Welcome to your guide on harnessing the power of the Amazon Bedrock service through pre-built code examples. This repository is designed to help developers dive deep into Bedrock and make the most out of its capabilities.

Introduction to Bedrock

Amazon Bedrock is a powerful platform for building and deploying machine learning models. It provides diverse tools and services for generative AI solutions. In this blog, we’ll not only cover the basics but also guide you through specific use cases and how to operationalize your projects.

Table of Contents

• Introduction to Bedrock
• Prompt Engineering
• Bedrock Fine-tuning
• Custom Model Import
• Generative AI Solutions
• Knowledge Bases
• Retrieval Augmented Generation (RAG)
• Agents
• Security and Governance
• Responsible AI
• Operational Tooling
• Multimodal

Getting Started

To begin your journey with the code examples, ensure you have access to Amazon Bedrock. After that, you can clone the repository and dive into the various folders as outlined in the table of contents. Each folder contains detailed instructions in its README file to help you get going.

Enable AWS IAM Permissions for Bedrock

Before you can use Amazon Bedrock, you need to ensure that the AWS Identity and Access Management (IAM) permissions are set correctly for the identity you assume in your environment.

Your role could be the Studio notebook Execution Role from SageMaker, or it might be a role or IAM User for self-managed notebooks. It’s important to ensure you have the right permissions. Here’s how you can grant the appropriate permissions:

1. Open the AWS IAM Console.
2. Locate your Role or User.
3. Select Add Permissions → Create Inline Policy to attach new inline permissions.
4. Open the JSON editor and paste in the following example policy:

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

Note: Keep in mind that with Amazon SageMaker, your notebook execution role is separate from the user or role you use to log in to the AWS Console. Thus, if you wish to explore the console for Amazon Bedrock, you must grant permissions accordingly.

Understanding the Code Example with an Analogy

Think of the Amazon Bedrock service as a pizza kitchen where you can create different types of pizzas (AI models) based on unique recipes (coding examples). The ingredients (code snippets) are ready to use, but first, you need to have access to the kitchen (Amazon Bedrock) and the necessary skills (IAM permissions) to create the pizza.

Once you have access, you can follow the recipe to create a variety of pizzas—from classic margherita (basic prompts) to gourmet creations with unique toppings (custom model imports). The more skills and permissions you have, the more delicious and intricate your pizzas (models) can be.

Troubleshooting Tips

Should you run into challenges, here are some troubleshooting ideas:

• Ensure your IAM permissions are correctly set and that they have been saved properly in the AWS console.
• Double-check that you are operating in the right AWS region where your Bedrock resources are set.
• Review the README files in each project folder for detailed instructions.
• If experiencing access issues, consider reviewing the policies applied to your role or user.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.