Access Jupyter AI Features - Amazon SageMaker AI
From the chat UIFrom notebook cells

Access Jupyter AI Features

You can access Jupyter AI capabilities through two distinct methods: using the chat UI or using magic commands within notebooks.

From the chat user interface AI assistant

The chat interface connects you with Jupyternaut, a conversational agent that uses the language model of your choice.

After launching a JupyterLab application installed with Jupyter AI, you can access the chat interface by choosing the chat icon ( Icon of a rectangular shape with a curved arrow pointing to the upper right corner. ) in the left navigation panel. First-time users are prompted to configure their model. See Configure your model provider in the chat UI for configuration instructions.

Using the chat UI, you can:

  • Answer questions: For instance, you can ask Jupyternaut to create a Python function that adds CSV files to an Amazon S3 bucket. Subsequently, you can refine your answer with a follow-up question, such as adding a parameter to the function to choose the path where the files are written.

  • Interact with files in JupyterLab: You can include a portion of your notebook in your prompt by selecting it. Then, you can either replace it with the model's suggested answer or manually copy the answer to your clipboard.

  • Generate entire notebooks from prompts: By starting your prompt with /generate, you trigger a notebook generation process in the background without interrupting your use of Jupyternaut. A message containing the link to the new file is displayed upon completion of the process.

  • Learn from and ask questions about local files: Using the /learn command, you can teach an embedding model of your choice about local files and then ask questions about those files using the /ask command. Jupyter AI stores the embedded content in a local FAISS vector database, then uses retrieval-augmented generation (RAG) to provide answers based on what it has learned. To erase all previously learned information from your embedding model, use /learn -d.

Note

Amazon Q developer doesn't have the capability to generate notebooks from scratch.

For a complete list of features and detailed instructions on their usage, see the Jupyter AI chat interface documentation. To learn about how to configure access to a model in Jupyternaut, see Configure your model provider in the chat UI.

From notebook cells

Using %%ai and %ai magic commands, you can interact with the language model of your choice from your notebook cells or any IPython command line interface. The %%ai command applies your instructions to the entire cell, whereas %ai apply them to the specific line.

The following example illustrates an %%ai magic command invoking an Anthropic Claude model to output an HTML file containing the image of a white square with black borders.

%%ai anthropic:claude-v1.2 -f html
Create a square using SVG with a black border and white fill.

To learn about the syntax of each command, use %ai help. To list the providers and models supported by the extension, run %ai list.

For a complete list of features and detailed instructions on their usage, see the Jupyter AI magic commands documentation. In particular, you can customize the output format of your model using the -f or --format parameter, allow variable interpolation in prompts, including special In and Out variables, and more.

To learn about how to configure the access to a model, see Configure your model provider in a notebook.