TN009 Review AI in Notebooks

Objective

• Review how other products/projects are creating AI enabled experiences in a notebook UX

TL;DR

There are lots of notebook products and notebook like interfaces. This doc tries to summarize how they are using AI to enhance the notebook experience. The goal is to identify features and patterns that could be useful incorporating into Foyle and RunMe.

JupyterLab AI

JupyterLab AI is a project that aims to bring AI capabilities to JupyterLab. It looks like it introduces a couple ways of interacting with AI’s

• GitHub copilot like chat window docs.
• AI Magic Commands
• Inline Completer uses ghost text to render completions to the current cell

The chat feature looks very similar to GitHub copilot chat. Presumably its more aware of the notebook structure. For example, it lets you select notebook cells and include them in your prompt (docs).

Chat offers built in RAG. Using the /learn command you can inject a bunch of documents into a datastore. These documents are then used to answer questions in the chat using RAG.

The AI magic commands let you directly invoke different AI models. The prompt is the text inside the cell. The model output is then rendered in a new cell. This is useful if your asking the model to produce code because you can then execute the code in the notebook.

In RunMe you achieve much of the same functionality as the magics by using a CLI like llm and just executing that in a bash cell.

Compared to what’s proposed in TN008 Autocompleting cells it looks like the inline completer only autocompletes the current cell; it doesn’t suggest new cells.

JupyterLab AI Links

• Early demo video of inline completion

Hex

Hex magic lets you describe in natural language what you want to do and then it adds the cells to the notebook to do that. For example, you can describe a graph you want and it will add an SQL cell to select the data Demo Video.

Notably, from a UX perspective prompting the AI is a “side conversation”. There is a hover window that lets you ask the AI to do something and then the AI will modify the notebook accordingly. For example, in their Demo Video you explicitly ask the AI to “Build a cohort analysis to look at customer churn”. The AI then adds the cells to the notebook. This is different from an AutoComplete like UX as proposed in TN008 Autocompleting cells.

In an Autocomplete like UX, a user might add a markdown cell containing a heading like “Customer Churn: Cohort Analysis”. The AI would then autocomplete these by inserting the cells to perform the analysis and render it. The user wouldn’t have to explicitly prompt the AI.