AWS DevOps Blog

Announcing Generative AI CDK Constructs

Announced by Werner Vogels in his 2023 re:Invent Keynote, Generative AI CDK Constructs, an open-source extension of the AWS Cloud Development Kit (AWS CDK), provides well-architected multi-service patterns to quickly and efficiently create repeatable infrastructure required for generative AI projects on AWS. Our initial release includes five CDK constructs enabling key generative AI capabilities like question and answering, summarization, data ingestion for Retrieval Augmented Generation (RAG), and model deployment capabilities.

Simplify and Accelerate the Development of Applications with Generative AI

Developing generative AI applications is particularly challenging due to the rapidly evolving nature of the underlying technologies. As a result, developers are faced with the challenge of keeping up with changing paradigms and best practices. This often leads to varied and disjointed patterns as developers try crafting their own solutions from scratch. For instance, there are already hundreds of implementations of patterns like retrieval augmented generation(RAG), summarization, or chatbots.

AWS introduced the Cloud Development Kit (CDK), an open-source software development framework to define cloud infrastructure in code and provision it through AWS CloudFormation. CDK empowers developers to use high-level construct libraries that encapsulate AWS best practices, allowing for the creation of cloud applications without needing to know every detail of the AWS services.

A ‘construct‘ in CDK terminology is a building block that represents an AWS resource or a combination of AWS resources configured together. These constructs can range from low-level (individual resources) to high-level (complete architectures), enabling developers to compose and share their cloud application models as code.

Our library harnesses the power of CDK, offering pre-built constructs designed to expedite the deployment of generative AI applications. These constructs use LLMs/FMs available in Amazon Bedrock facilitating easier modeling and rapid deployment of cloud architectures tailored for generative AI. With these constructs available in both Python and Typescript, developers can leverage AWS services to build industry-agnostic solutions swiftly, regardless of their expertise level in generative AI. The constructs are designed to integrate smoothly with existing CDK applications, offering a scalable approach to enhancing generative AI capabilities across various business verticals.

Getting Started with the Constructs


You can install the CDK constructs in your preferred language:

  • Typescript app: npm i @cdklabs/generative-ai-cdk-constructs
  • Python app: pip install cdklabs.generative-ai-cdk-constructs

Within your existing CDK application, import the new library and get access to available constructs:

  • Typescript app: import * as genai from '@cdklabs/generative-ai-cdk-constructs';
  • Python app: import cdklabs.generative-ai-cdk-constructs as genai


aws-qa-appsync-opensearch is one of the constructs available in the generative-ai-cdk-constructs library. This construct provides a question answering workflow using Amazon Bedrock and a provisioned Amazon OpenSearch cluster. Amazon Cognito is required to authenticate calls to the AWS AppSync GraphQL API created by the construct.

Typescript app:

import { Construct } from 'constructs';
import { Stack, StackProps } from 'aws-cdk-lib';
import * as os from 'aws-cdk-lib/aws-opensearchservice';
import * as cognito from 'aws-cdk-lib/aws-cognito';
import { QaAppsyncOpensearch, QaAppsyncOpensearchProps } from '@cdklabs/generative-ai-cdk-constructs';

class MyStack extends Stack {
    // get an existing OpenSearch provisioned cluster
    const osDomain = os.Domain.fromDomainAttributes(this, 'osdomain', {
        domainArn: 'arn:aws:es:us-east-1:XXXXXX',
        domainEndpoint: ''
    // get an existing userpool 
    const cognitoPoolId = 'us-east-1_XXXXX';
    const userPoolLoaded = cognito.UserPool.fromUserPoolId(this, 'myuserpool', cognitoPoolId);
    // Create a QA Appsync OpenSearch construct 
    const ragSource = new QaAppsyncOpensearch(
            existingOpensearchDomain: osDomain,
            openSearchIndexName: 'demoindex',
            cognitoUserPool: userPoolLoaded

Python app:

from aws_cdk import (
    aws_opensearchservice as os,
    aws_cognito as cognito
from constructs import Construct
from cdklabs.generative_ai_cdk_constructs import QaAppsyncOpensearch

class MyStack(Stack):
    def __init__(self, scope: Construct, id: str, **kwargs):
        super().__init__(scope, id, **kwargs)

        # Get an existing OpenSearch provisioned cluster
        os_domain = os.Domain.from_domain_attributes(self, 'osdomain',

        # Get an existing user pool
        cognito_pool_id = 'us-east-1_XXXXX'
        user_pool_loaded = cognito.UserPool.from_user_pool_id(self, 'myuserpool', cognito_pool_id)

        # Create a QA Appsync OpenSearch construct
        rag_source = QaAppsyncOpensearch(

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

Currently, the following constructs that are available:

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

Explore More Constructs

The library includes constructs for data ingestion pipelines, question answering workflows, document summarization, Lambda layer for generative AI applications, and SageMaker model deployment.

Next Steps

Visit the Generative AI CDK Constructs GitHub repository for a full list of constructs and documentation. For practical examples, check out the AWS samples repository. Your feedback and contributions are welcome on our GitHub repository to enhance the capabilities of Generative AI CDK Constructs.

Alain Krok

Alain Krok is a Senior Solutions Architect with a passion for emerging technologies. His past experience includes designing and implementing IIoT solutions for the oil and gas industry and working on robotics projects. He enjoys pushing the limits and indulging in extreme sports when he is not designing software.

Dinesh Sajwan

Dinesh Sajwan is a Senior Solutions Architect. His passion for emerging technologies allows him to stay on the cutting edge and identify new ways to apply the latest advancements to solve even the most complex business problems. His diverse expertise and enthusiasm for both technology and adventure position him as a uniquely creative problem-solver.

Michael Tran

Michael Tran is a Sr. Solutions Architect with Prototyping Acceleration team at Amazon Web Services. He provides technical guidance and helps customers innovate by showing the art of the possible on AWS. He specializes in building prototypes in the AI/ML space. You can contact him @Mike_Trann on Twitter.