@@ -6,23 +6,23 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 10/15/2024
+ms.date: 04/14/2025
 ms.custom:
   - devx-track-csharp
   - ignite-2023
 ---
 
 # Extract text and information from images by using AI enrichment
 
-Through [AI enrichment](cognitive-search-concept-intro.md), Azure AI Search gives you several options for creating and extracting searchable text from images, including:
+Images often contain useful information that's relevant in search scenarios. You can [vectorize images](search-get-started-portal-image-search.md) to represent visual content in your search index. Or, you can use [AI enrichment and skillsets](cognitive-search-concept-intro.md) to create and extract searchable *text* from images, including:
 
 + [OCR](cognitive-search-skill-ocr.md) for optical character recognition of text and digits
 + [Image Analysis](cognitive-search-skill-image-analysis.md) that describes images through visual features
 + [Custom skills](#passing-images-to-custom-skills) to invoke any external image processing that you want to provide
 
 By using OCR, you can extract text and from photos or pictures, such as the word *STOP* in a stop sign. Through image analysis, you can generate a text representation of an image, such as *dandelion* for a photo of a dandelion, or the color *yellow*. You can also extract metadata about the image, such as its size.
 
-This article covers the fundamentals of working with images, and also describes several common scenarios, such as working with embedded images, custom skills, and overlaying visualizations on original images.
+This article covers the fundamentals of working with images in skillsets, and also describes several common scenarios, such as working with embedded images, custom skills, and overlaying visualizations on original images.
 
 To work with image content in a skillset, you need:
 

@@ -7,8 +7,8 @@ ms.author: gimondra
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
-ms.date: 10/21/2024
+ms.topic: reference
+ms.date: 04/14/2025
 ---
 
 # Custom Web API skill in an Azure AI Search enrichment pipeline

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 10/15/2024
+ms.date: 04/14/2025
 ---
 
 # Map enriched output to fields in a search index in Azure AI Search

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 10/21/2024
+ms.date: 04/14/2025
 ms.custom:
   - devx-track-csharp
   - devx-track-dotnet
@@ -23,7 +23,7 @@ Matches on partial terms can be either an autocompleted query or a suggested mat
 
 ## Typeahead experiences in Azure AI Search
 
-Typeahead can use *autocomplete*, which completes a partial input for a whole term query, or *suggestions* that invite click through to a particular match. Autocomplete produces a query. Suggestions produce a matching document.
+Typeahead can be either *autocomplete*, which completes a partial input for a whole term query, or *suggestions* that invite click through to a particular match. Autocomplete produces a query. Suggestions produce a matching document.
 
 The following screenshot illustrates both. Autocomplete anticipates a potential term, finishing *tw* with *in*. Suggestions are mini search results, where a field like `hotel name` represents a matching hotel search document from the index. For suggestions, you can surface any field that provides descriptive information.
 

@@ -11,7 +11,7 @@ ms.custom:
   - ignite-2023
   - ignite-2024
 ms.topic: conceptual
-ms.date: 12/18/2024
+ms.date: 04/15/2025
 ---
 
 # Retrieval Augmented Generation (RAG) in Azure AI Search
@@ -41,7 +41,7 @@ Microsoft has several built-in implementations for using Azure AI Search in a RA
 + Azure OpenAI, [use a search index with or without vectors](/azure/ai-services/openai/concepts/use-your-data).
 + Azure Machine Learning, [use a search index as a vector store in a prompt flow](/azure/machine-learning/how-to-create-vector-index).
 
-Curated approaches make it simple to get started, but for more control over the architecture, you need a custom solution. These templates create end-to-end solutions in:
+Curated approaches make it simple to get started, but for more control over the architecture, you need a custom solution. The following templates create end-to-end solutions in:
 
 + [Python](https://aka.ms/azai/py)
 + [.NET](https://aka.ms/azai/net)
@@ -58,6 +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 RAG 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.
 
@@ -94,8 +95,6 @@ Since you probably know what kind of content you want to search over, consider t
 | text | vectors <sup>1</sup> | Text can be chunked and vectorized in an indexer pipeline, or handled externally and then [indexed as vector fields](vector-search-how-to-create-index.md) in your index. |
 | image | tokens, unaltered text <sup>2</sup> | [Skills](cognitive-search-working-with-skillsets.md) for OCR and Image Analysis can process images for text recognition or image characteristics. Image information is converted to searchable text and added to the index. Skills have an indexer requirement. |
 | image | vectors <sup>1</sup> | Images can be vectorized in an indexer pipeline, or handled externally for a mathematical representation of image content and then [indexed as vector fields](vector-search-how-to-create-index.md) in your index. You can use [Azure AI Vision multimodal](/azure/ai-services/computer-vision/how-to/image-retrieval) or an open source model like [OpenAI CLIP](https://github.com/openai/CLIP/blob/main/README.md) to vectorize text and images in the same embedding space.|
-<!-- | audio | vectors <sup>1</sup> | Vectorized audio content can be [indexed as vector fields](vector-search-how-to-create-index.md) in your index. Vectorization of audio content often requires intermediate processing that converts audio to text, and then text to vecctors. [Azure AI Speech](/azure/ai-services/speech-service/overview) and [OpenAI Whisper](https://platform.openai.com/docs/guides/speech-to-text) are two examples for this scenario. | 
-| video | vectors <sup>1</sup> | Vectorized video content can be [indexed as vector fields](vector-search-how-to-create-index.md) in your index. Similar to audio, vectorization of video content also requires extra processing, such as breaking up the video into frames or smaller chunks for vectorization. | -->
 
  <sup>1</sup> Azure AI Search provides [integrated data chunking and vectorization](vector-search-integrated-vectorization.md), but you must take a dependency on indexers and skillsets. If you can't use an indexer, Microsoft's [Semantic Kernel](/semantic-kernel/overview/) or other community offerings can help you with a full stack solution. For code samples showing both approaches, see [azure-search-vectors repo](https://github.com/Azure/azure-search-vector-samples).
 
@@ -156,7 +155,8 @@ In comparison and benchmark testing, hybrid queries with text and vector fields,
 
 The following Python code demonstrates the essential components of a RAG workflow in Azure AI Search. You need to set up the clients, define a system prompt, and provide a query. The prompt tells the LLM to use just the results from the query, and how to return the results. For more steps based on this example, see this [RAG quickstart](search-get-started-rag.md).
 
-For the Azure Government cloud, modify the API endpoint on the token provider to `"https://cognitiveservices.azure.us/.default"`.
+> [!NOTE]
+> For the Azure Government cloud, modify the API endpoint on the token provider to `"https://cognitiveservices.azure.us/.default"`.
 
 ```python
 # Set up the query for generating responses
@@ -223,39 +223,55 @@ A RAG solution that includes Azure AI Search can leverage [built-in data chunkin
 
 ## How to get started
 
+There are many ways to get started, including code-first solutions and demos.
+
+### [**Docs**](#tab/docs)
+
 + [Try this RAG quickstart](search-get-started-rag.md) for a demonstration of query integration with chat models over a search index.
 
 + [Tutorial: How to build a RAG solution in Azure AI Search](tutorial-rag-build-solution.md) for focused coverage on the features and pattern for RAG solutions that obtain grounding data from a search index.
 
-+ Start with solution accelerators:
++ [Review indexing concepts and strategies](search-what-is-an-index.md) to determine how you want to ingest and refresh data. Decide whether to use vector search, keyword search, or hybrid search. The kind of content you need to search over, and the type of queries you want to run, determines index design.
 
-  + [Chat with your data solution accelerator](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator)
-  
-  + [Conversational Knowledge Mining solution accelerator](https://github.com/microsoft/Customer-Service-Conversational-Insights-with-Azure-OpenAI-Services)
++ [Review creating queries](search-query-create.md) to learn more about search request syntax and requirements.
 
-  + [Document Knowledge Mining accelerator](https://github.com/microsoft/Document-Knowledge-Mining-Solution-Accelerator)
+> [!NOTE]
+> Some Azure AI Search features are intended for human interaction and aren't useful in a RAG pattern. Specifically, you can skip features like autocomplete and suggestions. Other features like facets and orderby might be useful, but would be uncommon in a RAG scenario.
 
-  + [Build your own copilot solution accelerator](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator)
+### [**Demos and code**](#tab/demos)
 
-    + [Client Advisor](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator/blob/main/ClientAdvisor/README.md)
+Check out the following GitHub repositories for code, documentation, and video demonstrations where applicable.
 
-    + [Research Assistant](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator/blob/main/ResearchAssistant/README.md)
++ [RAG Time Journeys](https://github.com/microsoft/rag-time)
 
-    + [Generic copilot](https://github.com/microsoft/Generic-Build-your-own-copilot-Solution-Accelerator)
++ [azure-search-vector-samples](https://github.com/Azure/azure-search-vector-samples)
 
-+ [Use enterprise chat app templates](https://aka.ms/azai) deploy Azure resources, code, and sample grounding data using fictitious health plan documents for Contoso and Northwind. This end-to-end solution gives you an operational chat app in as little as 15 minutes. Code for these templates is the **azure-search-openai-demo** featured in several presentations. The following links provide language-specific versions:
+### [**Templates**](#tab/templates)
 
-  + [.NET](https://aka.ms/azai/net)
-  + [Python](https://aka.ms/azai/py)
-  + [JavaScript](https://aka.ms/azai/js)
-  + [Java](https://aka.ms/azai/javat)
+[Use enterprise chat app templates](https://aka.ms/azai) deploy Azure resources, code, and sample grounding data using fictitious health plan documents for Contoso and Northwind. This end-to-end solution gives you an operational chat app in as little as 15 minutes. Code for these templates is the **azure-search-openai-demo** featured in several presentations. The following links provide language-specific versions:
 
-+ [Review indexing concepts and strategies](search-what-is-an-index.md) to determine how you want to ingest and refresh data. Decide whether to use vector search, keyword search, or hybrid search. The kind of content you need to search over, and the type of queries you want to run, determines index design.
++ [.NET](https://aka.ms/azai/net)
++ [Python](https://aka.ms/azai/py)
++ [JavaScript](https://aka.ms/azai/js)
++ [Java](https://aka.ms/azai/javat)
 
-+ [Review creating queries](search-query-create.md) to learn more about search request syntax and requirements.
+### [**Accelerators**](#tab/accelerators)
 
-> [!NOTE]
-> Some Azure AI Search features are intended for human interaction and aren't useful in a RAG pattern. Specifically, you can skip features like autocomplete and suggestions. Other features like facets and orderby might be useful, but would be uncommon in a RAG scenario.
++ [Chat with your data solution accelerator](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator)
+
++ [Conversational Knowledge Mining solution accelerator](https://github.com/microsoft/Customer-Service-Conversational-Insights-with-Azure-OpenAI-Services)
+
++ [Document Knowledge Mining accelerator](https://github.com/microsoft/Document-Knowledge-Mining-Solution-Accelerator)
+
++ [Build your own copilot solution accelerator](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator)
+
+  + [Client Advisor](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator/blob/main/ClientAdvisor/README.md)
+
+  + [Research Assistant](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator/blob/main/ResearchAssistant/README.md)
+
+  + [Generic copilot](https://github.com/microsoft/Generic-Build-your-own-copilot-Solution-Accelerator)
+
+---
 
 ## See also
 

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 10/22/2024
+ms.date: 04/14/2025
 ---
 
 # Add autocomplete and search suggestions in client apps

@@ -8,8 +8,8 @@ ms.author: heidist
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
-ms.date: 10/21/2024
+ms.topic: reference
+ms.date: 04/14/2025
 ---
 
 # Content metadata properties used in Azure AI Search

@@ -17,9 +17,7 @@ ms.date: 02/24/2025
 
 Azure AI Search provides information retrieval and uses optional AI integration to extract more value from text and vector content. 
 
-The following table summarizes features by category. For more information about how Azure AI Search compares with other search technologies, see [Compare search options](search-what-is-azure-search.md#compare-search-options).
-
-There's feature parity in all Azure public, private, and sovereign clouds, but some features aren't supported in specific regions. For more information, see [Choose a region](search-region-support.md).
+The following table summarizes features by category. There's feature parity in all Azure public, private, and sovereign clouds, but some features aren't supported in specific regions. For more information, see [Choose a region](search-region-support.md).
 
 > [!NOTE]
 > Looking for preview features? See the [preview features list](search-api-preview.md).

@@ -7,7 +7,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 11/01/2024
+ms.date: 04/14/2025
 ---
 
 # Create an index in Azure AI Search

@@ -8,12 +8,12 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 10/10/2024
+ms.date: 04/14/2025
 ---
 
 # Define an index projection for parent-child indexing
 
-For indexes containing chunked documents, an index projection specifies how parent-child content is mapped to fields in a search index for one-to-many indexing. Through an index projection, you can send content to:
+For indexes containing chunked documents, an *index projection* specifies how parent-child content is mapped to fields in a search index for one-to-many indexing. Through an index projection, you can send content to:
 
 - A single index, where the parent fields repeat for each chunk, but the grain of the index is at the chunk level. The [RAG tutorial](tutorial-rag-build-solution-index-schema.md) is an example of this approach.
 

@@ -1,15 +1,15 @@
 ---
-title: Use Azure.Search.Documents (v11) in .NET
+title: Use Azure.Search.Documents in .NET
 titleSuffix: Azure AI Search
-description: Learn how to create and manage search objects in a .NET application using C# and the Azure.Search.Documents (v11) client library.
+description: Learn how to create and manage search objects in a .NET application using C# and the Azure.Search.Documents client library.
 
 manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.devlang: csharp
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 10/23/2024
+ms.date: 04/14/2025
 ms.custom:
   - devx-track-csharp
   - devx-track-dotnet
@@ -18,13 +18,9 @@ ms.custom:
 
 # How to use Azure.Search.Documents in a .NET application
 
-This article explains how to create and manage search objects using C# and the [**Azure.Search.Documents**](/dotnet/api/overview/azure/search) (version 11) client library in the Azure SDK for .NET.
+This article explains how to create and manage search objects using C# and the [**Azure.Search.Documents**](/dotnet/api/overview/azure/search) client library in the Azure SDK for .NET.
 
-## About version 11
-
-Azure SDK for .NET includes an [**Azure.Search.Documents**](/dotnet/api/overview/azure/search) client library from the Azure SDK team that is functionally equivalent to the previous client library, [Microsoft.Azure.Search](/dotnet/api/microsoft.azure.search). Version 11 is more consistent in terms of Azure programmability. Some examples include [`AzureKeyCredential`](/dotnet/api/azure.azurekeycredential) key authentication, and [System.Text.Json.Serialization](/dotnet/api/system.text.json.serialization) for JSON serialization.
-
-As with previous versions, you can use this library to:
+You can use this library for data plane operations, including:
 
 + Create and manage search indexes, data sources, indexers, skillsets, and synonym maps
 + Load and manage search documents in an index
@@ -40,19 +36,13 @@ The client library defines classes like `SearchIndex`, `SearchField`, and `Searc
 + [`Azure.Search.Documents.Indexes.Models`](/dotnet/api/azure.search.documents.indexes.models)
 + [`Azure.Search.Documents.Models`](/dotnet/api/azure.search.documents.models)
 
-Version 11 targets the [2020-06-30 search service specification](https://github.com/Azure/azure-rest-api-specs/tree/main/specification/search/data-plane/Azure.Search/stable/2020-06-30). 
-
-The client library doesn't provide [service management operations](/rest/api/searchmanagement/), such as creating and scaling search services and managing API keys. If you need to manage your search resources from a .NET application, use the [Microsoft.Azure.Management.Search](/dotnet/api/microsoft.azure.management.search) library in the Azure SDK for .NET.
-
-## Upgrade to v11
-
-If you've been using the previous version of the .NET SDK and you'd like to upgrade to the current generally available version, see [Upgrade to Azure AI Search .NET SDK version 11](search-dotnet-sdk-migration-version-11.md).
+The **Azure.Search.Documents** client library doesn't provide [service management operations](/rest/api/searchmanagement/), such as creating and scaling search services and managing API keys. If you need to manage your search resources from a .NET application, use the [Azure.ResourceManager.Search](/dotnet/api/overview/azure/resourcemanager.search-readme) library in the Azure SDK for .NET.
 
 ## SDK requirements
 
 + Visual Studio 2019 or later.
 
-+ Your own Azure AI Search service. In order to use the SDK, you need the name of your service and one or more API keys. [Create a service in the Azure portal](search-create-service-portal.md) if you don't have one.
++ [Azure AI Search](search-create-service-portal.md)
 
 + Download the [NuGet package](https://www.nuget.org/packages/Azure.Search.Documents) using **Tools** > **NuGet Package Manager** > **Manage NuGet Packages for Solution** in Visual Studio. Search for the package name `Azure.Search.Documents`.
 

@@ -9,7 +9,7 @@ ms.author: heidist
 
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 03/21/2025
+ms.date: 04/14/2025
 ---
 
 # Load data into a search index in Azure AI Search

@@ -11,7 +11,7 @@ ms.custom:
   - ignite-2023
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 10/21/2024
+ms.date: 04/14/2025
 ---
 
 # Model complex data types in Azure AI Search

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: tutorial
-ms.date: 10/31/2024
+ms.date: 04/14/2025
 ---
 
 # Tutorial: Query an Azure AI Search index from Power Apps

@@ -1,5 +1,5 @@
 ---
-title: Service limits for tiers and skus
+title: Service Limits for Tiers and SKUs
 titleSuffix: Azure AI Search
 description: Service limits used for capacity planning and maximum limits on requests and responses for Azure AI Search.
 
@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 04/09/2025
+ms.date: 04/14/2025
 ms.custom:
   - references_regions
   - build-2024
@@ -107,7 +107,12 @@ This table shows the progression of vector quota increases in GB over time. The
 The service enforces a vector index size quota *for every partition* in your search service. Each extra partition increases the available vector index size quota. This quota is a hard limit to ensure your service remains healthy, which means that further indexing attempts once the limit is exceeded results in failure. You can resume indexing once you free up available quota by either deleting some vector documents or by scaling up in partitions.
 
 > [!IMPORTANT]
-> Higher vector limits are tied to larger partition sizes. Regions that run on older infrastructure are subject to the July-April limits. Review the [regions list](search-region-support.md) for status on partition storage limits.
+> Higher vector limits are tied to [larger partition sizes](#partition-storage-gb). Currently, higher vector limits aren't available in the following regions, which are subject to the July–April limits.
+> 
+> + Israel Central
+> + Qatar Central
+> + ⁠Spain Central
+> + South India
 
 ## Indexer limits
 

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 10/31/2024
+ms.date: 04/14/2025
 ---
 
 # Create a full text query in Azure AI Search
@@ -29,7 +29,7 @@ In Azure AI Search, a query is a read-only request against the docs collection o
 
 A full text query is specified in a `search` parameter and consists of terms, quote-enclosed phrases, and operators. Other parameters add more definition to the request.
 
-The following [Search POST REST API](/rest/api/searchservice/documents/search-post) call illustrates a query request using the mentioned parameters.
+The following [Search POST REST API](/rest/api/searchservice/documents/search-post) call illustrates a query request using `search` and other parameters.
 
 ```http
 POST https://[service name].search.windows.net/indexes/hotels-sample-index/docs/search?api-version=2024-07-01
@@ -46,7 +46,7 @@ POST https://[service name].search.windows.net/indexes/hotels-sample-index/docs/
 
 ### Key points
 
-+ **`search`** provides the match criteria, usually whole terms or phrases, with or without operators. Any field that is attributed as *searchable* in the index schema is a candidate for this parameter.
++ **`search`** provides the match criteria, usually whole terms or phrases, with or without operators. Any field that is attributed as *searchable* in the index schema is within scope for a search operation.
 
 + **`queryType`** sets the parser: *simple*, *full*. The [default simple query parser](search-query-simple-examples.md) is optimal for full text search. The [full Lucene query parser](search-query-lucene-examples.md) is for advanced query constructs like regular expressions, proximity search, fuzzy and wildcard search. This parameter can also be set to *semantic* for [semantic ranking](semantic-search-overview.md) for advanced semantic modeling on the query response.
 
@@ -66,7 +66,7 @@ Parameters used to shape the response:
 
 ## Choose a client
 
-For early development and proof-of-concept testing, start with the Azure portal or a REST client. Both approaches are interactive, useful for targeted testing, and help you assess the effects of different properties without having to write any code.
+For early development and proof-of-concept testing, start with the Azure portal or a REST client or a Jupyter notebook. These approaches are interactive, useful for targeted testing, and help you assess the effects of different properties without having to write any code.
 
 To call search from within an app, use the `Azure.Document.Search` client libraries in the Azure SDKs for .NET, Java, JavaScript, and Python.
 

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 10/31/2024
+ms.date: 04/14/2025
 ---
 # Fuzzy search to correct misspellings and typos
 

@@ -11,7 +11,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 10/31/2024
+ms.date: 04/14/2025
 ---
 
 # Examples of *full* Lucene search syntax (advanced queries)

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 10/21/2024
+ms.date: 04/14/2025
 ---
 
 # Partial term search and patterns with special characters (hyphens, wildcard, regex, patterns)

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 10/31/2024
+ms.date: 04/14/2025
 ---
 
 # Examples of *simple* search queries in Azure AI Search

@@ -6,7 +6,7 @@ author: mattgotteiner
 ms.author: magottei
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 03/21/2025
+ms.date: 04/14/2025
 ms.custom:
   - subject-reliability
   - references_regions

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 10/30/2024
+ms.date: 04/14/2025
 ---
 
 # Find encrypted objects and information
@@ -41,7 +41,7 @@ You must have [Search Service Contributor](search-security-rbac.md#built-in-role
 
 1. On the left side, select **Keys** and copy an admin API. 
 
-For the remaining steps, switch to PowerShell and the REST API. the Azure portal doesn't show encryption key information for any object.
+For the remaining steps, switch to PowerShell and the REST API. The Azure portal doesn't show encryption key information for any object.
 
 ## Retrieve object properties
 

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 10/28/2024
+ms.date: 04/14/2025
 ---
 
 # Add synonyms in Azure AI Search

@@ -11,7 +11,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 10/21/2024
+ms.date: 04/14/2025
 ---
 
 # Search indexes in Azure AI Search

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2024
 ms.topic: overview
-ms.date: 12/10/2024
+ms.date: 04/15/2025
 ---
 
 # What's Azure AI Search?
@@ -126,7 +126,7 @@ Or, try solution accelerators:
 > [!TIP]
 > For help with complex or custom solutions, [**contact a partner**](resource-partners-knowledge-mining.md) with deep expertise in Azure AI Search technology.
 
-## Compare search options
+<!-- ## Compare search options
 
 Customers often ask how Azure AI Search compares with other search-related solutions. The following table summarizes key differences.
 
@@ -145,4 +145,4 @@ Key strengths include:
 + Azure AI integration for transformations that make content text and vector searchable.
 + Microsoft Entra security for trusted connections, and Azure Private Link for private connections in no-internet scenarios.
 + [Full search experience](search-features-list.md): Linguistic and custom text analysis in 56 languages. Faceting, autocomplete queries and suggested results, and synonyms.
-+ Azure scale, reliability, and global reach.
++ Azure scale, reliability, and global reach. -->

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 09/24/2024
+ms.date: 04/14/2025
 ---
 
 # Configure network access and firewall rules for Azure AI Search

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 10/08/2024
+ms.date: 04/14/2025
 ---
 
 # Create a private endpoint for a secure connection to Azure AI Search
@@ -18,7 +18,7 @@ This article explains how to configure a private connection to Azure AI Search s
 
 + [Create an Azure virtual network](#create-the-virtual-network), or use an existing one
 + [Configure a search service to use a private endpoint](#create-a-search-service-with-a-private-endpoint)
-+ [Create an Azure virtual machine in the same virtual network](#create-a-virtual-machine)
++ [Create an Azure virtual machine (client) in the same virtual network](#create-a-virtual-machine)
 + [Test using a browser session on the virtual machine](#connect-to-the-vm)
 
 Other Azure resources that might privately connect to Azure AI Search include Azure OpenAI for "use your own data" scenarios. Azure AI Foundry doesn't run in a virtual network, but it can be configured on the backend to send requests over the Microsoft backbone network. Configuration for this traffic pattern is enabled by Microsoft when your request is submitted and approved. For this scenario:

@@ -8,13 +8,13 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: overview
-ms.date: 03/11/2024
+ms.date: 04/14/2024
 
 ---
 
 # How to build a RAG solution using Azure AI Search
 
-This tutorial series demonstrates a pattern for building RAG solutions on Azure AI Search. It covers the components built in Azure AI Search, dependencies, and optimizations for maximizing relevance and minimizing costs.
+This tutorial series demonstrates a pattern for building RAG solutions on Azure AI Search. It explains the components that you need to build, enumerates the dependencies, and provides optimizations for maximizing relevance and minimizing costs.
 
 Sample data is a [collection of PDFs](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/nasa-e-book/earth_book_2019_text_pages) uploaded to Azure Storage. The content is from [NASA's Earth free e-book](https://www.nasa.gov/ebooks/earth/).
 

@@ -7,7 +7,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: overview
-ms.date: 03/31/2025
+ms.date: 04/15/2025
 ms.custom:
   - references_regions
   - ignite-2024
@@ -20,6 +20,12 @@ Learn about the latest updates to Azure AI Search functionality, docs, and sampl
 > [!NOTE]
 > Preview features are announced here, but we also maintain a [preview features list](search-api-preview.md) so you can find them in one place.
 
+## April 2025
+
+| Item                         | Type |  Description |
+|-----------------------------|------|--------------|
+| [RAG Time Journey](https://github.com/microsoft/rag-time) | Demo code | Code and video demonstrations of Retrieval Augmented Generation (RAG) workflows that use Azure AI Search. Segments include fundamentals, patterns and use-cases, [vector indexing at scale](https://github.com/microsoft/rag-time/tree/main/Journey%203%20-%20Optimize%20your%20Vector%20Index%20for%20Scale), and [agentic search](https://github.com/microsoft/rag-time/tree/main/Journey%20Bonus%20-%20Agentic%20RAG) where you use an agent to evaluate a result and generate a better answer. |
+
 ## March 2025
 
 | Item                         | Type |  Description |