[thibautdr]

Hi everyone, thanks for posting Amphi :)

To give some context, Amphi is a low-code ETL tool for both structured and unstructured data. The key use cases include file integration, data preparation, data migration, and creating data pipelines for AI tasks like data extraction and RAG. What sets it apart from traditional ETL tools is that it generates Python code that you own and can deploy anywhere. Amphi is available as a standalone web app or as a JupyterLab extension.

Visit the GitHub: https://github.com/amphi-ai/amphi-etl Give it a try and let me know what you think

[OutOfHere]

You know what does not set it apart? AI-washing. Also, lying about being open source when it isn't.

[isjamesalive]

To be fair, the only place the words ‘open’ and ‘source‘ appear in the readme are once in a sub-heading, where it’s phrased ‘open-source’. It’s clearly labelled ELv2.

Possibly more of a subtle miscommunication or misunderstanding than a deliberate lie.

[slt2021]

i liked the idea of leveraging jupyterlab as server. data engineers/scientists already use jupyter, so this is neat idea.

custom extension for jupyterlab is a great way to leverage existing jupyterlab install base: not everyone will be willing to install and jump through hoops to install software X, but installing extension is one pip install away and no need to run separate process, since you are running inside jupyterlab server.

this reminds of ALTERYX (another drag and drop ETL tool)

[thibautdr]

Thanks! Being based on JupyterLab also allows Amphi to benefit from the vast ecosystem of extensions already available, such as the Git extension or using different file systems (S3).

Some users pointed out they were Alteryx users but liked the Python code generation from Amphi :)

[johhns4]

Wow amazing work! How does the inputs work, are they created for you or does it support custom as well?

[thibautdr]

Thank you! Inputs components are pre-built for now but the ability to add custom inputs is coming soon!