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articles/azure-functions/functions-bindings-kafka-output.md

@@ -3,7 +3,7 @@ title: Apache Kafka output binding for Azure Functions
 description: Use Azure Functions to write messages to an Apache Kafka stream.
 ms.topic: reference
 ms.custom: devx-track-extended-java, devx-track-js, devx-track-python
-ms.date: 05/14/2022
+ms.date: 06/14/2024
 zone_pivot_groups: programming-languages-set-functions-lang-workers
 ---
 
@@ -37,51 +37,51 @@ The attributes you use depend on the specific event provider.
 
 The following example shows a C# function that sends a single message to a Kafka topic, using data provided in HTTP GET request.
 
-:::code language="json" source="~/azure-functions-kafka-extension/samples/dotnet/Confluent/KafkaOutput.cs" range="12-32" :::
+:::code language="csharp" source="~/azure-functions-kafka-extension/samples/dotnet/Confluent/KafkaOutput.cs" range="12-32" :::
 
 To send events in a batch, use an array of `KafkaEventData` objects, as shown in the following example:
 
-:::code language="json" source="~/azure-functions-kafka-extension/samples/dotnet/Confluent/KafkaOutputMany.cs" range="12-30" :::
+:::code language="csharp" source="~/azure-functions-kafka-extension/samples/dotnet/Confluent/KafkaOutputMany.cs" range="12-30" :::
 
 The following function adds headers to the Kafka output data:
 
-:::code language="json" source="~/azure-functions-kafka-extension/samples/dotnet/Confluent/KafkaOutputWithHeaders.cs" range="11-31" :::
+:::code language="csharp" source="~/azure-functions-kafka-extension/samples/dotnet/Confluent/KafkaOutputWithHeaders.cs" range="11-31" :::
 
 For a complete set of working .NET examples, see the [Kafka extension repository](https://github.com/Azure/azure-functions-kafka-extension/blob/dev/samples/dotnet/Confluent/). 
 
 # [Event Hubs](#tab/event-hubs/in-process)
 
 The following example shows a C# function that sends a single message to a Kafka topic, using data provided in HTTP GET request.
 
-:::code language="json" source="~/azure-functions-kafka-extension/samples/dotnet/EventHub/KafkaOutput.cs" range="11-31" :::
+:::code language="csharp" source="~/azure-functions-kafka-extension/samples/dotnet/EventHub/KafkaOutput.cs" range="11-31" :::
 
 To send events in a batch, use an array of `KafkaEventData` objects, as shown in the following example:
 
-:::code language="json" source="~/azure-functions-kafka-extension/samples/dotnet/EventHub/KafkaOutputMany.cs" range="12-30" :::
+:::code language="csharp" source="~/azure-functions-kafka-extension/samples/dotnet/EventHub/KafkaOutputMany.cs" range="12-30" :::
 
 The following function adds headers to the Kafka output data:
 
-:::code language="json" source="~/azure-functions-kafka-extension/samples/dotnet/EventHub/KafkaOutputWithHeaders.cs" range="11-31" :::
+:::code language="csharp" source="~/azure-functions-kafka-extension/samples/dotnet/EventHub/KafkaOutputWithHeaders.cs" range="11-31" :::
 
 For a complete set of working .NET examples, see the [Kafka extension repository](https://github.com/Azure/azure-functions-kafka-extension/blob/dev/samples/dotnet/EventHub). 
 
 # [Confluent](#tab/confluent/isolated-process)
 
 The following example has a custom return type that is `MultipleOutputType`, which consists of an HTTP response and a Kafka output. 
 
-:::code language="json" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/confluent/KafkaOutput.cs" range="11-31" :::
+:::code language="csharp" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/confluent/KafkaOutput.cs" range="11-31" :::
 
 In the class `MultipleOutputType`, `Kevent` is the output binding variable for the Kafka binding.
 
-:::code language="json" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/confluent/KafkaOutput.cs" range="34-46" :::
+:::code language="csharp" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/confluent/KafkaOutput.cs" range="34-46" :::
 
 To send a batch of events, pass a string array to the output type, as shown in the following example:
 
-:::code language="json" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/confluent/KafkaOutputMany.cs" range="11-30" :::
+:::code language="csharp" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/confluent/KafkaOutputMany.cs" range="11-30" :::
 
 The string array is defined as `Kevents` property on the class, on which the output binding is defined:  
 
-:::code language="json" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/confluent/KafkaOutputMany.cs" range="33-45" :::
+:::code language="csharp" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/confluent/KafkaOutputMany.cs" range="33-45" :::
 
 The following function adds headers to the Kafka output data:
 
@@ -94,19 +94,19 @@ For a complete set of working .NET examples, see the [Kafka extension repository
 
 The following example has a custom return type that is `MultipleOutputType`, which consists of an HTTP response and a Kafka output. 
 
-:::code language="json" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/eventhub/KafkaOutput.cs" range="11-31" :::
+:::code language="csharp" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/eventhub/KafkaOutput.cs" range="11-31" :::
 
 In the class `MultipleOutputType`, `Kevent` is the output binding variable for the Kafka binding.
 
-:::code language="json" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/eventhub/KafkaOutput.cs" range="34-46" :::
+:::code language="csharp" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/eventhub/KafkaOutput.cs" range="34-46" :::
 
 To send a batch of events, pass a string array to the output type, as shown in the following example:
 
-:::code language="json" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/eventhub/KafkaOutputMany.cs" range="11-30" :::
+:::code language="csharp" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/eventhub/KafkaOutputMany.cs" range="11-30" :::
 
 The string array is defined as `Kevents` property on the class, on which the output binding is defined:  
 
-:::code language="json" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/eventhub/KafkaOutputMany.cs" range="33-45" :::
+:::code language="csharp" source="~/azure-functions-kafka-extension/samples/dotnet-isolated/eventhub/KafkaOutputMany.cs" range="33-45" :::
 
 The following function adds headers to the Kafka output data:
 

articles/azure-functions/functions-bindings-openai-textcompletion-input.md

@@ -68,11 +68,11 @@ The code simply returns the text from the completion API as the response:
 ::: zone pivot="programming-language-python"  
 This example demonstrates the _templating_ pattern, where the HTTP trigger function takes a `name` parameter and embeds it into a text prompt, which is then sent to the Azure OpenAI completions API by the extension. The response to the prompt is returned in the HTTP response.  
 
-:::code language="java" source="~/functions-openai-extension/samples/textcompletion/python/function_app.py" range="7-11" :::
+:::code language="python" source="~/functions-openai-extension/samples/textcompletion/python/function_app.py" range="7-11" :::
 
 This example takes a prompt as input, sends it directly to the completions API, and returns the response as the output.
 
-:::code language="csharp" source="~/functions-openai-extension/samples/textcompletion/python/function_app.py" range="14-18" ::: 
+:::code language="python" source="~/functions-openai-extension/samples/textcompletion/python/function_app.py" range="14-18" ::: 
 
 ::: zone-end  
 <!--- End code examples section -->  

articles/reliability/reliability-fabric.md

@@ -41,7 +41,7 @@ Fabric makes commercially reasonable efforts to provide availability zone suppor
 |:---------------------|:---------------------------------------------------------------------:|:---------------------------------------------------------------------:|:---------------------------------------------------------------------:|:---------------------------------------------------------------------:|:---------------------------------------------------------------------:|
 | Brazil South         | :::image type="icon" source="media/yes-icon.svg" border="false"::: |                                                                       | :::image type="icon" source="media/yes-icon.svg" border="false"::: | :::image type="icon" source="media/yes-icon.svg" border="false"::: |:::image type="icon" source="media/yes-icon.svg" border="false"::: | 
 | Canada Central       | :::image type="icon" source="media/yes-icon.svg" border="false"::: |                                                                       |                                                                       | :::image type="icon" source="media/yes-icon.svg" border="false"::: |:::image type="icon" source="media/yes-icon.svg" border="false"::: | 
-| Central US           | :::image type="icon" source="media/yes-icon.svg" border="false"::: |                                                                       |                                                                       |                                                                       |:::image type="icon" source="media/yes-icon.svg" border="false"::: | 
+| Central US           | :::image type="icon" source="media/yes-icon.svg" border="false"::: |                                                                       |                                                                       |                                                                       |                                                                | 
 | East US              | :::image type="icon" source="media/yes-icon.svg" border="false"::: | :::image type="icon" source="media/yes-icon.svg" border="false"::: | :::image type="icon" source="media/yes-icon.svg" border="false"::: | :::image type="icon" source="media/yes-icon.svg" border="false"::: |:::image type="icon" source="media/yes-icon.svg" border="false"::: | 
 | East US 2            | :::image type="icon" source="media/yes-icon.svg" border="false"::: | :::image type="icon" source="media/yes-icon.svg" border="false"::: |:::image type="icon" source="media/yes-icon.svg" border="false":::| :::image type="icon" source="media/yes-icon.svg" border="false"::: |:::image type="icon" source="media/yes-icon.svg" border="false"::: | 
 | South Central US     | :::image type="icon" source="media/yes-icon.svg" border="false"::: |                                                                       |                                                                       |                                                                       |:::image type="icon" source="media/yes-icon.svg" border="false"::: | 

articles/search/search-get-started-portal-image-search.md

@@ -1,7 +1,7 @@
 ---
 title: Quickstart image search
 titleSuffix: Azure AI Search
-description: Index and query images on Azure AI Search using the Azure portal. Run the Import and vectorize data wizard to vectorize images. Use Search Explorer to query images.
+description: Search for images on Azure AI Search index using the Azure portal. Run the Import and vectorize data wizard to vectorize images, then use Search Explorer to provide an image as your query input.
 
 author: HeidiSteen
 ms.author: heidist
@@ -100,13 +100,14 @@ Azure AI Vision provides text embeddings, so we'll use that resource for text ve
 
 Use Azure AI Vision to generate a vector representation of the image files. 
 
-In this step, you can also apply AI to extract text from images. The wizard uses OCR from Azure AI services to recognize text in image files. Two more outputs appear in the index when OCR is added to the workflow:
+In this step, you can also apply AI to extract text from images. The wizard uses OCR from Azure AI services to recognize text in image files. 
 
-+ First, the "chunk" field is populated with the OCR-generated string. 
+Two more outputs appear in the index when OCR is added to the workflow:
 
-+ Second, the "text_vector" field is populated with an embedding that represents the string. 
++ First, the "chunk" field is populated with an OCR-generated string of any text found in the image. 
++ Second, the "text_vector" field is populated with an embedding that represents the "chunk" string. 
 
-The inclusion of plain text in an index is useful if you want to use relevance features that operate on strings, such as semantic ranking and scoring profiles.
+The inclusion of plain text in the "chunk" field is useful if you want to use relevance features that operate on strings, such as [semantic ranking](semantic-search-overview.md) and [scoring profiles](index-add-scoring-profiles.md).
 
 1. On the **Vectorize your images** page, select the **Vectorize images** checkbox, and then select **AI Vision vectorization**.
 
@@ -136,27 +137,23 @@ The inclusion of plain text in an index is useful if you want to use relevance f
 
 1. Select **Create** to run the wizard. This step creates the following objects:
 
-   + Data source connection to blob storage.
+   + An indexer that drives the indexing pipeline.
 
-   + Index with vector fields, text fields, vectorizers, vector profiles, vector algorithms. You can't modify the default index during the wizard workflow. Indexes conform to the [2024-05-01-preview REST API](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true).
+   + A data source connection to blob storage.
 
-   + Skillset with the following five skills:
+   + An index with vector fields, text fields, vectorizers, vector profiles, vector algorithms. You can't modify the default index during the wizard workflow. Indexes conform to the [2024-05-01-preview REST API](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true).
 
-     + [OCR skill](cognitive-search-skill-ocr.md) recognizes text in image files.
+   + A skillset with the following five skills:
 
+     + [OCR skill](cognitive-search-skill-ocr.md) recognizes text in image files.
      + [Text Merger skill](cognitive-search-skill-textmerger.md) unifies the various outputs of OCR processing.
-
      + [Text Split skill](cognitive-search-skill-textsplit.md) adds data chunking. This skill is built into the wizard workflow.
-
      + [Azure AI Vision multimodal](cognitive-search-skill-vision-vectorize.md) is used to vectorize text generated from OCR.
-
      + [Azure AI Vision multimodal](cognitive-search-skill-vision-vectorize.md) is called again to vectorize images.
 
-   + Indexer with field mappings and output field mappings.
-
 ## Check results
 
-Search explorer accepts text, vectors, and images as query inputs. You can drag or select an image into the search area, and it will be vectorized for search. Image vectorization assumes that your index has a vectorizer definition, which **Import and vectorize data** creates from your inputs.
+Search Explorer accepts text, vectors, and images as query inputs. You can drag or select an image into the search area. Search Explorer vectorizes your image and sends the vector as a query input to the search engine. Image vectorization assumes that your index has a vectorizer definition, which **Import and vectorize data** creates based on your embedding model inputs.
 
 1. In the Azure portal, under **Search Management** and **Indexes**, select the index your created. An embedded Search Explorer is the first tab.
 
@@ -170,11 +167,11 @@ Search explorer accepts text, vectors, and images as query inputs. You can drag
 
    :::image type="content" source="media/search-get-started-portal-images/image-search.png" alt-text="Screenshot of search results.":::
 
-   The top match should be the image you searched for. Because a vector search matches on similar vectors, the search engine returns any document that is sufficiently similar to the query input. You can switch to JSON view for more advanced queries that include relevance tuning.
+   The top match should be the image you searched for. Because a [vector search](vector-search-overview.md) matches on similar vectors, the search engine returns any document that is sufficiently similar to the query input, up to *k*-number of results. You can switch to JSON view for more advanced queries that include relevance tuning.
 
 1. Try other query options to compare search outcomes:
 
-   + Hide vectors for more readable results.
+   + Hide vectors for more readable results (recommended).
    + Select a vector field to query over. The default is text vectors, but you can specify the image vector to exclude text vectors from query execution.
 
 ## Clean up

articles/search/whats-new.md

@@ -23,7 +23,7 @@ ms.custom:
 
 | Item                         | Type |  Description |
 |-----------------------------|------|--------------|
-| [Image search in the Azure portal](search-get-started-portal-image-search.md) | Feature | Search explorer now supports image search. In a vector index that has vectorized image content, you can drop images into Search Explorer to query for matching image content.
+| [Image search in the Azure portal](search-get-started-portal-image-search.md) | Feature | Search explorer now supports image search. In a vector index that has vectorized image content, you can drop images into Search Explorer to query for a match.
 
 ## May 2024
 

articles/storage/.openpublishing.redirection.storage.json

@@ -1,10 +1,5 @@
 {
     "redirections": [
-        {
-            "source_path_from_root": "/articles/storage/common/storage-failover-customer-managed-planned.md",
-            "redirect_url": "/articles/storage/common/storage-failover-customer-managed-unplanned",
-            "redirect_document_id": false
-        },
         {
             "source_path_from_root": "/articles/storage/common/azure-defender-storage-configure.md",
             "redirect_url": "/azure/defender-for-cloud/defender-for-storage-introduction",

articles/storage/blobs/TOC.yml

@@ -450,7 +450,9 @@ items:
       items:
       - name: Disaster recovery planning and failover
         href: ../common/storage-disaster-recovery-guidance.md?toc=/azure/storage/blobs/toc.json&bc=/azure/storage/blobs/breadcrumb/toc.json
-      - name: How customer-managed failover works
+      - name: How customer-managed planned failover (preview) works
+        href: ../common/storage-failover-customer-managed-planned.md?toc=/azure/storage/blobs/toc.json&bc=/azure/storage/blobs/breadcrumb/toc.json
+      - name: How customer-managed (unplanned) failover works
         href: ../common/storage-failover-customer-managed-unplanned.md?toc=/azure/storage/blobs/toc.json&bc=/azure/storage/blobs/breadcrumb/toc.json
       - name: Initiate account failover
         href: ../common/storage-initiate-account-failover.md?toc=/azure/storage/blobs/toc.json&bc=/azure/storage/blobs/breadcrumb/toc.json