Updated GH paths to samples

Author: HeidiSteen

articles/search/retrieval-augmented-generation-overview.md

@@ -58,7 +58,7 @@ A high-level summary of the pattern looks like this:
 + Send it to Azure AI Search to find relevant information.
 + Return the top ranked search results to an LLM.
 + Use the natural language understanding and reasoning capabilities of the LLM to generate a response to the initial prompt.
-+ Optionally, use agentic retrieival where an agent evaluates an answer and finds a better one if the original answer is incomplete or low quality.
++ Optionally, use agentic retrieval where an agent evaluates an answer and finds a better one if the original answer is incomplete or low quality.
 
 Azure AI Search provides inputs to the LLM prompt, but doesn't train the model. In RAG architecture, there's no extra training. The LLM is pretrained using public data, but it generates responses that are augmented by information from the retriever, in this case, Azure AI Search.
 

articles/search/search-agentic-retrieval-concept.md

@@ -143,11 +143,16 @@ You must use the preview REST APIs or a prerelease Azure SDK package that provid
 Choose any of these options for your next step.
 
 <!-- + Watch this demo. -->
-+ [Quickstart](search-get-started-agentic-retrieval.md). Learn the basic workflow using sample data and a prepared index and queries.
++ [Quickstart article: Run agentic retrieval in Azure AI Search](search-get-started-agentic-retrieval.md). Learn the basic workflow using sample data and a prepared index and queries.
 
-+ [(Sample code) Build an agentic retrieval pipeline using Azure AI Search and Azure AI Agent in the Foundry portal](https://github.com/Azure-Samples/azure-search-python-samples/agent-example)
++ Sample code:
 
-+ How-to guides for a closer look at building an agentic retrieval pipeline:
+  + [Quickstart-Agentic-Retrieval: Python](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Agentic-Retrieval)
+  + [Quickstart-Agentic-Retrieval: .NET](https://github.com/Azure-Samples/azure-search-dotnet-samples/blob/main/quickstart-agentic-retrieval)
+  + [Quickstart-Agentic-Retrieval: REST](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/agentic-retrieval)
+  + [End-to-end with Azure AI Search and Azure AI Agent Service](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/agentic-retrieval-pipeline-example)
+
++ How-to guides for a focused look at development tasks:
 
   + [Create an agent](search-agentic-retrieval-how-to-create.md)
   + [Use an agent to retrieve data](search-agentic-retrieval-how-to-retrieve.md)

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

@@ -17,8 +17,7 @@ ms.date: 05/10/2025
 
 This article describes an approach or pattern for building a solution that uses Azure AI Search for data retrieval and how to integrate the retrieval into a custom solution that includes Azure AI Agent.
 
-This article supports the [agent-example](https://github.com/Azure-Samples/azure-search-python-samples/agent-example
-) Python sample on GitHub.
+This article supports the [agent-example](https://github.com/Azure-Samples/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 determine whether to retrieve data from the index, and how it uses an agent tool for orchestration.
 

articles/search/search-document-level-access-overview.md

@@ -4,7 +4,7 @@ titleSuffix: Azure AI Search
 description: Conceptual overview of document-level permissions in Azure AI Search.    
 ms.service: azure-ai-search    
 ms.topic: conceptual    
-ms.date: 05/10/2025    
+ms.date: 05/19/2025    
 author: gmndrg    
 ms.author: gimondra    
 ---  
@@ -49,17 +49,13 @@ By offloading permission enforcement to Azure AI Search, developers can focus on
 
 Document-level permissions in Azure AI Search provide a structured framework for enforcing access controls that align with organizational policies. By using Microsoft Entra-based ACLs and RBAC roles, organizations can create systems that support robust compliance and promote trust among users. These built-in capabilities reduce the need for custom coding, offering a standardized approach to document-level security.  
 
-## Reference documents  
+## Related content
 
-To help you dive deeper into document-level access control in Azure AI Search, here’s a table of key resources:  
+To help you dive deeper into document-level access control in Azure AI Search, here are more articles and samples:  
 
-| Functionality                                   | Reference |  Sample |
+| Functionality                                   | Content |  Sample |
 |---|---|---|
-| **Index permissions using Push APIs**           | [Index permissions using REST API](search-index-access-control-lists-and-rbac-push-api.md)  |  [azure-search-rest-samples/Quickstart-ACL](https://github.com/Azure-Samples/azure-search-rest-samples-pr/tree/main/Quickstart-ACL) or [azure-search-python-samples/Quickstart-ACL](https://github.com/Azure-Samples/azure-search-python-samples-pr/blob/main/Quickstart-ACL) |
-| **Index ADLS Gen2 permissions metadata using built-in indexers** | [Index permissions using ADLS Gen2 indexer](search-indexer-access-control-lists-and-role-based-access.md) |   [azure-search-python-samples/Quickstart-Document-Permissions](https://github.com/Azure-Samples/azure-search-python-samples/blob/main/Quickstart-Document-Permissions) |
-| **Query using Microsoft Entra token-based permissions** | [Query using Microsoft Entra token-based permissions](https://aka.ms/azs-query-preserving-permissions) | See previous samples. |
+| **Index permissions using Push APIs**           | [How to index permissions using REST API](search-index-access-control-lists-and-rbac-push-api.md)  |  [azure-search-rest-samples/Quickstart-ACL](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-ACL) or [azure-search-python-samples/Quickstart-Document-Permissions-Push-API](https://github.com/Azure-Samples/azure-search-python-samples/blob/main/Quickstart-Document-Permissions-Push-API) |
+| **Index ADLS Gen2 permissions metadata using built-in indexers** | [How to index permissions using ADLS Gen2 indexer](search-indexer-access-control-lists-and-role-based-access.md) and [Tutorial: Index ADLS Gen2 permissions metadata using an indexer](tutorial-adls-gen2-indexer-acls.md) |  [azure-search-python-samples/Quickstart-Document-Permissions-Pull-API](https://github.com/Azure-Samples/azure-search-python-samples/blob/main/Quickstart-Document-Permissions-Pull-API) |
+| **Query using Microsoft Entra token-based permissions** | [How to query using Microsoft Entra token-based permissions](https://aka.ms/azs-query-preserving-permissions) | See previous samples. |
 | **Security trimming via filters**              | [Security trimming via filters](search-security-trimming-for-azure-search.md)               |  Not available. |
-  
-## Next steps  
-  
-- [Tutorial: Index ADLS Gen2 permissions metadata](tutorial-adls-gen2-indexer-acls.md)  

articles/search/whats-new.md

@@ -24,15 +24,15 @@ Learn about the latest updates to Azure AI Search functionality, docs, and sampl
 
 | Item                         | Type |  Description |
 |-----------------------------|------|--------------|
-| [Agentic retrieval (preview)](search-agentic-retrieval-concept.md) | Query | Create a conversational search experience powered by large language models (LLMs) and your proprietary data. Agentic retrieval breaks down complex user queries into subqueries, runs the subqueries in parallel, and extracts grounding data from documents indexed in Azure AI Search. The output is intended for agents and custom chat solutions. A new [knowledge agent](search-agentic-retrieval-how-to-create.md) object is introduced in this preview. Its [response payload](search-agentic-retrieval-how-to-retrieve.md) is designed for downstream agent and chat model consumption, with full transparency of the query plan and reference data. To get started, see [Quickstart: Agentic retrieval](search-get-started-agentic-retrieval.md). |
+| [Agentic retrieval (preview)](search-agentic-retrieval-concept.md) | Query | Create a conversational search experience powered by large language models (LLMs) and your proprietary data. Agentic retrieval breaks down complex user queries into subqueries, runs the subqueries in parallel, and extracts grounding data from documents indexed in Azure AI Search. The output is intended for agents and custom chat solutions. A new [knowledge agent](search-agentic-retrieval-how-to-create.md) object is introduced in this preview. Its [response payload](search-agentic-retrieval-how-to-retrieve.md) is designed for downstream agent and chat model consumption, with full transparency of the query plan and reference data. To get started in the portal, see [Quickstart: Agentic retrieval](search-get-started-agentic-retrieval.md). |
 | [Multivector support (preview)](vector-search-multi-vector-fields.md) | Indexing | Index multiple child vectors within a single document field. You can now use vector types in nested fields of complex collections, effectively allowing multiple vectors to be associated with a single document.|
 | [Scoring profiles with semantic ranking (preview)​](semantic-how-to-enable-scoring-profiles.md) | Relevance | Semantic ranker adds a new field, `@search.rerankerBoostedScore`, to help you maintain consistent relevance and greater control over final ranking outcomes in your search pipeline. |
-| [Azure Logic Apps integration (preview)](search-how-to-index-logic-apps-indexers.md) | Indexing | Create an automated indexing pipeline that retrieves content using a logic app workflow. Use the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) in the Azure portal to build an indexing pipeline based on Logic Apps. |
+| [Azure Logic Apps integration (preview)](search-how-to-index-logic-apps-indexers.md) | Indexing | Create an automated indexing pipeline that retrieves content using a logic app workflow. Use the [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) in the Azure portal to build an indexing pipeline based on Azure Logic Apps integration. |
 | [Document-level access control (preview)](search-document-level-access-overview.md) | Security | Flow document-level permissions from blobs in Azure Data Lake Storage (ADLS) Gen2 to searchable documents in an index. Queries can now filter results based on user identity for selected data sources. |
-| [Multimodal search (preview)](multimodal-search-overview.md) | Indexing, Query | Ingest, understand, and retrieve documents that contain text and images, enabling you to perform searches that combine various modalities, such as querying with text to find information embedded in relevant complex images. |
+| [Multimodal search (preview)](multimodal-search-overview.md) | Indexing, Query | Ingest, understand, and retrieve documents that contain text and images, enabling you to perform searches that combine various modalities, such as querying with text to find information embedded in relevant complex images. See [Quickstart: Search for multimodal content](search-get-started-portal-image-search.md) for portal wizard support and [Azure AI Search Multimodal RAG Demo](https://github.com/Azure-Samples/azure-ai-search-multimodal-sample) for a code-first approach. |
 | [GenAI prompt skill (preview)](cognitive-search-skill-genai-prompt.md) | Skills | A new skill that connects to a large language model (LLM) for information, using a prompt you provide. With this skill, you can populate a searchable field using content from an LLM. A primary use case for this skill is *image verbalization*, using an LLM to describe images and send the description to a searchable field in your index. |
 | [Document Layout skill (preview)](cognitive-search-skill-document-intelligence-layout.md)| Skills | New parameters are available for this skill if you use the 2025-05-01-preview API version. New parameters support image offset metadata that improves the image search experience. |
-| Import and vectorize data wizard enhancements | Portal | This wizard provides two paths for creating and populating vector indexes: [Retrieval Augmented Generation (RAG)](search-get-started-portal-import-vectors.md) and [Multimodal](search-get-started-portal-image-search.md). Logic apps integration is through the RAG path. |
+| Import and vectorize data wizard enhancements | Portal | This wizard provides two paths for creating and populating vector indexes: [Retrieval Augmented Generation (RAG)](search-get-started-portal-import-vectors.md) and [Multimodal RAG](search-get-started-portal-image-search.md). Logic apps integration is through the RAG path. |
 | [Index "description" support (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true#request-body) | REST | The latest preview API adds a description to an index. A description is useful in agentic solutions, where the agent reads the description to decide whether to run a query or move on to another index. |
 | [2025-05-01-preview](/rest/api/searchservice/operation-groups?view=rest-searchservice-2025-05-01-preview&preserve-view=true) | REST | New data plane preview REST API version providing programmatic access to the preview features announced in this release. |