Merge branch 'main' into code-expert

Author: krokoko

README.md

@@ -6,9 +6,6 @@ This repo provides samples to demonstrate how to build your own Generative AI so
 
 |Use Case|Description|Type|Language|
 |-|-|-|-|
-|[Document Explorer](samples/document_explorer/)| This sample provides an end-to-end experience that allows a user to ingest documents into a knowledge base, then summarize and ask questions against those documents.| Backend + Frontend |TypeScript for Backend, Python for Frontend ([Streamlit](https://streamlit.io/))|
-|[Content Generation](samples/content-generation/)| This sample provides an end-to-end experience that allows a user to generate images from text using Amazon titan-image-generator-v1 or stability stable-diffusion-xl model.| Backend + Frontend |TypeScript for Backend, Python for Frontend ([Streamlit](https://streamlit.io/))|
-|[Image Description](samples/image-description/)| This sample provides an end-to-end experience that allows a user to generate descriptive text for uploaded images.| Backend + Frontend |TypeScript for Backend, Python for Frontend ([Streamlit](https://streamlit.io/))|
 |[SageMaker JumpStart model](samples/sagemaker_jumpstart_model/)| This sample provides a sample application which deploys a SageMaker real-time endpoint hosting a Llama 2 foundation model developed by Meta from Amazon JumpStart, and an AWS Lambda function to run inference requests against that endpoint.|Backend|TypeScript|
 |[SageMaker Hugging Face model](samples/sagemaker_huggingface_model/)| This sample provides a sample application which deploys a SageMaker real-time endpoint hosting a model (Mistral 7B) from Hugging Face, and an AWS Lambda function to run inference requests against that endpoint.|Backend|TypeScript|
 |[SageMaker Hugging Face model on AWS Inferentia2](samples/sagemaker_huggingface_inferentia/)| This sample provides a sample application which deploys a SageMaker real-time endpoint hosting a model (Zephyr 7B) from Hugging Face, and an AWS Lambda function to run inference requests against that endpoint. This sample uses Inferentia 2 as the hardware accelerator.|Backend|TypeScript|
@@ -20,8 +17,6 @@ This repo provides samples to demonstrate how to build your own Generative AI so
 |[Python Samples](samples/python-samples/)| This project showcases the utilization of the 'generative-ai-cdk-constructs' package from the Python Package Index (PyPI).| Backend | Python|
 |[.NET Samples](samples/dotnet-samples/)| This project showcases the utilization of the 'Cdklabs.GenerativeAiCdkConstructs' package from nuget library.| Backend | .NET|
 |[Contract Compliance Analysis](samples/contract-compliance-analysis/)| This project automates the analysis of contracts by splitting them into clauses, determining clause types, evaluating compliance against a customer's legal guidelines, and assessing overall contract risk based on the number of compliant clauses. This is achieved through a workflow that leverages Large Language Models via Amazon Bedrock. | Backend + Frontend | Python for Backend, TypeScript (React) for Frontend |
-|[Text To SQL](samples/text-to-sql/)| The "Text To SQL" generative AI sample application solution 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. | Backend + Frontend | Python for Backend, TypeScript (React) for Frontend |
-|[LlamaIndex Basic DataLoader](samples/llamaindex-basic-data-loader/)| The "LlamaIndex Basic Data Loader" generative AI sample application solution demonstrates the `LlamaIndexDataLoader` from the [Generative AI CDK Constructs](https://github.com/awslabs/generative-ai-cdk-constructs) package. The default implementation uses 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). The solution expects [LlamaIndex Documents](https://docs.llamaindex.ai/en/stable/module_guides/loading/documents_and_nodes/) into an output S3 ready for downstream consumer generative AI solutions. | Backend | Python |
 |[RFP Answers with GenAI](samples/rfp-answer-generation/)| This project provides guidance on how you can use Knowledge Bases for Amazon Bedrock with custom transformations to create draft answers for Request for Proposal (RFP) documents, streamlining the answer of new potential contracts. This is achieved through a workflow that leverages Large Language Models via Amazon Bedrock. | Backend + Frontend | Python for Backend, TypeScript (React) for Frontend |
 
 ## Contributing