This article provides documentation, samples and other resources for learning how to develop applications that use Azure OpenAI Service and other Azure AI Services.
Azure AI reference templates
Azure AI reference templates provide you with well-maintained, easy to deploy reference implementations. These ensure a high-quality starting point for your intelligent applications. The end-to-end solutions provide popular, comprehensive reference applications. The building blocks are smaller-scale samples that focus on specific scenarios and tasks.
An article that walks you through deploying and using the Enterprise chat app sample for Python. This sample is a complete end-to-end solution demonstrating the Retrieval-Augmented Generation (RAG) pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
An Azure Functions sample that shows how to take a human prompt as HTTP Get or Post input, calculates the completions using chains of human input and templates. This is a starting point that can be used for more sophisticated chains.
A sample for creating ChatGPT Plugin using GitHub Codespaces, VS Code, and Azure. The sample includes templates to deploy the plugin to Azure Container Apps using the Azure Developer CLI.
For the full list of Azure AI templates, visit our gallery. All app templates in our gallery can be spun up and deployed using a single command: azd up.
The enterprise sample solution shows how to create an Azure API Management Policy to seamlessly expose a single endpoint to your applications while keeping an efficient logic to consume two or more OpenAI or any API backends based on availability and priority.
Evaluate a chat app's answers against a set of correct or ideal answers (known as ground truth). The evaulation tools can be used with any Chat API which conforms to the Chat protocol.
Use a Locust test to validate your chat app can handle the expected load. If your chat app doesn't scale on your App Service due to Azure OpenAI TPM limits, add a load balancer and test your load again. Smart load balancers include Azure API Management and Azure Container Apps.
An article that walks you through deploying and using the Enterprise chat app sample for Python. This sample is a complete end-to-end solution demonstrating the Retrieval-Augmented Generation (RAG) pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
A sample shows how to take a human prompt as HTTP Get or Post input, calculates the completions using chains of human input and templates. This is a starting point that can be used for more sophisticated chains.
A sample for creating ChatGPT Plugin using GitHub Codespaces, VS Code, and Azure. The sample includes templates to deploy the plugin to Azure Container Apps using the Azure Developer CLI.
An article discussing how Azure Database for PostgreSQL Flexible Server and Azure Cosmos DB for PostgreSQL supports the pgvector extension, along with an overview, scenarios, etc.
The GitHub source code version of the OpenAI Python library provides convenient access to the OpenAI API from applications written in the Python language.
A notebook containing example of getting chat completions to work using the Azure endpoints. This example focuses on chat completions but also touches on some other operations that are also available using the API.
A notebook demonstrating operations how to use embeddings that can be done using the Azure endpoints. This example focuses on embeddings but also touches some other operations that are also available using the API.
An article with more complex security scenarios requires Azure role-based access control (Azure RBAC). This document covers how to authenticate to your OpenAI resource using Microsoft Entra ID.
Use Azure AI Speech to converse with Azure OpenAI Service. The text recognized by the Speech service is sent to Azure OpenAI. The Speech service synthesizes the text response from Azure OpenAI.
A repo containing both a Command Line tool and Windows application that serves as a local interface to the Azure Document Translation service for Windows, macOS and Linux.
Azure AI Document Intelligence (formerly Form Recognizer) is a cloud service that uses machine learning to analyze text and structured data from documents. The Document Intelligence software development kit (SDK) is a set of libraries and tools that enable you to easily integrate Document Intelligence models and capabilities into your applications.
The client Library for Text Analytics. This is part of the Azure AI Language service, which provides Natural Language Processing (NLP) features for understanding and analyzing text.
A quickstart article that uses Document Translation to translate a source document into a target language while preserving structure and text formatting.
The client library for Conversational Language Understanding (CLU), a cloud-based conversational AI service, which can extract intents and entities in conversations and acts like an orchestrator to select the best candidate to analyze conversations to get best response from apps like Qna, Luis, and Conversation App.
Detects harmful user-generated and AI-generated content in applications and services. Content Safety includes text and image APIs that allow you to detect material that is harmful.
Coming soon: Throughout 2024 we will be phasing out GitHub Issues as the feedback mechanism for content and replacing it with a new feedback system. For more information see: https://aka.ms/ContentUserFeedback.