Merge pull request #5504 from MicrosoftDocs/main

6/12/2025 PM Publish

Author: Taojunshen

articles/ai-services/agents/concepts/standard-agent-setup.md

@@ -27,18 +27,17 @@ Both standard setup configurations are designed to give you complete control ove
 
 By bundling these BYO features (file storage, search, and thread storage), the standard setup guarantees that your deployment is secure by default. All data processed by Azure AI Foundry Agent Service is automatically stored at rest in your own Azure resources, helping you meet internal policies, compliance requirements, and enterprise security standards. 
 
-## Project-Level Data Isolation
+### Azure Cosmos DB for NoSQL
+
+Your existing Azure Cosmos DB for NoSQL Account used in standard setup must have a total throughput limit of at least **3000 RU/s**. Both **Provisioned Throughput** and **Serverless** modes are supported.
 
-Azure AI Foundry enforces project-level data isolation by default. When you configure your own resources in the project capability host: 
-* **Azure Storage**: Two Blob containers are automatically provisioned: 
-    * One for uploaded files 
-    * One for intermediate system data (for example, chunks, embeddings) 
-* **Azure Cosmos DB**: Three containers are provisioned under a dedicated enterprise_memory database: 
-    * thread-message-store: End-user conversations 
-    * system-thread-message-store: Internal system messages 
-    * agent-entity-store: Model inputs and outputs 
+When you use standard setup, **three containers** will be provisioned in your existing Cosmos DB account, and **each container requires 1000 RU/s**.
+* thread-message-store: End-user conversations
+* system-thread-message-store: Internal system messages
+* agent-entity-store: Agent metadata including their instructions, tools, name, etc.
 
-This default behavior was chosen to reduce configuration complexity while still enforcing strict data boundaries—ensuring each project has a clean, isolated storage footprint without requiring manual setup. 
+## Project-Level Data Isolation
+Standard setup enforces project-level data isolation by default. Two blob storage containers will automatically be provisioned in your storage account, one for files and one for intermediate system data (chunks, embeddings) and three containers will be provisioned in your Cosmos DB, one for user systems, one for system messages, and one for user inputs related to created agents such as their instructions, tools, name, etc. This default behavior was chosen to reduce setup complexity while still enforcing strict data boundaries between projects.
 
 ## Capability hosts
 **Capability hosts** are sub-resources on both the Account and Project, enabling interaction with the Azure AI Foundry Agent Service. 
@@ -85,4 +84,4 @@ This default behavior was chosen to reduce configuration complexity while still
         * Assign role: Cosmos DB Built-in Data Contributor 
     * Cosmos DB for NoSQL container: `<'${projectWorkspaceId}>-agent-entity-store'` 
         * Assign role: Cosmos DB Built-in Data Contributor 
-11. Once all resources are provisioned, all developers who want to create/edit agents in the project should be assigned the role: Azure AI User on the project scope. 
+11. Once all resources are provisioned, all developers who want to create/edit agents in the project should be assigned the role: Azure AI User on the project scope.

articles/ai-services/agents/environment-setup.md

@@ -18,14 +18,14 @@ Creating your first agent with Azure AI Foundry Agent Service is a two-step proc
 1. Set up your agent environment.
 1. Create and configure your agent using either the SDK of your choice or the Azure Foundry Portal. 
 
-Use this article to learn more about setting up your agents.
+Use this article to learn more about setting up your agent environment.
 
 ### Required permissions 
 
 | Action                                                                 | Required Role                   |
 |------------------------------------------------------------------------|----------------------------------|
 | Create an account and project                                          | Azure AI Account Owner           |
-| **Standard Setup Only:** Assign RBAC for required resources (Cosmos DB, Search, Storage, etc.) | Role Based Access Administrator  |
+| **Standard Setup Only:** Assign RBAC for required resources (Cosmos DB, Search, Storage, etc.) | Role Based Access Control Administrator  |
 | Create and edit agents                                                 | Azure AI User                    |
 
 ## Set up your agent environment
@@ -37,7 +37,7 @@ Agents are scoped at the project level, which ensures data isolation—agents wi
 * An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services).
 * Ensure that the individual creating the account and project has the **Azure AI Account Owner** role at the subscription scope
 * If configuring **Standard Setup**, the same individual must also have permissions to assign roles to required resources (Cosmos DB, Search, Storage).
-    * The built-in role needed is **Role Based Access Administrator**.
+    * The built-in role needed is **Role Based Access Control Administrator**.
     * Alternatively, having the **Owner** role at the subscription level also satisfies this requirement.
     * The key permission needed is: `Microsoft.Authorization/roleAssignments/write`
 

articles/ai-services/agents/how-to/use-your-own-resources.md

@@ -85,6 +85,9 @@ Use an existing AI Services / Azure OpenAI, Azure Storage account, Azure Cosmos
     ```
 
 ### Use an existing Azure Cosmos DB for NoSQL account for thread storage
+**Azure Cosmos DB for NoSQL**
+- Your existing Azure Cosmos DB for NoSQL Account used in standard setup must have at least a total throughput limit of at least 3000 RU/s. Both Provisioned Thoughtput and Serverless are supported.
+    - 3 containers will be provisioned in your existing Cosmos DB account and each need 1000 RU/s
 
 1. To get your Azure Cosmos DB account resource ID, sign in to the Azure CLI and select the subscription with your account: 
     

articles/ai-services/speech-service/includes/how-to/professional-voice/train-voice/speech-studio.md

@@ -54,6 +54,9 @@ To create a custom voice in Speech Studio, follow these steps for one of the fol
    :::image type="content" source="../../../../media/custom-voice/cnv-train-neural.png" alt-text="Screenshot that shows how to select neural training.":::
 
 1. Select a version of the training recipe for your model. The latest version is selected by default. The supported features and training time can vary by version. Normally, we recommend the latest version. In some cases, you can choose an earlier version to reduce training time. See [Bilingual training](#bilingual-training) for more information about bilingual training and differences between locales.
+
+   > [!NOTE]
+   > Model versions `V3.0`, `V7.0` and `V8.0` will be retired by July 25, 2025. The voice models already created on these retired versions won't be affected.
    
 1. Select the data that you want to use for training. Duplicate audio names are removed from the training. Make sure that the data you select doesn't contain the same audio names across multiple *.zip* files.
 
@@ -83,6 +86,11 @@ To create a custom voice in Speech Studio, follow these steps for one of the fol
 
    :::image type="content" source="../../../../media/custom-voice/cnv-train-neural-cross-lingual.png" alt-text="Screenshot that shows how to select neural cross lingual training.":::
 
+1. Select a version of the training recipe for your model. The latest version is selected by default. The supported features and training time can vary by version. Normally, we recommend the latest version.
+
+   > [!NOTE]
+   > Model versions `V3.0` will be retired by July 25, 2025. The voice models already created on these retired versions won't be affected.
+
 1. Select the **Target language** that your voice speaks. The voice speaks a different language from your training data. You can select only one target language for a voice model.
 1. Select the data that you want to use for training. Duplicate audio names are removed from the training. Make sure that the data you select doesn't contain the same audio names across multiple *.zip* files.
 

articles/machine-learning/how-to-secure-training-vnet.md

@@ -9,7 +9,7 @@ ms.topic: how-to
 ms.reviewer: None
 ms.author: larryfr
 author: Blackmist
-ms.date: 04/08/2024
+ms.date: 06/12/2025
 ms.custom:
   - tracking-python
   - references_regions

articles/machine-learning/v1/how-to-secure-training-vnet.md

@@ -9,7 +9,7 @@ ms.topic: how-to
 ms.reviewer: None
 ms.author: larryfr
 author: Blackmist
-ms.date: 07/26/2024
+ms.date: 06/12/2025
 ms.custom: UpdateFrequency5, tracking-python, references_regions, build-2023
 ---
 

articles/machine-learning/v1/how-to-secure-workspace-vnet.md

@@ -8,7 +8,7 @@ ms.subservice: enterprise-readiness
 ms.reviewer: None
 ms.author: larryfr
 author: Blackmist
-ms.date: 09/29/2023
+ms.date: 06/12/2025
 ms.topic: how-to
 ms.custom: UpdateFrequency5, tracking-python, security, cliv1, sdkv1, build-2023
 ---
@@ -121,8 +121,6 @@ Azure Machine Learning supports storage accounts configured to use either a priv
     * **Queue** - Only needed if you plan to use [ParallelRunStep](../tutorial-pipeline-batch-scoring-classification.md) in an Azure Machine Learning pipeline.
     * **Table** - Only needed if you plan to use [ParallelRunStep](../tutorial-pipeline-batch-scoring-classification.md) in an Azure Machine Learning pipeline.
 
-    :::image type="content" source="../media/how-to-enable-studio-virtual-network/configure-storage-private-endpoint.png" alt-text="Screenshot showing private endpoint configuration page with blob and file options":::
-
     > [!TIP]
     > When configuring a storage account that is **not** the default storage, select the **Target subresource** type that corresponds to the storage account you want to add.
 
@@ -132,8 +130,6 @@ Azure Machine Learning supports storage accounts configured to use either a priv
     > [!TIP]
     > Alternatively, you can select __Allow Azure services on the trusted services list to access this storage account__ to more broadly allow access from trusted services. For more information, see [Configure Azure Storage firewalls and virtual networks](/azure/storage/common/storage-network-security#trusted-microsoft-services).
 
-    :::image type="content" source="../media/how-to-enable-virtual-network/storage-firewalls-and-virtual-networks-no-vnet.png" alt-text="The networking area on the Azure Storage page in the Azure portal when using private endpoint":::
-
 1. Select __Save__ to save the configuration.
 
 > [!TIP]
@@ -155,8 +151,6 @@ Azure Machine Learning supports storage accounts configured to use either a priv
     > [!TIP]
     > Alternatively, you can select __Allow Azure services on the trusted services list to access this storage account__ to more broadly allow access from trusted services. For more information, see [Configure Azure Storage firewalls and virtual networks](/azure/storage/common/storage-network-security#trusted-microsoft-services).
 
-    :::image type="content" source="../media/how-to-enable-virtual-network/storage-firewalls-and-virtual-networks.png" alt-text="The networking area on the Azure Storage page in the Azure portal":::
-
 1. Select __Save__ to save the configuration.
 
 > [!TIP]
@@ -193,8 +187,6 @@ For information on using a private endpoint with Azure Key Vault, see [Integrate
     1. Under __Virtual networks__, select __Add a virtual network__, __Add existing virtual networks__, and add the virtual network/subnet where your experimentation compute resides.
     1. Verify that __Allow trusted Microsoft services to bypass this firewall__ is checked, and then select __Apply__.
 
-    :::image type="content" source="../media/how-to-enable-virtual-network/key-vault-firewalls-and-virtual-networks-page.png" alt-text="The Firewalls and virtual networks section in the Key Vault pane":::
-
 For more information, see [Configure Azure Key Vault network settings](/azure/key-vault/general/how-to-azure-key-vault-network-security).
 
 ---

articles/search/index-add-scoring-profiles.md

@@ -10,32 +10,28 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 02/25/2025
+ms.date: 06/10/2025
 ---
 
 # Add scoring profiles to boost search scores
 
-Scoring profiles are used to boost the ranking of matching documents based on criteria. In this article, learn how to specify and assign a scoring profile that boosts a search score based on parameters that you provide. 
+Scoring profiles are used to boost the ranking of matching documents based on criteria. In this article, learn how to specify and assign a scoring profile that boosts a search score based on parameters that you provide. You can create scoring profiles based on:
 
-You can use scoring profiles for [keyword search](search-lucene-query-architecture.md), [vector search](vector-search-overview.md), and [hybrid search](hybrid-search-overview.md). However, scoring profiles only apply to nonvector fields, so make sure your index has text or numeric fields that can be used in a scoring profile. 
++ Weighted fields, where boosting is based on a match found in a specific string field. For example, if matches found in a "Subject" field should be more relevant than the same match found in a "Description" field.
 
-## Prerequisites
-
-+ A new or existing search index with text or numeric fields.
-
-You can specify a scoring profile in the index designer in the Azure portal or through APIs like [Create or Update Index REST](/rest/api/searchservice/indexes/create-or-update) or equivalent APIs in any Azure SDK.
++ Functions for numeric data, including dates, ranges, and geographic coordinates. There's also a Tags function that operates on a field providing an arbitrary collection of strings. You can choose this approach over weighted fields if you want to boost a score based on whether a match is found in a tags field.
 
-Scoring profile support for vector and hybrid search is available in 2024-05-01-preview and 2024-07-01 REST APIs and in Azure SDK packages that targeting those releases.
+You can add a scoring profile to an index by editing its JSON definition in the Azure portal or programmatically through APIs like [Create or Update Index REST](/rest/api/searchservice/indexes/create-or-update) or equivalent APIs in any Azure SDK.
 
-## Key points about scoring profiles
+## Prerequisites
 
-Scoring profile parameters are either:
+You can use any API version or SDK package for scoring profiles in keyword search. For vector and hybrid search, use 2024-05-01-preview and 2024-07-01 REST APIs or Azure SDK packages that provide feature parity. For integration between scoring profiles and semantic ranker, use 2025-05-01-preview and later.
 
-+ Weighted fields, where a match is found in a specific string field. For example, you might want matches found in a "summary" field to be more relevant than the same match found in a "content" field.
+## Rules for scoring profiles
 
-+ Functions for numeric data, including dates, ranges, and geographic coordinates. There's also a Tags function that operates on a field providing an arbitrary collection of strings. You can choose this approach over weighted fields if you want to boost a score based on whether a match is found in a tags field.
+You must have a new or existing search index with text or numeric fields. 
 
-You can create multiple profiles and then modify query logic to choose which one is used.
+You can use scoring profiles in [keyword search](search-lucene-query-architecture.md), [vector search](vector-search-overview.md), and [hybrid search](hybrid-search-overview.md). However, scoring profiles only apply to nonvector fields, so make sure your index has text or numeric fields that can be boosted or weighted. 
 
 You can have up to 100 scoring profiles within an index (see [service Limits](search-limits-quotas-capacity.md)), but you can only specify one profile at time in any given query.
 
@@ -76,13 +72,7 @@ The following definition shows a simple profile named "geo". This example boosts
 ]
 ```  
 
-To use this scoring profile, your query is formulated to specify scoringProfile parameter in the request. If you're using the REST API, queries are specified through GET and POST requests. In the following example, "currentLocation" has a delimiter of a single dash (`-`). It's followed by longitude and latitude coordinates, where longitude is a negative value.
-
-```http
-GET /indexes/hotels/docs?search+inn&scoringProfile=geo&scoringParameter=currentLocation--122.123,44.77233&api-version=2024-07-01
-```
-
-Notice the syntax differences when using POST. In POST, "scoringParameters" is plural and it's an array.
+To use this scoring profile, your query is formulated to specify `scoringProfile` parameter in the request. If you're using the REST API, queries are specified through GET and POST requests. In the following example, "currentLocation" has a delimiter of a single dash (`-`). It's followed by longitude and latitude coordinates, where longitude is a negative value.
 
 ```http
 POST /indexes/hotels/docs&api-version=2024-07-01
@@ -119,15 +109,15 @@ For text queries in a hybrid query, scoring profiles identify the maximum 1,000
 
 1. Paste in the [template](#template) provided in this article.  
 
-1. Provide a name that adheres to [naming conventions](/rest/api/searchservice/naming-rules).
+1. Provide a name that adheres to [Azure AI Search naming conventions](/rest/api/searchservice/naming-rules).
 
 1. Specify boosting criteria. A single profile can contain [text weighted fields](#use-text-weighted-fields), [functions](#use-functions), or both. 
 
 You should work iteratively, using a data set that will help you prove or disprove the efficacy of a given profile.
 
 Scoring profiles can be defined in Azure portal as shown in the following screenshot, or programmatically through [REST APIs](/rest/api/searchservice/indexes/create-or-update) or in Azure SDKs, such as the [ScoringProfile](/dotnet/api/azure.search.documents.indexes.models.scoringprofile) class in the Azure SDK for .NET.
 
-   :::image type="content" source="media/scoring-profiles/portal-add-scoring-profile-small.png" alt-text="Add scoring profiles page" lightbox="media/scoring-profiles/portal-add-scoring-profile.png" border="true":::
+:::image type="content" source="media/scoring-profiles/portal-add-scoring-profile-small.png" alt-text="Screenshot showing the Add scoring profile option in the Azure portal." lightbox="media/scoring-profiles/portal-add-scoring-profile.png" border="true":::
 
 ## Use text-weighted fields