wifi-densepose/npm/packages/agentic-synth-examples/examples/intermediate/self-learning-system.js

"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);
    });
}
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