fixed links

Author: HeidiSteen

articles/search/cognitive-search-skill-textsplit.md

@@ -13,9 +13,12 @@ ms.date: 10/01/2024
 
 # Text split cognitive skill
 
+> [!IMPORTANT] 
+> Some parameters are in public preview under [Supplemental Terms of Use](https://azure.microsoft.com/support/legal/preview-supplemental-terms/). The [preview REST API](/rest/api/searchservice/index-preview) supports these parameters.
+
 The **Text Split** skill breaks text into chunks of text. You can specify whether you want to break the text into sentences or into pages of a particular length. This skill is useful if there are maximum text length requirements in other skills downstream, such as embedding skills that pass data chunks to embedding models on Azure OpenAI and other model providers. For more information about this scenario, see [Chunk documents for vector search](vector-search-how-to-chunk-documents.md).
 
-Several parameters are version-specific. The skills parameter table notes the API version in which a parameter was introduced so that you know whether a [version upgrade](search-api-migration.md) is required. To use version-specific features such as token chunking in **2024-09-01-preview**, you can use the Azure portal, or target a REST API version, or check an Azure SDK change log to see if it supports the feature.
+Several parameters are version-specific. The skills parameter table notes the API version in which a parameter was introduced so that you know whether a [version upgrade](search-api-migration.md) is required. To use version-specific features such as *token chunking* in **2024-09-01-preview**, you can use the Azure portal, or target a REST API version, or check an Azure SDK change log to see if it supports the feature.
 
 The Azure portal supports most preview features and can be used to create or update a skillset. For updates to the Text Split skill, edit the skillset JSON definition to add new preview parameters. 
 

articles/search/hybrid-search-how-to-query.md

@@ -24,7 +24,7 @@ In this article, learn how to:
 + 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 [scope filters to vector subqueries](#hybrid-search-with-filters-scoped-to-vector-subqueries-preview) in this article.
+> 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:
 
@@ -180,7 +180,7 @@ api-key: {{admin-api-key}}
 
 + When you postfilter query results, the number of results might be less than top-n.
 
-## Hybrid search with filters scoped to vector subqueries (preview)
+## Hybrid search with filters targeting vector subqueries (preview)
 
 Using [**2024-09-01-preview**](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-09-01-preview&preserve-view=true), you can override a global filter on the search request by applying a secondary filter that targets just the vector subqueries in a hybrid request.
 

articles/search/search-api-preview.md

@@ -25,9 +25,9 @@ Preview features are removed from this list if they're retired or transition to
 
 |Feature                          | Category | Description | Availability  |
 |---------|------------------|-------------|---------------|
-| [**Lower the dimension requirements for retrained text embedding models on Azure OpenAI**](vector-search-how-to-configure-compression-storage.md) | Feature | Text-embedding-3-small and Text-embedding-3-large are trained using Matryoshka Representation Learning (MRL), allowing you to truncate the embedding vectors to fewer dimensions, allowing you to adjust the balance between vector index size usage and retrieval quality. A new `truncationDimension` provides the MRL behaviors as an additional parameter in a vector compression configuration. This can only be configured for new vector fields. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-09-01-preview&preserve-view=true). |
+| [**Lower the dimension requirements for MRL-trained text embedding models on Azure OpenAI**](vector-search-how-to-configure-compression-storage.md#use-mrl-compression-and-truncated-dimensions-preview) | Feature | Text-embedding-3-small and Text-embedding-3-large are trained using Matryoshka Representation Learning (MRL). This allows you to truncate the embedding vectors to fewer dimensions, and adjust the balance between vector index size usage and retrieval quality. A new `truncationDimension` provides the MRL behaviors as an additional parameter in a vector compression configuration. This can only be configured for new vector fields. | [Create or Update Index (preview)](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-09-01-preview&preserve-view=true). |
 | [**Unpack `@search.score` to view subscores in hybrid search results**](hybrid-search-ranking.md#unpack-a-search-score-into-subscores-preview) | Feature | You can investigate Reciprocal Rank Fusion (RRF) ranked results by viewing the individual query subscores of the final merged and scored result. A new `debug` property unpacks the search score. `QueryResultDocumentSubscores`, `QueryResultDocumentRerankerInput`, and `QueryResultDocumentSemanticField` provide the extra detail. | [Search Documents (preview)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-09-01-preview&preserve-view=true). |
-| [**Target filters in a hybrid search to just the vector queries**](hybrid-search-how-to-query.md#hybrid-search-with-filters-scoped-to-vector-subqueries-preview) | Feature | A filter on a hybrid query involves all subqueries on the request, regardless of type. You can override the global filter to scope the filter to a specific subquery. A new `filterOverride` parameter provides the behaviors. | [Search Documents (preview)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-09-01-preview&preserve-view=true). |
+| [**Target filters in a hybrid search to just the vector queries**](hybrid-search-how-to-query.md#hybrid-search-with-filters-targeting-vector-subqueries-preview) | Feature | A filter on a hybrid query involves all subqueries on the request, regardless of type. You can override the global filter to scope the filter to a specific subquery. A new `filterOverride` parameter provides the behaviors. | [Search Documents (preview)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-09-01-preview&preserve-view=true). |
 | [**Text Split skill (token chunking)**](cognitive-search-skill-textsplit.md) | Applied AI (skills) | This skill has new parameters that improve data chunking for embedding models. A new `unit` parameter lets you specify token chunking. You can now chunk by token length, setting the length to a value that makes sense for your embedding model. You can also specify the tokenizer and any tokens that shouldn't be split during data chunking. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-09-01-preview&preserve-view=true). |
 | [**Azure AI Vision multimodal embedding skill**](cognitive-search-skill-vision-vectorize.md) | Applied AI (skills) | A new skill type that calls Azure AI Vision multimodal API to generate embeddings for text or images during indexing. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
 | [**Azure Machine Learning (AML) skill**](cognitive-search-aml-skill.md) | Applied AI (skills) | AML skill integrates an inferencing endpoint from Azure Machine Learning. | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true). In previous preview APIs, it supports connections to deployed custom models in an AML workspace. Starting in the 2024-05-01-preview, you can use this skill in workflows that connect to embedding models in the Azure AI Studio model catalog. It's also available in the portal, in skillset design, assuming Azure AI Search and Azure Machine Learning services are deployed in the same subscription. |

articles/search/vector-search-how-to-configure-compression-storage.md

@@ -197,7 +197,7 @@ This feature is in preview. It's available in `2024-09-01-preview` and in beta S
 #### Requirements
 
 - Text-embedding-3-small, Text-embedding-3-large (text content only).
-- Vector fields of type `Edm.Half` or `Edm.Single`.
+- New vector fields of type `Edm.Half` or `Edm.Single` (you can't add MRL compression to an existing field).
 - [HNSW algorithm](vector-search-ranking.md) (no support for exhaustive KNN in this preview).
 - [Configure scalar or binary quantization](#option-1-configure-quantization). We recommend binary quantization.
 

articles/search/whats-new.md

@@ -23,9 +23,9 @@ ms.custom:
 
 | Item                         | Type |  Description |
 |-----------------------------|------|--------------|
-| [**Lower the dimension requirements for retrained text embedding models on Azure OpenAI**](vector-search-how-to-configure-compression-storage.md#evaluate-the-options) | Feature | Text-embedding-3-small and Text-embedding-3-large are retrained using the Matryoshka Representation Learning (MRL) technique, allowing you to specify a lower dimension limit, saving on vector storage and increasing query performance. A new `truncationDimension` in the [2024-09-01-preview](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-09-01-preview&preserve-view=true) enables access to MRL compression in text embedding models. |
+| [**Lower the dimension requirements for MRL-trained text embedding models on Azure OpenAI**](vector-search-how-to-configure-compression-storage.md#use-mrl-compression-and-truncated-dimensions-preview) | Feature | Text-embedding-3-small and Text-embedding-3-large are trained using Matryoshka Representation Learning (MRL). This allows you to truncate the embedding vectors to fewer dimensions, and adjust the balance between vector index size usage and retrieval quality. A new `truncationDimension` in the [2024-09-01-preview](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2024-09-01-preview&preserve-view=true) enables access to MRL compression in text embedding models. This can only be configured for new vector fields. |
 | [**Unpack `@search.score` to view subscores in hybrid search results**](hybrid-search-ranking.md#unpack-a-search-score-into-subscores-preview) | Feature | You can investigate Reciprocal Rank Fusion (RRF) ranked results by viewing the individual query subscores of the final merged and scored result. A new `debug` property unpacks the search score. `QueryResultDocumentSubscores`, `QueryResultDocumentRerankerInput`, and `QueryResultDocumentSemanticField` provide the extra detail. These definitions are available in the [2024-09-01-preview](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-09-01-preview&preserve-view=true). |
-| [**Target filters in a hybrid search to just the vector queries**](hybrid-search-how-to-query.md#hybrid-search-with-filters-scoped-to-vector-subqueries-preview) | Feature | A filter on a hybrid query involves all subqueries on the request, regardless of type. You can override the global filter to scope the filter to a specific subquery. The new `filterOverride` parameter is available on hybrid queries using the [2024-09-01-preview](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-09-01-preview&preserve-view=true). |
+| [**Target filters in a hybrid search to just the vector queries**](hybrid-search-how-to-query.md#hybrid-search-with-filters-targeting-vector-subqueries-preview) | Feature | A filter on a hybrid query involves all subqueries on the request, regardless of type. You can override the global filter to scope the filter to a specific subquery. The new `filterOverride` parameter is available on hybrid queries using the [2024-09-01-preview](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-09-01-preview&preserve-view=true). |
 | [**Text Split skill (token chunking)**](cognitive-search-skill-textsplit.md) | Applied AI (skills) | This skill has new parameters that improve data chunking for embedding models. A new `unit` parameter lets you specify token chunking. You can now chunk by token length, setting the length to a value that makes sense for your embedding model. You can also specify the tokenizer and any tokens that shouldn't be split during data chunking. The new `unit` parameter and query subscore definitions are found in the [2024-09-01-preview](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-09-01-preview&preserve-view=true). |
 | [**2024-09-01-preview**](/rest/api/searchservice/search-service-api-versions?view=rest-searchservice-2024-09-01-preview&preserve-view=true) | API | Preview release of REST APIs for truncated dimensions in text-embedding-3 models, targeted vector filtering for hybrid queries, RRF subscore details for debugging, and token chunking for Text Split skill.|