AI-in-a-Box leverages the expertise of Microsoft across the globe to develop and provide AI and ML solutions to the technical community. Our intent is to present a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction.

This repository contains the implementation of an order processing workflow with Durable Functions in C#. The sample is deployed to Azure Functions Flex Consumption using the Azure Developer CLI (azd) and is configured with managed identity as the authentication mechanism.

This C# demo is based on azure-search-openai-demo and uses a static web app for the frontend and Azure functions for the backend API's. This solution uses the Azure Functions OpenAI triggers and binding extension for the backend capabilities.

This C# demo is based on azure-search-openai-demo and uses a static web app for the frontend and Azure functions for the backend API's. This solution uses the Azure Functions OpenAI triggers and binding extension for the backend capabilities.

This Quickstart uses Azure Developer command-line (azd) tools to create functions that respond to HTTP requests. After testing the code locally, you deploy it to a new serverless function app you create running in a Flex Consumption plan in Azure Functions. This follows current best practices for secure and scalable Azure Functions deployments

This Quickstart uses Azure Developer command-line (azd) tools to create functions that respond to HTTP requests. After testing the code locally, you deploy it to a new serverless function app you create running in a Flex Consumption plan in Azure Functions. This follows current best practices for secure and scalable Azure Functions deployments

This Quickstart uses Azure Developer command-line (azd) tools to create functions that respond to HTTP requests. After testing the code locally, you deploy it to a new serverless function app you create running in a Flex Consumption plan in Azure Functions. This follows current best practices for secure and scalable Azure Functions deployments

This Quickstart uses Azure Developer command-line (azd) tools to create functions that respond to HTTP requests. After testing the code locally, you deploy it to a new serverless function app you create running in a Flex Consumption plan in Azure Functions. This follows current best practices for secure and scalable Azure Functions deployments

This Quickstart uses Azure Developer command-line (azd) tools to create functions that respond to HTTP requests. After testing the code locally, you deploy it to a new serverless function app you create running in a Flex Consumption plan in Azure Functions. This follows current best practices for secure and scalable Azure Functions deployments

This Quickstart uses Azure Developer command-line (azd) tools to create functions that respond to HTTP requests. After testing the code locally, you deploy it to a new serverless function app you create running in a Flex Consumption plan in Azure Functions. This follows current best practices for secure and scalable Azure Functions deployments

Reusable OpenAI secure UI and infrastructure for AI Chat with Azure

Sample template for deploying generic integration components using Azure Developer CLI (azd)

Demo repository to show how to use Managed Identity on Azure Functions to connect to Power Apps Web API.

Simple starting point function to host LangChains with LLMs and other tools in an Azure Function.

This provides some reference use cases while using Azure API Center

This sample shows how to take text documents as a input via BlobTrigger, does Text Summarization & Sentiment Score processing using the AI Congnitive Language service, and then outputs to another text document using BlobOutput binding.

The Doc Intelligence in-a-Box project leverages Azure AI Document Intelligence to extract data from PDF forms and store the data in a Azure Cosmos DB. This solution, part of the AI-in-a-Box framework by Microsoft Customer Engineers and Architects, ensures quality, efficiency, and rapid deployment of AI and ML solutions across various industries.

This sample shows how to take text documents as a input via BlobTrigger, does Text Summarization & Sentiment Score processing using the AI Congnitive Language service, and then outputs to another text document using BlobOutput binding. Uses Azure Functions Python v2 programming model.

Packaged app samples to accelerate devs journey to cloud

Construct a high-performance, low-latency data sharing solution between different Azure Storage Accounts using Azure Functions Flex Consumption and the Copy Blob API.