Introduction to Amazon Bedrock Samples
The Amazon Bedrock Samples repository serves as an invaluable resource for anyone looking to dive into Amazon's Bedrock service, a tool designed to enhance the use of artificial intelligence and machine learning capabilities. This repository provides users with pre-built examples and guidelines to help them explore and effectively utilize the various features of the Amazon Bedrock service. A dedicated website powered by this GitHub repository helps guide users through the content, making it easy to navigate and implement.
Contents Overview
The repository is structured into several sections, each focusing on different aspects of the Bedrock service:
- Introduction to Bedrock: Users can kick off their journey by learning the basics of what Bedrock has to offer.
- Prompt Engineering: This section offers tips and best practices for crafting effective prompts to achieve the desired results from AI models.
- Agents: It explains how to implement Generative AI Agents and the components that make up these systems.
- Custom Model Import: Users can learn how to bring their custom models into the Bedrock environment to suit specific requirements.
- Multimodal: This part focuses on handling multimodal data, enabling users to work with a variety of data types using Amazon Bedrock.
- Generative AI Use Cases: Demonstrates various practical scenarios where generative AI can have significant impacts.
- Retrieval Augmented Generation (RAG): Guides users on implementing RAG, a technique that enhances AI by incorporating retrieval mechanisms.
- Responsible AI: Highlights the importance of using AI technology ethically, along with guidelines for responsible deployment.
- Workshop: Offers an example setup for conducting workshops using Amazon Bedrock.
- POC to Prod: Provides instructions on taking projects from proof of concept to full production deployment.
- Embeddings: Educates users on how to use embedding models available through Amazon Bedrock to enhance machine learning tasks.
- Observability & Evaluation: This section helps users understand how to improve observability and evaluate models and AI applications effectively.
Getting Started
To make use of the code examples provided, users first need access to Amazon Bedrock. Once they have access, they should clone the repository and navigate to the folder of interest to find detailed instructions in the README files.
Enabling AWS IAM Permissions for Bedrock
To utilize the features of Bedrock, users must have sufficient AWS Identity and Access Management (IAM) permissions. This involves setting permissions for either a role or an IAM user, especially for those using services like SageMaker. An exemplary policy for granting full access to Bedrock is provided, which can be added via the AWS IAM Console by creating an inline policy with the required permissions.
Contributing and License
The Amazon Bedrock Samples project encourages community contributions. Guidelines for contributing can be found in the project's CONTRIBUTING.md file. The project is licensed under the MIT-0 License, ensuring that users have the freedom to use the content with minimal restrictions.
Security
For users interested in the security aspects of the project, more information is available in the security section of the CONTRIBUTING document.
Overall, the Amazon Bedrock Samples repository is a compact, well-documented resource aimed at simplifying the learning and deployment process for AI and machine learning applications using Amazon Bedrock, catering to both new learners and experienced developers seeking to expand their AI capabilities.