Merge branch 'main' of https://github.com/MicrosoftDocs/azure-ai-docs-pr into mrb_06_17_2025_launch_001

Author: mrbullwinkle

articles/ai-foundry/concepts/model-lifecycle-retirement.md

@@ -245,6 +245,12 @@ The following tables list the timelines for models that are on track for retirem
 |-------|-------------------|------------------------|-----------------------|-----------------------------|
 | [DeepSeek-V3](https://aka.ms/azureai/landing/DeepSeek-V3) | April 10, 2025 | May 31, 2025 | August 31, 2025 | [DeepSeek-V3-0324](https://aka.ms/azureai/landing/DeepSeek-V3-0324) |
 
+#### Gretel
+
+| Model | Legacy date (UTC) | Deprecation date (UTC) | Retirement date (UTC) | Suggested replacement model |
+|-------|-------------------|------------------------|-----------------------|-----------------------------|
+| [Gretel-Navigator-Tabular](https://ai.azure.com/explore/models/Gretel-Navigator-Tabular/version/1/registry/azureml-gretel) | N/A | June 16, 2025 | September 16, 2025 | N/A |
+
 #### Meta
 
 | Model | Legacy date (UTC) | Deprecation date (UTC) | Retirement date (UTC) | Suggested replacement model |

articles/ai-services/openai/how-to/predicted-outputs.md

@@ -6,7 +6,7 @@ services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 04/14/2025
+ms.date: 06/17/2025
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -22,10 +22,12 @@ Predicted outputs can improve model response latency for chat completions calls
 - `gpt-4o` version: `2024-08-06`
 - `gpt-4o` version: `2024-11-20`
 - `gpt-4.1` version: `2025-04-14`
+- `gpt-4.1-nano` version: `2025-04-14`
+- `gpt-4.1-mini` version: `2025-04-14`
 
 ## API support
 
-- `2025-01-01-preview`
+First introduced in `2025-01-01-preview`. Supported in all subsequent releases. 
 
 ## Unsupported features
 

articles/ai-services/openai/quotas-limits.md

@@ -49,7 +49,7 @@ The following sections provide you with a quick guide to the default quotas and
 | Max file size for Assistants & fine-tuning | 512 MB<br/><br/>200 MB via [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) |
 | Max size for all uploaded files for Assistants |200 GB |
 | Assistants token limit | 2,000,000 token limit |
-| GPT-4o max images per request (# of images in the messages array/conversation history) | 50 |
+| GPT-4o and GPT-4.1 max images per request (# of images in the messages array/conversation history) | 50 |
 | GPT-4 `vision-preview` & GPT-4 `turbo-2024-04-09` default max tokens | 16 <br><br> Increase the `max_tokens` parameter value to avoid truncated responses. GPT-4o max tokens defaults to 4096. |
 | Max number of custom headers in API requests<sup>1</sup> | 10 |
 | Message character limit | 1048576 |

articles/search/keyless-connections.md

@@ -2,7 +2,7 @@
 title: Use keyless connections in search apps
 description: Use keyless connections with an Azure Identity library for Microsoft Entra ID authentication and authorization with Azure AI Search.
 ms.topic: how-to
-ms.date: 10/30/2024
+ms.date: 06/17/2025
 ms.service: azure-ai-search
 author: HeidiSteen
 ms.author: heidist
@@ -16,35 +16,31 @@ In your application code, you can set up a keyless connection to Azure AI Search
 
 Keyless connections are enabled with the following steps: 
 
-* Configure your authentication.
+* Enable role-based access on your search service
 * Set environment variables, as needed. 
 * Use an Azure Identity library credential type to create an Azure AI Search client object.
 
 ## Prerequisites
 
 The following steps need to be completed for both local development and production workloads:
 
-* [Create an AI Search resource](#create-an-ai-search-resource)
+* [Create an AI Search resource](search-create-service-portal.md)
 * [Enable role-based access on your search service](search-security-enable-roles.md)
 * [Install Azure Identity client library](#install-azure-identity-client-library)
 
-### Create an AI Search resource
-
-Before continuing with this article, you need an Azure AI Search resource to work with. If you don't have a resource, [create your resource](search-create-service-portal.md) now. [Enable role-based access control (RBAC)](search-security-enable-roles.md) for the resource.
-
-### Install Azure Identity client library
+## Install Azure Identity client library
 
 To use a keyless approach, update your AI Search enabled code with the Azure Identity client library.
 
-#### [.NET](#tab/csharp)
+### [.NET](#tab/csharp)
 
 Install the [Azure Identity client library for .NET](https://www.nuget.org/packages/Azure.Identity):
 
 ```dotnetcli
 dotnet add package Azure.Identity
 ```
 
-#### [Java](#tab/java)
+### [Java](#tab/java)
 
 Install the [Azure Identity client library for Java](https://mvnrepository.com/artifact/com.azure/azure-identity) with the following POM file:
 
@@ -60,15 +56,15 @@ Install the [Azure Identity client library for Java](https://mvnrepository.com/a
 </dependencyManagement>
 ```
 
-#### [JavaScript](#tab/javascript)
+### [JavaScript](#tab/javascript)
 
 Install the [Azure Identity client library for JavaScript](https://www.npmjs.com/package/@azure/identity):
 
 ```console
 npm install --save @azure/identity
 ```
 
-#### [Python](#tab/python)
+### [Python](#tab/python)
 
 Install the [Azure Identity client library for Python](https://pypi.org/project/azure-identity/):
 
@@ -82,7 +78,7 @@ pip install azure-identity
 
 The Azure Identity library's `DefaultAzureCredential` allows you to run the same code in the local development environment and in the Azure cloud. Create a single credential and reuse the credential instance as needed to take advantage of token caching.
 
-#### [.NET](#tab/csharp)
+### [.NET](#tab/csharp)
 
 For more information on `DefaultAzureCredential` for .NET, see [Azure Identity client library for .NET](/dotnet/api/overview/azure/identity-readme#defaultazurecredential). 
 
@@ -104,7 +100,7 @@ SearchClient searchClient = new(new Uri(endpoint), indexName, credential);
 SearchIndexClient searchIndexClient = new(endpoint, credential);
 ```
 
-#### [Java](#tab/java)
+### [Java](#tab/java)
 
 For more information on `DefaultAzureCredential` for Java, see [Azure Identity client library for Java](/java/api/overview/azure/identity-readme#defaultazurecredential).
 
@@ -148,7 +144,7 @@ SearchIndexAsyncClient searchIndexAsyncClient = new SearchIndexClientBuilder()
     .buildAsyncClient();
 ```
 
-#### [JavaScript](#tab/javascript)
+### [JavaScript](#tab/javascript)
 
 For more information on `DefaultAzureCredential` for JavaScript, see [Azure Identity client library for JavaScript](/javascript/api/overview/azure/identity-readme#defaultazurecredential).
 
@@ -178,7 +174,7 @@ const indexClient = new SearchIndexClient(
 );
 ```
 
-#### [Python](#tab/python)
+### [Python](#tab/python)
 
 For more information on `DefaultAzureCredential` for Python, see [Azure Identity client library for Python](/python/api/overview/azure/identity-readme#defaultazurecredential).
 

articles/search/knowledge-store-create-rest.md

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 12/10/2024
+ms.date: 06/17/2025
 ---
 
 # Create a knowledge store using REST

articles/search/knowledge-store-projection-example-long.md

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 06/17/2025
 ---
 
 # Detailed example of shapes and projections in a knowledge store

articles/search/knowledge-store-projection-overview.md

@@ -1,7 +1,7 @@
 ---
 title: Projection concepts
 titleSuffix: Azure AI Search
-description: Introduces projection concepts and best practices. If you are creating a knowledge store in Azure AI Search, projections determine the type, quantity, and composition of objects in Azure Storage.
+description: Introduces projection concepts and best practices. If you're creating a knowledge store in Azure AI Search, projections determine the type, quantity, and composition of objects in Azure Storage.
 
 manager: nitinme
 author: HeidiSteen
@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 06/17/2025
 ---
 
 # Knowledge store "projections" in Azure AI Search

articles/search/knowledge-store-projection-shape.md

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 12/10/2024
+ms.date: 06/17/2025
 ---
 
 # Shaping data for projection into a knowledge store
@@ -21,9 +21,9 @@ By itself, the enrichment tree doesn't include logic that would inform how its c
 
 ## Approaches for creating shapes
 
-There are two ways to shape enriched content to that it can be projected into a knowledge store:
+There are two ways to shape enriched content so that it can be projected into a knowledge store:
 
-+ Use the [Shaper skill](cognitive-search-skill-shaper.md) to create nodes in an enrichment tree that are used expressly for projection. Most skills create new content. In contrast, a Shaper skill work with existing nodes, usually to consolidate multiple nodes into a single complex object. This is useful for tables, where you want the output of multiple nodes to be physically expressed as columns in the table. 
++ Use the [Shaper skill](cognitive-search-skill-shaper.md) to create nodes in an enrichment tree that are used expressly for projection. Most skills create new content. In contrast, a Shaper skill works with existing nodes, usually to consolidate multiple nodes into a single complex object. This is useful for tables, where you want the output of multiple nodes to be physically expressed as columns in the table. 
 
 + Use an inline shape within the projection definition itself.
 
@@ -111,11 +111,11 @@ Within a Shaper skill, an input can have a `sourceContext` element. This same pr
 
 `sourceContext` is used to construct multi-level, nested objects in an enrichment pipeline. If the input is at a *different* context than the skill context, use the *sourceContext*. The *sourceContext* requires you to define a nested input with the specific element being addressed as the source. 
 
-In the example above, sentiment analysis and key phrases extraction was performed on text that was split into pages for more efficient analysis. Assuming you want the scores and phrases projected into a table, you'll now need to set the context to nested input that provides the score and phrase.
+In the previous example, sentiment analysis and key phrases extraction was performed on text that was split into pages for more efficient analysis. Assuming you want the scores and phrases projected into a table, you'll now need to set the context to nested input that provides the score and phrase.
 
 ### Projecting a shape into multiple tables
 
-With the `tableprojection` node defined in the `outputs` section above, you can slice parts of the `tableprojection` node into individual, related tables:
+With the `tableprojection` node defined in the `outputs` in the previous section, you can slice parts of the `tableprojection` node into individual, related tables:
 
 ```json
 "projections": [
@@ -145,8 +145,8 @@ With the `tableprojection` node defined in the `outputs` section above, you can
 
 Inline shaping is the ability to form new shapes within the projection definition itself. Inline shaping has these characteristics:
 
-+ The shape can be used only by the projection that contains it.
-+ The shape can be identical to what a Shaper skill would produce.
++ The shape is used only by the projection that contains it.
++ The shape can be identical to what a Shaper skill produces.
 
 An inline shape is created using `sourceContext` and `inputs`.