@@ -10,14 +10,14 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: concept-article
-ms.date: 09/19/2024
+ms.date: 03/11/2025
 ---
 
 # Skills for extra processing during indexing (Azure AI Search)
 
 This article describes the skills in Azure AI Search that you can include in a [skillset](cognitive-search-working-with-skillsets.md) to access external processing. 
 
-A *skill* provides an atomic operation that transforms content in some way. Often, it's an operation that recognizes or extracts text, but it can also be a utility skill that reshapes the enrichments that are already created. Typically, the output is text-based so that it can be used in [full text search](search-lucene-query-architecture.md) or vectors used in [vector search](vector-search-overview.md).
+A *skill* is an atomic operation that transforms content in some way. Often, it's an operation that recognizes or extracts text, but it can also be a utility skill that reshapes the enrichments that are already created. Typically, the output is either text-based so that it can be used in [full text search](search-lucene-query-architecture.md), or vectors used in [vector search](vector-search-overview.md).
 
 Skills are organized into categories:
 

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 10/01/2024
+ms.date: 03/11/2025
 ---
 
 # Create a hybrid query in Azure AI Search
@@ -19,19 +19,13 @@ ms.date: 10/01/2024
 In this article, learn how to:
 
 + Set up a basic request
-+ Formulate hybrid queries with more parameters and filters
++ Add parameters and filters
 + Improve relevance using semantic ranking or vector weights
 + Optimize query behaviors by controlling text and vector inputs
 
 > [!NOTE]
 > New in [**2024-09-01-preview**](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-09-01-preview&preserve-view=true) is the ability to target filters to just the vector subqueries in a hybrid request. This gives you more precision over how filters are applied. For more information, see [targeting filters to vector subqueries](#hybrid-search-with-filters-targeting-vector-subqueries-preview) in this article.
 
-<!-- To improve relevance in a hybrid query, use these parameters:
-
-+ [vector.queries.weight](vector-search-how-to-query.md#vector-weighting) lets you set the relative weight of the vector query. This feature is particularly useful in complex queries where two or more distinct result sets need to be combined, as is the case for hybrid search. This feature is generally available.
-
-+ [hybridsearch.maxTextRecallSize and countAndFacetMode (preview)](#set-maxtextrecallsize-and-countandfacetmode) give you more control over text inputs into a hybrid query. This feature requires a preview API version.
- -->
 ## Prerequisites
 
 + A search index containing `searchable` vector and nonvector fields. We recommend the [Import and vectorize data wizard](search-import-data-portal.md) to create an index quickly. Otherwise, see [Create an index](search-how-to-create-search-index.md) and [Add vector fields to a search index](vector-search-how-to-create-index.md).

@@ -9,12 +9,12 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 10/01/2024
+ms.date: 03/11/2025
 ---
 
 # Relevance scoring in hybrid search using Reciprocal Rank Fusion (RRF)
 
-Reciprocal Rank Fusion (RRF) is an algorithm that evaluates the search scores from multiple, previously ranked results to produce a unified result set. In Azure AI Search, RRF is used whenever there are two or more queries that execute in parallel. Each query produces a ranked result set, and RRF is used to merge and homogenize the rankings into a single result set, returned in the query response. Examples of scenarios where RRF is always used include [*hybrid search*](hybrid-search-overview.md) and multiple vector queries executing concurrently. 
+Reciprocal Rank Fusion (RRF) is an algorithm that evaluates the search scores from multiple, previously ranked results to produce a unified result set. In Azure AI Search, RRF is used whenever there are two or more queries that execute in parallel. Each query produces a ranked result set, and RRF merges and homogenizes the rankings into a single result set for the query response. Examples of scenarios where RRF is always used include [*hybrid search*](hybrid-search-overview.md) and multiple vector queries executing concurrently. 
 
 RRF is based on the concept of *reciprocal rank*, which is the inverse of the rank of the first relevant document in a list of search results. The goal of the technique is to take into account the position of the items in the original rankings, and give higher importance to items that are ranked higher in multiple lists. This can help improve the overall quality and reliability of the final ranking, making it more useful for the task of fusing multiple ordered search results.
 

@@ -11,7 +11,7 @@ ms.custom:
   - ignite-2023
   - ignite-2024
 ms.topic: conceptual
-ms.date: 10/02/2024
+ms.date: 03/11/2025
 ---
 
 # Estimate and manage capacity of a search service

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: concept-article
-ms.date: 09/19/2024
+ms.date: 03/11/2025
 ms.custom:
   - devx-track-csharp
   - ignite-2023

@@ -11,7 +11,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 10/23/2024
+ms.date: 03/11/2025
 ---
 
 # Index CSV blobs and files using delimitedText parsing mode

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 10/02/2024
+ms.date: 03/11/2025
 ---
 
 # Schedule an indexer in Azure AI Search

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: tutorial
-ms.date: 09/23/2024
+ms.date: 03/11/2025
 ms.custom:
   - devx-track-csharp
   - devx-track-dotnet

@@ -10,12 +10,12 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 09/17/2024
+ms.date: 03/11/2025
 ---
 
 # Data import in Azure AI Search
 
-In Azure AI Search, queries execute over user-owned content that's loaded into a [search index](search-what-is-an-index.md). This article describes the two basic workflows for populating an index: *push* your data into the index programmatically, or *pull* in the data using a [search indexer](search-indexer-overview.md).
+In Azure AI Search, queries execute over your content that's loaded into a [search index](search-what-is-an-index.md). This article describes the two basic workflows for populating an index: *push* your data into the index programmatically, or *pull* in the data using a [search indexer](search-indexer-overview.md).
 
 Both approaches load documents from an external data source. Although you can create an empty index, it's not queryable until you add the content.
 

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2024
 ms.topic: tutorial
-ms.date: 10/05/2024
+ms.date: 03/11/2025
 ---
 
 # Tutorial: Maximize relevance (RAG in Azure AI Search)

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: overview
-ms.date: 10/04/2024
+ms.date: 03/11/2024
 
 ---
 
@@ -34,8 +34,6 @@ Sample code can be found in [this Python notebook](https://github.com/Azure-Samp
 
 - Minimize storage and costs
 
-<!-- - Deploy and secure an app -->
-
 We omitted a few aspects of a RAG pattern to reduce complexity:
 
 - No management of chat history and context. Chat history is typically stored and managed separately from your grounding data, which means extra steps and code. This tutorial assumes atomic question and answers from the LLM and the default LLM experience.

@@ -9,30 +9,29 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 10/01/2024
+ms.date: 03/11/2025
 ---
 
 # Chunk large documents for vector search solutions in Azure AI Search
 
-Partitioning large documents into smaller chunks can help you stay under the maximum token input limits of embedding models. For example, the maximum length of input text for the [Azure OpenAI](/azure/ai-services/openai/how-to/embeddings) text-embedding-ada-002 model is 8,191 tokens. Given that each token is around four characters of text for common OpenAI models, this maximum limit is equivalent to around 6,000 words of text. If you're using these models to generate embeddings, it's critical that the input text stays under the limit. Partitioning your content into chunks ensures that your data can be processed by the embedding models and that you don't lose information due to truncation.
+Partitioning large documents into smaller chunks can help you stay under the maximum token input limits of embedding models. For example, the maximum length of input text for the [Azure OpenAI](/azure/ai-services/openai/how-to/embeddings) text-embedding-ada-002 model is 8,191 tokens. Given that each token is around four characters of text for common OpenAI models, this maximum limit is equivalent to around 6,000 words of text. If you're using these models to generate embeddings, it's critical that the input text stays under the limit. Partitioning your content into chunks helps you meet embedding model requirements and prevents data loss due to truncation.
 
-We recommend [integrated vectorization](vector-search-integrated-vectorization.md) for built-in data chunking and embedding. Integrated vectorization takes a dependency on indexers, skillsets, the [Text Split skill](cognitive-search-skill-textsplit.md), and an embedding skill like [Azure OpenAI Embedding skill](cognitive-search-skill-azure-openai-embedding.md). If you can't use integrated vectorization, this article describes some approaches for chunking your content.
+We recommend [integrated vectorization](vector-search-integrated-vectorization.md) for built-in data chunking and embedding. Integrated vectorization takes a dependency on indexers and skillsets that split text and generate embeddings. If you can't use integrated vectorization, this article describes some alternative approaches for chunking your content.
 
 ## Common chunking techniques
 
-Chunking is only required if the source documents are too large for the maximum input size imposed by models.
+Chunking is only required if the source documents are too large for the maximum input size imposed by models. Here are some common chunking techniques, associated with built-in features if you use [indexers](search-indexer-overview.md) and [skills](cognitive-search-working-with-skillsets.md).
 
-Here are some common chunking techniques, starting with the most widely used method:
-
-+ Fixed-size chunks: Define a fixed size that's sufficient for semantically meaningful paragraphs (for example, 200 words) and allows for some overlap (for example, 10-15% of the content) can produce good chunks as input for embedding vector generators.
-
-+ Variable-sized chunks based on content: Partition your data based on content characteristics, such as end-of-sentence punctuation marks, end-of-line markers, or using features in the Natural Language Processing (NLP) libraries. Markdown language structure can also be used to split the data.
-
-+ Customize or iterate over one of the above techniques. For example, when dealing with large documents, you might use variable-sized chunks, but also append the document title to chunks from the middle of the document to prevent context loss.
+| Approach | Usage | Built-in functionality |
+|----------|-------|-----------------|
+| Fixed-size chunks | Define a fixed size that's sufficient for semantically meaningful paragraphs (for example, 200 words or 600 characters) and allows for some overlap (for example, 10-15% of the content) can produce good chunks as input for embedding vector generators. | [Text Split skill](cognitive-search-skill-textsplit.md), splitting by pages (defined by character length) |
+| Variable-sized chunks based on content characteristics | Partition your data based end-of-sentence punctuation marks, end-of-line markers, or using features in the Natural Language Processing (NLP) libraries that detect document structure. Embedded markup, like HTML or Markdown, have heading syntax that can be used to chunk data by sections. | [Document Layout skill](cognitive-search-skill-document-intelligence-layout.md) or [Text Split skill](cognitive-search-skill-textsplit.md), splitting by sentences. |
+| Custom combinations | Use a combination of fixed and variable sized chunking, or extend an approach. For example, when dealing with large documents, you might use variable-sized chunks, but also append the document title to chunks from the middle of the document to prevent context loss. | None |
+| Document parsing | Indexers can parse larger source documents into smaller search documents for indexing. Strictly speaking, this approach isn't *chunking* but it can sometimes achieve the same objective. | [Index Markdown blobs and files](search-how-to-index-markdown-blobs.md) or [one-to-many indexing](search-howto-index-one-to-many-blobs.md) or [Index JSON blobs and files ](search-howto-index-json-blobs.md) |
 
 ### Content overlap considerations
 
-When you chunk data, overlapping a small amount of text between chunks can help preserve context. We recommend starting with an overlap of approximately 10%. For example, given a fixed chunk size of 256 tokens, you would begin testing with an overlap of 25 tokens. The actual amount of overlap varies depending on the type of data and the specific use case, but we have found that 10-15% works for many scenarios.
+When you chunk data based on fixed size, overlapping a small amount of text between chunks can help preserve context. We recommend starting with an overlap of approximately 10%. For example, given a fixed chunk size of 256 tokens, you would begin testing with an overlap of 25 tokens. The actual amount of overlap varies depending on the type of data and the specific use case, but we find that 10-15% works for many scenarios.
 
 ### Factors for chunking data
 
@@ -42,11 +41,11 @@ When it comes to chunking data, think about these factors:
 
 + User queries: Larger chunks and overlapping strategies help preserve context and semantic richness for queries that target specific information.
 
-+ Large Language Models (LLM) have performance guidelines for chunk size. you need to set a chunk size that works best for all of the models you're using. For instance, if you use models for summarization and embeddings, choose an optimal chunk size that works for both.
++ Large Language Models (LLM) have performance guidelines for chunk size. Find a chunk size that works best for all of the models you're using. For instance, if you use models for summarization and embeddings, choose an optimal chunk size that works for both.
 
 ### How chunking fits into the workflow
 
-If you have large documents, you must insert a chunking step into indexing and query workflows that breaks up large text. When using [integrated vectorization](vector-search-integrated-vectorization.md), a default chunking strategy using the [Text Split skill](./cognitive-search-skill-textsplit.md) is applied. You can also apply a custom chunking strategy using a [custom skill](cognitive-search-custom-skill-web-api.md). Some libraries that provide chunking include:
+If you have large documents, insert a chunking step into indexing and query workflows that breaks up large text. When using [integrated vectorization](vector-search-integrated-vectorization.md), a default chunking strategy using the [Text Split skill](./cognitive-search-skill-textsplit.md) is common. You can also apply a custom chunking strategy using a [custom skill](cognitive-search-custom-skill-web-api.md). Some external libraries that provide chunking include:
 
 + [LangChain Text Splitters](https://python.langchain.com/v0.1/docs/modules/data_connection/document_transformers/)
 + [Semantic Kernel TextChunker](/dotnet/api/microsoft.semantickernel.text.textchunker)
@@ -77,7 +76,7 @@ The `pages` parameter adds extra parameters:
 
 + `maximumPageLength` defines the maximum number of characters <sup>1</sup> or tokens <sup>2</sup> in each chunk. The text splitter avoids breaking up sentences, so the actual character count depends on the content.
 + `pageOverlapLength` defines how many characters from the end of the previous page are included at the start of the next page. If set, this must be less than half the maximum page length.
-+ `maximumPagesToTake` defines how many pages / chunks to take from a document. The default value is 0, which means taking all pages or chunks from the document.
++ `maximumPagesToTake` defines how many pages / chunks to take from a document. The default value is 0, which means to take all pages or chunks from the document.
 
 <sup>1</sup> Characters don't align to the definition of a [token](/azure/ai-services/openai/concepts/prompt-engineering#space-efficiency). The number of tokens measured by the LLM might be different than the character size measured by the Text Split skill.
 
@@ -95,23 +94,23 @@ The following table shows how the choice of parameters affects the total chunk c
 | `pages` | 5000 | 500 | 38 |
 | `sentences` | N/A | N/A | 13361 |
 
-Using a `textSplitMode` of `pages` results in a majority of chunks having total character counts close to `maximumPageLength`. Chunk character count varies due to differences on where sentence boundaries fall inside the chunk. Chunk token length varies due to differences in the contents of the chunk.
+Using a `textSplitMode` of `pages` results in most chunks having total character counts close to `maximumPageLength`. Chunk character count varies due to differences on where sentence boundaries fall inside the chunk. Chunk token length varies due to differences in the contents of the chunk.
 
 The following histograms show how the distribution of chunk character length compares to chunk token length for [gpt-35-turbo](/azure/ai-services/openai/how-to/chatgpt) when using a `textSplitMode` of `pages`, a `maximumPageLength` of 2000, and a `pageOverlapLength` of 500 on the Earth at Night e-book:
 
    :::image type="content" source="./media/vector-search-how-to-chunk-documents/maximumpagelength-2000-pageoverlap-500-characters.png" alt-text="Histogram of chunk character count for maximumPageLength 2000 and pageOverlapLength 500.":::
 
    :::image type="content" source="./media/vector-search-how-to-chunk-documents/maximumpagelength-2000-pageoverlap-500-tokens.png" alt-text="Histogram of chunk token count for maximumPageLength 2000 and pageOverlapLength 500.":::
 
-Using a `textSplitMode` of `sentences` results in a large number of chunks consisting of individual sentences. These chunks are significantly smaller than those produced by `pages`, and the token count of the chunks more closely matches the character counts.
+Using a `textSplitMode` of `sentences` results in a large number of chunks consisting of individual sentences. These chunks are smaller than those produced by `pages`, and the token count of the chunks more closely matches the character counts.
 
 The following histograms show how the distribution of chunk character length compares to chunk token length for [gpt-35-turbo](/azure/ai-services/openai/how-to/chatgpt) when using a `textSplitMode` of `sentences` on the Earth at Night e-book:
 
    :::image type="content" source="./media/vector-search-how-to-chunk-documents/sentences-characters.png" alt-text="Histogram of chunk character count for sentences.":::
 
    :::image type="content" source="./media/vector-search-how-to-chunk-documents/sentences-tokens.png" alt-text="Histogram of chunk token count for sentences.":::
 
-The optimal choice of parameters depends on how the chunks will be used. For most applications, it's recommended to start with the following default parameters:
+The optimal choice of parameters depends on how the chunks are used. For most applications, it's recommended to start with the following default parameters:
 
 | `textSplitMode` | `maximumPageLength` | `pageOverlapLength` |
 |-----------------|-----------------|-----------------|
@@ -164,7 +163,7 @@ print(f"Max: {max_token_count}")
 
 Output indicates that no pages have zero tokens, the average token length per page is 189 tokens, and the maximum token count of any page is 1,583.
 
-Knowing the average and maximum token size gives you insight into setting chunk size. Although you could use the standard recommendation of 2000 characters with a 500 character overlap, in this case it makes sense to go lower given the token counts of the sample document. In fact, setting an overlap value that's too large can result in no overlap appearing at all.
+Knowing the average and maximum token size gives you insight into setting chunk size. Although you could use the standard recommendation of 2,000 characters with a 500 character overlap, in this case it makes sense to go lower given the token counts of the sample document. In fact, setting an overlap value that's too large can result in no overlap appearing at all.
 
 ```python
 from langchain.text_splitter import RecursiveCharacterTextSplitter
@@ -195,6 +194,6 @@ A [fixed-sized chunking and embedding generation sample](https://github.com/Azur
 
 ## See also
 
-+ [Understanding embeddings in Azure OpenAI Service](/azure/ai-services/openai/concepts/understand-embeddings)
++ [Understand embeddings in Azure OpenAI Service](/azure/ai-services/openai/concepts/understand-embeddings)
 + [Learn how to generate embeddings](/azure/ai-services/openai/how-to/embeddings)
 + [Tutorial: Explore Azure OpenAI Service embeddings and document search](/azure/ai-services/openai/tutorials/embeddings)

@@ -9,14 +9,14 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 09/19/2024
+ms.date: 03/11/2025
 ---
 
 # Generate embeddings for search queries and documents
 
-Azure AI Search doesn't host vectorization models, so one of your challenges is creating embeddings for query inputs and outputs. You can use any supported embedding model, but this article assumes Azure OpenAI embedding models for the steps.
+Azure AI Search doesn't host embedding models, so one of your challenges is creating vectors for query inputs and outputs. You can use any supported embedding model, but this article assumes Azure OpenAI embedding models for illustration.
 
-We recommend [integrated vectorization](vector-search-integrated-vectorization.md), which provides built-in data chunking and vectorization. Integrated vectorization takes a dependency on indexers, skillsets, and built-in or custom skills that point to a model that executes externally from Azure AI Search.
+We recommend [integrated vectorization](vector-search-integrated-vectorization.md), which provides built-in data chunking and vectorization. Integrated vectorization takes a dependency on indexers, skillsets, and built-in or custom skills that point to a model that executes externally from Azure AI Search. Several built-in skills point to embedding models in Azure AI Foundry, which makes integrated vectorization your easiest solution for solving the embedding challenge.
 
 If you want to handle data chunking and vectorization yourself, we provide demos in the [sample repository](https://github.com/Azure/azure-search-vector-samples/tree/main) that show you how to integrate with other community solutions.
 
@@ -30,7 +30,7 @@ If you want to handle data chunking and vectorization yourself, we provide demos
 
 ## Create resources in the same region
 
-Integrated vectorization requires resources to be in the same region:
+Integrated vectorization usually requires resources to be in the same region:
 
 1. [Check regions for a text embedding model](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability).
 

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 09/24/2024
+ms.date: 03/11/2025
 ---
 
 # Create a vector query in Azure AI Search
@@ -497,7 +497,7 @@ Because nearest neighbor search always returns the requested `k` neighbors, it's
 
 This parameter is still in preview. We recommend preview REST API version [2024-05-01-preview](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true).
 
-In this example, all matches that score below 0.8 are excluded from vector search results, even if the number of results fall below `k`.
+In this example, all matches that score below 0.8 are excluded from vector search results, even if the number of results falls below `k`.
 
 ```http
 POST https://[service-name].search.windows.net/indexes/[index-name]/docs/search?api-version=2024-05-01-preview 

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: concept-article
-ms.date: 09/19/2024
+ms.date: 03/11/2025
 ---
 
 # Vector storage in Azure AI Search
@@ -29,9 +29,9 @@ Considerations for vector storage include the following points:
 
 In Azure AI Search, there are two patterns for working with search results. 
 
-+ Generative search. Language models formulate a response to the user's query using data from Azure AI Search. This pattern includes an orchestration layer to coordinate prompts and maintain context. In this pattern, search results are fed into prompt flows, received by chat models like GPT and Text-Davinci. This approach is based on [**Retrieval augmented generation (RAG)**](retrieval-augmented-generation-overview.md) architecture, where the search index provides the grounding data.
++ Generative search. Language models formulate a response to the user's query using grounding data from Azure AI Search. This pattern typically includes an orchestration layer to coordinate prompts and maintain context. In this pattern, search results are fed into prompt flows, received by chat models like GPT. This approach is based on [**Retrieval augmented generation (RAG)**](retrieval-augmented-generation-overview.md) architecture, where the search index provides the grounding data.
 
-+ Classic search using a search bar, query input string, and rendered results. The search engine accepts and executes the vector query, formulates a response, and you render those results in a client app. In Azure AI Search, results are returned in a flattened row set, and you can choose which fields to include search results. Since there's no chat model, it's expected that you would populate the vector store (search index) with nonvector content that's human readable in your response. Although the search engine matches on vectors, you should use nonvector values to populate the search results. [**Vector queries**](vector-search-how-to-query.md) and [**hybrid queries**](hybrid-search-how-to-query.md) cover the types of query requests you can formulate for classic search scenarios.
++ Classic search using a search bar, query input, and rendered results. The search engine formulates the response using verbatim content in the search index, with no extra reasoning or logic. At query time, your application code or the search engine vectorizes the user input into a vector. The search engine performs a vector search over vector fields and formulates a response. You render those results in a client app. In Azure AI Search, results are returned in a flattened row set, and you can choose which fields to include search results. Since there's no chat model, it's expected that you would populate the vector store (search index) with nonvector content that's human readable in your response. Although the search engine matches on vectors, you should use nonvector values to populate the search results. [**Vector queries**](vector-search-how-to-query.md) and [**hybrid queries**](hybrid-search-how-to-query.md) cover the types of query requests you can formulate for classic search scenarios.
 
 Your index schema should reflect your primary use case. The following section highlights the differences in field composition for solutions built for generative AI or classic search.
 
@@ -171,7 +171,7 @@ Vector index limits and estimations are covered in [another article](vector-sear
 This section introduces vector run time operations, including connecting to and securing a single index.
 
 > [!NOTE]
-> When managing an index, be aware that there is no portal or API support for moving or copying an index. Instead, customers typically point their application deployment solution at a different search service (if using the same index name), or revise the name to create a copy on the current search service, and then build it.
+> When managing an index, be aware that there's no portal or API support for moving or copying an index. Instead, customers typically point their application deployment solution at a different search service (if using the same index name), or revise the name to create a copy on the current search service, and then build it.
 
 ### Continuously available