Try a hosted version here: https://pretzelai.app
Jupyter is by far the most used Data Science tool. Despite its popularity, it still lacks good code-generation extensions. The flagship AI extension jupyter-ai lags far behind in features and UX compared to modern AI code generation and understanding tools (like https://www.continue.dev and https://www.cursor.com). Also, GitHub Copilot still isn’t supported in Jupyter, more than 2 years after its launch. We’re solving this with Pretzel.
Pretzel is a free and open-source fork of Jupyter. You can install it locally with “pip install pretzelai” and launch it with “pretzel lab”. We recommend creating a new python environment if you already have jupyter lab installed. Our GitHub README has more information: https://github.com/pretzelai/pretzelai
For our first iteration, we’ve shipped 3 features:
1. Inline Tab autocomplete: This works similar to GitHub Copilot. You can choose between Mistral Codestral or GPT-4o in the settings
2. Cell level code generation: Click Ask AI or press Cmd+K / Ctrl+K to instruct AI to generate code in the active Jupyter Cell. We provide relevant context from the current notebook to the LLM with RAG. You can refer to existing variables in the notebook using the @variable syntax (for dataframes, it will pass the column names to the LLM)
3. Sidebar chat: Clicking the blue Pretzel Icon on the right sidebar opens this chat (Ctrl+Cmd+B / Ctrl+Alt+B). This chat always has context of your current cell or any selected text. Here too, we use RAG to send any relevant context from the current notebook to the LLM
All of these features work out-of-the-box via our “AI Server” but you have the option of using your own OpenAI API Key. This can be configured in the settings (Menu Bar > Settings > Settings Editor > Search for Pretzel). If you use your own OpenAI API Key but don’t have a Mistral API key, be sure to select OpenAI as the inline code completion model in the settings.
These features are just a start. We're building a modern version of Jupyter. Our roadmap includes frictionless, realtime collaboration (think pair-programming, comments, version history), full-fledged SQL support (both in code cells and as a standalone SQL IDE), a visual analysis builder, a VSCode-like coding experience powered by Monaco, and 1-click dashboard creation and sharing straight from your notebooks.
We’d love for you to try Pretzel and send us any feedback, no matter how minor (see my bio for contact info, or file a GitHub issue here: https://github.com/pretzelai/pretzelai/issues)
LocalAI: https://github.com/mudler/LocalAI
E.g promptfoo and chainforge have multi-LLM workflows.
Promptfoo has a YAML configuration for prompts, providers,: https://www.promptfoo.dev/docs/configuration/guide/
What is the system prompt, and how does a system prompt also bias an analysis?
/? "system prompt" https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...
At the moment, the system prompt is hardcoded in code. I don't think it should bias analyses because our goal is usually returning code that does something specific (for eg "calculate normalized rates for column X grouped by column Y") as opposed to something generic ("why is our churn rate going up"). So, the idea would be that the operator is responsible for asking the right questions - the AI merely acts as a facilitator to generate and understand code.
Also tangentially, we do want to allow users some king of local prompt management and allow easy access, potentially via slash commands. In our testing so far, the existing prompt largely works (took us a fair bit of hit-and-trial to find something that mostly works for common data usecases) but we're hitting limitations of a fixed system prompt so these links will be useful.
I agree that I think starting out with Ollama is probably hits 80% of what people would be looking for out of the box.
Meanwhile, we also found a better speed/quality tradeoff with Codestral (since it has a fill-in-the-middle version, unlike a general LLM) so we decided to go with Codestral. This is inspired by continue.dev using Codestral for tab completion :)
[1] https://github.com/github/copilot.vim [2] https://zed.dev/blog/copilot
What do you mean by this? I've been using Copilot in VS Code .ipynb files for over a year now.
We pull all of your config from the ~/.jupyter folder so you should be able to switch between Jupyter and Pretzel from different python environments (though you might see some warnings)
Our goal is to make a modern literate programming tool. On a surface level, a tool like that would end up looking very similar to Jupyter, though with better features. We've mentioned some things we'd like to have in this final tool in our README and also in the post above.
Our first thought was to make a tool from scratch. The challenge was, it's very hard to get people to switch and so, we had to go where people already are - that meant Jupyter.
We could've made this one feature an extension with some difficulty (in-fact, our early experiments, we started by making an extension). It would have some downsides - we wouldn't have granular control over certain core Jupyter behaviours like we do right now (for eg, we wanted to allow creating hidden folders to store some files). But we probably could have made a 95% working version of Pretzel work as a jupyter extension.
The bigger reason we chose to fork was because down the line, we want to completely change the code execution model to being DAG based to allow for reproducible notebooks (similar to https://plutojl.org/ for eg). Similarly, we want to completely remove Codemirror and replace it with Monaco (the core editor engine in VSCode) to provide a more IDE like experience in Jupyter. These things simply couldn't have been done as extensions.
Im curious to see if you continue building out some other features. While these are great features (copilot, chat, etc), I’d think most users would expect their IDE to have it out of the box (or with an extension) these days
The seem to have taken a "BSD-3-Clause" licensed project and change it to AGPLv3 licensed one. That's not the same thing, but it's similar to what you're concerned about.
Selecting the license for an open-source tool backed by a company is tricky. You want your code to be open-source for it's benefits (for eg, for us one benefit is building trust with people working with sensitive data). But, the history of open-source tools is full of tools that another company just started reselling without doing any of the work (sentry, mongodb etc). So, you need to find a balance. AGPLv3 strikes the right balance for us.
You're right that it is not the same thing, which is why I wrote "That's not the same thing [...]" in the comment you're responding to. I have done the same as you guys (building an AGPLv3 or 'worse' product on top of BSD licensed code) many, many times! Anyway, what you're doing is really exciting!
Thanks for the kind words, we're exited to be building this :)
To your second point - completely agree that most users would expect these feature from their IDEs today. But, only two IDEs support Jupyter Notebooks: VSCode and PyCharm. You can certainly use them for notebook work but most AI extensions written for VSCode wouldn't be optimized for notebook work (for eg, GH Copilot apparently has difficulty completing code across different cells as per a friend). Secondly, to your point, this is just a start - we're going to be building a lot more Data Analysis specific features that don't exist in any IDE. I think there's a decent space for a tool a like this.
- https://noteable.io/ -- pretty good, but then they got acquirehired out of existence
- https://deepnote.com -- also extensive AI integration and realtime collaboration
- https://github.com/jupyterlab/jupyter-ai -- a very nice standard open source extension for gen AI in Jupyter, from an Amazon. JupyterLab of course also has fairly mature realtime collaboration now.
- https://colab.google/ -- has great AI integration but of course only with Google-hosted models
- https://cocalc.com -- very extensive AI integration everywhere with all the main hosted models, mostly free or pay as you go; also has realtime collaboration. (Disclaimer: I co-authored this.)
- VS Code has a great builtin Jupyter notebook, as other people have mentioned.
Am I missing any?
VS Code (and Cursor) has so nice Jupyter support that I find it much better to use it for my workflow, rather than using any dedicated solution for Jupiter Notebooks only.
I used Jupyter Notebook before it was popular and back when I was a PhD student. I pushed in a few places for the unorthodox way of exploring data in a browser. Now, I am back - but only thanks to wonderful code editors and their good support of Jupyter Notebooks. I recommend VSC (or, this year, Cursor) as the default environment for data sci.
I'd be curious to hear a bit more about the kind of work you do that made you switch. Also if there's anything you miss in VSC/Cursor vs Jupyter. If you don't mind a small email exchange, let me know and I'll drop you a message :)
My path is in "What I do or: science to data science" (https://p.migdal.pl/blog/2015/12/sci-to-data-sci)... and well, a bit more recent one in "Embodiment for nerds" (https://p.migdal.pl/blog/2021/09/embodiment-for-nerds).
Regarding the shortcomings of the Jupyter Notebook, I made a few notes in "How I learned to stop worrying and love the types & tests" (https://p.migdal.pl/blog/2020/03/types-tests-typescript).
Also, as we build more features, we're definitely going in the direction of more analytics workloads (live collaboration, leaving comments, google-doc type versioning, fully AI driven analyses similar to OpenAI Interpreter mode etc) and with these features, I think there will be a clear divergence of feature-set in VSCode/PyCharm vs Pretzel.
If I may ask, are you more on the engineering side (MLE) or more on the data side (Data Analyst)? EDIT: Just saw your other comment!
However, in general, I believe that any approach that works is good. And I don't think there is any reason to think that we need to settle with the current data science programming UIs. However, some went with mixed success, e.g. ObservableHQ never took over Jupiter.
In my view, PyCharm and VS Code are not that close to each other. PyCharm is a traditional IDE, while VS Code is more like an ecosystem of extensions. In particular, there is one (surprisingly good) for Jupyter Notebooks.
When developing any new way of interacting with code, there is a question of which ecosystem to use. Having it as a VS Code extension (or clone) has benefits and limitations. So is having it as a Jupyter extension or (the way you went) - clone.
If you want to talk more, happy to move it to emails.
- https://noteable.io/ - as you say, it doesn't exist anymore
- https://deepnote.com - Deepnote is closed source sadly - you can't run it locally, you can't tweak it, you need to learn a new interface and switch to it
- https://github.com/jupyterlab/jupyter-ai - I actually mentioned this in the post but in my experience, the UX and features are far behind what we've built already. I'd love to hear from anyone who's tried jupyter-ai to give us a shot and let me know what we're missing! The plus side of jupyter-ai is of course that it supports way more models and the codebase is a lot more hackable than what we've built.
- https://colab.google/ - closed-source, similar challenges as with Deepnote. Another big challenge is that if you want to use Colab as a company, AFAICT, you need use their enterprise version (so that you can have native data collectors, support guarantees etc) and that only works with GCP so if you're an AWS shop, this might be a deal-breaker.
- https://cocalc.com - hadn't used it so far but congrats on a great project! Will check it out. Didn't look in detail but first impressions makes it look like a fairly different interface from Jupyter. One of our goals was to go to where the users already are - that meant Jupyter. So that's definitely a major difference.
- VSCode - as I've mentioned elsewhere, we're targeting a more of an analytics usecase with the features we're building. VSCode has AI features of course! But we'll look quite different once we build more items on the roadmap :)
That is correct, in that it is a completely different implementation. Unlike Deepnote and Colab, we try to maintain the same keyboard shortcuts and other semantics as JupyterLab, as much as we can.
If you don't already, we would love it if you came to the JupyerLab weekly dev meeting and demoed pretzelai: https://hackmd.io/Y7fBMQPSQ1C08SDGI-fwtg?view People from Colab, VS Code, etc. regularly come to the meeting and demo their JupyterLab related notebook work, and it's really good for the community.
DataSpell: https://www.jetbrains.com/dataspell
Any plans to finetune Codestral for this specific usecase?
We haven't tried vs Sonnet 3.5 yet - my hunch is that on the speed/quality/cost space, Sonnet will end up doing better than Codestral for some folks.
Against general purpose local models (taking Llama-70B as a high-water mark), Codestral does better far better on code related tasks while being less than 1/3rd the size (22B!). That said, I'm definitely exited to try out DeepSeek Coder v2 - by all reports, it's amazing model for code completion and will likely also beat out Codestral.
I don't think we're planning to fine-tune Codestral though (or any model for that matter). The latest models keep on becoming faster, better and cheaper AND they already work quite well. My thinking at this time is that waiting it out and having a big AI lab make a more capable general model is a better strategy.
import pandas as pd
# read a csv in with a pipe delimiter
pd.read... # AI fills this in
The AI mode/box with Cmd + K is for more complex prompts, multi-line code chunks and such. We've tried to make everything completely keyboard accessible btw, including the sidebar so that you never have to use the mouse :)
I'd like to do things like organizing very rough notes, having them reformatted according to a general template, apply changes according to a prompt, maybe ask questions that refer to a collection of notes, ...
As of now though - there are plenty of people who do use Jupyter and we hope Pretzel - as it stands today - can already be of help to them.
Jupyter is a web application that lets a user execute python code in either a local or remote "kernel" - which is just a Python process with a communication layer.
You can then do literate programming (meaning run some code and immediately see results including plots and tables) within this web interface. Basically a much fancier REPL. The code can run either locally on your machine or remotely in the aforementioned python "kernel".
Here's a quick introduction to Notebooks that I found on YouTube: https://www.youtube.com/watch?v=jZ952vChhuI
there is a feeling that i can do anything and everything with AI but in reality I can't do anything because i can't prioritize and choose anymore due to choice fatigue
By making a fork that can be installed in one line of code, we're hoping that we don't make Jupyter user's go through decision fatigue for yet another dev tool. Instead, the idea is to simply make their existing tool better.
If you've used jupyter-ai, I'd really appreciate if you can try out Pretzel and tell us if you're missing anything. We definitely are behind on the number of integrations and as you say, jupyter-ai codebase is more mature but I really think we've a made a much more usable tool.
If you wouldn't mind, can you please comment on this issue so we can ask a few follow-up questions to help in debugging this issue?
I'll also copy what I said in another comment: In short, for now, yes might have been able to implement this functionality as an extension (though there are several other changes that we've made to core Jupyter behavior as well). But our roadmap calls for much more sweeping changes to the code execution model itself and that simply cannot be an extension.
In short, for now, yes might have been able to implement this functionality as an extension (though there are several other changes that we've made to core Jupyter behavior as well). But our roadmap calls for much more sweeping changes to the code execution model itself and that simply cannot be an extension.