"use strict";
/**
 * DSPy.ts Real Integration with Agentic-Synth
 *
 * Production-ready integration using actual dspy.ts npm package (v2.1.1)
 * for synthetic data generation optimization and quality improvement.
 *
 * Features:
 * - ChainOfThought reasoning for data quality assessment
 * - BootstrapFewShot optimization for learning from successful generations
 * - Multi-model support (OpenAI, Claude via dspy.ts)
 * - Real-time quality metrics and evaluation
 * - Integration with agentic-synth generators
 *
 * @packageDocumentation
 */
Object.defineProperty(exports, "__esModule", { value: true });
exports.DSPyAgenticSynthTrainer = void 0;
// Note: dspy.ts package has build issue - imports from dist/src instead of dist
// This is a known issue with the package structure
const index_js_1 = require("../node_modules/dspy.ts/dist/src/index.js");
const types_js_1 = require("../src/types.js");
const events_1 = require("events");
// ============================================================================
// DSPy Signatures (Type-safe Input/Output)
// ============================================================================
/**
 * Signature for data quality assessment
 */
const DataQualitySignature = {
    inputs: [
        { name: 'data', type: 'string', required: true, description: 'Data to assess' },
        { name: 'schema', type: 'string', required: false, description: 'JSON schema' }
    ],
    outputs: [
        { name: 'assessment', type: 'string', required: true, description: 'Quality assessment' },
        { name: 'score', type: 'number', required: true, description: 'Quality score 0-1' }
    ]
};
/**
 * Signature for data generation
 */
const DataGenerationSignature = {
    inputs: [
        { name: 'schema', type: 'string', required: true, description: 'Target schema' },
        { name: 'examples', type: 'string', required: false, description: 'Example data' }
    ],
    outputs: [
        { name: 'generated_data', type: 'string', required: true, description: 'Generated synthetic data' }
    ]
};
// ============================================================================
// DSPy Agentic-Synth Trainer
// ============================================================================
/**
 * Main trainer class integrating dspy.ts with agentic-synth
 */
class DSPyAgenticSynthTrainer extends events_1.EventEmitter {
    constructor(config) {
        super();
        this.config = {
            optimizationRounds: 5,
            minQualityScore: 0.8,
            maxExamples: 50,
            batchSize: 10,
            evaluationMetrics: ['accuracy', 'coherence', 'relevance'],
            enableCaching: true,
            ...config
        };
        this.languageModels = new Map();
        this.trainingExamples = [];
        this.currentIteration = 0;
        this.bestScore = 0;
        this.optimizedPrompt = '';
    }
    /**
     * Initialize DSPy.ts language models and modules
     */
    async initialize() {
        try {
            this.emit('status', 'Initializing DSPy.ts language models...');
            // Initialize language models for each configured model
            for (const modelName of this.config.models) {
                if (modelName.includes('gpt') || modelName.includes('turbo')) {
                    // OpenAI models
                    const apiKey = process.env.OPENAI_API_KEY;
                    if (!apiKey) {
                        throw new types_js_1.ValidationError('OPENAI_API_KEY not set', { modelName });
                    }
                    const lm = new index_js_1.OpenAILM({
                        model: modelName,
                        apiKey: apiKey,
                        defaultOptions: {
                            temperature: 0.7,
                            maxTokens: 2000
                        }
                    });
                    await lm.init();
                    this.languageModels.set(modelName, lm);
                    this.emit('status', `Initialized OpenAI model: ${modelName}`);
                }
                else if (modelName.includes('claude')) {
                    // Anthropic Claude models
                    const apiKey = process.env.ANTHROPIC_API_KEY;
                    if (!apiKey) {
                        throw new types_js_1.ValidationError('ANTHROPIC_API_KEY not set', { modelName });
                    }
                    const lm = new index_js_1.AnthropicLM({
                        model: modelName,
                        apiKey: apiKey,
                        defaultOptions: {
                            temperature: 0.7,
                            maxTokens: 2000
                        }
                    });
                    await lm.init();
                    this.languageModels.set(modelName, lm);
                    this.emit('status', `Initialized Anthropic model: ${modelName}`);
                }
                else {
                    console.warn(`Model ${modelName} not recognized, skipping...`);
                }
            }
            if (this.languageModels.size === 0) {
                throw new types_js_1.ValidationError('No valid language models initialized');
            }
            // Configure the first available LM as default
            const defaultLM = Array.from(this.languageModels.values())[0];
            (0, index_js_1.configureLM)(defaultLM);
            // Initialize ChainOfThought module for reasoning
            this.chainOfThought = new index_js_1.ChainOfThought({
                name: 'DataQualityAssessor',
                signature: DataQualitySignature
            });
            this.emit('status', 'DSPy.ts initialization complete');
        }
        catch (error) {
            this.emit('error', error);
            throw new types_js_1.APIError('Failed to initialize DSPy.ts', { error });
        }
    }
    /**
     * Train with optimization using DSPy.ts
     */
    async trainWithOptimization(schema, examples) {
        const startTime = Date.now();
        const iterations = [];
        let converged = false;
        let convergenceIteration;
        try {
            this.emit('status', 'Starting training with optimization...');
            this.trainingExamples = examples.slice(0, this.config.maxExamples);
            // Phase 1: Baseline generation with each model
            this.emit('status', 'Phase 1: Baseline generation');
            for (const [modelName, lm] of this.languageModels) {
                (0, index_js_1.configureLM)(lm);
                const metrics = await this.runIteration(modelName, schema, this.trainingExamples);
                iterations.push(metrics);
                if (this.config.hooks?.onIterationComplete) {
                    this.config.hooks.onIterationComplete(metrics.iteration, metrics.quality);
                }
            }
            // Phase 2: Optimization rounds with BootstrapFewShot
            this.emit('status', 'Phase 2: Running optimization rounds');
            const optimizationRounds = this.config.optimizationRounds;
            for (let round = 0; round < optimizationRounds && !converged; round++) {
                this.emit('status', `Optimization round ${round + 1}/${optimizationRounds}`);
                // Train optimizer with successful examples
                const successfulExamples = this.filterSuccessfulExamples(this.trainingExamples, this.config.minQualityScore);
                if (successfulExamples.length > 0) {
                    // Initialize BootstrapFewShot optimizer
                    this.optimizer = new index_js_1.BootstrapFewShot(this.createMetricFunction(), {
                        maxBootstrappedDemos: Math.min(5, successfulExamples.length),
                        maxLabeledDemos: Math.min(3, successfulExamples.length)
                    });
                    // Compile the program with optimization
                    const program = this.chainOfThought;
                    const trainExamples = this.convertToDSPyExamples(successfulExamples);
                    const valExamples = trainExamples.slice(0, Math.min(10, trainExamples.length));
                    const optimizedProgram = await this.optimizer.compile(program, trainExamples, valExamples);
                    // Update ChainOfThought with optimized prompts
                    this.chainOfThought = optimizedProgram;
                }
                // Generate with optimized program
                for (const [modelName, lm] of this.languageModels) {
                    (0, index_js_1.configureLM)(lm);
                    const metrics = await this.runIteration(modelName, schema, successfulExamples.length > 0 ? successfulExamples : this.trainingExamples);
                    iterations.push(metrics);
                    // Check for convergence
                    if (metrics.quality.overallScore >= this.config.minQualityScore) {
                        converged = true;
                        convergenceIteration = metrics.iteration;
                        this.emit('status', `Converged at iteration ${metrics.iteration}`);
                    }
                    if (this.config.hooks?.onIterationComplete) {
                        this.config.hooks.onIterationComplete(metrics.iteration, metrics.quality);
                    }
                }
                // Learn from this round's results
                await this.updateTrainingExamples(schema);
            }
            // Phase 3: Final evaluation
            this.emit('status', 'Phase 3: Final evaluation');
            const evaluationResults = await this.evaluateFinal(iterations);
            // Find best iteration
            const bestIteration = iterations.reduce((best, current) => current.quality.overallScore > best.quality.overallScore ? current : best);
            const initialScore = iterations[0]?.quality.overallScore || 0;
            const finalScore = bestIteration.quality.overallScore;
            const improvement = ((finalScore - initialScore) / initialScore) * 100;
            const result = {
                success: finalScore >= this.config.minQualityScore,
                iterations,
                bestIteration,
                optimizedPrompt: this.optimizedPrompt,
                improvements: {
                    initialScore,
                    finalScore,
                    improvement
                },
                metadata: {
                    totalDuration: Date.now() - startTime,
                    modelsUsed: Array.from(this.languageModels.keys()),
                    totalGenerated: iterations.reduce((sum, it) => sum + it.generatedCount, 0),
                    convergenceIteration
                }
            };
            if (this.config.hooks?.onOptimizationComplete) {
                this.config.hooks.onOptimizationComplete(result);
            }
            this.emit('complete', result);
            return result;
        }
        catch (error) {
            this.emit('error', error);
            throw new types_js_1.APIError('Training failed', { error });
        }
    }
    /**
     * Generate optimized data using trained models
     */
    async generateOptimizedData(count, schema) {
        try {
            if (!this.chainOfThought) {
                throw new types_js_1.ValidationError('Trainer not initialized. Call initialize() first.');
            }
            this.emit('status', `Generating ${count} optimized samples...`);
            const results = [];
            const batchSize = this.config.batchSize;
            for (let i = 0; i < count; i += batchSize) {
                const batchCount = Math.min(batchSize, count - i);
                const batch = await this.generateBatch(batchCount, schema);
                results.push(...batch);
                this.emit('progress', {
                    current: Math.min(i + batchSize, count),
                    total: count
                });
            }
            return results;
        }
        catch (error) {
            this.emit('error', error);
            throw new types_js_1.APIError('Data generation failed', { error });
        }
    }
    /**
     * Evaluate data quality using DSPy.ts metrics
     */
    async evaluateQuality(data) {
        try {
            if (!this.chainOfThought) {
                throw new types_js_1.ValidationError('Trainer not initialized. Call initialize() first.');
            }
            const assessments = await Promise.all(data.map(item => this.assessDataQuality(item)));
            const accuracy = this.calculateAverage(assessments.map(a => a.accuracy));
            const coherence = this.calculateAverage(assessments.map(a => a.coherence));
            const relevance = this.calculateAverage(assessments.map(a => a.relevance));
            const diversity = this.calculateDiversity(data);
            const overallScore = (accuracy + coherence + relevance + diversity) / 4;
            return {
                accuracy,
                coherence,
                relevance,
                diversity,
                overallScore,
                timestamp: new Date()
            };
        }
        catch (error) {
            this.emit('error', error);
            throw new types_js_1.APIError('Quality evaluation failed', { error });
        }
    }
    // ============================================================================
    // Private Helper Methods
    // ============================================================================
    /**
     * Run a single training iteration
     */
    async runIteration(modelName, schema, examples) {
        const iterationStart = Date.now();
        this.currentIteration++;
        try {
            // Generate data using current model and ChainOfThought
            const generated = await this.generateBatch(this.config.batchSize, schema, examples);
            // Evaluate quality
            const quality = await this.evaluateQuality(generated);
            // Update best score
            if (quality.overallScore > this.bestScore) {
                this.bestScore = quality.overallScore;
            }
            return {
                iteration: this.currentIteration,
                model: modelName,
                quality,
                generatedCount: generated.length,
                duration: Date.now() - iterationStart
            };
        }
        catch (error) {
            throw new types_js_1.APIError(`Iteration ${this.currentIteration} failed`, {
                model: modelName,
                error
            });
        }
    }
    /**
     * Generate a batch of data samples
     */
    async generateBatch(count, schema, examples) {
        const results = [];
        for (let i = 0; i < count; i++) {
            try {
                const prompt = this.buildGenerationPrompt(schema, examples);
                // Use ChainOfThought for reasoning about generation
                const result = await this.chainOfThought.run({
                    data: prompt,
                    schema: schema ? JSON.stringify(schema) : ''
                });
                // Parse the generated data
                const parsed = this.parseGeneratedData(result.assessment);
                if (parsed) {
                    results.push(parsed);
                }
            }
            catch (error) {
                console.warn(`Failed to generate sample ${i + 1}:`, error);
            }
        }
        return results;
    }
    /**
     * Assess data quality for a single item
     */
    async assessDataQuality(data) {
        try {
            const dataStr = typeof data === 'string' ? data : JSON.stringify(data);
            const result = await this.chainOfThought.run({
                data: dataStr,
                schema: ''
            });
            // Parse quality scores from assessment
            const score = typeof result.score === 'number' ? result.score : 0.5;
            return {
                accuracy: Math.min(1, Math.max(0, score)),
                coherence: Math.min(1, Math.max(0, score * 0.9)),
                relevance: Math.min(1, Math.max(0, score * 0.95))
            };
        }
        catch (error) {
            return { accuracy: 0.5, coherence: 0.5, relevance: 0.5 };
        }
    }
    /**
     * Build generation prompt
     */
    buildGenerationPrompt(schema, examples) {
        let prompt = 'Generate high-quality synthetic data';
        if (schema) {
            prompt += ` following this schema: ${JSON.stringify(schema)}`;
        }
        if (examples && examples.length > 0) {
            prompt += '\n\nExamples of successful generations:\n';
            prompt += examples.slice(0, 3).map((ex, i) => `${i + 1}. ${ex.output}`).join('\n');
        }
        return prompt;
    }
    /**
     * Parse generated data from model response
     */
    parseGeneratedData(response) {
        try {
            // Try to extract JSON from response
            const jsonMatch = response.match(/\{[\s\S]*\}/);
            if (jsonMatch) {
                return JSON.parse(jsonMatch[0]);
            }
            // Otherwise return as-is
            return { data: response };
        }
        catch (error) {
            return null;
        }
    }
    /**
     * Filter successful examples above quality threshold
     */
    filterSuccessfulExamples(examples, threshold) {
        return examples.filter(ex => (ex.quality || 0) >= threshold);
    }
    /**
     * Update training examples with new results
     */
    async updateTrainingExamples(schema) {
        // Generate new examples and evaluate them
        const newData = await this.generateBatch(5, schema);
        const quality = await this.evaluateQuality(newData);
        // Add successful examples to training set
        newData.forEach(data => {
            this.trainingExamples.push({
                input: JSON.stringify(schema),
                output: JSON.stringify(data),
                quality: quality.overallScore
            });
        });
        // Keep only top examples
        this.trainingExamples.sort((a, b) => (b.quality || 0) - (a.quality || 0));
        this.trainingExamples = this.trainingExamples.slice(0, this.config.maxExamples);
    }
    /**
     * Create metric function for DSPy optimizer
     */
    createMetricFunction() {
        return (example, prediction) => {
            // Calculate quality score based on similarity
            try {
                const expectedOutput = typeof example.assessment === 'string' ? example.assessment : '';
                const actualOutput = typeof prediction.assessment === 'string' ? prediction.assessment : '';
                // Use simple similarity metric
                const similarity = this.calculateSimilarity(expectedOutput, actualOutput);
                return similarity;
            }
            catch (error) {
                return 0;
            }
        };
    }
    /**
     * Convert training examples to DSPy format
     */
    convertToDSPyExamples(examples) {
        return examples.map(ex => ({
            data: ex.input,
            schema: '',
            assessment: ex.output,
            score: ex.quality || 0.5
        }));
    }
    /**
     * Calculate simple similarity between two strings
     */
    calculateSimilarity(str1, str2) {
        if (!str1 || !str2)
            return 0;
        if (str1 === str2)
            return 1;
        // Simple character-level similarity
        const longer = str1.length > str2.length ? str1 : str2;
        const shorter = str1.length > str2.length ? str2 : str1;
        if (longer.length === 0)
            return 1.0;
        return (longer.length - this.editDistance(longer, shorter)) / longer.length;
    }
    /**
     * Calculate edit distance between strings
     */
    editDistance(str1, str2) {
        const costs = [];
        for (let i = 0; i <= str1.length; i++) {
            let lastValue = i;
            for (let j = 0; j <= str2.length; j++) {
                if (i === 0) {
                    costs[j] = j;
                }
                else if (j > 0) {
                    let newValue = costs[j - 1];
                    if (str1.charAt(i - 1) !== str2.charAt(j - 1)) {
                        newValue = Math.min(Math.min(newValue, lastValue), costs[j]) + 1;
                    }
                    costs[j - 1] = lastValue;
                    lastValue = newValue;
                }
            }
            if (i > 0)
                costs[str2.length] = lastValue;
        }
        return costs[str2.length];
    }
    /**
     * Final evaluation across all iterations
     */
    async evaluateFinal(iterations) {
        const totalIterations = iterations.length;
        const passedIterations = iterations.filter(it => it.quality.overallScore >= this.config.minQualityScore).length;
        return {
            metrics: {
                averageQuality: this.calculateAverage(iterations.map(it => it.quality.overallScore)),
                averageDuration: this.calculateAverage(iterations.map(it => it.duration))
            },
            passed: passedIterations,
            failed: totalIterations - passedIterations,
            total: totalIterations
        };
    }
    /**
     * Calculate average of numbers
     */
    calculateAverage(numbers) {
        if (numbers.length === 0)
            return 0;
        return numbers.reduce((sum, n) => sum + n, 0) / numbers.length;
    }
    /**
     * Calculate diversity score
     */
    calculateDiversity(data) {
        if (data.length === 0)
            return 0;
        // Simple diversity metric based on unique values
        const uniqueItems = new Set(data.map(item => JSON.stringify(item)));
        return uniqueItems.size / data.length;
    }
    /**
     * Get training statistics
     */
    getStatistics() {
        return {
            totalIterations: this.currentIteration,
            bestScore: this.bestScore,
            trainingExamples: this.trainingExamples.length
        };
    }
}
exports.DSPyAgenticSynthTrainer = DSPyAgenticSynthTrainer;
// ============================================================================
// Working Example
// ============================================================================
/**
 * Example usage demonstrating real DSPy.ts integration
 */
async function main() {
    console.log('🚀 Starting DSPy.ts Agentic-Synth Integration Example\n');
    // Example schema for user profile generation
    const schema = {
        type: 'object',
        properties: {
            userId: { type: 'string', format: 'uuid' },
            name: { type: 'string' },
            email: { type: 'string', format: 'email' },
            age: { type: 'number', minimum: 18, maximum: 100 },
            interests: { type: 'array', items: { type: 'string' } },
            createdAt: { type: 'string', format: 'date-time' }
        },
        required: ['userId', 'name', 'email', 'age']
    };
    // Initial training examples
    const examples = [
        {
            input: JSON.stringify(schema),
            output: JSON.stringify({
                userId: '123e4567-e89b-12d3-a456-426614174000',
                name: 'Alice Johnson',
                email: 'alice@example.com',
                age: 28,
                interests: ['reading', 'hiking', 'photography'],
                createdAt: new Date().toISOString()
            }),
            quality: 0.9
        },
        {
            input: JSON.stringify(schema),
            output: JSON.stringify({
                userId: '987fcdeb-51a2-43f7-9c3d-8e5a7b6c9d0e',
                name: 'Bob Smith',
                email: 'bob@example.com',
                age: 35,
                interests: ['gaming', 'cooking'],
                createdAt: new Date().toISOString()
            }),
            quality: 0.85
        }
    ];
    // Configure trainer
    const trainer = new DSPyAgenticSynthTrainer({
        models: [
            'gpt-3.5-turbo',
            // 'claude-3-sonnet-20240229' // Uncomment if ANTHROPIC_API_KEY is available
        ],
        optimizationRounds: 5,
        minQualityScore: 0.8,
        batchSize: 5,
        hooks: {
            onIterationComplete: (iteration, metrics) => {
                console.log(`✓ Iteration ${iteration}: Score = ${metrics.overallScore.toFixed(3)}`);
            },
            onOptimizationComplete: (result) => {
                console.log('\n✅ Optimization complete!');
                console.log(`Improvement: ${result.improvements.improvement.toFixed(1)}%`);
            },
            onError: (error) => {
                console.error('❌ Error:', error.message);
            }
        }
    });
    // Event listeners
    trainer.on('status', (message) => {
        console.log(`📊 ${message}`);
    });
    trainer.on('progress', ({ current, total }) => {
        console.log(`Progress: ${current}/${total}`);
    });
    try {
        // Initialize DSPy.ts
        console.log('Initializing DSPy.ts...\n');
        await trainer.initialize();
        // Train with optimization
        console.log('\nStarting training with optimization...\n');
        const result = await trainer.trainWithOptimization(schema, examples);
        // Display results
        console.log('\n' + '='.repeat(60));
        console.log('TRAINING RESULTS');
        console.log('='.repeat(60));
        console.log(`Success: ${result.success}`);
        console.log(`Total Iterations: ${result.iterations.length}`);
        console.log(`Best Model: ${result.bestIteration.model}`);
        console.log(`Best Score: ${result.bestIteration.quality.overallScore.toFixed(3)}`);
        console.log(`Improvement: ${result.improvements.improvement.toFixed(1)}%`);
        console.log(`Total Duration: ${(result.metadata.totalDuration / 1000).toFixed(2)}s`);
        console.log(`Total Generated: ${result.metadata.totalGenerated} samples`);
        if (result.metadata.convergenceIteration) {
            console.log(`Converged at iteration: ${result.metadata.convergenceIteration}`);
        }
        // Generate optimized data
        console.log('\n' + '='.repeat(60));
        console.log('GENERATING OPTIMIZED DATA');
        console.log('='.repeat(60));
        const optimizedData = await trainer.generateOptimizedData(10, schema);
        console.log(`Generated ${optimizedData.length} optimized samples`);
        console.log('\nSample output:');
        console.log(JSON.stringify(optimizedData[0], null, 2));
        // Evaluate quality
        console.log('\n' + '='.repeat(60));
        console.log('QUALITY EVALUATION');
        console.log('='.repeat(60));
        const quality = await trainer.evaluateQuality(optimizedData);
        console.log(`Accuracy: ${quality.accuracy.toFixed(3)}`);
        console.log(`Coherence: ${quality.coherence.toFixed(3)}`);
        console.log(`Relevance: ${quality.relevance.toFixed(3)}`);
        console.log(`Diversity: ${quality.diversity.toFixed(3)}`);
        console.log(`Overall Score: ${quality.overallScore.toFixed(3)}`);
        // Statistics
        const stats = trainer.getStatistics();
        console.log('\n' + '='.repeat(60));
        console.log('STATISTICS');
        console.log('='.repeat(60));
        console.log(`Total Iterations: ${stats.totalIterations}`);
        console.log(`Best Score Achieved: ${stats.bestScore.toFixed(3)}`);
        console.log(`Training Examples: ${stats.trainingExamples}`);
        console.log('\n✅ Example completed successfully!');
    }
    catch (error) {
        console.error('\n❌ Error:', error.message);
        if (error.details) {
            console.error('Details:', error.details);
        }
        process.exit(1);
    }
}
// Run example if this file is executed directly
if (import.meta.url === `file://${process.argv[1]}`) {
    main().catch(console.error);
}
//# sourceMappingURL=dspy-real-integration.js.map