Eugene YurtsevandGitHub
f0f329d9e1
# Overview Adding conditional rendering logic to co-locate js and python documentation. * `:::` conditional syntax can be used to switch between python only or js only content. * Contains simple unit tests for `:::` * PR adds set up for a way to implement a context switch between languages, but it will not be enabled until JS content is merged in. * Contains a script that can add javascript documentation Implementation of: https://github.com/langchain-ai/langgraph/pull/5118 ## Example Example of conditional rendering / compilation. ```markdown ### Config (static context) Config is for immutable data like user metadata or API keys. Use when you have values that don't change mid-run. Specify configuration using a key called **"configurable"** which is reserved for this purpose: :::python This content will only be rendered for the python site. ::: :::js this content will only be rendered for the js / ts site. ::: ```
Setup
To setup requirements for building docs you can run:
uv sync --group test
Serving documentation locally
To run the documentation server locally you can run:
make serve-docs
This will start the documentation server on http://127.0.0.1:8000/langgraph/.
Execute notebooks
If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:
python _scripts/prepare_notebooks_for_ci.py
./_scripts/execute_notebooks.sh
Note: if you want to run the notebooks without %pip install cells, you can run:
python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
./_scripts/execute_notebooks.sh
prepare_notebooks_for_ci.py script will add VCR cassette context manager for each cell in the notebook, so that:
- when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
- when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes
Adding new notebooks
If you are adding a notebook with API requests, it's recommended to record network requests so that they can be subsequently replayed. If this is not done, the notebook runner will make API requests every time the notebook is run, which can be costly and slow.
To record network requests, please make sure to first run prepare_notebooks_for_ci.py script.
Then, run
jupyter execute <path_to_notebook>
Once the notebook is executed, you should see the new VCR cassettes recorded in cassettes directory and discard the updated notebook.
Updating existing notebooks
If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in cassettes directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
To delete cassettes for a notebook, you can run:
rm cassettes/<notebook_name>*