This tool simplifies creating a conversational chatbot that answers questions based on provided documentation. It's designed for developers and product teams looking to build knowledge-based AI assistants without deep ML expertise, offering a streamlined workflow from data generation to model testing.

How It Works

The system leverages OpenAI's API to generate a question-and-answer dataset from user-provided documentation and a service description. It uses a specified temperature to control the creativity of generated questions and answers, formatting this data for model training. The tool then trains a model on this dataset and allows for direct testing within the notebook environment.

Quick Start & Requirements

• Run via Google Colab or a local Jupyter notebook.
• Requires an OpenAI API key.
• Dependencies are managed within the notebook environment (likely standard Python libraries).
• Setup involves pasting documentation into a list, defining service details, temperature, and example count.

Highlighted Details

• Automated Q&A dataset generation from raw documentation.
• Integrated model training and testing capabilities.
• Configurable data generation via temperature parameter.
• Experimental approach to improve conversational accuracy over traditional methods.

Maintenance & Community

• Project initiated by Matt Shumer (@mattshumer_).
• Contributions are welcomed, with suggestions for improving data generation and training customization.
• Link to project: https://github.com/mshumer/gpt-oracle-trainer

Licensing & Compatibility

• MIT Licensed.
• Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The tool is described as experimental, and its effectiveness may depend heavily on the quality and structure of the input documentation. The reliance on OpenAI's API means costs are associated with data generation and model training.