Noteable ChatGPT Plugin

AI Tech IP319655



Noteable is a powerful plugin that allows you to create, manage, and execute Python, SQL, and Markdown notebooks. It’s designed to facilitate data exploration, visualization, and sharing of notebooks. It provides a wide range of functions, from creating new projects and notebooks to executing code cells and managing kernel sessions.

Available Function(s) and Parameters:

  • about: Provides information about the plugin.
  • create_project: Creates a new project to store Notebooks and associated data files. Parameters: name, description, git_url.
  • set_default_project: Configures the user’s default project. Parameter: new_default_project_id.
  • clear_default_project: Clears the user’s default project.
  • get_project_files: Gets a list of files in the project. Parameters: project_id, filename_contains, file_limit, sort_by, sort_order.
  • create_notebook: Creates a new notebook. Parameters: project_id, notebook_name, start_kernel.
  • get_notebook: Gets a summary of a notebook. Parameter: file_id.
  • get_text_file: Gets content of a markdown or plain text file. Parameter: file_id.
  • get_datasources: Gets the databases for a notebook file. Parameter: file_id.
  • run_multiple_cells: Executes multiple cells in a Notebook. Parameters: file_id, ids, before_id, after_id, all.
  • shutdown_kernel_from_file: Shuts down the kernel for a notebook. Parameter: file_id.
  • get_cell: Returns Cell model details. Parameters: file_id, cell_id.
  • update_cell: Replaces the source code of a cell. Parameters: file_id, cell_id, source, and_run.
  • change_cell_type: Changes the type of a cell. Parameters: file_id, cell_id, cell_type, db_connection, assign_results_to.
  • create_cell: Creates a code or markdown cell. Parameters: file_id, cell_id, cell_type, and_run, source, after_cell_id, datasource_id, assign_results_to.
  • run_cell: Runs a Cell within a Notebook by ID. Parameters: file_id, cell_id.
  • get_user_info: Gets details of the Plugin user’s Noteable account information.
  • get_active_kernel_sessions: Returns a list of the user’s active kernel sessions.
  • shutdown_kernel: Shuts down the kernel for a notebook. Parameter: kernel_session_id.

Basic Prompt:

“Create a new Python notebook in my default project and run a simple ‘Hello, World!’ code.”

Use Case Interpretation:

Noteable can be a game-changer for data scientists, researchers, and anyone who works with data. It allows you to create and manage notebooks right from your chat interface, run code or SQL queries, visualize data, and share your findings. It’s like having a full-fledged data science environment at your fingertips, without leaving your chat window.

Advanced Prompts:

  1. “Create a new project named ‘Climate Analysis’, then create a Python notebook within this project. In the notebook, import pandas and matplotlib libraries.”
  2. “In my ‘Stock Analysis’ notebook, create a new cell that fetches data from my SQL database using the query ‘SELECT * FROM stocks WHERE price > 100’, and assign the results to a variable named ‘high_price_stocks’.”
  3. “Get a list of all my active kernel sessions and shut down the one that’s been idle for the longest time.”

Unusual Prompts

  1. “Create a markdown cell in my ‘Fun Facts’ notebook that contains a list of all the planets in our solar system, then create a code cell that calculates the total number of planets.”
  2. “In my ‘Recipe Database’ project, create a SQL notebook that contains a query to find all recipes that include ‘chocolate’ in their ingredients.”
  3. “Create a Python notebook that generates a random inspirational quote each day and sends it to my email.”

Multi-Step Prompts:

Noteable is capable of executing multi-step prompts. Here are three unique examples:

  1. Using Noteable and WebPilot: “Create a Python notebook in Noteable that scrapes data from a list of URLs using BeautifulSoup. Then, use WebPilot to navigate to these URLs and extract the required data.”
  2. Using Noteable and VoxScript: “Create a Python notebook that analyzes YouTube transcript data. Use VoxScript to search for and retrieve the transcripts, then analyze the data in the notebook.”
  3. Using Noteable and BlockAtlas: “Create a Python notebook that visualizes US Census data. Use BlockAtlas to find and retrieve the data, then use matplotlib in the notebook to create the visualizations.”

For more information, visit the Noteable website or contact the support team at

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