Incognito Pilot: The Next-Gen AI Code Interpreter for Sensitive Data

Hello everyone! Today marks the first day of a new series of posts featuring projects in my GitHub Stars.

Most of these repos are FOSS & FOSAI focused, meaning they should be hackable, free, and (mostly) open-source.

We’re going to kick this series off by sharing Incognito Pilot. It’s like the ChatGPT Code Interpreter but for those who prioritize data privacy.

Project Summary from ChatGPT-4:

Features:

  • Powered by Large Language Models like GPT-4 and Llama 2.
  • Run code and execute tasks with Python interpreter.
  • Privacy: Interacts with cloud but sensitive data stays local.
  • Local or Remote: Choose between local LLMs (like Llama 2) or API (like GPT-4) with data approval mechanism.

You can use Incognito Pilot to:

  • Analyse data, create visualizations.
  • Convert files, e.g., video to gif.
  • Internet access for tasks like downloading data.

Incognito Pilot ensures data privacy while leveraging GPT-4’s capabilities.

Getting Started:

  1. Installation:

    • Use Docker (For Llama 2, check dedicated installation).
    • Create a folder for Incognito Pilot to access. Example: /home/user/ipilot.
    • Have an OpenAI account & API key.
    • Use the provided docker command to run.
    • Access via: http://localhost:3030
    • Bonus: Works with OpenAI’s free trial credits (For GPT-3.5).

  2. First Steps:

    • Chat with the interface: Start by saying “Hi”.
    • Get familiar: Command it to print “Hello World”.
    • Play around: Make it create a text file with numbers.

Notes:

  • Data you enter and approved code results are sent to cloud APIs.
  • All data is processed locally.
  • Advanced users can customize Python interpreter packages for added functionalities.

FAQs:

  • Comparison with ChatGPT Code Interpreter: Incognito Pilot offers a balance between privacy and functionality. It allows internet access, and can be run on powerful machines for larger tasks.

  • Why use Incognito Pilot over just ChatGPT: Multi-round code execution, tons of pre-installed dependencies, and a sandboxed environment.

  • Data Privacy with Cloud APIs: Your core data remains local. Only meta-data approved by you gets sent to the API, ensuring a controlled and conscious usage.

Personally, my only concern using ChatGPT has always been about data privacy. This explores an interesting way to solve that while still getting the state of the art performance that OpenAI has managed to maintain (so far).

I am all for these pro-privacy projects. I hope to see more emerge!

If you get a chance to try this, let us know your experience in the comments below!

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