Merge pull request #7689 from MicrosoftDocs/main

Auto Publish – main to live - 2025-10-15 22:09 UTC

Author: learn-build-service-prod[bot]

articles/ai-foundry/responsible-ai/document-intelligence/data-privacy-security.md

@@ -7,7 +7,7 @@ ms.author: lajanuar
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: article
-ms.date: 09/22/2025
+ms.date: 10/15/2025
 ---
 
 
@@ -41,6 +41,6 @@ The "Get Analyze Results" operation is authenticated against the same API key th
 
 **For customer trained models**: The Custom model feature allows customers to build custom models from training data stored in customer’s Azure blob storage locations. The interim outputs after analysis and labeling are stored in the same location. The trained custom models are stored in Azure storage in the same region and logically isolated with their Azure subscription and API credentials.
 
-**Deletes data**: Customers can delete the analysis response at any time by utilizing the [**Delete Analyze Result**](/rest/api/aiservices/document-models/delete-analyze-result?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true&tabs=HTTP) API. . After successfully retrieving the analysis results, calling the [**Delete Analyze Result**](/rest/api/aiservices/document-models/delete-analyze-result?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true&tabs=HTTP) API permanently purges those results.
+**Deletes data**: Analyze response is stored for 24 hours from when the operation completes for retrieval. Customers can delete the analysis response at any time by utilizing the [**Delete Analyze Result**](/rest/api/aiservices/document-models/delete-analyze-result?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true&tabs=HTTP) API. After successfully retrieving the analysis results, calling the [**Delete Analyze Result**](/rest/api/aiservices/document-models/delete-analyze-result?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true&tabs=HTTP) API permanently purges those results. This action applies to all models.
 
 To learn more about privacy and security commitments, see the [Microsoft Trust Center](https://www.microsoft.com/TrustCenter/CloudServices/Azure/default.aspx).

articles/ai-services/language-service/toc.yml

@@ -715,19 +715,65 @@ items:
           href: ../containers/disconnected-containers.md
         - name: Azure AI containers overview
           href: ../cognitive-services-container-support.md
+      - name: Responsible use of AI
+        items:
+        - name: Transparency note for summarization
+          href: ../../ai-foundry/responsible-ai/language-service/transparency-note-extractive-summarization.md
+          displayName: Transparency note for summarization, summarization transparency, Responsible AI, Responsible use of AI
+        - name: Integration and responsible use
+          href: ../../ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use-summarization.md
+          displayName: Responsible deployment, Responsible use, Responsible integration, AI deployment, AI use
+        - name: Characteristics and limitations
+          href: ../../ai-foundry/responsible-ai/language-service/characteristics-and-limitations-summarization.md
+        - name: Data, privacy, and security
+          href: ../../ai-foundry/responsible-ai/language-service/data-privacy.md
+          displayName: Data privacy, logging, data retention
+
+  - name: Text Analytics for health
+    items:
+    - name: Overview
+      href: text-analytics-for-health/overview.md
+      displayName: text analytics for health, healthcare nlp, medical text analysis, clinical text, health entities
+    - name: Quickstart
+      href: text-analytics-for-health/quickstart.md
+    - name: Language support
+      href: text-analytics-for-health/language-support.md
     - name: Responsible use of AI
       items:
-      - name: Transparency note for summarization
-        href: ../../ai-foundry/responsible-ai/language-service/transparency-note-extractive-summarization.md
-        displayName: Transparency note for summarization, summarization transparency, Responsible AI, Responsible use of AI
+      - name: Transparency note for Text Analytics for health
+        href: ../../ai-foundry/responsible-ai/language-service/transparency-note-health.md
+        displayName: Transparency note for Text Analytics health, Text Analytics for health transparency, Responsible AI, Responsible use of AI
       - name: Integration and responsible use
-        href: ../../ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use-summarization.md
+        href: ../../ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use.md
         displayName: Responsible deployment, Responsible use, Responsible integration, AI deployment, AI use
-      - name: Characteristics and limitations
-        href: ../../ai-foundry/responsible-ai/language-service/characteristics-and-limitations-summarization.md
       - name: Data, privacy, and security
         href: ../../ai-foundry/responsible-ai/language-service/data-privacy.md
         displayName: Data privacy, logging, data retention
+    - name: How-to guides
+      items:
+      - name: How to call the API
+        href: text-analytics-for-health/how-to/call-api.md
+      - name: Use containers
+        items:
+        - name: Use Docker containers
+          href: text-analytics-for-health/how-to/use-containers.md
+        - name: Configure Docker containers
+          href: text-analytics-for-health/how-to/configure-containers.md
+        - name: Use container instances
+          href: ../containers/azure-container-instance-recipe.md?context=/azure/ai-services/language-service/context/context
+        - name: Azure AI containers overview
+          href: ../cognitive-services-container-support.md
+    - name: Concepts
+      items:
+      - name: Recognized entity categories
+        href: text-analytics-for-health/concepts/health-entity-categories.md
+      - name: Relation extraction
+        href: text-analytics-for-health/concepts/relation-extraction.md
+      - name: Assertion detection
+        href: text-analytics-for-health/concepts/assertion-detection.md
+      - name: Fast Healthcare Interoperability Resources structuring
+        href: text-analytics-for-health/concepts/fhir.md
+        
 - name: Concepts
   items:
   - name: Developer guide
@@ -868,50 +914,7 @@ items:
         - name: Azure.AI.Language.QuestionAnswering namespace
           href: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-questionanswering
 
-  - name: Text Analytics for health
-    items:
-    - name: Overview
-      href: text-analytics-for-health/overview.md
-      displayName: text analytics for health, healthcare nlp, medical text analysis, clinical text, health entities
-    - name: Quickstart
-      href: text-analytics-for-health/quickstart.md
-    - name: Language support
-      href: text-analytics-for-health/language-support.md
-    - name: Responsible use of AI
-      items:
-      - name: Transparency note for Text Analytics for health
-        href: ../../ai-foundry/responsible-ai/language-service/transparency-note-health.md
-        displayName: Transparency note for Text Analytics health, Text Analytics for health transparency, Responsible AI, Responsible use of AI
-      - name: Integration and responsible use
-        href: ../../ai-foundry/responsible-ai/language-service/guidance-integration-responsible-use.md
-        displayName: Responsible deployment, Responsible use, Responsible integration, AI deployment, AI use
-      - name: Data, privacy, and security
-        href: ../../ai-foundry/responsible-ai/language-service/data-privacy.md
-        displayName: Data privacy, logging, data retention
-    - name: How-to guides
-      items:
-      - name: How to call the API
-        href: text-analytics-for-health/how-to/call-api.md
-      - name: Use containers
-        items:
-        - name: Use Docker containers
-          href: text-analytics-for-health/how-to/use-containers.md
-        - name: Configure Docker containers
-          href: text-analytics-for-health/how-to/configure-containers.md
-        - name: Use container instances
-          href: ../containers/azure-container-instance-recipe.md?context=/azure/ai-services/language-service/context/context
-        - name: Azure AI containers overview
-          href: ../cognitive-services-container-support.md
-    - name: Concepts
-      items:
-      - name: Recognized entity categories
-        href: text-analytics-for-health/concepts/health-entity-categories.md
-      - name: Relation extraction
-        href: text-analytics-for-health/concepts/relation-extraction.md
-      - name: Assertion detection
-        href: text-analytics-for-health/concepts/assertion-detection.md
-      - name: Fast Healthcare Interoperability Resources structuring
-        href: text-analytics-for-health/concepts/fhir.md
+
 - name: Resources
   items:
     - name: Support and help options

articles/search/agentic-retrieval-how-to-create-pipeline.md

@@ -1,29 +1,30 @@
 ---
-title: Build an agentic retrieval solution
+title: 'Tutorial: Build an agentic retrieval solution'
 titleSuffix: Azure AI Search
 description: Learn how to design and build a custom agentic retrieval solution where Azure AI Search handles data retrieval for your custom agents in AI Foundry.
 author: HeidiSteen
 ms.author: heidist
 manager: nitinme
 ms.date: 09/10/2025
 ms.service: azure-ai-search
-ms.topic: how-to
+ms.topic: tutorial
 ms.custom:
   - build-2025
 ---
 
-# Build an agent-to-agent retrieval solution using Azure AI Search
+# Tutorial: Build an agent-to-agent retrieval solution using Azure AI Search
 
 [!INCLUDE [Feature preview](./includes/previews/preview-generic.md)]
 
 This article describes an approach or pattern for building a solution that uses Azure AI Search for knowledge retrieval, and how to integrate knowledge retrieval into a custom solution that includes Azure AI Agent. This pattern uses an agent tool to invoke an agentic retrieval pipeline in Azure AI Search.
 
 :::image type="content" source="media/agentic-retrieval/agent-to-agent-pipeline.svg" alt-text="Diagram of Azure AI Search integration with Azure AI Agent service." lightbox="media/agentic-retrieval/agent-to-agent-pipeline.png" :::
 
-This article supports the [agentic-retrieval-pipeline-example](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/agentic-retrieval-pipeline-example) Python sample on GitHub.
-
 This exercise differs from the [Agentic Retrieval Quickstart](search-get-started-agentic-retrieval.md) in how it uses Azure AI Agent to retrieve data from the index, and how it uses an agent tool for orchestration. If you want to understand the retrieval pipeline in its simplest form, begin with the quickstart.
 
+> [!TIP]
+> To run the code for this tutorial, download the [agentic-retrieval-pipeline-example](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/agentic-retrieval-pipeline-example) Python sample on GitHub.
+
 ## Prerequisites
 
 The following resources are required for this design pattern:
@@ -315,6 +316,18 @@ Look at output tokens in the [activity array](agentic-retrieval-how-to-retrieve.
 
 + Set `maxOutputSize` in the [knowledge agent](agentic-retrieval-how-to-create-knowledge-base.md) to govern the size of the response, or `maxRuntimeInSeconds` for time-bound processing.
 
+## Clean up resources
+
+When you're working in your own subscription, at the end of a project, it's a good idea to remove the resources that you no longer need. Resources left running can cost you money. You can delete resources individually or delete the resource group to delete the entire set of resources.
+
+You can also delete individual objects:
+
++ [Delete a knowledge agent](agentic-retrieval-how-to-create-knowledge-base.md#delete-an-agent)
+
++ [Delete a knowledge source](agentic-knowledge-source-how-to-search-index.md#delete-a-knowledge-source)
+
++ [Delete an index](search-how-to-manage-index.md#delete-an-index)
+
 ## Related content
 
 + [Agentic retrieval in Azure AI Search](agentic-retrieval-overview.md)

articles/search/agentic-retrieval-how-to-retrieve.md

@@ -27,7 +27,7 @@ This article also explains the three components of the retrieval response:
 The retrieve request can include instructions for query processing that override the defaults set on the knowledge agent.
 
 > [!NOTE]
-> By default, there's no model-generated "answer" in the response and you should pass the extracted response to an LLM so that it can ground its answer based on the search results. For an end-to-end example that includes this step, see [Build an agent-to-agent retrieval solution ](agentic-retrieval-how-to-create-pipeline.md) or [Azure OpenAI Demo](https://github.com/Azure-Samples/azure-search-openai-demo).
+> By default, there's no model-generated "answer" in the response and you should pass the extracted response to an LLM so that it can ground its answer based on the search results. For an end-to-end example that includes this step, see [Tutorial: Build an agent-to-agent retrieval solution ](agentic-retrieval-how-to-create-pipeline.md) or [Azure OpenAI Demo](https://github.com/Azure-Samples/azure-search-openai-demo).
 >
 >Alternatively, you can use [answer synthesis](agentic-retrieval-how-to-answer-synthesis.md) to bring answer formulation into the agentic pipeline. In this workflow, the retriever response consists of LLM-formulated answers instead of the raw search results.