Merge pull request #209588 from HeidiSteen/heidist-fresh

[azure search] Freshness pass on c# quickstart plus screenshot updates

Author: Stacyrch140

articles/search/media/search-get-started-rest/get-url-key.png

@@ -13,7 +13,7 @@ ms.custom: devx-track-csharp, mode-api
 ---
 # Quickstart: Create a search index using the Azure.Search.Documents client library
 
-Use the [Azure.Search.Documents (version 11) client library](/dotnet/api/overview/azure/search.documents-readme) to create a .NET Core console application in C# that creates, loads, and queries a search index.
+Learn how to use the [Azure.Search.Documents (version 11) client library](/dotnet/api/overview/azure/search.documents-readme) to create a .NET Core console application in C# that creates, loads, and queries a search index.
 
 You can [download the source code](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/master/quickstart/v11) to start with a finished project or follow the steps in this article to create your own.
 
@@ -32,11 +32,10 @@ Before you begin, have the following tools and services:
 
 When setting up your project, you'll download the [Azure.Search.Documents NuGet package](https://www.nuget.org/packages/Azure.Search.Documents/).
 
-Azure SDK for .NET conforms to [.NET Standard 2.0](/dotnet/standard/net-standard#net-implementation-support), which means .NET Framework 4.6.1 and .NET Core 2.0 as minimum requirements.
-
+Azure SDK for .NET conforms to [.NET Standard 2.0](/dotnet/standard/net-standard#net-implementation-support), which means .NET Framework 4.6.1 and .NET Core 2.1 as minimum requirements.
 ## Set up your project
 
-Assemble service connection information, and then start Visual Studio to create a new Console App project that can run on .NET Core.
+Assemble service connection information, and then start Visual Studio to create a new Console App project that can run on. Select NET Core 3.1 for the run time.
 
 <a name="get-service-info"></a>
 
@@ -81,11 +80,11 @@ After the project is created, add the client library. The [Azure.Search.Document
 1. Create two clients: [SearchIndexClient](/dotnet/api/azure.search.documents.indexes.searchindexclient) creates the index, and [SearchClient](/dotnet/api/azure.search.documents.searchclient) loads and queries an existing index. Both need the service endpoint and an admin API key for authentication with create/delete rights.
 
    ```csharp
-   static void Main(string[] args)
-   {
-       string serviceName = "<YOUR-SERVICE-NAME>";
-       string indexName = "hotels-quickstart";
-       string apiKey = "<YOUR-ADMIN-API-KEY>";
+    static void Main(string[] args)
+    {
+        string serviceName = "<your-search-service-name>";
+        string apiKey = "<your-search-service-admin-api-key>";
+        string indexName = "hotels-quickstart";
 
         // Create a SearchIndexClient to send create/delete index commands
         Uri serviceEndpoint = new Uri($"https://{serviceName}.search.windows.net/");
@@ -94,6 +93,8 @@ After the project is created, add the client library. The [Azure.Search.Document
 
         // Create a SearchClient to load and query documents
         SearchClient srchclient = new SearchClient(serviceEndpoint, indexName, credential);
+        . . . 
+    }
     ```
 
 ## 1 - Create an index
@@ -364,6 +365,16 @@ The [SearchResults](/dotnet/api/azure.search.documents.models.searchresults-1) c
 
         Console.WriteLine();
     }
+
+    private static void WriteDocuments(AutocompleteResults autoResults)
+    {
+        foreach (AutocompleteItem result in autoResults.Results)
+        {
+            Console.WriteLine(result.Text);
+        }
+
+        Console.WriteLine();
+    }
     ```
 
 1. Create a **RunQueries** method to execute queries and return results. Results are Hotel objects. This sample shows the method signature and the first query. This query demonstrates the Select parameter that lets you compose the result using selected fields from the document.
@@ -374,7 +385,8 @@ The [SearchResults](/dotnet/api/azure.search.documents.models.searchresults-1) c
     {
         SearchOptions options;
         SearchResults<Hotel> response;
-
+        
+        // Query 1
         Console.WriteLine("Query #1: Search on empty term '*' to return all documents, showing a subset of fields...\n");
 
         options = new SearchOptions()
@@ -395,6 +407,7 @@ The [SearchResults](/dotnet/api/azure.search.documents.models.searchresults-1) c
 1. In the second query, search on a term, add a filter that selects documents where Rating is greater than 4, and then sort by Rating in descending order. Filter is a boolean expression that is evaluated over [IsFilterable](/dotnet/api/azure.search.documents.indexes.models.searchfield.isfilterable) fields in an index. Filter queries either include or exclude values. As such, there's no relevance score associated with a filter query. 
 
     ```csharp
+    // Query 2
     Console.WriteLine("Query #2: Search on 'hotels', filter on 'Rating gt 4', sort by Rating in descending order...\n");
 
     options = new SearchOptions()
@@ -414,6 +427,7 @@ The [SearchResults](/dotnet/api/azure.search.documents.models.searchresults-1) c
 1. The third query demonstrates searchFields, used to scope a full text search operation to specific fields.
 
     ```csharp
+    // Query 3
     Console.WriteLine("Query #3: Limit search to specific fields (pool in Tags field)...\n");
 
     options = new SearchOptions()
@@ -432,6 +446,7 @@ The [SearchResults](/dotnet/api/azure.search.documents.models.searchresults-1) c
 1. The fourth query demonstrates facets, which can be used to structure a faceted navigation structure. 
 
    ```csharp
+    // Query 4
     Console.WriteLine("Query #4: Facet on 'Category'...\n");
 
     options = new SearchOptions()
@@ -452,6 +467,7 @@ The [SearchResults](/dotnet/api/azure.search.documents.models.searchresults-1) c
 1. In the fifth query, return a specific document. A document lookup is a typical response to OnClick event in a result set.
 
    ```csharp
+    // Query 5
     Console.WriteLine("Query #5: Look up a specific document...\n");
 
     Response<Hotel> lookupResponse;
@@ -463,12 +479,12 @@ The [SearchResults](/dotnet/api/azure.search.documents.models.searchresults-1) c
 1. The last query shows the syntax for autocomplete, simulating a partial user input of "sa" that resolves to two possible matches in the sourceFields associated with the suggester you defined in the index.
 
    ```csharp
+    // Query 6
     Console.WriteLine("Query #6: Call Autocomplete on HotelName that starts with 'sa'...\n");
 
     var autoresponse = srchclient.Autocomplete("sa", "sg");
     WriteDocuments(autoresponse);
    ```
-
 1. Add **RunQueries** to Main().
 
     ```csharp

articles/search/media/tutorial-multiple-data-sources/blob-add-container.png

@@ -1,5 +1,5 @@
 ---
-title: 'Quickstart: Create a search index in Javas'
+title: 'Quickstart: Create a search index in Java'
 titleSuffix: Azure Cognitive Search
 description: In this Java quickstart, learn how to create an index, load data, and run queries using the Azure Cognitive Search client library for Java.
 manager: nitinme

articles/search/media/tutorial-multiple-data-sources/cosmos-add-container.png

@@ -22,7 +22,7 @@ ms.custom: devx-track-python, mode-api
 > * [Portal](search-get-started-portal.md)
 >
 
-Build a notebook that creates, loads, and queries an Azure Cognitive Search index using Python and the [azure-search-documents library](/python/api/overview/azure/search-documents-readme) in the Azure SDK for Python. This article explains how to build a notebook step by step. Alternatively, you can [download and run a finished Jupyter Python notebook](https://github.com/Azure-Samples/azure-search-python-samples).
+Build a Jupyter Notebook that creates, loads, and queries an Azure Cognitive Search index using Python and the [azure-search-documents library](/python/api/overview/azure/search-documents-readme) in the Azure SDK for Python. This article explains how to build a notebook step by step. Alternatively, you can [download and run a finished Jupyter Python notebook](https://github.com/Azure-Samples/azure-search-python-samples).
 
 If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/free/?WT.mc_id=A261C142F) before you begin.
 
@@ -57,8 +57,8 @@ In this task, start Jupyter Notebook and verify that you can connect to Azure Co
 1. In the first cell, load the libraries from the Azure SDK for Python, including [azure-search-documents](/python/api/azure-search-documents).
 
    ```python
-    !pip install azure-search-documents --pre
-    !pip show azure-search-documents
+    %pip install azure-search-documents --pre
+    %pip show azure-search-documents
 
     import os
     from azure.core.credentials import AzureKeyCredential
@@ -318,7 +318,7 @@ This step shows you how to query an index using the **search** method of the [se
         print("    {}".format(facet))
     ```
 
-1. In this example, look up a specific document based on its key. You would typically want to return a document when a user select on a document in a search result.
+1. In this example, look up a specific document based on its key. You would typically want to return a document when a user selects a document in a search result.
 
     ```python
     result = search_client.get_document(key="3")
@@ -331,7 +331,7 @@ This step shows you how to query an index using the **search** method of the [se
 
 1. In this example, we'll use the autocomplete function. Autocomplete is typically used in a search box to provide potential matches as the user types into the search box.
 
-   When the index was created, a suggester named "sg" was also created as part of the request. A suggester definition specifies which fields can be used to find potential matches to suggester requests. In this example, those fields are 'Tags', 'Address/City', 'Address/Country'. To simulate auto-complete, pass in the letters "sa" as a partial string. The autocomplete method of [SearchClient](/python/api/azure-search-documents/azure.search.documents.searchclient) sends back potential term matches.
+   When the index was created, a suggester named `sg` was also created as part of the request. A suggester definition specifies which fields can be used to find potential matches to suggester requests. In this example, those fields are 'Tags', 'Address/City', 'Address/Country'. To simulate auto-complete, pass in the letters "sa" as a partial string. The autocomplete method of [SearchClient](/python/api/azure-search-documents/azure.search.documents.searchclient) sends back potential term matches.
 
     ```python
     search_suggestion = 'sa'

articles/search/media/tutorial-multiple-data-sources/cosmos-dbname.png

@@ -64,19 +64,19 @@ This sample uses two small sets of data that describe seven fictional hotels. On
 
 1. Select **Data Explorer** and then select **New Database**.
 
-   :::image type="content" source="media/tutorial-multiple-data-sources/cosmos-newdb.png" alt-text="Create a new database" border="false":::
+   :::image type="content" source="media/tutorial-multiple-data-sources/cosmos-newdb.png" alt-text="Create a new database" border="true":::
 
 1. Enter the name **hotel-rooms-db**. Accept default values for the remaining settings.
 
-   :::image type="content" source="media/tutorial-multiple-data-sources/cosmos-dbname.png" alt-text="Configure database" border="false":::
+   :::image type="content" source="media/tutorial-multiple-data-sources/cosmos-dbname.png" alt-text="Configure database" border="true":::
 
 1. Create a new container. Use the existing database you just created. Enter **hotels** for the container name, and use **/HotelId** for the Partition key.
 
-   :::image type="content" source="media/tutorial-multiple-data-sources/cosmos-add-container.png" alt-text="Add container" border="false":::
+   :::image type="content" source="media/tutorial-multiple-data-sources/cosmos-add-container.png" alt-text="Add container" border="true":::
 
 1. Select **Items** under **hotels**, and then select **Upload Item** on the command bar. Navigate to and then select the file **cosmosdb/HotelsDataSubset_CosmosDb.json** in the project folder.
 
-   :::image type="content" source="media/tutorial-multiple-data-sources/cosmos-upload.png" alt-text="Upload to Azure Cosmos DB collection" border="false":::
+   :::image type="content" source="media/tutorial-multiple-data-sources/cosmos-upload.png" alt-text="Upload to Azure Cosmos DB collection" border="true":::
 
 1. Use the Refresh button to refresh your view of the items in the hotels collection. You should see seven new database documents listed.
 
@@ -88,7 +88,7 @@ This sample uses two small sets of data that describe seven fictional hotels. On
 
 1. [Create a blob container](../storage/blobs/storage-quickstart-blobs-portal.md) named **hotel-rooms** to store the sample hotel room JSON files. You can set the Public Access Level to any of its valid values.
 
-   :::image type="content" source="media/tutorial-multiple-data-sources/blob-add-container.png" alt-text="Create a blob container" border="false":::
+   :::image type="content" source="media/tutorial-multiple-data-sources/blob-add-container.png" alt-text="Create a blob container" border="true":::
 
 1. After the container is created, open it and select **Upload** on the command bar. Navigate to the folder containing the sample files. Select all of them and then select **Upload**.