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generative ai cdk constructs

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Basic Information

This repository provides an open-source library of AWS Cloud Development Kit (CDK) constructs specifically designed for building generative AI solutions on AWS. It offers high-level, multi-service architectural patterns implemented as reusable constructs so developers can define infrastructure-as-code for model deployment, inference workflows, monitoring, vector stores, and Bedrock integrations. The library is organized into L2 and L3 constructs that encapsulate well-architected defaults and multi-service configurations. It supports multiple programming languages and CDK apps and is intended for developers and architects who want predictable, repeatable infrastructure patterns to accelerate building, deploying, and operating generative AI applications on AWS.

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487
App URL
https://github.com/awslabs/generative-ai-cdk-constructs
Github Repository
https://github.com/awslabs/generative-ai-cdk-constructs/blob/main/README.md

App Details

Features
The project provides L3 constructs for common workflows such as SageMaker model deployments (JumpStart, Hugging Face, custom S3 models), Bedrock monitoring dashboards, Bedrock data automation, and batch model invocation via Step Functions. L2 constructs include Amazon Bedrock helpers, OpenSearch Serverless vector collections, and a vector index custom resource. The library is distributed across package managers for TypeScript, Python, Java, Go, and .NET. Documentation includes a catalog, usage examples, language-specific install steps, a samples repository for end-to-end use cases, design and developer guides, and operational metrics collection for anonymous usage telemetry. The constructs follow well-architected defaults and are under active development.
Use Cases
The constructs reduce boilerplate and operational complexity by encapsulating best-practice configurations for generative AI architectures, enabling faster prototyping and more consistent production deployments. Developers can deploy foundation models to SageMaker or integrate with Amazon Bedrock, create vector indexes and search collections, orchestrate batch inference with Step Functions, and add observability with CloudWatch dashboards. Language-specific packages and examples let teams use the CDK in their preferred language while relying on tested patterns. The samples, workshops, and documentation provide practical guidance and reference implementations, which help teams learn, extend, and combine constructs into CDK apps and stacks for real-world generative AI workloads.

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