More updates for fast-follow

Author: HeidiSteen

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

@@ -9,27 +9,26 @@ ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: concept-article
 ms.custom: references_regions
-ms.date: 05/15/2025
+ms.date: 05/19/2025
 ---
 
 # Agentic retrieval in Azure AI Search
 
 [!INCLUDE [Feature preview](./includes/previews/preview-generic.md)]
 
-In Azure AI Search, *agentic retrieval* is a new parallel query processing architecture that uses conversational language models to generate multiple subqueries for a single retrieval request, incorporating conversation history and semantic ranking to produce high-quality grounding data for custom chat and generative AI solutions that include agents.
+In Azure AI Search, *agentic retrieval* is a new parallel query processing architecture that incorporates user conversation history and Azure OpenAI models to plan, retrieve and synthesize queries for improved results. It produces high-quality grounding data for custom chat and generative AI solutions that include agents.
 
 Programmatically, agentic retrieval is supported through a new Knowledge Agents object (also known as a search agent) in the 2025-05-01-preview data plane REST API and in Azure SDK prerelease packages that provide the feature. An agent's retrieval response is designed for downstream consumption by other agents and chat apps.
 
 ## Why use agentic retrieval
 
-You should use agentic retrieval when you want to send data to an agent or customize a chat experience with high quality inputs that include your proprietary data.
+You should use agentic retrieval when you want to send high quality data to an agent or to ground a chat experience with inputs that include your proprietary content.
 
-The *agentic* aspect is a reasoning step in query planning processing that's performed by a supported large language model (LLM) that you provide. The LLM is tasked with designing multiple subqueries based on: user questions, chat history, and parameters on the request. The subqueries target your indexed documents (plain text and vectors) in Azure AI Search.
+The *agentic* aspect is a reasoning step in query planning processing that's performed by a supported large language model (LLM) that you provide. The LLM analyzes the entire chat thread to identify the underlying information need. Instead of a single, catch-all query, the model breaks down compound questions into focused subqueries based on: user questions, chat history, and parameters on the request. The subqueries target your indexed documents (plain text and vectors) in Azure AI Search.This hybrid approach ensures you surface both keyword matches and semantic similarities at once, dramatically improving recall. 
 
 The *retrieval* component is the ability to run subqueries simultaneously, merge results, semantically rank results, and return a three-part response that includes grounding data for the next conversation turn, reference data so that you can inspect the source content, and an activity plan that shows query execution steps.
 
 Query expansion and parallel execution, plus the retrieval response, are the key capabilities of agentic retrieval that make it the best choice for generative AI (RAG) applications.
-<!-- Queries target a new retrieval pipeline in AI Search supports parallel processing, expanding the scope of single request to include subqueries Query execution runs on your search service, utilizing the best and most effective relevance enhancements in Azure AI Search, including semantic ranker. Output is intended for integration into custom chat solutions, particularly those patterned after an agent-to-agent approach. -->
 
 Agentic retrieval adds latency to query processing, but it makes up for it by adding these capabilities:
 
@@ -83,7 +82,7 @@ Billing for agentic retrieval has two parts:
 
 + Billing for query planning is pay-as-you-go in Azure OpenAI. It's token based for both input and output tokens. The model you assign to the agent is the one charged for token usage. For example, if you use gpt-4o, the token charge appears in the bill for gpt-4o.
 
-+ Billing for semantic ranking during query execution. Billing is suspended during the initial roll-out phase, from May 19 through June 30, 2025. On July 1, billing will be token based and is pay-as-you-go on the Azure AI Search side through the semantic ranker. Semantic ranker, which is a premium billable feature, is an integral part of agentic retrieval. You're charged on the Azure AI Search side for token inputs to the semantic ranking models.
++ Billing for semantic ranking during query execution. Billing is suspended during the initial roll-out phase but then transitions to pay-as-you-go on the Azure AI Search side through the semantic ranker. Semantic ranker, which is a premium billable feature, is an integral part of agentic retrieval. You're charged on the Azure AI Search side for token inputs to the semantic ranking models.
 
 Semantic ranking is performed for every subquery in the plan. Semantic ranking charges are based on the number of tokens returned by each subquery.
 

articles/search/search-how-to-index-logic-apps-indexers.md

@@ -1,30 +1,30 @@
 ---
-title: Connect to Logic Apps
+title: Connect to Azure Logic Apps
 titleSuffix: Azure AI Search
-description: Use a Logic Apps workflow for indexer-based indexing in Azure AI Search.
+description: Use an Azure Logic Apps workflow for indexer-based indexing in Azure AI Search.
 manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.custom: references_regions
 ms.topic: how-to
-ms.date: 05/15/2025
+ms.date: 05/19/2025
 ---
 
-# Use a Logic Apps workflow for indexer-based indexing in Azure AI Search
+# Use an Azure Logic Apps workflow for indexer-based indexing in Azure AI Search
 
 [!INCLUDE [Feature preview](./includes/previews/preview-generic.md)]
 
-Support for Logic Apps integration is now in public preview, available in the Azure portal [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) only. In Azure AI Search, a logic apps workflow is used for indexing and vectorization, and it's equivalent to an indexer and data source in Azure AI Search. 
+Support for Azure Logic Apps integration is now in public preview, available in the Azure portal [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) only. In Azure AI Search, a logic app workflow is used for indexing and vectorization, and it's equivalent to an indexer and data source in Azure AI Search. 
 
-You can create a workflow in Azure AI Search using the Import and vectorize data wizard, and then manage the workflow in Logic Apps alongside your other workflows. Behind the scenes, the wizard follows a workflow template that pulls in (ingests) content from a source for indexing in AI Search. The connectors used in this scenario are prebuilt and already exist in Azure Logic Apps, so the workflow template just provides details for those connectors to create connections to the data source, AI Search, and other items to complete the ingestion workflow. 
+You can create a workflow in Azure AI Search using the Import and vectorize data wizard, and then manage the workflow in Azure Logic Apps alongside your other workflows. Behind the scenes, the wizard follows a workflow template that pulls in (ingests) content from a source for indexing in AI Search. The connectors used in this scenario are prebuilt and already exist in Azure Logic Apps, so the workflow template just provides details for those connectors to create connections to the data source, AI Search, and other items to complete the ingestion workflow. 
 
 > [!NOTE]
-> Logic Apps workflows are a billable resource. For more information, see [Azure Logic Apps pricing](/azure/logic-apps/logic-apps-pricing).
+> A logic app workflow is a billable resource. For more information, see [Azure Logic Apps pricing](/azure/logic-apps/logic-apps-pricing).
 
 ## Key features
 
-Logic Apps integration in Azure AI Search adds support for:
+Azure Logic Apps integration in Azure AI Search adds support for:
 
 + More data sources from Microsoft and other providers
 + Integrated vectorization
@@ -50,40 +50,41 @@ Review the following requirements before you start:
 
 + You must be an **Owner** or **Contributor** in your Azure subscription, with permissions to create resources.
 
-+ Azure AI Search in a [supported region](#supported-regions), basic tier or higher if you want to use a search service identity for connections to an Azure data source, otherwise you can use any tier, subject to tier limits. 
++ Azure AI Search, basic tier or higher if you want to use a search service identity for connections to an Azure data source, otherwise you can use any tier, subject to tier limits. 
 
 + Azure OpenAI, with a [supported embedding model](#supported-models) deployment. Vectorization is integrated into the workflow. If you don't need vectors, you can ignore the fields or try another indexing strategy.
 
-+ Logic Apps should have a [system-assigned managed identity](/azure/logic-apps/authenticate-with-managed-identity) if you want to use Microsoft Entra ID authentication on connections rather than API keys.
++ Azure Logic Apps is a [supported region](#supported-regions). It should have a [system-assigned managed identity](/azure/logic-apps/authenticate-with-managed-identity) if you want to use Microsoft Entra ID authentication on connections rather than API keys.
 
 ### Supported regions
 
-End-to-end functionality is available in the following regions, which provide the data source connection, document cracking, document chunks, support for Azure OpenAI embedding models, and the Azure AI indexer support for pulling the data.
+End-to-end functionality is available in the following regions, which provide the data source connection, document cracking, document chunks, support for Azure OpenAI embedding models, and the Azure AI indexer support for pulling the data. The following regions for Azure Logic Apps provide the `ParseDocument` action upon which Azure AI Search integration is based.
 
-+ Australia East
-+ Brazil South
-+ South Central US
 + East US
 + East US 2
++ South Central US
++ West US 2
++ West US 3
++ Brazil South
++ Australia East
 + East Asia
-+ North Europe
 + Southeast Asia
++ North Europe
 + Sweden Central
 + UK South
-+ West US 2
-+ West US 3
 
 ### Supported models
 
-Deploy one of the following [embedding models](/azure/ai-services/openai/concepts/models#embeddings) on Azure OpenAI.
+The logic app path through the **Import and vectorize data** wizard supports a selection of embedding models.
+Deploy one of the following [embedding models](/azure/ai-services/openai/concepts/models#embeddings) on Azure OpenAI for your end-to-end workflow.
 
 + text-embedding-3-small
 + text-embedding-3-large
 + text-embedding-ada-002
 
 ### Supported connectors
 
-The following connectors are helpful for indexing unstructured data, as a complement to classic indexers that primarily target structured data. 
+The following connectors are useful for indexing unstructured data, as a complement to classic indexers that primarily target structured data. 
 
 + [SharePoint](/connectors/sharepointonline/)
 + [OneDrive](/connectors/onedrive/)
@@ -101,13 +102,13 @@ Currently, the public preview has these limitations:
 + Deletion detection isn't supported. You must manually [delete orphaned documents](search-howto-reindex.md#delete-orphan-documents) from the index.
 + Duplicate documents in the search index are a known issue in this preview. Consider deleting objects and starting over if this becomes an issue.
 
-## Create a Logic Apps workflow
+## Create a logic app workflow
 
-Follow these steps to create a Logic Apps workflow for indexing content in Azure AI Search.
+Follow these steps to create a logic app workflow for indexing content in Azure AI Search.
 
 1. Start the Import and vectorize data wizard in the Azure portal.
 
-1. Choose a [supported Logic Apps indexer](#supported-connectors).
+1. Choose a [supported Azure Logic Apps indexer](#supported-connectors).
 
    :::image type="content" source="media/logic-apps-connectors/choose-data-source.png" alt-text="Screenshot of the chosen data source page in the Import and vectorize data wizard." lightbox="media/logic-apps-connectors/choose-data-source.png" :::
 

articles/search/whats-new.md

@@ -27,7 +27,7 @@ Learn about the latest updates to Azure AI Search functionality, docs, and sampl
 | [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). |
 | [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. |
-| [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 Logic Apps. |
 | [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. |
 | [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. |