@@ -20,7 +20,7 @@ Full-text search uses Apache Lucene for indexing and queries and the BM25 rankin
 
 - An [Azure AI Search service](../../search-create-service-portal.md). You can use a free service for this quickstart.
 
-- The latest version of the [.NET SDK](https://dotnet.microsoft.com/download).
+- [.NET 8](https://dotnet.microsoft.com/download) or later.
 
 - [Visual Studio Code](https://code.visualstudio.com/download).
 
@@ -40,33 +40,38 @@ Full-text search uses Apache Lucene for indexing and queries and the BM25 rankin
 
 1. Use Git to clone the sample repository.
 
-   ```console
-   git clone https://github.com/Azure-Samples/azure-search-dotnet-samples
-   ```
+    ```bash
+    git clone https://github.com/Azure-Samples/azure-search-dotnet-samples
+    ```
 
-1. Open the `azure-search-dotnet-samples/quickstart-keyword-search/AzureSearchQuickstart` folder in Visual Studio Code.
+1. Navigate to the quickstart folder and open it in Visual Studio Code.
 
-1. Open the `Program.cs` file.
+    ```bash
+    cd azure-search-dotnet-samples/quickstart-keyword-search/AzureSearchQuickstart
+    code .
+    ```
 
-1. Replace the placeholder value for `serviceEndpoint` with the URL you obtained in [Get endpoint](#get-endpoint).
+1. In `Program.cs`, replace the placeholder value for `serviceEndpoint` with the URL you obtained in [Get endpoint](#get-endpoint).
 
-1. Install the dependencies from the `AzureSearchQuickstart.csproj` file.
+1. Install the dependencies from `AzureSearchQuickstart.csproj`.
 
-   ```console
-   dotnet restore
-   ```
+    ```bash
+    dotnet restore
+    ```
+
+    When the restore completes, verify that no errors appear in the output.
 
 1. For keyless authentication with Microsoft Entra ID, sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service.
 
-    ```azurecli
-    az login
-    ```
+   ```azurecli
+   az login
+   ```
 
 ## Run the code
 
 Build and run the application.
 
-```console
+```bash
 dotnet run
 ```
 
@@ -154,6 +159,8 @@ Complete. Press any key to end this program...
 
 ## Understand the code
 
+[!INCLUDE [understand code note](../understand-code-note.md)]
+
 Now that you've run the code, let's break down the key steps:
 
 1. [Create a search client](#create-a-search-client)

@@ -40,37 +40,48 @@ Full-text search uses Apache Lucene for indexing and queries and the BM25 rankin
 
 1. Use Git to clone the sample repository.
 
-   ```console
-   git clone https://github.com/Azure-Samples/azure-search-java-samples
-   ```
+    ```bash
+    git clone https://github.com/Azure-Samples/azure-search-java-samples
+    ```
+
+1. Navigate to the quickstart folder and open it in Visual Studio Code.
 
-1. Open the `azure-search-java-samples/quickstart-keyword-search` folder in Visual Studio Code.
+    ```bash
+    cd azure-search-java-samples/quickstart-keyword-search
+    code .
+    ```
 
-1. Open the `App.java` file in the `src/main/java/azure/search/sample` folder.
+1. In `src/main/java/azure/search/sample/App.java`, replace the placeholder value for `searchServiceEndpoint` with the URL you obtained in [Get endpoint](#get-endpoint).
 
-1. Replace the placeholder value for `searchServiceEndpoint` with the URL you obtained in [Get endpoint](#get-endpoint).
+1. Install the dependencies.
 
-1. Use a terminal in Visual Studio Code to install the dependencies.
+    ```bash
+    mvn clean dependency:copy-dependencies
+    ```
 
-   ```console
-   mvn clean dependency:copy-dependencies
-   ```
+    When the build completes, verify that no errors appear in the output.
 
 1. For keyless authentication with Microsoft Entra ID, sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service.
 
-    ```azurecli
-    az login
-    ```
+   ```azurecli
+   az login
+   ```
 
 ## Run the code
 
 Compile and run the application.
 
-```console
+```bash
 javac -d target/classes -cp "target/dependency/*" src/main/java/azure/search/sample/*.java
 java -cp "target/classes;target/dependency/*" azure.search.sample.App
 ```
 
+On macOS or Linux, use `:` instead of `;` as the classpath separator.
+
+```bash
+java -cp "target/classes:target/dependency/*" azure.search.sample.App
+```
+
 ### Output
 
 The output should be similar to the following:
@@ -117,6 +128,8 @@ Complete.
 
 ## Understand the code
 
+[!INCLUDE [understand code note](../understand-code-note.md)]
+
 Now that you've run the code, let's break down the key steps:
 
 1. [Create a search client](#create-a-search-client)

@@ -20,7 +20,7 @@ Full-text search uses Apache Lucene for indexing and queries and the BM25 rankin
 
 - An [Azure AI Search service](../../search-create-service-portal.md). You can use a free service for this quickstart.
 
-- The latest LTS version of [Node.js](https://nodejs.org/en/download/).
+- [Node.js 20 LTS](https://nodejs.org/en/download/) or later.
 
 - [Visual Studio Code](https://code.visualstudio.com/download).
 
@@ -40,33 +40,44 @@ Full-text search uses Apache Lucene for indexing and queries and the BM25 rankin
 
 1. Use Git to clone the sample repository.
 
-   ```console
-   git clone https://github.com/Azure-Samples/azure-search-javascript-samples
-   ```
+    ```bash
+    git clone https://github.com/Azure-Samples/azure-search-javascript-samples
+    ```
+
+1. Navigate to the quickstart folder and open it in Visual Studio Code.
+
+    ```bash
+    cd azure-search-javascript-samples/quickstart-keyword-search
+    code .
+    ```
 
-1. Open the `azure-search-javascript-samples/quickstart-keyword-search` folder in Visual Studio Code.
+1. In `sample.env`, replace the placeholder value for `SEARCH_API_ENDPOINT` with the URL you obtained in [Get endpoint](#get-endpoint).
 
-1. Rename the `sample.env` file to `.env`, and then open the file.
+1. Rename `sample.env` to `.env`.
 
-1. Replace the placeholder value for `SEARCH_API_ENDPOINT` with the URL you obtained in [Get endpoint](#get-endpoint).
+    ```bash
+    mv sample.env .env
+    ```
 
-1. Use a terminal in Visual Studio Code to install the dependencies.
+1. Install the dependencies.
 
-    ```console
+    ```bash
     npm install
     ```
 
+    When the installation completes, you should see a `node_modules` folder in the project directory.
+
 1. For keyless authentication with Microsoft Entra ID, sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service.
 
-    ```azurecli
-    az login
-    ```
+   ```azurecli
+   az login
+   ```
 
 ## Run the code
 
 Run the application.
 
-```console
+```bash
 node index.js
 ```
 
@@ -109,6 +120,8 @@ HotelId: 3; HotelName: Gastronomic Landscape Hotel
 
 ## Understand the code
 
+[!INCLUDE [understand code note](../understand-code-note.md)]
+
 Now that you've run the code, let's break down the key steps:
 
 1. [Create a search client](#create-a-search-client)

@@ -20,7 +20,7 @@ Full-text search uses Apache Lucene for indexing and queries and the BM25 rankin
 
 - An [Azure AI Search service](../../search-create-service-portal.md). You can use a free service for this quickstart.
 
-- The latest version of [PowerShell](https://github.com/PowerShell/PowerShell).
+- [PowerShell 7](https://github.com/PowerShell/PowerShell) or later.
 
 - [Git](https://git-scm.com/downloads) to clone the sample repository.
 
@@ -38,25 +38,23 @@ Full-text search uses Apache Lucene for indexing and queries and the BM25 rankin
 
 1. Use Git to clone the sample repository.
 
-   ```powershell
-   git clone https://github.com/Azure-Samples/azure-search-powershell-samples
-   ```
+    ```powershell
+    git clone https://github.com/Azure-Samples/azure-search-powershell-samples
+    ```
 
-1. Navigate to the `Quickstart` folder.
+1. Navigate to the quickstart folder.
 
-   ```powershell
-   cd azure-search-powershell-samples/Quickstart
-   ```
+    ```powershell
+    cd azure-search-powershell-samples/Quickstart
+    ```
 
 1. For keyless authentication with Microsoft Entra ID, sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service.
 
    ```azurecli
    az login
    ```
 
-1. Open the `azure-search-powershell-samples/Quickstart/azure-search-quickstart.ps1` file in a text editor.
-
-1. Replace the placeholder value for `$baseUrl` with the URL you obtained in [Get endpoint](#get-endpoint).
+1. In `azure-search-quickstart.ps1`, replace the placeholder value for `$baseUrl` with the URL you obtained in [Get endpoint](#get-endpoint).
 
 ## Run the code
 
@@ -91,6 +89,8 @@ The script deletes any existing index, creates a new index, uploads documents, a
 
 ## Understand the code
 
+[!INCLUDE [understand code note](../understand-code-note.md)]
+
 Now that you've run the code, let's break down the key steps:
 
 1. [Create a search index](#create-a-search-index)

@@ -20,7 +20,7 @@ Full-text search uses Apache Lucene for indexing and queries and the BM25 rankin
 
 - An [Azure AI Search service](../../search-create-service-portal.md). You can use a free service for this quickstart.
 
-- The latest version of [Python](https://www.python.org/downloads/).
+- [Python 3.8](https://www.python.org/downloads/) or later.
 
 - [Visual Studio Code](https://code.visualstudio.com/download) with the [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python) and [Jupyter](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter) extensions.
 
@@ -40,25 +40,32 @@ Full-text search uses Apache Lucene for indexing and queries and the BM25 rankin
 
 1. Use Git to clone the sample repository.
 
-   ```console
-   git clone https://github.com/Azure-Samples/azure-search-python-samples
-   ```
+    ```bash
+    git clone https://github.com/Azure-Samples/azure-search-python-samples
+    ```
+
+1. Navigate to the quickstart folder and open it in Visual Studio Code.
+
+    ```bash
+    cd azure-search-python-samples/Quickstart-Keyword-Search
+    code .
+    ```
 
-1. Open the `azure-search-python-samples/Quickstart-Keyword-Search` folder in Visual Studio Code.
+1. Open `azure-search-quickstart.ipynb`.
 
-1. Open the `azure-search-quickstart.ipynb` file.
+1. Press **Ctrl+Shift+P**, select **Notebook: Select Notebook Kernel**, and follow the prompts to create a virtual environment.
 
-1. In the upper-right corner of the notebook, select **Select Kernel** > **Python Environments...** > **Create Python Environment**, and then follow the prompts to create a virtual environment.
+   When complete, you should see a `.venv` folder in the project directory.
 
 1. Run the first code cell to install the required packages.
 
-1. In the second code cell, replace the placeholder value for `search_endpoint` with the URL you obtained in [Get endpoint](#get-endpoint), and then run the cell.
+1. In the second code cell, replace the placeholder value for `search_endpoint` with the URL you obtained in [Get endpoint](#get-endpoint), and run the cell.
 
 1. For keyless authentication with Microsoft Entra ID, sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service.
 
-    ```azurecli
-    az login
-    ```
+   ```azurecli
+   az login
+   ```
 
 ## Run the code
 
@@ -90,6 +97,8 @@ Description: This classic hotel is fully-refurbished and ideally located on the
 
 ## Understand the code
 
+[!INCLUDE [understand code note](../understand-code-note.md)]
+
 Now that you've run the code, let's break down the key steps:
 
 1. [Create the clients](#create-the-clients)
@@ -151,7 +160,7 @@ print(f' {result.name} created')
 
 Azure AI Search searches over content stored in the service. In this step, you load JSON documents that conform to the hotel index you created.
 
-In Azure AI Search, documents are data structures that are both inputs to indexing and outputs from queries. The notebook defines a documents payload as a list of dictionaries containing hotel data:
+In Azure AI Search, documents are data structures that are both inputs to indexing and outputs from queries. The notebook defines a documents payload as a list of dictionaries containing hotel data.
 
 ```python
 # Create a documents payload

@@ -38,21 +38,24 @@ Full-text search uses Apache Lucene for indexing and queries and the BM25 rankin
 
 1. Use Git to clone the sample repository.
 
-   ```console
-   git clone https://github.com/Azure-Samples/azure-search-rest-samples
-   ```
+    ```bash
+    git clone https://github.com/Azure-Samples/azure-search-rest-samples
+    ```
 
-1. Open the `azure-search-rest-samples/Quickstart-keyword-search` folder in Visual Studio Code.
+1. Navigate to the quickstart folder and open it in Visual Studio Code.
 
-1. Open the `az-search-quickstart.rest` file.
+    ```bash
+    cd azure-search-rest-samples/Quickstart-keyword-search
+    code .
+    ```
 
-1. Replace the placeholder value for `@baseUrl` with the URL you obtained in [Get endpoint](#get-endpoint).
+1. In `az-search-quickstart.rest`, replace the placeholder value for `@baseUrl` with the URL you obtained in [Get endpoint](#get-endpoint).
 
 1. For keyless authentication with Microsoft Entra ID, sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service.
 
-    ```azurecli
-    az login
-    ```
+   ```azurecli
+   az login
+   ```
 
 1. For keyless authentication with Microsoft Entra ID, generate an access token.
 
@@ -66,7 +69,7 @@ Full-text search uses Apache Lucene for indexing and queries and the BM25 rankin
 
 1. Under `### List existing indexes by name`, select **Send Request** to verify your connection.
 
-   A response should appear in an adjacent pane. If you have existing indexes, they're listed. Otherwise, the list is empty. If the HTTP code is `200 OK`, you're ready to proceed.
+    A response should appear in an adjacent pane. If you have existing indexes, they're listed. Otherwise, the list is empty. If the HTTP code is `200 OK`, you're ready to proceed.
 
 1. Send the remaining requests sequentially to create an index, upload documents, and query the index.
 
@@ -94,6 +97,8 @@ Each request returns different JSON based on the operation.  The key output is f
 
 ## Understand the code
 
+[!INCLUDE [understand code note](../understand-code-note.md)]
+
 Now that you've run the code, let's break down the key steps:
 
 1. [Create a search index](#create-a-search-index)

@@ -20,7 +20,9 @@ Full-text search uses Apache Lucene for indexing and queries and the BM25 rankin
 
 - An [Azure AI Search service](../../search-create-service-portal.md). You can use a free service for this quickstart.
 
-- The latest LTS version of [Node.js](https://nodejs.org/en/download/).
+- [Node.js 20 LTS](https://nodejs.org/en/download/) or later to run the compiled code.
+
+- [TypeScript](https://www.typescriptlang.org/download/) to compile TypeScript to JavaScript.
 
 - [Visual Studio Code](https://code.visualstudio.com/download).
 
@@ -40,32 +42,45 @@ Full-text search uses Apache Lucene for indexing and queries and the BM25 rankin
 
 1. Use Git to clone the sample repository.
 
-   ```console
-   git clone https://github.com/Azure-Samples/azure-search-javascript-samples
-   ```
+    ```bash
+    git clone https://github.com/Azure-Samples/azure-search-javascript-samples
+    ```
+
+1. Navigate to the quickstart folder and open it in Visual Studio Code.
+
+    ```bash
+    cd azure-search-javascript-samples/quickstart-keyword-search
+    code .
+    ```
 
-1. Open the `azure-search-javascript-samples/quickstart-keyword-search` folder in Visual Studio Code.
+1. In `sample.env`, replace the placeholder value for `SEARCH_API_ENDPOINT` with the URL you obtained in [Get endpoint](#get-endpoint).
 
-1. Rename the `sample.env` file to `.env`, and then open the file.
+1. Rename `sample.env` to `.env`.
 
-1. Replace the placeholder value for `SEARCH_API_ENDPOINT` with the URL you obtained in [Get endpoint](#get-endpoint).
+    ```bash
+    mv sample.env .env
+    ```
 
-1. Use a terminal in Visual Studio Code to install the dependencies and initialize the project for TypeScript.
+1. Install the dependencies.
 
-    ```console
+    ```bash
     npm install
     npm install typescript @types/node --save-dev
     npm pkg set type=module
     ```
 
+    When the installation completes, you should see a `node_modules` folder in the project directory.
+
 1. For keyless authentication with Microsoft Entra ID, sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service.
 
-    ```azurecli
-    az login
-    ```
+   ```azurecli
+   az login
+   ```
 
 ## Run the code
 
+The sample code uses JavaScript by default. To run the code with TypeScript:
+
 1. Create a file named `tsconfig.json`, and then paste the following code into it.
 
     ```json
@@ -349,13 +364,13 @@ Full-text search uses Apache Lucene for indexing and queries and the BM25 rankin
 
 1. Transpile from TypeScript to JavaScript.
 
-    ```console
+    ```bash
     npx tsc
     ```
 
 1. Run the application.
 
-    ```console
+    ```bash
     node index.js
     ```
 
@@ -397,6 +412,8 @@ HotelId: 3; HotelName: Gastronomic Landscape Hotel
 
 ## Understand the code
 
+[!INCLUDE [understand code note](../understand-code-note.md)]
+
 Now that you've run the code, let's break down the key steps:
 
 1. [Create a search client](#create-a-search-client)
@@ -480,7 +497,7 @@ let index: SearchIndex = await searchIndexClient.createIndex(indexDefinition);
 
 In Azure AI Search, documents are data structures that are both inputs to indexing and outputs from queries. You can push such data to the index or use an [indexer](/azure/search/search-indexer-overview). In this quickstart, you programmatically push the documents to the index.
 
-Document inputs might be rows in a database, blobs in Azure Blob Storage, or JSON documents on disk, as in this quickstart. The hotel data is imported at the top of the file:
+Document inputs might be rows in a database, blobs in Azure Blob Storage, or JSON documents on disk, as in this quickstart. The hotel data is imported at the top of the file.
 
 ```typescript
 import hotelData from './hotels.json' with { type: "json" };
@@ -504,7 +521,7 @@ let indexDocumentsResult = await searchClient.mergeOrUploadDocuments(hotelData['
 
 With an index created and documents uploaded, you're ready to send queries to the index. This section sends five different queries to the search index to demonstrate different pieces of query functionality available to you.
 
-The queries are written in a `sendQueries()` function that is called in the main function:
+The queries are written in a `sendQueries()` function that is called in the main function.
 
 ```typescript
 await sendQueries(searchClient);

@@ -4,368 +4,463 @@ author: rotabor
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: include
-ms.date: 01/14/2026
+ms.date: 02/05/2026
 ms.custom: dev-focus
 ai-usage: ai-assisted
 ---
 
-In this quickstart, you use a .NET app to create, load, and query a [vector index](../../vector-store.md). The code performs these operations by using the [Azure AI Search client library for .NET](/dotnet/api/overview/azure/search), which provides an abstraction over the REST APIs to access index operations.
+In this quickstart, you use the [Azure AI Search client library for .NET](/dotnet/api/overview/azure/search) to create, load, and query a [vector index](../../vector-store.md). The .NET client library provides an abstraction over the REST APIs for index operations.
 
 In Azure AI Search, a vector index has an index schema that defines vector and nonvector fields, a vector search configuration for algorithms that create the embedding space, and settings on vector field definitions that are evaluated at query time. [Indexes - Create or Update](/rest/api/searchservice/indexes/create-or-update) (REST API) creates the vector index.
 
-> [!NOTE]
-> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import data (new)** wizard](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
+> [!TIP]
+> + Want to get started right away? Download the [source code](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/quickstart-vector-search) on GitHub.
+> + This quickstart omits the vectorization step and provides inline embeddings. For [integrated vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import data (new)** wizard](../../search-get-started-portal-import-vectors.md).
 
 ## Prerequisites
 
-+ An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn).
+- An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn).
 
-+ An [Azure AI Search service](../../search-create-service-portal.md).
+- An [Azure AI Search service](../../search-create-service-portal.md). You can use the Free tier for most of this quickstart, but we recommend Basic or higher for larger data files.
 
-    + You can use the Free tier for most of this quickstart, but we recommend Basic or higher for larger data files.
+- [Semantic ranker enabled on your search service](../../semantic-how-to-enable-disable.md) for the optional semantic hybrid query.
 
-    + For [keyless authentication](../../search-get-started-rbac.md) with Microsoft Entra ID, assign the **Search Index Data Contributor role** to your user account.
-    
-    + To run the semantic hybrid query, you must [enable semantic ranker](../../semantic-how-to-enable-disable.md).
+- [.NET 8](https://dotnet.microsoft.com/download) or later.
 
-+ [Visual Studio Code](https://code.visualstudio.com/download) or [Visual Studio](https://visualstudio.com) to run the code.
+- [Visual Studio Code](https://code.visualstudio.com/download).
 
-+ [Git](https://git-scm.com/downloads) to clone the sample repo.
+- [Git](https://git-scm.com/downloads) to clone the sample repository.
 
-## Get service information
+- The [Azure CLI](/cli/azure/install-azure-cli) for keyless authentication with Microsoft Entra ID.
 
-Requests to the search endpoint must be authenticated and authorized. While it's possible to use API keys for this task, we recommend [using a keyless connection via Microsoft Entra ID](../../search-get-started-rbac.md).
+## Configure access
 
-This quickstart uses `DefaultAzureCredential`, which simplifies authentication in both development and production scenarios. However, for production scenarios, you might have more advanced requirements that require a different approach. To understand all of your options, see [Authenticate .NET apps to Azure services by using the Azure SDK for .NET](/dotnet/azure/sdk/authentication/).
+[!INCLUDE [resource authentication](../resource-authentication.md)]
 
-## Clone the code and setup environment
+## Get endpoint
 
-1. Clone the repo containing the code for this quickstart.
+[!INCLUDE [resource endpoint](../resource-endpoint.md)]
+
+## Set up the environment
+
+1. Use Git to clone the sample repository.
 
    ```bash
    git clone https://github.com/Azure-Samples/azure-search-dotnet-samples
    ```
 
-1. Open the `quickstart-vector-search` folder in Visual Studio Code or double-click the `VectorSearchQuickstart.sln` file to open the solution in Visual Studio.
+1. Navigate to the quickstart folder and open it in Visual Studio Code.
 
-1. Open `appsettings.json` in both `VectorSearchCreatePopulateIndex` and `VectorSearchExamples` folders. 
-
-1. Set `Endpoint` to your search service URL, which should be similar to `https://mydemo.search.windows.net`.
+   ```bash
+   cd azure-search-dotnet-samples/quickstart-vector-search
+   code .
+   ```
 
-1. Set `IndexName` to a unique name for your index. You can also use the default `hotels-vector-quickstart` name.
+1. In `VectorSearchCreatePopulateIndex/appsettings.json`, replace the placeholder value for `Endpoint` with the URL you obtained in [Get endpoint](#get-endpoint).
 
-## Create a vector index and upload documents
+1. Repeat the previous step for `VectorSearchExamples/appsettings.json`.
 
-To run search queries against your Azure AI Search service, you must first create a search index and upload documents to the index.
+1. For keyless authentication with Microsoft Entra ID, sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service.
 
-To create and populate the index:
+   ```azurecli
+   az login
+   ```
 
-1. Open a terminal in the `VectorSearchCreatePopulateIndex` folder.
+## Run the code
 
-1. Use the `dotnet run` command to run the project.
+1. Run the first project to create and populate the index.
 
-    ```dotnetcli
+    ```bash
+    cd VectorSearchCreatePopulateIndex
     dotnet run
     ```
 
-    The following code executes to create an index:
-    
-    :::code language="csharp" source="~/azure-search-dotnet-samples/quickstart-vector-search/vectorsearchcreatepopulateindex/program.cs" id="CreateSearchindex":::
-    
-    The following code uploads JSON-formatted documents in the `hotel-samples.json` file to your search service.
-    
-    :::code language="csharp" source="~/azure-search-dotnet-samples/quickstart-vector-search/vectorsearchcreatepopulateindex/program.cs" id="UploadDocs":::
-    
-    After you run the project, the following output is printed.
-    
-    ```output
-    Key: 1, Succeeded: True
-    Key: 2, Succeeded: True
-    Key: 3, Succeeded: True
-    Key: 4, Succeeded: True
-    Key: 48, Succeeded: True
-    Key: 49, Succeeded: True
-    Key: 13, Succeeded: True
+1. In `VectorSearchExamples/Program.cs`, uncomment the query methods you want to run.
+
+1. Run the second project to execute those queries against the index.
+
+    ```bash
+    cd ..\VectorSearchExamples
+    dotnet run
     ```
-    
-    Key takeaways:
 
-    + Your code interacts with a specific search index hosted in your Azure AI Search service through the SearchClient, which is the main object provided by the azure-search-documents package. The SearchClient provides access to index operations, such as:
+### Output
 
-        + Data ingestion: `UploadDocuments()`, `MergeDocuments()`, `DeleteDocuments()`
+The output of the first project includes confirmation of index creation and successful document uploads.
 
-        + Search operations: `Search()`, `Autocomplete()`, `Suggest()`
-        
-        + Index management operations: `CreateOrUpdateIndex()`
-    
-    + Vector fields contain floating point values. The dimensions attribute has a minimum of 2 and a maximum of 4096 floating point values each. This quickstart sets the dimensions attribute to 1,536 because that's the size of embeddings generated by the `text-embedding-ada-002` model.
+```output
+Creating or updating index 'hotels-vector-quickstart'...
+Index 'hotels-vector-quickstart' updated.
 
-## Run queries
+Key: 1, Succeeded: True
+Key: 2, Succeeded: True
+Key: 3, Succeeded: True
+Key: 4, Succeeded: True
+Key: 48, Succeeded: True
+Key: 49, Succeeded: True
+Key: 13, Succeeded: True
+```
 
-To issue the vector queries in this section, open `Program.cs` in the `VectorSearchExamples` folder, and then open a terminal in the `VectorSearchExamples` folder.
+The output of the second project shows the search results for each enabled query method. The following example shows single vector search results.
 
-Queries in this section:
+```output
+Single Vector Search Results:
+Score: 0.6605852, HotelId: 48, HotelName: Nordick's Valley Motel
+Score: 0.6333684, HotelId: 13, HotelName: Luxury Lion Resort
+Score: 0.605672, HotelId: 4, HotelName: Sublime Palace Hotel
+Score: 0.6026341, HotelId: 49, HotelName: Swirling Currents Hotel
+Score: 0.57902366, HotelId: 2, HotelName: Old Century Hotel
+```
 
-+ [Single vector search](#single-vector-search)
-+ [Single vector search with filter](#single-vector-search-with-filter)
-+ [Hybrid search](#hybrid-search)
-+ [Semantic hybrid search with filter](#semantic-hybrid-search-with-a-filter)
+## Understand the code
 
-### Single vector search
+[!INCLUDE [understand code note](../understand-code-note.md)]
 
-The first query demonstrates a basic scenario where you want to find document descriptions that closely match the search string.
+Now that you've run the code, let's break down the key steps:
 
-To create a single vector search:
+1. [Create a vector index](#create-a-vector-index)
+1. [Upload documents to the index](#upload-documents-to-the-index)
+1. [Query the index](#query-the-index)
 
-1. In the `Program.cs` file of the `VectorSearchExamples` folder, uncomment the `SearchExamples.SearchSingleVector(searchClient, vectorizedResult);` method call.
+### Create a vector index
 
-    This method executes the following search function in the `SearchExamples.cs` class.
+Before you add content to Azure AI Search, you must create an index to define how the content is stored and structured.
 
-    :::code language="csharp" source="~/azure-search-dotnet-samples/quickstart-vector-search/vectorsearchexamples/SearchExamples.cs" id="SearchSingleVector":::
+The index schema is organized around hotel content. Sample data consists of vector and nonvector descriptions of fictitious hotels. The following code in `VectorSearchCreatePopulateIndex/Program.cs` creates the index schema, including the vector field `DescriptionVector`.
 
-1. Run `dotnet run` to execute the query, which returns the following results.
-    
-    ```output
-    Single Vector Search Results:
-    Score: 0.6605852, HotelId: 48, HotelName: Nordick's Valley Motel
-    Score: 0.6333684, HotelId: 13, HotelName: Luxury Lion Resort
-    Score: 0.605672, HotelId: 4, HotelName: Sublime Palace Hotel
-    Score: 0.6026341, HotelId: 49, HotelName: Swirling Currents Hotel
-    Score: 0.57902366, HotelId: 2, HotelName: Old Century Hotel
-    ```
+```csharp
+static async Task CreateSearchIndex(string indexName, SearchIndexClient indexClient)
+{
+    var addressField = new ComplexField("Address");
+    addressField.Fields.Add(new SearchableField("StreetAddress") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft });
+    addressField.Fields.Add(new SearchableField("City") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });
+    addressField.Fields.Add(new SearchableField("StateProvince") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });
+    addressField.Fields.Add(new SearchableField("PostalCode") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });
+    addressField.Fields.Add(new SearchableField("Country") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });
+
+    var allFields = new List<SearchField>()
+    {
+        new SimpleField("HotelId", SearchFieldDataType.String) { IsKey = true, IsFacetable = true, IsFilterable = true },
+        new SearchableField("HotelName") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft },
+        new SearchableField("Description") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft },
+        new VectorSearchField("DescriptionVector", 1536, "my-vector-profile"),
+        new SearchableField("Category") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true },
+        new SearchableField("Tags", collection: true) { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true },
+        new SimpleField("ParkingIncluded", SearchFieldDataType.Boolean) { IsFacetable = true, IsFilterable = true },
+        new SimpleField("LastRenovationDate", SearchFieldDataType.DateTimeOffset) { IsSortable = true },
+        new SimpleField("Rating", SearchFieldDataType.Double) { IsFacetable = true, IsFilterable = true, IsSortable = true },
+        addressField,
+        new SimpleField("Location", SearchFieldDataType.GeographyPoint) { IsFilterable = true, IsSortable = true },
+    };
+
+    // Create the suggester configuration
+    var suggester = new SearchSuggester("sg", new[] { "Address/City", "Address/Country" });
+
+    // Create the semantic search
+    var semanticSearch = new SemanticSearch()
+    {
+        Configurations =
+        {
+            new SemanticConfiguration(
+                name: "semantic-config",
+                prioritizedFields: new SemanticPrioritizedFields
+                {
+                    TitleField = new SemanticField("HotelName"),
+                    KeywordsFields = { new SemanticField("Category") },
+                    ContentFields = { new SemanticField("Description") }
+                })
+        }
+    };
 
-### Single vector search with filter
+    // Add vector search configuration
+    var vectorSearch = new VectorSearch();
+    vectorSearch.Algorithms.Add(new HnswAlgorithmConfiguration(name: "my-hnsw-vector-config-1"));
+    vectorSearch.Profiles.Add(new VectorSearchProfile(name: "my-vector-profile", algorithmConfigurationName: "my-hnsw-vector-config-1"));
 
-In Azure AI Search, [filters](../../vector-search-filters.md) apply to nonvector fields in an index. This example filters on the `Tags` field to filter out any hotels that don't provide free Wi-Fi.
+    var definition = new SearchIndex(indexName)
+    {
+        Fields = allFields,
+        Suggesters = { suggester },
+        VectorSearch = vectorSearch,
+        SemanticSearch = semanticSearch
+    };
 
-To create a single vector search with a filter:
+    // Create or update the index
+    Console.WriteLine($"Creating or updating index '{indexName}'...");
+    var result = await indexClient.CreateOrUpdateIndexAsync(definition);
+    Console.WriteLine($"Index '{result.Value.Name}' updated.");
+    Console.WriteLine();
+}
+```
 
-1. In the `Program.cs` file of the `VectorSearchExamples` folder, uncomment the `SearchExamples.SearchSingleVectorWithFilter(searchClient, vectorizedResult);` method call.
+Key takeaways:
 
-    This method executes the following search function in the `SearchExamples.cs` class.
++ You define an index by creating a list of fields.
 
-    :::code language="csharp" source="~/azure-search-dotnet-samples/quickstart-vector-search/vectorsearchexamples/SearchExamples.cs" id="SearchSingleVectorWithFilter":::
++ This particular index supports multiple search capabilities:
 
-1.  Run `dotnet run` to execute the query, which returns only hotels that provide free Wi-Fi. 
-    
-       ```output
-        Single Vector Search With Filter Results:
-        Score: 0.6605852, HotelId: 48, HotelName: Nordick's Valley Motel, Tags: continental breakfastair conditioningfree wifi
-        Score: 0.57902366, HotelId: 2, HotelName: Old Century Hotel, Tags: poolfree wifiair conditioningconcierge
-       ```
+    + [Full-text search](../../search-lucene-query-architecture.md) (`SearchableField`)
 
-1. For a geo filter, uncomment `SearchExamples.SingleSearchWithGeoFilter(searchClient, vectorizedResult);` in `Program.cs`.
+    + [Vector search](../../vector-search-overview.md) (`VectorSearchField`)
 
-    This method executes the following search function in the `SearchExamples.cs` class.
+    + [Semantic ranking](../../semantic-search-overview.md) (`SemanticSearch`)
 
-    :::code language="csharp" source="~/azure-search-dotnet-samples/quickstart-vector-search/vectorsearchexamples/SearchExamples.cs" id="SingleSearchWithGeoFilter":::
+    + [Faceted search](../../search-faceted-navigation.md) (fields marked with `IsFacetable`)
 
-1.  Run `dotnet run` to execute the query, which returns only hotels within 300 kilometers.
-   
-    ```output
-    Vector query with a geo filter:
-    -HotelId: 48
-    HotelName: Nordick's Valley Motel
-    Score: 0.6605852246284485
-    City/State: Washington D.C./null
-    Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
-    
-    -HotelId: 49
-    HotelName: Swirling Currents Hotel
-    Score: 0.602634072303772
-    City/State: Arlington/VA
-    Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.
-    ```
+    + [Geo-spatial search](../../search-query-odata-geo-spatial-functions.md) (`Location` field with `SearchFieldDataType.GeographyPoint`)
 
-### Hybrid search
-
-[Hybrid search](../../hybrid-search-overview.md) combines keyword and vector queries in one request. This example runs the following full-text and vector query strings concurrently:
-
-+ Search string: `historic hotel walk to restaurants and shopping`
-+ Vector query string: `quintessential lodging near running trails, eateries, retail` (vectorized into a mathematical representation)
-
-To create a hybrid search:
-
-1. In the `Program.cs` file, uncomment the `SearchExamples.SearchHybridVectorAndText(searchClient, vectorizedResult);` method call.
-
-    This method executes the following search function in the `SearchExamples.cs` class.
-
-    :::code language="csharp" source="~/azure-search-dotnet-samples/quickstart-vector-search/vectorsearchexamples/SearchExamples.cs" id="SearchHybridVectorAndText":::
-
-1.  Run `dotnet run` to execute the query, which returns the following results.
-
-       ```output
-       Hybrid search results:
-       Score: 0.03279569745063782
-       HotelId: 4
-       HotelName: Sublime Palace Hotel
-       Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th   century resort, updated for every modern convenience.
-       Category: Boutique
-       Tags: conciergeviewair conditioning
-       
-       Score: 0.032786883413791656
-       HotelId: 13
-       HotelName: Luxury Lion Resort
-       Description: Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.
-       Category: Luxury
-       Tags: barconciergerestaurant
-       
-       Score: 0.03205128386616707
-       HotelId: 48
-       HotelName: Nordick's Valley Motel
-       Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
-       Category: Boutique
-       Tags: continental breakfastair conditioningfree wifi
-       
-       Score: 0.0317460335791111
-       HotelId: 49
-       HotelName: Swirling Currents Hotel
-       Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
-       Category: Suite
-       Tags: air conditioninglaundry service24-hour front desk service
-       
-       Score: 0.03077651560306549
-       HotelId: 2
-       HotelName: Old Century Hotel
-       Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer   dinners, and live music.
-       Category: Boutique
-       Tags: poolfree wifiair conditioningconcierge
-       ```
-    
-    Because Reciprocal Rank Fusion (RRF) merges results, it helps to review the inputs. The following results are from the full-text query only. The top two results are Sublime Palace Hotel and Luxury Lion Resort. The Sublime Palace Hotel has a stronger BM25 relevance score.
-
-    ```json
-    {
-        "@search.score": 2.2626662,
-        "HotelName": "Sublime Palace Hotel",
-        "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace."
-    },
-    {
-        "@search.score": 0.86421645,
-        "HotelName": "Luxury Lion Resort",
-        "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort"
-    },
-    ```
+    + [Filtering](../../search-filters.md) and sorting (fields marked with `IsFilterable` and `IsSortable`)
+
++ The second parameter in `VectorSearchField` specifies `vectorSearchDimensions`, which must match the output size of your embedding model. This quickstart uses 1,536 dimensions to match the `text-embedding-ada-002` model.
 
-    In the vector-only query, which uses HNSW for finding matches, the Sublime Palace Hotel drops to the fourth position. Luxury Lion, which was second in the full-text search and third in the vector search, doesn't experience the same range of fluctuation, so it appears as a top match in a homogenized result set.
++ The `VectorSearch` configuration defines the Approximate Nearest Neighbor (ANN) algorithm. Supported algorithms include Hierarchical Navigable Small World (HNSW) and exhaustive K-Nearest Neighbor (KNN). For more information, see [Relevance in vector search](../../vector-search-ranking.md).
 
-    ```json
-    "value": [
+### Upload documents to the index
+
+Newly created indexes are empty. To populate an index and make it searchable, you must upload JSON documents that conform to the index schema.
+
+In Azure AI Search, documents serve as both inputs for indexing and outputs for queries. For simplicity, this quickstart provides sample hotel documents with precomputed vectors. In production scenarios, content is often pulled from connected data sources and transformed into JSON using [indexers](../../search-indexer-overview.md).
+
+The following code uploads documents from `HotelData.json` to your search service.
+
+```csharp
+static async Task UploadDocs(SearchClient searchClient)
+{
+    var jsonPath = Path.Combine(Directory.GetCurrentDirectory(), "HotelData.json");
+
+    // Read and parse hotel data
+    var json = await File.ReadAllTextAsync(jsonPath);
+    List<Hotel> hotels = new List<Hotel>();
+    try
+    {
+        using var doc = JsonDocument.Parse(json);
+        if (doc.RootElement.ValueKind != JsonValueKind.Array)
         {
-            "@search.score": 0.857736,
-            "HotelId": "48",
-            "HotelName": "Nordick's Valley Motel",
-            "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer.  Hiking? Wine Tasting? Exploring the caverns?  It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
-            "Category": "Boutique"
-        },
+            Console.WriteLine("HotelData.json root is not a JSON array.");
+        }
+        // Deserialize all hotel objects
+        hotels = doc.RootElement.EnumerateArray()
+            .Select(e => JsonSerializer.Deserialize<Hotel>(e.GetRawText()))
+            .Where(h => h != null)
+            .ToList();
+    }
+    catch (JsonException ex)
+    {
+        Console.WriteLine($"Failed to parse HotelData.json: {ex.Message}");
+    }
+
+    try
+    {
+        // Upload hotel documents to Azure Search
+        var result = await searchClient.UploadDocumentsAsync(hotels);
+        foreach (var r in result.Value.Results)
         {
-            "@search.score": 0.8399129,
-            "HotelId": "49",
-            "HotelName": "Swirling Currents Hotel",
-            "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.",
-            "Category": "Luxury"
-        },
+            Console.WriteLine($"Key: {r.Key}, Succeeded: {r.Succeeded}");
+        }
+    }
+    catch (Exception ex)
+    {
+        Console.WriteLine("Failed to upload documents: " + ex);
+    }
+}
+```
+
+Your code interacts with a specific search index hosted in your Azure AI Search service through the `SearchClient`, which is the main object provided by the [`Azure.Search.Documents`](/dotnet/api/overview/azure/search.documents-readme) package. The `SearchClient` provides access to index operations, such as:
+
++ Data ingestion: `UploadDocuments()`, `MergeDocuments()`, `DeleteDocuments()`
+
++ Search operations: `Search()`, `Autocomplete()`, `Suggest()`
+
+### Query the index
+
+The queries in `VectorSearchExamples` demonstrate different search patterns. The example vector queries are based on two strings:
+
++ Full-text search string: `"historic hotel walk to restaurants and shopping"`
+
++ Vector query string: `"quintessential lodging near running trails, eateries, retail"` (vectorized into a mathematical representation)
+
+The vector query string is semantically similar to the full-text search string, but it includes terms that don't exist in the index. A keyword-only search for the vector query string returns zero results. However, vector search finds relevant matches based on meaning rather than exact keywords.
+
+The following examples start with a basic vector query and progressively add filters, keyword search, and semantic reranking.
+
+#### Single vector search
+
+The `SearchSingleVector` method demonstrates a basic scenario where you want to find document descriptions that closely match the vector query string. `VectorizedQuery` configures the vector search:
+
++ `KNearestNeighborsCount` limits how many results are returned based on vector similarity.
++ `Fields` specifies the vector field to search against.
+
+```csharp
+public static async Task SearchSingleVector(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
+{
+    SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(
+        new SearchOptions
         {
-            "@search.score": 0.8383954,
-            "HotelId": "13",
-            "HotelName": "Luxury Lion Resort",
-            "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort",
-            "Category": "Resort and Spa"
-        },
+            VectorSearch = new()
+            {
+                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
+            },
+            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
+        });
+
+    Console.WriteLine($"Single Vector Search Results:");
+    await foreach (SearchResult<Hotel> result in response.GetResultsAsync())
+    {
+        Hotel doc = result.Document;
+        Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}");
+    }
+    Console.WriteLine();
+}
+```
+
+#### Single vector search with a filter
+
+In Azure AI Search, [filters](../../vector-search-filters.md) apply to nonvector fields in an index. The `SearchSingleVectorWithFilter` method filters on the `Tags` field to filter out any hotels that don't provide free Wi-Fi.
+
+```csharp
+public static async Task SearchSingleVectorWithFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
+{
+    SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
+        new SearchOptions
         {
-            "@search.score": 0.8254346,
-            "HotelId": "4",
-            "HotelName": "Sublime Palace Hotel",
-            "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace.",
-            "Category": "Boutique"
-        },
+            VectorSearch = new()
+            {
+                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
+            },
+            Filter = "Tags/any(tag: tag eq 'free wifi')",
+            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" }
+        });
+
+    Console.WriteLine($"Single Vector Search With Filter Results:");
+    await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
+    {
+        Hotel doc = result.Document;
+        Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}, Tags: {string.Join(String.Empty, doc.Tags)}");
+    }
+    Console.WriteLine();
+}
+```
+
+#### Single vector search with a geo filter
+
+You can specify a [geo-spatial filter](../../search-query-odata-geo-spatial-functions.md) to limit results to a specific geographic area. The `SingleSearchWithGeoFilter` method specifies a geographic point (Washington D.C., using longitude and latitude coordinates) and returns hotels within 300 kilometers. By default, filters run after the vector search.
+
+```csharp
+public static async Task SingleSearchWithGeoFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
+{
+    SearchResults<Hotel> responseWithGeoFilter = await searchClient.SearchAsync<Hotel>(
+        new SearchOptions
         {
-            "@search.score": 0.82380056,
-            "HotelId": "1",
-            "HotelName": "Stay-Kay City Hotel",
-            "Description": "The hotel is ideally located on the main commercial artery of the city in the heart of New York.",
-            "Category": "Boutique"
-        },
+            VectorSearch = new()
+            {
+                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
+            },
+            Filter = "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
+            Select = { "HotelId", "HotelName", "Description", "Address", "Category", "Tags" },
+            Facets = { "Address/StateProvince" },
+        });
+
+    Console.WriteLine($"Vector query with a geo filter:");
+    await foreach (SearchResult<Hotel> result in responseWithGeoFilter.GetResultsAsync())
+    {
+        Hotel doc = result.Document;
+        Console.WriteLine($"HotelId: {doc.HotelId}");
+        Console.WriteLine($"HotelName: {doc.HotelName}");
+        Console.WriteLine($"Score: {result.Score}");
+        Console.WriteLine($"City/State: {doc.Address.City}/{doc.Address.StateProvince}");
+        Console.WriteLine($"Description: {doc.Description}");
+        Console.WriteLine();
+    }
+    Console.WriteLine();
+}
+```
+
+#### Hybrid search
+
+[Hybrid search](../../hybrid-search-overview.md) combines full-text and vector queries in a single request. The `SearchHybridVectorAndText` method runs both query types concurrently, and then uses Reciprocal Rank Fusion (RRF) to merge the results into a unified ranking. RRF uses the inverse of result rankings from each result set to produce a merged ranking. Notice that hybrid search scores are uniformly smaller than single-query scores.
+
+```csharp
+public static async Task<SearchResults<Hotel>> SearchHybridVectorAndText(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
+{
+    SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
+        "historic hotel walk to restaurants and shopping",
+        new SearchOptions
         {
-            "@search.score": 0.81514084,
-            "HotelId": "2",
-            "HotelName": "Old Century Hotel",
-            "Description": "The hotel is situated in a  nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts.",
-            "Category": "Boutique"
-        },
+            VectorSearch = new()
+            {
+                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
+            },
+            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
+            Size = 5,
+        });
+
+    Console.WriteLine($"Hybrid search results:");
+    await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
+    {
+        Hotel doc = result.Document;
+        Console.WriteLine($"Score: {result.Score}");
+        Console.WriteLine($"HotelId: {doc.HotelId}");
+        Console.WriteLine($"HotelName: {doc.HotelName}");
+        Console.WriteLine($"Description: {doc.Description}");
+        Console.WriteLine($"Category: {doc.Category}");
+        Console.WriteLine($"Tags: {string.Join(String.Empty, doc.Tags)}");
+        Console.WriteLine();
+    }
+    Console.WriteLine();
+    return responseWithFilter;
+}
+```
+
+#### Semantic hybrid search
+
+The `SearchHybridVectorAndSemantic` method demonstrates [semantic ranking](../../semantic-search-overview.md), which reranks results based on language understanding.
+
+```csharp
+public static async Task SearchHybridVectorAndSemantic(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
+{
+    SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
+        "historic hotel walk to restaurants and shopping",
+        new SearchOptions
         {
-            "@search.score": 0.8133763,
-            "HotelId": "3",
-            "HotelName": "Gastronomic Landscape Hotel",
-            "Description": "The Hotel stands out for its gastronomic excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
-            "Category": "Resort and Spa"
-        }
-    ]
-    ```
+            IncludeTotalCount = true,
+            VectorSearch = new()
+            {
+                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
+            },
+            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
+            SemanticSearch = new SemanticSearchOptions
+            {
+                SemanticConfigurationName = "semantic-config"
+            },
+            QueryType = SearchQueryType.Semantic,
+            Size = 5
+        });
+
+    Console.WriteLine($"Hybrid search results:");
+    await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
+    {
+        Hotel doc = result.Document;
+        Console.WriteLine($"Score: {result.Score}");
+        Console.WriteLine($"HotelId: {doc.HotelId}");
+        Console.WriteLine($"HotelName: {doc.HotelName}");
+        Console.WriteLine($"Description: {doc.Description}");
+        Console.WriteLine($"Category: {doc.Category}");
+        Console.WriteLine();
+    }
+    Console.WriteLine();
+}
+```
 
-### Semantic hybrid search with a filter
-
-Add [semantic ranking](../../semantic-search-overview.md) to rerank results based on language understanding.
-
-To create a semantic hybrid search with a filter:
-
-1. In the `Program.cs` file, uncomment `SearchExamples.SearchHybridVectorAndSemantic(searchClient, vectorizedResult);` method call.
-
-    This method executes the following search function in the `SearchExamples.cs` class.
-
-    :::code language="csharp" source="~/azure-search-dotnet-samples/quickstart-vector-search/vectorsearchexamples/SearchExamples.cs" id="SearchHybridVectorAndSemantic":::
-
-1. Run `dotnet run` to execute the query, which returns the following hotels. The hotels are filtered by location, faceted by `StateProvince`, and semantically reranked to promote results that are closest to the search string query (`historic hotel walk to restaurants and shopping`).
-
-    The Swirling Currents Hotel now moves to the top spot. Without semantic ranking, Nordick's Valley Motel is number one. With semantic ranking, the machine comprehension models recognize that `historic` applies to "hotel within walking distance to dining (restaurants) and shopping."
-    
-    ```output
-    Total semantic hybrid results: 7
-    - Score: 0.0317460335791111
-      Re-ranker Score: 2.6550590991973877
-      HotelId: 49
-      HotelName: Swirling Currents Hotel
-      Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.
-      Category: Suite
-    - Score: 0.03279569745063782
-      Re-ranker Score: 2.599761724472046
-      HotelId: 4
-      HotelName: Sublime Palace Hotel
-      Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
-      Category: Boutique
-    - Score: 0.03125
-      Re-ranker Score: 2.3480887413024902
-      HotelId: 2
-      HotelName: Old Century Hotel
-      Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
-      Category: Boutique
-    - Score: 0.016393441706895828
-      Re-ranker Score: 2.2718777656555176
-      HotelId: 1
-      HotelName: Stay-Kay City Hotel
-      Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.
-      Category: Boutique
-    - Score: 0.01515151560306549
-      Re-ranker Score: 2.0582215785980225
-      HotelId: 3
-      HotelName: Gastronomic Landscape Hotel
-      Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel's restaurant services.
-      Category: Suite
-    ```
-    
-    Key takeaways:
+Compare these results with the hybrid search results from the previous query. Without semantic reranking, Sublime Palace Hotel ranks first because Reciprocal Rank Fusion (RRF) combines the text and vector scores to produce a merged result. After semantic reranking, Swirling Currents Hotel moves to the top spot.
 
-    + In a hybrid search, you can integrate vector search with full-text search over keywords. Filters, spell check, and semantic ranking apply to textual content only, and not vectors. In this final query, there's no semantic `answer` because the system didn't produce one that was sufficiently strong.
-    
-    + Actual results include more detail, including semantic captions and highlights. Results were modified for readability. To get the full structure of the response, run the request in the REST client.
+The semantic ranker uses machine comprehension models to evaluate how well each result matches the intent of the query. Swirling Currents Hotel's description mentions `"walking access to shopping, dining, entertainment and the city center"`, which aligns closely with the search query's `"walk to restaurants and shopping"`. This semantic match for nearby dining and shopping elevates it above Sublime Palace Hotel, which doesn't emphasize walkable amenities in its description.
 
-## Clean up resources
+Key takeaways:
 
-When you're working in your own subscription, it's a good idea at the end of a project to identify whether you still need the resources you created. Resources left running can cost you money. You can delete resources individually or delete the resource group to delete the entire set of resources.
++ In a hybrid search, you can integrate vector search with full-text search over keywords. Filters and semantic ranking apply to textual content only, not vectors.
 
-You can find and manage resources in the Azure portal by using the **All resources** or **Resource groups** link in the leftmost pane.
++ Actual results include more detail, including semantic captions and highlights. This quickstart modifies results for readability. To get the full structure of the response, use REST to run the request.
 
-## Next steps
+## Clean up resources
 
-+ Review the repository of code samples for vector search capabilities in Azure AI Search for [.NET](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-dotnet).
-+ Review the other .NET and Azure AI Search code samples in the [azure-search-dotnet-samples repo](https://github.com/Azure-Samples/azure-search-dotnet-samples).
+[!INCLUDE [resource-cleanup-paid](../resource-cleanup-paid.md)]

@@ -4,534 +4,437 @@ ms.author: karler
 ms.service: azure-ai-search
 ms.custom: devx-track-java, dev-focus
 ms.topic: include
-ms.date: 01/14/2026
+ms.date: 02/05/2026
 ai-usage: ai-assisted
 ---
 
-In this quickstart, you use Java to create, load, and query a [vector index](../../vector-store.md). The code performs these operations by using the [Azure AI Search client library for Java](/java/api/overview/azure/search-documents-readme), which provides an abstraction over the REST APIs to access index operations.
+In this quickstart, you use the [Azure AI Search client library for Java](/java/api/overview/azure/search-documents-readme) to create, load, and query a [vector index](../../vector-store.md). The Java client library provides an abstraction over the REST APIs for index operations.
 
 In Azure AI Search, a vector index has an index schema that defines vector and nonvector fields, a vector search configuration for algorithms that create the embedding space, and settings on vector field definitions that are evaluated at query time. [Indexes - Create or Update](/rest/api/searchservice/indexes/create-or-update) (REST API) creates the vector index.
 
-> [!NOTE]
-> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import data (new)** wizard](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
+> [!TIP]
+> + Want to get started right away? Download the [source code](https://github.com/Azure-Samples/azure-search-java-samples/tree/main/quickstart-vector-search) on GitHub.
+> + This quickstart omits the vectorization step and provides inline embeddings. For [integrated vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import data (new)** wizard](../../search-get-started-portal-import-vectors.md).
 
 ## Prerequisites
 
-+ An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn).
+- An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn).
 
-+ An [Azure AI Search service](../../search-create-service-portal.md).
+- An [Azure AI Search service](../../search-create-service-portal.md). You can use the Free tier for most of this quickstart, but we recommend Basic or higher for larger data files.
 
-    + You can use the Free tier for most of this quickstart, but we recommend Basic or higher for larger data files.
+- [Semantic ranker enabled on your search service](../../semantic-how-to-enable-disable.md) for the optional semantic hybrid query.
 
-    + For [keyless authentication](../../search-get-started-rbac.md) with Microsoft Entra ID, assign the **Search Index Data Contributor role** to your user account.
-    
-    + To run the semantic hybrid query, you must [enable semantic ranker](../../semantic-how-to-enable-disable.md).
+- [Java 21 (LTS)](/java/openjdk/install) and [Maven](https://maven.apache.org/download.cgi).
 
-+ [Java 21 (LTS)](/java/openjdk/install).
+- [Visual Studio Code](https://code.visualstudio.com/download).
 
-+ [Maven](https://maven.apache.org/download.cgi).
+- [Git](https://git-scm.com/downloads) to clone the sample repository.
 
-## Get service information
+- The [Azure CLI](/cli/azure/install-azure-cli) for keyless authentication with Microsoft Entra ID.
 
-In the remaining sections, you set up API calls to Azure OpenAI and Azure AI Search. Get the service endpoints so that you can provide them as variables in your code.
+## Configure access
 
-To get the service endpoint:
+[!INCLUDE [resource authentication](../resource-authentication.md)]
 
-1. Sign in to the [Azure portal](https://portal.azure.com).
+## Get endpoint
 
-1. [Find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
+[!INCLUDE [resource endpoint](../resource-endpoint.md)]
 
-1. On the **Overview** home page, copy the URL. An example endpoint might look like `https://example.search.windows.net`.
+## Set up the environment
 
-## Set up the Java project
-
-1. Create a new Maven project directory.
+1. Use Git to clone the sample repository.
 
    ```bash
-   mkdir vector-quickstart && cd vector-quickstart
+   git clone https://github.com/Azure-Samples/azure-search-java-samples
    ```
 
-1. Create a `pom.xml` file with required dependencies.
-
-   :::code language="xml" source="~/azure-search-java-samples/quickstart-vector-search/pom.xml" :::
-
-1. Compile the project to resolve the dependencies.
-
-    ```bash
-    mvn compile
-    ```
-
-1. Create the source directory structure.
+1. Navigate to the quickstart folder and open it in Visual Studio Code.
 
    ```bash
-   mkdir -p src/main/java/com/example/search
-   mkdir -p src/main/resources
+   cd azure-search-java-samples/quickstart-vector-search
+   code .
    ```
 
-1. Create an `application.properties` file in the `src/main/resources` directory and provide your search service endpoint.
-
-    ```properties
-    azure.search.endpoint=<YOUR AZURE AI SEARCH ENDPOINT>
-    azure.search.index-name=hotels-vector-quickstart
-    ```
-
-## Sign in to Azure
-
-You're using Microsoft Entra ID and role assignments for the connection. Make sure you're logged in to the same tenant and subscription as Azure AI Search and Azure OpenAI. You can use the Azure CLI on the command line to show current properties, change properties, and to sign in. For more information, see [Connect without keys](../../search-get-started-rbac.md).
+1. In `src/main/resources/application.properties`, replace the placeholder value for `azure.search.endpoint` with the URL you obtained in [Get endpoint](#get-endpoint).
 
-Run each of the following commands in sequence.
+1. Install the dependencies.
 
-```azurecli
-az account show
-
-az account set --subscription <PUT YOUR SUBSCRIPTION ID HERE>
-
-az login --tenant <PUT YOUR TENANT ID HERE>
-```
-
-You should now be logged in to Azure from your local device.
-
-## Create a vector index
-
-In this section, you create a vector index in Azure AI Search with [SearchIndexClient](/java/api/com.azure.search.documents.indexes.searchindexclient).[createOrUpdateIndex](/java/api/com.azure.search.documents.indexes.searchindexclient#com-azure-search-documents-indexes-searchindexclient-createorupdateindex(com-azure-search-documents-indexes-models-searchindex)). The index schema defines the fields, including the vector field `DescriptionVector`.
-
-To create a vector index:
+   ```bash
+   mvn clean dependency:copy-dependencies
+   ```
 
-1. Create a `CreateIndex.java` file in the `src/main/java/com/example/search` directory.
+   When the build completes, you should see a `target/dependency` folder in the project directory.
 
-1. Copy the following code into the file.
+1. For keyless authentication with Microsoft Entra ID, sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service.
 
-   :::code language="java" source="~/azure-search-java-samples/quickstart-vector-search/src/main/java/com/example/search/CreateIndex.java" :::
+   ```azurecli
+   az login
+   ```
 
-   The code file creates the index and defines the index schema, including the `DescriptionVector` vector field.
+## Run the code
 
-1. Run the file.
+1. Create a vector index.
 
     ```bash
-    mvn compile exec:java -Dexec.mainClass="com.example.search.CreateIndex"
+    mvn compile exec:java "-Dexec.mainClass=com.example.search.CreateIndex"
     ```
 
-    The output of this code shows that the index is created successfully.
+1. Load documents that contain precomputed embeddings.
 
-    ```output
-    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
-    Using Azure Search index: <vector-index-name>
-    Creating index...
-    hotels-vector-quickstart created
+    ```bash
+    mvn compile exec:java "-Dexec.mainClass=com.example.search.UploadDocuments"
     ```
 
-   Key takeaways:
-
-   + You define an index by creating a list of fields.
-
-   + This particular index supports multiple search capabilities, such as:
-
-      + Full-text keyword search (`setSearchable`)
-
-      + Vector search (enables hybrid search by collocating vector and nonvector fields) fields (`DescriptionVector` with `setVectorSearchProfileName`)
-      
-      + Semantic search 
-
-      + Faceted search (`SearchSuggester`)
-
-      + Geo-spatial search (`Location` field with `SearchFieldDataType.GEOGRAPHY_POINT`)
-
-      + Filtering, sorting (many fields marked filterable and sortable)
-
-## Upload documents to the index
-
-Creating and loading the index are separate steps. You created the index schema in the previous step. You now need to load documents into the index with [SearchClient](/java/api/com.azure.search.documents.searchclient).[uploadDocuments](/java/api/com.azure.search.documents.searchclient#com-azure-search-documents-searchclient-uploaddocuments(java-lang-iterable(-))). The documents contain the vectorized version of the article's description, which enables similarity search based on meaning rather than exact keywords.
-
-In Azure AI Search, the index stores all searchable content, while the search engine executes queries against that index.
-
-To upload documents to the index:
-
-1. Create an `UploadDocuments.java` file in the `src/main/java/com/example/search` directory.
-
-1. Copy the following code into the file.
-
-   :::code language="java" source="~/azure-search-java-samples/quickstart-vector-search/src/main/java/com/example/search/UploadDocuments.java" :::
-
-    This code loads a collection of documents. Each document includes the vectorized version of the article's `Description`. This vector enables similarity search, where matching is based on meaning rather than exact keywords.
-
-    Because this quickstart article searches the index immediately, the `waitUntilIndexed` method waits until the index is ready for search operations. This is important because the index needs to be fully populated with documents before you can perform searches.
-
-1. Build and run the file.
+1. Run a vector search query.
 
     ```bash
-    mvn compile exec:java -Dexec.mainClass="com.example.search.UploadDocuments"
+    mvn compile exec:java "-Dexec.mainClass=com.example.search.SearchSingle"
     ```
 
-    The output of this code shows that the documents are indexed and ready for search.
-
-    ```output
-    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
-    Using Azure Search index: <vector-index-name>
-    Uploading documents...
-    Key: 1, Succeeded: true, ErrorMessage: none
-    Key: 2, Succeeded: true, ErrorMessage: none
-    Key: 3, Succeeded: true, ErrorMessage: none
-    Key: 4, Succeeded: true, ErrorMessage: none
-    Key: 48, Succeeded: true, ErrorMessage: none
-    Key: 49, Succeeded: true, ErrorMessage: none
-    Key: 13, Succeeded: true, ErrorMessage: none
-    Waiting for indexing... Current count: 0
-    All documents indexed successfully.
-    ```
-
-    Key takeaways:
-
-    * Your code interacts with a specific search index hosted in your Azure AI Search service through the `SearchClient`. The `SearchClient` provides access to operations such as:
+1. (Optional) Run additional query variations.
 
-        * Data ingestion: `uploadDocuments`, `mergeDocuments`, `deleteDocuments`, and so on.
-
-        * Search operations: `search`, `autocomplete`, `suggest`
-
-    * Vector fields contain floating point values. The dimensions attribute has a minimum of 2 and a maximum of 4096 floating point values each. This quickstart sets the dimensions attribute to 1,536 because that's the size of embeddings generated by the `text-embedding-ada-002` model.
+    ```bash
+    mvn compile exec:java "-Dexec.mainClass=com.example.search.SearchSingleWithFilter"
+    mvn compile exec:java "-Dexec.mainClass=com.example.search.SearchSingleWithFilterGeo"
+    mvn compile exec:java "-Dexec.mainClass=com.example.search.SearchHybrid"
+    mvn compile exec:java "-Dexec.mainClass=com.example.search.SearchSemanticHybrid"
+    ```
 
-## Create the query as a vector
+### Output
 
-The example vector queries are based on two strings:
+The output of `CreateIndex.java` shows the index name and confirmation.
 
-+ Search string: `historic hotel walk to restaurants and shopping`
-+ Vector query string: `quintessential lodging near running trails, eateries, retail` (vectorized into a mathematical representation)
+```output
+Using Azure Search endpoint: https://<search-service-name>.search.windows.net
+Using Azure Search index: hotels-vector-quickstart
+Creating index...
+hotels-vector-quickstart created
+```
 
-The vector query string is semantically similar to the search string, but it includes terms that don't exist in the search index. If you do a keyword search for `quintessential lodging near running trails, eateries, retail`, results are zero. We use this example to show how you can get relevant results even if there are no matching terms.
+The output of `UploadDocuments.java` shows the success status for each indexed document.
+
+```output
+Uploading documents...
+Key: 1, Succeeded: true, ErrorMessage: none
+Key: 2, Succeeded: true, ErrorMessage: none
+Key: 3, Succeeded: true, ErrorMessage: none
+Key: 4, Succeeded: true, ErrorMessage: none
+Key: 48, Succeeded: true, ErrorMessage: none
+Key: 49, Succeeded: true, ErrorMessage: none
+Key: 13, Succeeded: true, ErrorMessage: none
+Waiting for indexing... Current count: 0
+All documents indexed successfully.
+```
 
-Create a `QueryVector.java` file in the `src/main/java/com/example/search` directory and add the code to create the query vector.
+The output of `SearchSingle.java` shows vector search results ranked by similarity score.
 
-:::code language="java" source="~/azure-search-java-samples/quickstart-vector-search/src/main/java/com/example/search/QueryVector.java" :::
+```output
+Single Vector search found 5
+- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
+- HotelId: 13, HotelName: Luxury Lion Resort, Tags: ["bar","concierge","restaurant"], Score 0.6333684
+- HotelId: 4, HotelName: Sublime Palace Hotel, Tags: ["concierge","view","air conditioning"], Score 0.605672
+- HotelId: 49, HotelName: Swirling Currents Hotel, Tags: ["air conditioning","laundry service","24-hour front desk service"], Score 0.6026341
+- HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366
+```
 
-This code is used in the following sections to perform vector searches. The query vector is created using an embedding model from Azure OpenAI.
+## Understand the code
+
+[!INCLUDE [understand code note](../understand-code-note.md)]
+
+Now that you've run the code, let's break down the key steps:
+
+1. [Create a vector index](#create-a-vector-index)
+1. [Upload documents to the index](#upload-documents-to-the-index)
+1. [Query the index](#query-the-index)
+
+### Create a vector index
+
+Before you add content to Azure AI Search, you must create an index to define how the content is stored and structured.
+
+The index schema is organized around hotel content. Sample data consists of vector and nonvector descriptions of fictitious hotels. The following code in `CreateIndex.java` creates the index schema, including the vector field `DescriptionVector`.
+
+```java
+// Define fields
+List<SearchField> fields = Arrays.asList(
+    new SearchField("HotelId", SearchFieldDataType.STRING)
+        .setKey(true)
+        .setFilterable(true),
+    new SearchField("HotelName", SearchFieldDataType.STRING)
+        .setSortable(true)
+        .setSearchable(true),
+    new SearchField("Description", SearchFieldDataType.STRING)
+        .setSearchable(true),
+    new SearchField("DescriptionVector",
+        SearchFieldDataType.collection(SearchFieldDataType.SINGLE))
+        .setSearchable(true)
+        .setVectorSearchDimensions(1536)
+        .setVectorSearchProfileName("my-vector-profile"),
+    new SearchField("Category", SearchFieldDataType.STRING)
+        .setSortable(true)
+        .setFilterable(true)
+        .setFacetable(true)
+        .setSearchable(true),
+    new SearchField("Tags", SearchFieldDataType.collection(
+        SearchFieldDataType.STRING))
+        .setSearchable(true)
+        .setFilterable(true)
+        .setFacetable(true),
+    // Additional fields: ParkingIncluded, LastRenovationDate, Rating, Address, Location
+);
+
+var searchIndex = new SearchIndex(indexName, fields);
+
+// Define vector search configuration
+var hnswParams = new HnswParameters()
+    .setM(16)
+    .setEfConstruction(200)
+    .setEfSearch(128);
+var hnsw = new HnswAlgorithmConfiguration("hnsw-vector-config");
+hnsw.setParameters(hnswParams);
+
+var vectorProfile = new VectorSearchProfile(
+    "my-vector-profile",
+    "hnsw-vector-config");
+var vectorSearch = new VectorSearch()
+    .setAlgorithms(Arrays.asList(hnsw))
+    .setProfiles(Arrays.asList(vectorProfile));
+searchIndex.setVectorSearch(vectorSearch);
+
+// Define semantic configuration
+var prioritizedFields = new SemanticPrioritizedFields()
+    .setTitleField(new SemanticField("HotelName"))
+    .setContentFields(Arrays.asList(new SemanticField("Description")))
+    .setKeywordsFields(Arrays.asList(new SemanticField("Category")));
+var semanticConfig = new SemanticConfiguration(
+    "semantic-config",
+    prioritizedFields);
+var semanticSearch = new SemanticSearch()
+    .setConfigurations(Arrays.asList(semanticConfig));
+searchIndex.setSemanticSearch(semanticSearch);
+
+// Define suggesters
+var suggester = new SearchSuggester("sg", Arrays.asList("HotelName"));
+searchIndex.setSuggesters(Arrays.asList(suggester));
+
+// Create the search index
+SearchIndex result = searchIndexClient.createOrUpdateIndex(searchIndex);
+```
 
-## Create a single vector search
+Key takeaways:
 
-The first example demonstrates a basic scenario where you want to find document descriptions that closely match the search string using the [SearchClient](/java/api/com.azure.search.documents.searchclient).[search](/java/api/com.azure.search.documents.searchasyncclient#com-azure-search-documents-searchasyncclient-search(java-lang-string)) and the [VectorQuery](/java/api/com.azure.search.documents.models.vectorquery) and [SearchOptions](/java/api/com.azure.search.documents.models.searchoptions).
++ You define an index by creating a list of fields.
 
-To create a single vector search:
++ This particular index supports multiple search capabilities:
 
-1. Create a `SearchSingle.java` file in the `src/main/java/com/example/search` directory.
+    + [Full-text search](../../search-lucene-query-architecture.md) (`setSearchable`)
 
-1. Copy the following code into the file.
+    + [Vector search](../../vector-search-overview.md) (`DescriptionVector` with `setVectorSearchProfileName`)
 
-    :::code language="java" source="~/azure-search-java-samples/quickstart-vector-search/src/main/java/com/example/search/SearchSingle.java" :::
+    + [Semantic ranking](../../semantic-search-overview.md) (`SemanticConfiguration`)
 
-    The `vectorQuery` contains the configuration of the vectorized query including the vectorized text of the query input as retrieved through `QueryVector.getVectorList`. `setFields` determines which vector fields are searched and `setKNearestNeighborsCount` specifies the number of nearest neighbors to return.
+    + [Faceted search](../../search-faceted-navigation.md) (fields marked with `setFacetable`)
 
-    The vector query string is `quintessential lodging near running trails, eateries, retail`, which is vectorized into 1,536 embeddings for this query.
+    + [Geo-spatial search](../../search-query-odata-geo-spatial-functions.md) (`Location` field with `SearchFieldDataType.GEOGRAPHY_POINT`)
 
-1. Build and run the file.
+    + [Filtering](../../search-filters.md) and sorting (fields marked with `setFilterable` and `setSortable`)
 
-    ```bash
-    mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingle"
-    ```
++ The `setVectorSearchDimensions()` value must match the output size of your embedding model. This quickstart uses 1,536 dimensions to match the `text-embedding-ada-002` model.
 
-    The output of this code shows the relevant docs for the query `quintessential lodging near running trails, eateries, retail`.
-
-    ```output
-    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
-    Using Azure Search index: <vector-index-name>
-    
-    Single Vector search found 5
-    - HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
-    - HotelId: 13, HotelName: Luxury Lion Resort, Tags: ["bar","concierge","restaurant"], Score 0.6333684
-    - HotelId: 4, HotelName: Sublime Palace Hotel, Tags: ["concierge","view","air conditioning"], Score 0.605672
-    - HotelId: 49, HotelName: Swirling Currents Hotel, Tags: ["air conditioning","laundry service","24-hour front desk service"], Score 0.6026341
-    - HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366
-    ```
++ The `VectorSearch` configuration defines the Approximate Nearest Neighbor (ANN) algorithm. Supported algorithms include Hierarchical Navigable Small World (HNSW) and exhaustive K-Nearest Neighbor (KNN). For more information, see [Relevance in vector search](../../vector-search-ranking.md).
 
-    The response for the vector equivalent of `quintessential lodging near running trails, eateries, retail` consists of 5 results with only the fields specified by the select returned.
+### Upload documents to the index
 
-## Create a single vector search with a filter
+Newly created indexes are empty. To populate an index and make it searchable, you must upload JSON documents that conform to the index schema.
 
-You can add filters, but the filters are applied to the nonvector content in your index. In this example, the filter applies to the `Tags` field to filter out any hotels that don't provide free Wi-Fi. This search uses [SearchClient](/java/api/com.azure.search.documents.searchclient).[SearchClient](/java/api/com.azure.search.documents.searchclient) and [SearchOptions](/java/api/com.azure.search.documents.models.searchoptions). 
+In Azure AI Search, documents serve as both inputs for indexing and outputs for queries. For simplicity, this quickstart provides sample hotel documents with precomputed vectors. In production scenarios, content is often pulled from connected data sources and transformed into JSON using [indexers](../../search-indexer-overview.md).
 
-To create a single vector search with a filter:
+The following code in `UploadDocuments.java` uploads documents to your search service.
 
-1. Create a `SearchSingleWithFilter.java` file in the `src/main/java/com/example/search` directory.
+```java
+// Documents contain hotel data with 1536-dimension vectors for DescriptionVector
+static final List<Map<String, Object>> DOCUMENTS = Arrays.asList(
+    new HashMap<>() {{
+        put("@search.action", "mergeOrUpload");
+        put("HotelId", "1");
+        put("HotelName", "Stay-Kay City Hotel");
+        put("Description", "This classic hotel is fully-refurbished...");
+        put("DescriptionVector", Arrays.asList(/* 1536 float values */));
+        put("Category", "Boutique");
+        put("Tags", Arrays.asList("view", "air conditioning", "concierge"));
+        // Additional fields...
+    }}
+    // Additional hotel documents
+);
 
-1. Copy the following code into the file.
+// Upload documents to the index
+IndexDocumentsResult result = searchClient.uploadDocuments(DOCUMENTS);
+for (IndexingResult r : result.getResults()) {
+    System.out.println("Key: %s, Succeeded: %s".formatted(r.getKey(), r.isSucceeded()));
+}
+```
 
-    :::code language="java" source="~/azure-search-java-samples/quickstart-vector-search/src/main/java/com/example/search/SearchSingleWithFilter.java" :::
+Your code interacts with a specific search index hosted in your Azure AI Search service through the `SearchClient`, which is the main object provided by the [`azure-search-documents`](/java/api/overview/azure/search-documents-readme) package. The `SearchClient` provides access to operations such as:
 
-    This code has the same search functionality as the previous search, but it adds a post-processing exclusion filter for hotels with `free wifi`.
++ Data ingestion: `uploadDocuments`, `mergeDocuments`, `deleteDocuments`
 
-1. Build and run the file.
++ Search operations: `search`, `autocomplete`, `suggest`
 
-    ```bash
-    mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingleWithFilter"
-    ```
+### Query the index
 
-    The output of this code shows the relevant documents for the query with the filter for `free wifi` applied.
+The queries in the search files demonstrate different search patterns. The example vector queries are based on two strings:
 
-    ```output
-    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
-    Using Azure Search index: <vector-index-name>
-    
-    Single Vector search found 2
-    - HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
-    - HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366
-    ```
++ Full-text search string: `"historic hotel walk to restaurants and shopping"`
 
-## Create a search with a geospatial filter
++ Vector query string: `"quintessential lodging near running trails, eateries, retail"` (vectorized into a mathematical representation)
 
-You can specify a geospatial filter to limit results to a specific geographic area. In this example, the filter limits results to hotels within 300 kilometers of Washington D.C. (coordinates: `-77.03241 38.90166`). Geospatial distances are always in kilometers. This search uses [SearchClient](/java/api/com.azure.search.documents.searchclient).[SearchClient](/java/api/com.azure.search.documents.searchclient) and [SearchOptions](/java/api/com.azure.search.documents.models.searchoptions).
+The vector query string is semantically similar to the full-text search string, but it includes terms that don't exist in the index. A keyword-only search for the vector query string returns zero results. However, vector search finds relevant matches based on meaning rather than exact keywords.
 
-To create a single vector search with a geospatial filter:
+The following examples start with a basic vector query and progressively add filters, keyword search, and semantic reranking.
 
-1. Create a `SearchSingleWithFilterGeo.java` file in the `src/main/java/com/example/search` directory.
+#### Single vector search
 
-1. Copy the following code into the file.
+`SearchSingle.java` demonstrates a basic scenario where you want to find document descriptions that closely match the vector query string. `VectorizedQuery` configures the vector search:
 
-    :::code language="java" source="~/azure-search-java-samples/quickstart-vector-search/src/main/java/com/example/search/SearchSingleWithFilterGeo.java" :::
++ `setKNearestNeighborsCount()` limits how many results are returned based on vector similarity.
++ `setFields()` specifies the vector field to search against.
 
-1. Build and run the file.
+```java
+var vectorQuery = new VectorizedQuery(QueryVector.getVectorList())
+    .setKNearestNeighborsCount(5)
+    .setFields("DescriptionVector")
+    .setExhaustive(true);
 
-    ```bash
-    mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingleWithFilterGeo"
-    ```
+var vectorSearchOptions = new VectorSearchOptions()
+    .setQueries(vectorQuery)
+    .setFilterMode(VectorFilterMode.POST_FILTER);
 
-    The output of this code shows the relevant documents for the query with the geospatial post-processing exclusion filter applied.
+var searchOptions = new SearchOptions()
+    .setTop(7)
+    .setIncludeTotalCount(true)
+    .setSelect("HotelId", "HotelName", "Description", "Category", "Tags")
+    .setVectorSearchOptions(vectorSearchOptions);
 
-    ```output
-    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
-    Using Azure Search index: <vector-index-name>
-    
-    Single Vector search found 2
-    - HotelId: 48, HotelName: Nordick's Valley Motel, Tags: N/A, Score 0.6605852246284485
-    - HotelId: 49, HotelName: Swirling Currents Hotel, Tags: N/A, Score 0.602634072303772
-    ```
+var results = searchClient.search("*", searchOptions, Context.NONE);
 
-## Create a hybrid search
+for (SearchResult result : results) {
+    SearchDocument document = result.getDocument(SearchDocument.class);
+    System.out.println("HotelId: %s, HotelName: %s, Score: %s".formatted(
+        document.get("HotelId"), document.get("HotelName"), result.getScore()));
+}
+```
 
-[Hybrid search](../../hybrid-search-overview.md) combines keyword and vector queries in one request. This example runs the following full-text and vector query strings concurrently:
+#### Single vector search with a filter
 
-+ Search string: `historic hotel walk to restaurants and shopping`
-+ Vector query string: `quintessential lodging near running trails, eateries, retail` (vectorized into a mathematical representation)
+In Azure AI Search, [filters](../../vector-search-filters.md) apply to nonvector fields in an index. `SearchSingleWithFilter.java` filters on the `Tags` field to filter out any hotels that don't provide free Wi-Fi.
 
-This search uses [SearchClient](/java/api/com.azure.search.documents.searchclient).[SearchClient](/java/api/com.azure.search.documents.searchclient) and [SearchOptions](/java/api/com.azure.search.documents.models.searchoptions).
+```java
+var vectorQuery = new VectorizedQuery(QueryVector.getVectorList())
+    .setKNearestNeighborsCount(5)
+    .setFields("DescriptionVector")
+    .setExhaustive(true);
 
-Tp create a hybrid search:
+var vectorSearchOptions = new VectorSearchOptions()
+    .setQueries(vectorQuery)
+    .setFilterMode(VectorFilterMode.POST_FILTER);
 
-1. Create a `SearchHybrid.java` file in the `src/main/java/com/example/search` directory.
+// Add filter for "free wifi" tag
+var searchOptions = new SearchOptions()
+    .setTop(7)
+    .setIncludeTotalCount(true)
+    .setSelect("HotelId", "HotelName", "Description", "Category", "Tags")
+    .setFilter("Tags/any(tag: tag eq 'free wifi')")
+    .setVectorSearchOptions(vectorSearchOptions);
 
-1. Copy the following code into the file.
+var results = searchClient.search("*", searchOptions, Context.NONE);
+```
 
-    :::code language="java" source="~/azure-search-java-samples/quickstart-vector-search/src/main/java/com/example/search/SearchHybrid.java" :::
+#### Single vector search with a geo filter
 
-1. Build and run the file.
+You can specify a [geo-spatial filter](../../search-query-odata-geo-spatial-functions.md) to limit results to a specific geographic area. `SearchSingleWithGeoFilter.java` specifies a geographic point (Washington D.C., using longitude and latitude coordinates) and returns hotels within 300 kilometers. The `setFilterMode` method, called on `VectorSearchOptions`, determines when the filter runs. In this case, `POST_FILTER` runs the filter after the vector search.
 
-    ```bash
-    mvn compile exec:java -Dexec.mainClass="com.example.search.SearchHybrid"
-    ```
+```java
+var searchOptions = new SearchOptions()
+    .setTop(5)
+    .setIncludeTotalCount(true)
+    .setSelect("HotelId", "HotelName", "Category", "Description",
+               "Address/City", "Address/StateProvince")
+    .setFacets("Address/StateProvince")
+    .setFilter("geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300")
+    .setVectorSearchOptions(vectorSearchOptions);
+```
 
-    The output of this code shows the relevant documents for the hybrid search.
-
-    ```output
-    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
-    Using Azure Search index: <vector-index-name>
-    
-    Hybrid search found 7 then limited to top 5
-    - Score: 0.03279569745063782
-      HotelId: 4
-      HotelName: Sublime Palace Hotel
-      Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
-      Category: Boutique
-      Tags: ["concierge","view","air conditioning"]
-    
-    - Score: 0.032522473484277725
-      HotelId: 13
-      HotelName: Luxury Lion Resort
-      Description: Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.
-      Category: Luxury
-      Tags: ["bar","concierge","restaurant"]
-    
-    - Score: 0.03205128386616707
-      HotelId: 48
-      HotelName: Nordick's Valley Motel
-      Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
-      Category: Boutique
-      Tags: ["continental breakfast","air conditioning","free wifi"]
-    
-    - Score: 0.0317460335791111
-      HotelId: 49
-      HotelName: Swirling Currents Hotel
-      Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
-      Category: Suite
-      Tags: ["air conditioning","laundry service","24-hour front desk service"]
-    
-    - Score: 0.03125
-      HotelId: 2
-      HotelName: Old Century Hotel
-      Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
-      Category: Boutique
-      Tags: ["pool","free wifi","air conditioning","concierge"]
-    ```
+#### Hybrid search
 
-    Because Reciprocal Rank Fusion (RRF) merges results, it helps to review the inputs. The following results are from the full-text query only. The top two results are Sublime Palace Hotel and Luxury Lion Resort. The Sublime Palace Hotel has a stronger BM25 relevance score.
-
-    ```json
-       {
-           "@search.score": 2.2626662,
-           "HotelName": "Sublime Palace Hotel",
-           "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace."
-       },
-       {
-           "@search.score": 0.86421645,
-           "HotelName": "Luxury Lion Resort",
-           "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort"
-       },
-    ```
+[Hybrid search](../../hybrid-search-overview.md) combines full-text and vector queries in a single request. `SearchHybrid.java` runs both query types concurrently, and then uses Reciprocal Rank Fusion (RRF) to merge the results into a unified ranking. RRF uses the inverse of result rankings from each result set to produce a merged ranking. Notice that hybrid search scores are uniformly smaller than single-query scores.
 
-    In the vector-only query, which uses HNSW for finding matches, the Sublime Palace Hotel drops to the fourth position. Luxury Lion, which was second in the full-text search and third in the vector search, doesn't experience the same range of fluctuation, so it appears as a top match in a homogenized result set.
-
-   ```json
-   "value": [
-       {
-           "@search.score": 0.857736,
-           "HotelId": "48",
-           "HotelName": "Nordick's Valley Motel",
-           "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer.  Hiking? Wine Tasting? Exploring the caverns?  It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
-           "Category": "Boutique"
-       },
-       {
-           "@search.score": 0.8399129,
-           "HotelId": "49",
-           "HotelName": "Swirling Currents Hotel",
-           "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.",
-           "Category": "Luxury"
-       },
-       {
-           "@search.score": 0.8383954,
-           "HotelId": "13",
-           "HotelName": "Luxury Lion Resort",
-           "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort",
-           "Category": "Resort and Spa"
-       },
-       {
-           "@search.score": 0.8254346,
-           "HotelId": "4",
-           "HotelName": "Sublime Palace Hotel",
-           "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace.",
-           "Category": "Boutique"
-       },
-       {
-           "@search.score": 0.82380056,
-           "HotelId": "1",
-           "HotelName": "Stay-Kay City Hotel",
-           "Description": "The hotel is ideally located on the main commercial artery of the city in the heart of New York.",
-           "Category": "Boutique"
-       },
-       {
-           "@search.score": 0.81514084,
-           "HotelId": "2",
-           "HotelName": "Old Century Hotel",
-           "Description": "The hotel is situated in a  nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts.",
-           "Category": "Boutique"
-       },
-       {
-           "@search.score": 0.8133763,
-           "HotelId": "3",
-           "HotelName": "Gastronomic Landscape Hotel",
-           "Description": "The Hotel stands out for its gastronomic excellence under the management of William Dough, who advises on and oversees all of the Hotel's restaurant services.",
-           "Category": "Resort and Spa"
-       }
-   ]
-   ```
+```java
+var vectorQuery = new VectorizedQuery(QueryVector.getVectorList())
+    .setKNearestNeighborsCount(5)
+    .setFields("DescriptionVector")
+    .setExhaustive(true);
 
-## Create a semantic hybrid search
+var vectorSearchOptions = new VectorSearchOptions()
+    .setQueries(vectorQuery)
+    .setFilterMode(VectorFilterMode.POST_FILTER);
 
-Here's the last query in the collection to extend the semantic hybrid search with the additional search text `historic hotel walk to restaurants and shopping`.
+var searchOptions = new SearchOptions()
+    .setTop(5)
+    .setIncludeTotalCount(true)
+    .setSelect("HotelId", "HotelName", "Description", "Category", "Tags")
+    .setVectorSearchOptions(vectorSearchOptions);
 
-This search uses [SearchClient](/java/api/com.azure.search.documents.searchclient).[SearchClient](/java/api/com.azure.search.documents.searchclient) and [SearchOptions](/java/api/com.azure.search.documents.models.searchoptions). 
+// Pass both text query and vector search options
+var results = searchClient.search(
+    "historic hotel walk to restaurants and shopping",
+    searchOptions, Context.NONE);
+```
 
-To create a semantic hybrid search:
+#### Semantic hybrid search
 
-1. Create a `SearchSemanticHybrid.java` file in the `src/main/java/com/example/search` directory.
+`SearchSemanticHybrid.java` demonstrates [semantic ranking](../../semantic-search-overview.md), which reranks results based on language understanding.
 
-1. Copy the following code into the file.
+```java
+var vectorQuery = new VectorizedQuery(QueryVector.getVectorList())
+    .setKNearestNeighborsCount(5)
+    .setFields("DescriptionVector")
+    .setExhaustive(true);
 
-    :::code language="java" source="~/azure-search-java-samples/quickstart-vector-search/src/main/java/com/example/search/SearchSemanticHybrid.java" :::
+var vectorSearchOptions = new VectorSearchOptions()
+    .setQueries(vectorQuery)
+    .setFilterMode(VectorFilterMode.POST_FILTER);
 
-1. Build and run the file.
+SemanticSearchOptions semanticSearchOptions = new SemanticSearchOptions()
+    .setSemanticConfigurationName("semantic-config");
 
-    ```bash
-    mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSemanticHybrid"
-    ```
+var searchOptions = new SearchOptions()
+    .setTop(5)
+    .setIncludeTotalCount(true)
+    .setSelect("HotelId", "HotelName", "Category", "Description")
+    .setQueryType(QueryType.SEMANTIC)
+    .setSemanticSearchOptions(semanticSearchOptions)
+    .setVectorSearchOptions(vectorSearchOptions);
 
-    The output of this code shows the relevant documents for the semantic hybrid search.
-
-    ```output
-    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
-    Using Azure Search index: <vector-index-name>
-    
-    Semantic hybrid search found 7 then limited to top 5
-    - Score: 0.0317460335791111
-      Re-ranker Score: 2.6550590991973877
-      HotelId: 49
-      HotelName: Swirling Currents Hotel
-      Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
-      Category: Suite
-    
-    - Score: 0.03279569745063782
-      Re-ranker Score: 2.599761724472046
-      HotelId: 4
-      HotelName: Sublime Palace Hotel
-      Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
-      Category: Boutique
-    
-    - Score: 0.03125
-      Re-ranker Score: 2.3480887413024902
-      HotelId: 2
-      HotelName: Old Century Hotel
-      Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
-      Category: Boutique
-    
-    - Score: 0.016393441706895828
-      Re-ranker Score: 2.2718777656555176
-      HotelId: 1
-      HotelName: Stay-Kay City Hotel
-      Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.
-      Category: Boutique
-    
-    - Score: 0.01515151560306549
-      Re-ranker Score: 2.0582215785980225
-      HotelId: 3
-      HotelName: Gastronomic Landscape Hotel
-      Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel's restaurant services.
-      Category: Suite
-    ```
+var results = searchClient.search(
+    "historic hotel walk to restaurants and shopping",
+    searchOptions, Context.NONE);
+```
 
-    You can think of the semantic ranking as a way to improve the relevance of search results by understanding the meaning behind the words in the query and the content of the documents. In this case, the semantic ranking helps to identify hotels that are not only relevant to the keywords but also match the intent of the query:
+Compare these results with the hybrid search results from the previous query. Without semantic reranking, Sublime Palace Hotel ranks first because Reciprocal Rank Fusion (RRF) combines the text and vector scores to produce a merged result. After semantic reranking, Swirling Currents Hotel moves to the top spot.
 
-    Key takeaways:
+The semantic ranker uses machine comprehension models to evaluate how well each result matches the intent of the query. Swirling Currents Hotel's description mentions `"walking access to shopping, dining, entertainment and the city center"`, which aligns closely with the search query's `"walk to restaurants and shopping"`. This semantic match for nearby dining and shopping elevates it above Sublime Palace Hotel, which doesn't emphasize walkable amenities in its description.
 
-    - Vector search is specified through the `VectorSearchOptions` class. Keyword search is specified through the `SemanticSearchOptions` class.
+Key takeaways:
 
-    - In a hybrid search, you can integrate vector search with full-text search over keywords. Filters, spell check, and semantic ranking apply to textual content only, and not vectors. In this final query, there's no semantic `answer` because the system didn't produce one that was sufficiently strong.
++ In a hybrid search, you can integrate vector search with full-text search over keywords. Filters and semantic ranking apply to textual content only, not vectors.
 
-    - Actual results include more detail, including semantic captions and highlights. Results were modified for readability. To get the full structure of the response, run the request in the REST client.
++ Actual results include more detail, including semantic captions and highlights. This quickstart modifies results for readability. To get the full structure of the response, use REST to run the request.
 
 ## Clean up resources
 
-When you're working in your own subscription, it's a good idea at the end of a project to identify whether you still need the resources you created. Resources left running can cost you money. You can delete resources individually or delete the resource group to delete the entire set of resources.
+[!INCLUDE [resource-cleanup-paid](../resource-cleanup-paid.md)]
 
-You can find and manage resources in the Azure portal by using the **All resources** or **Resource groups** link in the leftmost pane.
+Otherwise, run the following command to delete the index you created in this quickstart.
 
-Alternatively, to delete the vector index you created in this quickstart programmatically:
-
-1. Create a `DeleteIndex.java` file in the `src/main/java/com/example/search` directory.
-
-1. Add the following code to the file.
-
-    :::code language="java" source="~/azure-search-java-samples/quickstart-vector-search/src/main/java/com/example/search/DeleteIndex.java" :::
-
-1. Build and run the file.
-
-    ```bash
-    mvn compile exec:java -Dexec.mainClass="com.example.search.DeleteIndex"
-    ```
-
-## Next steps
-
-+ Review the repository of code samples for vector search capabilities in Azure AI Search for [Java](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-java).
+```bash
+mvn compile exec:java "-Dexec.mainClass=com.example.search.DeleteIndex"
+```

@@ -4,545 +4,424 @@ author: diberry
 ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: include
-ms.date: 01/14/2026
+ms.date: 02/05/2026
 ms.custom: dev-focus
 ai-usage: ai-assisted
 ---
 
-In this quickstart, you use JavaScript to create, load, and query a [vector index](../../vector-store.md). The code performs these operations by using the [Azure AI Search client library for JavaScript](/javascript/api/overview/azure/search-documents-readme), which provides an abstraction over the REST APIs to access index operations.
+In this quickstart, you use the [Azure AI Search client library for JavaScript](/javascript/api/overview/azure/search-documents-readme) to create, load, and query a [vector index](../../vector-store.md). The JavaScript client library provides an abstraction over the REST APIs for index operations.
 
 In Azure AI Search, a vector index has an index schema that defines vector and nonvector fields, a vector search configuration for algorithms that create the embedding space, and settings on vector field definitions that are evaluated at query time. [Indexes - Create or Update](/rest/api/searchservice/indexes/create-or-update) (REST API) creates the vector index.
 
-> [!NOTE]
-> This quickstart omits the vectorization step and provides inline embeddings. If you want to add [built-in data chunking and vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import data (new)** wizard](../../search-get-started-portal-import-vectors.md) for an end-to-end walkthrough.
+> [!TIP]
+> + Want to get started right away? Download the [source code](https://github.com/Azure-Samples/azure-search-javascript-samples/tree/main/quickstart-vector-js) on GitHub.
+> + This quickstart omits the vectorization step and provides inline embeddings. For [integrated vectorization](../../vector-search-integrated-vectorization.md) over your own content, try the [**Import data (new)** wizard](../../search-get-started-portal-import-vectors.md).
 
 ## Prerequisites
 
-+ An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn).
+- An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn).
 
-+ An [Azure AI Search service](../../search-create-service-portal.md).
+- An [Azure AI Search service](../../search-create-service-portal.md). You can use the Free tier for most of this quickstart, but we recommend Basic or higher for larger data files.
 
-    + You can use the Free tier for most of this quickstart, but we recommend Basic or higher for larger data files.
+- [Semantic ranker enabled on your search service](../../semantic-how-to-enable-disable.md) for the optional semantic hybrid query.
 
-    + For [keyless authentication](../../search-get-started-rbac.md) with Microsoft Entra ID, assign the **Search Index Data Contributor role** to your user account.
-    
-    + To run the semantic hybrid query, you must [enable semantic ranker](../../semantic-how-to-enable-disable.md).
+- [Node.js 20 LTS](https://nodejs.org/en/download/) or later.
 
-+ [Visual Studio Code](https://code.visualstudio.com/download).
+- [Visual Studio Code](https://code.visualstudio.com/download).
 
-+ [Node.JS with LTS](https://nodejs.org/en/download/).
+- [Git](https://git-scm.com/downloads) to clone the sample repository.
 
-## Get service endpoints
+- The [Azure CLI](/cli/azure/install-azure-cli) for keyless authentication with Microsoft Entra ID.
 
-In the remaining sections, you set up API calls to Azure OpenAI and Azure AI Search. Get the service endpoints so that you can provide them as variables in your code.
+## Configure access
 
-To get the service endpoint:
+[!INCLUDE [resource authentication](../resource-authentication.md)]
 
-1. Sign in to the [Azure portal](https://portal.azure.com).
+## Get endpoint
 
-1. [Find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices).
+[!INCLUDE [resource endpoint](../resource-endpoint.md)]
 
-1. On the **Overview** home page, copy the URL. An example endpoint might look like `https://example.search.windows.net`.
+## Set up the environment
 
-## Set up the Node.JS project
-
-1. Start Visual Studio Code in a new directory.
+1. Use Git to clone the sample repository.
 
    ```bash
-   mkdir vector-quickstart && cd vector-quickstart
-   code .
+   git clone https://github.com/Azure-Samples/azure-search-javascript-samples
    ```
-1. Create a new package for ESM modules in your project directory.
+
+1. Navigate to the quickstart folder and open it in Visual Studio Code.
 
    ```bash
-   npm init -y
-   npm pkg set type=module
+   cd azure-search-javascript-samples/quickstart-vector-js
+   code .
    ```
 
-   This creates a `package.json` file with default values.
+1. In `sample.env`, replace the placeholder value for `AZURE_SEARCH_ENDPOINT` with the URL you obtained in [Get endpoint](#get-endpoint).
 
-1. Install the following `npm` packages.
+1. Rename `sample.env` to `.env`.
 
    ```bash
-   npm install @azure/identity @azure/search-documents dotenv
-   ``` 
+   mv sample.env .env
+   ```
 
-1. Create a `src` directory in your project directory.
+1. Install the dependencies.
 
    ```bash
-   mkdir src
+   npm install
    ```
 
-## Set up environment variables for local development
-
-1. Create a `.env` file in your `vector-quickstart` project directory.
+   When the installation completes, you should see a `node_modules` folder in the project directory.
 
-1. Add the following environment variables to the `.env` file, replacing the values with your own service endpoints and keys.
+1. For keyless authentication with Microsoft Entra ID, sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service.
 
-   ```plaintext
-   AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
-   AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart
+   ```azurecli
+   az login
    ```
-## Sign in to Azure
-
-You're using Microsoft Entra ID and role assignments for the connection. Make sure you're logged in to the same tenant and subscription as Azure AI Search and Azure OpenAI. You can use the Azure CLI on the command line to show current properties, change properties, and to sign in. For more information, see [Connect without keys](../../search-get-started-rbac.md). 
-
-Run each of the following commands in sequence.
-
-```azurecli
-az account show
-
-az account set --subscription <PUT YOUR SUBSCRIPTION ID HERE>
-
-az login --tenant <PUT YOUR TENANT ID HERE>
-```
-
-You should now be logged in to Azure from your local device.
 
-## Create a vector index
+## Run the code
 
-In this section, you create a vector index in Azure AI Search with [SearchIndexClient](/javascript/api/@azure/search-documents/searchindexclient).[createOrUpdateIndex](/javascript/api/@azure/search-documents/searchindexclient#@azure-search-documents-searchindexclient-createorupdateindex). The index schema defines the fields, including the vector field `DescriptionVector`. 
-
-To create a vector index:
-
-1. Create a `createIndex.js` file in the `src` directory.
-
-1. Copy the following code into the file. 
-
-    :::code language="javascript" source="~/azure-search-javascript-samples/quickstart-vector-js/src/createIndex.js":::
-
-    The code file adds the dependencies, environment variables, and JavaScript type for `HotelDocument` to the top of the file. Add the `createIndex` function to create the index. The function defines the index schema, including the vector field `DescriptionVector`.
-
-1. Run the file.
+1. Create a vector index.
 
     ```bash
     node -r dotenv/config src/createIndex.js
     ```
 
-   The output of this code shows that the index is created successfully.
-
-    ```output
-    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
-    Using Azure Search index: <vector-index-name>
-    Creating index...
-    hotels-vector-quickstart created
-    ```
-
-   Key takeaways:
-
-   + You define an index by creating a list of fields. 
-
-   + This particular index supports multiple search capabilities, such as:
-
-      + Full-text keyword search (`searchable`)
-
-      + Vector search (enables hybrid search by collocating vector and nonvector fields) fields (`DescriptionVector` with `vectorSearchProfileName`)
-
-      + Semantic search 
-
-      + Faceted search (`searchSuggester`)
-
-      + Geo-spatial search (`Location` field with `geo.distance`)
-
-      + Filtering, sorting (many fields marked filterable and sortable)
-
-## Upload documents to the index
-
-Creating and loading the index are separate steps. You created the index schema in the previous step. You now need to load documents into the index with [SearchClient](/javascript/api/@azure/search-documents/searchclient).[uploadDocuments](/javascript/api/%40azure/search-documents/searchclient#@azure-search-documents-searchclient-uploaddocuments). The documents contain the vectorized version of the article's description, which enables similarity search based on meaning rather than exact keywords.
-
-In Azure AI Search, the index stores all searchable content, while the search engine executes queries against that index.
-
-To upload documents to the index:
-
-1. Create an `uploadDocuments.js` file in the `src` directory.
-1. Copy the following code into the file.
-
-    :::code language="javascript" source="~/azure-search-javascript-samples/quickstart-vector-js/src/uploadDocuments.js" :::
-
-    This code loads an array of JSON objects. Each document includes the vectorized version of the article's `description`. This vector enables similarity search, where matching is based on meaning rather than exact keywords.
-
-    Because this quickstart article searches the index immediately, the `waitUntilIndexed` function waits until the index is ready for search operations. This is important because the index needs to be fully populated with documents before you can perform searches.
-
-1. Build and run the file.
+1. Load documents that contain precomputed embeddings.
 
     ```bash
     node -r dotenv/config src/uploadDocuments.js
     ```
 
-   The output of this code shows that the documents are indexed and ready for search.
-
-    ```output
-    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
-    Using Azure Search index: <vector-index-name>
-    Uploading documents...
-    Key: 1, Succeeded: true, ErrorMessage: none
-    Key: 2, Succeeded: true, ErrorMessage: none
-    Key: 3, Succeeded: true, ErrorMessage: none
-    Key: 4, Succeeded: true, ErrorMessage: none
-    Key: 48, Succeeded: true, ErrorMessage: none
-    Key: 49, Succeeded: true, ErrorMessage: none
-    Key: 13, Succeeded: true, ErrorMessage: none
-    Waiting for indexing... Current count: 0
-    All documents indexed successfully.
-    ```
-
-    Key takeaways:
-
-    * Your code interacts with a specific search index hosted in your Azure AI Search service through the `SearchClient`, which is the main object provided by the @azure/search-documents package. The `SearchClient` provides access to index operations, such as:
-
-        * Data ingestion: `uploadDocuments`, `mergeDocuments`, `deleteDocuments`, and so on.
-
-        * Search operations: `search`, `autocomplete`, `suggest`
-
-        * Index management operations: `createIndex`, `deleteIndex`, `getIndex`, and so on.
-
-    * Vector fields contain floating point values. The dimensions attribute has a minimum of 2 and a maximum of 4096 floating point values each. This quickstart sets the dimensions attribute to 1,536 because that's the size of embeddings generated by the `text-embedding-ada-002` model.
-
-## Create the query as a vector
-
-The example vector queries are based on two strings:
-
-+ Search string: `historic hotel walk to restaurants and shopping`
-+ Vector query string: `quintessential lodging near running trails, eateries, retail` (vectorized into a mathematical representation)
-
-The vector query string is semantically similar to the search string, but it includes terms that don't exist in the search index. If you do a keyword search for `quintessential lodging near running trails, eateries, retail`, results are zero. We use this example to show how you can get relevant results even if there are no matching terms.
-
-Create a `queryVector.js` file in the `src` directory and add the code to create the query vector.
-
-:::code language="javascript" source="~/azure-search-javascript-samples/quickstart-vector-js/src/queryVector.js" :::
-
-This code is used in the following sections to perform vector searches. The query vector is created using an embedding model from Azure OpenAI.
-
-## Create a single vector search
-
-The first example demonstrates a basic scenario where you want to find document descriptions that closely match the search string using the [SearchClient](/javascript/api/@azure/search-documents/searchclient).[search](/javascript/api/@azure/search-documents/searchclient#@azure-search-documents-searchclient-search) and the [VectorQuery](/javascript/api/@azure/search-documents/vectorquery) and [SearchOptions](/javascript/api/@azure/search-documents/searchoptions).
-
-To create a single vector search:
-
-1. Create a `searchSingle.js` file in the `src` directory.
-
-1. Copy the following code into the file.
-
-    :::code language="javascript" source="~/azure-search-javascript-samples/quickstart-vector-js/src/searchSingle.js" :::
-
-    The vectorQuery contains the configuration of the vectorized query including the vectorized text of the query input as `vector`,  `fields` determines which vector fields are searched, and `kNearestNeighborsCount` specifies the number of nearest neighbors to return.
-
-    The vector query string is `quintessential lodging near running trails, eateries, retail`, which is vectorized into 1,536 embeddings for this query.
-
-1. Build and run the file.
+1. Run a vector search query.
 
     ```bash
     node -r dotenv/config src/searchSingle.js
     ```
 
-   The output of this code shows the relevant docs for the query `quintessential lodging near running trails, eateries, retail`.
-
-    ```output
-    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
-    Using Azure Search index: <vector-index-name>
-    
-    
-    Single Vector search found 5
-    - HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
-    - HotelId: 13, HotelName: Luxury Lion Resort, Tags: ["bar","concierge","restaurant"], Score 0.6333684
-    - HotelId: 4, HotelName: Sublime Palace Hotel, Tags: ["concierge","view","air conditioning"], Score 0.605672
-    - HotelId: 49, HotelName: Swirling Currents Hotel, Tags: ["air conditioning","laundry service","24-hour front desk service"], Score 0.6026341
-    - HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366
+1. (Optional) Run additional query variations.
+
+    ```bash
+    node -r dotenv/config src/searchSingleWithFilter.js
+    node -r dotenv/config src/searchSingleWithFilterGeo.js
+    node -r dotenv/config src/searchHybrid.js
+    node -r dotenv/config src/searchSemanticHybrid.js
     ```
 
-    The response for the vector equivalent of `quintessential lodging near running trails, eateries, retail` consists of 5 results with only the fields specified by the select returned.
+### Output
 
+The output of `createIndex.js` shows the index name and confirmation.
 
-## Create a single vector search with a filter
+```output
+Using Azure Search endpoint: https://<search-service-name>.search.windows.net
+Using Azure Search index: hotels-vector-quickstart
+Creating index...
+hotels-vector-quickstart created
+```
 
-You can add filters, but the filters are applied to the nonvector content in your index. In this example, the filter applies to the `Tags` field to filter out any hotels that don't provide free Wi-Fi. This search uses [SearchClient](/javascript/api/@azure/search-documents/searchclient).[search](/javascript/api/@azure/search-documents/searchclient#@azure-search-documents-searchclient-search) and the [VectorQuery](/javascript/api/@azure/search-documents/vectorquery) and [SearchOptions](/javascript/api/@azure/search-documents/searchoptions). 
+The output of `uploadDocuments.js` shows the success status for each indexed document.
+
+```output
+Uploading documents...
+Key: 1, Succeeded: true, ErrorMessage: none
+Key: 2, Succeeded: true, ErrorMessage: none
+Key: 3, Succeeded: true, ErrorMessage: none
+Key: 4, Succeeded: true, ErrorMessage: none
+Key: 48, Succeeded: true, ErrorMessage: none
+Key: 49, Succeeded: true, ErrorMessage: none
+Key: 13, Succeeded: true, ErrorMessage: none
+Waiting for indexing... Current count: 0
+All documents indexed successfully.
+```
 
-To create a single vector search with a filter:
+The output of `searchSingle.js` shows vector search results ranked by similarity score.
 
-1. Create a `searchSingleWithFilter.js` file in the `src` directory.
+```output
+Single Vector search found 5
+- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
+- HotelId: 13, HotelName: Luxury Lion Resort, Tags: ["bar","concierge","restaurant"], Score 0.6333684
+- HotelId: 4, HotelName: Sublime Palace Hotel, Tags: ["concierge","view","air conditioning"], Score 0.605672
+- HotelId: 49, HotelName: Swirling Currents Hotel, Tags: ["air conditioning","laundry service","24-hour front desk service"], Score 0.6026341
+- HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366
+```
 
-1. Copy the following code into the file.
+## Understand the code
+
+[!INCLUDE [understand code note](../understand-code-note.md)]
+
+Now that you've run the code, let's break down the key steps:
+
+1. [Create a vector index](#create-a-vector-index)
+1. [Upload documents to the index](#upload-documents-to-the-index)
+1. [Query the index](#query-the-index)
+
+### Create a vector index
+
+Before you add content to Azure AI Search, you must create an index to define how the content is stored and structured.
+
+The index schema is organized around hotel content. Sample data consists of vector and nonvector descriptions of fictitious hotels. The following code in `createIndex.js` creates the index schema, including the vector field `DescriptionVector`.
+
+```javascript
+const searchFields = [
+    { name: "HotelId", type: "Edm.String", key: true, sortable: true, filterable: true, facetable: true },
+    { name: "HotelName", type: "Edm.String", searchable: true, filterable: true },
+    { name: "Description", type: "Edm.String", searchable: true },
+    {
+        name: "DescriptionVector",
+        type: "Collection(Edm.Single)",
+        searchable: true,
+        vectorSearchDimensions: 1536,
+        vectorSearchProfileName: "vector-profile"
+    },
+    { name: "Category", type: "Edm.String", filterable: true, facetable: true },
+    { name: "Tags", type: "Collection(Edm.String)", filterable: true },
+    // Additional fields: ParkingIncluded, LastRenovationDate, Rating, Address, Location
+];
+
+const vectorSearch = {
+    profiles: [
+        {
+            name: "vector-profile",
+            algorithmConfigurationName: "vector-search-algorithm"
+        }
+    ],
+    algorithms: [
+        {
+            name: "vector-search-algorithm",
+            kind: "hnsw",
+            parameters: { m: 4, efConstruction: 400, efSearch: 1000, metric: "cosine" }
+        }
+    ]
+};
+
+const semanticSearch = {
+    configurations: [
+        {
+            name: "semantic-config",
+            prioritizedFields: {
+                contentFields: [{ name: "Description" }],
+                keywordsFields: [{ name: "Category" }],
+                titleField: { name: "HotelName" }
+            }
+        }
+    ]
+};
+
+const searchIndex = {
+    name: indexName,
+    fields: searchFields,
+    vectorSearch: vectorSearch,
+    semanticSearch: semanticSearch,
+    suggesters: [{ name: "sg", searchMode: "analyzingInfixMatching", sourceFields: ["HotelName"] }]
+};
+
+const result = await indexClient.createOrUpdateIndex(searchIndex);
+```
 
-    :::code language="javascript" source="~/azure-search-javascript-samples/quickstart-vector-js/src/searchSingleWithFilter.js" :::
+Key takeaways:
 
-    Add the dependencies, environment variables, and the same search functionality as the previous search with a post-processing exclusion filter added for hotels with `free wifi`.
++ You define an index by creating a list of fields.
 
-1. Build and run the file.
++ This particular index supports multiple search capabilities:
 
-    ```bash
-    node -r dotenv/config src/searchSingleWithFilter.js
-    ```
+    + [Full-text search](../../search-lucene-query-architecture.md) (`searchable: true`)
 
-   The output of this code shows the relevant documents for the query with the filter for `free wifi` applied.
+    + [Vector search](../../vector-search-overview.md) (`DescriptionVector` with `vectorSearchProfileName`)
 
-    ```output
-    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
-    Using Azure Search index: <vector-index-name>
-    
-    
-    Single Vector search found 2
-    - HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
-    - HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366
-    ```
+    + [Semantic ranking](../../semantic-search-overview.md) (`semanticSearch`)
 
-## Create a search with a geospatial filter    
+    + [Faceted search](../../search-faceted-navigation.md) (fields marked with `facetable`)
 
-You can specify a geospatial filter to limit results to a specific geographic area. In this example, the filter limits results to hotels within 300 kilometers of Washington D.C. (coordinates: `-77.03241 38.90166`). Geospatial distances are always in kilometers. This search uses [SearchClient](/javascript/api/@azure/search-documents/searchclient).[search](/javascript/api/@azure/search-documents/searchclient#@azure-search-documents-searchclient-search) and the [VectorQuery](/javascript/api/@azure/search-documents/vectorquery) and [SearchOptions](/javascript/api/@azure/search-documents/searchoptions). 
+    + [Geo-spatial search](../../search-query-odata-geo-spatial-functions.md) (`Location` field with `Edm.GeographyPoint`)
 
-To create a search with a geospatial filter:
+    + [Filtering](../../search-filters.md) and sorting (fields marked with `filterable` and `sortable`)
 
-1. Create a `searchSingleWithFilterGeo.js` file in the `src` directory.
++ The `vectorSearchDimensions` property must match the output size of your embedding model. This quickstart uses 1,536 dimensions to match the `text-embedding-ada-002` model.
 
-1. Copy the following code into the file.
++ The `vectorSearch` configuration defines the Approximate Nearest Neighbor (ANN) algorithm. Supported algorithms include Hierarchical Navigable Small World (HNSW) and exhaustive K-Nearest Neighbor (KNN). For more information, see [Relevance in vector search](../../vector-search-ranking.md).
 
-    :::code language="javascript" source="~/azure-search-javascript-samples/quickstart-vector-js/src/searchSingleWithFilterGeo.js" :::
-1. Build and run the file.
+### Upload documents to the index
 
-    ```bash
-    node -r dotenv/config src/searchSingleWithFilterGeo.js
-    ```
+Newly created indexes are empty. To populate an index and make it searchable, you must upload JSON documents that conform to the index schema.
 
-   The output of this code shows the relevant documents for the query with the geospatial post-processing exclusion filter applied.
+In Azure AI Search, documents serve as both inputs for indexing and outputs for queries. For simplicity, this quickstart provides sample hotel documents with precomputed vectors. In production scenarios, content is often pulled from connected data sources and transformed into JSON using [indexers](../../search-indexer-overview.md).
 
-    ```output
-    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
-    Using Azure Search index: <vector-index-name>
-    
-    
-    Single Vector search found 2
-    - HotelId: 48, HotelName: Nordick's Valley Motel, Tags: N/A, Score 0.6605852246284485
-    - HotelId: 49, HotelName: Swirling Currents Hotel, Tags: N/A, Score 0.602634072303772
-    ```
+The following code in `uploadDocuments.js` uploads documents to your search service.
 
-## Create a hybrid search
+```javascript
+const DOCUMENTS = [
+    // Array of hotel documents with embedded 1536-dimension vectors
+    // Each document contains: HotelId, HotelName, Description, DescriptionVector,
+    // Category, Tags, ParkingIncluded, LastRenovationDate, Rating, Address, Location
+];
 
-Hybrid search consists of keyword queries and vector queries in a single search request. This example runs the vector query and full-text search concurrently:
+const searchClient = new SearchClient(searchEndpoint, indexName, credential);
 
-+ Search string: `historic hotel walk to restaurants and shopping`
-+ Vector query string: `quintessential lodging near running trails, eateries, retail` (vectorized into a mathematical representation)
+const result = await searchClient.uploadDocuments(DOCUMENTS);
+for (const r of result.results) {
+    console.log(`Key: ${r.key}, Succeeded: ${r.succeeded}`);
+}
+```
 
-This search uses [SearchClient](/javascript/api/@azure/search-documents/searchclient).[search](/javascript/api/@azure/search-documents/searchclient#@azure-search-documents-searchclient-search) and the [VectorQuery](/javascript/api/@azure/search-documents/vectorquery) and [SearchOptions](/javascript/api/@azure/search-documents/searchoptions). 
+Your code interacts with a specific search index hosted in your Azure AI Search service through the `SearchClient`, which is the main object provided by the [`@azure/search-documents`](/javascript/api/overview/azure/search-documents-readme) package. The `SearchClient` provides access to index operations, such as:
 
-To create a hybrid search:
++ Data ingestion: `uploadDocuments`, `mergeDocuments`, `deleteDocuments`
 
-1. Create a `searchHybrid.js` file in the `src` directory.
-1. Copy the following code into the file.
++ Search operations: `search`, `autocomplete`, `suggest`
 
-    :::code language="javascript" source="~/azure-search-javascript-samples/quickstart-vector-js/src/searchHybrid.js" :::
-1. Build and run the file.
+### Query the index
 
-    ```bash
-    node -r dotenv/config src/searchHybrid.js
-    ```
+The queries in the search files demonstrate different search patterns. The example vector queries are based on two strings:
 
-   The output of this code shows the relevant documents for the hybrid search.
-
-    ```output
-    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
-    Using Azure Search index: <vector-index-name>
-    
-    
-    Hybrid search found 7 then limited to top 5
-    - Score: 0.03279569745063782
-      HotelId: 4
-      HotelName: Sublime Palace Hotel
-      Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
-      Category: Boutique
-      Tags: ["concierge","view","air conditioning"]
-    
-    - Score: 0.032522473484277725
-      HotelId: 13
-      HotelName: Luxury Lion Resort
-      Description: Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.
-      Category: Luxury
-      Tags: ["bar","concierge","restaurant"]
-    
-    - Score: 0.03205128386616707
-      HotelId: 48
-      HotelName: Nordick's Valley Motel
-      Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
-      Category: Boutique
-      Tags: ["continental breakfast","air conditioning","free wifi"]
-    
-    - Score: 0.0317460335791111
-      HotelId: 49
-      HotelName: Swirling Currents Hotel
-      Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
-      Category: Suite
-      Tags: ["air conditioning","laundry service","24-hour front desk service"]
-    
-    - Score: 0.03125
-      HotelId: 2
-      HotelName: Old Century Hotel
-      Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
-      Category: Boutique
-      Tags: ["pool","free wifi","air conditioning","concierge"]
-    ```
++ Full-text search string: `"historic hotel walk to restaurants and shopping"`
 
-    Because Reciprocal Rank Fusion (RRF) merges results, it helps to review the inputs. The following results are from the full-text query only. The top two results are Sublime Palace Hotel and Luxury Lion Resort. The Sublime Palace Hotel has a stronger BM25 relevance score.
-
-    ```json
-       {
-           "@search.score": 2.2626662,
-           "HotelName": "Sublime Palace Hotel",
-           "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace."
-       },
-       {
-           "@search.score": 0.86421645,
-           "HotelName": "Luxury Lion Resort",
-           "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort"
-       },
-    ```
++ Vector query string: `"quintessential lodging near running trails, eateries, retail"` (vectorized into a mathematical representation)
 
-    In the vector-only query, which uses HNSW for finding matches, the Sublime Palace Hotel drops to the fourth position. Luxury Lion, which was second in the full-text search and third in the vector search, doesn't experience the same range of fluctuation, so it appears as a top match in a homogenized result set.
-   
-   ```json
-   "value": [
-       {
-           "@search.score": 0.857736,
-           "HotelId": "48",
-           "HotelName": "Nordick's Valley Motel",
-           "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer.  Hiking? Wine Tasting? Exploring the caverns?  It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
-           "Category": "Boutique"
-       },
-       {
-           "@search.score": 0.8399129,
-           "HotelId": "49",
-           "HotelName": "Swirling Currents Hotel",
-           "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.",
-           "Category": "Luxury"
-       },
-       {
-           "@search.score": 0.8383954,
-           "HotelId": "13",
-           "HotelName": "Luxury Lion Resort",
-           "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort",
-           "Category": "Resort and Spa"
-       },
-       {
-           "@search.score": 0.8254346,
-           "HotelId": "4",
-           "HotelName": "Sublime Palace Hotel",
-           "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace.",
-           "Category": "Boutique"
-       },
-       {
-           "@search.score": 0.82380056,
-           "HotelId": "1",
-           "HotelName": "Stay-Kay City Hotel",
-           "Description": "The hotel is ideally located on the main commercial artery of the city in the heart of New York.",
-           "Category": "Boutique"
-       },
-       {
-           "@search.score": 0.81514084,
-           "HotelId": "2",
-           "HotelName": "Old Century Hotel",
-           "Description": "The hotel is situated in a  nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts.",
-           "Category": "Boutique"
-       },
-       {
-           "@search.score": 0.8133763,
-           "HotelId": "3",
-           "HotelName": "Gastronomic Landscape Hotel",
-           "Description": "The Hotel stands out for its gastronomic excellence under the management of William Dough, who advises on and oversees all of the Hotel's restaurant services.",
-           "Category": "Resort and Spa"
-       }
-   ]
-   ```
+The vector query string is semantically similar to the full-text search string, but it includes terms that don't exist in the index. A keyword-only search for the vector query string returns zero results. However, vector search finds relevant matches based on meaning rather than exact keywords.
 
-## Create a semantic hybrid search
+The following examples start with a basic vector query and progressively add filters, keyword search, and semantic reranking.
 
-Here's the last query in the collection to extend the semantic hybrid search with the additional search text `historic hotel walk to restaurants and shopping`.
+#### Single vector search
 
-This search uses [SearchClient](/javascript/api/@azure/search-documents/searchclient).[search](/javascript/api/@azure/search-documents/searchclient#@azure-search-documents-searchclient-search) and the [VectorQuery](/javascript/api/@azure/search-documents/vectorquery) and [SearchOptions](/javascript/api/@azure/search-documents/searchoptions). 
+`searchSingle.js` demonstrates a basic scenario where you want to find document descriptions that closely match the vector query string. The `vectorQuery` object configures the vector search:
 
-To create a semantic hybrid search:
++ `kNearestNeighborsCount` limits how many results are returned based on vector similarity.
++ `fields` specifies the vector field to search against.
 
-1. Create a `searchSemanticHybrid.js` file in the `src` directory.
+```javascript
+const vectorQuery = {
+    vector: vector,
+    kNearestNeighborsCount: 5,
+    fields: ["DescriptionVector"],
+    kind: "vector",
+    exhaustive: true
+};
 
-1. Copy the following code into the file.
+const searchOptions = {
+    top: 7,
+    select: ["HotelId", "HotelName", "Description", "Category", "Tags"],
+    includeTotalCount: true,
+    vectorSearchOptions: {
+        queries: [vectorQuery],
+        filterMode: "postFilter"
+    }
+};
 
-    :::code language="javascript" source="~/azure-search-javascript-samples/quickstart-vector-js/src/searchSemanticHybrid.js" :::
+const results = await searchClient.search("*", searchOptions);
 
-1. Build and run the file.
+for await (const result of results.results) {
+    const doc = result.document;
+    console.log(`HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Score: ${result.score}`);
+}
+```
 
-    ```bash
-    node -r dotenv/config src/searchSemanticHybrid.js
-    ```
+#### Single vector search with a filter
 
-   The output of this code shows the relevant documents for the semantic hybrid search.
-
-    ```output
-    Using Azure Search endpoint: https://<search-service-name>.search.windows.net
-    Using Azure Search index: <vector-index-name>
-
-    
-    Semantic hybrid search found 7 then limited to top 5
-    - Score: 0.0317460335791111
-      Re-ranker Score: 2.6550590991973877
-      HotelId: 49
-      HotelName: Swirling Currents Hotel
-      Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
-      Category: Suite
-    
-    - Score: 0.03279569745063782
-      Re-ranker Score: 2.599761724472046
-      HotelId: 4
-      HotelName: Sublime Palace Hotel
-      Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
-      Category: Boutique
-    
-    - Score: 0.03125
-      Re-ranker Score: 2.3480887413024902
-      HotelId: 2
-      HotelName: Old Century Hotel
-      Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
-      Category: Boutique
-    
-    - Score: 0.016393441706895828
-      Re-ranker Score: 2.2718777656555176
-      HotelId: 1
-      HotelName: Stay-Kay City Hotel
-      Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.
-      Category: Boutique
-    
-    - Score: 0.01515151560306549
-      Re-ranker Score: 2.0582215785980225
-      HotelId: 3
-      HotelName: Gastronomic Landscape Hotel
-      Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel's restaurant services.
-      Category: Suite
-    ```
+In Azure AI Search, [filters](../../vector-search-filters.md) apply to nonvector fields in an index. `searchSingleWithFilter.js` filters on the `Tags` field to filter out any hotels that don't provide free Wi-Fi.
 
-    You can think of the semantic ranking as a way to improve the relevance of search results by understanding the meaning behind the words in the query and the content of the documents. In this case, the semantic ranking helps to identify hotels that are not only relevant to the keywords but also match the intent of the query:
-    
-    Key takeaways: 
+```javascript
+const searchOptions = {
+    top: 7,
+    select: ["HotelId", "HotelName", "Description", "Category", "Tags"],
+    includeTotalCount: true,
+    filter: "Tags/any(tag: tag eq 'free wifi')", // Adding filter for "free wifi" tag
+    vectorSearchOptions: {
+        queries: [vectorQuery],
+        filterMode: "postFilter"
+    }
+};
 
-    - Vector search is specified through the `vectorSearchOptions` property. Keyword search is specified through the `semanticSearchOptions` property.
+const results = await searchClient.search("*", searchOptions);
+```
 
-    - In a hybrid search, you can integrate vector search with full-text search over keywords. Filters, spell check, and semantic ranking apply to textual content only, and not vectors. In this final query, there's no semantic `answer` because the system didn't produce one that was sufficiently strong.
+#### Single vector search with a geo filter
+
+You can specify a [geo-spatial filter](../../search-query-odata-geo-spatial-functions.md) to limit results to a specific geographic area. `searchSingleWithGeoFilter.js` specifies a geographic point (Washington D.C., using longitude and latitude coordinates) and returns hotels within 300 kilometers. The `filterMode` property determines when the filter runs. In this case, `postFilter` runs the filter after the vector search.
+
+```javascript
+const searchOptions = {
+    top: 5,
+    includeTotalCount: true,
+    select: ["HotelId", "HotelName", "Category", "Description", "Address/City", "Address/StateProvince"],
+    facets: ["Address/StateProvince"],
+    filter: "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
+    vectorSearchOptions: {
+        queries: [vectorQuery],
+        filterMode: "postFilter"
+    }
+};
+```
 
-    - Actual results include more detail, including semantic captions and highlights. Results were modified for readability. To get the full structure of the response, run the request in the REST client.
+#### Hybrid search
+
+[Hybrid search](../../hybrid-search-overview.md) combines full-text and vector queries in a single request. `searchHybrid.js` runs both query types concurrently, and then uses Reciprocal Rank Fusion (RRF) to merge the results into a unified ranking. RRF uses the inverse of result rankings from each result set to produce a merged ranking. Notice that hybrid search scores are uniformly smaller than single-query scores.
+
+```javascript
+const vectorQuery = {
+    vector: vector,
+    kNearestNeighborsCount: 5,
+    fields: ["DescriptionVector"],
+    kind: "vector",
+    exhaustive: true
+};
+
+const searchOptions = {
+    top: 5,
+    includeTotalCount: true,
+    select: ["HotelId", "HotelName", "Description", "Category", "Tags"],
+    vectorSearchOptions: {
+        queries: [vectorQuery],
+        filterMode: "postFilter"
+    }
+};
+
+// Use search text for keyword search (hybrid search = vector + keyword)
+const searchText = "historic hotel walk to restaurants and shopping";
+const results = await searchClient.search(searchText, searchOptions);
+```
 
-## Clean up resources
+#### Semantic hybrid search
+
+`searchSemanticHybrid.js` demonstrates [semantic ranking](../../semantic-search-overview.md), which reranks results based on language understanding.
+
+```javascript
+const searchOptions = {
+    top: 5,
+    includeTotalCount: true,
+    select: ["HotelId", "HotelName", "Category", "Description"],
+    queryType: "semantic",
+    semanticSearchOptions: {
+        configurationName: "semantic-config"
+    },
+    vectorSearchOptions: {
+        queries: [vectorQuery],
+        filterMode: "postFilter"
+    }
+};
+
+const searchText = "historic hotel walk to restaurants and shopping";
+const results = await searchClient.search(searchText, searchOptions);
+
+for await (const result of results.results) {
+    console.log(`Score: ${result.score}, Re-ranker Score: ${result.rerankerScore}`);
+}
+```
 
-When you're working in your own subscription, it's a good idea at the end of a project to identify whether you still need the resources you created. Resources left running can cost you money. You can delete resources individually or delete the resource group to delete the entire set of resources.
+Compare these results with the hybrid search results from the previous query. Without semantic reranking, Sublime Palace Hotel ranks first because Reciprocal Rank Fusion (RRF) combines the text and vector scores to produce a merged result. After semantic reranking, Swirling Currents Hotel moves to the top spot.
 
-You can find and manage resources in the Azure portal by using the **All resources** or **Resource groups** link in the leftmost pane.
+The semantic ranker uses machine comprehension models to evaluate how well each result matches the intent of the query. Swirling Currents Hotel's description mentions `"walking access to shopping, dining, entertainment and the city center"`, which aligns closely with the search query's `"walk to restaurants and shopping"`. This semantic match for nearby dining and shopping elevates it above Sublime Palace Hotel, which doesn't emphasize walkable amenities in its description.
 
-Alternatively, to delete the vector index you created in this quickstart programmatically:
+Key takeaways:
 
-1. Create a `deleteIndex.js` file in the `src` directory.
-1. Add the dependencies, environment variables, and code to delete the index.
++ In a hybrid search, you can integrate vector search with full-text search over keywords. Filters and semantic ranking apply to textual content only, not vectors.
 
-    :::code language="javascript" source="~/azure-search-javascript-samples/quickstart-vector-js/src/deleteIndex.js" :::
++ Actual results include more detail, including semantic captions and highlights. This quickstart modifies results for readability. To get the full structure of the response, use REST to run the request.
 
-1. Build and run the file.
+## Clean up resources
 
-    ```bash
-    node -r dotenv/config src/deleteIndex.js
-    ```
+[!INCLUDE [resource-cleanup-paid](../resource-cleanup-paid.md)]
 
-## Next steps
+Otherwise, run the following command to delete the index you created in this quickstart.
 
-+ Review the repository of code samples for vector search capabilities in Azure AI Search for [JavaScript](https://github.com/Azure/azure-search-vector-samples/tree/main/demo-javascript).
\ No newline at end of file
+```bash
+node -r dotenv/config src/deleteIndex.js
+```