Amazon Bedrock Workshop

This hands-on workshop, aimed at developers and solution builders, introduces how to leverage foundation models (FMs) through Amazon Bedrock.

Amazon Bedrock is a fully managed service that provides access to FMs from third-party providers and Amazon; available via an API. With Bedrock, you can choose from a variety of models to find the one that’s best suited for your use case.

Within this series of labs, you'll explore some of the most common usage patterns we are seeing with our customers for Generative AI. We will show techniques for generating text and images, creating value for organizations by improving productivity. This is achieved by leveraging foundation models to help in composing emails, summarizing text, answering questions, building chatbots, and creating images. You will gain hands-on experience implementing these patterns via Bedrock APIs and SDKs, as well as open-source software like LangChain and FAISS.

Labs include:

• Text Generation [Estimated time to complete - 30 mins]
• Text Summarization [Estimated time to complete - 30 mins]
• Questions Answering [Estimated time to complete - 45 mins]

Getting started

Choose a notebook environment

This workshop is presented as a series of Python notebooks, which you can run from the environment of your choice:

• For a fully-managed environment with rich AI/ML features, we'd recommend using SageMaker Studio. To get started quickly, you can refer to the instructions for domain quick setup.
• For a fully-managed but more basic experience, you could instead create a SageMaker Notebook Instance.
• If you prefer to use your existing (local or other) notebook environment, make sure it has credentials for calling AWS.

Enable AWS IAM permissions for Bedrock

The AWS identity you assume from your notebook environment (which is the Studio/notebook Execution Role from SageMaker, or could be a role or IAM User for self-managed notebooks), must have sufficient AWS IAM permissions to call the Amazon Bedrock service.

To grant Bedrock access to your identity, you can:

• Open the AWS IAM Console
• Find your Role (if using SageMaker or otherwise assuming an IAM Role), or else User
• Select Add Permissions > Create Inline Policy to attach new inline permissions, open the JSON editor and paste in the below example policy:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "BedrockFullAccess",
            "Effect": "Allow",
            "Action": ["bedrock:*"],
            "Resource": "*"
        }
    ]
}

⚠️ Note: With Amazon SageMaker, your notebook execution role will typically be separate from the user or role that you log in to the AWS Console with. If you'd like to explore the AWS Console for Amazon Bedrock, you'll need to grant permissions to your Console user/role too.

For more information on the fine-grained action and resource permissions in Bedrock, check out the Bedrock Developer Guide.

Clone and use the notebooks

ℹ️ Note: In SageMaker Studio, you can open a "System Terminal" to run these commands by clicking File > New > Terminal

Once your notebook environment is set up, clone this workshop repository into it.

git clone https://github.com/SherryXDing/FINRA-Bedrock-Workshop
cd amazon-bedrock-workshop

You're now ready to explore the lab notebooks! Start with 00_Intro/bedrock_boto3_setup.ipynb for details on how to install the Bedrock SDKs, create a client, and start calling the APIs from Python.

Content

This repository contains notebook examples for the Bedrock Architecture Patterns workshop. The notebooks are organised by module as follows:

Intro

• Simple Bedrock Usage: This notebook shows setting up the boto3 client and some basic usage of bedrock.

Generation

• Generation with additional context: We then take this further by enhancing the prompt with additional context in order to improve the response.

Summarization

• Small text summarization: In this notebook, you use use Bedrock to perform a simple task of summarizing a small piece of text.
• Long text summarization: The above approach may not work as the content to be summarized gets larger and exceeds the max tokens of the model. In this notebook we show an approach of breaking the file up into smaller chunks, summarizing each chunk, and then summarizing the summaries.

Question Answering

• Simple questions with context: This notebook shows a simple example answering a question with given context by calling the model directly.