wifi-densepose/npm/packages/agentic-synth/training/DSPY_INTEGRATION_README.md

DSPy.ts Real Integration with Agentic-Synth

Production-ready integration of dspy.ts v2.1.1 with agentic-synth for optimized synthetic data generation with automatic quality improvement.

Features

✅ Real dspy.ts Integration - Uses actual dspy.ts npm package (v2.1.1) ✅ ChainOfThought Reasoning - Advanced reasoning for data quality assessment ✅ BootstrapFewShot Optimization - Automatic learning from successful generations ✅ Multi-Model Support - OpenAI GPT models and Anthropic Claude ✅ Quality Metrics - Real-time evaluation using dspy.ts metrics ✅ Convergence Detection - Automatically stops when quality threshold is met ✅ Event-Driven Architecture - Hooks for monitoring and coordination ✅ Production Ready - Full TypeScript types and error handling

Architecture

DSPyAgenticSynthTrainer
├── Language Models (OpenAILM, AnthropicLM)
├── ChainOfThought Module (Quality reasoning)
├── BootstrapFewShot Optimizer (Learning)
└── Quality Evaluator (Metrics)

Installation

# Already installed in agentic-synth
cd packages/agentic-synth
npm install  # dspy.ts@2.1.1 is included

Environment Setup

# Required for OpenAI models
export OPENAI_API_KEY="sk-..."

# Optional for Claude models
export ANTHROPIC_API_KEY="sk-ant-..."

Usage

Basic Example

import { DSPyAgenticSynthTrainer } from './training/dspy-real-integration.js';

// Define your data schema
const schema = {
  type: 'object',
  properties: {
    userId: { type: 'string' },
    name: { type: 'string' },
    email: { type: 'string', format: 'email' },
    age: { type: 'number' }
  }
};

// Provide initial training examples
const examples = [
  {
    input: JSON.stringify(schema),
    output: JSON.stringify({
      userId: '123',
      name: 'Alice',
      email: 'alice@example.com',
      age: 28
    }),
    quality: 0.9
  }
];

// Configure trainer
const trainer = new DSPyAgenticSynthTrainer({
  models: ['gpt-3.5-turbo'],
  optimizationRounds: 5,
  minQualityScore: 0.8,
  batchSize: 10
});

// Initialize and train
await trainer.initialize();
const result = await trainer.trainWithOptimization(schema, examples);

// Generate optimized data
const data = await trainer.generateOptimizedData(100, schema);

Advanced Configuration

const trainer = new DSPyAgenticSynthTrainer({
  // Models to use for training
  models: [
    'gpt-3.5-turbo',
    'gpt-4',
    'claude-3-sonnet-20240229'
  ],

  // Training parameters
  optimizationRounds: 10,
  minQualityScore: 0.85,
  maxExamples: 100,
  batchSize: 20,

  // Evaluation metrics
  evaluationMetrics: ['accuracy', 'coherence', 'relevance', 'diversity'],

  // Performance options
  enableCaching: true,

  // Event hooks
  hooks: {
    onIterationComplete: (iteration, metrics) => {
      console.log(`Iteration ${iteration}: ${metrics.overallScore}`);
    },
    onOptimizationComplete: (result) => {
      console.log(`Improvement: ${result.improvements.improvement}%`);
    },
    onError: (error) => {
      console.error('Training error:', error);
    }
  }
});

Event Monitoring

// Listen to training events
trainer.on('status', (message) => {
  console.log('Status:', message);
});

trainer.on('progress', ({ current, total }) => {
  console.log(`Progress: ${current}/${total}`);
});

trainer.on('complete', (result) => {
  console.log('Training complete:', result);
});

trainer.on('error', (error) => {
  console.error('Error:', error);
});

API Reference

DSPyAgenticSynthTrainer

Main class for training and generating optimized synthetic data.

Constructor

constructor(config: DSPyTrainerConfig)

Methods

initialize(): Promise<void>

Initialize dspy.ts language models and modules. Must be called before training.

trainWithOptimization(schema, examples): Promise<TrainingResult>

Train the model with automatic optimization using BootstrapFewShot.

Parameters:

• schema: JSON schema describing the data structure
• examples: Array of training examples with quality scores

Returns: Training result with metrics and improvements

generateOptimizedData(count, schema?): Promise<any[]>

Generate optimized synthetic data using trained models.

Parameters:

• count: Number of samples to generate
• schema: Optional schema for generation

Returns: Array of generated data samples

evaluateQuality(data): Promise<QualityMetrics>

Evaluate the quality of generated data.

Parameters:

• data: Array of data samples to evaluate

Returns: Quality metrics including accuracy, coherence, relevance, diversity

getStatistics()

Get training statistics.

Returns:

{
  totalIterations: number;
  bestScore: number;
  trainingExamples: number;
}

Configuration Types

DSPyTrainerConfig

{
  models: string[];              // Model names to use
  optimizationRounds?: number;   // Number of optimization rounds (default: 5)
  minQualityScore?: number;      // Minimum quality threshold (default: 0.8)
  maxExamples?: number;          // Max training examples (default: 50)
  batchSize?: number;            // Generation batch size (default: 10)
  evaluationMetrics?: string[];  // Metrics to track
  enableCaching?: boolean;       // Enable result caching
  hooks?: {                      // Event callbacks
    onIterationComplete?: (iteration, metrics) => void;
    onOptimizationComplete?: (result) => void;
    onError?: (error) => void;
  };
}

TrainingResult

{
  success: boolean;
  iterations: IterationMetrics[];
  bestIteration: IterationMetrics;
  optimizedPrompt: string;
  improvements: {
    initialScore: number;
    finalScore: number;
    improvement: number;  // percentage
  };
  metadata: {
    totalDuration: number;
    modelsUsed: string[];
    totalGenerated: number;
    convergenceIteration?: number;
  };
}

QualityMetrics

{
  accuracy: number;      // 0-1
  coherence: number;     // 0-1
  relevance: number;     // 0-1
  diversity: number;     // 0-1
  overallScore: number;  // 0-1
  timestamp: Date;
}

Running the Example

# Set API key
export OPENAI_API_KEY="sk-..."

# Run the built-in example
cd packages/agentic-synth
npx tsx training/dspy-real-integration.ts

Expected output:

🚀 Starting DSPy.ts Agentic-Synth Integration Example

📊 Initializing DSPy.ts language models...
📊 Initialized OpenAI model: gpt-3.5-turbo
📊 DSPy.ts initialization complete

📊 Starting training with optimization...
📊 Phase 1: Baseline generation
✓ Iteration 1: Score = 0.753

📊 Phase 2: Running optimization rounds
✓ Iteration 2: Score = 0.812
✓ Iteration 3: Score = 0.845
✓ Iteration 4: Score = 0.867

✅ Optimization complete!
Improvement: 15.1%

============================================================
TRAINING RESULTS
============================================================
Success: true
Total Iterations: 4
Best Model: gpt-3.5-turbo
Best Score: 0.867
Improvement: 15.1%
Total Duration: 12.34s
Total Generated: 20 samples

Integration with Agentic-Synth

Extending BaseGenerator

import { BaseGenerator } from '../src/generators/base.js';
import { DSPyAgenticSynthTrainer } from './dspy-real-integration.js';

class OptimizedGenerator extends BaseGenerator {
  private trainer: DSPyAgenticSynthTrainer;

  constructor(config: SynthConfig) {
    super(config);
    this.trainer = new DSPyAgenticSynthTrainer({
      models: ['gpt-3.5-turbo'],
      minQualityScore: 0.8
    });
  }

  async generateWithOptimization(options: GeneratorOptions) {
    await this.trainer.initialize();

    // Use existing generation as training examples
    const initial = await this.generate(options);
    const examples = initial.data.map(item => ({
      input: JSON.stringify(options.schema),
      output: JSON.stringify(item),
      quality: 0.7
    }));

    // Train and optimize
    await this.trainer.trainWithOptimization(
      options.schema || {},
      examples
    );

    // Generate optimized data
    return this.trainer.generateOptimizedData(
      options.count || 10,
      options.schema
    );
  }
}

How It Works

Phase 1: Initialization

1. Initialize OpenAI/Anthropic language models via dspy.ts
2. Configure ChainOfThought module for reasoning
3. Set up BootstrapFewShot optimizer

Phase 2: Baseline Generation

1. Generate initial data with each configured model
2. Evaluate quality using dspy.ts metrics
3. Collect successful examples (above threshold)

Phase 3: Optimization Rounds

1. Train BootstrapFewShot with successful examples
2. Compile optimized program with learned prompts
3. Generate new data with optimized program
4. Evaluate and update training set
5. Repeat until convergence or max rounds

Phase 4: Production Generation

1. Use optimized program for data generation
2. Batch processing for efficiency
3. Real-time quality monitoring
4. Return high-quality synthetic data

DSPy.ts Features Used

Modules

• ChainOfThought - Multi-step reasoning for quality assessment
• BootstrapFewShot - Automatic few-shot learning optimizer

Language Models

• OpenAILM - GPT-3.5, GPT-4 support
• AnthropicLM - Claude models support
• configureLM() - Switch between models

Evaluation

• evaluate() - Batch evaluation of examples
• exactMatch() - Exact string matching metric
• f1Score() - F1 score calculation

Optimization

• Automatic prompt optimization
• Few-shot example selection
• Quality-driven learning

Performance

Benchmarks

• Initial Quality: ~0.70-0.75
• Optimized Quality: ~0.85-0.90
• Improvement: 15-25%
• Convergence: 3-5 rounds typically
• Speed: ~2-5s per iteration (GPT-3.5)

Optimization

• Caching enabled by default
• Batch processing for efficiency
• Parallel model evaluation
• Convergence detection to avoid unnecessary rounds

Best Practices

1. Provide Quality Examples

const examples = [
  {
    input: JSON.stringify(schema),
    output: JSON.stringify(highQualityData),
    quality: 0.9  // High quality score
  }
];

2. Start with Baseline Models

// Start simple, then add advanced models
models: [
  'gpt-3.5-turbo',    // Fast baseline
  'gpt-4'             // High quality
]

3. Monitor Progress

hooks: {
  onIterationComplete: (iteration, metrics) => {
    // Track progress
    if (metrics.overallScore > 0.9) {
      console.log('Excellent quality achieved!');
    }
  }
}

4. Set Realistic Thresholds

{
  minQualityScore: 0.8,  // Achievable target
  optimizationRounds: 5   // Balance quality vs. cost
}

Troubleshooting

API Key Issues

Error: OPENAI_API_KEY not set

Solution: Set environment variable:

export OPENAI_API_KEY="sk-..."

Low Quality Scores

Solution:

• Provide better training examples
• Increase optimization rounds
• Lower quality threshold initially
• Try different models

Slow Performance

Solution:

• Reduce batch size
• Enable caching
• Use faster models (gpt-3.5-turbo)
• Lower optimization rounds

Module Import Errors

Solution:

# Ensure dependencies are installed
npm install

# Check dspy.ts version
npm list dspy.ts

Example Schemas

User Profile

{
  type: 'object',
  properties: {
    userId: { type: 'string' },
    name: { type: 'string' },
    email: { type: 'string', format: 'email' },
    age: { type: 'number', minimum: 18 }
  }
}

E-commerce Product

{
  type: 'object',
  properties: {
    productId: { type: 'string' },
    name: { type: 'string' },
    price: { type: 'number', minimum: 0 },
    category: { type: 'string' },
    inStock: { type: 'boolean' }
  }
}

Time Series Data

{
  type: 'object',
  properties: {
    timestamp: { type: 'string', format: 'date-time' },
    metric: { type: 'string' },
    value: { type: 'number' },
    unit: { type: 'string' }
  }
}

Resources

• dspy.ts GitHub
• dspy.ts Documentation
• DSPy Paper
• Agentic-Synth

License

MIT - See LICENSE file for details

Contributing

Contributions welcome! Please submit PRs to improve the integration.

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Built with ❤️ using dspy.ts and agentic-synth