Learn Azure AI Search with C# — index data, run vector and hybrid queries, and add RAG to your .NET app. Start building intelligent search today. If your application's search box still runs a LIKE '%term%' query against SQL Server, your users are quietly suffering. They type "cheap laptop for uni" and get zero results because your catalogue says "affordable notebook for students." Azure AI Search with C# fixes exactly this problem: it combines classic keyword search, vector embeddings, and semantic reranking into a single managed service that you can drive from .NET with a few dozen lines of code. In this tutorial you'll build a working search index from scratch, run keyword, vector, and hybrid queries, and finish with a Retrieval Augmented Generation (RAG) pattern that grounds an LLM in your own data. This guide targets .NET 9 and the Azure.Search.Documents v11 SDK. Every snippet is runnable. We'll explain why each design choice matter...
How to build basic CRUD app with ReactJS Here's an example of building a simple CRUD application for managing a list of books. Step 1: Set up the React application To set up the React application, you'll need to have Node.js and NPM installed on your computer. Open a terminal or command prompt and create a new directory for your project. mkdir react-crud-app cd react-crud-app Next, initialize a new React application using the create-react-app CLI. npx create -react-app . Step 2: Create a list of books In this step, you'll create a list of books and display them in a table. Create a new file BookList.js in the src directory and add the following code: import React from 'react' ; const BookList = ( { books } ) => { return ( < table > < thead > < tr > < th > Title </ th > < th > Author </ th > < th > Actions </ th > </ tr > ...