AI Image


API Documentation

Noteable is an innovative platform that streamlines the process of creating, exploring, and sharing digital notebooks in Python, SQL, and Markdown formats. Embedded on, it allows users to run Jupyter notebooks with a blend of code, markdown, and SQL cells. The platform offers a distinctive URL structure for notebooks, projects, and spaces, with projects containing notebooks and data files and spaces encompassing projects. Noteable further simplifies data exploration with notebook runtimes or kernels, which are docker images with preinstalled data science stacks. Users can choose additional kernelspecs at notebook creation or kernel launch, and can even use their environment variables as secrets for modules requiring API tokens or user credentials. Uniquely, Noteable's Python kernel supports top-level async-await and allows for display of images from disk or buffer in the assistant response. It also facilitates installation of libraries, making it a versatile tool for data scientists and analysts. Moreover, the Noteable user interface supports configuration of RBAC permissions, Secrets, Data Sources, and Databases, as well as interaction with notebooks, making it a comprehensive tool for data exploration and visualization.




Example Prompts


Create a new notebook called "DataAnalysis.ipynb"


List all files in my default project


Show the available kernels for my project


Get the content of a notebook with file ID "


Update the source code of a cell with cell ID "xyz-


Run all cells in a notebook with file ID "abcd-


Change the cell type of cell ID "abc-


Get the metadata of a file with file ID "


Show the active kernel sessions


Start a kernel for a notebook with file ID "abcd-efgh-


Shutdown the kernel with kernel session ID "


Get information about my user account

Description for AI

On, create and run Jupyter notebooks with code, markdown, and SQL cells.


  • Notebook URL, CellID optional:<file_id>/<decorative_file_name>?cellID=<cell_id>
  • Project URL:<project_id>/<decorative_project_name>
  • Space URL:<space_id>/<decorative_space_name>

project_id, space_id, and file_id are UUIDs; cell_id is a string

Spaces contain projects, projects contain notebooks and data files.


Notebook runtimes (kernels) are docker images with the Project files volume mounted into the current working directory. The default docker image is python with the data science stack preinstalled. Additional kernelspecs can be chosen on notebook creation as well as kernel launch.

User configured secrets are available as environment variables. For libraries and modules that use API tokens or user credentials, prefer to use environment variables from Secrets over other configuration methods.

Python Kernel

IPython supports top level async-await. To display images from disk or buffer in the assistant response, use IPython.display.Image with embed=True. Matplotlib animations and other GIFs can also be shown in chat and the notebook using IPython.display.Image(gif_path_or_buffer).

The assistant is allowed to !pip install libraries. Good etiquette is placing all the pip installs at the top of the Notebook and installing quietly (!pip install -q).

R Kernel

The R kernel (ir) comes with a robust set of pre-installed packages, including the full tidyverse suite, machine learning packages like caret and randomForest, forecast for time series analysis, lme4 for mixed-effects models, and more. Additional packages can be installed as needed using either install.packages or devtools.

Noteable UI

Direct the user to the Noteable UI to configure RBAC permissions, Secrets, Data Sources, and Databases. IPython widgets and other interactive widgets are supported in the Noteable UI. Additionally, the assistant can guide users to the UI for viewing and interacting with notebooks, especially when dealing with IPython widgets and other interactive elements that may not be fully supported in the assistant's response.

Similar Plugins and Alternatives