generativeaicdkconstructs

package module

v0.1.314 Latest Latest Go to latest Published: Jan 22, 2026 License: Apache-2.0 Imports: 9 Imported by: 0

README ¶

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 This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.

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

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';

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

NuGet

Create or use an existing CDK application in Python
- cdk init app --language csharp
Install the package while in the Visual Studio project:
- dotnet add package CdkLabs.GenerativeAICdkConstructs
Use the namespace:
- using Cdklabs.GenerativeAiCdkConstructs;

Go

Create or use an existing CDK application in Python
- cdk init app --language go
Get the module:
- go get github.com/cdklabs/generative-ai-cdk-constructs-go/generative-ai-cdk-constructs
Import the library:
- import "github.com/cdklabs/generative-ai-cdk-constructs-go/generative-ai-cdk-constructs"

NOTE: The Go distribution repository, distributes the JSII tar gzipped versioned source from the source repository

Java

Create or use an existing CDK application in Java
- cdk init app --language java
Add the dependency into the pom.xml

<dependency>
    <groupId>io.github.cdklabs</groupId>
    <artifactId>generative-ai-cdk-constructs</artifactId>
    <version>Get the latest version and insert it here</version>
</dependency>

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

Catalog

The following constructs are available in the library:

L3 constructs

Construct	Description	AWS Services used
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
Amazon Bedrock Monitoring (Amazon CloudWatch Dashboard)	Amazon CloudWatch dashboard to monitor model usage from Amazon Bedrock.	Amazon CloudWatch
Bedrock Data Automation	Use Amazon bedrock data automation client to to build and manage intelligent document processing, media analysis, and other multimodal data-centric automation solutions	AWS Lambda, Amazon S3 bucket
Bedrock Batch Step Functions	Manage Bedrock model invocation jobs(batch inference) in AWS Step Functions state machines	AWS Step Functions, AWS Lambda, AWS EventBridge, Amazon Bedrock, AWS IAM

L2 Constructs

[!WARNING] Important: Amazon Bedrock L2 constructs are transitioning to the AWS CDK core repository. You can now find these constructs at: https://github.com/aws/aws-cdk/tree/main/packages/%40aws-cdk/aws-bedrock-alpha. Please migrate to the alpha package, as Bedrock L2 constructs in this repository are now deprecated and will no longer receive updates.

Construct	Description	AWS Services used
Amazon Bedrock	CDK L2 Constructs for Amazon Bedrock.	Amazon Bedrock, Amazon OpenSearch Serverless, AWS Lambda
Amazon OpenSearch Serverless Vector Collection	CDK L2 Constructs to create a vector collection.	Amazon OpenSearch Vector Index
Amazon OpenSearch Vector Index	CDK L1 Custom Resource to create a vector index.	Amazon OpenSearch Serverless, AWS Lambda
Amazon Aurora DSQL	CDK L2 Constructs for Amazon Aurora DSQL.	Amazon Aurora DSQL
Amazon S3 Vectors	CDK L2 Constructs for Amazon S3 vectors.	Amazon S3 Vectors

Sample Use Cases

The official samples repository 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	Type	Description
AWS re:Invent 2023 - Keynote with Dr. Werner Vogels	Keynote	Dr. Werner Vogels, Amazon.com's VP and CTO, announces the AWS Generative AI CDK Constructs during his AWS re:Invent 2023 keynote.
Workshop - Building Generative AI Apps on AWS with CDK	Workshop	In this workshop, you will explore how to build a sample generative AI app on AWS using CDK and Generative AI CDK Constructs.
Workshop - Hands on AWS CDK Generative AI Constructs	Workshop	In this workshop you will deploy projects that use CDK constructs from this library. Projects are from the amazon-bedrock-samples Github Repository.
Build generative AI applications with Amazon Titan Text Premier, Amazon Bedrock, and AWS CDK	Blog post + Code sample	Blog post exploring building and deploying two sample applications powered by Amazon Titan Text Premier using the Generative AI CDK constructs.
aws-cdk-stack-builder-tool	Code sample	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).
CDK Live! Building generative AI applications and architectures leveraging AWS CDK Constructs!	Video	CDK Live! episode focused on building and deploying generative AI applications and architectures on AWS using the AWS Cloud Development Kit (CDK) and the AWS Generative AI CDK Constructs.
Announcing AWS Generative AI CDK Constructs!	Blog post	Blog post announcing the release of the AWS Generative AI CDK Constructs.
Streamline insurance underwriting with generative AI using Amazon Bedrock	Blog post + Code sample	Blog post and code sample discussing how to use AWS generative artificial intelligence (AI) solutions like Amazon Bedrock to improve the underwriting process, including rule validation, underwriting guidelines adherence, and decision justification.
aws-genai-llm-chatbot	Code sample	Multi-Model and Multi-RAG Powered Chatbot Using AWS CDK on AWS allowing you to experiment with a variety of Large Language Models and Multimodal Language Models, settings and prompts in your own AWS account.
bedrock-claude-chat	Code sample	AWS-native chatbot using Bedrock + Claude (+Mistral).
amazon-bedrock-rag	Code sample	Fully managed RAG solution using Knowledge Bases for Amazon Bedrock.
Amazon Bedrock Multimodal Search	Code sample	Multimodal product search app built using Amazon Titan Multimodal Embeddings model.
Amazon Bedrock Knowledge Bases with Private Data	Blog post + Code sample	Blog post and associated code sample demonstrating how to integrate Knowledge Bases into Amazon Bedrock to provide foundational models with contextual data from private data sources.
Automating tasks using Amazon Bedrock Agents and AI	Blog post + Code sample	Blog post and associated code sample demonstrating how to deploy an Amazon Bedrock Agent and a Knowledge Base through a hotel and spa use case.
Agents for Amazon Bedrock - Powertools for AWS Lambda (Python)	Code sample	Create Agents for Amazon Bedrock using event handlers and auto generation of OpenAPI schemas.
Text to SQL Bedrock Agent	Code sample	Harnessing the power of natural language processing, the "Text to SQL Bedrock Agent" facilitates the automatic transformation of natural language questions into executable SQL queries.
Dynamic Text-to-SQL for Enterprise Workloads with Amazon Bedrock Agent	Code sample	Elevate your data analysis with an end-to-end agentic Text-to-SQL solution, built on AWS for enterprise-scale adaptability and resilience. Ideal for complex scenarios like fraud detection in financial services.

Contributors

Operational Metrics Collection

Generative AI CDK Constructs may collect anonymous operational metrics, including: the region a construct is deployed, the name and version of the construct deployed, and related information. We may use the metrics to maintain, provide, develop, and improve the constructs and AWS services.

Roadmap

Roadmap is available through the GitHub Project

Deprecation

To understand our deprecation process, please refer to the dedicated documentation

License

Apache-2.0

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.

Documentation ¶

Overview ¶

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

Index ¶

func BaseClass_IsConstruct(x interface{}) *bool
func BaseClass_SetUsageMetricMap(val *map[string]*float64)
func BaseClass_UsageMetricMap() *map[string]*float64
func NewBaseClass_Override(b BaseClass, scope constructs.Construct, id *string)
type BaseClass
- func NewBaseClass(scope constructs.Construct, id *string) BaseClass
type BaseClassProps
type ConstructName

Constants ¶

This section is empty.

Variables ¶

This section is empty.

Functions ¶

func BaseClass_IsConstruct ¶

func BaseClass_IsConstruct(x interface{}) *bool

Checks if `x` is a construct.

Use this method instead of `instanceof` to properly detect `Construct` instances, even when the construct library is symlinked.

Explanation: in JavaScript, multiple copies of the `constructs` library on disk are seen as independent, completely different libraries. As a consequence, the class `Construct` in each copy of the `constructs` library is seen as a different class, and an instance of one class will not test as `instanceof` the other class. `npm install` will not create installations like this, but users may manually symlink construct libraries together or use a monorepo tool: in those cases, multiple copies of the `constructs` library can be accidentally installed, and `instanceof` will behave unpredictably. It is safest to avoid using `instanceof`, and using this type-testing method instead.

Returns: true if `x` is an object created from a class which extends `Construct`. Experimental.

func BaseClass_SetUsageMetricMap ¶

func BaseClass_SetUsageMetricMap(val *map[string]*float64)

func BaseClass_UsageMetricMap ¶

func BaseClass_UsageMetricMap() *map[string]*float64

func NewBaseClass_Override ¶

func NewBaseClass_Override(b BaseClass, scope constructs.Construct, id *string)

Experimental.

Types ¶

type BaseClass ¶

type BaseClass interface {
	constructs.Construct
	// construct usage metric , added in template description.
	// Experimental.
	ConstructUsageMetric() *string
	// enable disable xray tracing.
	// Default: true.
	//
	// Experimental.
	Enablexray() *bool
	// Experimental.
	SetEnablexray(val *bool)
	// Default  log config for all constructs.
	// Experimental.
	FieldLogLevel() awsappsync.FieldLogLevel
	// Experimental.
	SetFieldLogLevel(val awsappsync.FieldLogLevel)
	// enable disable lambda tracing.
	// Default: Active.
	//
	// Experimental.
	LambdaTracing() awslambda.Tracing
	// Experimental.
	SetLambdaTracing(val awslambda.Tracing)
	// The tree node.
	// Experimental.
	Node() constructs.Node
	// Default  log retention config for all constructs.
	// Experimental.
	Retention() awslogs.RetentionDays
	// Experimental.
	SetRetention(val awslogs.RetentionDays)
	// Value will be appended to resources name.
	// Default: _dev.
	//
	// Experimental.
	Stage() *string
	// Experimental.
	SetStage(val *string)
	// Experimental.
	AddObservabilityToConstruct(props *BaseClassProps)
	// Returns a string representation of this construct.
	// Experimental.
	ToString() *string
	// update template description with construct usage metric and add AWS_SDK_UA_APP_ID to user agent on aws sdk.
	// Experimental.
	UpdateConstructUsageMetricCode(props *BaseClassProps, scope constructs.Construct, lambdaFunctions *[]awslambda.DockerImageFunction)
	// Experimental.
	UpdateEnvSuffix(props *BaseClassProps)
}

Experimental.

func NewBaseClass ¶

func NewBaseClass(scope constructs.Construct, id *string) BaseClass

Experimental.

type BaseClassProps ¶

type BaseClassProps struct {
	// construct id.
	// Experimental.
	ConstructId *string `field:"required" json:"constructId" yaml:"constructId"`
	// name of the construct.
	// Experimental.
	ConstructName ConstructName `field:"required" json:"constructName" yaml:"constructName"`
	// Enable observability.
	//
	// Warning: associated cost with the services
	// used. Best practice to enable by default.
	// Default: true.
	//
	// Experimental.
	Observability *bool `field:"optional" json:"observability" yaml:"observability"`
	// Value will be appended to resources name.
	// Default: _dev.
	//
	// Experimental.
	Stage *string `field:"optional" json:"stage" yaml:"stage"`
}

Experimental.

type ConstructName ¶

type ConstructName string

Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with the License. A copy of the License is located at

http://www.apache.org/licenses/LICENSE-2.0

or in the 'license' file accompanying this file. This file is distributed on an 'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, express or implied. See the License for the specific language governing permissions and limitations under the License. Experimental.

const (
	// Experimental.
	ConstructName_AWSMODELDEPLOYMENTSAGEMAKER ConstructName = "AWSMODELDEPLOYMENTSAGEMAKER"
	// Experimental.
	ConstructName_CUSTOMSAGEMAKERENDPOINT ConstructName = "CUSTOMSAGEMAKERENDPOINT"
	// Experimental.
	ConstructName_HUGGINGFACESAGEMAKERENDPOINT ConstructName = "HUGGINGFACESAGEMAKERENDPOINT"
	// Experimental.
	ConstructName_JUMPSTARTSAGEMAKERENDPOINT ConstructName = "JUMPSTARTSAGEMAKERENDPOINT"
)

Source Files ¶

View all Source files

Directories ¶

Path	Synopsis
amazonaurora
internal
aosscwdashboard
internal
auroradsql
internal
bda
internal
bedrock
internal
piitype
bedrockbatchstepfn
internal
bedrockcwdashboard
internal
internal
jsii Package jsii contains the functionaility needed for jsii packages to initialize their dependencies and themselves.	Package jsii contains the functionaility needed for jsii packages to initialize their dependencies and themselves.
kendra
internal
mongodbatlas
neptune
internal
opensearchmanagedcluster
opensearchserverless
internal
opensearchvectorindex
internal
pinecone
s3vectors
internal
sagemakerdeployment
internal

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README ¶

AWS Generative AI CDK Constructs

Table of contents

Introduction

CDK Versions

Contributing

Design guidelines and Development guide

Getting Started

Catalog

L3 constructs

L2 Constructs

Sample Use Cases

Additional Resources

Contributors

Operational Metrics Collection

Roadmap

Deprecation

License

Legal Disclaimer

Documentation ¶

Overview ¶

Index ¶

Constants ¶

Variables ¶

Functions ¶

func BaseClass_IsConstruct ¶

func BaseClass_SetUsageMetricMap ¶

func BaseClass_UsageMetricMap ¶

func NewBaseClass_Override ¶

Types ¶

type BaseClass ¶

func NewBaseClass ¶

type BaseClassProps ¶

type ConstructName ¶

Source Files ¶

Directories ¶