Merge pull request #974 from cdpark/refresh-oct-heidist3

Feature 322546: Q&M: Freshness - AI Services & Search 180d target - Oct sprint - heidist3

Author: prmerger-automator[bot]

articles/search/includes/quickstarts/dotnet-semantic.md

@@ -5,12 +5,12 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: include
-ms.date: 01/02/2024
+ms.date: 10/22/2024
 ---
 
-Build a console application using the [**Azure.Search.Documents**](/dotnet/api/overview/azure/search.documents-readme) client library to add semantic ranking to an existing search index. 
+Build a console application by using the [**Azure.Search.Documents**](/dotnet/api/overview/azure/search.documents-readme) client library to add semantic ranking to an existing search index.
 
-Alternatively, you can [download the source code](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/quickstart-semantic-search/SemanticSearchQuickstart) to start with a finished project or follow these steps to create your own.
+Alternatively, you can [download the source code](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/quickstart-semantic-search/SemanticSearchQuickstart) to start with a finished project.
 
 #### Set up your environment
 
@@ -20,13 +20,13 @@ Alternatively, you can [download the source code](https://github.com/Azure-Sampl
 
 1. Select **Browse**.
 
-1. Search for [Azure.Search.Documents package](https://www.nuget.org/packages/Azure.Search.Documents/) and select the latest stable version.
+1. Search for the [Azure.Search.Documents package](https://www.nuget.org/packages/Azure.Search.Documents/) and select the latest stable version.
 
 1. Select **Install** to add the assembly to your project and solution.
 
 #### Create a search client
 
-1. In **Program.cs**, add the following `using` directives.
+1. In *Program.cs*, add the following `using` directives.
 
    ```csharp
    using Azure;
@@ -36,9 +36,9 @@ Alternatively, you can [download the source code](https://github.com/Azure-Sampl
    using Azure.Search.Documents.Models;
    ```
 
-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.
+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.
 
-   Because the code builds out the URI for you, specify just the search service name in the "serviceName" property.
+    Both clients need the service endpoint and an admin API key for authentication with create/delete rights. However, the code builds out the URI for you, so specify only the search service name for the `serviceName` property. Don't include `https://` or `.search.windows.net`.
 
    ```csharp
     static void Main(string[] args)

articles/search/includes/quickstarts/python-semantic.md

@@ -5,12 +5,12 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: include
-ms.date: 03/11/2024
+ms.date: 10/22/2024
 ---
 
-Use a Jupyter notebook and the [**azure-search-documents**](/python/api/overview/azure/search-documents-readme) library in the Azure SDK for Python to learn about semantic ranking. 
+Use a Jupyter notebook and the [**azure-search-documents**](/python/api/overview/azure/search-documents-readme) library in the Azure SDK for Python to learn about semantic ranking.
 
-Alternatively, [download and run a finished notebook](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Semantic-Search).
+Alternatively, you can [download and run a finished notebook](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Semantic-Search).
 
 #### Set up your environment
 
@@ -22,7 +22,7 @@ We recommend a virtual environment for this quickstart:
 
 1. Create a new ipynb file.
 
-1. Open the Command Palette (Ctrl+Shift+P).
+1. Open the Command Palette by using **Ctrl+Shift+P**.
 
 1. Search for **Python: Create Environment**.
 
@@ -42,7 +42,7 @@ It can take a minute to set up. If you run into problems, see [Python environmen
     ! pip install python-dotenv --quiet
     ```
 
-1. Provide endpoint and API keys:
+1. Provide your endpoint and API keys:
 
     ```python
     search_endpoint: str = "PUT-YOUR-SEARCH-SERVICE-ENDPOINT-HERE"
@@ -238,9 +238,9 @@ for result in results:
 
 #### Run a text query
 
-For comparison purposes, run text query with BM25 relevance scoring. Full text search is invoked when you provide a query string. The response consists of ranked results, where higher scores are awarded to documents having more instances of matching terms, or more important terms.
+For comparison purposes, run a text query with BM25 relevance scoring. Full text search is invoked when you provide a query string. The response consists of ranked results, where higher scores are awarded to documents having more instances of matching terms, or more important terms.
 
-In this query for "what hotel has a good restaurant on site", Sublime Cliff Hotel comes out on top because its description includes "site". Terms that occur infrequently raise the search score of the document. 
+In this query for *what hotel has a good restaurant on site*, Sublime Cliff Hotel comes out on top because its description includes *site*. Terms that occur infrequently raise the search score of the document. 
 
 ```python
 # Run a text query (returns a BM25-scored result set)