closedloop / typedllm

TypedLLM helps developers efficiently transform unstructured text outputs from large language models into strongly typed dataclasses, like Pydantic, enabling seamless integration with applications and streamlined data handling for improved consistency and maintainability.

TypedLLM: Large Language Model to Typed Dataclass Conversion

TypedLLM is a Python repository that streamlines the process of converting unstructured text outputs from large language models into strongly typed dataclasses, like Pydantic, facilitating seamless integration with applications and enhancing data handling for improved consistency and maintainability.

Table of Contents

• Installation
• Usage
• Configuration
• API Documentation
• Contributing
• Testing
• License
• Credits/Acknowledgements
• Changelog
• Contact Information

Installation

To install TypedLLM, follow these steps:

1. Pip install.

   pip install typedllm
   

2. Clone the repository:

   git clone https://github.com/username/TypedLLM.git
   

   Change to the project directory:

   cd TypedLLM
   

   Install the required packages:

   pip install -r requirements.txt
   

Usage

To use TypedLLM, first import the necessary modules:

from typedllm import TypedLLM, PydanticConverter

Then, create an instance of the converter and process your text output:

converter = PydanticConverter()
typed_output = converter.convert(text_output)

For more detailed examples, please refer to the examples directory.

Configuration

No specific configuration is required to use TypedLLM. However, you can customize the behavior of the converter by extending the PydanticConverter class and overriding its methods, if needed.

API Documentation

Please refer to the API documentation for a complete list of available functions, classes, methods, and parameters.

Contributing

We welcome contributions to TypedLLM! To contribute, please follow these guidelines:

1. Fork the repository and create your branch from the main branch.
2. Make your changes, ensuring you follow the project's coding style and documentation standards.
3. Submit a pull request with a clear description of your changes.

Development Environment

Set up a virtual environment:

python3 -m venv venv
source venv/bin/activate

# install dependencies
pip install -r requirements.txt
pip install -r requirements-dev.txt

# install typedllm
pip install -e .

Testing

To run tests for TypedLLM, follow these steps:

Install the required testing packages:

pip install -r requirements-dev.txt

Run the tests:

pytest

License

TypedLLM is released under the MIT License.

Credits/Acknowledgements

We would like to thank the following libraries and resources that have been instrumental in the development of TypedLLM:

• Pydantic: https://github.com/pydantic/pydantic
• LangChain: https://python.langchain.com/

Changelog

Please refer to the CHANGELOG.md file for a summary of notable changes in each release of TypedLLM.

Contact Information

For any questions or suggestions, please reach out to the TypedLLM maintainers:

• Email: [email protected]
• Twitter: @seankruzel
• GitHub Issues: https://github.com/closedloop/TypedLLM/issues