data4ai 0.2.3

Production-ready AI-powered dataset generation for instruction tuning and model fine-tuning

Data4AI 🚀

AI-powered dataset generation for instruction tuning and model fine-tuning

Generate high-quality synthetic datasets using state-of-the-art language models through OpenRouter API. Perfect for creating training data for LLM fine-tuning.

✨ Key Features

• 🤖 100+ AI Models - Access to GPT-4, Claude, Llama, and more via OpenRouter
• 📊 Multiple Formats - Support for ChatML (default) and Alpaca schemas
• 🔮 DSPy Integration - Dynamic prompt optimization for better quality
• 📄 Document Support - Generate datasets from PDFs, Word docs, Markdown, and text files
• 🎯 Quality Features - Optional Bloom's taxonomy, provenance tracking, and quality verification
• 🤖 Smart Generation - Both prompt-based and document-based dataset creation
• ☁️ HuggingFace Hub - Direct dataset publishing
• ⚡ Production Ready - Rate limiting, checkpointing, deduplication

🚀 Quick Start

Installation

pip install data4ai              # All features included
pip install data4ai[all]         # All features

Set Up Environment Variables

Data4AI requires environment variables to be set in your terminal:

Option 1: Quick Setup (Current Session)

# Get your API key from https://openrouter.ai/keys
export OPENROUTER_API_KEY="your_key_here"

# Optional: Set a specific model (default: openai/gpt-4o-mini)
export OPENROUTER_MODEL="anthropic/claude-3-5-sonnet"  # Or another model

# Optional: Set default dataset schema (default: chatml)
export DEFAULT_SCHEMA="chatml"  # Options: chatml, alpaca

# Optional: For publishing to HuggingFace
export HF_TOKEN="your_huggingface_token"

Option 2: Using .env File

# Create a .env file in your project directory
echo 'OPENROUTER_API_KEY=your_key_here' > .env
# The tool will automatically load from .env

Option 3: Permanent Setup

# Add to your shell config (~/.bashrc, ~/.zshrc, or ~/.profile)
echo 'export OPENROUTER_API_KEY="your_key_here"' >> ~/.bashrc
source ~/.bashrc

Check Your Setup

# Verify environment variables are set
echo "OPENROUTER_API_KEY: ${OPENROUTER_API_KEY:0:10}..." # Shows first 10 chars

Generate Your First Dataset

# Generate from description
data4ai prompt \
  --repo my-dataset \
  --description "Create 10 Python programming questions with answers" \
  --count 10

# View results
cat my-dataset/data.jsonl

📚 Common Use Cases

1. Generate from Natural Language

data4ai prompt \
  --repo customer-support \
  --description "Create customer support Q&A for a SaaS product" \
  --count 100

2. Generate from Documents

# From single PDF document
data4ai doc research-paper.pdf \
  --repo paper-qa \
  --type qa \
  --count 100

# From entire folder of documents
data4ai doc /path/to/docs/folder \
  --repo multi-doc-dataset \
  --type qa \
  --count 500 \
  --recursive

# Process only specific file types in folder
data4ai doc /path/to/docs \
  --repo pdf-only-dataset \
  --file-types pdf \
  --count 200

# From Word document with summaries
data4ai doc manual.docx \
  --repo manual-summaries \
  --type summary \
  --count 50

# From Markdown with advanced extraction
data4ai doc README.md \
  --repo docs-dataset \
  --type instruction \
  --advanced

# Generate with optional quality features
data4ai doc document.pdf \
  --repo high-quality-dataset \
  --count 200 \
  --taxonomy balanced \    # Use Bloom's taxonomy for diverse questions
  --provenance \           # Include source references
  --verify \               # Verify quality (2x API calls)
  --long-context           # Merge chunks for better coherence

4. High-Quality Generation

# Basic generation (simple and fast)
data4ai doc document.pdf --repo basic-dataset --count 100

# With cognitive diversity using Bloom's Taxonomy
data4ai doc document.pdf \
  --repo taxonomy-dataset \
  --count 100 \
  --taxonomy balanced  # Creates questions at all cognitive levels

# With source tracking for verifiable datasets
data4ai doc research-papers/ \
  --repo cited-dataset \
  --count 500 \
  --provenance  # Includes character offsets for each answer

# Full quality mode for production datasets
data4ai doc documents/ \
  --repo production-dataset \
  --count 1000 \
  --chunk-tokens 250 \     # Token-based chunking
  --taxonomy balanced \    # Cognitive diversity
  --provenance \          # Source tracking
  --verify \              # Quality verification
  --long-context          # Optimized context usage

5. Publish to HuggingFace

# Generate and publish
data4ai prompt \
  --repo my-public-dataset \
  --description "Educational content about machine learning" \
  --count 200 \
  --huggingface

📚 Available Commands

data4ai prompt

Generate dataset from natural language description using AI.

data4ai prompt --repo <name> --description <text> [options]

data4ai doc

Generate dataset from document(s) - supports PDF, DOCX, MD, and TXT files.

data4ai doc <file_or_folder> --repo <name> [options]

data4ai push

Upload existing dataset to HuggingFace Hub.

data4ai push --repo <name> [options]

🐍 Python API

from data4ai import generate_from_description, generate_from_document

# Generate from description (uses ChatML by default)
result = generate_from_description(
    description="Create Python interview questions",
    repo="python-interviews",
    count=50,
    schema="chatml"  # Optional, ChatML is default
)

# Generate from document with quality features
result = generate_from_document(
    document_path="research-paper.pdf",
    repo="paper-qa",
    extraction_type="qa",
    count=100,
    taxonomy="balanced",      # Optional: Bloom's taxonomy
    include_provenance=True,   # Optional: Source tracking
    verify_quality=True        # Optional: Quality verification
)

print(f"Generated {result['row_count']} examples")

📋 Supported Schemas

ChatML (Default - OpenAI format)

{
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is machine learning?"},
    {"role": "assistant", "content": "Machine learning is..."}
  ]
}

Alpaca (Instruction tuning)

{
  "instruction": "What is machine learning?",
  "input": "Explain in simple terms",
  "output": "Machine learning is..."
}

🎯 Quality Features (Optional)

All quality features are optional - use them when you need higher quality datasets:

Feature	Flag	Description	Performance Impact
Token Chunking	--chunk-tokens N	Use token count instead of characters	Minimal
Bloom's Taxonomy	--taxonomy balanced	Create cognitively diverse questions	None
Provenance	--provenance	Include source references	Minimal
Quality Verification	--verify	Verify and improve examples	2x API calls
Long Context	--long-context	Merge chunks for coherence	May reduce API calls

When to Use Quality Features

• Quick Prototyping: No features needed - fast and simple
• Production Datasets: Use --taxonomy and --verify
• Academic/Research: Use all features for maximum quality
• Citation Required: Always use --provenance

⚙️ Configuration

Create .env file:

OPENROUTER_API_KEY=your_key_here
OPENROUTER_MODEL=openai/gpt-4o-mini  # Optional (this is the default)
DEFAULT_SCHEMA=chatml                # Optional (this is the default)
HF_TOKEN=your_huggingface_token      # For publishing

📖 Documentation

• Detailed Usage Guide - Complete CLI reference
• Examples - Code examples and recipes
• API Documentation - Python API reference
• Publishing Guide - PyPI publishing instructions
• All Documentation - Complete documentation index

🛠️ Development

# Clone repository
git clone https://github.com/zysec/data4ai.git
cd data4ai

# Install for development
pip install -e ".[dev]"

# Run tests
pytest

# Check code quality
ruff check .
black --check .

🤝 Contributing

Contributions welcome! Please check our Contributing Guide.

📄 License

MIT License - see LICENSE file.

🔗 Links

• PyPI Package
• GitHub Repository
• Documentation
• Issue Tracker

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