fixing conflict

Author: aahill

.openpublishing.publish.config.json

@@ -1330,6 +1330,7 @@
   "articles/spatial-anchors/.openpublishing.redirection.spatial-anchors.json",
   "articles/spring-apps/.openpublishing.redirection.spring-apps.json",
   "articles/static-web-apps/.openpublishing.redirection.static-web-apps.json",
+  "articles/storage/.openpublishing.redirection.storage.json",
   "articles/storsimple/.openpublishing.redirection.storsimple.json",
   "articles/stream-analytics/.openpublishing.redirection.stream-analytics.json",
   "articles/synapse-analytics/.openpublishing.redirection.synapse-analytics.json",

articles/advisor/advisor-alerts-bicep.md

@@ -1,9 +1,7 @@
 ---
 title: Create Azure Advisor alerts for new recommendations using Bicep
 description: Learn how to set up an alert for new recommendations from Azure Advisor using Bicep.
-author: orspod
 ms.topic: quickstart
-ms.author: orspodek
 ms.custom: subject-armqs, mode-arm, devx-track-bicep
 ms.date: 04/26/2022
 ---

articles/advisor/advisor-azure-resource-graph.md

@@ -1,11 +1,8 @@
 ---
 title: Advisor data in Azure Resource Graph
 description: Make queries for Advisor data in Azure Resource Graph
-author: orspod
 ms.topic: article
 ms.date: 03/12/2020
-ms.author: orspodek
-
 ---
 
 # Query for Advisor data in Resource Graph Explorer (Azure Resource Graph)

articles/advisor/advisor-quick-fix.md

@@ -1,11 +1,8 @@
 ---
 title: Quick Fix remediation for Advisor recommendations
 description: Perform bulk remediation using Quick Fix in Advisor
-author: orspod
 ms.topic: article
 ms.date: 03/13/2020
-ms.author: orspodek
-
 ---
 
 # Quick Fix remediation for Advisor

articles/advisor/advisor-recommendations-digest.md

@@ -1,12 +1,8 @@
 ---
-
 title: Recommendation digest for Azure Advisor
 description: Get periodic summary for your active recommendations
-author: orspod
 ms.topic: article
 ms.date: 03/16/2020
-ms.author: orspodek
-
 ---
 
 # Configure periodic summary for recommendations

articles/ai-services/cognitive-services-virtual-networks.md

@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: azure-ai-services
 ms.custom: devx-track-azurepowershell, devx-track-azurecli
 ms.topic: how-to
-ms.date: 02/13/2024
+ms.date: 03/25/2024
 ms.author: aahi
 ---
 
@@ -595,11 +595,21 @@ curl -i -X PATCH https://management.azure.com$rid?api-version=2023-10-01-preview
 '
 ```
 
-To revoke the exception, set `networkAcls.bypass` to `None`. 
-
 > [!NOTE]
 > The trusted service feature is only available using the command line described above, and cannot be done using the Azure portal.
 
+To revoke the exception, set `networkAcls.bypass` to `None`. 
+
+To verify if the trusted service has been enabled from the Azure portal, 
+
+1. Use the **JSON View** from the Azure OpenAI resource overview page
+
+    :::image type="content" source="media/vnet/azure-portal-json-view.png" alt-text="A screenshot showing the JSON view option for resources in the Azure portal." lightbox="media/vnet/azure-portal-json-view.png":::
+
+1.  Choose your latest API version under **API versions**. Only the latest API version is supported, `2023-10-01-preview` .
+
+    :::image type="content" source="media/vnet/virtual-network-trusted-service.png" alt-text="A screenshot showing the trusted service is enabled." lightbox="media/vnet/virtual-network-trusted-service.png":::
+
 ### Pricing
 
 For pricing details, see [Azure Private Link pricing](https://azure.microsoft.com/pricing/details/private-link).

articles/ai-services/computer-vision/how-to/background-removal.md

@@ -70,7 +70,7 @@ Where we used this helper function to read the value of an environment variable:
 #### [REST API](#tab/rest)
 -->
 
-Authentication is done by adding the HTTP request header **Ocp-Apim-Subscription-Key** and setting it to your vision key. The call is made to the URL `https://<endpoint>/computervision/imageanalysis:segment?api-version=2023-02-01-preview`, where `<endpoint>` is your unique Computer Vision endpoint URL. See [Select a mode ](./background-removal.md#select-a-mode) section for another query string you add to this URL.
+Authentication is done by adding the HTTP request header **Ocp-Apim-Subscription-Key** and setting it to your vision key. The call is made to the URL `<endpoint>/computervision/imageanalysis:segment?api-version=2023-02-01-preview`, where `<endpoint>` is your unique Computer Vision endpoint URL. See [Select a mode ](./background-removal.md#select-a-mode) section for another query string you add to this URL.
 
 
 ## Select the image to analyze
@@ -164,7 +164,7 @@ Set the query string *mode* to one of these two values. This query string is man
 | `mode`       | `backgroundRemoval` | Outputs an image of the detected foreground object with a transparent background. |
 | `mode`       | `foregroundMatting` | Outputs a gray-scale alpha matte image showing the opacity of the detected foreground object. |
 
-A populated URL for backgroundRemoval would look like this: `https://<endpoint>/computervision/imageanalysis:segment?api-version=2023-02-01-preview&mode=backgroundRemoval`
+A populated URL for backgroundRemoval would look like this: `<endpoint>/computervision/imageanalysis:segment?api-version=2023-02-01-preview&mode=backgroundRemoval`
 
 
 ## Get results from the service

articles/ai-services/computer-vision/how-to/call-analyze-image.md

@@ -86,7 +86,7 @@ You can specify which features you want to use by setting the URL query paramete
 
 A populated URL might look like this:
 
-`https://<endpoint>/vision/v3.2/analyze?visualFeatures=Tags`
+`<endpoint>/vision/v3.2/analyze?visualFeatures=Tags`
 
 #### [C#](#tab/csharp)
 
@@ -135,7 +135,7 @@ The following URL query parameter specifies the language. The default value is `
 
 A populated URL might look like this:
 
-`https://<endpoint>/vision/v3.2/analyze?visualFeatures=Tags&language=en`
+`<endpoint>/vision/v3.2/analyze?visualFeatures=Tags&language=en`
 
 #### [C#](#tab/csharp)
 
@@ -183,7 +183,7 @@ This section shows you how to parse the results of the API call. It includes the
 > [!NOTE]
 > **Scoped API calls**
 >
-> Some of the features in Image Analysis can be called directly as well as through the Analyze API call. For example, you can do a scoped analysis of only image tags by making a request to `https://<endpoint>/vision/v3.2/tag` (or to the corresponding method in the SDK). See the [reference documentation](https://westus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f21b) for other features that can be called separately.
+> Some of the features in Image Analysis can be called directly as well as through the Analyze API call. For example, you can do a scoped analysis of only image tags by making a request to `<endpoint>/vision/v3.2/tag` (or to the corresponding method in the SDK). See the [reference documentation](https://westus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f21b) for other features that can be called separately.
 
 #### [REST](#tab/rest)
 

articles/ai-services/computer-vision/how-to/image-retrieval.md

@@ -17,6 +17,8 @@ ms.custom: references_regions
 
 The Multimodal embeddings APIs enable the _vectorization_ of images and text queries. They convert images to coordinates in a multi-dimensional vector space. Then, incoming text queries can also be converted to vectors, and images can be matched to the text based on semantic closeness. This allows the user to search a set of images using text, without the need to use image tags or other metadata. Semantic closeness often produces better results in search.
 
+The `2024-02-01` API includes a multi-lingual model that supports text search in 102 languages. The original English-only model is still available, but it cannot be combined with the new model in the same search index. If you vectorized text and images using the English-only model, these vectors won’t be compatible with multi-lingual text and image vectors.
+
 > [!IMPORTANT]
 > These APIs are only available in the following geographic regions: East US, France Central, Korea Central, North Europe, Southeast Asia, West Europe, West US.
 
@@ -46,7 +48,7 @@ The `retrieval:vectorizeImage` API lets you convert an image's data to a vector.
 1. Optionally, change the `model-version` parameter to an older version. `2022-04-11` is the legacy model that supports only English text. Images and text that are vectorized with a certain model aren't compatible with other models, so be sure to use the same model for both. 
 
 ```bash
-curl.exe -v -X POST "https://<endpoint>/computervision/retrieval:vectorizeImage?api-version=2023-02-01-preview&model-version=2023-04-15" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: <subscription-key>" --data-ascii "
+curl.exe -v -X POST "<endpoint>/computervision/retrieval:vectorizeImage?api-version=2024-02-01&model-version=2023-04-15" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: <subscription-key>" --data-ascii "
 {
 'url':'https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png'
 }"
@@ -73,7 +75,7 @@ The `retrieval:vectorizeText` API lets you convert a text string to a vector. To
 1. Optionally, change the `model-version` parameter to an older version. `2022-04-11` is the legacy model that supports only English text. Images and text that are vectorized with a certain model aren't compatible with other models, so be sure to use the same model for both. 
 
 ```bash
-curl.exe -v -X POST "https://<endpoint>/computervision/retrieval:vectorizeText?api-version=2023-02-01-preview&model-version=2023-04-15" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: <subscription-key>" --data-ascii "
+curl.exe -v -X POST "<endpoint>/computervision/retrieval:vectorizeText?api-version=2024-02-01&model-version=2023-04-15" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: <subscription-key>" --data-ascii "
 {
 'text':'cat jumping'
 }"

articles/ai-services/computer-vision/how-to/model-customization.md

@@ -235,7 +235,6 @@ prediction = prediction_client.predict(model_name, img, content_type='image/png'
 logging.info(f'Prediction: {prediction}')
 ```
 
-<!-- nbend -->
 -->
 
 #### [Vision Studio](#tab/studio)
@@ -369,7 +368,7 @@ The `datasets/<dataset-name>` API lets you create a new dataset object that refe
 1. In the request body, set the `"annotationFileUris"` array to an array of string(s) that show the URI location(s) of your COCO file(s) in blob storage.
 
 ```bash
-curl.exe -v -X PUT "https://<endpoint>/computervision/datasets/<dataset-name>?api-version=2023-02-01-preview" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: <subscription-key>" --data-ascii "
+curl.exe -v -X PUT "<endpoint>/computervision/datasets/<dataset-name>?api-version=2023-02-01-preview" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: <subscription-key>" --data-ascii "
 {
 'annotationKind':'imageClassification',
 'annotationFileUris':['<URI>']
@@ -387,7 +386,7 @@ The `models/<model-name>` API lets you create a new custom model and associate i
 1. In the request body, set `"modelKind"` to either `"Generic-Classifier"` or `"Generic-Detector"`, depending on your project.
 
 ```bash
-curl.exe -v -X PUT "https://<endpoint>/computervision/models/<model-name>?api-version=2023-02-01-preview" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: <subscription-key>" --data-ascii "
+curl.exe -v -X PUT "<endpoint>/computervision/models/<model-name>?api-version=2023-02-01-preview" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: <subscription-key>" --data-ascii "
 {
 'trainingParameters': {
     'trainingDatasetName':'<dataset-name>',
@@ -408,7 +407,7 @@ The `models/<model-name>/evaluations/<eval-name>` API evaluates the performance
 1. In the request body, set `"testDatasetName"` to the name of the dataset you want to use for evaluation. If you don't have a dedicated dataset, you can use the same dataset you used for training.
 
 ```bash
-curl.exe -v -X PUT "https://<endpoint>/computervision/models/<model-name>/evaluations/<eval-name>?api-version=2023-02-01-preview" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: <subscription-key>" --data-ascii "
+curl.exe -v -X PUT "<endpoint>/computervision/models/<model-name>/evaluations/<eval-name>?api-version=2023-02-01-preview" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: <subscription-key>" --data-ascii "
 {
 'evaluationParameters':{
     'testDatasetName':'<dataset-name>'
@@ -431,7 +430,7 @@ The `imageanalysis:analyze` API does ordinary Image Analysis operations. By spec
 1. In the request body, set `"url"` to the URL of a remote image you want to test your model on.
 
 ```bash
-curl.exe -v -X POST "https://<endpoint>/computervision/imageanalysis:analyze?model-name=<model-name>&api-version=2023-02-01-preview" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: <subscription-key>" --data-ascii "
+curl.exe -v -X POST "<endpoint>/computervision/imageanalysis:analyze?model-name=<model-name>&api-version=2023-02-01-preview" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: <subscription-key>" --data-ascii "
 {'url':'https://learn.microsoft.com/azure/ai-services/computer-vision/media/quickstarts/presentation.png'
 }"
 ```