Merge pull request #5911 from MicrosoftDocs/main

learn-build-service-prod[bot] · web-flow · commit 1d05d595f148 · 2025-07-07T22:09:51.000Z
Merged by Learn.Build PR Management system
diff --git a/articles/ai-foundry/agents/how-to/tools/deep-research-samples.md b/articles/ai-foundry/agents/how-to/tools/deep-research-samples.md
@@ -17,6 +17,9 @@ Use this article to learn how to use the Deep Research tool with the Azure AI Pr
 
 ## Prerequisites
 
+> [!NOTE]
+> * The  `o3-deep-research` model and the GPT model deployments should be part of your AI Foundry project resulting in all three resources in the same Azure subscription and same region. Supported regions are **West US** and **Norway East**.
+
 * The requirements in the [Deep Research overview](./deep-research.md).
 * The Deep Research tool requires the latest prerelease versions of the `azure-ai-projects` library. You can install it with the following command:
 
diff --git a/articles/ai-foundry/agents/how-to/tools/deep-research.md b/articles/ai-foundry/agents/how-to/tools/deep-research.md
@@ -35,24 +35,28 @@ The deep research tool is a **code-only release** and available for use using th
 ## Knowledge source support
 The deep research tool is tightly integrated with Grounding with Bing Search and only supports web-based research.
 
-## How Deep Research works
-
-At its core, the Deep Research tool orchestrates a multi-step research pipeline uses the Azure OpenAI's `o3-deep-research` model together with Grounding with Bing Search to autonomously search for and read information from multiple online sources appropriate to the user prompt. This enables the Deep Research tool to generate thorough, documented, and cited reports on complex topics. 
+## Region support
+The Deep Research tool is supported in the following regions where the deep research model is available for deployment.
+|West US  | Norway East |
+|---------|---------|
+| ✔️ | ✔️ | 
 
-### Deep research model deployment
+## Deep research model
+The Deep Research tool uses the Azure OpenAI `o3-deep-research` model for its research tasks. The model was fine-tuned on the Azure OpenAI `o3` reasoning model. Key features:
 
-The Deep Research tool uses the Azure OpenAI `o3-deep-research` model for its research tasks. The model was fine-tuned on the Azure OpenAI `o3` reasoning model.
-
-**Key features**:
 - Handles data as part of its research tasks.
 - 200-K context length, 100-K completion tokens, and May 31, 2024 knowledge cutoff.
 - Outputs its thinking as reasoning summary as it analyzes information.
 - Delivers a synthesized report at the end of the research task.
 
-**Deployment information**:
-- Deployment type: Global Standard
-- Available regions: West US, Norway East
-- Quotas and limits: Enterprise: `30K RPS / 30M TPM`, Default: `3K RPS / 3M TPM`
+### Deployment information
+- **Deployment type**: Global Standard
+- **Available regions**: West US, Norway East
+- **Quotas and limits**: Enterprise: `30K RPS / 30M TPM`, Default: `3K RPS / 3M TPM`
+  
+## How Deep Research works
+
+At its core, the Deep Research tool orchestrates a multi-step research pipeline uses the Azure OpenAI's `o3-deep-research` model and any GPT model together with Grounding with Bing Search to autonomously search for and read information from multiple online sources appropriate to the user prompt. This enables the Deep Research tool to generate thorough, documented, and cited reports on complex topics. 
 
 ### GPT model deployment for clarifying intent
 
@@ -74,16 +78,19 @@ The output is a structured report that documents not only the comprehensive answ
 
 ## Prerequisites
 - If you already have access to the Azure OpenAI `o3` model, no request is required to access the `o3-deep-research`  model. Otherwise, fill out the [request form](https://aka.ms/OAI/deepresearchaccess). 
-- An Azure subscription with the ability to create resources [Set up your environment](../../environment-setup.md)
+- An Azure subscription with the ability to create AI Foundry project, Grounding with Bing Search, deep research model and GPT model resources [Set up your environment](../../environment-setup.md) in the **West US** and **Norway East** regions.
 - [Grounding with Bing Search tool](./bing-grounding.md) resource for connecting to your Azure AI Foundry project.
 - [Model deployments](../../../model-inference/how-to/create-model-deployments.md) for the following models
     - `o3-deep-research` version `2025-06-26`. This model is available in `West US` and `Norway East`.
-    - Any Azure OpenAI GPT model like `gpt-4o` for intent clarification.
+    - Any Azure OpenAI GPT model like `gpt-4o` for intent clarification. Deploy in the same regions.
 
 ## Setup 
 
 To use the Deep Research tool, you need to create the Azure AI Foundry type project, add your Grounding with Bing Search resource as a new connection, deploy the `o3-deep-research-model`, and deploy the selected Azure OpenAI GPT model. 
 
+> [!NOTE]
+> * The  `o3-deep-research` model and the GPT model deployments should be part of your AI Foundry project resulting in all three resources in the same Azure subscription and same region. Supported regions are **West US** and **Norway East**.
+
 :::image type="content" source="../../media/tools/deep-research/setup-deep-research-tool.png" alt-text="A diagram of the steps to set up the deep research tool." lightbox="../../media/tools/deep-research/setup-deep-research-tool.png":::
 
 1. Navigate to the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) and create a new project.
diff --git a/articles/ai-foundry/concepts/fine-tuning-overview.md b/articles/ai-foundry/concepts/fine-tuning-overview.md
@@ -10,8 +10,8 @@ ms.custom:
 ms.topic: concept-article
 ms.date: 05/14/2025
 ms.reviewer: keli19
-ms.author: sgilley
-author: sdgilley
+ms.author: ssalgado
+author: ssalgadodev
 #customer intent: As a developer, I want to learn what it means to fine-tune a model.
 ---
 
diff --git a/articles/ai-foundry/openai/concepts/content-filter-personal-information.md b/articles/ai-foundry/openai/concepts/content-filter-personal-information.md
diff --git a/articles/ai-foundry/openai/concepts/content-filter.md b/articles/ai-foundry/openai/concepts/content-filter.md
@@ -47,7 +47,6 @@ The following table summarizes the risk categories supported by Azure OpenAI's c
 | [Groundedness](/azure/ai-services/openai/concepts/content-filter-groundedness)<sup>2</sup> | Groundedness detection flags whether the text responses of large language models (LLMs) are grounded in the source materials provided by the users. Ungrounded material refers to instances where the LLMs produce information that is non-factual or inaccurate from what was present in the source materials. Requires [document embedding and formatting](./content-filter-document-embedding.md). |
 | [Protected Material for Text](/azure/ai-services/openai/concepts/content-filter-protected-material)<sup>1</sup> | Protected material text describes known text content (for example, song lyrics, articles, recipes, and selected web content) that can be outputted by large language models.|
 | [Protected Material for Code](/azure/ai-services/openai/concepts/content-filter-protected-material) | Protected material code describes source code that matches a set of source code from public repositories, which can be outputted by large language models without proper citation of source repositories.|
-| [Personally identifiable information (PII)](/azure/ai-services/openai/concepts/content-filter-personal-information) | Personally identifiable information (PII) refers to any information that can be used to identify a particular individual. PII detection involves analyzing text content in LLM completions and filtering any PII that was returned. |
 
 
 <sup>1</sup> If you're an owner of text material and want to submit text content for protection, [file a request](https://aka.ms/protectedmaterialsform).
diff --git a/articles/ai-foundry/openai/how-to/content-filters.md b/articles/ai-foundry/openai/how-to/content-filters.md
@@ -48,7 +48,6 @@ You can configure the following filter categories in addition to the default har
 | Protected material - code |GA| On | Completion | Filters protected code or gets the example citation and license information in annotations for code snippets that match any public code sources, powered by GitHub Copilot. For more information about consuming annotations, see the [Protected material concepts guide](/azure/ai-services/openai/concepts/content-filter-protected-material) |
 | Protected material - text | GA| On | Completion | Identifies and blocks known text content from being displayed in the model output (for example, song lyrics, recipes, and selected web content).  |
 | Groundedness | Preview |Off | Completion |Detects whether the text responses of large language models (LLMs) are grounded in the source materials provided by the users. Ungroundedness refers to instances where the LLMs produce information that is non-factual or inaccurate from what was present in the source materials. Requires: [Document embedding and formatting](/azure/ai-services/openai/concepts/content-filter?tabs=warning%2Cuser-prompt%2Cpython-new#embedding-documents-in-your-prompt).|
-| Personally identifiable information (PII) | Preview | Off | Completion | Filters information that can be used to identify a particular individual, such as a name, address, phone number, email address, social security number, driver's license number, passport number, or similar information. |
 
 
 [!INCLUDE [create-content-filter](../../../ai-foundry/includes/create-content-filter.md)]
diff --git a/articles/ai-foundry/openai/whats-new.md b/articles/ai-foundry/openai/whats-new.md
@@ -20,9 +20,6 @@ This article provides a summary of the latest releases and major documentation u
 
 ## June 2025
 
-### PII detection content filter
-
-Personally identifiable information (PII) detection is now available as a built-in content filter. This feature allows you to identify and block sensitive information in LLM outputs, enhancing data privacy. For more information, see the [PII detection](./concepts/content-filter-personal-information.md) documentation.
 
 ### codex-mini & o3-pro models released
 
diff --git a/articles/ai-foundry/toc.yml b/articles/ai-foundry/toc.yml
@@ -251,8 +251,6 @@ items:
             href: ./openai/concepts/content-filter-groundedness.md
           - name: Protected material detection
             href: ./openai/concepts/content-filter-protected-material.md
-          - name: Personally identifiable information (PII) detection
-            href: ./openai/concepts/content-filter-personal-information.md
           - name: Content filter configurability
             href: ./openai/concepts/content-filter-configurability.md
           - name: Content filter annotations
diff --git a/articles/ai-services/.openpublishing.redirection.ai-services.json b/articles/ai-services/.openpublishing.redirection.ai-services.json
@@ -1173,7 +1173,7 @@
     {
       "source_path_from_root": "/articles/ai-services/openai/concepts/content-filter-personal-information.md",
       "redirect_url": "/azure/ai-services/openai/concepts/content-filter",
-      "redirect_document_id": true
+      "redirect_document_id": false
     },
     {
       "source_path_from_root": "/articles/ai-services/agents/concepts/agent-catalog.md",
diff --git a/articles/search/samples-dotnet.md b/articles/search/samples-dotnet.md
@@ -79,7 +79,7 @@ A demo repo provides proof-of-concept source code for examples or scenarios show
 
 | Samples | Repository | Description |
 |---------|------------|-------------|
-| Covid-19 search app | [covid19search](https://github.com/liamca/covid19search) | Source code repository for the Azure AI Search based [Covid-19 Search App](https://covid19search.azurewebsites.net/). |
+| Covid-19 search app | [covid19search](https://github.com/liamca/covid19search) | Source code repository for the Azure AI Search based [Covid-19 Search App](https://github.com/liamca/covid19search). |
 | JFK demo | [AzureSearch JFK Files](https://github.com/Microsoft/AzureSearch_JFK_Files) | Learn more about the [JFK solution](https://www.microsoft.com/ai/ai-lab-jfk-files). |
 
 ## Other samples
diff --git a/articles/search/search-how-to-upgrade.md b/articles/search/search-how-to-upgrade.md
@@ -8,7 +8,7 @@ ms.author: haileytapia
 ms.service: azure-ai-search
 ms.topic: how-to
 ms.custom: references_regions
-ms.date: 04/29/2025
+ms.date: 07/07/2025
 ---
 
 # Upgrade your Azure AI Search service in the Azure portal
@@ -76,7 +76,9 @@ The date you created your service partially determines its [upgrade eligibility]
 
 ## Upgrade your service
 
-You can’t undo a service upgrade. Before you proceed, make sure that you want to permanently increase the [storage limit](#higher-storage-limits) and [vector index size](#higher-vector-limits) of your search service. We recommend that you test this operation in a nonproduction environment.
+You can't undo a service upgrade. Before you proceed, make sure that you want to permanently increase the [storage limit](#higher-storage-limits) and [vector index size](#higher-vector-limits) of your search service. We recommend that you test this operation in a nonproduction environment.
+
+The availability of your search service during an upgrade depends on how many replicas you've provisioned. With two or more replicas, your service remains available while one replica is updated. For more information, see [Reliability in Azure AI Search](search-reliability.md).
 
 To upgrade your service:
 
@@ -100,9 +102,7 @@ To upgrade your service:
 
 1. Check your notifications to confirm that the operation started.
 
-   The upgrade is an asynchronous operation, so you can continue using your service. Depending on the size of your service, the upgrade can take several hours to complete.
-
-   If the upgrade fails, your service returns to its original state.
+   Depending on the size of your service, this operation can take several hours to complete. If the upgrade fails, your service returns to its original state.
 
 ## Next step
 
diff --git a/articles/search/toc.yml b/articles/search/toc.yml
@@ -14,6 +14,62 @@ items:
   - name: FAQ
     href: search-faq-frequently-asked-questions.yml
   expanded: true
+- name: Concepts
+  items:
+  - name: Data
+    items:
+    - name: Search index
+      href: search-what-is-an-index.md
+    - name: Vector index
+      href: vector-store.md
+    - name: Knowledge store
+      href: knowledge-store-concept-intro.md
+    - name: Data import strategies
+      href: search-what-is-data-import.md
+    - name: Indexers
+      href: search-indexer-overview.md
+  - name: Applied AI
+    items:
+    - name: Multimodal search
+      href: multimodal-search-overview.md
+    - name: Built-in vectorization
+      href: vector-search-integrated-vectorization.md
+    - name: AI enrichment during indexing
+      href: cognitive-search-concept-intro.md
+    - name: Enrichment cache
+      href: cognitive-search-incremental-indexing-conceptual.md
+    - name: Skillsets
+      href: cognitive-search-working-with-skillsets.md
+  - name: Retrieval
+    items:
+    - name: Agentic search
+      href: search-agentic-retrieval-concept.md
+    - name: Full-text search
+      href: search-lucene-query-architecture.md
+    - name: Vector search
+      href: vector-search-overview.md
+    - name: Hybrid search
+      href: hybrid-search-overview.md
+    - name: Retrieval Augmented Generation (RAG)
+      href: retrieval-augmented-generation-overview.md
+    - name: Other query types
+      href: search-query-overview.md
+  - name: Relevance
+    items:
+    - name: Semantic ranking
+      href: semantic-search-overview.md
+    - name: BM25 ranking
+      href: index-similarity-and-scoring.md
+    - name: Vector ranking
+      href: vector-search-ranking.md
+    - name: Hybrid ranking (RRF)
+      href: hybrid-search-ranking.md
+  - name: Security
+    items:
+    - name: Security overview
+      href: search-security-overview.md
+    - name: Secure access to external data
+      href: search-indexer-securing-resources.md
 - name: Quickstarts
   items:
   - name: Agentic search
@@ -130,76 +186,6 @@ items:
       href: cognitive-search-tutorial-blob.md
     - name: Debug a skillset
       href: cognitive-search-tutorial-debug-sessions.md
-- name: Samples
-  items:
-  - name: C# samples
-    href: samples-dotnet.md
-  - name: Java samples
-    href: samples-java.md
-  - name: JavaScript samples
-    href: samples-javascript.md
-  - name: Python samples
-    href: samples-python.md
-  - name: REST samples
-    href: samples-rest.md
-  - name: Vector samples
-    href: https://github.com/Azure/azure-search-vector-samples
-- name: Concepts
-  items:
-  - name: Data
-    items:
-    - name: Search index
-      href: search-what-is-an-index.md
-    - name: Vector index
-      href: vector-store.md
-    - name: Knowledge store
-      href: knowledge-store-concept-intro.md
-    - name: Data import strategies
-      href: search-what-is-data-import.md
-    - name: Indexers
-      href: search-indexer-overview.md
-  - name: Applied AI
-    items:
-    - name: Multimodal search
-      href: multimodal-search-overview.md
-    - name: Built-in vectorization
-      href: vector-search-integrated-vectorization.md
-    - name: AI enrichment during indexing
-      href: cognitive-search-concept-intro.md
-    - name: Enrichment cache
-      href: cognitive-search-incremental-indexing-conceptual.md
-    - name: Skillsets
-      href: cognitive-search-working-with-skillsets.md
-  - name: Retrieval
-    items:
-    - name: Agentic search
-      href: search-agentic-retrieval-concept.md
-    - name: Full-text search
-      href: search-lucene-query-architecture.md
-    - name: Vector search
-      href: vector-search-overview.md
-    - name: Hybrid search
-      href: hybrid-search-overview.md
-    - name: Retrieval Augmented Generation (RAG)
-      href: retrieval-augmented-generation-overview.md
-    - name: Other query types
-      href: search-query-overview.md
-  - name: Relevance
-    items:
-    - name: Semantic ranking
-      href: semantic-search-overview.md
-    - name: BM25 ranking
-      href: index-similarity-and-scoring.md
-    - name: Vector ranking
-      href: vector-search-ranking.md
-    - name: Hybrid ranking (RRF)
-      href: hybrid-search-ranking.md
-  - name: Security
-    items:
-    - name: Security overview
-      href: search-security-overview.md
-    - name: Secure access to external data
-      href: search-indexer-securing-resources.md
 - name: How-to guides
   items:
   - name: Service management
@@ -605,11 +591,25 @@ items:
       href: knowledge-store-projection-example-long.md
     - name: Connect with Power BI
       href: knowledge-store-connect-power-bi.md
+- name: Samples
+  items:
+  - name: C# samples
+    href: samples-dotnet.md
+  - name: Java samples
+    href: samples-java.md
+  - name: JavaScript samples
+    href: samples-javascript.md
+  - name: Python samples
+    href: samples-python.md
+  - name: REST samples
+    href: samples-rest.md
+  - name: Vector samples
+    href: https://github.com/Azure/azure-search-vector-samples
 - name: Responsible AI
   items:
   - name: Transparency note
     href: /azure/ai-foundry/responsible-ai/search/transparency-note
-- name: Reference
+- name: References
   items:
   - name: REST API reference
     items:
@@ -795,4 +795,4 @@ items:
     - name: Tools and accelerators
       href: resource-tools.md
     - name: Training
-      href: resource-training.md
+      href: resource-training.md
diff --git a/articles/search/vector-search-integrated-vectorization-ai-studio.md b/articles/search/vector-search-integrated-vectorization-ai-studio.md

Original file line number	Diff line number	Diff line change
`@@ -1173,7 +1173,7 @@`
`1173`	`1173`	`{`
`1174`	`1174`	`"source_path_from_root": "/articles/ai-services/openai/concepts/content-filter-personal-information.md",`
`1175`	`1175`	`"redirect_url": "/azure/ai-services/openai/concepts/content-filter",`
`1176`		`- "redirect_document_id": true`
	`1176`	`+ "redirect_document_id": false`
`1177`	`1177`	`},`
`1178`	`1178`	`{`
`1179`	`1179`	`"source_path_from_root": "/articles/ai-services/agents/concepts/agent-catalog.md",`