Merge pull request #6402 from PatrickFarley/minor-updates

Stacyrch140 · web-flow · commit 5fb00a5840af · 2025-08-08T10:48:03.000-04:00
Minor updates
diff --git a/articles/ai-foundry/openai/how-to/dall-e.md b/articles/ai-foundry/openai/how-to/dall-e.md
@@ -108,7 +108,8 @@ The following is a sample request body. You specify a number of options, defined
 
 #### [GPT-image-1](#tab/gpt-image-1)
 
-The response from a successful image generation API call looks like the following example. The `b64_json` field contains the output image data. 
+
+The response from a successful image generation API call looks like the following example. The `b64_json` field contains the output image data.
 
 ```json
 { 
@@ -140,6 +141,7 @@ The response from a successful image generation API call looks like the followin
 ```
 ---
 
+
 ### API call rejection
 
 Prompts and images are filtered based on our content policy, returning an error when a prompt or image is flagged.
@@ -316,8 +318,7 @@ The response from a successful image editing API call looks like the following e
     "created": 1698116662, 
     "data": [ 
         { 
-            "b64_json": "<base64 image data>",
-            "revised_prompt": "<prompt_that_was_used>" 
+            "b64_json": "<base64 image data>"
         }
     ]
 } 
diff --git a/articles/ai-services/computer-vision/computer-vision-how-to-install-containers.md b/articles/ai-services/computer-vision/computer-vision-how-to-install-containers.md
@@ -345,6 +345,6 @@ In this article, you learned concepts and workflow for downloading, installing,
 
 * Review [Configure containers](computer-vision-resource-container-config.md) for configuration settings
 * Review the [OCR overview](overview-ocr.md) to learn more about recognizing printed and handwritten text
-* Refer to the [Read API](/rest/api/computervision/operation-groups?view=rest-computervision-v3.2-preview) for details about the methods supported by the container.
+* Refer to the [Read API](/rest/api/computervision/operation-groups) for details about the methods supported by the container.
 * Refer to [Frequently asked questions (FAQ)](FAQ.yml) to resolve issues related to Azure AI Vision functionality.
 * Use more [Azure AI containers](../cognitive-services-container-support.md)
diff --git a/articles/ai-services/computer-vision/concept-background-removal.md b/articles/ai-services/computer-vision/concept-background-removal.md
@@ -54,7 +54,7 @@ It's important to note the limitations of background removal:
 
 ## Use the API
 
-The background removal feature is available through the [Segment](/rest/api/computervision/image-analysis/segment?view=rest-computervision-2023-02-01-preview&tabs=HTTP) API (`imageanalysis:segment`). See the [Background removal how-to guide](./how-to/background-removal.md) for more information.
+The background removal feature is available through the [Segment](/rest/api/computervision/image-analysis/segment?tabs=HTTP) API (`imageanalysis:segment`). See the [Background removal how-to guide](./how-to/background-removal.md) for more information.
 
 ## Next step
 
diff --git a/articles/ai-services/computer-vision/concept-categorizing-images.md b/articles/ai-services/computer-vision/concept-categorizing-images.md
@@ -57,7 +57,7 @@ The following table illustrates a typical image set and the category returned by
 
 ## Use the API
 
-The categorization feature is part of the [Analyze Image 3.2](/rest/api/computervision/analyze-image?view=rest-computervision-v3.2) API. You can call this API through a native SDK or through REST calls. Include `Categories` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"categories"` section.
+The categorization feature is part of the [Analyze Image 3.2](/rest/api/computervision/analyze-image?view=rest-computervision-v3.2&preserve-view=true) API. You can call this API through a native SDK or through REST calls. Include `Categories` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"categories"` section.
 
 * [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
 
diff --git a/articles/ai-services/computer-vision/concept-describing-images.md b/articles/ai-services/computer-vision/concept-describing-images.md
@@ -57,7 +57,7 @@ The following JSON response illustrates what the Analyze Image API returns when
 
 ## Use the API
 
-The image description feature is part of the [Analyze Image](/rest/api/computervision/analyze-image?view=rest-computervision-v3.2) API. You can call this API through a native SDK or through REST calls. Include `Description` in the **visualFeatures** query parameter. Then, when you get the full JSON response, parse the string for the contents of the `"description"` section.
+The image description feature is part of the [Analyze Image](/rest/api/computervision/analyze-image) API. You can call this API through a native SDK or through REST calls. Include `Description` in the **visualFeatures** query parameter. Then, when you get the full JSON response, parse the string for the contents of the `"description"` section.
 
 * [Quickstart: Image Analysis REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
 
diff --git a/articles/ai-services/computer-vision/concept-detecting-adult-content.md b/articles/ai-services/computer-vision/concept-detecting-adult-content.md
@@ -35,7 +35,7 @@ The "adult" classification contains several different categories:
 
 ## Use the API
 
-You can detect adult content with the [Analyze Image 3.2](/rest/api/computervision/analyze-image?view=rest-computervision-v3.2) API. When you add the value of `Adult` to the **visualFeatures** query parameter, the API returns three boolean properties&mdash;`isAdultContent`, `isRacyContent`, and `isGoryContent`&mdash;in its JSON response. The method also returns corresponding properties&mdash;`adultScore`, `racyScore`, and `goreScore`&mdash;which represent confidence scores between zero and one for each respective category.
+You can detect adult content with the [Analyze Image 3.2](/rest/api/computervision/analyze-image?view=rest-computervision-v3.2&preserve-view=true) API. When you add the value of `Adult` to the **visualFeatures** query parameter, the API returns three boolean properties&mdash;`isAdultContent`, `isRacyContent`, and `isGoryContent`&mdash;in its JSON response. The method also returns corresponding properties&mdash;`adultScore`, `racyScore`, and `goreScore`&mdash;which represent confidence scores between zero and one for each respective category.
 
 ## Next step
 
diff --git a/articles/ai-services/computer-vision/concept-detecting-color-schemes.md b/articles/ai-services/computer-vision/concept-detecting-color-schemes.md
@@ -85,7 +85,7 @@ The following table shows Azure AI Vision's black and white evaluation in the sa
 
 ## Use the API
 
-The color scheme detection feature is part of the [Analyze Image 3.2](/rest/api/computervision/analyze-image?view=rest-computervision-v3.2) API. You can call this API through a native SDK or through REST calls. Include `Color` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"color"` section.
+The color scheme detection feature is part of the [Analyze Image 3.2](/rest/api/computervision/analyze-image?view=rest-computervision-v3.2&preserve-view=true) API. You can call this API through a native SDK or through REST calls. Include `Color` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"color"` section.
 
 ## Next step
 
diff --git a/articles/ai-services/computer-vision/concept-detecting-domain-content.md b/articles/ai-services/computer-vision/concept-detecting-domain-content.md
@@ -21,7 +21,7 @@ There are two ways to use the domain-specific models: by themselves (scoped anal
 
 ### Scoped analysis
 
-You can analyze an image using only the chosen domain-specific model by calling the [Models/\<model\>/Analyze](/rest/api/computervision/analyze-image?view=rest-computervision-v3.2) API.
+You can analyze an image using only the chosen domain-specific model by calling the [Models/\<model\>/Analyze](/rest/api/computervision/analyze-image) API.
 
 The following is a sample JSON response returned by the `models/celebrities/analyze` API for the given image:
 
@@ -52,7 +52,7 @@ The following is a sample JSON response returned by the `models/celebrities/anal
 
 ### Enhanced categorization analysis
 
-You can also use domain-specific models to supplement general image analysis. You do this as part of [high-level categorization](concept-categorizing-images.md) by specifying domain-specific models in the *details* parameter of the [Analyze Image](/rest/api/computervision/analyze-image?view=rest-computervision-v3.2) API call.
+You can also use domain-specific models to supplement general image analysis. You do this as part of [high-level categorization](concept-categorizing-images.md) by specifying domain-specific models in the *details* parameter of the [Analyze Image](/rest/api/computervision/analyze-image) API call.
 
 In this case, the 86-category taxonomy classifier is called first. If any of the detected categories have a matching domain-specific model, the image is passed through that model as well and the results are added.
 
@@ -100,7 +100,7 @@ Currently, Azure AI Vision supports the following domain-specific models:
 | celebrities | Celebrity recognition, supported for images classified in the `people_` category |
 | landmarks | Landmark recognition, supported for images classified in the `outdoor_` or `building_` categories |
 
-Calling the [Models](/rest/api/computervision/list-models/list-models?view=rest-computervision-v3.2&tabs=HTTP) API returns this information along with the categories to which each model can apply:
+Calling the [Models](/rest/api/computervision/list-models/list-models) API returns this information along with the categories to which each model can apply:
 
 ```json
 {
@@ -134,6 +134,6 @@ Calling the [Models](/rest/api/computervision/list-models/list-models?view=rest-
 
 ## Use the API
 
-This feature is available through the [Analyze Image 3.2 API](/rest/api/computervision/analyze-image/analyze-image?view=rest-computervision-v3.2&tabs=HTTP). You can call this API through a native SDK or through REST calls. Include `Celebrities` or `Landmarks` in the **details** query parameter. Then, when you get the full JSON response, parse the string for the contents of the `"details"` section.
+This feature is available through the [Analyze Image 3.2 API](/rest/api/computervision/analyze-image/analyze-image). You can call this API through a native SDK or through REST calls. Include `Celebrities` or `Landmarks` in the **details** query parameter. Then, when you get the full JSON response, parse the string for the contents of the `"details"` section.
 
 * [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
diff --git a/articles/ai-services/computer-vision/concept-detecting-faces.md b/articles/ai-services/computer-vision/concept-detecting-faces.md
@@ -109,7 +109,7 @@ The next example demonstrates the JSON response returned for an image containing
 
 ## Use the API
 
-The face detection feature is part of the [Analyze Image 3.2](/rest/api/computervision/analyze-image/analyze-image?view=rest-computervision-v3.2&tabs=HTTP) API. You can call this API through a native SDK or through REST calls. Include `Faces` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"faces"` section.
+The face detection feature is part of the [Analyze Image 3.2](/rest/api/computervision/analyze-image/analyze-image?view=rest-computervision-v3.2&tabs=HTTP&preserve-view=true) API. You can call this API through a native SDK or through REST calls. Include `Faces` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"faces"` section.
 
 ## Next step
 
diff --git a/articles/ai-services/computer-vision/concept-detecting-image-types.md b/articles/ai-services/computer-vision/concept-detecting-image-types.md
@@ -15,7 +15,7 @@ ms.author: pafarley
 
 # Image type detection
 
-With the [Analyze Image 3.2 API](/rest/api/computervision/analyze-image/analyze-image?view=rest-computervision-v3.2&tabs=HTTP), Azure AI Vision can analyze the content type of images and indicate whether an image is clip art or a line drawing.
+With the [Analyze Image 3.2 API](/rest/api/computervision/analyze-image/analyze-image?view=rest-computervision-v3.2&tabs=HTTP&preserve-view=true), Azure AI Vision can analyze the content type of images and indicate whether an image is clip art or a line drawing.
 
 ## Clip art detection
 
@@ -110,7 +110,7 @@ The following JSON responses illustrates what Azure AI Vision returns when indic
 
 ## Use the API
 
-The image type detection feature is part of the [Analyze Image 3.2 API](/rest/api/computervision/analyze-image/analyze-image?view=rest-computervision-v3.2&tabs=HTTP). You can call this API through a native SDK or through REST calls. Include `ImageType` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"imageType"` section.
+The image type detection feature is part of the [Analyze Image 3.2 API](/rest/api/computervision/analyze-image/analyze-image). You can call this API through a native SDK or through REST calls. Include `ImageType` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"imageType"` section.
 
 ## Next step
 
diff --git a/articles/ai-services/computer-vision/concept-generating-thumbnails.md b/articles/ai-services/computer-vision/concept-generating-thumbnails.md
@@ -46,6 +46,6 @@ The following table illustrates thumbnails defined by smart-cropping for the exa
 
 ## Use the API
 
-The generate thumbnail feature is available through the [Get Thumbnail](/rest/api/computervision/generate-thumbnail/generate-thumbnail?view=rest-computervision-v3.2&tabs=HTTP) and [Get Area of Interest](/rest/api/computervision/get-area-of-interest/get-area-of-interest?view=rest-computervision-v3.2&tabs=HTTP) API. You can call this API through a native SDK or through REST calls. 
+The generate thumbnail feature is available through the [Get Thumbnail](/rest/api/computervision/generate-thumbnail/generate-thumbnail) and [Get Area of Interest](/rest/api/computervision/get-area-of-interest/get-area-of-interest) API. You can call this API through a native SDK or through REST calls. 
 
 * [Generate a thumbnail (how-to)](./how-to/generate-thumbnail.md)
diff --git a/articles/ai-services/computer-vision/concept-shelf-analysis.md b/articles/ai-services/computer-vision/concept-shelf-analysis.md
@@ -186,4 +186,4 @@ It returns a JSON response that accounts for each position in the planogram docu
 Get started with Product Recognition by trying out the stitching and rectification APIs. Then do basic analysis with the Product Understanding API.
 * [Prepare images for Product Recognition](./how-to/shelf-modify-images.md)
 * [Analyze a shelf image](./how-to/shelf-analyze.md)
-* [API reference](/rest/api/computervision/operation-groups?view=rest-computervision-2023-04-01-preview)
+* [API reference](/rest/api/computervision/operation-groups)
diff --git a/articles/ai-services/computer-vision/concept-tagging-images.md b/articles/ai-services/computer-vision/concept-tagging-images.md
@@ -138,7 +138,7 @@ The following JSON response illustrates what Azure AI Vision returns when taggin
 
 ## Use the API
 
-The tagging feature is part of the [Analyze Image](/rest/api/computervision/analyze-image/analyze-image?view=rest-computervision-v3.2&tabs=HTTP) API. You can call this API through a native SDK or through REST calls. Include `Tags` in the **visualFeatures** query parameter. Then, when you get the full JSON response, parse the string for the contents of the `"tags"` section.
+The tagging feature is part of the [Analyze Image](/rest/api/computervision/analyze-image/analyze-image?view=rest-computervision-v3.2&tabs=HTTP&preserve-view=true) API. You can call this API through a native SDK or through REST calls. Include `Tags` in the **visualFeatures** query parameter. Then, when you get the full JSON response, parse the string for the contents of the `"tags"` section.
 
 
 * [Quickstart: Image Analysis REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
diff --git a/articles/ai-services/computer-vision/how-to/call-analyze-image.md b/articles/ai-services/computer-vision/how-to/call-analyze-image.md
@@ -106,7 +106,7 @@ Specify which visual features you'd like to extract in your analysis. See the [V
 
 #### [Python](#tab/python)
 
-Specify which visual features you'd like to extract in your analysis. See the [VisualFeatureTypes](/python/api/azure-cognitiveservices-vision-computervision/azure.cognitiveservices.vision.computervision.models.visualfeaturetypes?view=azure-python&preserve-view=true) enum for a complete list.
+Specify which visual features you'd like to extract in your analysis. See the [VisualFeatureTypes](/python/api/azure-cognitiveservices-vision-computervision/azure.cognitiveservices.vision.computervision.models.visualfeaturetypes) enum for a complete list.
 
 [!code-python[](~/cognitive-services-quickstart-code/python/ComputerVision/ImageAnalysisQuickstart.py?name=snippet_features_remote)]
 
@@ -218,7 +218,7 @@ 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 either directly or 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](/rest/api/computervision/operation-groups?view=rest-computervision-v3.2&preserve-view=true) for other features that can be called separately.
+> Some of the features in Image Analysis can be called either directly or 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](/rest/api/computervision/operation-groups) for other features that can be called separately.
 
 #### [REST](#tab/rest)
 
diff --git a/articles/ai-services/computer-vision/how-to/shelf-analyze.md b/articles/ai-services/computer-vision/how-to/shelf-analyze.md
@@ -147,4 +147,4 @@ In this guide, you learned how to make a basic analysis call using the pretraine
 > [Train a custom model for Product Recognition](../how-to/shelf-model-customization.md)
 
 * [Image Analysis overview](../overview-image-analysis.md)
-* [API reference](/rest/api/computervision/operation-groups?view=rest-computervision-2023-04-01-preview)
+* [API reference](/rest/api/computervision/operation-groups)
diff --git a/articles/ai-services/computer-vision/how-to/shelf-model-customization.md b/articles/ai-services/computer-vision/how-to/shelf-model-customization.md
@@ -72,4 +72,4 @@ In this guide, you learned how to use a custom Product Recognition model to bett
 > [Planogram matching](shelf-planogram.md)
 
 * [Image Analysis overview](../overview-image-analysis.md)
-* [API reference](/rest/api/computervision/operation-groups?view=rest-computervision-2023-04-01-preview)
+* [API reference](/rest/api/computervision/operation-groups)
diff --git a/articles/ai-services/computer-vision/how-to/shelf-planogram.md b/articles/ai-services/computer-vision/how-to/shelf-planogram.md
@@ -304,4 +304,4 @@ Paired planogram position ID and corresponding detected object from product unde
 ## Related content
 
 * [Image Analysis overview](../overview-image-analysis.md)
-* [API reference](/rest/api/computervision/operation-groups?view=rest-computervision-2023-04-01-preview)
+* [API reference](/rest/api/computervision/operation-groups)
diff --git a/articles/ai-services/computer-vision/identity-api-reference.md b/articles/ai-services/computer-vision/identity-api-reference.md
@@ -18,7 +18,7 @@ feedback_help_link_url: https://learn.microsoft.com/answers/tags/156/azure-face
 Azure AI Face is a cloud-based service that provides algorithms for face detection and recognition. The Face APIs comprise the following categories:
 
 - Face Algorithm APIs: Cover core functions such as [Detection](/rest/api/face/face-detection-operations/detect), [Find Similar](/rest/api/face/face-recognition-operations/find-similar-from-large-face-list), [Verification](/rest/api/face/face-recognition-operations/verify-face-to-face), [Identification](/rest/api/face/face-recognition-operations/identify-from-large-person-group), and [Group](/rest/api/face/face-recognition-operations/group).
-- [DetectLiveness session APIs](/rest/api/face/liveness-session-operations?view=rest-face-v1.2): Used to create and manage a Liveness Detection session. See the [Liveness Detection](/azure/ai-services/computer-vision/tutorials/liveness) tutorial.
+- [DetectLiveness session APIs](/rest/api/face/liveness-session-operations): Used to create and manage a Liveness Detection session. See the [Liveness Detection](/azure/ai-services/computer-vision/tutorials/liveness) tutorial.
 - [FaceList APIs](/rest/api/face/face-list-operations): Used to manage a FaceList for [Find Similar From Face List](/rest/api/face/face-recognition-operations/find-similar-from-face-list).
 - [LargeFaceList APIs](/rest/api/face/face-list-operations): Used to manage a LargeFaceList for [Find Similar From Large Face List](/rest/api/face/face-recognition-operations/find-similar-from-large-face-list).
 - [PersonGroup APIs](/rest/api/face/person-group-operations): Used to manage a PersonGroup dataset for [Identification From Person Group](/rest/api/face/face-recognition-operations/identify-from-person-group).
diff --git a/articles/ai-services/computer-vision/language-support.md b/articles/ai-services/computer-vision/language-support.md
diff --git a/articles/ai-services/computer-vision/overview-ocr.md b/articles/ai-services/computer-vision/overview-ocr.md
diff --git a/articles/ai-services/computer-vision/read-container-migration-guide.md b/articles/ai-services/computer-vision/read-container-migration-guide.md
diff --git a/articles/ai-services/computer-vision/sdk/overview-sdk.md b/articles/ai-services/computer-vision/sdk/overview-sdk.md
diff --git a/articles/ai-services/computer-vision/whats-new.md b/articles/ai-services/computer-vision/whats-new.md

Original file line number	Diff line number	Diff line change
`@@ -108,7 +108,8 @@ The following is a sample request body. You specify a number of options, defined`
`108`	`108`
`109`	`109`	`#### [GPT-image-1](#tab/gpt-image-1)`
`110`	`110`
`111`		-The response from a successful image generation API call looks like the following example. The `b64_json` field contains the output image data.
	`111`	`+`
	`112`	+The response from a successful image generation API call looks like the following example. The `b64_json` field contains the output image data.
`112`	`113`
`113`	`114`	```json
`114`	`115`	`{`
`@@ -140,6 +141,7 @@ The response from a successful image generation API call looks like the followin`
`140`	`141`	```
`141`	`142`	`---`
`142`	`143`
	`144`	`+`
`143`	`145`	`### API call rejection`
`144`	`146`
`145`	`147`	`Prompts and images are filtered based on our content policy, returning an error when a prompt or image is flagged.`
`@@ -316,8 +318,7 @@ The response from a successful image editing API call looks like the following e`
`316`	`318`	`"created": 1698116662,`
`317`	`319`	`"data": [`
`318`	`320`	`{`
`319`		`- "b64_json": "<base64 image data>",`
`320`		`- "revised_prompt": "<prompt_that_was_used>"`
	`321`	`+ "b64_json": "<base64 image data>"`
`321`	`322`	`}`
`322`	`323`	`]`
`323`	`324`	`}`