This repo hosts samples meant to help design AI applications built on data from an Azure SQL Database. We illustrate key technical concepts and demonstrate workflows that integrate Azure SQL data with other popular AI application components inside and outside of Azure.

The AzureSQL_CogSearch_IntegratedVectorization sample notebook shows a simple AI application that recommends products based on a database of user reviews, using Azure Cognitive Search to store and search the relevant data. It highlights new preview features of Azure Cognitive Search, including automatic chunking and integrated vectorization of user queries.

The AzureSQL_PromptFlow sample shows an E2E example of how to build AI applications with Prompt Flow, Azure Cognitive Search, and your own data in Azure SQL database. It includes instructions on how to index your data with Azure Cognitive Search, a sample Prompt Flow local development that links everything together with Azure OpenAI connections, and also how to create an endpoint of the flow to an Azure ML workspace.

See the description in each sample for instructions (projects will have either a README file or instructions in the notebooks themselves.)

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