Merge pull request #276741 from HeidiSteen/heidist-may28

[azure search] portal updates for build

Author: ShannonLeavitt

articles/search/search-explorer.md

@@ -7,10 +7,9 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: quickstart
-ms.date: 02/22/2024
+ms.date: 05/30/2024
 ms.custom:
   - mode-ui
-  - ignite-2023
 ---
 
 # Quickstart: Use Search explorer to run queries in the Azure portal
@@ -47,17 +46,20 @@ Before you begin, have the following prerequisites in place:
 
 There are two approaches for querying in Search explorer. 
 
-+ The default search bar accepts an empty query or free text query with booleans. For example, `seattle condo +parking`.
++ Query view provides a default search bar. It accepts an empty query or free text query with booleans. For example, `seattle condo +parking`.
 
 + JSON view supports parameterized queries. Filters, orderby, select, count, searchFields, and all other parameters must be set in JSON view.
 
+  > [!TIP]
+  > JSON view provides intellisense for parameter name completion. Place the cursor inside the JSON view and type a space character to show a list of all query parameters, or type a single letter like "s" to show just the query parameters starting with "s". Intellisense doesn't exclude invalid parameters so use your best judgement.
+
   Switch to **JSON view** for parameterized queries. The examples in this article assume JSON view throughout. You can paste JSON examples from this article into the text area.
 
    :::image type="content" source="media/search-explorer/search-explorer-json-view.png" alt-text="Screenshot of the JSON view selector." border="true":::
 
 ## Run an unspecified query
 
-In Search explorer, POST requests are formulated internally using the [Search POST REST API](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2023-10-01-preview&preserve-view=true), with responses returned as verbose JSON documents.
+In Search explorer, POST requests are formulated internally using the [Search POST REST API](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true), with responses returned as verbose JSON documents.
 
 For a first look at content, execute an empty search by clicking **Search** with no terms provided. An empty search is useful as a first query because it returns entire documents so that you can review document composition. On an empty search, there's no search score and documents are returned in arbitrary order (`"@search.score": 1` for all documents). By default, 50 documents are returned in a search request.
 

articles/search/search-get-started-portal-import-vectors.md

@@ -7,20 +7,20 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: quickstart
-ms.date: 11/30/2023
+ms.date: 05/30/2024
 ms.custom:
   - mode-ui
   - ignite-2023
 ---
 
 # Quickstart: Create a search index in the Azure portal
 
-In this Azure AI Search quickstart, create your first _search index_ by using the [**Import data** wizard](search-import-data-portal.md) and a built-in sample data source consisting of fictitious hotel data hosted by Microsoft. The wizard guides you through the creation of a no-code search index to help you write interesting queries within minutes. 
+In this Azure AI Search quickstart, create your first _search index_ by using the [**Import data** wizard](search-import-data-portal.md) and a built-in sample data source consisting of fictitious hotel data hosted by Microsoft. The wizard guides you through the no-code creation of a search index to help you write interesting queries within minutes. 
 
 The wizard creates multiple objects on your search service - [searchable index](search-what-is-an-index.md) - but also an [indexer](search-indexer-overview.md) and data source connection for automated data retrieval. At the end of this quickstart, we review each object. 
 
 > [!NOTE]
-> The **Import data** wizard includes options for OCR, text translation, and other AI enrichments that aren't covered in this quickstart. For a similar walkthrough that focuses on AI enrichment, see [Quickstart: Create a skillset in the Azure portal](cognitive-search-quickstart-blob.md).
+> The **Import data** wizard includes options for OCR, text translation, and other AI enrichments that aren't covered in this quickstart. For a similar walkthrough that focuses on applied AI, see [Quickstart: Create a skillset in the Azure portal](cognitive-search-quickstart-blob.md).
 
 ## Prerequisites
 
@@ -81,10 +81,6 @@ The wizard infers a schema for the built-in hotels-sample index. Follow these st
 
 1. Accept the system-generated values for all field attributes.
 
-   > [!IMPORTANT]
-   > If you rerun the wizard and use an existing hotels-sample data source, the index isn't configured with default attributes.
-   > You have to manually select attributes on future imports. 
-
 1. Select **Next: Create an indexer** to continue.
 
 :::image type="content" source="media/search-get-started-portal/hotels-sample-generated-index.png" alt-text="Screenshot that shows the generated index definition for the hotels-sample data source in the Import data wizard.":::
@@ -153,7 +149,7 @@ To clearly understand what you can and can't edit during index design, take a mi
 
 ## Query with Search explorer
 
-You now have a search index that can be queried with [**Search explorer**](search-explorer.md). **Search explorer** sends REST calls that conform to the [Search POST REST API](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2023-10-01-preview&preserve-view=true). The tool supports [simple query syntax](/rest/api/searchservice/simple-query-syntax-in-azure-search) and [full Lucene query syntax](/rest/api/searchservice/lucene-query-syntax-in-azure-search).
+You now have a search index that can be queried with [**Search explorer**](search-explorer.md). **Search explorer** sends REST calls that conform to the [Search POST REST API](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-05-01-preview&preserve-view=true). The tool supports [simple query syntax](/rest/api/searchservice/simple-query-syntax-in-azure-search) and [full Lucene query syntax](/rest/api/searchservice/lucene-query-syntax-in-azure-search).
 
 1. On the **Search explorer** tab, enter text to search on.
 
@@ -169,7 +165,10 @@ You now have a search index that can be queried with [**Search explorer**](searc
 
 ## Example queries for hotels sample index
 
-The following examples assume the JSON view and the 2023-11-01 REST API version.
+The following examples assume the JSON view and the 2024-05-01-preview REST API version.
+
+> [!TIP]
+> JSON view now supports intellisense for parameter name completion. Place the cursor inside the JSON view and type a space character to show a list of all query parameters, or type a single letter like "s" to show just the query parameters starting with "s". Intellisense doesn't exclude invalid parameters so use your best judgement.
 
 ### Filter examples
 

articles/search/search-get-started-portal.md

@@ -9,7 +9,7 @@ ms.service: cognitive-search
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 05/21/2024
+ms.date: 05/30/2024
 ---
 
 # Index data from OneLake files and shortcuts
@@ -20,7 +20,7 @@ Use this indexer for the following tasks:
 
 + **Data indexing and incremental indexing:** The indexer can index files and associated metadata from data paths within a lakehouse. It detects new and updated files and metadata through built-in change detection. You can configure data refresh on a schedule or on demand. 
 + **Deletion detection:** The indexer can [detect deletions via custom metadata](#detect-deletions-via-custom-metadata) for most files and shortcuts. This requires adding metadata to files to signify that they have been "soft deleted", enabling their removal from the search index. Currently, it's not possible to detect deletions in Google Cloud Storage or Amazon S3 shortcut files because custom metadata isn't supported for those data sources.
-+ **Applied AI through skillsets:** [Skillsets](cognitive-search-concept-intro.md) are fully supported by the OneLake files indexer. This includes key features like [integrated vectorization](vector-search-integrated-vectorization.md).
++ **Applied AI through skillsets:** [Skillsets](cognitive-search-concept-intro.md) are fully supported by the OneLake files indexer. This includes key features like [integrated vectorization](vector-search-integrated-vectorization.md) that adds data chunking and embedding steps.
 + **Parsing modes:** The indexer supports [JSON parsing modes](search-howto-index-json-blobs.md) if you want to parse JSON arrays or lines into individual search documents.
 + **Compatibility with other features:** The OneLake indexer is designed to work seamlessly with other indexer features, such as [debug sessions](cognitive-search-debug-session.md), [indexer cache for incremental enrichments](search-howto-incremental-index.md), and [knowledge store](knowledge-store-concept-intro.md). 
 
@@ -30,8 +30,6 @@ This article uses the REST APIs to illustrate each step.
 
 ## Prerequisites  
 
-Before you begin, you need to have:  
-  
 + A Fabric workspace. Follow this tutorial to [create a Fabric workspace](/fabric/data-engineering/tutorial-lakehouse-get-started). 
 
 + A lakehouse in a Fabric workspace. Follow this tutorial to [create a lakehouse](/fabric/data-engineering/tutorial-build-lakehouse).

articles/search/search-how-to-index-onelake-files.md

@@ -9,7 +9,7 @@ ms.service: cognitive-search
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 05/21/2024
+ms.date: 05/30/2024
 ---
 
 # Index binary vectors for vector search
@@ -23,7 +23,7 @@ There are three steps to configuring an index for binary vectors:
 > + Add a vector profile that points to the algorithm
 > + Add the vector profile to your binary field definition
 
-This article assumes you're familiar with [creating an index in Azure AI Search](search-how-to-create-search-index.md). It uses the REST APIs to illustrate each step.
+This article assumes you're familiar with [creating an index in Azure AI Search](search-how-to-create-search-index.md). It uses the REST APIs to illustrate each step. You can also add a binary field type to an index in the Azure portal.
 
 ## Prerequisites
 

articles/search/vector-search-how-to-index-binary-data.md

@@ -9,7 +9,7 @@ ms.service: cognitive-search
 ms.custom:
   - build-2024
 ms.topic: how-to
-ms.date: 05/21/2024
+ms.date: 05/30/2024
 ---
 
 # Create a vector query in Azure AI Search
@@ -25,7 +25,9 @@ In Azure AI Search, if you have a [vector index](vector-search-how-to-create-ind
 > + [Set MaxTextSizeRecall to control the number of results (preview)](#maxtextsizerecall-for-hybrid-search-preview)
 > + [Set vector weights (preview)](#vector-weighting-preview)
 
-This article uses REST for illustration. For code samples in other languages, see the [azure-search-vector-samples](https://github.com/Azure/azure-search-vector-samples) GitHub repository for end-to-end solutions that include vector queries.
+This article uses REST for illustration. For code samples in other languages, see the [azure-search-vector-samples](https://github.com/Azure/azure-search-vector-samples) GitHub repository for end-to-end solutions that include vector queries. 
+
+You can also use [Search explorer](search-explorer.md) in the Azure portal if you [configure a vectorizer](vector-search-how-to-configure-vectorizer.md) that converts strings into embeddings.
 
 ## Prerequisites
 

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

@@ -24,13 +24,14 @@ ms.custom:
 | Item                         | Type |  Description |
 |-----------------------------|------|--------------|
 | [Higher capacity and more vector quota at every tier (same billing rate)](search-limits-quotas-capacity.md#service-limits) | Infrastructure | Partition sizes are now even larger for Standard 2 (S2), Standard 3 (S3), and Standard 3 High Density (S3 HD) for all services created after April 3, 2024. If you create a new service now, you get the larger partitions. If you created a new service between April 3 and May 17, you get the larger partitions automatically. <br><br>Storage Optimized tiers (L1 and L2) also have more capacity. L1 and L2 customers must create a new service to benefit from the higher capacity. There's no in-place upgrade at this time. <br><br>Extra capacity is now available in [more regions](search-limits-quotas-capacity.md#supported-regions-with-higher-storage-limits): Germany North​, Germany West Central​, South Africa North​, Switzerland West​, and Azure Government (Texas, Arizona, and Virginia).|
-| [OneLake files and shortcuts integration (preview)](search-how-to-index-onelake-files.md) | Feature | New indexer for OneLake files and OneLake shortcuts. If you use Microsoft Fabric and OneLake for data access to Amazon Web Services (AWS) and Google data sources, use this indexer to import external data into a search index. This indexer is available through the Azure portal, the [2024-05-01-preview REST API](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true), and Azure SDK beta packages. |
-| [Relevance tuning and search results customization](vector-search-how-to-query.md) | Feature | Three enhancements improve vector search relevance. <br><br>First, you can now set thresholds on vector search results to exclude low-scoring results. <br><br>Second, you can set `MaxSizeTextRecall` and `countAndFacetMode` in hybrid queries to specify the maximum number of documents that can be recalled using text query in hybrid (text and vector) search. Previously, the maximum was fixed at 1,000. If you have more matches, you can now specify a higher limit to get more results back. <br><br>Third, for hybrid queries, you can set a weight on vector queries to have more or less importance than the nonvector query. |
+| [OneLake integration (preview)](search-how-to-index-onelake-files.md) | Feature | New indexer for OneLake files and OneLake shortcuts. If you use Microsoft Fabric and OneLake for data access to Amazon Web Services (AWS) and Google data sources, use this indexer to import external data into a search index. This indexer is available through the Azure portal, the [2024-05-01-preview REST API](/rest/api/searchservice/data-sources/create-or-update?view=rest-searchservice-2024-05-01-preview&preserve-view=true), and Azure SDK beta packages. |
+| [Vector relevance tuning and search results customization](vector-search-how-to-query.md) | Feature | Three enhancements improve vector search relevance. <br><br>First, you can now set thresholds on vector search results to exclude low-scoring results. <br><br>Second, you can set `MaxSizeTextRecall` and `countAndFacetMode` in hybrid queries to specify the maximum number of documents that can be recalled using text query in hybrid (text and vector) search. Previously, the maximum was fixed at 1,000. If you have more matches, you can now specify a higher limit to get more results back. <br><br>Third, for hybrid queries, you can set a weight on vector queries to have more or less importance than the nonvector query. |
 | [Binary vectors support](/rest/api/searchservice/supported-data-types) | Feature | `Collection(Edm.Byte)` is a new supported data type. This data type opens up integration with the [Cohere v3 binary embedding models](https://cohere.com/blog/int8-binary-embeddings) and custom binary quantization. Narrow data types lower the cost of large vector datasets. See [Index binary data for vector search](vector-search-how-to-index-binary-data.md) for more information.| 
 | [Azure AI Vision multimodal embeddings skill (preview)](cognitive-search-skill-vision-vectorize.md) | Skill | New skill that's bound to the [multimodal embeddings API of Azure AI Vision](../ai-services/computer-vision/concept-image-retrieval.md). You can generate embeddings for text or images during indexing. This skill is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true).|
 | [Azure AI Vision vectorizer (preview)](vector-search-vectorizer-ai-services-vision.md) | Vectorizer | New vectorizer connects to an Azure AI Vision resource using the [multimodal embeddings API](../ai-services/computer-vision/concept-image-retrieval.md) to generate embeddings at query time. This vectorizer is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true). |
 | [Azure AI Studio model catalog vectorizer (preview)](vector-search-vectorizer-azure-machine-learning-ai-studio-catalog.md) | Vectorizer | New vectorizer connects to an embedding model deployed from the [Azure AI Studio model catalog](../ai-studio/how-to/model-catalog.md). This vectorizer is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true). <br><br>[**How to implement integrated vectorization using models from Azure AI Studio**](vector-search-integrated-vectorization-ai-studio.md).|
 | [AzureOpenAIEmbedding skill (preview) supports more models on Azure OpenAI](cognitive-search-skill-azure-openai-embedding.md) | Skill | Updates to this skill add support for more embedding models on Azure OpenAI. New `dimensions` and `modelName` properties are used for specifying models. Previously, the dimensions limits were fixed at 1,536 dimensions. It's now configurable. This update is available through the Azure portal and the [2024-05-01-preview REST API](/rest/api/searchservice/operation-groups?view=rest-searchservice-2024-05-01-preview&preserve-view=true).|
+| Azure portal updates | Portal | [Import and vectorize data wizard](search-get-started-portal-import-vectors.md) is updated to support [OneLake indexers](search-how-to-index-onelake-files.md) and the [Azure AI Vision multimodal skill](cognitive-search-skill-vision-vectorize.md). [Search explorer](search-explorer.md) now defaults to 2024-05-01-preview and supports the new preview features for vector and hybrid queries. When adding a field, you can now choose a [binary data type](vector-search-how-to-index-binary-data.md). |
 | [2024-05-01-preview Search REST API](/rest/api/searchservice/search-service-api-versions#2024-05-01-preview) | API | New preview version of the Search REST APIs provides new skills and vectorizers, new binary data type, OneLake files indexer, and new query parameters for more relevant results. See [Upgrade REST APIs](search-api-migration.md) if you have existing code written against the 2023-07-01-preview and need to migrate to this version.|
 | Azure SDK beta packages for new features | API | Review the changelogs of the following Azure SDK beta packages for new feature support: [Azure SDK for Python](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/CHANGELOG.md), [Azure SDK for .NET](https://github.com/Azure/azure-sdk-for-net/blob/Azure.Search.Documents_11.6.0-beta.4/sdk/search/Azure.Search.Documents/CHANGELOG.md), [Azure SDK for Java](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/search/azure-search-documents/CHANGELOG.md) |
 | [Python code samples](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/readme.md)  | Samples | New end-to-end samples demonstrate [integration with Cohere Embed v3](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/community-integration/cohere/azure-search-cohere-embed-v3-sample.ipynb), [integration with OneLake and cloud data platforms on Google and AWS](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/e2e-demos/azure-ai-search-e2e-build-demo.ipynb), and [integration with Azure AI Vision multimodal APIs](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/embeddings/multimodal-embeddings/multimodal-embeddings.ipynb). |