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Merge pull request #155 from aws-samples/deprecation_announcement
chore(doc): announce deprecation of some samples
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‎samples/content-generation/README.md‎

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# Content Generation
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> [!WARNING]
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> This code sample is deprecated: it is no longer maintained and will not receive further updates. This sample will be removed in an upcoming release.
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## Overview
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The "Content Generation" sample generative AI application demonstrates how to build end-to-end solutions with generative ai multimodals leveraging AWS services and [AWS Generative AI CDK Constructs](https://github.com/awslabs/generative-ai-cdk-constructs).

‎samples/document_explorer/README.md‎

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# Document Explorer
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> [!WARNING]
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> This code sample is deprecated: it is no longer maintained and will not receive further updates. This sample will be removed in an upcoming release.
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## Overview
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The "Document Explorer" sample generative AI application demonstrates how to build end-to-end solutions leveraging AWS services and [AWS Generative AI CDK Constructs](https://github.com/awslabs/generative-ai-cdk-constructs).

‎samples/image-description/README.md‎

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# Overview
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> [!WARNING]
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> This code sample is deprecated: it is no longer maintained and will not receive further updates. This sample will be removed in an upcoming release.
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The "Image Description" generative AI application showcases the capability of generating accurate and detailed multilingual textual descriptions for multiple images by leveraging the power of AWS services and the [AWS Generative AI Cloud Development Kit (CDK) Constructs](https://github.com/awslabs/generative-ai-cdk-constructs/blob/main/src/patterns/gen-ai/aws-summarization-appsync-stepfn/README.md). This application harnesses the potential of state-of-the-art generative AI models to provide users with valuable insights and a comprehensive understanding of visual data.
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The application features a user-friendly Streamlit interface, enabling users to authenticate securely via Amazon Cognito, a robust and highly scalable identity management service. Upon successful authentication, users can seamlessly upload images, and the application will generate descriptive text for the uploaded visual data. The description can be generated in different languages by selecting the desired language option in the sidebar. Multiple images can be uploaded simultaneously, and the application will generate descriptions for all of them.

‎samples/llamaindex-basic-data-loader/README.md‎

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# LlamaIndex Data Loader
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> [!WARNING]
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> This code sample is deprecated: it is no longer maintained and will not receive further updates. This sample will be removed in an upcoming release.
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This example can be used to sample the `LlamaIndexDataLoader` directly from the [Generative AI CDK Constructs](https://github.com/awslabs/generative-ai-cdk-constructs) package. It demonstrates the [S3 File or Directory Loader](https://github.com/run-llama/llama_index/tree/main/llama-index-integrations/readers/llama-index-readers-s3), and can be extended for other [LlamaHub Readers](https://llamahub.ai/?tab=readers).
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_See more information at [LlamaIndex](https://llamaindex.ai/)_

‎samples/text-to-sql/README.md‎

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# Text To SQL
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> This code sample is deprecated: it is no longer maintained and will not receive further updates. This sample will be removed in an upcoming release.
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The "Text To SQL" generative AI sample application is a cutting-edge solution that enables users to interact with databases through natural language queries, eliminating the need for extensive SQL knowledge. This application leverages the powerful Anthropic Claude 3 model, hosted on Amazon Bedrock, to translate natural language queries into executable SQL statements seamlessly.
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At the core of the application lies a sophisticated query interpretation process. The application first analyzes the user's natural language query, disambiguating any ambiguities by reformulating the question based on the database schema. This crucial step ensures that the query is accurately interpreted and aligned with the underlying data structure, ensuring precise and relevant results.

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