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Name: Ryan Doty
Type: User
Company: AWS
Bio: The contents of my repositories represent my viewpoints and not of my past or current employers, including Amazon Web Services (AWS).
Location: New York, New York
Name: Ryan Doty
Type: User
Company: AWS
Bio: The contents of my repositories represent my viewpoints and not of my past or current employers, including Amazon Web Services (AWS).
Location: New York, New York
This is sample code demonstrating the use of Amazon Bedrock and Generative AI to use natural language questions to query relational data stores, specifically Amazon Athena. This example leverages the MOMA Open Source Database: https://github.com/MuseumofModernArt/collection.
This is sample code demonstrating the use of Amazon Bedrock and Generative AI to use natural language questions to query relational data stores, specifically Amazon RDS.
This is sample code demonstrating the use of Amazon Bedrock and Generative AI to use natural language questions to query relational data stores, specifically Amazon Redshift. This example leverages the MOMA Open Source Database: https://github.com/MuseumofModernArt/collection.
This is sample code demonstrating the use of Amazon Bedrock and Generative AI to implement a ChatGPT alternative. The application is constructed with a simple streamlit frontend where users can input zero shot prompt requests to satisfy a broad range of use cases.
This is sample code demonstrating the use of Amazon Bedrock and Anthropic Claude 3 to satisfy multi-modal use cases. The application is constructed with a simple streamlit frontend where users can input zero shot requests to satisfy a broad range of use cases, including image to text multi-modal style use cases.
This is sample code demonstrating the use of Amazon Bedrock and Generative AI to implement a document comparison use case. The application is constructed with a simple streamlit frontend where users can upload 2 versions of a document and get all changes between documents listed.
This is sample code demonstrating the use of Amazon Bedrock and Generative AI to implement a document generation use case. The application is constructed with a simple streamlit frontend where users can provide details and create a document in the exact format that the you specify.
This is sample code that can be used to provide high-level pricing estimates for Amazon Bedrock Gen AI based applications. The application is constructed with a simple streamlit frontend where users can calculate a pricing estimate for their Gen AI based application that leverages Amazon Bedrock and has support for RAG based architectures.
This is sample code that can be used to provide a hands on explanation as to how Dynamic Prompting works in relation to Gen AI. The application is constructed with a simple streamlit frontend where users can ask questions against a Amazon Bedrock supported LLM and get a deeper understanding of how few-shot and dynamic prompting works.
This is sample code demonstrating the use of Amazon Bedrock and Generative AI to implement a image generation use case. The application is constructed with a simple streamlit frontend where users can input text requests to generate images based on the text input.
This is sample code demonstrating the use of Amazon Bedrock and Generative AI to implement a multi-model micro-services oriented architecture. The application is constructed with a simple streamlit frontend where users can input zero shot queries to satisfy a broad range of use cases against multiple disparate datasources.
This is sample code demonstrating the use of Amazon Bedrock and Generative AI to implement a RAG based architecture with Amazon Kendra. The application is constructed with a simple streamlit frontend where users can ask questions against documents stored in Amazon Kendra.
This is sample code demonstrating the use of Amazon Bedrock and Generative AI to create custom embeddings stored in Amazon OpenSearch Serverless. The application is constructed with a RAG based architecture where users can ask questions against the indexed embeddings within OpenSearch Serverless.
This is sample code demonstrating the use of Amazon Bedrock and Generative AI to take natural language questions to query relational data stores, specifically Snowflake.
This is sample code demonstrating the use of Amazon Bedrock and Generative AI to implement streaming responses. The application is constructed with a simple streamlit frontend where users can input zero shot requests directly against the LLM of their choice, leveraging a streaming response technique.
This is sample code demonstrating the use of Amazon Bedrock and Generative AI to implement a long document summarization use case. The application is constructed with a simple streamlit frontend where users can upload large documents and get them summarized.
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