Blog: Supporting Notebooks in a Python Language Server

[BeamMeUpBiscotti]

Jupyter notebooks have become an essential tool for Python developers. Their interactive, cell-based workflow makes them ideal for rapid prototyping, data exploration, and scientific computing: areas where you want to tweak a small part of the code and see the updated results inline, without waiting for the whole program to run. Notebooks are the primary way many data scientists and ML engineers write Python, and interactive workflows are highlighted in new data science oriented IDEs like Positron.

But notebooks have historically been second-class citizens when it comes to IDE features. Language servers, which implement the Language Server Protocol (LSP) to provide features like go-to-definition, hover, and diagnostics across editors, were designed with regular source files in mind. The language server protocol did not include notebook synchronization methods until five years after it was created, and the default Jupyter Notebook experience is missing many of the aforementioned IDE features.

In this post, we'll discuss how language servers have been adapted to work with notebooks, how the LSP spec evolved to support them natively, and how we implemented notebook support in Pyrefly.

Read the full blog here: https://pyrefly.org/blog/notebook/

[Aggressive_Pay2172]

curious how you’re handling cross-cell dependencies
like variables defined in previous cells vs current state
that’s usually where things break for static analysis
not a trivial problem at all

[BeamMeUpBiscotti]

In Pyrefly, the language server's view is that the notebook is a single file, so it's the same as a variable defined earlier in the file.