Skip to content

Latest commit

 

History

History
154 lines (107 loc) · 4.84 KB

README.md

File metadata and controls

154 lines (107 loc) · 4.84 KB
Raw

Code Interpreter API

A LangChain implementation of the ChatGPT Code Interpreter. Using CodeBoxes as backend for sandboxed python code execution. CodeBox is the simplest cloud infrastructure for your LLM Apps. You can run everything local except the LLM using your own OpenAI API Key.

Features

  • Dataset Analysis, Stock Charting, Image Manipulation, ....
  • Internet access and auto Python package installation
  • Input text + files -> Receive text + files
  • Conversation Memory: respond based on previous inputs
  • Run everything local except the OpenAI API (OpenOrca or others maybe soon)
  • Use CodeBox API for easy scaling in production (coming soon)

Installation

Get your OpenAI API Key here and install the package.

pip install "codeinterpreterapi[all]"

Everything for local experiments are installed with the all extra. For deployments, you can use pip install codeinterpreterapi instead which does not install the additional dependencies.

Usage

To configure OpenAI and Azure OpenAI, ensure that you set the appropriate environment variables (or use a .env file):

For OpenAI, set the OPENAI_API_KEY environment variable:

export OPENAI_API_KEY=your_openai_api_key

For Azure OpenAI, set the following environment variables:

export OPENAI_API_TYPE=azure
export OPENAI_API_VERSION=your_api_version
export OPENAI_API_BASE=your_api_base
export OPENAI_API_KEY=your_azure_openai_api_key
export DEPLOYMENT_NAME=your_deployment_name

Remember to replace the placeholders with your actual API keys and other required information.

from codeinterpreterapi import CodeInterpreterSession, settings

# set api key (or automatically loads from env vars)
settings.OPENAI_API_KEY = "sk-***************"

# create a session
with CodeInterpreterSession() as session:
    # generate a response based on user input
    response = session.generate_response(
        "Plot the bitcoin chart of year 2023"
    )

    # output the response
    response.show()

Bitcoin YTD Bitcoin YTD Chart Output

Dataset Analysis

from codeinterpreterapi import CodeInterpreterSession, File


async def main():
    # context manager for auto start/stop of the session
    async with CodeInterpreterSession() as session:
        # define the user request
        user_request = "Analyze this dataset and plot something interesting about it."
        files = [
            File.from_path("examples/assets/iris.csv"),
        ]

        # generate the response
        response = await session.generate_response(
            user_request, files=files
        )

        # output to the user
        print("AI: ", response.content)
        for file in response.files:
            file.show_image()


if __name__ == "__main__":
    import asyncio

    asyncio.run(main())

Iris Dataset Analysis Iris Dataset Analysis Output

Production

In case you want to deploy to production, you can utilize the CodeBox API for seamless scalability.

Please contact me if you are interested in this, as it is still in the early stages of development.

Contributing

There are some remaining TODOs in the code. So, if you want to contribute, feel free to do so. You can also suggest new features. Code refactoring is also welcome. Just open an issue or pull request and I will review it.

Please also submit any bugs you find as an issue with a minimal code example or screenshot. This helps me a lot in improving the code.

Thanks!

Streamlit WebApp

To start the web application created with streamlit:

streamlit run frontend/app.py --browser.gatherUsageStats=False

License

MIT

Contact

You can contact me at contact@shroominic.com. But I prefer to use Twitter or Discord DMs.

Support this project

If you would like to help this project with a donation, you can click here. Thanks, this helps a lot! ❤️

Star History

Star History Chart