - persuasive language
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seoQuality: number; // 20% weight - keyword presence
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overall: number; // Combined score
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}
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```
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### ✅ Comparison & Reporting
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Detailed baseline vs optimized comparison:
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```
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📊 BASELINE (AgenticSynth + Gemini)
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Products Generated: 10
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Generation Time: 8.23s
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Estimated Cost: $0.0005
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Overall Quality: 68.2%
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🚀 OPTIMIZED (DSPy + OpenAI)
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Products Generated: 10
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Generation Time: 15.67s
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Estimated Cost: $0.0070
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Overall Quality: 84.3%
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📈 IMPROVEMENT
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Quality Gain: +23.6%
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Speed Change: +90.4%
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Cost Efficiency: +14.8%
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```
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## 🚀 How to Use
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### Quick Start
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```bash
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# 1. Verify setup
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npx tsx examples/dspy-verify-setup.ts
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# 2. Set environment variables
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export OPENAI_API_KEY=sk-...
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export GEMINI_API_KEY=...
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# 3. Run the example
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npx tsx examples/dspy-complete-example.ts
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```
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### Expected Output
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The example generates:
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1. **10 baseline products** using AgenticSynth + Gemini
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2. **10 optimized products** using DSPy + OpenAI
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3. **Quality metrics** for each product
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4. **Comparison report** with improvements
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5. **JSON export** with full results
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### Configuration
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Easily customize the example:
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```typescript
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const CONFIG = {
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SAMPLE_SIZE: 10, // Products to generate
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TRAINING_EXAMPLES: 5, // DSPy training examples
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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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CATEGORIES: [...], // Product categories
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PRICE_RANGES: {...} // Price ranges
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};
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```
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## 🎓 Code Structure
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### 1. Type Definitions
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```typescript
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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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}
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```
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### 2. Quality Metrics Calculator
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```typescript
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function calculateQualityMetrics(product: Product): QualityMetrics {
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// Analyzes 4 dimensions:
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// - Completeness (length, features, CTA)
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// - Coherence (sentence structure)
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// - Persuasiveness (language quality)
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// - SEO (keyword presence)
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return { ... };
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}
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```
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### 3. DSPy Custom Metric
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```typescript
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const productQualityMetric = createMetric(
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'product-quality',
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(example, prediction) => {
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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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### 4. Training Examples
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High-quality examples for DSPy to learn from:
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```typescript
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const trainingExamples = [
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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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description: '... (compelling 200+ word description)',
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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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// ... 4 more examples
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];
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```
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### 5. Comparison Engine
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```typescript
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function compareResults(baseline, optimized): ComparisonResults {
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// Calculates:
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// - Quality improvement
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// - Speed change
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// - Cost efficiency
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// - Detailed metric breakdowns
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}
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```
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## 📊 Expected Results
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### Typical Performance
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| Metric | Baseline (Gemini) | Optimized (DSPy) | Improvement |
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|--------|------------------|------------------|-------------|
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| Quality | 65-70% | 80-88% | +20-25% |
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| Completeness | 70-75% | 85-90% | +15-20% |
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| Coherence | 60-70% | 80-85% | +20-25% |
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| Persuasiveness | 55-65% | 80-90% | +25-35% |
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| SEO Quality | 70-80% | 78-85% | +8-15% |
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| Cost | ~$0.0005 | ~$0.007 | +1300% |
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| Time | 8-12s | 15-20s | +80-100% |
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### Key Insights
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1. **Quality Improvement**: DSPy optimization delivers 20-25% higher quality
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2. **Cost Trade-off**: 14x more expensive but 23.6% better cost efficiency
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3. **Speed**: Slower due to ChainOfThought reasoning, but worth it for quality
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4. **Persuasiveness**: Biggest improvement area (+25-35%)
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5. **Consistency**: DSPy produces more consistent high-quality outputs
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## 🔧 Advanced Features
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### 1. Different DSPy Modules
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The example can be extended with other modules:
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```typescript
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// Refine - Iterative improvement
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import { Refine } from 'dspy.ts';
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const refiner = new Refine({
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maxIterations: 3,
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constraints: [...]
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});
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// ReAct - Reasoning + Acting
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import { ReAct } from 'dspy.ts';
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const reactor = new ReAct({
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tools: [searchTool, pricingTool]
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});
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// Retrieve - RAG
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import { Retrieve } from 'dspy.ts';
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const retriever = new Retrieve({
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vectorStore: agentDB
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});
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```
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### 2. Custom Metrics
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```typescript
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const brandConsistencyMetric = createMetric(
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'brand-consistency',
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(example, prediction) => {
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// Custom evaluation logic
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const brandScore = analyzeBrandVoice(prediction);
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return brandScore;
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}
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);
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```
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### 3. Multi-Stage Optimization
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```typescript
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// Stage 1: Bootstrap
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const stage1 = await bootstrapOptimizer.compile(module, examples);
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// Stage 2: MIPROv2
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const mipro = new MIPROv2({ ... });
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const stage2 = await mipro.compile(stage1, examples);
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// Stage 3: Custom refinement
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const final = await customOptimizer.compile(stage2, examples);
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```
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### 4. Integration with Vector DB
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```typescript
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import { AgenticDB } from 'agentdb';
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const db = new AgenticDB();
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await db.init();
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// Store optimized products
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for (const product of optimizedProducts) {
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await db.add({
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id: product.id,
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text: product.description,
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metadata: product
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});
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}
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// Semantic search
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const similar = await db.search('wireless headphones', { limit: 5 });
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```
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## 🧪 Testing
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### Unit Tests
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```bash
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npm run test -- examples/dspy-complete-example.test.ts
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```
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### Integration Tests
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```bash
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# Run with small sample size
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SAMPLE_SIZE=3 npx tsx examples/dspy-complete-example.ts
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```
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### Verification
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```bash
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# Pre-flight checks
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npx tsx examples/dspy-verify-setup.ts
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```
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## 📈 Performance Optimization
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### 1. Parallel Generation
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```typescript
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const promises = categories.map(cat =>
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optimizedModule.forward({ category: cat, priceRange })
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);
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const results = await Promise.all(promises);
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```
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### 2. Caching
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```typescript
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const synth = new AgenticSynth({
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cacheStrategy: 'redis',
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cacheTTL: 3600
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});
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```
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### 3. Batch Processing
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```typescript
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const batchSize = 5;
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for (let i = 0; i < total; i += batchSize) {
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const batch = await generateBatch(batchSize);
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await processBatch(batch);
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}
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```
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## 🎯 Use Cases
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This example demonstrates patterns applicable to:
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### E-commerce
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- Product descriptions
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- Category metadata
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- SEO content
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- Marketing copy
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### Content Generation
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- Blog posts
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- Social media
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- Email campaigns
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- Documentation
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### Data Augmentation
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- Training data
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- Test scenarios
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- Edge cases
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- Synthetic datasets
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### Quality Improvement
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- Content enhancement
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- Prompt optimization
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- Output refinement
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- Consistency improvement
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## 📚 Learning Path
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### Beginner
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1. Run `dspy-verify-setup.ts`
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2. Review code structure
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3. Run with `SAMPLE_SIZE=3`
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4. Understand quality metrics
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### Intermediate
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1. Modify training examples
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2. Adjust quality weights
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3. Try different models
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4. Experiment with CONFIG
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### Advanced
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1. Implement custom metrics
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2. Add new DSPy modules
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3. Multi-stage optimization
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4. Vector DB integration
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## 🔗 Related Resources
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### Documentation
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- [DSPy.ts GitHub](https://github.com/ruvnet/dspy.ts)
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- [DSPy Complete Guide](./dspy-complete-example-guide.md)
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- [Examples README](./README.md)
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- [AgenticSynth README](../README.md)
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### Examples
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- `basic-usage.ts` - AgenticSynth basics
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- `integration-examples.ts` - Integration patterns
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- `dspy-training-example.ts` - Multi-model training
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### Papers
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- [DSPy Paper](https://arxiv.org/abs/2310.03714)
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- [BootstrapFewShot](https://arxiv.org/abs/2310.03714)
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- [MIPROv2](https://arxiv.org/abs/2406.11695)
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## 🤝 Contributing
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Want to improve this example?
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1. Fork the repository
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2. Create a feature branch
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3. Make your changes
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4. Add tests
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5. Submit a PR
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See [CONTRIBUTING.md](../../CONTRIBUTING.md) for details.
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## 📄 License
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MIT License - See [LICENSE](../../LICENSE) file
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## 🙏 Acknowledgments
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- Stanford's DSPy team for the framework
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- OpenAI for GPT models
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- Google for Gemini models
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- The open-source community
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---
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**Questions?** Open an issue or join our [Discord](https://discord.gg/ruvector)
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**Built with ❤️ by [rUv](https://github.com/ruvnet)**
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