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Jupyter AI

jupyter-ai.readthedocs.io

21–30 of 37 posts

Re: Jupyter AI

#21
Self plug. If you're looking for something less-integrated into JupyterLab (with support for ipython and Jupyter Notebooks), check out: https://github.com/santiagobasulto/ipython-gpt

I wrote the package to solve my own issue, I need a really lightweight interface to GPT and primarily from ipython.

Re: Jupyter AI

#22
post #7

This looks useful, but not quite what I hoped. GPT4 with Code Interpreter is a fun, frustrating experience where you’re writing a dialog about writing some code, sort of like pair programming or a code interview. Compared to a notebook, it’s terrible. The sandbox environment resets if you take a break. There’s also a quota, and if you hit that it forces taking a break, causing a reset. In a notebook, you could rerun…

You might find it interesting to try my open source ai coding tool “aider”. It lets you pair program with gpt-4 like you are describing. But the source code lives in your local git repo. You can start a new project or work with an existing repo. You can fluidly switch back and forth between a coding chat where you ask gpt to edit the code and your own editor to make edits yourself. https://github.com/paul-gauthier/ai…

Seems very good! In the edit a whole repo example how do you account for many files i.e. many tokens? Can it also have a vector search?

Re: Jupyter AI

#24
post #14
post #11

I've tried a number of notebook AIs, jupyter ai, hex, deepnote, einblick. The one that worked best for me was einblick probably because it's data-aware. For AIs that don't support that you need to be overly specific when writing prompts, which is annoying, and you keep having to rename/reference the correct dataframes and variables (even more annoying).

Can you expand on this "data awareness"? What does it mean, and what are its benefits?

By data-aware I mean that the AI leverages additional context about the data to generate code for a given prompt. Let's say you're asking an AI to "build a regression model for column X". To give you a targeted, executable response, the AI needs to know: which dataframes contain a column named "X"? if there are many such dataframes, which one should be referenced for the regression task? Is X a numeric column, and if not can it be converted to numeric column? Does the data need to be normalized beforehand? If the AI is unable to answer such questions on its own, it will only ever be able to return a generic answer. That's equivalent to typing it into ChatGPT, requiring the user to modify the returned code before it actually does what the user asked for. That clearly isn't a great for an AI that operates on data. A data-aware AI on the other hand is able to provide more targeted responses that require much less user intervention because it has access to the broader context.

A couple of other benefits: - the AI will have an easier time automatically fixing runtime errors - it knows how to fix and transform user input into the correct data format, e.g., "san fancisco" => "San Francisco"

Re: Jupyter AI

#25
post #7

This looks useful, but not quite what I hoped. GPT4 with Code Interpreter is a fun, frustrating experience where you’re writing a dialog about writing some code, sort of like pair programming or a code interview. Compared to a notebook, it’s terrible. The sandbox environment resets if you take a break. There’s also a quota, and if you hit that it forces taking a break, causing a reset. In a notebook, you could rerun…

You might find it interesting to try my open source ai coding tool “aider”. It lets you pair program with gpt-4 like you are describing. But the source code lives in your local git repo. You can start a new project or work with an existing repo. You can fluidly switch back and forth between a coding chat where you ask gpt to edit the code and your own editor to make edits yourself. https://github.com/paul-gauthier/ai…

This looks like it might be quite nice for practical use. Does it work for a Jupyter notebook, including plotting things and making images?

I see someone make it work in Colab, though it looks like a bit of a hack and how they are handling credentials looks iffy.

Ultimately, I'd like the final result to be a tutorial-style blog post, so git isn't strictly required for my purposes. The conversation is as important as the code.

Re: Jupyter AI

#26
post #7

This looks useful, but not quite what I hoped. GPT4 with Code Interpreter is a fun, frustrating experience where you’re writing a dialog about writing some code, sort of like pair programming or a code interview. Compared to a notebook, it’s terrible. The sandbox environment resets if you take a break. There’s also a quota, and if you hit that it forces taking a break, causing a reset. In a notebook, you could rerun…

You might find it interesting to try my open source ai coding tool “aider”. It lets you pair program with gpt-4 like you are describing. But the source code lives in your local git repo. You can start a new project or work with an existing repo. You can fluidly switch back and forth between a coding chat where you ask gpt to edit the code and your own editor to make edits yourself. https://github.com/paul-gauthier/ai…

This looks like it might be quite nice for practical use. Does it work for a Jupyter notebook, including plotting things and making images?

I see someone make it work in Colab, though it looks like a bit of a hack and how they are handling credentials looks iffy.

Ultimately, I'd like the final result to be a tutorial-style blog post, so git isn't strictly required for my purposes. The conversation is as important as the code.

Re: Jupyter AI

#27

Earlier quoted context omitted.

You might find it interesting to try my open source ai coding tool “aider”. It lets you pair program with gpt-4 like you are describing. But the source code lives in your local git repo. You can start a new project or work with an existing repo. You can fluidly switch back and forth between a coding chat where you ask gpt to edit the code and your own editor to make edits yourself. https://github.com/paul-gauthier/ai…

This looks like it might be quite nice for practical use. Does it work for a Jupyter notebook, including plotting things and making images? I see someone make it work in Colab, though it looks like a bit of a hack and how they are handling credentials looks iffy. Ultimately, I'd like the final result to be a tutorial-style blog post, so git isn't strictly required for my purposes. The conversation is as important as…

Right now aider isn't integrated with jupyter notebooks, but it is certainly on the roadmap.

I have been sharing aider conversations [0] to help folks understand what it's like to pair program with GPT-4. I've had some users asking how they can share aider chat transcripts like this, so I'm hoping to add that capability soon. I don't think it's a full solution to your needs, but it might be helpful?

[0] https://aider.chat/examples/2048-game.html

Re: Jupyter AI

#28
post #22

Earlier quoted context omitted.

You might find it interesting to try my open source ai coding tool “aider”. It lets you pair program with gpt-4 like you are describing. But the source code lives in your local git repo. You can start a new project or work with an existing repo. You can fluidly switch back and forth between a coding chat where you ask gpt to edit the code and your own editor to make edits yourself. https://github.com/paul-gauthier/ai…

Seems very good! In the edit a whole repo example how do you account for many files i.e. many tokens? Can it also have a vector search?

Aider scans the repo for all the important identifiers/symbols and condenses them down to make a "repo map" [0]. You tell aider which files you want it to edit, and it uses the repo map to augment them with all the relevant code context from the rest of the repo. This way when GPT makes code changes, it is able to respect and utilize the existing modules and abstractions present in the codebase.

[0] https://aider.chat/docs/ctags.html

Re: Jupyter AI

#29
post #7

This looks useful, but not quite what I hoped. GPT4 with Code Interpreter is a fun, frustrating experience where you’re writing a dialog about writing some code, sort of like pair programming or a code interview. Compared to a notebook, it’s terrible. The sandbox environment resets if you take a break. There’s also a quota, and if you hit that it forces taking a break, causing a reset. In a notebook, you could rerun…

You might find it interesting to try my open source ai coding tool “aider”. It lets you pair program with gpt-4 like you are describing. But the source code lives in your local git repo. You can start a new project or work with an existing repo. You can fluidly switch back and forth between a coding chat where you ask gpt to edit the code and your own editor to make edits yourself. https://github.com/paul-gauthier/ai…

This is great, thank you! I can see myself using this whenever I program, though I can feel my skills atrophy whenever I have GPT write code.

Re: Jupyter AI

#30
Installation section:

"Installation via pip within Conda environment (recommended)"

This is one of the signs of Python's package management being too messy. I learnt NOT to use pip to install packages inside of conda environments after considerable pain. Now this guide says that's recommended?

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