Merge pull request #266344 from HeidiSteen/heidist-docs

[azure search] Misc edits

Author: prmerger-automator[bot]

articles/search/cognitive-search-concept-intro.md

@@ -17,7 +17,7 @@ ms.date: 01/30/2024
 
 In Azure AI Search, *AI enrichment* refers to integration with [Azure AI services](/azure/ai-services/what-are-ai-services) to process content that isn't searchable in its raw form. Through enrichment, analysis and inference are used to create searchable content and structure where none previously existed. 
 
-Because Azure AI Search is a text and vector search solution, the purpose of AI enrichment is to improve the utility of your content in search-related scenarios. Source content must be textual (you can't enrich vectors), but the content created by an enrichment pipeline can be vectorized and indexed in a vector store using skills like [Text Split skill](cognitive-search-skill-textsplit.md) for chunking and [AzureOpenAiEmbedding skill](cognitive-search-skill-azure-openai-embedding.md) for encoding. 
+Because Azure AI Search is a text and vector search solution, the purpose of AI enrichment is to improve the utility of your content in search-related scenarios. Source content must be textual (you can't enrich vectors), but the content created by an enrichment pipeline can be vectorized and indexed in a vector store using skills like [Text Split skill](cognitive-search-skill-textsplit.md) for chunking and [AzureOpenAIEmbedding skill](cognitive-search-skill-azure-openai-embedding.md) for encoding. 
 
 AI enrichment is based on [*skills*](cognitive-search-working-with-skillsets.md).
 

articles/search/search-indexer-howto-access-private.md

@@ -282,11 +282,11 @@ This section assumes manual approval and the portal for this step, but you can a
 
 After the private endpoint is approved, Azure AI Search creates the necessary DNS zone mappings in the DNS zone that's created for it.
 
-The private endpoint link on the page only resolves to the private link definition in Azure AI Search if there's shared tenancy between Azure AI Search backend private link and the Azure PaaS resource.
+Although the private endpoint link on the **Networking** page is active, it won't resolve. 
 
 :::image type="content" source="media/search-indexer-howto-secure-access/private-endpoint-link.png" alt-text="Screenshot of the private endpoint link in the Azure PaaS networking page.":::
 
-A status message of `"The access token is from the wrong issuer"` and `must match the tenant associated with this subscription` appears because the backend private endpoint resource is provisioned in a Microsoft-managed tenant, while the linked resource (Azure AI Search) is in your tenant. It's by design you can't access the private endpoint resource by selecting the private endpoint connection link. 
+Selecting the link produces an error. A status message of `"The access token is from the wrong issuer"` and `must match the tenant associated with this subscription` appears because the backend private endpoint resource is provisioned by Microsoft in a Microsoft-managed tenant, while the linked resource (Azure AI Search) is in your tenant. It's by design you can't access the private endpoint resource by selecting the private endpoint connection link. 
 
 Follow the instructions in the next section to check the status of your shared private link.
 

articles/search/search-what-is-an-index.md

@@ -170,12 +170,14 @@ All indexing and query requests target an index. Endpoints are usually one of th
 | `<your-service>.search.windows.net/indexes` | Targets the indexes collection. Used when creating, listing, or deleting an index. Admin rights are required for these operations, available through admin [API keys](search-security-api-keys.md) or a [Search Contributor role](search-security-rbac.md#built-in-roles-used-in-search). |
 | `<your-service>.search.windows.net/indexes/<your-index>/docs` | Targets the documents collection of a single index. Used when querying an index or data refresh. For queries, read rights are sufficient, and available through query API keys or a data reader role. For data refresh, admin rights are required. |
 
-Search subscribers, or the person who created the search service, can manage the search service in the Azure portal. An Azure subscription requires Contributor or above permissions to create or delete services. You can [sign in to the Azure portal](https://portal.azure.com) for a direct connection to your search service.
+#### How to connect to Azure AI Search
 
-For other clients, we recommend reviewing the quickstarts for connection steps:
+1. [Start with the Azure portal](https://portal.azure.com). Azure subscribers, or the person who created the search service, can manage the search service in the Azure portal. An Azure subscription requires Contributor or above permissions to create or delete services. This permission level is sufficient for fully managing a search service in the Azure portal.
 
-+ [Quickstart: REST](search-get-started-rest.md)
-+ [Quickstart: Azure SDKs](search-get-started-text.md)
+1. Try other clients for programmatic access. We recommend the quickstarts for first steps:
+
+   + [Quickstart: REST](search-get-started-rest.md)
+   + [Quickstart: Azure SDKs](search-get-started-text.md)
 
 ## Next steps
 
@@ -184,11 +186,7 @@ You can get hands-on experience creating an index using almost any sample or wal
 But you'll also want to become familiar with methodologies for loading an index with data. Index definition and data import strategies are defined in tandem. The following articles provide more information about creating and loading an index.
 
 + [Create a search index](search-how-to-create-search-index.md)
-
 + [Create a vector store](vector-search-how-to-create-index.md)
-
 + [Create an index alias](search-how-to-alias.md)
-
 + [Data import overview](search-what-is-data-import.md)
-
 + [Load an index](search-how-to-load-search-index.md)

articles/search/vector-search-how-to-create-index.md

@@ -23,7 +23,7 @@ Follow these steps to index vector data:
 > + Add one or more vector fields
 > + Load prevectorized data [as a separate step](#load-vector-data-for-indexing), or use [integrated vectorization (preview)](vector-search-integrated-vectorization.md) for data chunking and encoding during indexing.
 
-This article applies to the generally available, non-preview version of [vector search](vector-search-overview.md), which assumes your application code calls external resources for chunking and encoding. 
+This article applies to the generally available non-preview version of [vector search](vector-search-overview.md), which assumes your application code calls external resources for chunking and encoding. 
 
 > [!NOTE]
 > Looking for migration guidance from 2023-07-01-preview? See [Upgrade REST APIs](search-api-migration.md). 
@@ -40,7 +40,7 @@ This article applies to the generally available, non-preview version of [vector
 
 ## Prepare documents for indexing
 
-Prior to indexing, assemble a document payload that includes fields of vector and non-vector data. The document structure must conform to the index schema. 
+Prior to indexing, assemble a document payload that includes fields of vector and nonvector data. The document structure must conform to the index schema. 
 
 Make sure your documents:
 
@@ -52,9 +52,9 @@ Make sure your documents:
 
 1. Provide other fields with human-readable alphanumeric content for the query response, and for hybrid query scenarios that include full text search or semantic ranking in the same request. 
 
-Your search index should include fields and content for all of the query scenarios you want to support. Suppose you want to search or filter over product names, versions, metadata, or addresses. In this case, similarity search isn't especially helpful. Keyword search, geo-search, or filters would be a better choice. A search index that includes a comprehensive field collection of vector and non-vector data provides maximum flexibility for query construction and response composition.
+Your search index should include fields and content for all of the query scenarios you want to support. Suppose you want to search or filter over product names, versions, metadata, or addresses. In this case, similarity search isn't especially helpful. Keyword search, geo-search, or filters would be a better choice. A search index that includes a comprehensive field collection of vector and nonvector data provides maximum flexibility for query construction and response composition.
 
-A short example of a documents payload that includes vector and non-vector fields is in the [load vector data](#load-vector-data-for-indexing) section of this article.
+A short example of a documents payload that includes vector and nonvector fields is in the [load vector data](#load-vector-data-for-indexing) section of this article.
 
 ## Add a vector search configuration
 
@@ -271,7 +271,7 @@ Use this version if you want generally available features only.
    + `retrievable` can be true or false. True returns the raw vectors (1536 of them) as plain text and consumes storage space. Set to true if you're passing a vector result to a downstream app.
    + `filterable`, `facetable`, `sortable` must be false. 
 
-1. Add filterable non-vector fields to the collection, such as "title" with `filterable` set to true, if you want to invoke [prefiltering or postfiltering](vector-search-filters.md) on the [vector query](vector-search-how-to-query.md).
+1. Add filterable nonvector fields to the collection, such as "title" with `filterable` set to true, if you want to invoke [prefiltering or postfiltering](vector-search-filters.md) on the [vector query](vector-search-how-to-query.md).
 
 1. Add other fields that define the substance and structure of the textual content you're indexing. At a minimum, you need a document key. 
 
@@ -361,7 +361,7 @@ In the following REST API example, "title" and "content" contain textual content
    + `retrievable` can be true or false. True returns the raw vectors (1536 of them) as plain text and consumes storage space. Set to true if you're passing a vector result to a downstream app.
    + `filterable`, `facetable`, `sortable` must be false. 
 
-1. Add filterable non-vector fields to the collection, such as "title" with `filterable` set to true, if you want to invoke [prefiltering or postfiltering](vector-search-filters.md) on the [vector query](vector-search-how-to-query.md
+1. Add filterable nonvector fields to the collection, such as "title" with `filterable` set to true, if you want to invoke [prefiltering or postfiltering](vector-search-filters.md) on the [vector query](vector-search-how-to-query.md
 
 1. Add other fields that define the substance and structure of the textual content you're indexing. At a minimum, you need a document key. 
 
@@ -589,7 +589,7 @@ Although you can add a field to an index, there's no portal (Import data wizard)
 
 ## Load vector data for indexing
 
-Content that you provide for indexing must conform to the index schema and include a unique string value for the document key. Pre-vectorized data is loaded into one or more vector fields, which can coexist with other fields containing alphanumeric content.
+Content that you provide for indexing must conform to the index schema and include a unique string value for the document key. Prevectorized data is loaded into one or more vector fields, which can coexist with other fields containing alphanumeric content.
 
 You can use either [push or pull methodologies](search-what-is-data-import.md) for data ingestion. 
 
@@ -672,7 +672,7 @@ You can use [Search Explorer](search-explorer.md) to query an index. Search expl
 
 ### [**REST API**](#tab/rest-check-index)
 
-The following REST API example is a vector query, but it returns only non-vector fields (title, content, category). Only fields marked as "retrievable" can be returned in search results.
+The following REST API example is a vector query, but it returns only nonvector fields (title, content, category). Only fields marked as "retrievable" can be returned in search results.
 
 ```http
 POST https://my-search-service.search.windows.net/indexes/my-index/docs/search?api-version=2023-11-01

articles/search/vector-search-index-size.md

@@ -43,7 +43,7 @@ The following table shows vector quotas by partition, and by service if all part
 
 ## How to determine service creation date
 
-Find out whether your search service was created before July 1, 2023. If it's an older service, consider creating a new search service to benefit from the higher limits. Newer services at the same tier offer at least twice as much vector storage.
+Services created after July 1, 2023 offer at least twice as much vector storage as older ones at the same tier.
 
 1. In Azure portal, open the resource group. 
 
@@ -74,13 +74,13 @@ Usage information can be found on the **Overview** page's **Usage** tab. Portal
 
 The following screenshot is for a newer Standard 1 (S1) tier, configured for one partition and one replica. Vector index quota, measured in megabytes, refers to the internal vector indexes created for each vector field. Overall, indexes consume almost 460 megabytes of available storage, but the vector index component takes up just 93 megabytes of the 460 used on this search service.
 
-:::image type="content" source="media/vector-search-index-size/portal-vector-index-size.png" alt-text="Screenshot of the Overview page's usage tab showing vector index consumption against quota.":::
+:::image type="content" source="media/vector-search-index-size/portal-vector-index-size.png" lightbox="media/vector-search-index-size/portal-vector-index-size.png" alt-text="Screenshot of the Overview page's usage tab showing vector index consumption against quota.":::
 
 The tile on the Usage tab tracks vector index consumption at the search service level. If you increase or decrease search service capacity, the tile reflects the changes accordingly.
 
 ### [**REST**](#tab/rest-vector-quota)
 
-Use the following data plane REST APIs (version 2023-11-01 or later) for vector usage statistics:
+Use the following data plane REST APIs (version 2023-10-01-preview, 2023-11-01, and later) for vector usage statistics:
 
 + [GET Index Statistics](/rest/api/searchservice/indexes/get-statistics) returns usage for a given index.
 + [GET Service Statistics](/rest/api/searchservice/get-service-statistics/get-service-statistics) returns quota and usage for the search service all-up.

articles/search/vector-store.md

@@ -115,7 +115,7 @@ Fields from the chat index that support generative search experience:
 ]
 ```
 
-Here's a screenshot showing [Search explorer](search-explorer.md) search results for the conversations index. The search score is 1.00 because the search was unqualified. Notice the fields that exist to support orchestration and prompt flows. A conversation ID identifies a specific chat. `"type"` indicates whether the content is from the user or the assistant. Dates are used to age out chats from the history.
+Here's a screenshot showing search results in [Search Explorer](search-explorer.md) for the conversations index. The search score is 1.00 because the search was unqualified. Notice the fields that exist to support orchestration and prompt flows. A conversation ID identifies a specific chat. `"type"` indicates whether the content is from the user or the assistant. Dates are used to age out chats from the history.
 
 :::image type="content" source="media/vector-search-overview/vector-schema-search-results.png" alt-text="Screenshot of Search Explorer with results from an index designed for RAG apps.":::
 
@@ -154,18 +154,35 @@ Notice that query continuity exists for document operations (refreshing or delet
 
 To avoid an [index rebuild](search-howto-reindex.md), some customers who are making small changes choose to "version" a field by creating a new one that coexists alongside a previous version. Over time, this leads to orphaned content in the form of obsolete fields or obsolete custom analyzer definitions, especially in a production index that is expensive to replicate. You can address these issues on planned updates to the index as part of index lifecycle management.
 
+### Endpoint connection
+
+All vector indexing and query requests target an index. Endpoints are usually one of the following:
+
+| Endpoint | Connection and access control |
+|----------|-------------------------------|
+| `<your-service>.search.windows.net/indexes` | Targets the indexes collection. Used when creating, listing, or deleting an index. Admin rights are required for these operations, available through admin [API keys](search-security-api-keys.md) or a [Search Contributor role](search-security-rbac.md#built-in-roles-used-in-search). |
+| `<your-service>.search.windows.net/indexes/<your-index>/docs` | Targets the documents collection of a single index. Used when querying an index or data refresh. For queries, read rights are sufficient, and available through query API keys or a data reader role. For data refresh, admin rights are required. |
+
+#### How to connect to Azure AI Search
+
+1. [Start with the Azure portal](https://portal.azure.com). Azure subscribers, or the person who created the search service, can manage the search service in the Azure portal. An Azure subscription requires Contributor or above permissions to create or delete services. This permission level is sufficient for fully managing a search service in the Azure portal.
+
+1. Try other clients for programmatic access. We recommend the quickstarts and samples for first steps:
+
+   + [Quickstart: REST](search-get-started-vector.md)
+   + [Vector samples](https://github.com/Azure/azure-search-vector-samples/blob/main/README.md)
+
 ### Secure access to vector data
 
-<!-- Azure AI Search supports comprehensive security. Authentication and authorization -->
+Azure AI Search implements data encryption, private connections for no-internet connections, and role assignments for secure access through Microsoft Entra ID. The full range of enterprise security features are outlined in [Security in Azure AI Search](search-security-overview.md).
 
 ### Manage vector stores
 
-Azure provides a monitoring platform that includes diagnostic logging and alerting.
+Azure provides a [monitoring platform](monitor-azure-cognitive-search.md) that includes diagnostic logging and alerting. We recommend the following best practices:
 
-+ Enable logging
-+ Set up alerts
-+ Back up and restore isn't natively supported but there are samples.
-+ Scale
++ [Enable diagnostic logging](/azure/azure-monitor/essentials/create-diagnostic-settings)
++ [Set up alerts](/azure/azure-monitor/alerts/tutorial-metric-alert)
++ [Analyze query and index performance](search-performance-analysis.md)
 
 ## See also