opendatagen 0.0.34

Data preparation system to build controllable AI system

⬜️ Open Datagen ⬜️

Open Datagen is a Data Preparation Tool designed to build Controllable AI Systems

It offers improvements for:

RAG: Generate large Q&A datasets to improve your Retrieval strategies.

Evals: Create unique, “unseen” datasets to robustly test your models and avoid overfitting.

Fine-Tuning: Produce large, low-bias, and high-quality datasets to get better models after the fine-tuning process.

Guardrails: Generate red teaming datasets to strengthen the security and robustness of your Generative AI applications against attack.

Additional Features

• Use external sources to generate high-quality synthetic data (Local files, Hugging Face datasets and Internet)

• Data anonymization

• Open-source model support + local inference

• Decontamination

• Tree of thought

• (SOON) No-code dataset generation

• (SOON) Multimodality

Installation

pip install --upgrade opendatagen

Setting up your API keys

export OPENAI_API_KEY='your_openai_api_key' #(using openai>=1.2)
export MISTRAL_API_KEY='your_mistral_api_key'
export TOGETHER_API_KEY='your_together_api_key'
export ANYSCALE_API_KEY='your_anyscale_api_key'
export ELEVENLABS_API_KEY='your_elevenlabs_api_key'
export SERPLY_API_KEY='your_serply_api_key' #Google Search API 

Usage

Example: Generate a low-biased FAQ dataset based on Wikipedia content

from opendatagen.template import TemplateManager
from opendatagen.data_generator import DataGenerator

output_path = "opendatagen.csv"
template_name = "opendatagen"
manager = TemplateManager(template_file_path="faq_wikipedia.json")
template = manager.get_template(template_name=template_name)

if template:

    generator = DataGenerator(template=template)

    data, data_decontaminated = generator.generate_data(output_path=output_path, output_decontaminated_path=None)

where faq_wikipedia.json is here

Contribution

We welcome contributions to Open Datagen! Whether you're looking to fix bugs, add templates, new features, or improve documentation, your help is greatly appreciated.

Acknowledgements

We would like to express our gratitude to the following open source projects and individuals that have inspired and helped us:

• Textbooks are all you need (Read the paper)

• Evol-Instruct Paper (Read the paper) by WizardLM_AI

• Textbook Generation by VikParuchuri

Connect

If you need help for your Generative AI strategy, implementation, and infrastructure, reach us on

Linkedin: @Thomas. Twitter: @thoddnn.