corrected vector relevance doc bug

Author: HeidiSteen

articles/search/search-relevance-overview.md

@@ -17,10 +17,15 @@ In a query operation, the relevance of any given result is determined by a ranki
 
 Ranking occurs whenever the query request includes full text or vector queries. It doesn't occur if the query invokes strict pattern matching, such as a filter-only query or a specialized query form like autocomplete, suggestions, geospatial search, fuzzy search, or regular expression search. A uniform search score of 1.0 indicates the absence of a ranking algorithm.
 
-***Relevance tuning*** can be used to boost search scores based on extra criteria such as freshness or proximity. In Azure AI Search, relevance tuning is primarily directed at textual and numeric (nonvector) content when you apply a [scoring profile](#custom-boosting-logic-using-scoring-profiles) or invoke the [semantic ranker](semantic-search-overview.md). 
+## Relevance tuning
 
-> [!NOTE]
-> In Azure AI Search, there's no explicit relevance tuning capabilities that apply to vector fields, but you can experiment between Hierarchical Navigable Small World (HNSW) and exhaustive K-nearest neighbors (KNN) to see if one algorithm outperforms the other for your scenario. HNSW graphing with an exhaustive KNN override at query time is the most flexible approach for comparison testing. You can also experiment with various embedding models to see which ones produce higher quality results. Finally, remember that a hybrid query or a vector query on documents that include nonvector fields are in-scope for relevance tuning, so it's just the vector fields themselves that can't participate in a relevance tuning effort.
+***Relevance tuning*** is a technique for boosting search scores based on extra criteria such as weighted fields, freshness, or proximity. In Azure AI Search, relevance tuning options vary based on query type:
+
++ For textual and numeric (nonvector) content in keyword or hybrid search, you can tune relevance through [scoring profiles](#custom-boosting-logic-using-scoring-profiles) or invoking the [semantic ranker](semantic-search-overview.md).
+
++ For vector content in a hybrid query, you can [weight a vector field](hybrid-search-ranking.md#weighted-scores) to boost the importance of the vector component relative to the text component of the hybrid query.
+
++ For pure vector queries, you can experiment between Hierarchical Navigable Small World (HNSW) and exhaustive K-nearest neighbors (KNN) to see if one algorithm outperforms the other for your scenario. HNSW graphing with an exhaustive KNN override at query time is the most flexible approach for comparison testing. You can also experiment with various embedding models to see which ones produce higher quality results. Finally, remember that a hybrid query or a vector query on documents that include nonvector fields are in-scope for relevance tuning, so it's just the vector fields themselves that can't participate in a relevance tuning effort.
 
 ## Levels of ranking
 

articles/search/tutorial-document-extraction-image-verbalization.md

@@ -16,9 +16,9 @@ ms.date: 05/29/2025
 
 # Tutorial: Verbalize images using generative AI
 
-Azure AI Search can extract and index both text and images from PDF documents stored in Azure Blob Storage. This tutorial shows you how to build a multimodal indexing pipeline by describing visual content in natural language and embedding it alongside document text.
+Azure AI Search can extract and index both text and images from PDF documents stored in Azure Blob Storage. This tutorial shows you how to build a multimodal indexing pipeline that includes steps for describing visual content in natural language and using the generated descriptions in your searchable index.
 
-From the source document, each image is passed to the [GenAI Prompt skill (preview)](cognitive-search-skill-genai-prompt.md) to generate a concise textual description. These descriptions, along with the original document text, are then embedded into vector representations using Azure OpenAI’s text-embedding-3-large model. The result is a single index containing semantically searchable content from both modalities: text and verbalized images.
+From the source document, each image is passed to the [GenAI Prompt skill (preview)](cognitive-search-skill-genai-prompt.md) that calls a chat completion model to generate a concise textual description. These descriptions, along with the original document text, are then embedded into vector representations using Azure OpenAI’s text-embedding-3-large model. The result is a single index containing semantically searchable content from both modalities: text and verbalized images.
 
 In this tutorial, you use:
 
@@ -39,9 +39,9 @@ This tutorial demonstrates a lower-cost approach for indexing multimodal content
 
 ## Prerequisites
 
-+ [Azure Storage](/azure/storage/common/storage-account-create), used for storing sample data.
++ [Azure AI Search](search-create-service-portal.md). [Configure your search service](search-manage.md) for role-based access control and a managed identity. Your service must be on the Basic tier or higher. This tutorial isn't supported on the Free tier. It must also be in the same region as your multi-service account.
 
-+ [Azure AI Search](search-what-is-azure-search.md), Basic pricing tier or higher, with a managed identity. [Create a service](search-create-service-portal.md) or [find an existing service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) in your current subscription.
++ [Azure Storage](/azure/storage/common/storage-account-create), used for storing sample data and for creating a [knowledge store](knowledge-store-concept-intro.md).
 
 + A chat completion model hosted in Azure AI Foundry or another source. The model is used to verbalize image content. You provide the URI to the hosted model in the GenAI Prompt skill definition.
 

articles/search/tutorial-document-extraction-multimodal-embeddings.md

@@ -35,24 +35,13 @@ This tutorial demonstrates a lower-cost approach for indexing multimodal content
 > [!NOTE]
 > Setting `imageAction` to `generateNormalizedImages` results in image extraction, which is an extra charge. For more information, see [Azure AI Search pricing](https://azure.microsoft.com/pricing/details/search/) for image extraction.
 
-<!-- Using a REST client and the [Search REST APIs](/rest/api/searchservice/) you will:
-
-> [!div class="checklist"]
-> + Set up sample data and configure an `azureblob` data source
-> + Create an index with support for text and image embeddings
-> + Define a skillset with extraction and embedding steps
-> + Create and run an indexer to process and index content
-> + Search the index you just created
- -->
-
 ## Prerequisites
 
-+ [Azure Storage](/azure/storage/common/storage-account-create), used for storing sample data.
++ [Azure AI Search](search-create-service-portal.md). [Configure your search service](search-manage.md) for role-based access control and a managed identity. Your service must be on the Basic tier or higher. This tutorial isn't supported on the Free tier. It must also be in the same region as your multi-service account.
 
-+ An [Azure AI services multi-service account](/azure/ai-services/multi-service-resource#azure-ai-services-resource-for-azure-ai-search-skills) that provides Azure AI Vision for multimodal embeddings. You must use an Azure AI multi-service account for this task. For an updated list of regions that provide multimodal embeddings, see the [Azure AI Vision documentation](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
++ [Azure Storage](/azure/storage/common/storage-account-create), used for storing sample data and for creating a [knowledge store](knowledge-store-concept-intro.md).
 
-+ [Azure AI Search](search-create-service-portal.md), with a managed identity. [Create a service](search-create-service-portal.md) or [find an existing service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) in your current subscription.  
-  > Your service must be on the Basic tier or higher—this tutorial isn't supported on the Free tier. It must also be in the same region as your multi-service account.
++ An [Azure AI services multi-service account](/azure/ai-services/multi-service-resource#azure-ai-services-resource-for-azure-ai-search-skills) that provides Azure AI Vision for multimodal embeddings. You must use an Azure AI multi-service account for this task. For an updated list of regions that provide multimodal embeddings, see the [Azure AI Vision documentation](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
 
 + [Visual Studio Code](https://code.visualstudio.com/download) with a [REST client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client).
 

articles/search/tutorial-document-layout-image-verbalization.md

@@ -34,9 +34,9 @@ In this tutorial, you use:
 
 ## Prerequisites
 
-+ [Azure Storage](/azure/storage/common/storage-account-create), used for storing sample data.
++ [Azure AI Search](search-create-service-portal.md). [Configure your search service](search-manage.md) for role-based access control and a managed identity. Your service must be on the Basic tier or higher. This tutorial isn't supported on the Free tier. It must also be in the same region as your multi-service account.
 
-+ [Azure AI Search](search-what-is-azure-search.md). [Create a service](search-create-service-portal.md) or [find an existing service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) in your current subscription. Your service must be on the Basic tier or higher. This tutorial isn't supported on the Free tier.
++ [Azure Storage](/azure/storage/common/storage-account-create), used for storing sample data and for creating a [knowledge store](knowledge-store-concept-intro.md).
 
 + A chat completion model hosted in Azure AI Foundry or another source. The model is used to verbalize image content. You provide the URI to the hosted model in the GenAI Prompt skill definition.
 

articles/search/tutorial-document-layout-multimodal-embeddings.md

@@ -31,22 +31,13 @@ In this tutorial, you use:
 
 + A search index configured to store extracted text and image verbalizations. Some content is vectorized for vector-based similarity search.
 
-<!-- Using a REST client and the [Search REST APIs](/rest/api/searchservice/), you will:
-
-> [!div class="checklist"]
-> + Set up sample data and configure an `azureblob` data source
-> + Create an index with support for text and image embeddings
-> + Define a skillset with extraction, embedding and knowleage store file projection steps
-> + Create and run an indexer to process and index content
-> + Search the index you just created -->
-
 ## Prerequisites
 
-+ [Azure Storage](/azure/storage/common/storage-account-create), used for storing sample data.
++ [Azure AI Search](search-create-service-portal.md). [Configure your search service](search-manage.md) for role-based access control and a managed identity. Your service must be on the Basic tier or higher. This tutorial isn't supported on the Free tier. It must also be in the same region as your multi-service account.
 
-+ An [Azure AI services multi-service account](/azure/ai-services/multi-service-resource#azure-ai-services-resource-for-azure-ai-search-skills) that provides Azure AI Vision for multimodal embeddings. You must use an Azure AI multi-service account for this task. For an updated list of regions that provide multimodal embeddings, see the [Azure AI Vision documentation](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
++ [Azure Storage](/azure/storage/common/storage-account-create), used for storing sample data and for creating a [knowledge store](knowledge-store-concept-intro.md).
 
-+ [Azure AI Search](search-what-is-azure-search.md), with a managed identity. [Create a service](search-create-service-portal.md) or [find an existing service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices) in your current subscription. Your service must be on the Basic tier or higher—this tutorial isn't supported on the Free tier. It must also be in the same region as your multi-service account.
++ An [Azure AI services multi-service account](/azure/ai-services/multi-service-resource#azure-ai-services-resource-for-azure-ai-search-skills) that provides Azure AI Vision for multimodal embeddings. You must use an Azure AI multi-service account for this task. For an updated list of regions that provide multimodal embeddings, see the [Azure AI Vision documentation](/azure/ai-services/computer-vision/overview-image-analysis#region-availability).
 
 + [Visual Studio Code](https://code.visualstudio.com/download) with a [REST client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client).