# Customize Amazon Q Developer in Amazon SageMaker Studio applications
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You can customize Amazon Q Developer in the JupyterLab and Code Editor applications in Amazon SageMaker Studio. When you customize Q Developer, it provides suggestions and answers based on examples from your codebase. If you use Amazon Q Developer Pro, you can load any customizations that you've created with that service. 

## Customize in JupyterLab
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In JupyterLab, you can load any customizations that you've created with Amazon Q Developer Pro. Or, in your JupyterLab space, you can customize Q Developer locally with files that you upload to the space.

### To use customizations that you've created in Amazon Q Developer Pro
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When you load a customization, Q Developer provides suggestions based on the codebase that you used to create the customization. Also, when you use the chat in the **Amazon Q** panel, you interact with your customization.

For more information about setting up customizations, see [Customizing suggestions](https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/customizations.html) in the *Amazon Q Developer User Guide*.

**To load your customization**

Open your JupyterLab space and complete the following steps.

1. In the status bar at the bottom of JupyterLab, choose **Amazon Q**. A menu opens.

1. In the menu, choose **Other Features**. The **Amazon Q Features** tab opens in the main work area.

1. In the **Amazon Q Features** tab, under **Select Customization**, choose your Q Developer customization.

1. Interact with your customization in either of the following ways:
   + Create a notebook, and write code in it. As you do, Q Developer automatically provides tailored inline suggestions based on your customization.
   + Chat with Q Developer in the **Amazon Q** panel by following these steps:

     1. In the left sidebar in JupyterLab, choose the **Jupyter AI Chat** icon. The **Amazon Q** panel opens.

     1. Use the **Ask Amazon Q** chat box to interact with your customization.

### To customize Amazon Q Developer with files in your JupyterLab space
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In JupyterLab, you can customize Q Developer with files that you upload to your space. Then, in the chat in the **Amazon Q** panel, you can use a command to ask Q Developer about those files.

When you customize Q Developer with files in your space, the customization exists only in your space. You can't load the customization elsewhere, such as in other spaces or in the Amazon Q Developer console.

You can customize Q Developer with files in JupyterLab if you use either Amazon Q Developer Pro or Amazon Q Developer at the Free tier.

**To customize with your files**

Open your JupyterLab space and complete the following steps.

1. Check whether your space is configured with the required embedding model. You can customize Q Developer in JupyterLab only if you use the default embedding model, which is **CodeSage :: codesage-small**. To check, do the following:

   1. In the left sidebar in JupyterLab, choose the **Jupyter AI Chat** icon. The **Amazon Q** panel opens.

   1. Choose the settings icon in the upper-right corner of the panel.

   1. For **Embedding model**, if necessary, choose **CodeSage :: codesage-small**, and choose **Save Changes**.

   1. In the upper-right corner of the panel, choose the back icon. 

1. To upload files that you want to customize Q Developer with, in the **File Browser** panel, choose the **Upload Files** icon.

1. After you upload your files, in the **Ask Amazon Q** chat box, type `/learn file path/`. Replace *file path/* with the path to your files in your JupyterLab space. When Amazon Q finishes processing your files, it confirms with a chat message in the Amazon Q panel.

1. To ask Q Developer a question about your files, type `/ask` in the chat box, and follow the command with your question. Amazon Q generates an answer based on your files, and it responds in the chat.

For more information about the `/learn` and `/ask` commands, such as their options and supported arguments, see [Learning about local data](https://jupyter-ai.readthedocs.io/en/latest/users/index.html#learning-about-local-data) in the Jupyter AI user documentation. That page explains how to use the commands with the Jupyternaut AI chatbot. JupyterLab in Amazon SageMaker Studio supports the same command syntax.

## Customize in Code Editor
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If you've created a customization in Amazon Q Developer Pro, you can load it in Code Editor. Then, when Q Developer provides suggestions for your code, it bases them on the codebase that you used to create the customization. Also, when you use the chat in the **Amazon Q: Chat** panel, you interact with your customization.

**To use customizations that you've created in Amazon Q Developer Pro**

Open your Code Editor space and complete the following steps.

1. In the Code Editor menu, choose **View**, and choose **Command Pallette**.

1. In the command pallet, begin typing **>Amazon Q: Select Customization**, and choose that option in the filtered list of commands when it appears. The command pallet shows your Q Developer customizations.

1. Choose your customization.

1. Interact with your customization in either of the following ways:
   + Create a Python file or a Jupyter notebook, and write code in it. As you do, Q Developer automatically provides tailored inline suggestions based on your customization.
   + Chat with Q Developer in the **Amazon Q** panel by following these steps:

     1. In the left sidebar in Code Editor, choose the **Amazon Q** icon. The **Amazon Q: Chat** panel opens.

     1. Use the chat box to interact with your customization.

For more information about the capabilities of Q Developer, see [Using Amazon Q Developer in the IDE](https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/q-in-IDE.html) in the *Amazon Q Developer User Guide*.