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[release build azure search] Logic Apps connector doc
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articles/search/search-get-started-portal-import-vectors.md

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1. Select **Next**.
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### [Logic Apps](#tab/connect-logic-apps)
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The current preview adds support for Logic Apps connectors. For a list of supported connectors and operations:
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+ [Use a Logic Apps connector for indexer-based indexing](search-how-to-index-logic-apps-indexers.md)
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## Vectorize your text
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---
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title: Connect to Logic Apps
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titleSuffix: Azure AI Search
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description: Use a Logic Apps workflow for indexer-based indexing in Azure AI Search.
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manager: nitinme
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author: HeidiSteen
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ms.author: heidist
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ms.service: azure-ai-search
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ms.custom: references_regions
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ms.topic: how-to
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ms.date: 05/15/2025
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---
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# Use a Logic Apps workflow for indexer-based indexing in Azure AI Search
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[!INCLUDE [Feature preview](./includes/previews/preview-generic.md)]
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Support for Logic Apps integration is now in public preview, available in the Azure portal [Quickstart wizard](search-get-started-portal-import-vectors.md) only.
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A Logic apps workflow is equivalent to an indexer in Azure AI Search, inclusive of both the data source and indexer definition. Logic Apps integration adds support for more data sources, and extends to Azure OpenAI models for vectorizing data during indexing.
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You can create a workflow in Azure AI Search using the Quickstart wizard, and then manage it 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.
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After you're done with the wizard, you've got a logic app resource and workflow that's live and running. You can view the running workflow, or you can open the designer in Azure Logic Apps to edit the workflow, as you regularly do if you'd started from Azure Logic Apps instead.
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Logic Apps workflows are a billable resource. For more information, see [Azure Logic Apps pricing](/azure/logic-apps/logic-apps-pricing).
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## Key features
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The Quickstart wizard generates a Logic Apps template one each for on-demand or scheduled indexing, and a search index. This capability provides:
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+ Support for more data sources
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+ Integrated vectorization
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+ Scheduled or on-demand indexing
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## Supported regions
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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:
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+ Australia East
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+ Brazil South
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+ South Central US
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+ East US
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+ East US 2
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+ East Asia
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+ North Europe
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+ Southeast Asia
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+ Sweden Central
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+ UK South
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+ West US 2
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+ West US 3
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## Create a Logic Apps workflow
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Follow these steps to create a Logic Apps workflow for indexing content in Azure AI Search.
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1. Start the Quickstart wizard in the Azure portal.
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1. Choose a Logic Apps indexer.
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:::image type="content" source="media/logic-apps-connectors/choose-data-source.png" alt-text="Screenshot of the choose data source page in the Quickstart wizard." lightbox="media/logic-apps-connectors/choose-data-source.png" :::
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## Template management
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Templates are created by the wizard when you specify a Logic Apps indexer. To create and manage templates, including template deletion, do this through Logic Apps. The Azure portal search service dashboard doesn't provide template management, and currently there's no programmatic support in Azure AI Search APIs.
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## Related content
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+ [Indexers](search-indexer-overview.md)
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articles/search/toc.yml

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href: search-index-access-control-lists-and-rbac-push-api.md
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- name: Create a custom analyzer
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href: tutorial-create-custom-analyzer.md
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- name: Multimodal indexing tutorials
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items:
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- name: Index multimodal content using semantic chunking
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href: tutorial-multimodal-index-embeddings-skill.md
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- name: Index image verbalizations using semantic chunking
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href: tutorial-multimodal-index-image-verbalization-skill.md
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- name: Index multimodal content using embedding and document extraction skill
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- name: Index multimodal content using document extraction
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href: tutorial-multimodal-indexing-with-embedding-and-doc-extraction.md
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- name: Index multimodal content using image verbalization and document extraction skill
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- name: Index image verbalizations using document extraction
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href: tutorial-multimodal-indexing-with-image-verbalization-and-doc-extraction.md
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- name: RAG tutorials
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items:
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href: search-how-to-alias.md
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- name: Import large data sets
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href: search-how-to-large-index.md
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- name: Logic Apps workflows
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items:
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- name: Create a workflow
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href: search-how-to-index-logic-apps-indexers.md
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- name: Indexers
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items:
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- name: Create an indexer
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- name: Filter on vector queries
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displayName: query
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href: vector-search-filters.md
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- name: Keyword search
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- name: Full text query

articles/search/whats-new.md

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ms.author: heidist
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ms.service: azure-ai-search
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ms.topic: overview
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ms.date: 04/15/2025
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ms.date: 05/15/2025
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| Item                         | Type | Description |
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|-----------------------------|------|--------------|
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| [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 integration with custom chat solutions. A new [knowledge agent](search-agentic-retrieval-how-to-create.md) 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. For developer guidance, see [Build an agent-to-agent retrieval solution](search-agentic-retrieval-how-to-pipeline.md). |
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| 2025-05-01-preview | REST | New data plane preview REST API version providing programmatic access to the preview features announced in this release. |
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| [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 integration with custom chat solutions. A new [knowledge agent](search-agentic-retrieval-how-to-create.md) 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). |
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| [Logic Apps integration (preview)](search-how-to-index-logic-apps-indexers.md) | Indexing | Create an automated indexing pipeline that retrieves content using a Logic Apps workflow. Use the [Quickstart wizard](search-get-started-portal-import-vectors.md) in the Azure portal to build the indexing pipeline.|
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| [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. |
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## April 2025
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