cadCAD GPT is an open-source LLM agents framework to support token systems simulations. cadCAD GPT can be used on top of any Python model following the cadCAD/radCAD model structure. Via Python functions, it can integrate today’s most powerful data analysis and machine learning libraries (Tools) and can access data in various formats (Memory). Finally, cadCAD GPT allows users to control and track the agents’ workflow for verifiable and reproducible results.
Learn more about cadCAD GPT in this series of articles:
- Part I: Hello, cadCAD GPT! Requirements and conceptual design of LLMs to support token system simulations
- Part II: This is me, cadCAD GPT! A deep-dive into cadCAD GPT’s powerful, customizable components
- Part III: Let’s chat! Experiments and further development of cadCAD GPT
- Installation
- Quickstart
- Contributing
- Community and Support
- Future Additions
- License
- Acknowledgements
- Citations
Include instructions on how to download, install dependencies, and set up the project. This could include:
- How to download the project files:
git clone https://github.com/TE-Academy/cadCAD-GPT.git - Installation of dependencies:
or just run the setup.ipynb file which will install the requirements for you.
pip install -r requirements.txt
- Check the project_template folder in the examples folder. This folder has all the components to get started building your own chatbot.
- Copy your radcad code into the example_radcad.py file.
- Go to the example_project.ipynb file, paste your openAI key in the openai_key variable and run all the code.
Guidelines for contributors:
- Instructions on how to contribute (TBD).
- Code styling conventions (write about pep257 format here).
- Contribution process (TBD).
For any ideas, discussions or queries join the TE Academy Discord!
You'll find us here
- Outline upcoming features or enhancements planned for the project.
- Encourage users to suggest improvements or features.
This project is licensed under the MIT License - see the LICENSE file for details.
Depending on the complexity or specific requirements of your project, you might want to include additional sections such as:
- Troubleshooting: Common issues and their solutions.
- Examples: Links or snippets of example code demonstrating the project's capabilities.
- Tests: Instructions on how to run tests included in the project.
- Acknowledgments: Recognition of contributors, libraries, or resources used in the project.
Remember, the README should be clear, concise, and informative to help users and contributors understand and engage with your project easily. Adjust and expand the sections as needed for your specific project.
cadCAD GPT was kickstarted by funding received from Token Engineering Commons. We thank the TE Commons community, and Gideon Rosenblatt in particular, who encouraged us to embark on this exciting journey. Big thank you to our advisors Roderick McKinley, Richard Blythman, and Robert Koschig for ongoing support and feedback. Shoutout to Dr. Achim Struve, Dimitrios Chatzianagnostou, Stephanie Tramicheck, Ivan Bermejo, Rohan Sundar, and Lukasz Szymanski for the most valuable alpha user feedback and insights, and Kaidlyne Neukam for her tireless support in publishing this work.
If you find cadCAD GPT useful, please consider citing the project:
@software {cadCAD GPT, author = Rohan Mehta, Angela Kreitenweis, license = MIT License, month = Nov, title = cadCAD GPT - an LLM agents framework to support token systems simulations, url = https://github.com/TE-Academy/cadCAD-GPT, year = 2023 }
a language model interface for cadcad/radcad simulations using autonomous LLM agents
- how to use
- how to download
- how to how to add
- discord
- Future additions