Integrating Generative AI Models into Your Apps with Amazon Bedrock

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Getting Started

To code with the instructor, create a fork of this repository, then create a GitHub Codespace on the fork. The Fork button is in the upper-right-hand corner of this page. Once forked, create the green Code button on your forked version to access the ability to create a Codespace.

GitHub provides a generous amount of free time for Codespaces each month (currently, 120 hours). You are responsible for any charges incurred if you exceed the free time. You can monitor your usage in the GitHub settings.

The AWS Bedrock Python demos are located in the demos folder. To run the demos, a virtual environment will need to be created.

1. Open a terminal window, and log into AWS with the AWS CLI.

aws configure sso

After answering a few question, a web browser will open. After logging in through the web browser, it will attempt to redirect to localhost:SOME_PORT or 127.0.0.1:SOME_PORT. This will localhost URL not work. Click on the PORTS tab, and update the localhost URL with the URL to the GitHub Codespace container and complete logging in.

1. In Visual Studio Code, open the intro_to_bedrock.ipynb in the demos/aistylist folder. In the upper right corner of the notebook, select a kernel. To create a new kernel, create a new virtual environment. The path to the Python interpreter is /usr/bin/python.

2. Follow the instructions in the intro_to_bedrock.ipynb notebook to configure the environment.

3. You are now ready to work through the lab exercise in the aistylist.ipynb notebook located in the demos/aistylist folder.

Special thanks to Amazon for providing this excellent demonstration: https://github.com/aws-samples/amazon-bedrock-aistylist-lab

License

The content of this repository is made available under the following license.

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