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Copy file name to clipboardExpand all lines: README.md
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|[Data ingestion pipeline](./src/patterns/gen-ai/aws-rag-appsync-stepfn-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 |
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|[Question answering](./src/patterns/gen-ai/aws-qa-appsync-opensearch/)| Question answering with a large language model (Anthropic Claude V2.1) using a RAG (retrieval augmented generation) source and/or long context. | Amazon OpenSearch, AWS Lambda, Amazon Bedrock, AWS AppSync |
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|[Summarization](./src/patterns/gen-ai/aws-summarization-appsync-stepfn/)| Document summarization with a large language model (Anthropic Claude V2.1). | AWS Lambda, Amazon Bedrock, AWS AppSync and Amazon Elasticache for Redis. |
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|[Summarization](./src/patterns/gen-ai/aws-summarization-appsync-stepfn/)| Document summarization with a large language model (Anthropic Claude V2.1). | AWS Lambda, Amazon Bedrock, AWS AppSync and Amazon ElastiCache for Redis. |
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|[Lambda layer](./src/patterns/gen-ai/aws-langchain-common-layer/)| Python Lambda layer providing dependencies and utilities to develop generative AI applications on AWS. | AWS Lambda, Amazon Bedrock, Amazon SageMaker |
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|[SageMaker model deployment (Jumpstart)](./src/patterns/gen-ai/aws-model-deployment-sagemaker/README_jumpstart.md)| Deploy a foundation model from Amazon SageMaker JumpStart to an Amazon SageMaker endpoint. | Amazon SageMaker |
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|[SageMaker model deployment (JumpStart)](./src/patterns/gen-ai/aws-model-deployment-sagemaker/README_jumpstart.md)| Deploy a foundation model from Amazon SageMaker JumpStart to an Amazon SageMaker endpoint. | Amazon SageMaker |
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|[SageMaker model deployment (Hugging Face)](./src/patterns/gen-ai/aws-model-deployment-sagemaker/README_hugging_face.md)| Deploy a foundation model from Hugging Face to an Amazon SageMaker endpoint. | Amazon SageMaker |
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|[SageMaker model deployment (Custom)](./src/patterns/gen-ai/aws-model-deployment-sagemaker/README_custom_sagemaker_endpoint.md)| Deploy a foundation model from an S3 location to an Amazon SageMaker endpoint. | Amazon SageMaker |
|[Amazon OpenSearch Serverless Vector Collection](./src/cdk-lib/opensearchserverless/README.md)| CDK L2 Constructs to create a vector collection.| Amazon OpenSearch Vector Index |
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|[Amazon OpenSearch Vector Index](./src/cdk-lib/opensearch-vectorindex/README.md)| CDK L1 Custom Resource to create a vector index.| Amazon OpenSearch Serverless, AWS Lambda |
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# Sample Use Cases
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|[aws-cdk-stack-builder-tool](https://github.com/aws-samples/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). |
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|[CDK Live! Building generative AI applications and architectures leveraging AWS CDK Constructs!](https://www.youtube.com/watch?v=NI1F4Xxqyr8)| 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. |
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|[Announcing AWS Generative AI CDK Constructs!](https://aws.amazon.com/blogs/devops/announcing-generative-ai-cdk-constructs/)| Blog post announcing the release of the AWS Generative AI CDK Constructs. |
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|[aws-genai-llm-chatbot](https://github.com/aws-samples/aws-genai-llm-chatbot/tree/main)| 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 |
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|[aws-genai-llm-chatbot](https://github.com/aws-samples/aws-genai-llm-chatbot/tree/main)| 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.|
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