cdklabs.generative-ai-cdk-constructs 0.1.23

AWS Generative AI CDK Constructs is a library for well-architected generative AI patterns.

AWS Generative AI CDK Constructs

All classes are under active development and subject to non-backward compatible changes or removal in any future version. These are not subject to the Semantic Versioning model. This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.

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Table of contents

• Introduction
• CDK Versions
• Contributing
• Adding new constructs
• Getting Started
• Catalog
• Sample Use Cases
• Additional Resources
• Operational Metrics Collection
• Roadmap
• Legal Disclaimer

Introduction

The AWS Generative AI Constructs Library is an open-source extension of the AWS Cloud Development Kit (AWS CDK) that provides multi-service, well-architected patterns for quickly defining solutions in code to create predictable and repeatable infrastructure, called constructs. The goal of AWS Generative AI CDK Constructs is to help developers build generative AI solutions using pattern-based definitions for their architecture.

The patterns defined in AWS Generative AI CDK Constructs are high level, multi-service abstractions of AWS CDK constructs that have default configurations based on well-architected best practices. The library is organized into logical modules using object-oriented techniques to create each architectural pattern model.

CDK Versions

AWS Generative AI CDK Constructs and the AWS CDK are independent teams and have different release schedules. Each release of AWS Generative AI CDK Constructs is built against a specific version of the AWS CDK. The CHANGELOG.md file lists the CDK version associated with each AWS Generative AI Constructs release. For instance, AWS Generative AI CDK Constructs v0.0.0 was built against AWS CDK v2.96.2. This means that to use AWS Generative AI CDK Constructs v0.0.0, your application must include AWS CDK v2.96.2 or later. You can continue to use the latest AWS CDK versions and upgrade the your AWS Generative AI CDK Constructs version when new releases become available.

Contributing

Contributions of all kinds are welcome! Check out our contributor guide

Design guidelines and Development guide

If you want to add a new construct to the library, check out our design guidelines, then follow the development guide

Getting Started

For TypeScript:

• Create or use an existing CDK application in TypeScript.

  • cdk init app --language typescript

• Run npm install @cdklabs/generative-ai-cdk-constructs

• The package should be added to your package.json.

• Import the library:

  • import * as genai from '@cdklabs/generative-ai-cdk-constructs';

For Python:

• Create or use an existing CDK application in Python

  • cdk init app --language python

• Install the package:

  • pip install cdklabs.generative-ai-cdk-constructs

• Import the library:

  • import cdklabs.generative_ai_cdk_constructs

Refer to the documentation for additional guidance on a particular construct: Catalog

Catalog

The following constructs are available in the library:

Construct	Description	AWS Services used
Data ingestion pipeline	Ingestion pipeline providing a RAG (retrieval augmented generation) source for storing documents in a knowledge base.	Amazon OpenSearch, AWS Step Functions, Amazon Bedrock, AWS AppSync, AWS Lambda
Question answering	Question answering with a large language model (Anthropic Claude V2) using a RAG (retrieval augmented generation) source and/or long context.	Amazon OpenSearch, AWS Lambda, Amazon Bedrock, AWS AppSync
Summarization	Document summarization with a large language model (Anthropic Claude V2).	AWS Lambda, Amazon Bedrock, AWS AppSync and Amazon Elasticache for Redis.
Lambda layer	Python Lambda layer providing dependencies and utilities to develop generative AI applications on AWS.	AWS Lambda, Amazon Bedrock, Amazon SageMaker
SageMaker model deployment (Jumpstart)	Deploy a foundation model from Amazon SageMaker JumpStart to an Amazon SageMaker endpoint.	Amazon SageMaker
SageMaker model deployment (Hugging Face)	Deploy a foundation model from Hugging Face to an Amazon SageMaker endpoint.	Amazon SageMaker
SageMaker model deployment (Custom)	Deploy a foundation model from an S3 location to an Amazon SageMaker endpoint.	Amazon SageMaker

Sample Use Cases

The official samples repository https://github.com/aws-samples/generative-ai-cdk-constructs-samples includes a collection of functional use case implementations to demonstrate the usage of AWS Generative AI CDK Constructs. These can be used in the same way as architectural patterns, and can be conceptualized as an additional "higher-level" abstraction of those patterns. Those patterns (constructs) are composed together into stacks, forming a "CDK app".

Additional Resources

Resource	Description
AWS re:Invent 2023 - Keynote with Dr. Werner Vogels	Dr. Werner Vogels, Amazon.com's VP and CTO, announces the AWS Generative AI CDK Constructs during his AWS re:Invent 2023 keynote.
aws-cdk-stack-builder-tool	AWS CDK Builder is a browser-based tool designed to streamline bootstrapping of Infrastructure as Code (IaC) projects using the AWS Cloud Development Kit (CDK).

Additional Resources

Resource	Description
AWS re:Invent 2023 - Keynote with Dr. Werner Vogels	Dr. Werner Vogels, Amazon.com's VP and CTO, announces the AWS Generative AI CDK Constructs during his AWS re:Invent 2023 keynote.
aws-cdk-stack-builder-tool	AWS CDK Builder is a browser-based tool designed to streamline bootstrapping of Infrastructure as Code (IaC) projects using the AWS Cloud Development Kit (CDK).

Operational Metrics Collection

Provided CDK constructs collect anonymous operational metrics to help AWS improve the quality and features of the constructs. Data collection is subject to the AWS Privacy Policy (https://aws.amazon.com/privacy/). To opt out of this feature, simply disable it by setting the construct property "enableOperationalMetric" to false for each construct used. Defaults to true.

Roadmap

Roadmap is available through the GitHub Project

Legal Disclaimer

You should consider doing your own independent assessment before using the content in this library for production purposes. This may include (amongst other things) testing, securing, and optimizing the CDK constructs and other content, provided in this library, based on your specific quality control practices and standards.

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