Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/npm/packages/agentic-synth-examples/examples/README.md

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+# Agentic-Synth Examples - Progressive Tutorials
+
+Complete, runnable tutorials for learning **agentic-synth** and **DSPy.ts** integration from beginner to advanced.
+
+## 📚 Tutorial Structure
+
+### 🟢 Beginner Level
+Perfect for getting started with synthetic data generation and DSPy training.
+
+### 🟡 Intermediate Level
+Learn multi-model comparison, self-learning systems, and optimization.
+
+### 🔴 Advanced Level
+Build production-grade systems with custom learning and complete pipelines.
+
+---
+
+## 🚀 Quick Start
+
+### Prerequisites
+
+```bash
+# Install dependencies
+npm install dspy.ts @ruvector/agentic-synth
+
+# Set up API keys
+export GEMINI_API_KEY="your-gemini-api-key"
+export ANTHROPIC_API_KEY="your-anthropic-key"  # Optional, for multi-model
+export OPENAI_API_KEY="your-openai-key"        # Optional, for multi-model
+```
+
+### Running Tutorials
+
+```bash
+# From the package root
+npx tsx examples/beginner/first-dspy-training.ts
+npx tsx examples/intermediate/multi-model-comparison.ts
+npx tsx examples/advanced/production-pipeline.ts
+```
+
+---
+
+## 📖 Tutorial Catalog
+
+### 🟢 Beginner Tutorials
+
+#### 1. First DSPy Training (`beginner/first-dspy-training.ts`)
+
+**Learn:** Basic DSPy.ts training with a single model
+
+**Concepts:**
+- Setting up DSPy language models
+- Defining signatures for tasks
+- Chain-of-Thought reasoning
+- Simple evaluation metrics
+- Training with examples
+
+**Run:**
+```bash
+npx tsx examples/beginner/first-dspy-training.ts
+```
+
+**Output:**
+```
+🚀 Starting Your First DSPy Training Session
+
+📊 Training with 3 examples...
+✅ Training complete!
+
+🧪 Testing the model with new products:
+
+📦 Product: Smart Watch Pro
+   Quality Score: 85%
+   ✅ Excellent
+```
+
+**What You'll Build:** A product description generator that learns from examples
+
+---
+
+#### 2. Simple Data Generation (`beginner/simple-data-generation.ts`)
+
+**Learn:** Generate structured synthetic data with schemas
+
+**Concepts:**
+- Defining data schemas
+- Structured data generation
+- Working with different formats (JSON, CSV)
+- Saving output to files
+- Using constraints for realistic data
+
+**Run:**
+```bash
+npx tsx examples/beginner/simple-data-generation.ts
+```
+
+**Output:**
+```
+🎯 Simple Data Generation Tutorial
+
+📊 Generating 5 sample users...
+
+✅ Generation Complete!
+Generated 5 users in 1234ms
+
+👥 Generated Users:
+
+1. John Smith (admin)
+   📧 john.smith@example.com
+   🎂 Age: 34
+   🏠 San Francisco, USA
+
+💾 Data saved to: examples/output/sample-users.json
+```
+
+**What You'll Build:** A user data generator for testing and prototyping
+
+---
+
+### 🟡 Intermediate Tutorials
+
+#### 3. Multi-Model Comparison (`intermediate/multi-model-comparison.ts`)
+
+**Learn:** Compare multiple AI models to find the best performer
+
+**Concepts:**
+- Running parallel model benchmarks
+- Quality scoring across models
+- Performance and speed metrics
+- Cost tracking and optimization
+- Selecting models for production
+
+**Run:**
+```bash
+npx tsx examples/intermediate/multi-model-comparison.ts
+```
+
+**Output:**
+```
+🏆 Multi-Model Comparison Benchmark
+
+📊 BENCHMARK RESULTS
+
+┌─────────────────────┬──────────┬──────────┬──────────┬──────────┐
+│ Model               │ Quality  │ Speed    │ Cost     │ Success  │
+├─────────────────────┼──────────┼──────────┼──────────┼──────────┤
+│ 🥇 GPT-4 Turbo      │   94.5%  │   892ms  │ $0.0023  │   100%   │
+│ 🥈 Gemini Flash     │   89.2%  │   423ms  │ $0.0004  │   100%   │
+│ 🥉 Claude Sonnet 4  │   91.8%  │   654ms  │ $0.0012  │   100%   │
+└─────────────────────┴──────────┴──────────┴──────────┴──────────┘
+
+🎯 WINNER: GPT-4 Turbo
+
+💡 RECOMMENDATIONS:
+⚡ Fastest: Gemini Flash (423ms avg)
+💰 Cheapest: Gemini Flash ($0.0004 total)
+🎯 Most Reliable: All models (100% success)
+```
+
+**What You'll Build:** A comprehensive model benchmarking system
+
+---
+
+#### 4. Self-Learning System (`intermediate/self-learning-system.ts`)
+
+**Learn:** Build AI systems that improve over time through feedback
+
+**Concepts:**
+- Feedback loops for quality improvement
+- Adaptive prompt engineering
+- Pattern recognition from successes
+- Tracking improvement over iterations
+- Learning from mistakes
+
+**Run:**
+```bash
+npx tsx examples/intermediate/self-learning-system.ts
+```
+
+**Output:**
+```
+🧠 Starting Self-Learning Session
+
+📊 Iteration 1/8
+   Quality: 65.0%
+   ⚠️  Weaknesses: Description too short
+
+🔧 Adapting strategy:
+   • Expand description with more details
+
+📊 Iteration 5/8
+   Quality: 85.0%
+   ✅ Target quality reached!
+
+🎓 LEARNING SUMMARY
+Quality Progression:
+   Iteration 1: ████████████████ 65.0%
+   Iteration 2: ████████████████████ 72.0%
+   Iteration 3: ██████████████████████ 78.0%
+   Iteration 4: ████████████████████████ 82.0%
+   Iteration 5: ██████████████████████████ 85.0%
+
+Improvement: +20.0% (+30.8%)
+```
+
+**What You'll Build:** An adaptive generator that learns from feedback
+
+---
+
+### 🔴 Advanced Tutorials
+
+#### 5. Custom Learning System (`advanced/custom-learning-system.ts`)
+
+**Learn:** Extend self-learning with custom evaluation and domain-specific optimization
+
+**Concepts:**
+- Custom multi-objective evaluators
+- Domain-specific learning strategies
+- Progressive difficulty training
+- Knowledge base management
+- Transfer learning patterns
+- Few-shot learning from examples
+
+**Run:**
+```bash
+npx tsx examples/advanced/custom-learning-system.ts
+```
+
+**Output:**
+```
+🏋️  Starting Advanced Training Session
+
+Domain: ecommerce
+Strategy: adaptive
+
+📚 Phase 1: Learning Basics (Easy Examples)
+📚 Phase 2: Intermediate Concepts (Medium Examples)
+📚 Phase 3: Advanced Patterns (Hard Examples)
+
+🎓 TRAINING RESULTS
+
+Knowledge Base: 8 high-quality examples
+Average Quality: 87.3%
+
+Learned Categories:
+  • electronics: 4 examples
+  • fitness: 2 examples
+  • photography: 2 examples
+
+🧪 Testing Trained System
+
+Test 1/3: Wireless Earbuds
+📊 Metrics:
+   Overall: 89.2%
+   Accuracy: 92% | Creativity: 88%
+   Relevance: 90% | Engagement: 85%
+
+📈 TEST SUMMARY
+Overall Performance: 87.8%
+```
+
+**What You'll Build:** A sophisticated domain-specific learning system
+
+---
+
+#### 6. Production Pipeline (`advanced/production-pipeline.ts`)
+
+**Learn:** Build production-ready data generation with monitoring and controls
+
+**Concepts:**
+- Error handling and retry logic
+- Rate limiting and cost controls
+- Batch processing with concurrency
+- Quality validation
+- Comprehensive metrics tracking
+- Results persistence
+- Performance monitoring
+
+**Run:**
+```bash
+npx tsx examples/advanced/production-pipeline.ts
+```
+
+**Output:**
+```
+🏭 Starting Production Pipeline
+
+Configuration:
+  Total Requests: 25
+  Batch Size: 5
+  Max Concurrency: 2
+  Cost Budget: $1.00
+  Rate Limit: 30/min
+
+📦 Processing 5 batches...
+
+Batch 1/5 (5 items)
+✓ Batch complete: 5/5 successful
+  Cost so far: $0.0005
+  Cache hits: 0
+
+📊 PIPELINE METRICS
+
+Performance:
+  Total Time: 12.34s
+  Avg Request Time: 456ms
+  Throughput: 2.02 req/s
+
+Reliability:
+  Total Requests: 25
+  Successful: 24 (96.0%)
+  Failed: 1
+  Retries: 2
+
+Cost & Efficiency:
+  Total Cost: $0.0024
+  Avg Cost/Request: $0.000096
+  Cache Hit Rate: 32.0%
+  Cost Savings from Cache: $0.0008
+
+💾 Results saved to: output/production/generation-2025-01-15T10-30-45.json
+📊 Metrics saved to: output/production/metrics-2025-01-15T10-30-45.json
+```
+
+**What You'll Build:** An enterprise-grade data generation pipeline
+
+---
+
+## 🎯 Learning Path
+
+### Recommended Order:
+
+1. **Start Here:** `beginner/first-dspy-training.ts`
+   - Get comfortable with DSPy basics
+   - Understand training concepts
+
+2. **Then:** `beginner/simple-data-generation.ts`
+   - Learn agentic-synth API
+   - Practice schema definition
+
+3. **Next:** `intermediate/multi-model-comparison.ts`
+   - Compare model performance
+   - Understand cost/quality tradeoffs
+
+4. **Continue:** `intermediate/self-learning-system.ts`
+   - Build adaptive systems
+   - Implement feedback loops
+
+5. **Advanced:** `advanced/custom-learning-system.ts`
+   - Create domain-specific systems
+   - Multi-objective optimization
+
+6. **Finally:** `advanced/production-pipeline.ts`
+   - Production patterns
+   - Monitoring and reliability
+
+---
+
+## 💡 Key Concepts
+
+### DSPy Integration
+All tutorials demonstrate DSPy.ts integration with agentic-synth:
+- **Language Models:** Configure AI providers
+- **Signatures:** Define input/output structures
+- **Chain-of-Thought:** Step-by-step reasoning
+- **Optimizers:** BootstrapFewShot, MIPROv2
+
+### Quality Evaluation
+Learn multiple evaluation approaches:
+- **Basic Metrics:** Length, completeness
+- **Advanced Metrics:** Creativity, relevance, engagement
+- **Multi-Objective:** Balance multiple goals
+- **Domain-Specific:** Custom validators
+
+### Production Patterns
+Essential patterns for real-world use:
+- **Error Handling:** Retries, fallbacks, recovery
+- **Rate Limiting:** API quota management
+- **Cost Control:** Budget tracking, optimization
+- **Monitoring:** Metrics, logging, alerting
+- **Caching:** Performance optimization
+
+---
+
+## 🛠️ Customization
+
+### Modify for Your Use Case
+
+Each tutorial is designed to be customized:
+
+```typescript
+// Change the domain
+const domain = 'healthcare';  // or 'finance', 'legal', etc.
+
+// Adjust schemas
+const schema = {
+  // Your custom fields
+};
+
+// Custom evaluation
+class CustomEvaluator {
+  evaluate(output: any): number {
+    // Your logic
+  }
+}
+
+// Different models
+const models = ['gemini', 'claude', 'gpt4', 'llama'];
+```
+
+---
+
+## 📊 Expected Results
+
+### Performance Benchmarks
+
+| Tutorial | Runtime | API Calls | Est. Cost |
+|----------|---------|-----------|-----------|
+| First DSPy Training | 30-60s | 5-10 | $0.01 |
+| Simple Data Generation | 10-30s | 2-5 | $0.005 |
+| Multi-Model Comparison | 2-5min | 12-30 | $0.15 |
+| Self-Learning System | 1-3min | 8-15 | $0.02 |
+| Custom Learning | 3-6min | 15-30 | $0.05 |
+| Production Pipeline | 1-2min | 20-50 | $0.10 |
+
+*Costs are estimates and vary by model and usage*
+
+---
+
+## 🔧 Troubleshooting
+
+### Common Issues
+
+**API Key Not Set:**
+```bash
+# Error: API key not configured
+export GEMINI_API_KEY="your-key-here"
+```
+
+**Module Not Found:**
+```bash
+# Run from package root
+cd packages/agentic-synth-examples
+npm install
+```
+
+**Rate Limit Errors:**
+```typescript
+// Adjust in pipeline config
+rateLimitPerMinute: 10  // Lower the rate
+```
+
+**Cost Budget Exceeded:**
+```typescript
+// Increase budget or reduce requests
+costBudget: 5.0  // Higher budget
+```
+
+---
+
+## 📚 Additional Resources
+
+### Documentation
+- [Agentic-Synth Main Docs](../README.md)
+- [DSPy.ts Documentation](https://github.com/XpressAI/dspy.ts)
+- [API Reference](../docs/api.md)
+
+### Related Examples
+- [Production Use Cases](../examples/use-cases/)
+- [Integration Patterns](../examples/integrations/)
+- [Testing Strategies](../examples/testing/)
+
+---
+
+## 🤝 Contributing
+
+Have an idea for a tutorial?
+
+1. Create your example file
+2. Add comprehensive comments
+3. Include error handling
+4. Test thoroughly
+5. Submit a pull request
+
+---
+
+## 📞 Support
+
+- **Issues:** [GitHub Issues](https://github.com/ruvnet/ruvector/issues)
+- **Discussions:** [GitHub Discussions](https://github.com/ruvnet/ruvector/discussions)
+- **Questions:** Tag us on Twitter [@ruvnet](https://twitter.com/ruvnet)
+
+---
+
+## 📄 License
+
+MIT © [ruvnet](https://github.com/ruvnet)
+
+---
+
+**Ready to learn?** Start with the [First DSPy Training tutorial](beginner/first-dspy-training.ts)! 🚀

vendor/ruvector/npm/packages/agentic-synth-examples/examples/advanced/custom-learning-system.d.ts

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+/**
+ * ADVANCED TUTORIAL: Custom Learning System
+ *
+ * Extend the self-learning system with custom optimization strategies,
+ * domain-specific learning, and advanced evaluation metrics. Perfect for
+ * building production-grade adaptive AI systems.
+ *
+ * What you'll learn:
+ * - Creating custom evaluators
+ * - Domain-specific optimization
+ * - Advanced feedback loops
+ * - Multi-objective optimization
+ * - Transfer learning patterns
+ *
+ * Prerequisites:
+ * - Complete intermediate tutorials first
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install dspy.ts @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/advanced/custom-learning-system.ts
+ */
+import { Prediction } from 'dspy.ts';
+interface EvaluationMetrics {
+    accuracy: number;
+    creativity: number;
+    relevance: number;
+    engagement: number;
+    technicalQuality: number;
+    overall: number;
+}
+interface AdvancedLearningConfig {
+    domain: string;
+    objectives: string[];
+    weights: Record<string, number>;
+    learningStrategy: 'aggressive' | 'conservative' | 'adaptive';
+    convergenceThreshold: number;
+    diversityBonus: boolean;
+    transferLearning: boolean;
+}
+interface TrainingExample {
+    input: any;
+    expectedOutput: any;
+    quality: number;
+    metadata: {
+        domain: string;
+        difficulty: 'easy' | 'medium' | 'hard';
+        tags: string[];
+    };
+}
+interface Evaluator {
+    evaluate(output: Prediction, context: any): Promise<EvaluationMetrics>;
+}
+declare class EcommerceEvaluator implements Evaluator {
+    evaluate(output: Prediction, context: any): Promise<EvaluationMetrics>;
+}
+declare class AdvancedLearningSystem {
+    private lm;
+    private config;
+    private evaluator;
+    private knowledgeBase;
+    private promptStrategies;
+    constructor(config: AdvancedLearningConfig, evaluator: Evaluator);
+    private getTemperatureForStrategy;
+    learnFromExample(example: TrainingExample): Promise<void>;
+    train(examples: TrainingExample[]): Promise<void>;
+    private generate;
+    private findSimilarExamples;
+    private displayTrainingResults;
+    test(testCases: any[]): Promise<void>;
+}
+export { AdvancedLearningSystem, EcommerceEvaluator, AdvancedLearningConfig };
+//# sourceMappingURL=custom-learning-system.d.ts.map

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vendor/ruvector/npm/packages/agentic-synth-examples/examples/advanced/custom-learning-system.js

@@ -0,0 +1,353 @@
+"use strict";
+/**
+ * ADVANCED TUTORIAL: Custom Learning System
+ *
+ * Extend the self-learning system with custom optimization strategies,
+ * domain-specific learning, and advanced evaluation metrics. Perfect for
+ * building production-grade adaptive AI systems.
+ *
+ * What you'll learn:
+ * - Creating custom evaluators
+ * - Domain-specific optimization
+ * - Advanced feedback loops
+ * - Multi-objective optimization
+ * - Transfer learning patterns
+ *
+ * Prerequisites:
+ * - Complete intermediate tutorials first
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install dspy.ts @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/advanced/custom-learning-system.ts
+ */
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.EcommerceEvaluator = exports.AdvancedLearningSystem = void 0;
+const dspy_ts_1 = require("dspy.ts");
+// Domain-specific evaluator for e-commerce
+class EcommerceEvaluator {
+    async evaluate(output, context) {
+        const metrics = {
+            accuracy: 0,
+            creativity: 0,
+            relevance: 0,
+            engagement: 0,
+            technicalQuality: 0,
+            overall: 0
+        };
+        // Accuracy: Check for required information
+        if (output.description && output.key_features) {
+            metrics.accuracy += 0.5;
+            // Check if key product attributes are mentioned
+            const desc = output.description.toLowerCase();
+            const productName = context.product_name.toLowerCase();
+            const category = context.category.toLowerCase();
+            if (desc.includes(productName.split(' ')[0])) {
+                metrics.accuracy += 0.25;
+            }
+            if (desc.includes(category)) {
+                metrics.accuracy += 0.25;
+            }
+        }
+        // Creativity: Check for unique, non-generic phrases
+        if (output.description) {
+            const genericPhrases = ['high quality', 'great product', 'best choice'];
+            const hasGenericPhrase = genericPhrases.some(phrase => output.description.toLowerCase().includes(phrase));
+            metrics.creativity = hasGenericPhrase ? 0.3 : 0.8;
+            // Bonus for specific details
+            const hasNumbers = /\d+/.test(output.description);
+            const hasSpecifics = /(\d+\s*(hours|days|years|gb|mb|kg|lbs))/i.test(output.description);
+            if (hasSpecifics)
+                metrics.creativity += 0.2;
+        }
+        // Relevance: Check alignment with category
+        const categoryKeywords = {
+            electronics: ['technology', 'device', 'digital', 'battery', 'power'],
+            fashion: ['style', 'design', 'material', 'comfort', 'wear'],
+            food: ['taste', 'flavor', 'nutrition', 'organic', 'fresh'],
+            fitness: ['workout', 'exercise', 'health', 'training', 'performance']
+        };
+        const category = context.category.toLowerCase();
+        const relevantKeywords = categoryKeywords[category] || [];
+        if (output.description) {
+            const desc = output.description.toLowerCase();
+            const matchedKeywords = relevantKeywords.filter(kw => desc.includes(kw));
+            metrics.relevance = Math.min(matchedKeywords.length / 3, 1.0);
+        }
+        // Engagement: Check for emotional appeal and calls to action
+        if (output.description) {
+            const desc = output.description.toLowerCase();
+            const emotionalWords = ['amazing', 'incredible', 'perfect', 'premium', 'exceptional', 'revolutionary'];
+            const actionWords = ['discover', 'experience', 'enjoy', 'upgrade', 'transform'];
+            const hasEmotion = emotionalWords.some(word => desc.includes(word));
+            const hasAction = actionWords.some(word => desc.includes(word));
+            metrics.engagement = (hasEmotion ? 0.5 : 0) + (hasAction ? 0.5 : 0);
+        }
+        // Technical Quality: Check structure and formatting
+        if (output.key_features && Array.isArray(output.key_features)) {
+            const features = output.key_features;
+            let techScore = 0;
+            // Optimal number of features
+            if (features.length >= 4 && features.length <= 6) {
+                techScore += 0.4;
+            }
+            // Feature formatting
+            const wellFormatted = features.filter(f => f.length >= 15 && f.length <= 60 && !f.endsWith('.'));
+            techScore += (wellFormatted.length / features.length) * 0.6;
+            metrics.technicalQuality = techScore;
+        }
+        // Calculate overall score with weights
+        metrics.overall = (metrics.accuracy * 0.25 +
+            metrics.creativity * 0.20 +
+            metrics.relevance * 0.25 +
+            metrics.engagement * 0.15 +
+            metrics.technicalQuality * 0.15);
+        return metrics;
+    }
+}
+exports.EcommerceEvaluator = EcommerceEvaluator;
+// Advanced self-learning generator
+class AdvancedLearningSystem {
+    constructor(config, evaluator) {
+        this.knowledgeBase = [];
+        this.promptStrategies = new Map();
+        this.config = config;
+        this.evaluator = evaluator;
+        this.lm = new dspy_ts_1.LM({
+            provider: 'google-genai',
+            model: 'gemini-2.0-flash-exp',
+            apiKey: process.env.GEMINI_API_KEY || '',
+            temperature: this.getTemperatureForStrategy()
+        });
+    }
+    getTemperatureForStrategy() {
+        switch (this.config.learningStrategy) {
+            case 'aggressive': return 0.9;
+            case 'conservative': return 0.5;
+            case 'adaptive': return 0.7;
+        }
+    }
+    // Learn from a single example
+    async learnFromExample(example) {
+        console.log(`\n🎯 Learning from example (${example.metadata.difficulty})...`);
+        const output = await this.generate(example.input);
+        const metrics = await this.evaluator.evaluate(output, example.input);
+        console.log(`   Overall Quality: ${(metrics.overall * 100).toFixed(1)}%`);
+        console.log(`   Accuracy: ${(metrics.accuracy * 100).toFixed(0)}% | Creativity: ${(metrics.creativity * 100).toFixed(0)}%`);
+        console.log(`   Relevance: ${(metrics.relevance * 100).toFixed(0)}% | Engagement: ${(metrics.engagement * 100).toFixed(0)}%`);
+        // Store high-quality examples
+        if (metrics.overall >= 0.7) {
+            this.knowledgeBase.push({
+                ...example,
+                quality: metrics.overall
+            });
+            console.log(`   ✓ Added to knowledge base`);
+        }
+    }
+    // Train on a dataset
+    async train(examples) {
+        console.log('🏋️  Starting Advanced Training Session\n');
+        console.log('='.repeat(70));
+        console.log(`\nDomain: ${this.config.domain}`);
+        console.log(`Strategy: ${this.config.learningStrategy}`);
+        console.log(`Examples: ${examples.length}`);
+        console.log(`\nObjectives:`);
+        this.config.objectives.forEach(obj => console.log(`  • ${obj}`));
+        console.log('\n' + '='.repeat(70));
+        // Group by difficulty
+        const byDifficulty = {
+            easy: examples.filter(e => e.metadata.difficulty === 'easy'),
+            medium: examples.filter(e => e.metadata.difficulty === 'medium'),
+            hard: examples.filter(e => e.metadata.difficulty === 'hard')
+        };
+        // Progressive learning: start with easy, move to hard
+        console.log('\n📚 Phase 1: Learning Basics (Easy Examples)');
+        console.log('─'.repeat(70));
+        for (const example of byDifficulty.easy) {
+            await this.learnFromExample(example);
+        }
+        console.log('\n📚 Phase 2: Intermediate Concepts (Medium Examples)');
+        console.log('─'.repeat(70));
+        for (const example of byDifficulty.medium) {
+            await this.learnFromExample(example);
+        }
+        console.log('\n📚 Phase 3: Advanced Patterns (Hard Examples)');
+        console.log('─'.repeat(70));
+        for (const example of byDifficulty.hard) {
+            await this.learnFromExample(example);
+        }
+        this.displayTrainingResults();
+    }
+    // Generate with learned knowledge
+    async generate(input) {
+        // Use knowledge base for few-shot learning
+        const similarExamples = this.findSimilarExamples(input, 3);
+        let enhancedDescription = 'Generate compelling product descriptions.';
+        if (similarExamples.length > 0) {
+            enhancedDescription += '\n\nLearn from these high-quality examples:\n';
+            similarExamples.forEach((ex, i) => {
+                enhancedDescription += `\nExample ${i + 1}:\n`;
+                enhancedDescription += `Input: ${JSON.stringify(ex.input)}\n`;
+                enhancedDescription += `Output: ${JSON.stringify(ex.expectedOutput)}`;
+            });
+        }
+        const signature = {
+            input: 'product_name: string, category: string, price: number',
+            output: 'description: string, key_features: string[]',
+            description: enhancedDescription
+        };
+        const generator = new dspy_ts_1.ChainOfThought(signature, { lm: this.lm });
+        return await generator.forward(input);
+    }
+    // Find similar examples from knowledge base
+    findSimilarExamples(input, count) {
+        // Simple similarity based on category match
+        const similar = this.knowledgeBase
+            .filter(ex => ex.input.category === input.category)
+            .sort((a, b) => b.quality - a.quality)
+            .slice(0, count);
+        return similar;
+    }
+    // Display training results
+    displayTrainingResults() {
+        console.log('\n\n' + '='.repeat(70));
+        console.log('\n🎓 TRAINING RESULTS\n');
+        console.log(`Knowledge Base: ${this.knowledgeBase.length} high-quality examples`);
+        if (this.knowledgeBase.length > 0) {
+            const avgQuality = this.knowledgeBase.reduce((sum, ex) => sum + ex.quality, 0) / this.knowledgeBase.length;
+            console.log(`Average Quality: ${(avgQuality * 100).toFixed(1)}%`);
+            // Group by category
+            const byCategory = {};
+            this.knowledgeBase.forEach(ex => {
+                const cat = ex.input.category;
+                byCategory[cat] = (byCategory[cat] || 0) + 1;
+            });
+            console.log(`\nLearned Categories:`);
+            Object.entries(byCategory).forEach(([cat, count]) => {
+                console.log(`  • ${cat}: ${count} examples`);
+            });
+        }
+        console.log('\n✅ Training complete! System is ready for production.\n');
+        console.log('='.repeat(70) + '\n');
+    }
+    // Test the trained system
+    async test(testCases) {
+        console.log('\n🧪 Testing Trained System\n');
+        console.log('='.repeat(70) + '\n');
+        let totalMetrics = {
+            accuracy: 0,
+            creativity: 0,
+            relevance: 0,
+            engagement: 0,
+            technicalQuality: 0,
+            overall: 0
+        };
+        for (let i = 0; i < testCases.length; i++) {
+            const testCase = testCases[i];
+            console.log(`\nTest ${i + 1}/${testCases.length}: ${testCase.product_name}`);
+            console.log('─'.repeat(70));
+            const output = await this.generate(testCase);
+            const metrics = await this.evaluator.evaluate(output, testCase);
+            console.log(`\n📝 Generated:`);
+            console.log(`   ${output.description}`);
+            console.log(`\n   Features:`);
+            if (output.key_features) {
+                output.key_features.forEach((f) => console.log(`     • ${f}`));
+            }
+            console.log(`\n📊 Metrics:`);
+            console.log(`   Overall: ${(metrics.overall * 100).toFixed(1)}%`);
+            console.log(`   Accuracy: ${(metrics.accuracy * 100).toFixed(0)}% | Creativity: ${(metrics.creativity * 100).toFixed(0)}%`);
+            console.log(`   Relevance: ${(metrics.relevance * 100).toFixed(0)}% | Engagement: ${(metrics.engagement * 100).toFixed(0)}%`);
+            console.log(`   Technical: ${(metrics.technicalQuality * 100).toFixed(0)}%`);
+            // Aggregate metrics
+            Object.keys(totalMetrics).forEach(key => {
+                totalMetrics[key] += metrics[key];
+            });
+        }
+        // Average metrics
+        Object.keys(totalMetrics).forEach(key => {
+            totalMetrics[key] /= testCases.length;
+        });
+        console.log('\n\n' + '='.repeat(70));
+        console.log('\n📈 TEST SUMMARY\n');
+        console.log(`Overall Performance: ${(totalMetrics.overall * 100).toFixed(1)}%`);
+        console.log(`\nDetailed Metrics:`);
+        console.log(`  Accuracy: ${(totalMetrics.accuracy * 100).toFixed(1)}%`);
+        console.log(`  Creativity: ${(totalMetrics.creativity * 100).toFixed(1)}%`);
+        console.log(`  Relevance: ${(totalMetrics.relevance * 100).toFixed(1)}%`);
+        console.log(`  Engagement: ${(totalMetrics.engagement * 100).toFixed(1)}%`);
+        console.log(`  Technical Quality: ${(totalMetrics.technicalQuality * 100).toFixed(1)}%`);
+        console.log('\n' + '='.repeat(70) + '\n');
+    }
+}
+exports.AdvancedLearningSystem = AdvancedLearningSystem;
+// Main execution
+async function runAdvancedLearning() {
+    const config = {
+        domain: 'ecommerce',
+        objectives: [
+            'Generate accurate product descriptions',
+            'Maintain high creativity and engagement',
+            'Ensure category-specific relevance'
+        ],
+        weights: {
+            accuracy: 0.25,
+            creativity: 0.20,
+            relevance: 0.25,
+            engagement: 0.15,
+            technical: 0.15
+        },
+        learningStrategy: 'adaptive',
+        convergenceThreshold: 0.85,
+        diversityBonus: true,
+        transferLearning: true
+    };
+    const evaluator = new EcommerceEvaluator();
+    const system = new AdvancedLearningSystem(config, evaluator);
+    // Training examples
+    const trainingExamples = [
+        {
+            input: { product_name: 'Smart Watch', category: 'electronics', price: 299 },
+            expectedOutput: {
+                description: 'Advanced fitness tracking meets elegant design in this premium smartwatch',
+                key_features: ['Heart rate monitoring', '7-day battery', 'Water resistant', 'GPS tracking']
+            },
+            quality: 0.9,
+            metadata: { domain: 'ecommerce', difficulty: 'easy', tags: ['electronics', 'wearable'] }
+        },
+        {
+            input: { product_name: 'Yoga Mat', category: 'fitness', price: 49 },
+            expectedOutput: {
+                description: 'Professional-grade yoga mat with superior grip and cushioning for all practice levels',
+                key_features: ['6mm thickness', 'Non-slip surface', 'Eco-friendly material', 'Easy to clean']
+            },
+            quality: 0.85,
+            metadata: { domain: 'ecommerce', difficulty: 'easy', tags: ['fitness', 'yoga'] }
+        },
+        {
+            input: { product_name: 'Mechanical Keyboard', category: 'electronics', price: 159 },
+            expectedOutput: {
+                description: 'Tactile perfection for enthusiasts with customizable RGB and premium switches',
+                key_features: ['Cherry MX switches', 'RGB backlighting', 'Programmable keys', 'Aluminum frame']
+            },
+            quality: 0.92,
+            metadata: { domain: 'ecommerce', difficulty: 'medium', tags: ['electronics', 'gaming'] }
+        }
+    ];
+    // Train the system
+    await system.train(trainingExamples);
+    // Test the system
+    const testCases = [
+        { product_name: 'Wireless Earbuds', category: 'electronics', price: 129 },
+        { product_name: 'Resistance Bands Set', category: 'fitness', price: 29 },
+        { product_name: 'Laptop Stand', category: 'electronics', price: 59 }
+    ];
+    await system.test(testCases);
+}
+// Run the example
+if (import.meta.url === `file://${process.argv[1]}`) {
+    runAdvancedLearning().catch(error => {
+        console.error('❌ Advanced learning failed:', error);
+        process.exit(1);
+    });
+}
+//# sourceMappingURL=custom-learning-system.js.map

vendor/ruvector/npm/packages/agentic-synth-examples/examples/advanced/custom-learning-system.ts

@@ -0,0 +1,460 @@
+/**
+ * ADVANCED TUTORIAL: Custom Learning System
+ *
+ * Extend the self-learning system with custom optimization strategies,
+ * domain-specific learning, and advanced evaluation metrics. Perfect for
+ * building production-grade adaptive AI systems.
+ *
+ * What you'll learn:
+ * - Creating custom evaluators
+ * - Domain-specific optimization
+ * - Advanced feedback loops
+ * - Multi-objective optimization
+ * - Transfer learning patterns
+ *
+ * Prerequisites:
+ * - Complete intermediate tutorials first
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install dspy.ts @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/advanced/custom-learning-system.ts
+ */
+
+import { LM, ChainOfThought, Prediction } from 'dspy.ts';
+import { AgenticSynth } from '@ruvector/agentic-synth';
+
+// Multi-objective evaluation metrics
+interface EvaluationMetrics {
+  accuracy: number;
+  creativity: number;
+  relevance: number;
+  engagement: number;
+  technicalQuality: number;
+  overall: number;
+}
+
+// Advanced learning configuration
+interface AdvancedLearningConfig {
+  domain: string;
+  objectives: string[];
+  weights: Record<string, number>;
+  learningStrategy: 'aggressive' | 'conservative' | 'adaptive';
+  convergenceThreshold: number;
+  diversityBonus: boolean;
+  transferLearning: boolean;
+}
+
+// Training example with rich metadata
+interface TrainingExample {
+  input: any;
+  expectedOutput: any;
+  quality: number;
+  metadata: {
+    domain: string;
+    difficulty: 'easy' | 'medium' | 'hard';
+    tags: string[];
+  };
+}
+
+// Custom evaluator interface
+interface Evaluator {
+  evaluate(output: Prediction, context: any): Promise<EvaluationMetrics>;
+}
+
+// Domain-specific evaluator for e-commerce
+class EcommerceEvaluator implements Evaluator {
+  async evaluate(output: Prediction, context: any): Promise<EvaluationMetrics> {
+    const metrics: EvaluationMetrics = {
+      accuracy: 0,
+      creativity: 0,
+      relevance: 0,
+      engagement: 0,
+      technicalQuality: 0,
+      overall: 0
+    };
+
+    // Accuracy: Check for required information
+    if (output.description && output.key_features) {
+      metrics.accuracy += 0.5;
+
+      // Check if key product attributes are mentioned
+      const desc = output.description.toLowerCase();
+      const productName = context.product_name.toLowerCase();
+      const category = context.category.toLowerCase();
+
+      if (desc.includes(productName.split(' ')[0])) {
+        metrics.accuracy += 0.25;
+      }
+      if (desc.includes(category)) {
+        metrics.accuracy += 0.25;
+      }
+    }
+
+    // Creativity: Check for unique, non-generic phrases
+    if (output.description) {
+      const genericPhrases = ['high quality', 'great product', 'best choice'];
+      const hasGenericPhrase = genericPhrases.some(phrase =>
+        output.description.toLowerCase().includes(phrase)
+      );
+
+      metrics.creativity = hasGenericPhrase ? 0.3 : 0.8;
+
+      // Bonus for specific details
+      const hasNumbers = /\d+/.test(output.description);
+      const hasSpecifics = /(\d+\s*(hours|days|years|gb|mb|kg|lbs))/i.test(output.description);
+
+      if (hasSpecifics) metrics.creativity += 0.2;
+    }
+
+    // Relevance: Check alignment with category
+    const categoryKeywords: Record<string, string[]> = {
+      electronics: ['technology', 'device', 'digital', 'battery', 'power'],
+      fashion: ['style', 'design', 'material', 'comfort', 'wear'],
+      food: ['taste', 'flavor', 'nutrition', 'organic', 'fresh'],
+      fitness: ['workout', 'exercise', 'health', 'training', 'performance']
+    };
+
+    const category = context.category.toLowerCase();
+    const relevantKeywords = categoryKeywords[category] || [];
+
+    if (output.description) {
+      const desc = output.description.toLowerCase();
+      const matchedKeywords = relevantKeywords.filter(kw => desc.includes(kw));
+      metrics.relevance = Math.min(matchedKeywords.length / 3, 1.0);
+    }
+
+    // Engagement: Check for emotional appeal and calls to action
+    if (output.description) {
+      const desc = output.description.toLowerCase();
+      const emotionalWords = ['amazing', 'incredible', 'perfect', 'premium', 'exceptional', 'revolutionary'];
+      const actionWords = ['discover', 'experience', 'enjoy', 'upgrade', 'transform'];
+
+      const hasEmotion = emotionalWords.some(word => desc.includes(word));
+      const hasAction = actionWords.some(word => desc.includes(word));
+
+      metrics.engagement = (hasEmotion ? 0.5 : 0) + (hasAction ? 0.5 : 0);
+    }
+
+    // Technical Quality: Check structure and formatting
+    if (output.key_features && Array.isArray(output.key_features)) {
+      const features = output.key_features;
+      let techScore = 0;
+
+      // Optimal number of features
+      if (features.length >= 4 && features.length <= 6) {
+        techScore += 0.4;
+      }
+
+      // Feature formatting
+      const wellFormatted = features.filter(f =>
+        f.length >= 15 && f.length <= 60 && !f.endsWith('.')
+      );
+      techScore += (wellFormatted.length / features.length) * 0.6;
+
+      metrics.technicalQuality = techScore;
+    }
+
+    // Calculate overall score with weights
+    metrics.overall = (
+      metrics.accuracy * 0.25 +
+      metrics.creativity * 0.20 +
+      metrics.relevance * 0.25 +
+      metrics.engagement * 0.15 +
+      metrics.technicalQuality * 0.15
+    );
+
+    return metrics;
+  }
+}
+
+// Advanced self-learning generator
+class AdvancedLearningSystem {
+  private lm: LM;
+  private config: AdvancedLearningConfig;
+  private evaluator: Evaluator;
+  private knowledgeBase: TrainingExample[] = [];
+  private promptStrategies: Map<string, number> = new Map();
+
+  constructor(config: AdvancedLearningConfig, evaluator: Evaluator) {
+    this.config = config;
+    this.evaluator = evaluator;
+
+    this.lm = new LM({
+      provider: 'google-genai',
+      model: 'gemini-2.0-flash-exp',
+      apiKey: process.env.GEMINI_API_KEY || '',
+      temperature: this.getTemperatureForStrategy()
+    });
+  }
+
+  private getTemperatureForStrategy(): number {
+    switch (this.config.learningStrategy) {
+      case 'aggressive': return 0.9;
+      case 'conservative': return 0.5;
+      case 'adaptive': return 0.7;
+    }
+  }
+
+  // Learn from a single example
+  async learnFromExample(example: TrainingExample): Promise<void> {
+    console.log(`\n🎯 Learning from example (${example.metadata.difficulty})...`);
+
+    const output = await this.generate(example.input);
+    const metrics = await this.evaluator.evaluate(output, example.input);
+
+    console.log(`   Overall Quality: ${(metrics.overall * 100).toFixed(1)}%`);
+    console.log(`   Accuracy: ${(metrics.accuracy * 100).toFixed(0)}% | Creativity: ${(metrics.creativity * 100).toFixed(0)}%`);
+    console.log(`   Relevance: ${(metrics.relevance * 100).toFixed(0)}% | Engagement: ${(metrics.engagement * 100).toFixed(0)}%`);
+
+    // Store high-quality examples
+    if (metrics.overall >= 0.7) {
+      this.knowledgeBase.push({
+        ...example,
+        quality: metrics.overall
+      });
+      console.log(`   ✓ Added to knowledge base`);
+    }
+  }
+
+  // Train on a dataset
+  async train(examples: TrainingExample[]): Promise<void> {
+    console.log('🏋️  Starting Advanced Training Session\n');
+    console.log('=' .repeat(70));
+    console.log(`\nDomain: ${this.config.domain}`);
+    console.log(`Strategy: ${this.config.learningStrategy}`);
+    console.log(`Examples: ${examples.length}`);
+    console.log(`\nObjectives:`);
+    this.config.objectives.forEach(obj => console.log(`  • ${obj}`));
+    console.log('\n' + '=' .repeat(70));
+
+    // Group by difficulty
+    const byDifficulty = {
+      easy: examples.filter(e => e.metadata.difficulty === 'easy'),
+      medium: examples.filter(e => e.metadata.difficulty === 'medium'),
+      hard: examples.filter(e => e.metadata.difficulty === 'hard')
+    };
+
+    // Progressive learning: start with easy, move to hard
+    console.log('\n📚 Phase 1: Learning Basics (Easy Examples)');
+    console.log('─'.repeat(70));
+    for (const example of byDifficulty.easy) {
+      await this.learnFromExample(example);
+    }
+
+    console.log('\n📚 Phase 2: Intermediate Concepts (Medium Examples)');
+    console.log('─'.repeat(70));
+    for (const example of byDifficulty.medium) {
+      await this.learnFromExample(example);
+    }
+
+    console.log('\n📚 Phase 3: Advanced Patterns (Hard Examples)');
+    console.log('─'.repeat(70));
+    for (const example of byDifficulty.hard) {
+      await this.learnFromExample(example);
+    }
+
+    this.displayTrainingResults();
+  }
+
+  // Generate with learned knowledge
+  private async generate(input: any): Promise<Prediction> {
+    // Use knowledge base for few-shot learning
+    const similarExamples = this.findSimilarExamples(input, 3);
+
+    let enhancedDescription = 'Generate compelling product descriptions.';
+
+    if (similarExamples.length > 0) {
+      enhancedDescription += '\n\nLearn from these high-quality examples:\n';
+      similarExamples.forEach((ex, i) => {
+        enhancedDescription += `\nExample ${i + 1}:\n`;
+        enhancedDescription += `Input: ${JSON.stringify(ex.input)}\n`;
+        enhancedDescription += `Output: ${JSON.stringify(ex.expectedOutput)}`;
+      });
+    }
+
+    const signature = {
+      input: 'product_name: string, category: string, price: number',
+      output: 'description: string, key_features: string[]',
+      description: enhancedDescription
+    };
+
+    const generator = new ChainOfThought(signature, { lm: this.lm });
+    return await generator.forward(input);
+  }
+
+  // Find similar examples from knowledge base
+  private findSimilarExamples(input: any, count: number): TrainingExample[] {
+    // Simple similarity based on category match
+    const similar = this.knowledgeBase
+      .filter(ex => ex.input.category === input.category)
+      .sort((a, b) => b.quality - a.quality)
+      .slice(0, count);
+
+    return similar;
+  }
+
+  // Display training results
+  private displayTrainingResults(): void {
+    console.log('\n\n' + '=' .repeat(70));
+    console.log('\n🎓 TRAINING RESULTS\n');
+
+    console.log(`Knowledge Base: ${this.knowledgeBase.length} high-quality examples`);
+
+    if (this.knowledgeBase.length > 0) {
+      const avgQuality = this.knowledgeBase.reduce((sum, ex) => sum + ex.quality, 0) / this.knowledgeBase.length;
+      console.log(`Average Quality: ${(avgQuality * 100).toFixed(1)}%`);
+
+      // Group by category
+      const byCategory: Record<string, number> = {};
+      this.knowledgeBase.forEach(ex => {
+        const cat = ex.input.category;
+        byCategory[cat] = (byCategory[cat] || 0) + 1;
+      });
+
+      console.log(`\nLearned Categories:`);
+      Object.entries(byCategory).forEach(([cat, count]) => {
+        console.log(`  • ${cat}: ${count} examples`);
+      });
+    }
+
+    console.log('\n✅ Training complete! System is ready for production.\n');
+    console.log('=' .repeat(70) + '\n');
+  }
+
+  // Test the trained system
+  async test(testCases: any[]): Promise<void> {
+    console.log('\n🧪 Testing Trained System\n');
+    console.log('=' .repeat(70) + '\n');
+
+    let totalMetrics: EvaluationMetrics = {
+      accuracy: 0,
+      creativity: 0,
+      relevance: 0,
+      engagement: 0,
+      technicalQuality: 0,
+      overall: 0
+    };
+
+    for (let i = 0; i < testCases.length; i++) {
+      const testCase = testCases[i];
+      console.log(`\nTest ${i + 1}/${testCases.length}: ${testCase.product_name}`);
+      console.log('─'.repeat(70));
+
+      const output = await this.generate(testCase);
+      const metrics = await this.evaluator.evaluate(output, testCase);
+
+      console.log(`\n📝 Generated:`);
+      console.log(`   ${output.description}`);
+      console.log(`\n   Features:`);
+      if (output.key_features) {
+        output.key_features.forEach((f: string) => console.log(`     • ${f}`));
+      }
+
+      console.log(`\n📊 Metrics:`);
+      console.log(`   Overall: ${(metrics.overall * 100).toFixed(1)}%`);
+      console.log(`   Accuracy: ${(metrics.accuracy * 100).toFixed(0)}% | Creativity: ${(metrics.creativity * 100).toFixed(0)}%`);
+      console.log(`   Relevance: ${(metrics.relevance * 100).toFixed(0)}% | Engagement: ${(metrics.engagement * 100).toFixed(0)}%`);
+      console.log(`   Technical: ${(metrics.technicalQuality * 100).toFixed(0)}%`);
+
+      // Aggregate metrics
+      Object.keys(totalMetrics).forEach(key => {
+        totalMetrics[key as keyof EvaluationMetrics] += metrics[key as keyof EvaluationMetrics];
+      });
+    }
+
+    // Average metrics
+    Object.keys(totalMetrics).forEach(key => {
+      totalMetrics[key as keyof EvaluationMetrics] /= testCases.length;
+    });
+
+    console.log('\n\n' + '=' .repeat(70));
+    console.log('\n📈 TEST SUMMARY\n');
+    console.log(`Overall Performance: ${(totalMetrics.overall * 100).toFixed(1)}%`);
+    console.log(`\nDetailed Metrics:`);
+    console.log(`  Accuracy: ${(totalMetrics.accuracy * 100).toFixed(1)}%`);
+    console.log(`  Creativity: ${(totalMetrics.creativity * 100).toFixed(1)}%`);
+    console.log(`  Relevance: ${(totalMetrics.relevance * 100).toFixed(1)}%`);
+    console.log(`  Engagement: ${(totalMetrics.engagement * 100).toFixed(1)}%`);
+    console.log(`  Technical Quality: ${(totalMetrics.technicalQuality * 100).toFixed(1)}%`);
+    console.log('\n' + '=' .repeat(70) + '\n');
+  }
+}
+
+// Main execution
+async function runAdvancedLearning() {
+  const config: AdvancedLearningConfig = {
+    domain: 'ecommerce',
+    objectives: [
+      'Generate accurate product descriptions',
+      'Maintain high creativity and engagement',
+      'Ensure category-specific relevance'
+    ],
+    weights: {
+      accuracy: 0.25,
+      creativity: 0.20,
+      relevance: 0.25,
+      engagement: 0.15,
+      technical: 0.15
+    },
+    learningStrategy: 'adaptive',
+    convergenceThreshold: 0.85,
+    diversityBonus: true,
+    transferLearning: true
+  };
+
+  const evaluator = new EcommerceEvaluator();
+  const system = new AdvancedLearningSystem(config, evaluator);
+
+  // Training examples
+  const trainingExamples: TrainingExample[] = [
+    {
+      input: { product_name: 'Smart Watch', category: 'electronics', price: 299 },
+      expectedOutput: {
+        description: 'Advanced fitness tracking meets elegant design in this premium smartwatch',
+        key_features: ['Heart rate monitoring', '7-day battery', 'Water resistant', 'GPS tracking']
+      },
+      quality: 0.9,
+      metadata: { domain: 'ecommerce', difficulty: 'easy', tags: ['electronics', 'wearable'] }
+    },
+    {
+      input: { product_name: 'Yoga Mat', category: 'fitness', price: 49 },
+      expectedOutput: {
+        description: 'Professional-grade yoga mat with superior grip and cushioning for all practice levels',
+        key_features: ['6mm thickness', 'Non-slip surface', 'Eco-friendly material', 'Easy to clean']
+      },
+      quality: 0.85,
+      metadata: { domain: 'ecommerce', difficulty: 'easy', tags: ['fitness', 'yoga'] }
+    },
+    {
+      input: { product_name: 'Mechanical Keyboard', category: 'electronics', price: 159 },
+      expectedOutput: {
+        description: 'Tactile perfection for enthusiasts with customizable RGB and premium switches',
+        key_features: ['Cherry MX switches', 'RGB backlighting', 'Programmable keys', 'Aluminum frame']
+      },
+      quality: 0.92,
+      metadata: { domain: 'ecommerce', difficulty: 'medium', tags: ['electronics', 'gaming'] }
+    }
+  ];
+
+  // Train the system
+  await system.train(trainingExamples);
+
+  // Test the system
+  const testCases = [
+    { product_name: 'Wireless Earbuds', category: 'electronics', price: 129 },
+    { product_name: 'Resistance Bands Set', category: 'fitness', price: 29 },
+    { product_name: 'Laptop Stand', category: 'electronics', price: 59 }
+  ];
+
+  await system.test(testCases);
+}
+
+// Run the example
+if (import.meta.url === `file://${process.argv[1]}`) {
+  runAdvancedLearning().catch(error => {
+    console.error('❌ Advanced learning failed:', error);
+    process.exit(1);
+  });
+}
+
+export { AdvancedLearningSystem, EcommerceEvaluator, AdvancedLearningConfig };

vendor/ruvector/npm/packages/agentic-synth-examples/examples/advanced/production-pipeline.d.ts

@@ -0,0 +1,83 @@
+/**
+ * ADVANCED TUTORIAL: Production Pipeline
+ *
+ * Build a complete production-ready data generation pipeline with:
+ * - Error handling and retry logic
+ * - Monitoring and metrics
+ * - Rate limiting and cost controls
+ * - Batch processing and caching
+ * - Quality validation
+ *
+ * What you'll learn:
+ * - Production-grade error handling
+ * - Performance monitoring
+ * - Cost optimization
+ * - Scalability patterns
+ * - Deployment best practices
+ *
+ * Prerequisites:
+ * - Complete previous tutorials
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/advanced/production-pipeline.ts
+ */
+import { GenerationResult } from '@ruvector/agentic-synth';
+interface PipelineConfig {
+    maxRetries: number;
+    retryDelay: number;
+    batchSize: number;
+    maxConcurrency: number;
+    qualityThreshold: number;
+    costBudget: number;
+    rateLimitPerMinute: number;
+    enableCaching: boolean;
+    outputDirectory: string;
+}
+interface PipelineMetrics {
+    totalRequests: number;
+    successfulRequests: number;
+    failedRequests: number;
+    totalDuration: number;
+    totalCost: number;
+    averageQuality: number;
+    cacheHits: number;
+    retries: number;
+    errors: Array<{
+        timestamp: Date;
+        error: string;
+        context: any;
+    }>;
+}
+interface QualityValidator {
+    validate(data: any): {
+        valid: boolean;
+        score: number;
+        issues: string[];
+    };
+}
+declare class ProductionPipeline {
+    private config;
+    private synth;
+    private metrics;
+    private requestsThisMinute;
+    private minuteStartTime;
+    constructor(config?: Partial<PipelineConfig>);
+    private checkRateLimit;
+    private checkCostBudget;
+    private generateWithRetry;
+    private processBatch;
+    run(requests: any[], validator?: QualityValidator): Promise<GenerationResult[]>;
+    private saveResults;
+    private displayMetrics;
+    getMetrics(): PipelineMetrics;
+}
+declare class ProductQualityValidator implements QualityValidator {
+    validate(data: any[]): {
+        valid: boolean;
+        score: number;
+        issues: string[];
+    };
+}
+export { ProductionPipeline, ProductQualityValidator, PipelineConfig, PipelineMetrics };
+//# sourceMappingURL=production-pipeline.d.ts.map

vendor/ruvector/npm/packages/agentic-synth-examples/examples/advanced/production-pipeline.d.ts.map

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vendor/ruvector/npm/packages/agentic-synth-examples/examples/advanced/production-pipeline.js

@@ -0,0 +1,341 @@
+"use strict";
+/**
+ * ADVANCED TUTORIAL: Production Pipeline
+ *
+ * Build a complete production-ready data generation pipeline with:
+ * - Error handling and retry logic
+ * - Monitoring and metrics
+ * - Rate limiting and cost controls
+ * - Batch processing and caching
+ * - Quality validation
+ *
+ * What you'll learn:
+ * - Production-grade error handling
+ * - Performance monitoring
+ * - Cost optimization
+ * - Scalability patterns
+ * - Deployment best practices
+ *
+ * Prerequisites:
+ * - Complete previous tutorials
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/advanced/production-pipeline.ts
+ */
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.ProductQualityValidator = exports.ProductionPipeline = void 0;
+const agentic_synth_1 = require("@ruvector/agentic-synth");
+const fs_1 = require("fs");
+const path_1 = require("path");
+// Production-grade pipeline
+class ProductionPipeline {
+    constructor(config = {}) {
+        this.requestsThisMinute = 0;
+        this.minuteStartTime = Date.now();
+        this.config = {
+            maxRetries: config.maxRetries || 3,
+            retryDelay: config.retryDelay || 1000,
+            batchSize: config.batchSize || 10,
+            maxConcurrency: config.maxConcurrency || 3,
+            qualityThreshold: config.qualityThreshold || 0.7,
+            costBudget: config.costBudget || 10.0,
+            rateLimitPerMinute: config.rateLimitPerMinute || 60,
+            enableCaching: config.enableCaching !== false,
+            outputDirectory: config.outputDirectory || './output'
+        };
+        this.synth = new agentic_synth_1.AgenticSynth({
+            provider: 'gemini',
+            apiKey: process.env.GEMINI_API_KEY,
+            model: 'gemini-2.0-flash-exp',
+            cacheStrategy: this.config.enableCaching ? 'memory' : 'none',
+            cacheTTL: 3600,
+            maxRetries: this.config.maxRetries,
+            timeout: 30000
+        });
+        this.metrics = {
+            totalRequests: 0,
+            successfulRequests: 0,
+            failedRequests: 0,
+            totalDuration: 0,
+            totalCost: 0,
+            averageQuality: 0,
+            cacheHits: 0,
+            retries: 0,
+            errors: []
+        };
+        // Ensure output directory exists
+        if (!(0, fs_1.existsSync)(this.config.outputDirectory)) {
+            (0, fs_1.mkdirSync)(this.config.outputDirectory, { recursive: true });
+        }
+    }
+    // Rate limiting check
+    async checkRateLimit() {
+        const now = Date.now();
+        const elapsedMinutes = (now - this.minuteStartTime) / 60000;
+        if (elapsedMinutes >= 1) {
+            // Reset counter for new minute
+            this.requestsThisMinute = 0;
+            this.minuteStartTime = now;
+        }
+        if (this.requestsThisMinute >= this.config.rateLimitPerMinute) {
+            const waitTime = 60000 - (now - this.minuteStartTime);
+            console.log(`⏳ Rate limit reached, waiting ${Math.ceil(waitTime / 1000)}s...`);
+            await new Promise(resolve => setTimeout(resolve, waitTime));
+            this.requestsThisMinute = 0;
+            this.minuteStartTime = Date.now();
+        }
+    }
+    // Cost check
+    checkCostBudget() {
+        if (this.metrics.totalCost >= this.config.costBudget) {
+            throw new Error(`Cost budget exceeded: $${this.metrics.totalCost.toFixed(4)} >= $${this.config.costBudget}`);
+        }
+    }
+    // Generate with retry logic
+    async generateWithRetry(options, attempt = 1) {
+        try {
+            await this.checkRateLimit();
+            this.checkCostBudget();
+            this.requestsThisMinute++;
+            this.metrics.totalRequests++;
+            const startTime = Date.now();
+            const result = await this.synth.generateStructured(options);
+            const duration = Date.now() - startTime;
+            this.metrics.totalDuration += duration;
+            this.metrics.successfulRequests++;
+            if (result.metadata.cached) {
+                this.metrics.cacheHits++;
+            }
+            // Estimate cost (rough approximation)
+            const estimatedCost = result.metadata.cached ? 0 : 0.0001;
+            this.metrics.totalCost += estimatedCost;
+            return result;
+        }
+        catch (error) {
+            const errorMsg = error instanceof Error ? error.message : 'Unknown error';
+            if (attempt < this.config.maxRetries) {
+                this.metrics.retries++;
+                console.log(`⚠️  Attempt ${attempt} failed, retrying... (${errorMsg})`);
+                await new Promise(resolve => setTimeout(resolve, this.config.retryDelay * attempt));
+                return this.generateWithRetry(options, attempt + 1);
+            }
+            else {
+                this.metrics.failedRequests++;
+                this.metrics.errors.push({
+                    timestamp: new Date(),
+                    error: errorMsg,
+                    context: options
+                });
+                throw error;
+            }
+        }
+    }
+    // Process a single batch
+    async processBatch(requests, validator) {
+        const results = [];
+        // Process with concurrency control
+        for (let i = 0; i < requests.length; i += this.config.maxConcurrency) {
+            const batch = requests.slice(i, i + this.config.maxConcurrency);
+            const batchResults = await Promise.allSettled(batch.map(req => this.generateWithRetry(req)));
+            batchResults.forEach((result, idx) => {
+                if (result.status === 'fulfilled') {
+                    const genResult = result.value;
+                    // Validate quality if validator provided
+                    if (validator) {
+                        const validation = validator.validate(genResult.data);
+                        if (validation.valid) {
+                            results.push(genResult);
+                        }
+                        else {
+                            console.log(`⚠️  Quality validation failed (score: ${validation.score.toFixed(2)})`);
+                            console.log(`   Issues: ${validation.issues.join(', ')}`);
+                        }
+                    }
+                    else {
+                        results.push(genResult);
+                    }
+                }
+                else {
+                    console.error(`❌ Batch item ${i + idx} failed:`, result.reason);
+                }
+            });
+        }
+        return results;
+    }
+    // Main pipeline execution
+    async run(requests, validator) {
+        console.log('🏭 Starting Production Pipeline\n');
+        console.log('='.repeat(70));
+        console.log(`\nConfiguration:`);
+        console.log(`  Total Requests: ${requests.length}`);
+        console.log(`  Batch Size: ${this.config.batchSize}`);
+        console.log(`  Max Concurrency: ${this.config.maxConcurrency}`);
+        console.log(`  Max Retries: ${this.config.maxRetries}`);
+        console.log(`  Cost Budget: $${this.config.costBudget}`);
+        console.log(`  Rate Limit: ${this.config.rateLimitPerMinute}/min`);
+        console.log(`  Caching: ${this.config.enableCaching ? 'Enabled' : 'Disabled'}`);
+        console.log(`  Output: ${this.config.outputDirectory}`);
+        console.log('\n' + '='.repeat(70) + '\n');
+        const startTime = Date.now();
+        const allResults = [];
+        // Split into batches
+        const batches = [];
+        for (let i = 0; i < requests.length; i += this.config.batchSize) {
+            batches.push(requests.slice(i, i + this.config.batchSize));
+        }
+        console.log(`📦 Processing ${batches.length} batches...\n`);
+        // Process each batch
+        for (let i = 0; i < batches.length; i++) {
+            console.log(`\nBatch ${i + 1}/${batches.length} (${batches[i].length} items)`);
+            console.log('─'.repeat(70));
+            try {
+                const batchResults = await this.processBatch(batches[i], validator);
+                allResults.push(...batchResults);
+                console.log(`✓ Batch complete: ${batchResults.length}/${batches[i].length} successful`);
+                console.log(`  Cost so far: $${this.metrics.totalCost.toFixed(4)}`);
+                console.log(`  Cache hits: ${this.metrics.cacheHits}`);
+            }
+            catch (error) {
+                console.error(`✗ Batch failed:`, error instanceof Error ? error.message : 'Unknown error');
+                if (error instanceof Error && error.message.includes('budget')) {
+                    console.log('\n⚠️  Cost budget exceeded, stopping pipeline...');
+                    break;
+                }
+            }
+        }
+        const totalTime = Date.now() - startTime;
+        // Save results
+        await this.saveResults(allResults);
+        // Display metrics
+        this.displayMetrics(totalTime);
+        return allResults;
+    }
+    // Save results to disk
+    async saveResults(results) {
+        try {
+            const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
+            const filename = `generation-${timestamp}.json`;
+            const filepath = (0, path_1.join)(this.config.outputDirectory, filename);
+            const output = {
+                timestamp: new Date(),
+                results: results.map(r => r.data),
+                metadata: {
+                    count: results.length,
+                    metrics: this.metrics
+                }
+            };
+            (0, fs_1.writeFileSync)(filepath, JSON.stringify(output, null, 2));
+            console.log(`\n💾 Results saved to: ${filepath}`);
+            // Save metrics separately
+            const metricsFile = (0, path_1.join)(this.config.outputDirectory, `metrics-${timestamp}.json`);
+            (0, fs_1.writeFileSync)(metricsFile, JSON.stringify(this.metrics, null, 2));
+            console.log(`📊 Metrics saved to: ${metricsFile}`);
+        }
+        catch (error) {
+            console.error('⚠️  Failed to save results:', error instanceof Error ? error.message : 'Unknown error');
+        }
+    }
+    // Display comprehensive metrics
+    displayMetrics(totalTime) {
+        console.log('\n\n' + '='.repeat(70));
+        console.log('\n📊 PIPELINE METRICS\n');
+        const successRate = (this.metrics.successfulRequests / this.metrics.totalRequests) * 100;
+        const avgDuration = this.metrics.totalDuration / this.metrics.successfulRequests;
+        const cacheHitRate = (this.metrics.cacheHits / this.metrics.totalRequests) * 100;
+        console.log('Performance:');
+        console.log(`  Total Time: ${(totalTime / 1000).toFixed(2)}s`);
+        console.log(`  Avg Request Time: ${avgDuration.toFixed(0)}ms`);
+        console.log(`  Throughput: ${(this.metrics.successfulRequests / (totalTime / 1000)).toFixed(2)} req/s`);
+        console.log('\nReliability:');
+        console.log(`  Total Requests: ${this.metrics.totalRequests}`);
+        console.log(`  Successful: ${this.metrics.successfulRequests} (${successRate.toFixed(1)}%)`);
+        console.log(`  Failed: ${this.metrics.failedRequests}`);
+        console.log(`  Retries: ${this.metrics.retries}`);
+        console.log('\nCost & Efficiency:');
+        console.log(`  Total Cost: $${this.metrics.totalCost.toFixed(4)}`);
+        console.log(`  Avg Cost/Request: $${(this.metrics.totalCost / this.metrics.totalRequests).toFixed(6)}`);
+        console.log(`  Cache Hit Rate: ${cacheHitRate.toFixed(1)}%`);
+        console.log(`  Cost Savings from Cache: $${(this.metrics.cacheHits * 0.0001).toFixed(4)}`);
+        if (this.metrics.errors.length > 0) {
+            console.log(`\n⚠️  Errors (${this.metrics.errors.length}):`);
+            this.metrics.errors.slice(0, 5).forEach((err, i) => {
+                console.log(`  ${i + 1}. ${err.error}`);
+            });
+            if (this.metrics.errors.length > 5) {
+                console.log(`  ... and ${this.metrics.errors.length - 5} more`);
+            }
+        }
+        console.log('\n' + '='.repeat(70) + '\n');
+    }
+    // Get metrics
+    getMetrics() {
+        return { ...this.metrics };
+    }
+}
+exports.ProductionPipeline = ProductionPipeline;
+// Example quality validator
+class ProductQualityValidator {
+    validate(data) {
+        const issues = [];
+        let score = 1.0;
+        if (!Array.isArray(data) || data.length === 0) {
+            return { valid: false, score: 0, issues: ['No data generated'] };
+        }
+        data.forEach((item, idx) => {
+            if (!item.description || item.description.length < 50) {
+                issues.push(`Item ${idx}: Description too short`);
+                score -= 0.1;
+            }
+            if (!item.key_features || !Array.isArray(item.key_features) || item.key_features.length < 3) {
+                issues.push(`Item ${idx}: Insufficient features`);
+                score -= 0.1;
+            }
+        });
+        score = Math.max(0, score);
+        const valid = score >= 0.7;
+        return { valid, score, issues };
+    }
+}
+exports.ProductQualityValidator = ProductQualityValidator;
+// Main execution
+async function runProductionPipeline() {
+    const pipeline = new ProductionPipeline({
+        maxRetries: 3,
+        retryDelay: 2000,
+        batchSize: 5,
+        maxConcurrency: 2,
+        qualityThreshold: 0.7,
+        costBudget: 1.0,
+        rateLimitPerMinute: 30,
+        enableCaching: true,
+        outputDirectory: (0, path_1.join)(process.cwd(), 'examples', 'output', 'production')
+    });
+    const validator = new ProductQualityValidator();
+    // Generate product data for e-commerce catalog
+    const requests = [
+        {
+            count: 2,
+            schema: {
+                id: { type: 'string', required: true },
+                name: { type: 'string', required: true },
+                description: { type: 'string', required: true },
+                key_features: { type: 'array', items: { type: 'string' }, required: true },
+                price: { type: 'number', required: true, minimum: 10, maximum: 1000 },
+                category: { type: 'string', enum: ['Electronics', 'Clothing', 'Home', 'Sports'] }
+            }
+        }
+    ];
+    // Duplicate requests to test batching
+    const allRequests = Array(5).fill(null).map(() => requests[0]);
+    const results = await pipeline.run(allRequests, validator);
+    console.log(`\n✅ Pipeline complete! Generated ${results.length} batches of products.\n`);
+}
+// Run the example
+if (import.meta.url === `file://${process.argv[1]}`) {
+    runProductionPipeline().catch(error => {
+        console.error('❌ Pipeline failed:', error);
+        process.exit(1);
+    });
+}
+//# sourceMappingURL=production-pipeline.js.map

vendor/ruvector/npm/packages/agentic-synth-examples/examples/advanced/production-pipeline.ts

@@ -0,0 +1,444 @@
+/**
+ * ADVANCED TUTORIAL: Production Pipeline
+ *
+ * Build a complete production-ready data generation pipeline with:
+ * - Error handling and retry logic
+ * - Monitoring and metrics
+ * - Rate limiting and cost controls
+ * - Batch processing and caching
+ * - Quality validation
+ *
+ * What you'll learn:
+ * - Production-grade error handling
+ * - Performance monitoring
+ * - Cost optimization
+ * - Scalability patterns
+ * - Deployment best practices
+ *
+ * Prerequisites:
+ * - Complete previous tutorials
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/advanced/production-pipeline.ts
+ */
+
+import { AgenticSynth, GenerationResult } from '@ruvector/agentic-synth';
+import { writeFileSync, existsSync, mkdirSync } from 'fs';
+import { join } from 'path';
+
+// Pipeline configuration
+interface PipelineConfig {
+  maxRetries: number;
+  retryDelay: number;
+  batchSize: number;
+  maxConcurrency: number;
+  qualityThreshold: number;
+  costBudget: number;
+  rateLimitPerMinute: number;
+  enableCaching: boolean;
+  outputDirectory: string;
+}
+
+// Metrics tracking
+interface PipelineMetrics {
+  totalRequests: number;
+  successfulRequests: number;
+  failedRequests: number;
+  totalDuration: number;
+  totalCost: number;
+  averageQuality: number;
+  cacheHits: number;
+  retries: number;
+  errors: Array<{ timestamp: Date; error: string; context: any }>;
+}
+
+// Quality validator
+interface QualityValidator {
+  validate(data: any): { valid: boolean; score: number; issues: string[] };
+}
+
+// Production-grade pipeline
+class ProductionPipeline {
+  private config: PipelineConfig;
+  private synth: AgenticSynth;
+  private metrics: PipelineMetrics;
+  private requestsThisMinute: number = 0;
+  private minuteStartTime: number = Date.now();
+
+  constructor(config: Partial<PipelineConfig> = {}) {
+    this.config = {
+      maxRetries: config.maxRetries || 3,
+      retryDelay: config.retryDelay || 1000,
+      batchSize: config.batchSize || 10,
+      maxConcurrency: config.maxConcurrency || 3,
+      qualityThreshold: config.qualityThreshold || 0.7,
+      costBudget: config.costBudget || 10.0,
+      rateLimitPerMinute: config.rateLimitPerMinute || 60,
+      enableCaching: config.enableCaching !== false,
+      outputDirectory: config.outputDirectory || './output'
+    };
+
+    this.synth = new AgenticSynth({
+      provider: 'gemini',
+      apiKey: process.env.GEMINI_API_KEY,
+      model: 'gemini-2.0-flash-exp',
+      cacheStrategy: this.config.enableCaching ? 'memory' : 'none',
+      cacheTTL: 3600,
+      maxRetries: this.config.maxRetries,
+      timeout: 30000
+    });
+
+    this.metrics = {
+      totalRequests: 0,
+      successfulRequests: 0,
+      failedRequests: 0,
+      totalDuration: 0,
+      totalCost: 0,
+      averageQuality: 0,
+      cacheHits: 0,
+      retries: 0,
+      errors: []
+    };
+
+    // Ensure output directory exists
+    if (!existsSync(this.config.outputDirectory)) {
+      mkdirSync(this.config.outputDirectory, { recursive: true });
+    }
+  }
+
+  // Rate limiting check
+  private async checkRateLimit(): Promise<void> {
+    const now = Date.now();
+    const elapsedMinutes = (now - this.minuteStartTime) / 60000;
+
+    if (elapsedMinutes >= 1) {
+      // Reset counter for new minute
+      this.requestsThisMinute = 0;
+      this.minuteStartTime = now;
+    }
+
+    if (this.requestsThisMinute >= this.config.rateLimitPerMinute) {
+      const waitTime = 60000 - (now - this.minuteStartTime);
+      console.log(`⏳ Rate limit reached, waiting ${Math.ceil(waitTime / 1000)}s...`);
+      await new Promise(resolve => setTimeout(resolve, waitTime));
+      this.requestsThisMinute = 0;
+      this.minuteStartTime = Date.now();
+    }
+  }
+
+  // Cost check
+  private checkCostBudget(): void {
+    if (this.metrics.totalCost >= this.config.costBudget) {
+      throw new Error(`Cost budget exceeded: $${this.metrics.totalCost.toFixed(4)} >= $${this.config.costBudget}`);
+    }
+  }
+
+  // Generate with retry logic
+  private async generateWithRetry(
+    options: any,
+    attempt: number = 1
+  ): Promise<GenerationResult> {
+    try {
+      await this.checkRateLimit();
+      this.checkCostBudget();
+
+      this.requestsThisMinute++;
+      this.metrics.totalRequests++;
+
+      const startTime = Date.now();
+      const result = await this.synth.generateStructured(options);
+      const duration = Date.now() - startTime;
+
+      this.metrics.totalDuration += duration;
+      this.metrics.successfulRequests++;
+
+      if (result.metadata.cached) {
+        this.metrics.cacheHits++;
+      }
+
+      // Estimate cost (rough approximation)
+      const estimatedCost = result.metadata.cached ? 0 : 0.0001;
+      this.metrics.totalCost += estimatedCost;
+
+      return result;
+
+    } catch (error) {
+      const errorMsg = error instanceof Error ? error.message : 'Unknown error';
+
+      if (attempt < this.config.maxRetries) {
+        this.metrics.retries++;
+        console.log(`⚠️  Attempt ${attempt} failed, retrying... (${errorMsg})`);
+
+        await new Promise(resolve =>
+          setTimeout(resolve, this.config.retryDelay * attempt)
+        );
+
+        return this.generateWithRetry(options, attempt + 1);
+      } else {
+        this.metrics.failedRequests++;
+        this.metrics.errors.push({
+          timestamp: new Date(),
+          error: errorMsg,
+          context: options
+        });
+        throw error;
+      }
+    }
+  }
+
+  // Process a single batch
+  private async processBatch(
+    requests: any[],
+    validator?: QualityValidator
+  ): Promise<GenerationResult[]> {
+    const results: GenerationResult[] = [];
+
+    // Process with concurrency control
+    for (let i = 0; i < requests.length; i += this.config.maxConcurrency) {
+      const batch = requests.slice(i, i + this.config.maxConcurrency);
+
+      const batchResults = await Promise.allSettled(
+        batch.map(req => this.generateWithRetry(req))
+      );
+
+      batchResults.forEach((result, idx) => {
+        if (result.status === 'fulfilled') {
+          const genResult = result.value;
+
+          // Validate quality if validator provided
+          if (validator) {
+            const validation = validator.validate(genResult.data);
+
+            if (validation.valid) {
+              results.push(genResult);
+            } else {
+              console.log(`⚠️  Quality validation failed (score: ${validation.score.toFixed(2)})`);
+              console.log(`   Issues: ${validation.issues.join(', ')}`);
+            }
+          } else {
+            results.push(genResult);
+          }
+        } else {
+          console.error(`❌ Batch item ${i + idx} failed:`, result.reason);
+        }
+      });
+    }
+
+    return results;
+  }
+
+  // Main pipeline execution
+  async run(
+    requests: any[],
+    validator?: QualityValidator
+  ): Promise<GenerationResult[]> {
+    console.log('🏭 Starting Production Pipeline\n');
+    console.log('=' .repeat(70));
+    console.log(`\nConfiguration:`);
+    console.log(`  Total Requests: ${requests.length}`);
+    console.log(`  Batch Size: ${this.config.batchSize}`);
+    console.log(`  Max Concurrency: ${this.config.maxConcurrency}`);
+    console.log(`  Max Retries: ${this.config.maxRetries}`);
+    console.log(`  Cost Budget: $${this.config.costBudget}`);
+    console.log(`  Rate Limit: ${this.config.rateLimitPerMinute}/min`);
+    console.log(`  Caching: ${this.config.enableCaching ? 'Enabled' : 'Disabled'}`);
+    console.log(`  Output: ${this.config.outputDirectory}`);
+    console.log('\n' + '=' .repeat(70) + '\n');
+
+    const startTime = Date.now();
+    const allResults: GenerationResult[] = [];
+
+    // Split into batches
+    const batches = [];
+    for (let i = 0; i < requests.length; i += this.config.batchSize) {
+      batches.push(requests.slice(i, i + this.config.batchSize));
+    }
+
+    console.log(`📦 Processing ${batches.length} batches...\n`);
+
+    // Process each batch
+    for (let i = 0; i < batches.length; i++) {
+      console.log(`\nBatch ${i + 1}/${batches.length} (${batches[i].length} items)`);
+      console.log('─'.repeat(70));
+
+      try {
+        const batchResults = await this.processBatch(batches[i], validator);
+        allResults.push(...batchResults);
+
+        console.log(`✓ Batch complete: ${batchResults.length}/${batches[i].length} successful`);
+        console.log(`  Cost so far: $${this.metrics.totalCost.toFixed(4)}`);
+        console.log(`  Cache hits: ${this.metrics.cacheHits}`);
+
+      } catch (error) {
+        console.error(`✗ Batch failed:`, error instanceof Error ? error.message : 'Unknown error');
+
+        if (error instanceof Error && error.message.includes('budget')) {
+          console.log('\n⚠️  Cost budget exceeded, stopping pipeline...');
+          break;
+        }
+      }
+    }
+
+    const totalTime = Date.now() - startTime;
+
+    // Save results
+    await this.saveResults(allResults);
+
+    // Display metrics
+    this.displayMetrics(totalTime);
+
+    return allResults;
+  }
+
+  // Save results to disk
+  private async saveResults(results: GenerationResult[]): Promise<void> {
+    try {
+      const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
+      const filename = `generation-${timestamp}.json`;
+      const filepath = join(this.config.outputDirectory, filename);
+
+      const output = {
+        timestamp: new Date(),
+        results: results.map(r => r.data),
+        metadata: {
+          count: results.length,
+          metrics: this.metrics
+        }
+      };
+
+      writeFileSync(filepath, JSON.stringify(output, null, 2));
+      console.log(`\n💾 Results saved to: ${filepath}`);
+
+      // Save metrics separately
+      const metricsFile = join(this.config.outputDirectory, `metrics-${timestamp}.json`);
+      writeFileSync(metricsFile, JSON.stringify(this.metrics, null, 2));
+      console.log(`📊 Metrics saved to: ${metricsFile}`);
+
+    } catch (error) {
+      console.error('⚠️  Failed to save results:', error instanceof Error ? error.message : 'Unknown error');
+    }
+  }
+
+  // Display comprehensive metrics
+  private displayMetrics(totalTime: number): void {
+    console.log('\n\n' + '=' .repeat(70));
+    console.log('\n📊 PIPELINE METRICS\n');
+
+    const successRate = (this.metrics.successfulRequests / this.metrics.totalRequests) * 100;
+    const avgDuration = this.metrics.totalDuration / this.metrics.successfulRequests;
+    const cacheHitRate = (this.metrics.cacheHits / this.metrics.totalRequests) * 100;
+
+    console.log('Performance:');
+    console.log(`  Total Time: ${(totalTime / 1000).toFixed(2)}s`);
+    console.log(`  Avg Request Time: ${avgDuration.toFixed(0)}ms`);
+    console.log(`  Throughput: ${(this.metrics.successfulRequests / (totalTime / 1000)).toFixed(2)} req/s`);
+
+    console.log('\nReliability:');
+    console.log(`  Total Requests: ${this.metrics.totalRequests}`);
+    console.log(`  Successful: ${this.metrics.successfulRequests} (${successRate.toFixed(1)}%)`);
+    console.log(`  Failed: ${this.metrics.failedRequests}`);
+    console.log(`  Retries: ${this.metrics.retries}`);
+
+    console.log('\nCost & Efficiency:');
+    console.log(`  Total Cost: $${this.metrics.totalCost.toFixed(4)}`);
+    console.log(`  Avg Cost/Request: $${(this.metrics.totalCost / this.metrics.totalRequests).toFixed(6)}`);
+    console.log(`  Cache Hit Rate: ${cacheHitRate.toFixed(1)}%`);
+    console.log(`  Cost Savings from Cache: $${(this.metrics.cacheHits * 0.0001).toFixed(4)}`);
+
+    if (this.metrics.errors.length > 0) {
+      console.log(`\n⚠️  Errors (${this.metrics.errors.length}):`);
+      this.metrics.errors.slice(0, 5).forEach((err, i) => {
+        console.log(`  ${i + 1}. ${err.error}`);
+      });
+      if (this.metrics.errors.length > 5) {
+        console.log(`  ... and ${this.metrics.errors.length - 5} more`);
+      }
+    }
+
+    console.log('\n' + '=' .repeat(70) + '\n');
+  }
+
+  // Get metrics
+  getMetrics(): PipelineMetrics {
+    return { ...this.metrics };
+  }
+}
+
+// Example quality validator
+class ProductQualityValidator implements QualityValidator {
+  validate(data: any[]): { valid: boolean; score: number; issues: string[] } {
+    const issues: string[] = [];
+    let score = 1.0;
+
+    if (!Array.isArray(data) || data.length === 0) {
+      return { valid: false, score: 0, issues: ['No data generated'] };
+    }
+
+    data.forEach((item, idx) => {
+      if (!item.description || item.description.length < 50) {
+        issues.push(`Item ${idx}: Description too short`);
+        score -= 0.1;
+      }
+
+      if (!item.key_features || !Array.isArray(item.key_features) || item.key_features.length < 3) {
+        issues.push(`Item ${idx}: Insufficient features`);
+        score -= 0.1;
+      }
+    });
+
+    score = Math.max(0, score);
+    const valid = score >= 0.7;
+
+    return { valid, score, issues };
+  }
+}
+
+// Main execution
+async function runProductionPipeline() {
+  const pipeline = new ProductionPipeline({
+    maxRetries: 3,
+    retryDelay: 2000,
+    batchSize: 5,
+    maxConcurrency: 2,
+    qualityThreshold: 0.7,
+    costBudget: 1.0,
+    rateLimitPerMinute: 30,
+    enableCaching: true,
+    outputDirectory: join(process.cwd(), 'examples', 'output', 'production')
+  });
+
+  const validator = new ProductQualityValidator();
+
+  // Generate product data for e-commerce catalog
+  const requests = [
+    {
+      count: 2,
+      schema: {
+        id: { type: 'string', required: true },
+        name: { type: 'string', required: true },
+        description: { type: 'string', required: true },
+        key_features: { type: 'array', items: { type: 'string' }, required: true },
+        price: { type: 'number', required: true, minimum: 10, maximum: 1000 },
+        category: { type: 'string', enum: ['Electronics', 'Clothing', 'Home', 'Sports'] }
+      }
+    }
+  ];
+
+  // Duplicate requests to test batching
+  const allRequests = Array(5).fill(null).map(() => requests[0]);
+
+  const results = await pipeline.run(allRequests, validator);
+
+  console.log(`\n✅ Pipeline complete! Generated ${results.length} batches of products.\n`);
+}
+
+// Run the example
+if (import.meta.url === `file://${process.argv[1]}`) {
+  runProductionPipeline().catch(error => {
+    console.error('❌ Pipeline failed:', error);
+    process.exit(1);
+  });
+}
+
+export { ProductionPipeline, ProductQualityValidator, PipelineConfig, PipelineMetrics };

vendor/ruvector/npm/packages/agentic-synth-examples/examples/beginner/first-dspy-training.d.ts

@@ -0,0 +1,25 @@
+/**
+ * BEGINNER TUTORIAL: First DSPy Training
+ *
+ * This tutorial demonstrates the basics of training a single model using DSPy.ts
+ * with agentic-synth for synthetic data generation.
+ *
+ * What you'll learn:
+ * - How to set up a DSPy module
+ * - Basic configuration options
+ * - Training a model with examples
+ * - Evaluating output quality
+ *
+ * Prerequisites:
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install dspy.ts @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/beginner/first-dspy-training.ts
+ */
+import { ChainOfThought } from 'dspy.ts';
+declare class ProductDescriptionGenerator extends ChainOfThought {
+    constructor();
+}
+declare function runTraining(): Promise<void>;
+export { runTraining, ProductDescriptionGenerator };
+//# sourceMappingURL=first-dspy-training.d.ts.map

vendor/ruvector/npm/packages/agentic-synth-examples/examples/beginner/first-dspy-training.d.ts.map

@@ -0,0 +1 @@
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vendor/ruvector/npm/packages/agentic-synth-examples/examples/beginner/first-dspy-training.js

@@ -0,0 +1,158 @@
+"use strict";
+/**
+ * BEGINNER TUTORIAL: First DSPy Training
+ *
+ * This tutorial demonstrates the basics of training a single model using DSPy.ts
+ * with agentic-synth for synthetic data generation.
+ *
+ * What you'll learn:
+ * - How to set up a DSPy module
+ * - Basic configuration options
+ * - Training a model with examples
+ * - Evaluating output quality
+ *
+ * Prerequisites:
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install dspy.ts @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/beginner/first-dspy-training.ts
+ */
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.ProductDescriptionGenerator = void 0;
+exports.runTraining = runTraining;
+const dspy_ts_1 = require("dspy.ts");
+// Step 1: Configure the language model
+// We'll use Gemini as it's fast and cost-effective for learning
+const lm = new dspy_ts_1.LM({
+    provider: 'google-genai',
+    model: 'gemini-2.0-flash-exp',
+    apiKey: process.env.GEMINI_API_KEY || '',
+    temperature: 0.7, // Controls randomness (0 = deterministic, 1 = creative)
+});
+// Step 2: Define the signature for our task
+// This tells DSPy what inputs we expect and what outputs we want
+const productDescriptionSignature = {
+    input: 'product_name: string, category: string',
+    output: 'description: string, key_features: string[]',
+    description: 'Generate compelling product descriptions for e-commerce'
+};
+// Step 3: Create a DSPy module using Chain of Thought
+// CoT helps the model reason through the task step-by-step
+class ProductDescriptionGenerator extends dspy_ts_1.ChainOfThought {
+    constructor() {
+        super(productDescriptionSignature, { lm });
+    }
+}
+exports.ProductDescriptionGenerator = ProductDescriptionGenerator;
+// Step 4: Prepare training examples
+// These examples teach the model what good output looks like
+const trainingExamples = [
+    {
+        product_name: 'Wireless Bluetooth Headphones',
+        category: 'Electronics',
+        description: 'Premium wireless headphones with active noise cancellation and 30-hour battery life',
+        key_features: ['ANC Technology', '30h Battery', 'Bluetooth 5.0', 'Comfortable Design']
+    },
+    {
+        product_name: 'Organic Green Tea',
+        category: 'Beverages',
+        description: 'Hand-picked organic green tea leaves from high-altitude gardens, rich in antioxidants',
+        key_features: ['100% Organic', 'High Antioxidants', 'Mountain Grown', 'Fair Trade']
+    },
+    {
+        product_name: 'Leather Laptop Bag',
+        category: 'Accessories',
+        description: 'Handcrafted genuine leather laptop bag with padded compartment for 15-inch laptops',
+        key_features: ['Genuine Leather', 'Padded Protection', '15" Laptop Fit', 'Professional Style']
+    }
+];
+// Step 5: Simple evaluation function
+// This measures how good the generated descriptions are
+function evaluateDescription(prediction) {
+    let score = 0;
+    // Check if description exists and has good length (50-200 chars)
+    if (prediction.description &&
+        prediction.description.length >= 50 &&
+        prediction.description.length <= 200) {
+        score += 0.5;
+    }
+    // Check if key features are provided (at least 3)
+    if (prediction.key_features &&
+        Array.isArray(prediction.key_features) &&
+        prediction.key_features.length >= 3) {
+        score += 0.5;
+    }
+    return score;
+}
+// Step 6: Main training function
+async function runTraining() {
+    console.log('🚀 Starting Your First DSPy Training Session\n');
+    console.log('='.repeat(60));
+    // Initialize the generator
+    const generator = new ProductDescriptionGenerator();
+    console.log('\n📊 Training with', trainingExamples.length, 'examples...\n');
+    // Train the model by showing it examples
+    // In a real scenario, you'd use DSPy's optimizers like BootstrapFewShot
+    for (let i = 0; i < trainingExamples.length; i++) {
+        const example = trainingExamples[i];
+        console.log(`Example ${i + 1}/${trainingExamples.length}:`);
+        console.log(`  Product: ${example.product_name}`);
+        console.log(`  Category: ${example.category}`);
+        console.log(`  ✓ Learned pattern\n`);
+    }
+    console.log('✅ Training complete!\n');
+    console.log('='.repeat(60));
+    // Step 7: Test the trained model
+    console.log('\n🧪 Testing the model with new products:\n');
+    const testCases = [
+        { product_name: 'Smart Watch Pro', category: 'Wearables' },
+        { product_name: 'Yoga Mat', category: 'Fitness' },
+        { product_name: 'Coffee Maker', category: 'Kitchen Appliances' }
+    ];
+    let totalScore = 0;
+    for (const testCase of testCases) {
+        try {
+            console.log(`\n📦 Product: ${testCase.product_name}`);
+            console.log(`   Category: ${testCase.category}`);
+            // Generate description
+            const result = await generator.forward(testCase);
+            // Evaluate quality
+            const score = evaluateDescription(result);
+            totalScore += score;
+            console.log(`\n   Generated Description:`);
+            console.log(`   ${result.description}`);
+            console.log(`\n   Key Features:`);
+            if (Array.isArray(result.key_features)) {
+                result.key_features.forEach(feature => {
+                    console.log(`   • ${feature}`);
+                });
+            }
+            console.log(`\n   Quality Score: ${(score * 100).toFixed(0)}%`);
+            console.log(`   ${score >= 0.8 ? '✅' : score >= 0.5 ? '⚠️' : '❌'} ${score >= 0.8 ? 'Excellent' : score >= 0.5 ? 'Good' : 'Needs Improvement'}`);
+        }
+        catch (error) {
+            console.error(`   ❌ Error: ${error instanceof Error ? error.message : 'Unknown error'}`);
+        }
+    }
+    // Step 8: Summary
+    const avgScore = totalScore / testCases.length;
+    console.log('\n' + '='.repeat(60));
+    console.log('\n📈 Training Summary:');
+    console.log(`   Average Quality: ${(avgScore * 100).toFixed(1)}%`);
+    console.log(`   Tests Passed: ${testCases.length}`);
+    console.log(`   Model: ${lm.model}`);
+    console.log(`   Provider: ${lm.provider}`);
+    console.log('\n💡 Next Steps:');
+    console.log('   1. Try the multi-model comparison example');
+    console.log('   2. Experiment with different temperatures');
+    console.log('   3. Add more training examples');
+    console.log('   4. Customize the evaluation function\n');
+}
+// Run the training
+if (import.meta.url === `file://${process.argv[1]}`) {
+    runTraining().catch(error => {
+        console.error('❌ Training failed:', error);
+        process.exit(1);
+    });
+}
+//# sourceMappingURL=first-dspy-training.js.map

vendor/ruvector/npm/packages/agentic-synth-examples/examples/beginner/first-dspy-training.js.map

@@ -0,0 +1 @@
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vendor/ruvector/npm/packages/agentic-synth-examples/examples/beginner/first-dspy-training.ts

@@ -0,0 +1,178 @@
+/**
+ * BEGINNER TUTORIAL: First DSPy Training
+ *
+ * This tutorial demonstrates the basics of training a single model using DSPy.ts
+ * with agentic-synth for synthetic data generation.
+ *
+ * What you'll learn:
+ * - How to set up a DSPy module
+ * - Basic configuration options
+ * - Training a model with examples
+ * - Evaluating output quality
+ *
+ * Prerequisites:
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install dspy.ts @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/beginner/first-dspy-training.ts
+ */
+
+import { ChainOfThought, LM, Prediction } from 'dspy.ts';
+
+// Step 1: Configure the language model
+// We'll use Gemini as it's fast and cost-effective for learning
+const lm = new LM({
+  provider: 'google-genai',
+  model: 'gemini-2.0-flash-exp',
+  apiKey: process.env.GEMINI_API_KEY || '',
+  temperature: 0.7, // Controls randomness (0 = deterministic, 1 = creative)
+});
+
+// Step 2: Define the signature for our task
+// This tells DSPy what inputs we expect and what outputs we want
+const productDescriptionSignature = {
+  input: 'product_name: string, category: string',
+  output: 'description: string, key_features: string[]',
+  description: 'Generate compelling product descriptions for e-commerce'
+};
+
+// Step 3: Create a DSPy module using Chain of Thought
+// CoT helps the model reason through the task step-by-step
+class ProductDescriptionGenerator extends ChainOfThought {
+  constructor() {
+    super(productDescriptionSignature, { lm });
+  }
+}
+
+// Step 4: Prepare training examples
+// These examples teach the model what good output looks like
+const trainingExamples = [
+  {
+    product_name: 'Wireless Bluetooth Headphones',
+    category: 'Electronics',
+    description: 'Premium wireless headphones with active noise cancellation and 30-hour battery life',
+    key_features: ['ANC Technology', '30h Battery', 'Bluetooth 5.0', 'Comfortable Design']
+  },
+  {
+    product_name: 'Organic Green Tea',
+    category: 'Beverages',
+    description: 'Hand-picked organic green tea leaves from high-altitude gardens, rich in antioxidants',
+    key_features: ['100% Organic', 'High Antioxidants', 'Mountain Grown', 'Fair Trade']
+  },
+  {
+    product_name: 'Leather Laptop Bag',
+    category: 'Accessories',
+    description: 'Handcrafted genuine leather laptop bag with padded compartment for 15-inch laptops',
+    key_features: ['Genuine Leather', 'Padded Protection', '15" Laptop Fit', 'Professional Style']
+  }
+];
+
+// Step 5: Simple evaluation function
+// This measures how good the generated descriptions are
+function evaluateDescription(prediction: Prediction): number {
+  let score = 0;
+
+  // Check if description exists and has good length (50-200 chars)
+  if (prediction.description &&
+      prediction.description.length >= 50 &&
+      prediction.description.length <= 200) {
+    score += 0.5;
+  }
+
+  // Check if key features are provided (at least 3)
+  if (prediction.key_features &&
+      Array.isArray(prediction.key_features) &&
+      prediction.key_features.length >= 3) {
+    score += 0.5;
+  }
+
+  return score;
+}
+
+// Step 6: Main training function
+async function runTraining() {
+  console.log('🚀 Starting Your First DSPy Training Session\n');
+  console.log('=' .repeat(60));
+
+  // Initialize the generator
+  const generator = new ProductDescriptionGenerator();
+
+  console.log('\n📊 Training with', trainingExamples.length, 'examples...\n');
+
+  // Train the model by showing it examples
+  // In a real scenario, you'd use DSPy's optimizers like BootstrapFewShot
+  for (let i = 0; i < trainingExamples.length; i++) {
+    const example = trainingExamples[i];
+    console.log(`Example ${i + 1}/${trainingExamples.length}:`);
+    console.log(`  Product: ${example.product_name}`);
+    console.log(`  Category: ${example.category}`);
+    console.log(`  ✓ Learned pattern\n`);
+  }
+
+  console.log('✅ Training complete!\n');
+  console.log('=' .repeat(60));
+
+  // Step 7: Test the trained model
+  console.log('\n🧪 Testing the model with new products:\n');
+
+  const testCases = [
+    { product_name: 'Smart Watch Pro', category: 'Wearables' },
+    { product_name: 'Yoga Mat', category: 'Fitness' },
+    { product_name: 'Coffee Maker', category: 'Kitchen Appliances' }
+  ];
+
+  let totalScore = 0;
+
+  for (const testCase of testCases) {
+    try {
+      console.log(`\n📦 Product: ${testCase.product_name}`);
+      console.log(`   Category: ${testCase.category}`);
+
+      // Generate description
+      const result = await generator.forward(testCase);
+
+      // Evaluate quality
+      const score = evaluateDescription(result);
+      totalScore += score;
+
+      console.log(`\n   Generated Description:`);
+      console.log(`   ${result.description}`);
+      console.log(`\n   Key Features:`);
+      if (Array.isArray(result.key_features)) {
+        result.key_features.forEach(feature => {
+          console.log(`   • ${feature}`);
+        });
+      }
+      console.log(`\n   Quality Score: ${(score * 100).toFixed(0)}%`);
+      console.log(`   ${score >= 0.8 ? '✅' : score >= 0.5 ? '⚠️' : '❌'} ${score >= 0.8 ? 'Excellent' : score >= 0.5 ? 'Good' : 'Needs Improvement'}`);
+
+    } catch (error) {
+      console.error(`   ❌ Error: ${error instanceof Error ? error.message : 'Unknown error'}`);
+    }
+  }
+
+  // Step 8: Summary
+  const avgScore = totalScore / testCases.length;
+  console.log('\n' + '='.repeat(60));
+  console.log('\n📈 Training Summary:');
+  console.log(`   Average Quality: ${(avgScore * 100).toFixed(1)}%`);
+  console.log(`   Tests Passed: ${testCases.length}`);
+  console.log(`   Model: ${lm.model}`);
+  console.log(`   Provider: ${lm.provider}`);
+
+  console.log('\n💡 Next Steps:');
+  console.log('   1. Try the multi-model comparison example');
+  console.log('   2. Experiment with different temperatures');
+  console.log('   3. Add more training examples');
+  console.log('   4. Customize the evaluation function\n');
+}
+
+// Run the training
+if (import.meta.url === `file://${process.argv[1]}`) {
+  runTraining().catch(error => {
+    console.error('❌ Training failed:', error);
+    process.exit(1);
+  });
+}
+
+export { runTraining, ProductDescriptionGenerator };

vendor/ruvector/npm/packages/agentic-synth-examples/examples/beginner/simple-data-generation.d.ts

@@ -0,0 +1,24 @@
+/**
+ * BEGINNER TUTORIAL: Simple Data Generation
+ *
+ * Learn how to generate structured synthetic data with agentic-synth.
+ * Perfect for creating test data, mock APIs, or prototyping.
+ *
+ * What you'll learn:
+ * - Defining data schemas
+ * - Generating structured data
+ * - Saving output to files
+ * - Working with different formats
+ *
+ * Prerequisites:
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/beginner/simple-data-generation.ts
+ */
+import { AgenticSynth } from '@ruvector/agentic-synth';
+declare const synth: AgenticSynth;
+declare function generateUserData(): Promise<void>;
+declare function generateWithConstraints(): Promise<void>;
+export { generateUserData, generateWithConstraints, synth };
+//# sourceMappingURL=simple-data-generation.d.ts.map

vendor/ruvector/npm/packages/agentic-synth-examples/examples/beginner/simple-data-generation.d.ts.map

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vendor/ruvector/npm/packages/agentic-synth-examples/examples/beginner/simple-data-generation.js

@@ -0,0 +1,240 @@
+"use strict";
+/**
+ * BEGINNER TUTORIAL: Simple Data Generation
+ *
+ * Learn how to generate structured synthetic data with agentic-synth.
+ * Perfect for creating test data, mock APIs, or prototyping.
+ *
+ * What you'll learn:
+ * - Defining data schemas
+ * - Generating structured data
+ * - Saving output to files
+ * - Working with different formats
+ *
+ * Prerequisites:
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/beginner/simple-data-generation.ts
+ */
+var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {
+    if (k2 === undefined) k2 = k;
+    var desc = Object.getOwnPropertyDescriptor(m, k);
+    if (!desc || ("get" in desc ? !m.__esModule : desc.writable || desc.configurable)) {
+      desc = { enumerable: true, get: function() { return m[k]; } };
+    }
+    Object.defineProperty(o, k2, desc);
+}) : (function(o, m, k, k2) {
+    if (k2 === undefined) k2 = k;
+    o[k2] = m[k];
+}));
+var __setModuleDefault = (this && this.__setModuleDefault) || (Object.create ? (function(o, v) {
+    Object.defineProperty(o, "default", { enumerable: true, value: v });
+}) : function(o, v) {
+    o["default"] = v;
+});
+var __importStar = (this && this.__importStar) || (function () {
+    var ownKeys = function(o) {
+        ownKeys = Object.getOwnPropertyNames || function (o) {
+            var ar = [];
+            for (var k in o) if (Object.prototype.hasOwnProperty.call(o, k)) ar[ar.length] = k;
+            return ar;
+        };
+        return ownKeys(o);
+    };
+    return function (mod) {
+        if (mod && mod.__esModule) return mod;
+        var result = {};
+        if (mod != null) for (var k = ownKeys(mod), i = 0; i < k.length; i++) if (k[i] !== "default") __createBinding(result, mod, k[i]);
+        __setModuleDefault(result, mod);
+        return result;
+    };
+})();
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.synth = void 0;
+exports.generateUserData = generateUserData;
+exports.generateWithConstraints = generateWithConstraints;
+const agentic_synth_1 = require("@ruvector/agentic-synth");
+const fs_1 = require("fs");
+const path_1 = require("path");
+// Step 1: Define your data schema
+// This is like a blueprint for the data you want to generate
+const userSchema = {
+    // Basic fields with types
+    id: { type: 'string', required: true },
+    name: { type: 'string', required: true },
+    email: { type: 'string', required: true },
+    age: { type: 'number', required: true, minimum: 18, maximum: 80 },
+    // Enum fields (restricted choices)
+    role: {
+        type: 'string',
+        required: true,
+        enum: ['user', 'admin', 'moderator']
+    },
+    // Nested object
+    address: {
+        type: 'object',
+        required: false,
+        properties: {
+            street: { type: 'string' },
+            city: { type: 'string' },
+            country: { type: 'string' },
+            postalCode: { type: 'string' }
+        }
+    },
+    // Array field
+    interests: {
+        type: 'array',
+        required: false,
+        items: { type: 'string' }
+    }
+};
+// Step 2: Initialize AgenticSynth
+// We're using Gemini because it's fast and cost-effective
+const synth = new agentic_synth_1.AgenticSynth({
+    provider: 'gemini',
+    apiKey: process.env.GEMINI_API_KEY,
+    model: 'gemini-2.0-flash-exp',
+    cacheStrategy: 'memory', // Cache results to save API calls
+    cacheTTL: 3600 // Cache for 1 hour
+});
+exports.synth = synth;
+// Step 3: Main generation function
+async function generateUserData() {
+    console.log('🎯 Simple Data Generation Tutorial\n');
+    console.log('='.repeat(60));
+    // Step 3a: Generate a small batch first (5 users)
+    console.log('\n📊 Generating 5 sample users...\n');
+    try {
+        const result = await synth.generateStructured({
+            count: 5,
+            schema: userSchema,
+            format: 'json', // Can also be 'csv' or 'array'
+            constraints: {
+                // Additional constraints for more realistic data
+                emailDomain: '@example.com',
+                nameFormat: 'FirstName LastName',
+                countryList: ['USA', 'UK', 'Canada', 'Australia']
+            }
+        });
+        // Step 4: Display the results
+        console.log('✅ Generation Complete!\n');
+        console.log(`Generated ${result.metadata.count} users in ${result.metadata.duration}ms`);
+        console.log(`Provider: ${result.metadata.provider}`);
+        console.log(`Model: ${result.metadata.model}`);
+        console.log(`Cached: ${result.metadata.cached ? 'Yes ⚡' : 'No'}\n`);
+        // Show the generated data
+        console.log('👥 Generated Users:\n');
+        result.data.forEach((user, index) => {
+            console.log(`${index + 1}. ${user.name} (${user.role})`);
+            console.log(`   📧 ${user.email}`);
+            console.log(`   🎂 Age: ${user.age}`);
+            if (user.address) {
+                console.log(`   🏠 ${user.address.city}, ${user.address.country}`);
+            }
+            if (user.interests && user.interests.length > 0) {
+                console.log(`   ❤️  Interests: ${user.interests.join(', ')}`);
+            }
+            console.log('');
+        });
+        // Step 5: Save to file
+        const outputDir = (0, path_1.join)(process.cwd(), 'examples', 'output');
+        const outputFile = (0, path_1.join)(outputDir, 'sample-users.json');
+        try {
+            // Create output directory if it doesn't exist
+            const { mkdirSync } = await Promise.resolve().then(() => __importStar(require('fs')));
+            mkdirSync(outputDir, { recursive: true });
+            // Save the data
+            (0, fs_1.writeFileSync)(outputFile, JSON.stringify(result.data, null, 2));
+            console.log(`💾 Data saved to: ${outputFile}\n`);
+        }
+        catch (error) {
+            console.warn('⚠️  Could not save file:', error instanceof Error ? error.message : 'Unknown error');
+        }
+        // Step 6: Generate a larger batch
+        console.log('='.repeat(60));
+        console.log('\n📈 Now generating 20 users (to demonstrate scaling)...\n');
+        const largeResult = await synth.generateStructured({
+            count: 20,
+            schema: userSchema,
+            format: 'json'
+        });
+        console.log('✅ Large batch complete!');
+        console.log(`   Generated: ${largeResult.metadata.count} users`);
+        console.log(`   Time: ${largeResult.metadata.duration}ms`);
+        console.log(`   Cached: ${largeResult.metadata.cached ? 'Yes ⚡' : 'No'}\n`);
+        // Step 7: Demonstrate CSV format
+        console.log('='.repeat(60));
+        console.log('\n📄 Generating data in CSV format...\n');
+        const csvResult = await synth.generateStructured({
+            count: 3,
+            schema: {
+                id: { type: 'string', required: true },
+                name: { type: 'string', required: true },
+                email: { type: 'string', required: true },
+                role: { type: 'string', required: true }
+            },
+            format: 'csv'
+        });
+        console.log('CSV Output (first 3 users):');
+        console.log('─'.repeat(60));
+        // Note: CSV format will be in the data array as strings
+        console.log('✅ CSV generation successful\n');
+        // Step 8: Show statistics
+        console.log('='.repeat(60));
+        console.log('\n📊 Session Statistics:');
+        console.log(`   Total users generated: ${result.data.length + largeResult.data.length + csvResult.data.length}`);
+        console.log(`   Total API calls: ${result.metadata.cached ? '1 (cached)' : '2'}`);
+        console.log(`   Total time: ${result.metadata.duration + largeResult.metadata.duration}ms`);
+        // Step 9: Next steps
+        console.log('\n💡 What You Can Do Next:');
+        console.log('   1. Modify the schema to match your use case');
+        console.log('   2. Try different data types (timeseries, events)');
+        console.log('   3. Experiment with constraints for more realistic data');
+        console.log('   4. Generate thousands of records for load testing');
+        console.log('   5. Integrate with your test suite or mock API\n');
+    }
+    catch (error) {
+        console.error('❌ Generation failed:', error instanceof Error ? error.message : 'Unknown error');
+        // Helpful error messages
+        if (error instanceof Error) {
+            if (error.message.includes('API key')) {
+                console.error('\n💡 Tip: Make sure GEMINI_API_KEY is set in your environment');
+            }
+            else if (error.message.includes('schema')) {
+                console.error('\n💡 Tip: Check your schema definition for errors');
+            }
+        }
+        process.exit(1);
+    }
+}
+// Additional helper: Generate with custom constraints
+async function generateWithConstraints() {
+    console.log('\n🎨 Example: Custom Constraints\n');
+    const result = await synth.generateStructured({
+        count: 3,
+        schema: {
+            productName: { type: 'string', required: true },
+            price: { type: 'number', required: true, minimum: 10, maximum: 1000 },
+            category: {
+                type: 'string',
+                enum: ['Electronics', 'Clothing', 'Books', 'Food']
+            },
+            inStock: { type: 'boolean', required: true }
+        },
+        constraints: {
+            priceFormat: 'USD',
+            includeDiscounts: true,
+            realistic: true
+        }
+    });
+    console.log('Generated products:', result.data);
+}
+// Run the example
+if (import.meta.url === `file://${process.argv[1]}`) {
+    generateUserData().catch(error => {
+        console.error('Fatal error:', error);
+        process.exit(1);
+    });
+}
+//# sourceMappingURL=simple-data-generation.js.map

vendor/ruvector/npm/packages/agentic-synth-examples/examples/beginner/simple-data-generation.ts

@@ -0,0 +1,228 @@
+/**
+ * BEGINNER TUTORIAL: Simple Data Generation
+ *
+ * Learn how to generate structured synthetic data with agentic-synth.
+ * Perfect for creating test data, mock APIs, or prototyping.
+ *
+ * What you'll learn:
+ * - Defining data schemas
+ * - Generating structured data
+ * - Saving output to files
+ * - Working with different formats
+ *
+ * Prerequisites:
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/beginner/simple-data-generation.ts
+ */
+
+import { AgenticSynth } from '@ruvector/agentic-synth';
+import { writeFileSync } from 'fs';
+import { join } from 'path';
+
+// Step 1: Define your data schema
+// This is like a blueprint for the data you want to generate
+const userSchema = {
+  // Basic fields with types
+  id: { type: 'string', required: true },
+  name: { type: 'string', required: true },
+  email: { type: 'string', required: true },
+  age: { type: 'number', required: true, minimum: 18, maximum: 80 },
+
+  // Enum fields (restricted choices)
+  role: {
+    type: 'string',
+    required: true,
+    enum: ['user', 'admin', 'moderator']
+  },
+
+  // Nested object
+  address: {
+    type: 'object',
+    required: false,
+    properties: {
+      street: { type: 'string' },
+      city: { type: 'string' },
+      country: { type: 'string' },
+      postalCode: { type: 'string' }
+    }
+  },
+
+  // Array field
+  interests: {
+    type: 'array',
+    required: false,
+    items: { type: 'string' }
+  }
+};
+
+// Step 2: Initialize AgenticSynth
+// We're using Gemini because it's fast and cost-effective
+const synth = new AgenticSynth({
+  provider: 'gemini',
+  apiKey: process.env.GEMINI_API_KEY,
+  model: 'gemini-2.0-flash-exp',
+  cacheStrategy: 'memory', // Cache results to save API calls
+  cacheTTL: 3600 // Cache for 1 hour
+});
+
+// Step 3: Main generation function
+async function generateUserData() {
+  console.log('🎯 Simple Data Generation Tutorial\n');
+  console.log('=' .repeat(60));
+
+  // Step 3a: Generate a small batch first (5 users)
+  console.log('\n📊 Generating 5 sample users...\n');
+
+  try {
+    const result = await synth.generateStructured({
+      count: 5,
+      schema: userSchema,
+      format: 'json', // Can also be 'csv' or 'array'
+      constraints: {
+        // Additional constraints for more realistic data
+        emailDomain: '@example.com',
+        nameFormat: 'FirstName LastName',
+        countryList: ['USA', 'UK', 'Canada', 'Australia']
+      }
+    });
+
+    // Step 4: Display the results
+    console.log('✅ Generation Complete!\n');
+    console.log(`Generated ${result.metadata.count} users in ${result.metadata.duration}ms`);
+    console.log(`Provider: ${result.metadata.provider}`);
+    console.log(`Model: ${result.metadata.model}`);
+    console.log(`Cached: ${result.metadata.cached ? 'Yes ⚡' : 'No'}\n`);
+
+    // Show the generated data
+    console.log('👥 Generated Users:\n');
+    result.data.forEach((user: any, index: number) => {
+      console.log(`${index + 1}. ${user.name} (${user.role})`);
+      console.log(`   📧 ${user.email}`);
+      console.log(`   🎂 Age: ${user.age}`);
+      if (user.address) {
+        console.log(`   🏠 ${user.address.city}, ${user.address.country}`);
+      }
+      if (user.interests && user.interests.length > 0) {
+        console.log(`   ❤️  Interests: ${user.interests.join(', ')}`);
+      }
+      console.log('');
+    });
+
+    // Step 5: Save to file
+    const outputDir = join(process.cwd(), 'examples', 'output');
+    const outputFile = join(outputDir, 'sample-users.json');
+
+    try {
+      // Create output directory if it doesn't exist
+      const { mkdirSync } = await import('fs');
+      mkdirSync(outputDir, { recursive: true });
+
+      // Save the data
+      writeFileSync(outputFile, JSON.stringify(result.data, null, 2));
+      console.log(`💾 Data saved to: ${outputFile}\n`);
+    } catch (error) {
+      console.warn('⚠️  Could not save file:', error instanceof Error ? error.message : 'Unknown error');
+    }
+
+    // Step 6: Generate a larger batch
+    console.log('=' .repeat(60));
+    console.log('\n📈 Now generating 20 users (to demonstrate scaling)...\n');
+
+    const largeResult = await synth.generateStructured({
+      count: 20,
+      schema: userSchema,
+      format: 'json'
+    });
+
+    console.log('✅ Large batch complete!');
+    console.log(`   Generated: ${largeResult.metadata.count} users`);
+    console.log(`   Time: ${largeResult.metadata.duration}ms`);
+    console.log(`   Cached: ${largeResult.metadata.cached ? 'Yes ⚡' : 'No'}\n`);
+
+    // Step 7: Demonstrate CSV format
+    console.log('=' .repeat(60));
+    console.log('\n📄 Generating data in CSV format...\n');
+
+    const csvResult = await synth.generateStructured({
+      count: 3,
+      schema: {
+        id: { type: 'string', required: true },
+        name: { type: 'string', required: true },
+        email: { type: 'string', required: true },
+        role: { type: 'string', required: true }
+      },
+      format: 'csv'
+    });
+
+    console.log('CSV Output (first 3 users):');
+    console.log('─'.repeat(60));
+    // Note: CSV format will be in the data array as strings
+    console.log('✅ CSV generation successful\n');
+
+    // Step 8: Show statistics
+    console.log('=' .repeat(60));
+    console.log('\n📊 Session Statistics:');
+    console.log(`   Total users generated: ${result.data.length + largeResult.data.length + csvResult.data.length}`);
+    console.log(`   Total API calls: ${result.metadata.cached ? '1 (cached)' : '2'}`);
+    console.log(`   Total time: ${result.metadata.duration + largeResult.metadata.duration}ms`);
+
+    // Step 9: Next steps
+    console.log('\n💡 What You Can Do Next:');
+    console.log('   1. Modify the schema to match your use case');
+    console.log('   2. Try different data types (timeseries, events)');
+    console.log('   3. Experiment with constraints for more realistic data');
+    console.log('   4. Generate thousands of records for load testing');
+    console.log('   5. Integrate with your test suite or mock API\n');
+
+  } catch (error) {
+    console.error('❌ Generation failed:', error instanceof Error ? error.message : 'Unknown error');
+
+    // Helpful error messages
+    if (error instanceof Error) {
+      if (error.message.includes('API key')) {
+        console.error('\n💡 Tip: Make sure GEMINI_API_KEY is set in your environment');
+      } else if (error.message.includes('schema')) {
+        console.error('\n💡 Tip: Check your schema definition for errors');
+      }
+    }
+
+    process.exit(1);
+  }
+}
+
+// Additional helper: Generate with custom constraints
+async function generateWithConstraints() {
+  console.log('\n🎨 Example: Custom Constraints\n');
+
+  const result = await synth.generateStructured({
+    count: 3,
+    schema: {
+      productName: { type: 'string', required: true },
+      price: { type: 'number', required: true, minimum: 10, maximum: 1000 },
+      category: {
+        type: 'string',
+        enum: ['Electronics', 'Clothing', 'Books', 'Food']
+      },
+      inStock: { type: 'boolean', required: true }
+    },
+    constraints: {
+      priceFormat: 'USD',
+      includeDiscounts: true,
+      realistic: true
+    }
+  });
+
+  console.log('Generated products:', result.data);
+}
+
+// Run the example
+if (import.meta.url === `file://${process.argv[1]}`) {
+  generateUserData().catch(error => {
+    console.error('Fatal error:', error);
+    process.exit(1);
+  });
+}
+
+export { generateUserData, generateWithConstraints, synth };

vendor/ruvector/npm/packages/agentic-synth-examples/examples/intermediate/multi-model-comparison.d.ts

@@ -0,0 +1,42 @@
+/**
+ * INTERMEDIATE TUTORIAL: Multi-Model Comparison
+ *
+ * Compare multiple AI models (Gemini, Claude, GPT-4) to find the best
+ * performer for your specific task. Includes benchmarking, cost tracking,
+ * and performance metrics.
+ *
+ * What you'll learn:
+ * - Running parallel model comparisons
+ * - Benchmarking quality and speed
+ * - Tracking costs per model
+ * - Selecting the best model for production
+ *
+ * Prerequisites:
+ * - Set API keys: GEMINI_API_KEY, ANTHROPIC_API_KEY, OPENAI_API_KEY
+ * - npm install dspy.ts @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/intermediate/multi-model-comparison.ts
+ */
+import { Prediction } from 'dspy.ts';
+interface ModelConfig {
+    name: string;
+    provider: string;
+    model: string;
+    apiKey: string;
+    costPer1kTokens: number;
+    capabilities: string[];
+}
+declare const models: ModelConfig[];
+interface BenchmarkResult {
+    modelName: string;
+    qualityScore: number;
+    avgResponseTime: number;
+    estimatedCost: number;
+    successRate: number;
+    outputs: Prediction[];
+    errors: string[];
+}
+declare function benchmarkModel(config: ModelConfig): Promise<BenchmarkResult>;
+declare function runComparison(): Promise<BenchmarkResult[]>;
+export { runComparison, benchmarkModel, models };
+//# sourceMappingURL=multi-model-comparison.d.ts.map

vendor/ruvector/npm/packages/agentic-synth-examples/examples/intermediate/multi-model-comparison.d.ts.map

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vendor/ruvector/npm/packages/agentic-synth-examples/examples/intermediate/multi-model-comparison.js

@@ -0,0 +1,274 @@
+"use strict";
+/**
+ * INTERMEDIATE TUTORIAL: Multi-Model Comparison
+ *
+ * Compare multiple AI models (Gemini, Claude, GPT-4) to find the best
+ * performer for your specific task. Includes benchmarking, cost tracking,
+ * and performance metrics.
+ *
+ * What you'll learn:
+ * - Running parallel model comparisons
+ * - Benchmarking quality and speed
+ * - Tracking costs per model
+ * - Selecting the best model for production
+ *
+ * Prerequisites:
+ * - Set API keys: GEMINI_API_KEY, ANTHROPIC_API_KEY, OPENAI_API_KEY
+ * - npm install dspy.ts @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/intermediate/multi-model-comparison.ts
+ */
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.models = void 0;
+exports.runComparison = runComparison;
+exports.benchmarkModel = benchmarkModel;
+const dspy_ts_1 = require("dspy.ts");
+// Available models to compare
+const models = [
+    {
+        name: 'Gemini Flash',
+        provider: 'google-genai',
+        model: 'gemini-2.0-flash-exp',
+        apiKey: process.env.GEMINI_API_KEY || '',
+        costPer1kTokens: 0.001, // Very cheap
+        capabilities: ['fast', 'cost-effective', 'reasoning']
+    },
+    {
+        name: 'Claude Sonnet 4',
+        provider: 'anthropic',
+        model: 'claude-sonnet-4-20250514',
+        apiKey: process.env.ANTHROPIC_API_KEY || '',
+        costPer1kTokens: 0.003, // Medium cost
+        capabilities: ['high-quality', 'reasoning', 'code']
+    },
+    {
+        name: 'GPT-4 Turbo',
+        provider: 'openai',
+        model: 'gpt-4-turbo-preview',
+        apiKey: process.env.OPENAI_API_KEY || '',
+        costPer1kTokens: 0.01, // More expensive
+        capabilities: ['versatile', 'high-quality', 'creative']
+    }
+];
+exports.models = models;
+// Test cases for comparison
+const testCases = [
+    {
+        task: 'product_description',
+        input: {
+            product_name: 'Wireless Noise-Cancelling Headphones',
+            category: 'Electronics',
+            price: 299
+        },
+        expectedFeatures: ['noise cancellation', 'wireless', 'battery life']
+    },
+    {
+        task: 'product_description',
+        input: {
+            product_name: 'Organic Herbal Tea Collection',
+            category: 'Beverages',
+            price: 24
+        },
+        expectedFeatures: ['organic', 'herbal', 'health benefits']
+    },
+    {
+        task: 'product_description',
+        input: {
+            product_name: 'Professional Camera Tripod',
+            category: 'Photography',
+            price: 149
+        },
+        expectedFeatures: ['stability', 'adjustable', 'professional']
+    },
+    {
+        task: 'product_description',
+        input: {
+            product_name: 'Smart Fitness Tracker',
+            category: 'Wearables',
+            price: 79
+        },
+        expectedFeatures: ['fitness tracking', 'smart features', 'health monitoring']
+    }
+];
+// Quality evaluation function
+function evaluateQuality(prediction, testCase) {
+    let score = 0;
+    const weights = {
+        hasDescription: 0.3,
+        descriptionLength: 0.2,
+        hasFeatures: 0.2,
+        featureCount: 0.15,
+        relevance: 0.15
+    };
+    // Check if description exists and is well-formed
+    if (prediction.description && typeof prediction.description === 'string') {
+        score += weights.hasDescription;
+        // Optimal length is 80-200 characters
+        const length = prediction.description.length;
+        if (length >= 80 && length <= 200) {
+            score += weights.descriptionLength;
+        }
+        else if (length >= 50 && length <= 250) {
+            score += weights.descriptionLength * 0.5;
+        }
+    }
+    // Check features
+    if (prediction.key_features && Array.isArray(prediction.key_features)) {
+        score += weights.hasFeatures;
+        // More features is better (up to 5)
+        const featureCount = Math.min(prediction.key_features.length, 5);
+        score += weights.featureCount * (featureCount / 5);
+    }
+    // Check relevance to expected features
+    if (prediction.description) {
+        const descLower = prediction.description.toLowerCase();
+        const relevantFeatures = testCase.expectedFeatures.filter(feature => descLower.includes(feature.toLowerCase()));
+        score += weights.relevance * (relevantFeatures.length / testCase.expectedFeatures.length);
+    }
+    return score;
+}
+// Run benchmark for a single model
+async function benchmarkModel(config) {
+    console.log(`\n🔄 Testing ${config.name}...`);
+    const result = {
+        modelName: config.name,
+        qualityScore: 0,
+        avgResponseTime: 0,
+        estimatedCost: 0,
+        successRate: 0,
+        outputs: [],
+        errors: []
+    };
+    if (!config.apiKey) {
+        console.log(`   ⚠️  API key not found, skipping...`);
+        result.errors.push('API key not configured');
+        return result;
+    }
+    const lm = new dspy_ts_1.LM({
+        provider: config.provider,
+        model: config.model,
+        apiKey: config.apiKey,
+        temperature: 0.7
+    });
+    const signature = {
+        input: 'product_name: string, category: string, price: number',
+        output: 'description: string, key_features: string[]'
+    };
+    const generator = new dspy_ts_1.ChainOfThought(signature, { lm });
+    const times = [];
+    let totalScore = 0;
+    let successCount = 0;
+    // Run all test cases
+    for (let i = 0; i < testCases.length; i++) {
+        const testCase = testCases[i];
+        try {
+            const startTime = Date.now();
+            const prediction = await generator.forward(testCase.input);
+            const duration = Date.now() - startTime;
+            times.push(duration);
+            result.outputs.push(prediction);
+            const score = evaluateQuality(prediction, testCase);
+            totalScore += score;
+            successCount++;
+            console.log(`   ✓ Test ${i + 1}/${testCases.length} - Score: ${(score * 100).toFixed(0)}% - ${duration}ms`);
+        }
+        catch (error) {
+            const errorMsg = error instanceof Error ? error.message : 'Unknown error';
+            result.errors.push(`Test ${i + 1}: ${errorMsg}`);
+            console.log(`   ✗ Test ${i + 1}/${testCases.length} - Failed: ${errorMsg}`);
+        }
+    }
+    // Calculate metrics
+    result.avgResponseTime = times.length > 0
+        ? times.reduce((a, b) => a + b, 0) / times.length
+        : 0;
+    result.qualityScore = successCount > 0 ? totalScore / testCases.length : 0;
+    result.successRate = successCount / testCases.length;
+    // Estimate cost (rough approximation based on avg tokens)
+    const avgTokens = 500; // Rough estimate
+    result.estimatedCost = (avgTokens / 1000) * config.costPer1kTokens * testCases.length;
+    return result;
+}
+// Main comparison function
+async function runComparison() {
+    console.log('🏆 Multi-Model Comparison Benchmark\n');
+    console.log('='.repeat(70));
+    console.log('\nComparing models:');
+    models.forEach((m, i) => {
+        console.log(`${i + 1}. ${m.name} - $${m.costPer1kTokens}/1K tokens`);
+        console.log(`   Capabilities: ${m.capabilities.join(', ')}`);
+    });
+    console.log(`\nRunning ${testCases.length} test cases per model...\n`);
+    console.log('='.repeat(70));
+    // Run all benchmarks in parallel
+    const results = await Promise.all(models.map(config => benchmarkModel(config)));
+    // Display results
+    console.log('\n' + '='.repeat(70));
+    console.log('\n📊 BENCHMARK RESULTS\n');
+    // Sort by quality score
+    const sortedResults = [...results].sort((a, b) => b.qualityScore - a.qualityScore);
+    console.log('┌─────────────────────┬──────────┬──────────┬──────────┬──────────┐');
+    console.log('│ Model               │ Quality  │ Speed    │ Cost     │ Success  │');
+    console.log('├─────────────────────┼──────────┼──────────┼──────────┼──────────┤');
+    sortedResults.forEach((result, index) => {
+        const quality = `${(result.qualityScore * 100).toFixed(1)}%`;
+        const speed = `${result.avgResponseTime.toFixed(0)}ms`;
+        const cost = `$${result.estimatedCost.toFixed(4)}`;
+        const success = `${(result.successRate * 100).toFixed(0)}%`;
+        const modelName = result.modelName.padEnd(19);
+        const qualityPad = quality.padStart(8);
+        const speedPad = speed.padStart(8);
+        const costPad = cost.padStart(8);
+        const successPad = success.padStart(8);
+        const medal = index === 0 ? '🥇' : index === 1 ? '🥈' : index === 2 ? '🥉' : '  ';
+        console.log(`│ ${medal} ${modelName}│${qualityPad}│${speedPad}│${costPad}│${successPad}│`);
+    });
+    console.log('└─────────────────────┴──────────┴──────────┴──────────┴──────────┘\n');
+    // Winner analysis
+    const winner = sortedResults[0];
+    console.log('🎯 WINNER: ' + winner.modelName);
+    console.log(`   Quality Score: ${(winner.qualityScore * 100).toFixed(1)}%`);
+    console.log(`   Avg Response: ${winner.avgResponseTime.toFixed(0)}ms`);
+    console.log(`   Total Cost: $${winner.estimatedCost.toFixed(4)}`);
+    console.log(`   Success Rate: ${(winner.successRate * 100).toFixed(0)}%\n`);
+    // Recommendations
+    console.log('💡 RECOMMENDATIONS:\n');
+    const fastest = [...results].sort((a, b) => a.avgResponseTime - b.avgResponseTime)[0];
+    const cheapest = [...results].sort((a, b) => a.estimatedCost - b.estimatedCost)[0];
+    const mostReliable = [...results].sort((a, b) => b.successRate - a.successRate)[0];
+    console.log(`⚡ Fastest: ${fastest.modelName} (${fastest.avgResponseTime.toFixed(0)}ms avg)`);
+    console.log(`💰 Cheapest: ${cheapest.modelName} ($${cheapest.estimatedCost.toFixed(4)} total)`);
+    console.log(`🎯 Most Reliable: ${mostReliable.modelName} (${(mostReliable.successRate * 100).toFixed(0)}% success)\n`);
+    console.log('Use case suggestions:');
+    console.log('  • High-volume/cost-sensitive → ' + cheapest.modelName);
+    console.log('  • Latency-critical/real-time → ' + fastest.modelName);
+    console.log('  • Quality-critical/production → ' + winner.modelName + '\n');
+    // Error report
+    const errorsExist = results.some(r => r.errors.length > 0);
+    if (errorsExist) {
+        console.log('⚠️  ERRORS:\n');
+        results.forEach(result => {
+            if (result.errors.length > 0) {
+                console.log(`${result.modelName}:`);
+                result.errors.forEach(err => console.log(`  • ${err}`));
+                console.log('');
+            }
+        });
+    }
+    console.log('='.repeat(70));
+    console.log('\n✅ Benchmark complete!\n');
+    console.log('Next steps:');
+    console.log('  1. Configure your production app with the winning model');
+    console.log('  2. Set up fallback chains for reliability');
+    console.log('  3. Monitor performance in production');
+    console.log('  4. Re-run benchmarks periodically as models improve\n');
+    return results;
+}
+// Run the comparison
+if (import.meta.url === `file://${process.argv[1]}`) {
+    runComparison().catch(error => {
+        console.error('❌ Benchmark failed:', error);
+        process.exit(1);
+    });
+}
+//# sourceMappingURL=multi-model-comparison.js.map

vendor/ruvector/npm/packages/agentic-synth-examples/examples/intermediate/multi-model-comparison.ts

@@ -0,0 +1,338 @@
+/**
+ * INTERMEDIATE TUTORIAL: Multi-Model Comparison
+ *
+ * Compare multiple AI models (Gemini, Claude, GPT-4) to find the best
+ * performer for your specific task. Includes benchmarking, cost tracking,
+ * and performance metrics.
+ *
+ * What you'll learn:
+ * - Running parallel model comparisons
+ * - Benchmarking quality and speed
+ * - Tracking costs per model
+ * - Selecting the best model for production
+ *
+ * Prerequisites:
+ * - Set API keys: GEMINI_API_KEY, ANTHROPIC_API_KEY, OPENAI_API_KEY
+ * - npm install dspy.ts @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/intermediate/multi-model-comparison.ts
+ */
+
+import { LM, ChainOfThought, Prediction } from 'dspy.ts';
+import { AgenticSynth } from '@ruvector/agentic-synth';
+
+// Model configuration with pricing
+interface ModelConfig {
+  name: string;
+  provider: string;
+  model: string;
+  apiKey: string;
+  costPer1kTokens: number; // Approximate pricing
+  capabilities: string[];
+}
+
+// Available models to compare
+const models: ModelConfig[] = [
+  {
+    name: 'Gemini Flash',
+    provider: 'google-genai',
+    model: 'gemini-2.0-flash-exp',
+    apiKey: process.env.GEMINI_API_KEY || '',
+    costPer1kTokens: 0.001, // Very cheap
+    capabilities: ['fast', 'cost-effective', 'reasoning']
+  },
+  {
+    name: 'Claude Sonnet 4',
+    provider: 'anthropic',
+    model: 'claude-sonnet-4-20250514',
+    apiKey: process.env.ANTHROPIC_API_KEY || '',
+    costPer1kTokens: 0.003, // Medium cost
+    capabilities: ['high-quality', 'reasoning', 'code']
+  },
+  {
+    name: 'GPT-4 Turbo',
+    provider: 'openai',
+    model: 'gpt-4-turbo-preview',
+    apiKey: process.env.OPENAI_API_KEY || '',
+    costPer1kTokens: 0.01, // More expensive
+    capabilities: ['versatile', 'high-quality', 'creative']
+  }
+];
+
+// Benchmark results interface
+interface BenchmarkResult {
+  modelName: string;
+  qualityScore: number;
+  avgResponseTime: number;
+  estimatedCost: number;
+  successRate: number;
+  outputs: Prediction[];
+  errors: string[];
+}
+
+// Test cases for comparison
+const testCases = [
+  {
+    task: 'product_description',
+    input: {
+      product_name: 'Wireless Noise-Cancelling Headphones',
+      category: 'Electronics',
+      price: 299
+    },
+    expectedFeatures: ['noise cancellation', 'wireless', 'battery life']
+  },
+  {
+    task: 'product_description',
+    input: {
+      product_name: 'Organic Herbal Tea Collection',
+      category: 'Beverages',
+      price: 24
+    },
+    expectedFeatures: ['organic', 'herbal', 'health benefits']
+  },
+  {
+    task: 'product_description',
+    input: {
+      product_name: 'Professional Camera Tripod',
+      category: 'Photography',
+      price: 149
+    },
+    expectedFeatures: ['stability', 'adjustable', 'professional']
+  },
+  {
+    task: 'product_description',
+    input: {
+      product_name: 'Smart Fitness Tracker',
+      category: 'Wearables',
+      price: 79
+    },
+    expectedFeatures: ['fitness tracking', 'smart features', 'health monitoring']
+  }
+];
+
+// Quality evaluation function
+function evaluateQuality(prediction: Prediction, testCase: typeof testCases[0]): number {
+  let score = 0;
+  const weights = {
+    hasDescription: 0.3,
+    descriptionLength: 0.2,
+    hasFeatures: 0.2,
+    featureCount: 0.15,
+    relevance: 0.15
+  };
+
+  // Check if description exists and is well-formed
+  if (prediction.description && typeof prediction.description === 'string') {
+    score += weights.hasDescription;
+
+    // Optimal length is 80-200 characters
+    const length = prediction.description.length;
+    if (length >= 80 && length <= 200) {
+      score += weights.descriptionLength;
+    } else if (length >= 50 && length <= 250) {
+      score += weights.descriptionLength * 0.5;
+    }
+  }
+
+  // Check features
+  if (prediction.key_features && Array.isArray(prediction.key_features)) {
+    score += weights.hasFeatures;
+
+    // More features is better (up to 5)
+    const featureCount = Math.min(prediction.key_features.length, 5);
+    score += weights.featureCount * (featureCount / 5);
+  }
+
+  // Check relevance to expected features
+  if (prediction.description) {
+    const descLower = prediction.description.toLowerCase();
+    const relevantFeatures = testCase.expectedFeatures.filter(feature =>
+      descLower.includes(feature.toLowerCase())
+    );
+    score += weights.relevance * (relevantFeatures.length / testCase.expectedFeatures.length);
+  }
+
+  return score;
+}
+
+// Run benchmark for a single model
+async function benchmarkModel(config: ModelConfig): Promise<BenchmarkResult> {
+  console.log(`\n🔄 Testing ${config.name}...`);
+
+  const result: BenchmarkResult = {
+    modelName: config.name,
+    qualityScore: 0,
+    avgResponseTime: 0,
+    estimatedCost: 0,
+    successRate: 0,
+    outputs: [],
+    errors: []
+  };
+
+  if (!config.apiKey) {
+    console.log(`   ⚠️  API key not found, skipping...`);
+    result.errors.push('API key not configured');
+    return result;
+  }
+
+  const lm = new LM({
+    provider: config.provider as any,
+    model: config.model,
+    apiKey: config.apiKey,
+    temperature: 0.7
+  });
+
+  const signature = {
+    input: 'product_name: string, category: string, price: number',
+    output: 'description: string, key_features: string[]'
+  };
+
+  const generator = new ChainOfThought(signature, { lm });
+
+  const times: number[] = [];
+  let totalScore = 0;
+  let successCount = 0;
+
+  // Run all test cases
+  for (let i = 0; i < testCases.length; i++) {
+    const testCase = testCases[i];
+
+    try {
+      const startTime = Date.now();
+      const prediction = await generator.forward(testCase.input);
+      const duration = Date.now() - startTime;
+
+      times.push(duration);
+      result.outputs.push(prediction);
+
+      const score = evaluateQuality(prediction, testCase);
+      totalScore += score;
+      successCount++;
+
+      console.log(`   ✓ Test ${i + 1}/${testCases.length} - Score: ${(score * 100).toFixed(0)}% - ${duration}ms`);
+
+    } catch (error) {
+      const errorMsg = error instanceof Error ? error.message : 'Unknown error';
+      result.errors.push(`Test ${i + 1}: ${errorMsg}`);
+      console.log(`   ✗ Test ${i + 1}/${testCases.length} - Failed: ${errorMsg}`);
+    }
+  }
+
+  // Calculate metrics
+  result.avgResponseTime = times.length > 0
+    ? times.reduce((a, b) => a + b, 0) / times.length
+    : 0;
+  result.qualityScore = successCount > 0 ? totalScore / testCases.length : 0;
+  result.successRate = successCount / testCases.length;
+
+  // Estimate cost (rough approximation based on avg tokens)
+  const avgTokens = 500; // Rough estimate
+  result.estimatedCost = (avgTokens / 1000) * config.costPer1kTokens * testCases.length;
+
+  return result;
+}
+
+// Main comparison function
+async function runComparison() {
+  console.log('🏆 Multi-Model Comparison Benchmark\n');
+  console.log('=' .repeat(70));
+  console.log('\nComparing models:');
+  models.forEach((m, i) => {
+    console.log(`${i + 1}. ${m.name} - $${m.costPer1kTokens}/1K tokens`);
+    console.log(`   Capabilities: ${m.capabilities.join(', ')}`);
+  });
+  console.log(`\nRunning ${testCases.length} test cases per model...\n`);
+  console.log('=' .repeat(70));
+
+  // Run all benchmarks in parallel
+  const results = await Promise.all(
+    models.map(config => benchmarkModel(config))
+  );
+
+  // Display results
+  console.log('\n' + '=' .repeat(70));
+  console.log('\n📊 BENCHMARK RESULTS\n');
+
+  // Sort by quality score
+  const sortedResults = [...results].sort((a, b) => b.qualityScore - a.qualityScore);
+
+  console.log('┌─────────────────────┬──────────┬──────────┬──────────┬──────────┐');
+  console.log('│ Model               │ Quality  │ Speed    │ Cost     │ Success  │');
+  console.log('├─────────────────────┼──────────┼──────────┼──────────┼──────────┤');
+
+  sortedResults.forEach((result, index) => {
+    const quality = `${(result.qualityScore * 100).toFixed(1)}%`;
+    const speed = `${result.avgResponseTime.toFixed(0)}ms`;
+    const cost = `$${result.estimatedCost.toFixed(4)}`;
+    const success = `${(result.successRate * 100).toFixed(0)}%`;
+
+    const modelName = result.modelName.padEnd(19);
+    const qualityPad = quality.padStart(8);
+    const speedPad = speed.padStart(8);
+    const costPad = cost.padStart(8);
+    const successPad = success.padStart(8);
+
+    const medal = index === 0 ? '🥇' : index === 1 ? '🥈' : index === 2 ? '🥉' : '  ';
+
+    console.log(`│ ${medal} ${modelName}│${qualityPad}│${speedPad}│${costPad}│${successPad}│`);
+  });
+
+  console.log('└─────────────────────┴──────────┴──────────┴──────────┴──────────┘\n');
+
+  // Winner analysis
+  const winner = sortedResults[0];
+  console.log('🎯 WINNER: ' + winner.modelName);
+  console.log(`   Quality Score: ${(winner.qualityScore * 100).toFixed(1)}%`);
+  console.log(`   Avg Response: ${winner.avgResponseTime.toFixed(0)}ms`);
+  console.log(`   Total Cost: $${winner.estimatedCost.toFixed(4)}`);
+  console.log(`   Success Rate: ${(winner.successRate * 100).toFixed(0)}%\n`);
+
+  // Recommendations
+  console.log('💡 RECOMMENDATIONS:\n');
+
+  const fastest = [...results].sort((a, b) => a.avgResponseTime - b.avgResponseTime)[0];
+  const cheapest = [...results].sort((a, b) => a.estimatedCost - b.estimatedCost)[0];
+  const mostReliable = [...results].sort((a, b) => b.successRate - a.successRate)[0];
+
+  console.log(`⚡ Fastest: ${fastest.modelName} (${fastest.avgResponseTime.toFixed(0)}ms avg)`);
+  console.log(`💰 Cheapest: ${cheapest.modelName} ($${cheapest.estimatedCost.toFixed(4)} total)`);
+  console.log(`🎯 Most Reliable: ${mostReliable.modelName} (${(mostReliable.successRate * 100).toFixed(0)}% success)\n`);
+
+  console.log('Use case suggestions:');
+  console.log('  • High-volume/cost-sensitive → ' + cheapest.modelName);
+  console.log('  • Latency-critical/real-time → ' + fastest.modelName);
+  console.log('  • Quality-critical/production → ' + winner.modelName + '\n');
+
+  // Error report
+  const errorsExist = results.some(r => r.errors.length > 0);
+  if (errorsExist) {
+    console.log('⚠️  ERRORS:\n');
+    results.forEach(result => {
+      if (result.errors.length > 0) {
+        console.log(`${result.modelName}:`);
+        result.errors.forEach(err => console.log(`  • ${err}`));
+        console.log('');
+      }
+    });
+  }
+
+  console.log('=' .repeat(70));
+  console.log('\n✅ Benchmark complete!\n');
+  console.log('Next steps:');
+  console.log('  1. Configure your production app with the winning model');
+  console.log('  2. Set up fallback chains for reliability');
+  console.log('  3. Monitor performance in production');
+  console.log('  4. Re-run benchmarks periodically as models improve\n');
+
+  return results;
+}
+
+// Run the comparison
+if (import.meta.url === `file://${process.argv[1]}`) {
+  runComparison().catch(error => {
+    console.error('❌ Benchmark failed:', error);
+    process.exit(1);
+  });
+}
+
+export { runComparison, benchmarkModel, models };

vendor/ruvector/npm/packages/agentic-synth-examples/examples/intermediate/self-learning-system.d.ts

@@ -0,0 +1,57 @@
+/**
+ * INTERMEDIATE TUTORIAL: Self-Learning System
+ *
+ * Build an adaptive AI system that improves its output quality over time
+ * through feedback loops and pattern recognition. This demonstrates how
+ * to create systems that learn from their mistakes and successes.
+ *
+ * What you'll learn:
+ * - Building feedback loops
+ * - Tracking quality improvements
+ * - Adaptive prompt engineering
+ * - Learning from examples
+ *
+ * Prerequisites:
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install dspy.ts @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/intermediate/self-learning-system.ts
+ */
+import { Prediction } from 'dspy.ts';
+interface LearningConfig {
+    targetQualityThreshold: number;
+    maxIterations: number;
+    improvementRate: number;
+    minImprovement: number;
+}
+interface Feedback {
+    quality: number;
+    strengths: string[];
+    weaknesses: string[];
+    suggestions: string[];
+}
+interface LearningEntry {
+    iteration: number;
+    quality: number;
+    output: Prediction;
+    feedback: Feedback;
+    promptModifications: string[];
+    timestamp: Date;
+}
+declare class SelfLearningGenerator {
+    private lm;
+    private history;
+    private config;
+    private basePrompt;
+    private currentPromptAdditions;
+    constructor(config?: Partial<LearningConfig>);
+    private evaluateOutput;
+    private adaptPrompt;
+    private generate;
+    learn(input: any, criteria?: any): Promise<void>;
+    private displaySummary;
+    getLearnedImprovements(): string[];
+    getHistory(): LearningEntry[];
+}
+export { SelfLearningGenerator, LearningConfig, LearningEntry };
+//# sourceMappingURL=self-learning-system.d.ts.map

vendor/ruvector/npm/packages/agentic-synth-examples/examples/intermediate/self-learning-system.d.ts.map

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vendor/ruvector/npm/packages/agentic-synth-examples/examples/intermediate/self-learning-system.js

@@ -0,0 +1,300 @@
+"use strict";
+/**
+ * INTERMEDIATE TUTORIAL: Self-Learning System
+ *
+ * Build an adaptive AI system that improves its output quality over time
+ * through feedback loops and pattern recognition. This demonstrates how
+ * to create systems that learn from their mistakes and successes.
+ *
+ * What you'll learn:
+ * - Building feedback loops
+ * - Tracking quality improvements
+ * - Adaptive prompt engineering
+ * - Learning from examples
+ *
+ * Prerequisites:
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install dspy.ts @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/intermediate/self-learning-system.ts
+ */
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.SelfLearningGenerator = void 0;
+const dspy_ts_1 = require("dspy.ts");
+// Self-learning generator class
+class SelfLearningGenerator {
+    constructor(config = {}) {
+        this.history = [];
+        this.currentPromptAdditions = [];
+        this.config = {
+            targetQualityThreshold: config.targetQualityThreshold || 0.9,
+            maxIterations: config.maxIterations || 10,
+            improvementRate: config.improvementRate || 0.15,
+            minImprovement: config.minImprovement || 0.02
+        };
+        this.lm = new dspy_ts_1.LM({
+            provider: 'google-genai',
+            model: 'gemini-2.0-flash-exp',
+            apiKey: process.env.GEMINI_API_KEY || '',
+            temperature: 0.8 // Higher temperature for creativity during learning
+        });
+        this.basePrompt = '';
+    }
+    // Evaluate the quality of generated output
+    evaluateOutput(prediction, criteria) {
+        let quality = 0;
+        const strengths = [];
+        const weaknesses = [];
+        const suggestions = [];
+        // Check description quality
+        if (prediction.description) {
+            const desc = prediction.description;
+            const length = desc.length;
+            if (length >= 100 && length <= 200) {
+                quality += 0.3;
+                strengths.push('Description length is optimal');
+            }
+            else if (length < 50) {
+                weaknesses.push('Description too short');
+                suggestions.push('Expand description with more details');
+            }
+            else if (length > 250) {
+                weaknesses.push('Description too verbose');
+                suggestions.push('Make description more concise');
+            }
+            else {
+                quality += 0.15;
+            }
+            // Check for emotional/engaging language
+            const emotionalWords = ['amazing', 'powerful', 'innovative', 'premium', 'exceptional'];
+            const hasEmotionalLanguage = emotionalWords.some(word => desc.toLowerCase().includes(word));
+            if (hasEmotionalLanguage) {
+                quality += 0.2;
+                strengths.push('Uses engaging language');
+            }
+            else {
+                weaknesses.push('Could be more engaging');
+                suggestions.push('Add more descriptive and emotional words');
+            }
+        }
+        else {
+            weaknesses.push('Missing description');
+            suggestions.push('Generate a complete description');
+        }
+        // Check features
+        if (prediction.key_features && Array.isArray(prediction.key_features)) {
+            const features = prediction.key_features;
+            if (features.length >= 4 && features.length <= 6) {
+                quality += 0.3;
+                strengths.push('Optimal number of features');
+            }
+            else if (features.length < 3) {
+                weaknesses.push('Too few features');
+                suggestions.push('Include at least 4 key features');
+            }
+            else {
+                quality += 0.15;
+            }
+            // Check feature quality (should be concise)
+            const wellFormedFeatures = features.filter(f => f.length >= 10 && f.length <= 50);
+            if (wellFormedFeatures.length === features.length) {
+                quality += 0.2;
+                strengths.push('All features are well-formed');
+            }
+            else {
+                weaknesses.push('Some features need better formatting');
+                suggestions.push('Keep features concise (10-50 chars)');
+            }
+        }
+        else {
+            weaknesses.push('Missing features');
+            suggestions.push('Generate key features list');
+        }
+        return { quality, strengths, weaknesses, suggestions };
+    }
+    // Adapt prompt based on feedback
+    adaptPrompt(feedback) {
+        const modifications = [];
+        // Add specific instructions based on weaknesses
+        feedback.suggestions.forEach(suggestion => {
+            if (suggestion.includes('short')) {
+                modifications.push('Write detailed descriptions (100-200 characters)');
+            }
+            else if (suggestion.includes('verbose')) {
+                modifications.push('Keep descriptions concise and focused');
+            }
+            else if (suggestion.includes('engaging')) {
+                modifications.push('Use descriptive, engaging language');
+            }
+            else if (suggestion.includes('features')) {
+                modifications.push('Include 4-6 specific, measurable key features');
+            }
+            else if (suggestion.includes('concise')) {
+                modifications.push('Format features as short, punchy statements');
+            }
+        });
+        // Remove duplicates
+        return [...new Set(modifications)];
+    }
+    // Generate with current prompt
+    async generate(input) {
+        // Build enhanced signature with learned improvements
+        const enhancedInstructions = this.currentPromptAdditions.length > 0
+            ? '\n\nImportant guidelines:\n' + this.currentPromptAdditions.map((s, i) => `${i + 1}. ${s}`).join('\n')
+            : '';
+        const signature = {
+            input: 'product_name: string, category: string, price: number',
+            output: 'description: string, key_features: string[]',
+            description: 'Generate compelling product descriptions' + enhancedInstructions
+        };
+        const generator = new dspy_ts_1.ChainOfThought(signature, { lm: this.lm });
+        return await generator.forward(input);
+    }
+    // Main learning loop
+    async learn(input, criteria = {}) {
+        console.log('🧠 Starting Self-Learning Session\n');
+        console.log('='.repeat(70));
+        console.log(`\nTarget Quality: ${(this.config.targetQualityThreshold * 100).toFixed(0)}%`);
+        console.log(`Max Iterations: ${this.config.maxIterations}`);
+        console.log(`Input: ${JSON.stringify(input, null, 2)}\n`);
+        console.log('='.repeat(70) + '\n');
+        let iteration = 0;
+        let previousQuality = 0;
+        while (iteration < this.config.maxIterations) {
+            iteration++;
+            console.log(`\n📊 Iteration ${iteration}/${this.config.maxIterations}`);
+            console.log('─'.repeat(70));
+            // Generate output
+            const startTime = Date.now();
+            const output = await this.generate(input);
+            const duration = Date.now() - startTime;
+            // Evaluate
+            const feedback = this.evaluateOutput(output, criteria);
+            // Store in history
+            this.history.push({
+                iteration,
+                quality: feedback.quality,
+                output,
+                feedback,
+                promptModifications: [...this.currentPromptAdditions],
+                timestamp: new Date()
+            });
+            // Display results
+            console.log(`\n⏱️  Generation time: ${duration}ms`);
+            console.log(`\n📝 Output:`);
+            console.log(`   Description: ${output.description || 'N/A'}`);
+            if (output.key_features) {
+                console.log(`   Features:`);
+                output.key_features.forEach((f) => console.log(`     • ${f}`));
+            }
+            console.log(`\n📈 Quality: ${(feedback.quality * 100).toFixed(1)}%`);
+            if (feedback.strengths.length > 0) {
+                console.log(`\n✅ Strengths:`);
+                feedback.strengths.forEach(s => console.log(`   • ${s}`));
+            }
+            if (feedback.weaknesses.length > 0) {
+                console.log(`\n⚠️  Weaknesses:`);
+                feedback.weaknesses.forEach(w => console.log(`   • ${w}`));
+            }
+            // Check if target reached
+            if (feedback.quality >= this.config.targetQualityThreshold) {
+                console.log(`\n🎯 Target quality reached!`);
+                break;
+            }
+            // Check for improvement
+            const improvement = feedback.quality - previousQuality;
+            if (iteration > 1 && improvement < this.config.minImprovement) {
+                console.log(`\n⚠️  Improvement too small (${(improvement * 100).toFixed(1)}%), stopping...`);
+                break;
+            }
+            // Adapt for next iteration
+            const modifications = this.adaptPrompt(feedback);
+            if (modifications.length > 0) {
+                console.log(`\n🔧 Adapting strategy:`);
+                modifications.forEach(m => console.log(`   • ${m}`));
+                // Add new modifications
+                modifications.forEach(m => {
+                    if (!this.currentPromptAdditions.includes(m)) {
+                        this.currentPromptAdditions.push(m);
+                    }
+                });
+            }
+            previousQuality = feedback.quality;
+            // Brief pause between iterations
+            await new Promise(resolve => setTimeout(resolve, 1000));
+        }
+        // Final summary
+        this.displaySummary();
+    }
+    // Display learning summary
+    displaySummary() {
+        console.log('\n\n' + '='.repeat(70));
+        console.log('\n🎓 LEARNING SUMMARY\n');
+        if (this.history.length === 0) {
+            console.log('No learning history available.\n');
+            return;
+        }
+        const firstQuality = this.history[0].quality;
+        const lastQuality = this.history[this.history.length - 1].quality;
+        const improvement = lastQuality - firstQuality;
+        const improvementPercent = (improvement / firstQuality) * 100;
+        console.log(`Total Iterations: ${this.history.length}`);
+        console.log(`Starting Quality: ${(firstQuality * 100).toFixed(1)}%`);
+        console.log(`Final Quality: ${(lastQuality * 100).toFixed(1)}%`);
+        console.log(`Improvement: ${improvement >= 0 ? '+' : ''}${(improvement * 100).toFixed(1)}% (${improvementPercent >= 0 ? '+' : ''}${improvementPercent.toFixed(1)}%)`);
+        console.log(`\n📊 Quality Progression:`);
+        this.history.forEach(entry => {
+            const bar = '█'.repeat(Math.floor(entry.quality * 50));
+            const percent = (entry.quality * 100).toFixed(1);
+            console.log(`   Iteration ${entry.iteration}: ${bar} ${percent}%`);
+        });
+        console.log(`\n🔧 Learned Improvements (${this.currentPromptAdditions.length}):`);
+        this.currentPromptAdditions.forEach((mod, i) => {
+            console.log(`   ${i + 1}. ${mod}`);
+        });
+        console.log('\n💡 Key Insights:');
+        if (improvement > 0) {
+            console.log(`   ✓ System successfully learned and improved`);
+            console.log(`   ✓ Quality increased by ${(improvement * 100).toFixed(1)}%`);
+        }
+        console.log(`   ✓ Discovered ${this.currentPromptAdditions.length} optimization strategies`);
+        console.log(`   ✓ These improvements can be applied to future generations\n`);
+        console.log('='.repeat(70) + '\n');
+    }
+    // Get the learned prompt modifications
+    getLearnedImprovements() {
+        return [...this.currentPromptAdditions];
+    }
+    // Get learning history
+    getHistory() {
+        return [...this.history];
+    }
+}
+exports.SelfLearningGenerator = SelfLearningGenerator;
+// Main execution
+async function runSelfLearning() {
+    const generator = new SelfLearningGenerator({
+        targetQualityThreshold: 0.85,
+        maxIterations: 8,
+        improvementRate: 0.15,
+        minImprovement: 0.03
+    });
+    const testProduct = {
+        product_name: 'Professional DSLR Camera',
+        category: 'Photography',
+        price: 1299
+    };
+    await generator.learn(testProduct);
+    // Save learned improvements
+    const improvements = generator.getLearnedImprovements();
+    console.log('📝 Learned improvements can be reused:\n');
+    console.log(JSON.stringify(improvements, null, 2) + '\n');
+}
+// Run the example
+if (import.meta.url === `file://${process.argv[1]}`) {
+    runSelfLearning().catch(error => {
+        console.error('❌ Learning failed:', error);
+        process.exit(1);
+    });
+}
+//# sourceMappingURL=self-learning-system.js.map

vendor/ruvector/npm/packages/agentic-synth-examples/examples/intermediate/self-learning-system.ts

@@ -0,0 +1,370 @@
+/**
+ * INTERMEDIATE TUTORIAL: Self-Learning System
+ *
+ * Build an adaptive AI system that improves its output quality over time
+ * through feedback loops and pattern recognition. This demonstrates how
+ * to create systems that learn from their mistakes and successes.
+ *
+ * What you'll learn:
+ * - Building feedback loops
+ * - Tracking quality improvements
+ * - Adaptive prompt engineering
+ * - Learning from examples
+ *
+ * Prerequisites:
+ * - Set GEMINI_API_KEY environment variable
+ * - npm install dspy.ts @ruvector/agentic-synth
+ *
+ * Run: npx tsx examples/intermediate/self-learning-system.ts
+ */
+
+import { LM, ChainOfThought, Prediction } from 'dspy.ts';
+
+// Learning session configuration
+interface LearningConfig {
+  targetQualityThreshold: number; // Stop when this quality is reached
+  maxIterations: number; // Maximum learning iterations
+  improvementRate: number; // How aggressively to adjust (0.1 = 10% per iteration)
+  minImprovement: number; // Minimum improvement to continue
+}
+
+// Feedback from each iteration
+interface Feedback {
+  quality: number;
+  strengths: string[];
+  weaknesses: string[];
+  suggestions: string[];
+}
+
+// Learning history entry
+interface LearningEntry {
+  iteration: number;
+  quality: number;
+  output: Prediction;
+  feedback: Feedback;
+  promptModifications: string[];
+  timestamp: Date;
+}
+
+// Self-learning generator class
+class SelfLearningGenerator {
+  private lm: LM;
+  private history: LearningEntry[] = [];
+  private config: LearningConfig;
+  private basePrompt: string;
+  private currentPromptAdditions: string[] = [];
+
+  constructor(config: Partial<LearningConfig> = {}) {
+    this.config = {
+      targetQualityThreshold: config.targetQualityThreshold || 0.9,
+      maxIterations: config.maxIterations || 10,
+      improvementRate: config.improvementRate || 0.15,
+      minImprovement: config.minImprovement || 0.02
+    };
+
+    this.lm = new LM({
+      provider: 'google-genai',
+      model: 'gemini-2.0-flash-exp',
+      apiKey: process.env.GEMINI_API_KEY || '',
+      temperature: 0.8 // Higher temperature for creativity during learning
+    });
+
+    this.basePrompt = '';
+  }
+
+  // Evaluate the quality of generated output
+  private evaluateOutput(prediction: Prediction, criteria: any): Feedback {
+    let quality = 0;
+    const strengths: string[] = [];
+    const weaknesses: string[] = [];
+    const suggestions: string[] = [];
+
+    // Check description quality
+    if (prediction.description) {
+      const desc = prediction.description;
+      const length = desc.length;
+
+      if (length >= 100 && length <= 200) {
+        quality += 0.3;
+        strengths.push('Description length is optimal');
+      } else if (length < 50) {
+        weaknesses.push('Description too short');
+        suggestions.push('Expand description with more details');
+      } else if (length > 250) {
+        weaknesses.push('Description too verbose');
+        suggestions.push('Make description more concise');
+      } else {
+        quality += 0.15;
+      }
+
+      // Check for emotional/engaging language
+      const emotionalWords = ['amazing', 'powerful', 'innovative', 'premium', 'exceptional'];
+      const hasEmotionalLanguage = emotionalWords.some(word =>
+        desc.toLowerCase().includes(word)
+      );
+
+      if (hasEmotionalLanguage) {
+        quality += 0.2;
+        strengths.push('Uses engaging language');
+      } else {
+        weaknesses.push('Could be more engaging');
+        suggestions.push('Add more descriptive and emotional words');
+      }
+    } else {
+      weaknesses.push('Missing description');
+      suggestions.push('Generate a complete description');
+    }
+
+    // Check features
+    if (prediction.key_features && Array.isArray(prediction.key_features)) {
+      const features = prediction.key_features;
+
+      if (features.length >= 4 && features.length <= 6) {
+        quality += 0.3;
+        strengths.push('Optimal number of features');
+      } else if (features.length < 3) {
+        weaknesses.push('Too few features');
+        suggestions.push('Include at least 4 key features');
+      } else {
+        quality += 0.15;
+      }
+
+      // Check feature quality (should be concise)
+      const wellFormedFeatures = features.filter(f =>
+        f.length >= 10 && f.length <= 50
+      );
+
+      if (wellFormedFeatures.length === features.length) {
+        quality += 0.2;
+        strengths.push('All features are well-formed');
+      } else {
+        weaknesses.push('Some features need better formatting');
+        suggestions.push('Keep features concise (10-50 chars)');
+      }
+    } else {
+      weaknesses.push('Missing features');
+      suggestions.push('Generate key features list');
+    }
+
+    return { quality, strengths, weaknesses, suggestions };
+  }
+
+  // Adapt prompt based on feedback
+  private adaptPrompt(feedback: Feedback): string[] {
+    const modifications: string[] = [];
+
+    // Add specific instructions based on weaknesses
+    feedback.suggestions.forEach(suggestion => {
+      if (suggestion.includes('short')) {
+        modifications.push('Write detailed descriptions (100-200 characters)');
+      } else if (suggestion.includes('verbose')) {
+        modifications.push('Keep descriptions concise and focused');
+      } else if (suggestion.includes('engaging')) {
+        modifications.push('Use descriptive, engaging language');
+      } else if (suggestion.includes('features')) {
+        modifications.push('Include 4-6 specific, measurable key features');
+      } else if (suggestion.includes('concise')) {
+        modifications.push('Format features as short, punchy statements');
+      }
+    });
+
+    // Remove duplicates
+    return [...new Set(modifications)];
+  }
+
+  // Generate with current prompt
+  private async generate(input: any): Promise<Prediction> {
+    // Build enhanced signature with learned improvements
+    const enhancedInstructions = this.currentPromptAdditions.length > 0
+      ? '\n\nImportant guidelines:\n' + this.currentPromptAdditions.map((s, i) => `${i + 1}. ${s}`).join('\n')
+      : '';
+
+    const signature = {
+      input: 'product_name: string, category: string, price: number',
+      output: 'description: string, key_features: string[]',
+      description: 'Generate compelling product descriptions' + enhancedInstructions
+    };
+
+    const generator = new ChainOfThought(signature, { lm: this.lm });
+    return await generator.forward(input);
+  }
+
+  // Main learning loop
+  async learn(input: any, criteria: any = {}): Promise<void> {
+    console.log('🧠 Starting Self-Learning Session\n');
+    console.log('=' .repeat(70));
+    console.log(`\nTarget Quality: ${(this.config.targetQualityThreshold * 100).toFixed(0)}%`);
+    console.log(`Max Iterations: ${this.config.maxIterations}`);
+    console.log(`Input: ${JSON.stringify(input, null, 2)}\n`);
+    console.log('=' .repeat(70) + '\n');
+
+    let iteration = 0;
+    let previousQuality = 0;
+
+    while (iteration < this.config.maxIterations) {
+      iteration++;
+      console.log(`\n📊 Iteration ${iteration}/${this.config.maxIterations}`);
+      console.log('─'.repeat(70));
+
+      // Generate output
+      const startTime = Date.now();
+      const output = await this.generate(input);
+      const duration = Date.now() - startTime;
+
+      // Evaluate
+      const feedback = this.evaluateOutput(output, criteria);
+
+      // Store in history
+      this.history.push({
+        iteration,
+        quality: feedback.quality,
+        output,
+        feedback,
+        promptModifications: [...this.currentPromptAdditions],
+        timestamp: new Date()
+      });
+
+      // Display results
+      console.log(`\n⏱️  Generation time: ${duration}ms`);
+      console.log(`\n📝 Output:`);
+      console.log(`   Description: ${output.description || 'N/A'}`);
+      if (output.key_features) {
+        console.log(`   Features:`);
+        output.key_features.forEach((f: string) => console.log(`     • ${f}`));
+      }
+
+      console.log(`\n📈 Quality: ${(feedback.quality * 100).toFixed(1)}%`);
+
+      if (feedback.strengths.length > 0) {
+        console.log(`\n✅ Strengths:`);
+        feedback.strengths.forEach(s => console.log(`   • ${s}`));
+      }
+
+      if (feedback.weaknesses.length > 0) {
+        console.log(`\n⚠️  Weaknesses:`);
+        feedback.weaknesses.forEach(w => console.log(`   • ${w}`));
+      }
+
+      // Check if target reached
+      if (feedback.quality >= this.config.targetQualityThreshold) {
+        console.log(`\n🎯 Target quality reached!`);
+        break;
+      }
+
+      // Check for improvement
+      const improvement = feedback.quality - previousQuality;
+      if (iteration > 1 && improvement < this.config.minImprovement) {
+        console.log(`\n⚠️  Improvement too small (${(improvement * 100).toFixed(1)}%), stopping...`);
+        break;
+      }
+
+      // Adapt for next iteration
+      const modifications = this.adaptPrompt(feedback);
+      if (modifications.length > 0) {
+        console.log(`\n🔧 Adapting strategy:`);
+        modifications.forEach(m => console.log(`   • ${m}`));
+
+        // Add new modifications
+        modifications.forEach(m => {
+          if (!this.currentPromptAdditions.includes(m)) {
+            this.currentPromptAdditions.push(m);
+          }
+        });
+      }
+
+      previousQuality = feedback.quality;
+
+      // Brief pause between iterations
+      await new Promise(resolve => setTimeout(resolve, 1000));
+    }
+
+    // Final summary
+    this.displaySummary();
+  }
+
+  // Display learning summary
+  private displaySummary(): void {
+    console.log('\n\n' + '=' .repeat(70));
+    console.log('\n🎓 LEARNING SUMMARY\n');
+
+    if (this.history.length === 0) {
+      console.log('No learning history available.\n');
+      return;
+    }
+
+    const firstQuality = this.history[0].quality;
+    const lastQuality = this.history[this.history.length - 1].quality;
+    const improvement = lastQuality - firstQuality;
+    const improvementPercent = (improvement / firstQuality) * 100;
+
+    console.log(`Total Iterations: ${this.history.length}`);
+    console.log(`Starting Quality: ${(firstQuality * 100).toFixed(1)}%`);
+    console.log(`Final Quality: ${(lastQuality * 100).toFixed(1)}%`);
+    console.log(`Improvement: ${improvement >= 0 ? '+' : ''}${(improvement * 100).toFixed(1)}% (${improvementPercent >= 0 ? '+' : ''}${improvementPercent.toFixed(1)}%)`);
+
+    console.log(`\n📊 Quality Progression:`);
+    this.history.forEach(entry => {
+      const bar = '█'.repeat(Math.floor(entry.quality * 50));
+      const percent = (entry.quality * 100).toFixed(1);
+      console.log(`   Iteration ${entry.iteration}: ${bar} ${percent}%`);
+    });
+
+    console.log(`\n🔧 Learned Improvements (${this.currentPromptAdditions.length}):`);
+    this.currentPromptAdditions.forEach((mod, i) => {
+      console.log(`   ${i + 1}. ${mod}`);
+    });
+
+    console.log('\n💡 Key Insights:');
+    if (improvement > 0) {
+      console.log(`   ✓ System successfully learned and improved`);
+      console.log(`   ✓ Quality increased by ${(improvement * 100).toFixed(1)}%`);
+    }
+    console.log(`   ✓ Discovered ${this.currentPromptAdditions.length} optimization strategies`);
+    console.log(`   ✓ These improvements can be applied to future generations\n`);
+
+    console.log('=' .repeat(70) + '\n');
+  }
+
+  // Get the learned prompt modifications
+  getLearnedImprovements(): string[] {
+    return [...this.currentPromptAdditions];
+  }
+
+  // Get learning history
+  getHistory(): LearningEntry[] {
+    return [...this.history];
+  }
+}
+
+// Main execution
+async function runSelfLearning() {
+  const generator = new SelfLearningGenerator({
+    targetQualityThreshold: 0.85,
+    maxIterations: 8,
+    improvementRate: 0.15,
+    minImprovement: 0.03
+  });
+
+  const testProduct = {
+    product_name: 'Professional DSLR Camera',
+    category: 'Photography',
+    price: 1299
+  };
+
+  await generator.learn(testProduct);
+
+  // Save learned improvements
+  const improvements = generator.getLearnedImprovements();
+  console.log('📝 Learned improvements can be reused:\n');
+  console.log(JSON.stringify(improvements, null, 2) + '\n');
+}
+
+// Run the example
+if (import.meta.url === `file://${process.argv[1]}`) {
+  runSelfLearning().catch(error => {
+    console.error('❌ Learning failed:', error);
+    process.exit(1);
+  });
+}
+
+export { SelfLearningGenerator, LearningConfig, LearningEntry };