LANGUAGESERVERS.md

Language Servers

jupyter-lsp does not come with any Language Servers! However, we will try to use them if they are installed and we know about them (you can disable this behavior by configuring autodetect).

Use a package manager to install a language server (also this list) from the tables below: these implementations are tested to work with jupyter-lsp.

language	jupyter labextension link npm install (-g) yarn/jlpm add (-g)
bash	bash-language-server
css less sass	vscode-css-languageserver-bin
docker	dockerfile-language-server-nodejs
html	vscode-html-languageserver-bin
javascript typescript	javascript-typescript-langserver
json	vscode-json-languageserver-bin
yaml	yaml-language-server

language	conda install -c conda-forge	language-specific package manager
python	python-language-server	pip install python-language-server
r	r-languageserver	Rscript -e 'install.packages("languageserver")'

Don't see an implementation for the language server you need? You can bring your own language server.

Please consider contributing your language server spec to jupyter-lsp!

Adding custom language servers

Jupyter config via traitlets

Like the Jupyter Notebook server, JupyterHub and other Jupyter interactive computing tools, jupyter-lsp can be configured via Python or JSON files in well-known locations. You can find out where to put them on your system with:

jupyter --paths

They will be merged from bottom to top, and the directory where you launch your notebook server wins, making it easy to check in to version control. The absolute minimum listing requires argv (a list of shell tokens to launch the server) and languages (which languages to respond to), but many number of other options to enrich the user experience are available in the schema.

# ./jupyter_notebook_config.json                 ---------- unique! -----------
#                                               |                              |
# or e.g.                                       V                              V
# $PREFIX/etc/jupyter/jupyter_notebook_config.d/a-language-server-implementation.json
{
  "LanguageServerManager": {
    "language_servers": {
      "a-language-server-implementation": {
        "argv": ["/absolute/path/to/a-language-server", "--stdio"],
        "languages": ["a-language"]
      }
    }
  }
}

More complex configurations that can't be hard-coded may benefit from the python approach:

# jupyter_notebook_config.py
import shutil

# c is a magic, lazy variable
c.LanguageServerManager.language_servers = {
    "a-language-server-implementation": {
        # if installed as a binary
        "argv": [shutil.which("a-language-server")],
        "languages": ["a-language"]
    },
    "another-language-implementation": {
        # if run like a script
        "argv": [shutil.which("another-language-interpreter"), "another-language-server"],
        "languages": ["another-language"]
    }
}

Python entry_points

pip-installable packages in the same environment as the Jupyter notebook server can be automatically detected as providing a language server spec. These are a little more involved: see CONTRIBUTING.

Configuration Options

Like language_servers, these can be configured via jupyter_notebook_config.json (or .py) or via command line arguments to jupyter notebook or jupyter lab.

nodejs

default: autodetect

A custom absolute path to your nodejs executable.

autodetect

default: True

jupyter-lsp will look for all known language servers. User-configured language_servers of the same implementation will be preferred over autodetected ones.

node_roots

default: []

Absolute paths to search for node_modules, such as nodejs-backed language servers. The order is, roughly:

• the folder where notebook or lab was launched
• the JupyterLab staging folder
• wherever conda puts global node modules
• wherever some other conventions put it

extra_node_roots

default: []

Additional places jupyter-lsp will look for node_modules. These will be checked before node_roots.