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  • Azure OpenAI client library for . NET - Azure for . NET Developers
    The Azure OpenAI client library for NET is a companion to the official OpenAI client library for NET The Azure OpenAI library configures a client for use with Azure OpenAI and provides additional strongly typed extension support for request and response models specific to Azure OpenAI scenarios
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    Azure OpenAI is a service provided by Microsoft that allows developers to integrate OpenAI's powerful language models into their applications via APIs These models can perform a variety of tasks, such as natural language processing (NLP), content generation, summarization, and much more
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    Azure OpenAI Service provides access to OpenAI's powerful language and embedding models with the security and enterprise promise of Azure The NET Aspire Azure OpenAI integration enables you to connect to Azure OpenAI Service or OpenAI's API from your NET applications
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    Today, the OpenAI team released their first beta, version 2 0 0-beta 1, of the official OpenAI library for NET Features include: This official NET library ensures a smooth and supported integration with OpenAI and Azure OpenAI It also complements OpenAI’s official libraries for Python and TypeScript JavaScript developers
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    In this guide, we’ll walk through how to integrate ChatGPT (via Azure OpenAI) into your ASP NET Core app step-by-step—with code examples, use cases, and best practices Why Integrate AI into Your NET Apps? Prerequisites Step-by-Step Integration 1️⃣ Setup Azure OpenAI in Azure Portal 2️⃣ Add HTTP Client in ASP NET Core
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    Semantic Kernel is a powerful tool that allows developers to integrate cutting-edge language models into their applications with ease Semantic Kernel provides a unified interface to access various language models from different sources, such as OpenAI, AzureOpenAI, and Hugging Face
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    Microsoft Extensions AI provides unified abstractions and middleware for integrating AI services into NET applications This means you can work with AI capabilities like chat features, embedding generation, and tool calling without worrying about specific platform implementations
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    In this post I want to go through the steps to implement a basic chat with an LLM hosted in Azure using C#, by taking advantage of the new Microsoft Extensions AI NuGet package, which aims to simplify the process




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