split full text quickstart via pivots

eric-urban · eric-urban · commit c60c18b943e3 · 2025-02-08T07:56:49.000-08:00
diff --git a/articles/search/includes/quickstarts/full-text-csharp.md b/articles/search/includes/quickstarts/full-text-csharp.md
@@ -13,7 +13,7 @@ Build a console application using the [Azure.Search.Documents](/dotnet/api/overv
 
 Alternatively, you can [download the source code](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/quickstart/v11) to start with a finished project or follow these steps to create your own.
 
-##Set up your environment
+## Set up your environment
 
 1. Start Visual Studio and create a new project for a console app.
 
@@ -25,7 +25,7 @@ Alternatively, you can [download the source code](https://github.com/Azure-Sampl
 
 1. Select **Install** to add the assembly to your project and solution.
 
-##Create a search client
+## Create a search client
 
 1. In *Program.cs*, change the namespace to `AzureSearch.SDK.Quickstart.v11` and then add the following `using` directives.
 
@@ -59,7 +59,7 @@ Alternatively, you can [download the source code](https://github.com/Azure-Sampl
     }
     ```
 
-##Create an index
+## Create an index
 
 This quickstart builds a Hotels index that you'll load with hotel data and execute queries against. In this step, define the fields in the index. Each field definition includes a name, data type, and attributes that determine how the field is used.
 
@@ -168,7 +168,7 @@ In this example, synchronous methods of the *Azure.Search.Documents* library are
     }
    ```
 
-##Load documents
+## Load documents
 
 Azure AI Search searches over content stored in the service. In this step, you'll load JSON documents that conform to the hotel index you just created.
 
@@ -304,7 +304,7 @@ When uploading documents, you must use an [IndexDocumentsBatch](/dotnet/api/azur
 
     The 2-second delay compensates for indexing, which is asynchronous, so that all documents can be indexed before the queries are executed. Coding in a delay is typically only necessary in demos, tests, and sample applications.
 
-##Search an index
+## Search an index
 
 You can get query results as soon as the first document is indexed, but actual testing of your index should wait until all documents are indexed.
 
@@ -462,7 +462,7 @@ The previous queries show multiple [ways of matching terms in a query](/azure/se
 
 Full text search and filters are performed using the [SearchClient.Search](/dotnet/api/azure.search.documents.searchclient.search) method. A search query can be passed in the `searchText` string, while a filter expression can be passed in the [Filter](/dotnet/api/azure.search.documents.searchoptions.filter) property of the [SearchOptions](/dotnet/api/azure.search.documents.searchoptions) class. To filter without searching, just pass `"*"` for the `searchText` parameter of the [Search](/dotnet/api/azure.search.documents.searchclient.search) method. To search without filtering, leave the `Filter` property unset, or don't pass in a `SearchOptions` instance at all.
 
-##Run the program
+## Run the program
 
 Press **F5** to rebuild the app and run the program in its entirety.
 
diff --git a/articles/search/includes/quickstarts/full-text-java.md b/articles/search/includes/quickstarts/full-text-java.md
@@ -13,15 +13,15 @@ Build a Java console application using the [Azure.Search.Documents](/java/api/ov
 
 Alternatively, you can [download the source code](https://github.com/Azure-Samples/azure-search-java-samples/tree/main/quickstart) to start with a finished project or follow these steps to create your own.
 
-##Set up your environment
+## Set up your environment
 
 Use the following tools to create this quickstart.
 
 + [Visual Studio Code with the Java extension](https://code.visualstudio.com/docs/java/extensions)
 
 + [Java 11 SDK](/java/azure/jdk/)
 
-##Create the project
+## Create the project
 
 1. Start Visual Studio Code.
 
@@ -57,7 +57,7 @@ Use the following tools to create this quickstart.
 
 1. Open the folder you created the project in.
 
-##Specify Maven dependencies
+## Specify Maven dependencies
 
 1. Open the *pom.xml* file and add the following dependencies.
 
@@ -89,7 +89,7 @@ Use the following tools to create this quickstart.
     <maven.compiler.target>1.11</maven.compiler.target>
     ```
 
-##Create a search client
+## Create a search client
 
 1. Open the `App` class under **src**, **main**, **java**, **azure**, **search**, **sample**. Add the following import directives.
 
@@ -138,7 +138,7 @@ Use the following tools to create this quickstart.
     }
     ```
 
-##Create an index
+## Create an index
 
 This quickstart builds a Hotels index that you'll load with hotel data and execute queries against. In this step, define the fields in the index. Each field definition includes a name, data type, and attributes that determine how the field is used.
 
@@ -325,7 +325,7 @@ Whether you use the basic `SearchField` API or either one of the helper models,
         .setSuggesters(new SearchSuggester("sg", Arrays.asList("HotelName"))));
     ```
 
-##Load documents
+## Load documents
 
 Azure AI Search searches over content stored in the service. In this step, you'll load JSON documents that conform to the hotel index you just created.
 
@@ -454,7 +454,7 @@ Once you initialize the `IndexDocumentsBatch` object, you can send it to the ind
 
 The 2-second delay compensates for indexing, which is asynchronous, so that all documents can be indexed before the queries are executed. Coding in a delay is typically only necessary in demos, tests, and sample applications.
 
-##Search an index
+## Search an index
 
 You can get query results as soon as the first document is indexed, but actual testing of your index should wait until all documents are indexed.
 
@@ -584,7 +584,7 @@ The previous queries show multiple ways of matching terms in a query: full-text
 
 Full text search and filters are performed using the [SearchClient.search](/java/api/com.azure.search.documents.searchclient#com-azure-search-documents-searchclient-search(java-lang-string)) method. A search query can be passed in the `searchText` string, while a filter expression can be passed in the `filter` property of the [SearchOptions](/java/api/com.azure.search.documents.models.searchoptions) class. To filter without searching, just pass "*" for the `searchText` parameter of the `search` method. To search without filtering, leave the `filter` property unset, or don't pass in a `SearchOptions` instance at all.
 
-### Run the program
+## Run the program
 
 Press F5 to rebuild the app and run the program in its entirety.
 
diff --git a/articles/search/includes/quickstarts/full-text-javascript.md b/articles/search/includes/quickstarts/full-text-javascript.md
@@ -13,15 +13,15 @@ Build a Node.js application using the [@azure/search-documents](/javascript/api/
 
 Alternatively, you can [download the source code](https://github.com/Azure-Samples/azure-search-javascript-samples/tree/main/quickstart) to start with a finished project or follow these steps to create your own.
 
-##Set up your environment
+## Set up your environment
 
 We used the following tools to create this quickstart.
 
 + [Visual Studio Code](https://code.visualstudio.com), which has built-in support for creating JavaScript apps
 
 + [Node.js](https://nodejs.org) and [npm](https://www.npmjs.com)
 
-##Create the project
+## Create the project
 
 1. Start Visual Studio Code.
 
@@ -85,7 +85,7 @@ We used the following tools to create this quickstart.
 
 Replace the `YOUR-SEARCH-SERVICE-URL` value with the name of your search service endpoint URL. Replace `<YOUR-SEARCH-ADMIN-API-KEY>` with the admin key you recorded earlier. 
 
-##Create index.js file
+## Create index.js file
 
 Next we create an *index.js* file, which is the main file that hosts our code.
 
@@ -128,7 +128,7 @@ main().catch((err) => {
 
 With that in place, we're ready to create an index.
 
-##Create index 
+## Create index 
 
 Create a file *hotels_quickstart_index.json*. This file defines how Azure AI Search works with the documents you'll be loading in the next step. Each field will be identified by a `name` and have a specified `type`. Each field also has a series of index attributes that specify whether Azure AI Search can search, filter, sort, and facet upon the field. Most of the fields are simple data types, but some, like `AddressType` are complex types that allow you to create rich data structures in your index. You can read more about [supported data types](/rest/api/searchservice/supported-data-types) and index attributes described in [Create Index (REST)](/rest/api/searchservice/indexes/create). 
 
@@ -311,7 +311,7 @@ let index = await indexClient.createIndex(indexDefinition);
 console.log(`Index named ${index.name} has been created.`);
 ```
 
-##Run the sample
+## Run the sample
 
 At this point, you're ready to run the sample. Use a terminal window to run the following command:
 
@@ -329,7 +329,7 @@ Open the **Overview** of your search service in the Azure portal. Select the **I
 
 In the next step, you'll add data to index. 
 
-##Load documents 
+## Load documents 
 
 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 case, we'll programatically push the documents to the index.
 
@@ -459,7 +459,7 @@ To have the program wait for one second, call the `sleep` function like below:
 sleep(1000);
 ```
 
-##Search an index
+## Search an index
 
 With an index created and documents uploaded, you're ready to send queries to the index. In this section, we send five different queries to the search index to demonstrate different pieces of query functionality available to you.
 
@@ -552,6 +552,6 @@ let documentResult = await searchClient.getDocument(key='3')
 console.log(`HotelId: ${documentResult.HotelId}; HotelName: ${documentResult.HotelName}`)
 ```
 
-##Run the sample
+## Run the sample
 
 Run the program by using `node index.js`. Now, in addition to the previous steps, the queries are sent and the results written to the console.
diff --git a/articles/search/includes/quickstarts/full-text-python.md b/articles/search/includes/quickstarts/full-text-python.md
@@ -13,7 +13,7 @@ Use a Jupyter notebook and the [azure-search-documents](/python/api/overview/azu
 
 Alternatively, you can download and run a [finished notebook](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart).
 
-##Set up your environment
+## Set up your environment
 
 Use [Visual Studio Code with the Python extension](https://code.visualstudio.com/docs/languages/python), or an equivalent IDE, with Python 3.10 or later.
 
@@ -31,7 +31,7 @@ We recommend a virtual environment for this quickstart:
 
 It can take a minute to set up. If you run into problems, see [Python environments in VS Code](https://code.visualstudio.com/docs/python/environments).
 
-##Install packages and set variables
+## Install packages and set variables
 
 1. Install packages, including [azure-search-documents](/python/api/azure-search-documents). 
 
@@ -49,7 +49,7 @@ It can take a minute to set up. If you run into problems, see [Python environmen
     index_name: str = "hotels-quickstart"
     ```
 
-##Create an index
+## Create an index
 
 ```python
 from azure.core.credentials import AzureKeyCredential
@@ -99,7 +99,7 @@ result = index_client.create_or_update_index(index)
 print(f' {result.name} created')
 ```
 
-##Create a documents payload
+## Create a documents payload
 
 Use an [index action](/python/api/azure-search-documents/azure.search.documents.models.indexaction) for the operation type, such as upload or merge-and-upload. Documents originate from the [HotelsData](https://github.com/Azure-Samples/azure-search-sample-data/blob/main/hotels/HotelsData_toAzureSearch.JSON) sample on GitHub.
 
@@ -185,7 +185,7 @@ documents = [
 ]
 ```
 
-##Upload documents
+## Upload documents
 
 ```python
 # Upload documents to the index
@@ -202,7 +202,7 @@ except Exception as ex:
     endpoint=search_endpoint, credential=credential)
 ```
 
-##Run your first query
+## Run your first query
 
 Use the *search* method of the [search.client class](/python/api/azure-search-documents/azure.search.documents.searchclient).
 
@@ -222,7 +222,7 @@ for result in results:
     print(f"Description: {result['Description']}")
 ```
 
-##Run a term query
+## Run a term query
 
 The next query adds whole terms to the search expression ("wifi"). This query specifies that results contain only those fields in the `select` statement. Limiting the fields that come back minimizes the amount of data sent back over the wire and reduces search latency.
 
@@ -239,7 +239,7 @@ for result in results:
     print(f"Description: {result['Description']}")
 ```
 
-##Add a filter
+## Add a filter
 
 Add a filter expression, returning only those hotels with a rating greater than four, sorted in descending order.
 
@@ -255,7 +255,7 @@ for result in results:
     print("{}: {} - {} rating".format(result["HotelId"], result["HotelName"], result["Rating"]))
 ```
 
-##Add field scoping
+## Add field scoping
 
 Add `search_fields` to scope query execution to specific fields.
 
@@ -270,7 +270,7 @@ for result in results:
     print("{}: {}".format(result["HotelId"], result["HotelName"]))
 ```
 
-##Add facets
+## Add facets
 
 Facets are generated for positive matches found in search results. There are no zero matches. If search results don't include the term *wifi*, then *wifi* doesn't appear in the faceted navigation structure.
 
@@ -284,7 +284,7 @@ for facet in facets["Category"]:
     print("    {}".format(facet))
 ```
 
-##Look up a document
+## Look up a document
 
 Return a document based on its key. This operation is useful if you want to provide drill through when a user selects an item in a search result.
 
@@ -298,7 +298,7 @@ print("Rating: {}".format(result["Rating"]))
 print("Category: {}".format(result["Category"]))
 ```
 
-##Add autocomplete
+## Add autocomplete
 
 Autocomplete can provide potential matches as the user types into the search box.
 
diff --git a/articles/search/includes/quickstarts/full-text-typescript.md b/articles/search/includes/quickstarts/full-text-typescript.md
@@ -13,7 +13,7 @@ Build a Node.js application using the [@azure/search-documents](/javascript/api/
 
 Alternatively, you can [download the source code](https://github.com/Azure-Samples/azure-search-javascript-samples/tree/main/quickstart) to start with a finished project or follow these steps to create your own.
 
-##Set up your environment
+## Set up your environment
 
 We used the following tools to create this quickstart.
 
@@ -23,7 +23,7 @@ We used the following tools to create this quickstart.
 
 * [TypeScript](https://www.typescriptlang.org/download/)
 
-##Create the project
+## Create the project
 
 1. Start Visual Studio Code.
 
@@ -87,7 +87,7 @@ We used the following tools to create this quickstart.
 
 Replace the `YOUR-SEARCH-SERVICE-URL` value with the name of your search service endpoint URL. Replace `<YOUR-SEARCH-ADMIN-API-KEY>` with the admin key you recorded earlier. 
 
-##Create index.ts file
+## Create index.ts file
 
 Next we create an *index.ts* file, which is the main file that hosts our code.
 
@@ -141,7 +141,7 @@ main().catch((err) => {
 
 With that in place, we're ready to create an index.
 
-##Create index
+## Create index
 
 Create a file *hotels_quickstart_index.json*. This file defines how Azure AI Search works with the documents you'll be loading in the next step. Each field will be identified by a `name` and have a specified `type`. Each field also has a series of index attributes that specify whether Azure AI Search can search, filter, sort, and facet upon the field. Most of the fields are simple data types, but some, like `AddressType` are complex types that allow you to create rich data structures in your index. You can read more about [supported data types](/rest/api/searchservice/supported-data-types) and index attributes described in [Create Index (REST)](/rest/api/searchservice/indexes/create). 
 
@@ -333,7 +333,7 @@ let index = await indexClient.createIndex(hotelIndexDefinition);
 console.log(`Index named ${index.name} has been created.`);
 ```
 
-##Run the sample
+## Run the sample
 
 At this point, you're ready to build and run the sample. Use a terminal window to run the following commands to build your source with `tsc` then run your source with `node`:
 
@@ -352,7 +352,7 @@ Open the **Overview** of your search service in the Azure portal. Select the **I
 
 In the next step, you'll add data to index. 
 
-##Load documents 
+## Load documents 
 
 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 case, we'll programatically push the documents to the index.
 
@@ -498,7 +498,7 @@ To have the program wait for one second, call the `sleep` function:
 sleep(1000);
 ```
 
-##Search an index
+## Search an index
 
 With an index created and documents uploaded, you're ready to send queries to the index. In this section, we send five different queries to the search index to demonstrate different pieces of query functionality available to you.
 
@@ -598,6 +598,6 @@ let documentResult = await searchClient.getDocument('3')
 console.log(`HotelId: ${documentResult.HotelId}; HotelName: ${documentResult.HotelName}`)
 ```
 
-##Rerun the sample
+## Rerun the sample
 
 Build and run the program with `tsc && node index.ts`. Now, in addition to the previous steps, the queries are sent and the results written to the console.
