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 This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.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
- CDK Versions
- Contributing
- Design guidelines and Development guide
- Getting Started
- Catalog
- Sample Use Cases
- Additional Resources
- Contributors
- Operational Metrics Collection
- Roadmap
- License
- Legal Disclaimer
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.
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.
Contributions of all kinds are welcome! Check out our contributor guide
If you want to add a new construct to the library, check out our design guidelines, then follow the development guide
- 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';
- 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
- 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;
- 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
Refer to the documentation for additional guidance on a particular construct: Catalog
The following constructs are available in the library:
| Construct | Description | AWS Services used |
|---|---|---|
| Data ingestion pipeline - OpenSearch | 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 |
| Data ingestion pipeline - Kendra | Ingestion pipeline providing a RAG (retrieval augmented generation) source for storing documents in a knowledge base. | Amazon Kendra, AWS Step Functions, AWS AppSync, AWS Lambda |
| Question answering | Utilizing Large Language Models (Anthropic Claude V2.1.) for Question Answering on PDF documents with RAG (retrieval augmented generation) source and/or long context. Additionally, leveraging Anthropic Claude 3 for visual question answering on images. | Amazon OpenSearch, AWS Lambda, Amazon Bedrock, AWS AppSync |
| Summarization | Document summarization with a large language model (Anthropic Claude V2.1). | AWS Lambda, Amazon Bedrock, AWS AppSync and Amazon ElastiCache for Redis. |
| 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 |
| Content Generation | Generate images from text using Amazon titan-image-generator-v1 or stability.stable-diffusion-xl model. | AWS Lambda, Amazon Bedrock, AWS AppSync |
| Web crawler | Crawl websites and RSS feeds on a schedule and store changeset data in an Amazon Simple Storage Service bucket. | AWS Lambda, AWS Batch, AWS Fargate, Amazon DynamoDB |
| Amazon Bedrock Monitoring (Amazon CloudWatch Dashboard) | Amazon CloudWatch dashboard to monitor model usage from Amazon Bedrock. | Amazon CloudWatch |
| Construct | Description | AWS Services used |
|---|---|---|
| Lambda layer | Python Lambda layer providing dependencies and utilities to develop generative AI applications on AWS. | AWS Lambda, Amazon Bedrock, Amazon SageMaker |
| 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 |
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".
| 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. |
| 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. |
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 is available through the GitHub Project
Apache-2.0
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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