Merge pull request #274296 from HeidiSteen/heidist-bug

[azure search] Preview upgrade

Author: prmerger-automator[bot]

articles/search/search-api-migration.md

@@ -10,27 +10,27 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 04/30/2024
+ms.date: 05/04/2024
 ---
 
 # Upgrade to the latest REST API in Azure AI Search
 
 Use this article to migrate data plane calls to newer versions of the [**Search REST API**](/rest/api/searchservice/).
 
-+ [**2023-11-01**](/rest/api/searchservice/search-service-api-versions#2023-11-01) is the most recent stable version. Semantic ranking and support for indexing and querying vectors are generally available in this version.
++ [**2023-11-01**](/rest/api/searchservice/search-service-api-versions#2023-11-01) is the most recent stable version. Semantic ranking and support for vector indexing and queries are generally available in this version.
 
-+ [**2023-10-01-preview**](/rest/api/searchservice/search-service-api-versions#2023-10-01-preview) is the most recent preview version. Preview features include [built-in query vectorization](vector-search-how-to-configure-vectorizer.md), [built-in data chunking and vectorization during indexing](vector-search-integrated-vectorization.md) (uses the [Text Split](cognitive-search-skill-textsplit.md) skill and [Azure OpenAI Embedding](cognitive-search-skill-azure-openai-embedding.md) skill). Refer to [code samples](https://github.com/Azure/azure-search-vector-samples) and [walkthroughs](vector-search-how-to-configure-vectorizer.md) for help with new features.
++ [**2023-10-01-preview**](/rest/api/searchservice/search-service-api-versions#2023-10-01-preview) is the most recent preview version. Preview features include [built-in query vectorization](vector-search-how-to-configure-vectorizer.md), [built-in data chunking and vectorization during indexing](vector-search-integrated-vectorization.md) (uses the [Text Split](cognitive-search-skill-textsplit.md) skill and [Azure OpenAI Embedding](cognitive-search-skill-azure-openai-embedding.md) skill). 
 
 + **2023-07-01-preview** was the first REST API for vector support. It's now deprecated and you should migrate to either **2023-11-01** or **2023-10-01-preview** immediately.
 
 > [!NOTE]
-> API reference docs are now versioned. To get the right content, open a reference page and then filter by version, using the selector located above the table of contents.
+> REST API reference docs are now versioned. To get the right content, open a reference page and then filter by version, using the selector located above the table of contents.
 
 ## When to upgrade
 
 Azure AI Search breaks backward compatibility as a last resort. Upgrade is necessary when:
 
-+ Your code references a retired or deprecated API version and is subject to one or more of the breaking changes. API versions that fall into this category include 2023-07-10-preview for vectors and [2019-05-06](#upgrade-to-2019-05-06). 
++ Your code references a retired or deprecated API version and is subject to one or more of the breaking changes. API versions that fall into this category include [2023-07-10-preview](/rest/api/searchservice/index-preview) for vectors and [2019-05-06](#upgrade-to-2019-05-06). 
 
 + Your code fails when unrecognized properties are returned in an API response. As a best practice, your application should ignore properties that it doesn't understand.
 
@@ -48,9 +48,17 @@ Effective March 29, 2024 and applies to all [supported REST APIs](/rest/api/sear
 
 ## Upgrade to 2023-10-01-preview
 
-This version is identical to 2023-11-01 but has extra features in public preview: [built-in query vectorizer](vector-search-how-to-configure-vectorizer.md) and [vector prefilter mode](vector-search-filters.md). If you want to use those features, you should upgrade to the latest preview version.
+This section explains the migration path from 2023-07-01-preview to 2023-10-01-preview. You should migrate to 2023-10-01 if you want to continue using vector features that are still in public preview. If you don't need the preview features, we recommend upgrading to the stable release, 2023-11-01.
 
-The vector search algorithm configuration inside a search index is identical to 2023-11-01. To fix breaking changes from 2023-07-01-preview, follow the instructions in the next section.
+Preview features include:
+
++ [built-in text-to-vector indexing](search-get-started-portal-import-vectors.md)
++ [built-in text-to-vector queries](vector-search-how-to-configure-vectorizer.md)
++ [vector prefilter mode](vector-search-filters.md). 
+
+Because these features are new in 2023-10-01-preview, there's no migration path and you should add these capabilities to your code if you want to use them. To learn more about the preview  features, see [code samples](https://github.com/Azure/azure-search-vector-samples) and [walkthroughs](vector-search-how-to-configure-vectorizer.md).
+
+In contrast, the vector field definitions, vector search algorithm configuration, and vector query syntax that were first introduced in 2023-07-01-preview have changed. The 2023-10-01-preview syntax for vector fields, algorithms, and vector queries is identical to the 2023-11-01 syntax. Migration steps for vectors are explained in [upgrade to 2023-11-01](#upgrade-to-2023-11-01).
 
 ## Upgrade to 2023-11-01
 

articles/search/search-indexer-securing-resources.md

@@ -77,11 +77,11 @@ For any given indexer run, Azure AI Search determines the best environment in wh
 |-----------------------|-------------|
 | Private | Internal to a search service. Indexers running in the private environment share computing resources with other indexing and query workloads on the same search service. Typically, only indexers that perform text-based indexing (without skillsets) run in this environment. If you set up a private connection between an indexer and your data, this is the only execution enriovnment you can use. |
 |  multitenant | Managed and secured by Microsoft at no extra cost. It isn't subject to any network provisions under your control. This environment is used to offload computationally intensive processing, leaving service-specific resources available for routine operations. Examples of resource-intensive indexer jobs include attaching skillsets, processing large documents, or processing a high volume of documents. |
-  
-The following section explains the IP configuration for admitting requests from either execution environment.
 
 ### Setting up IP ranges for indexer execution
 
+This section explains IP firewall configuration for admitting requests from either execution environment.
+
 If your Azure resource is behind a firewall, set up [inbound rules that admit indexer connections](search-indexer-howto-access-ip-restricted.md) for all of the IPs from which an indexer request can originate. This includes the IP address used by the search service, and the IP addresses used by the multitenant environment.
 
 - To obtain the IP address of the search service (and the private execution environment), use `nslookup` (or `ping`) to find the fully qualified domain name (FQDN) of your search service. The FQDN of a search service in the public cloud would be `<service-name>.search.windows.net`.
@@ -108,7 +108,7 @@ A search service can't be provisioned into a specific virtual network, running n
 
 | Approach | Details |
 |----------|---------|
-| Inbound connection to your Azure resource | Configure an inbound firewall rule on your Azure resource that admits indexer requests for your data. Your firewall configuration should include the service tag for multitenant execution and the IP address of your search service. |
+| Secure the inbound connection to your Azure resource | Configure an inbound firewall rule on your Azure resource that admits indexer requests for your data. Your firewall configuration should include the service tag for multitenant execution and the IP address of your search service. See [Configure firewall rules to allow indexer access](search-indexer-howto-access-ip-restricted.md). |
 | Private connection between Azure AI Search and your Azure resource | Configure a shared private link used exclusively by your search service for connections to your resource. Connections travel over the internal network and bypass the public internet. If your resources are fully locked down (running on a protected virtual network, or otherwise not available over a public connection), a private endpoint is your only choice. See [Make outbound connections through a private endpoint](search-indexer-howto-access-private.md).|
 
 Connections through a private endpoint must originate from the search service's private execution environment.