Create and connect local Jupyter notebooks to remote Wherobots compute with automatic kernel selection. Create, edit, and execute spatial notebooks using natural language prompts within your editor.
Public PreviewThe Wherobots VS Code Extension provides Jupyter integration that connects your local notebooks
to remote Wherobots compute with automatic kernel selection, all within your editor.
The steps below use GitHub Copilot Chat in VS Code. Other editors have an equivalent feature — Cursor (Chat / Composer), Windsurf (Cascade), Kiro (AI Chat), Positron (Copilot Chat), Trae (Side chat).Consult your editor’s documentation for details on accessing chat, switching modes, and selecting models.
AI-assisted notebook development with the Wherobots VS Code Extension and your editor’s AI assistant offers several key benefits:
Faster notebook creation
Generate code, data loading, and analysis steps with natural language prompts.Work within your editor without needing to switch contexts between different tools for notebook development and Wherobots interaction.
Enhanced productivity
Focus on analysis and insights while the AI assistant handles code generation and execution details.
Learning and discovery
Explore datasets and analysis techniques with AI guidance, making it easier to get started with new data or methods.
Full use of Wherobots ecosystem
Leverage the Wherobots MCP server for dataset discovery and execution, unlocking the full potential of Wherobots Cloud in your notebooks and then turn those notebooks into repeatable workflows and jobs.
An active GitHub Copilot subscription (or your editor’s equivalent AI assistant). For more information about Copilot, see Microsoft’s GitHub Copilot.
Use advanced modelsAI-assisted notebook generation works best with the latest advanced AI models (for example, the latest Claude Opus, Gemini Pro, or GPT Codex models) — ensure your subscription includes access to these models for optimal results.
To create and run a Jupyter notebook connected to Wherobots compute using your editor’s AI assistant:
1
Open the Command Palette
To open the Command Palette, click the search bar at the top of your editor and type >wherobots:.
You can also press ⌘ + Shift + P on Mac, Ctrl + Shift + P on Windows/Linux to open the Command Palette.
2
Sign in to Wherobots
If you haven’t already, sign in to Wherobots Cloud:
Type Wherobots: Sign In and click Enter.
A browser window opens for you to sign in to Wherobots and select your Organization; when you’re done, return to your editor.
Prefer an API key? Type Wherobots: Set API Key and enter your Wherobots API key when prompted. If you haven’t created an API key yet, run Wherobots: Generate API Key on Wherobots Cloud to create one.
3
Create a workspace
Open the Command Palette and type Wherobots: Create Workspace.Click Enter to run the command.
4
Choose a runtime
Choose the runtime configuration for your workspace, including Region, Runtime Size, and Idle Timeout.
Click Create & Start.
Professional, Innovation, and Enterprise Organizations can select from additional compute tiers.
5
Open the chat panel
Open your editor’s AI assistant. In VS Code, open GitHub Copilot Chat via View > Chat.
In VS Code, you can also open Copilot Chat by pressing ⌘ + Shift + I on macOS or Ctrl + Shift + I on Windows/Linux. Other editors have their own shortcuts — consult your editor’s documentation.
6
Select Agent mode
Select Agent mode from the chat mode dropdown at the top of the chat panel.
Selecting Agent mode in Copilot Chat in VS Code. Consult the documentation for your chosen editor on how to change modes and models.
7
Choose a high-performance model
Select a high-performance AI model for optimal results. This includes the latest versions of advanced models such as Gemini Pro, Claude Opus, Claude Sonnet, or GPT Codex.
8
Confirm the MCP server is connected
Ask a simple question to confirm the MCP server is connected:
List the available tables in wherobots_pro_data.
Shortly, you should see a response listing available tables in the wherobots_pro_data dataset.If no SQL session is running, the next SQL query you run through the MCP server starts one, and a cold SQL session can take several minutes to start. While it starts, submit_query_tool returns a query_id, your AI assistant polls get_query_status_tool until the query finishes, and then your AI assistant fetches the rows with get_query_results_tool. See How queries run.
Enter a natural language prompt describing the notebook you want. For example:
Create a notebook that finds all parks within 1 mile of subway stations in Manhattan using the Overture Maps dataset. Run each cell.
When your editor requests permissions for the Wherobots MCP server, click Allow (or Allow in this session to avoid repeated prompts).
At this point, the AI assistant will locally generate a notebook (an .ipynb file) based on your prompt. You can open and run this notebook in your editor.
Now that your notebook is generated from the previous step, you can run it and see the results of each cell directly within your editor.
You can use these steps to run any local Jupyter notebook against Wherobots compute, not just AI-generated notebooks.
This allows you to leverage Wherobots compute and datasets in your existing notebooks as well.
1
Open the Command Palette
To open the Command Palette, click the search bar at the top of your editor and type >wherobots:.
You can also press ⌘ + Shift + P on Mac, Ctrl + Shift + P on Windows/Linux to open the Command Palette.
2
Open the Workspaces view
Click Wherobots: Focus on Workspaces View.
3
Create a workspace
Click Create WorkspaceCreating a workspace provisions a runtime — a dedicated compute cluster — in Wherobots Cloud. Once the runtime is running, your local notebook can connect to it and execute code remotely.
4
Open the local notebook
If the notebook is already open in your editor, skip to the next step.Otherwise, do the following:
Hover over the running workspace you started and a plug icon () will appear.
Click the plug icon to find and open the notebook file on your local machine. The notebook will open in a new tab in your editor.
5
Run all cells
Within the notebook, click Run All to execute all cells.
6
Select kernel source
When prompted for the kernel source, select Wherobots.
7
Select a running workspace
Select a running workspace from the list. Each workspace corresponds to a runtime provisioned in Wherobots Cloud.
8
Choose the Jupyter kernel
Select the appropriate Jupyter kernel. For most workflows, choose Python 3 (ipykernel).
The quality of AI-generated notebooks depends heavily on the prompts you provide. Use the following strategies for best results:
Strategy
Example prompt
Be specific about the dataset
”Use the Overture Maps buildings dataset to find all hospitals in Chicago”
Request validation
”Check that each query works at a small scale before adding it to the notebook”
Request iteration on failures
”If a query fails, debug and fix it, then try again”
Specify output format
”Display the results on a map”
AI-assisted development is meant to be iterative. You may need to refine your prompts or guide the AI assistant
through multi-step analyses. The experience improves as you develop prompting patterns
that work well with your datasets.
Use advanced AI models like the latest versions of the following for optimal results with AI-assisted notebook development:
Claude Sonnet
Claude Opus
Gemini Pro
GPT Codex
These models have enhanced reasoning capabilities and better contextual understanding, making them more effective at generating accurate code and handling complex notebook creation tasks.
