sysgen 0.2.1

SysGen - High-quality synthetic datasets creating tool using Gemini API

SysGen

SysGen is a powerful CLI tool that creates high-quality synthetic datasets from documents using the Gemini API. It intelligently chunks documents, generates comprehensive questions, and produces detailed answers for machine learning training datasets.

Features

• Smart Document Chunking: Automatically splits large documents into manageable chunks with overlap
• Comprehensive Question Generation: Extracts ALL possible questions from content using advanced AI prompting
• High-Quality Answer Generation: Creates detailed 4-5 sentence answers with supporting evidence
• Multiple Output Formats: Supports Alpaca, ChatML, and Conversation formats
• Semantic Duplicate Detection: Automatically removes duplicate questions using sentence embeddings
• Token-Aware Processing: Uses tiktoken for accurate token counting and chunking
• Batch Processing: Process multiple markdown/text files in a single run
• Quality Validation: Ensures answer length and content quality standards

Installation

Install from pip

pip install sysgen

Set Up Environment Variables

Before running sysgen, set the API key in your terminal:

# Windows
set GEMINI_API_KEY=your_gemini_api_key_here

# Linux/Mac
export GEMINI_API_KEY=your_gemini_api_key_here

Usage

Basic Usage

sysgen --input-folder md --output dataset.json --format alpaca

Advanced Usage

sysgen --input-folder documents --output training_data.json --format chatml --similarity-threshold 0.85

Arguments

• --input-folder: Folder containing markdown/text files (default: md)
• --output: Output JSON file (default: output.json)
• --format: Output format - alpaca, chatml, or conversation (default: alpaca)
• --similarity-threshold: Similarity threshold for duplicate detection, 0.0-1.0 (default: 0.85)

Output Formats

Alpaca Format

{
  "instruction": "What is the main concept discussed in this section?",
  "input": "",
  "output": "The main concept discussed is the implementation of neural networks...",
  "source_document": "document.md"
}

ChatML Format

{
  "messages": [
    {"role": "user", "content": "What is the main concept discussed in this section?"},
    {"role": "assistant", "content": "The main concept discussed is the implementation of neural networks..."}
  ],
  "source_document": "document.md"
}

Conversation Format

{
  "conversations": [
    {"from": "human", "value": "What is the main concept discussed in this section?"},
    {"from": "gpt", "value": "The main concept discussed is the implementation of neural networks..."}
  ],
  "source_document": "document.md"
}

How It Works

1. Document Chunking: Splits documents into 3000-token chunks with 200-token overlap
2. Question Extraction: Uses advanced AI prompting to extract ALL possible questions from each chunk
3. Answer Generation: Creates comprehensive 4-5 sentence answers with supporting evidence
4. Quality Filtering: Validates answer length (3-6 sentences) and content quality
5. Duplicate Detection: Uses sentence embeddings to identify semantically similar questions
6. Format Conversion: Converts to specified output format (Alpaca/ChatML/Conversation)
7. Batch Processing: Processes multiple files and combines results

Advanced Features

Smart Chunking

• Token-Aware: Uses tiktoken for accurate token counting
• Sentence Preservation: Keeps sentences intact during chunking
• Overlap Management: Maintains context between chunks with configurable overlap

Comprehensive Question Generation

• Multi-Level Questions: Generates factual, conceptual, analytical, and application questions
• Exhaustive Extraction: Extracts ALL possible questions from content
• Quality Standards: Ensures questions are clear, specific, and answerable

Semantic Duplicate Detection

• Embedding-Based: Uses sentence-transformers for semantic similarity
• Configurable Threshold: Adjust sensitivity with similarity_threshold parameter
• Quality Preservation: Keeps highest quality version from duplicate groups

Dependencies

• google-genai: Gemini API client for question and answer generation
• sentence-transformers: Semantic similarity detection for duplicate removal
• scikit-learn: Cosine similarity calculations
• tiktoken: Token counting for document chunking
• torch: PyTorch backend for sentence transformers
• transformers: Hugging Face transformers library
• numpy: Numerical operations
• scipy: Scientific computing utilities

Contributing

We welcome contributions! Please feel free to submit issues, feature requests, or pull requests.

How to Contribute

1. Fork the Repository: Start by forking the project on GitHub
2. Clone the Repository: Clone it to your local machine
3. Create a Branch: Create a new branch for your changes
4. Make Changes: Implement your improvements or bug fixes
5. Test Your Changes: Ensure the tool works correctly with your modifications
6. Submit a Pull Request: Open a PR describing your changes

License

This project is licensed under the MIT License. See LICENSE for details.

Contact

• Author: Adhishtanaka
• Email: [e-mail]