Merge pull request #274751 from MicrosoftDocs/main

5/8/2024 PM Publish

Author: Taojunshen

articles/active-directory-b2c/partner-saviynt.md

@@ -18,7 +18,7 @@ ms.subservice: B2C
 
 Learn to integrate Azure Active Directory B2C (Azure AD B2C) with the Saviynt Security Manager platform, which has visibility, security, and governance. Saviynt incorporates application risk and governance, infrastructure management, privileged account management, and customer risk analysis.
 
-Learn more: [Saviynt for Azure AD B2C](https://saviynt.com/integrations/old-version-azure-ad/for-b2c/)
+Learn more: [Saviynt for Azure AD B2C](https://saviynt.com/fr/integrations/entra-id/for-b2c)
 
 Use the following instructions to set up access control delegated administration for Azure AD B2C users. Saviynt determines if a user is authorized to manage Azure AD B2C users with:
 
@@ -46,7 +46,7 @@ The Saviynt integration includes the following components:
 * **Azure AD B2C** – identity as a service for custom control of customer sign-up, sign-in, and profile management
   * See, [Azure AD B2C, Get started](https://azure.microsoft.com/services/active-directory/external-identities/b2c/) 
 * **Saviynt for Azure AD B2C** – identity governance for delegated administration of user life-cycle management and access governance
-  * See, [Saviynt for Azure AD B2C](https://saviynt.com/integrations/old-version-azure-ad/for-b2c/)
+  * See, [Saviynt for Azure AD B2C](https://saviynt.com/fr/integrations/entra-id/for-b2c)
 * **Microsoft Graph API** – interface for Saviynt to manage Azure AD B2C users and their access
   * See, [Use the Microsoft Graph API](/graph/use-the-api)
 

articles/ai-services/content-safety/concepts/jailbreak-detection.md

@@ -6,7 +6,7 @@ description: Learn about User Prompt injection attacks and the Prompt Shields fe
 author: PatrickFarley
 manager: nitinme
 ms.service: azure-ai-content-safety
-ms.custom: build-2023
+ms.custom: build-2023, references_regions
 ms.topic: conceptual
 ms.date: 03/15/2024
 ms.author: pafarley
@@ -73,6 +73,12 @@ Currently, the Prompt Shields API supports the English language. While our API d
 
 The maximum character limit for Prompt Shields allows for a user prompt of up to 10,000 characters, while the document array is restricted to a maximum of 5 documents with a combined total not exceeding 10,000 characters.
 
+### Regions
+To use this API, you must create your Azure AI Content Safety resource in the supported regions. Currently, it's available in the following Azure regions:
+
+- East US
+- West Europe
+
 ### TPS limitations
 
 | Pricing Tier | Requests per 10 seconds |

articles/ai-services/language-service/concepts/developer-guide.md

@@ -121,7 +121,7 @@ It additionally enables you to use the following features, without creating any
 * [Conversation summarization](../summarization/quickstart.md?pivots=rest-api&tabs=conversation-summarization)
 * [Personally Identifiable Information (PII) detection for conversations](../personally-identifiable-information/how-to-call-for-conversations.md?tabs=rest-api#examples)
 
-As you use this API in your application, see the [reference documentation](/rest/api/language/2023-04-01/conversation-analysis-runtime) for additional information.
+As you use this API in your application, see the [reference documentation](/rest/api/language) for additional information.
 
 
 ### Text analysis authoring API

articles/ai-services/language-service/concepts/role-based-access-control.md

@@ -79,7 +79,7 @@ A user that should only be validating and reviewing the Language apps, typically
       Only Export POST operation under: 
          * [Question Answering Projects](/rest/api/cognitiveservices/questionanswering/question-answering-projects/export)
       All the Batch Testing Web APIs
-         *[Language Runtime CLU APIs](/rest/api/language/2023-04-01/conversation-analysis-runtime)
+         *[Language Runtime CLU APIs](/rest/api/language)
          *[Language Runtime Text Analysis APIs](https://go.microsoft.com/fwlink/?linkid=2239169)
     :::column-end:::
 :::row-end:::

articles/ai-services/language-service/custom-named-entity-recognition/how-to/call-api.md

@@ -17,7 +17,7 @@ ms.custom: language-service-custom-ner
 # Query your custom model
 
 After the deployment is added successfully, you can query the deployment to extract entities from your text based on the model you assigned to the deployment.
-You can query the deployment programmatically using the [Prediction API](https://aka.ms/ct-runtime-api) or through the client libraries (Azure SDK). 
+You can query the deployment programmatically using the [Prediction API](/rest/api/language/text-analysis-runtime/analyze-text) or through the client libraries (Azure SDK). 
 
 ## Test deployed model
 

articles/ai-services/language-service/custom-text-analytics-for-health/how-to/call-api.md

@@ -16,7 +16,7 @@ ms.custom: language-service-custom-ta4h
 # Send queries to your custom Text Analytics for health model
 
 After the deployment is added successfully, you can query the deployment to extract entities from your text based on the model you assigned to the deployment.
-You can query the deployment programmatically using the [Prediction API](https://aka.ms/ct-runtime-api). 
+You can query the deployment programmatically using the [Prediction API](/rest/api/language/text-analysis-runtime/analyze-text). 
 
 ## Test deployed model
 

articles/ai-services/language-service/custom-text-classification/how-to/call-api.md

@@ -17,7 +17,7 @@ ms.custom: language-service-clu
 # Send text classification requests to your model
 
 After you've successfully deployed a model, you can query the deployment to classify text based on the model you assigned to the deployment.
-You can query the deployment programmatically [Prediction API](https://aka.ms/ct-runtime-api) or through the client libraries (Azure SDK). 
+You can query the deployment programmatically [Prediction API](/rest/api/language/text-analysis-runtime/analyze-text) or through the client libraries (Azure SDK). 
 
 ## Test deployed model
 

articles/ai-services/language-service/sentiment-opinion-mining/custom/how-to/call-api.md

@@ -17,7 +17,7 @@ ms.custom: language-service-custom-ner
 # Send a Custom sentiment analysis request to your custom model
 
 After the deployment is added successfully, you can query the deployment to extract entities from your text based on the model you assigned to the deployment.
-You can query the deployment programmatically using the [Prediction API](https://aka.ms/ct-runtime-api) or through the client libraries (Azure SDK). 
+You can query the deployment programmatically using the [Prediction API](/rest/api/language/text-analysis-runtime/analyze-text) or through the client libraries (Azure SDK). 
 
 ## Test a deployed Custom sentiment analysis model
 

articles/ai-services/openai/concepts/content-filter.md

@@ -982,7 +982,7 @@ data: [DONE]
 ```
 
 > [!IMPORTANT]
-> When content filtering is triggered for a prompt and a `"status": 400` is received as part of the response there may be a charge for this request as the prompt was evaluated by the service. [Charges will also occur](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) when a `"status":200` is received with `"finish_reason": "content_filter"`. In this case the prompt did not have any issues, but the completion generated by the model was detected to violate the content filtering rules which results in the completion being filtered.
+> When content filtering is triggered for a prompt and a `"status": 400` is received as part of the response there will be a charge for this request as the prompt was evaluated by the service. [Charges will also occur](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) when a `"status":200` is received with `"finish_reason": "content_filter"`. In this case the prompt did not have any issues, but the completion generated by the model was detected to violate the content filtering rules which results in the completion being filtered.
 
 ## Best practices
 

articles/ai-services/openai/faq.yml

@@ -113,6 +113,15 @@ sections:
           How do I fix InternalServerError - 500 - Failed to create completion as the model generated invalid Unicode output?
         answer:
           You can minimize the occurrence of these errors by reducing the temperature of your prompts to less than 1 and ensuring you're using a client with retry logic. Reattempting the request often results in a successful response.
+      - question: |
+          We noticed charges associated with API calls that failed to complete with status code 400. Why are failed API calls generating a charge? 
+        answer:
+          If the service performs processing, you will be charged even if the status code is not successful (not 200).
+          Common examples of this are, a 400 error due to a content filter or input limit, or a 408 error due to a timeout. Charges will also occur when a `status 200` is received with a `finish_reason` of `content_filter`.
+          In this case the prompt did not have any issues, but the completion generated by the model was detected to violate the content filtering rules which results in the completion being filtered. 
+
+          If the service doesn't perform processing, you won't be charged.
+          For example, a 401 error due to authentication or a 429 error due to exceeding the Rate Limit.
           
   - name: Getting access to Azure OpenAI Service
     questions: