Merge pull request #1300 from HeidiSteen/heidist-hnsw

misc edits

Author: Jill Grant

articles/search/retrieval-augmented-generation-overview.md

@@ -155,6 +155,8 @@ In comparison and benchmark testing, hybrid queries with text and vector fields,
 
 The following Python code demonstrates the essential components of a RAG workflow in Azure AI Search. You need to set up the clients, define a system prompt, and provide a query. The prompt tells the LLM to use just the results from the query, and how to return the results. For more steps based on this example, see this [RAG quickstart](search-get-started-rag.md).
 
+For the Azure Government cloud, modify the API endpoint on the token provider to `"https://cognitiveservices.azure.us/.default"`.
+
 ```python
 # Set up the query for generating responses
 from azure.identity import DefaultAzureCredential

articles/search/search-api-preview.md

@@ -27,7 +27,7 @@ Preview features are removed from this list if they're retired or transition to
 |---------|------------------|-------------|---------------|
 | [**Query rewrite in the semantic reranker**](semantic-how-to-query-rewrite.md) | Relevance (scoring) | You can set options on a semantic query to rewrite the query input into a revised or expanded query that generates more relevant results from the L2 ranker. | [Search Documents (preview)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-11-01-preview&preserve-view=true).|
 | [**Document Layout skill**](cognitive-search-skill-document-intelligence-layout.md) | Applied AI (skills) | A new skill used to analyze a document for structure and provide [structure-aware chunking](search-how-to-semantic-chunking.md). | [Create or Update Skillset (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-11-01-preview&preserve-view=true). |
-| [**eyless billing for Azure AI skills processing**](cognitive-search-attach-cognitive-services.md). | Applied AI (skills) | You can now use a managed identity and roles for a keyless connection to Azure AI services for built-in skills processing. This capability removes restrictions for having both search and AI services in the same region.  | [Create or Update Skillset  (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-11-01-preview&preserve-view=true).|
+| [**Keyless billing for Azure AI skills processing**](cognitive-search-attach-cognitive-services.md). | Applied AI (skills) | You can now use a managed identity and roles for a keyless connection to Azure AI services for built-in skills processing. This capability removes restrictions for having both search and AI services in the same region.  | [Create or Update Skillset  (preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-11-01-preview&preserve-view=true).|
 | [**Markdown parsing mode**](search-how-to-index-markdown-blobs.md) | Indexer data source | With this parsing mode, indexers can generate one-to-one or one-to-many search documents from Markdown files in Azure Storage. | [Create or Update Indexer (preview)](/rest/api/searchservice/indexers/create-or-update?view=rest-searchservice-2024-11-01-preview&preserve-view=true). |
 | [**Rescoring options for compressed vectors**](vector-search-how-to-quantization.md) | Relevance (scoring) | You can set options to rescore with original vectors instead of compressed vectors. Applies to HNSW and exhaustive KNN vector algorithms, using binary and scalar compression. | [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-truncate-dimensions.md) | Index | 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 extra 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). |
@@ -56,7 +56,7 @@ Preview features are removed from this list if they're retired or transition to
 
 |Feature                          | Category | Description | Availability  |
 |---------|------------------|-------------|---------------|
-| [**Add Azure AI Search to a network security perimeter**](search-security-network-security-perimiter.md) | Service | Join a search service to a [network security perimeter](/azure/private-link/network-security-perimeter-concepts) to control network access to your search service. | The Azure portal and the [Network Security Perimeter APIs 2024-06-01-preview](/rest/api/searchmanagement/network-security-perimeter-configurations?view=rest-searchmanagement-2024-06-01-preview&preserve-view=true). |
+| [**Network security perimeter**](search-security-network-security-perimiter.md) | Service | Join a search service to a [network security perimeter](/azure/private-link/network-security-perimeter-concepts) to control network access to your search service. | The Azure portal and the [Network Security Perimeter APIs 2024-06-01-preview](/rest/api/searchmanagement/network-security-perimeter-configurations?view=rest-searchmanagement-2024-06-01-preview&preserve-view=true). |
 | [**Search service under a user-assigned managed identity**](search-howto-managed-identities-data-sources.md) | Service | Configures a search service to use a previously created user-assigned managed identity. | [Services - Update](/rest/api/searchmanagement/services/update?view=rest-searchmanagement-2024-06-01-preview&preserve-view=true#identity), 2021-04-01-preview, or the latest preview version. We recommend using the latest preview version. |
 
 ## Preview features in Azure SDKs

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

@@ -226,6 +226,8 @@ This section uses Visual Studio Code and Python to call the chat completion APIs
 
 1. Set up clients, the prompt, query, and response.
 
+   For the Azure Government cloud, modify the API endpoint on the token provider to `"https://cognitiveservices.azure.us/.default"`.
+
    ```python
    # Set up the query for generating responses
     from azure.identity import DefaultAzureCredential

articles/search/toc.yml

@@ -349,9 +349,9 @@ items:
       items:
       - name: Understand vector quotas and limits
         href: vector-search-index-size.md
-      - name: Choose a vector optimization strategy
+      - name: Choose an optimization strategy
         href: vector-search-how-to-configure-compression-storage.md
-      - name: Use binary or scalar quantization
+      - name: Compress using binary or scalar quantization
         href: vector-search-how-to-quantization.md
       - name: Index binary data for vector search
         href: vector-search-how-to-index-binary-data.md

articles/search/tutorial-rag-build-solution-maximize-relevance.md

@@ -47,6 +47,8 @@ Let's start with a new query, "Are there any cloud formations specific to oceans
 
 To compare outcomes after adding relevance features, run the query against the existing index schema, before you add semantic ranking or a scoring profile.
 
+For the Azure Government cloud, modify the API endpoint on the token provider to `"https://cognitiveservices.azure.us/.default"`.
+
 ```python
 from azure.search.documents import SearchClient
 from openai import AzureOpenAI
@@ -200,6 +202,8 @@ In a previous tutorial, you [ran queries](tutorial-rag-build-solution-query.md)
 
 This example modifies the query request to include the semantic configuration and scoring profile.
 
+For the Azure Government cloud, modify the API endpoint on the token provider to `"https://cognitiveservices.azure.us/.default"`.
+
 ```python
 # Import libraries
 from azure.search.documents import SearchClient

articles/search/tutorial-rag-build-solution-query.md

@@ -56,6 +56,8 @@ AZURE_OPENAI_ACCOUNT: str = "PUR YOUR AZURE OPENAI ENDPOINT HERE"
 
 Here's the Python script that instantiates the clients, defines the prompt, and sets up the query. You can run this script in the notebook to generate a response from your chat model deployment.
 
+For the Azure Government cloud, modify the API endpoint on the token provider to `"https://cognitiveservices.azure.us/.default"`.
+
 ```python
 # Import libraries
 from azure.search.documents import SearchClient

articles/search/whats-new.md

@@ -23,7 +23,7 @@ ms.custom:
 
 | Item                         | Type |  Description |
 |-----------------------------|------|--------------|
-| [**Add Azure AI Search to a network security perimeter**](search-security-network-security-perimiter.md) | Security | Join a search service to a [network security perimeter](/azure/private-link/network-security-perimeter-concepts) to control network access to your search service. The Azure portal and the Management REST APIs in the [2024-06-01-preview](/rest/api/searchmanagement/network-security-perimeter-configurations?view=rest-searchmanagement-2024-06-01-preview&preserve-view=true) can be used to view and reconcile network security perimeter configurations. |
+| [**Network security perimeter**](search-security-network-security-perimiter.md) | Security | Join a search service to a [network security perimeter](/azure/private-link/network-security-perimeter-concepts) to control network access to your search service. The Azure portal and the Management REST APIs in the [2024-06-01-preview](/rest/api/searchmanagement/network-security-perimeter-configurations?view=rest-searchmanagement-2024-06-01-preview&preserve-view=true) can be used to view and reconcile network security perimeter configurations. |
 | [**Query rewrite in the semantic reranker**](semantic-how-to-query-rewrite.md) | Relevance (scoring) | You can set options on a semantic query to rewrite the query input into a revised or expanded query that generates more relevant results from the L2 ranker. Available in the [Search Documents (2024-11-01-preview)](/rest/api/searchservice/documents/search-post?view=rest-searchservice-2024-11-01-preview&preserve-view=true), the Azure portal, and in the Azure SDK beta packages that provide this feature.|
 | [**New semantic ranker models**](semantic-search-overview.md) | Relevance (scoring) | Semantic ranker runs with improved models in all supported regions. There is no change to APIs or the portal experience. |
 | [**Document Layout skill**](cognitive-search-skill-document-intelligence-layout.md) | Applied AI (skills) | A new skill used to analyze a document for structure and provide [structure-aware chunking](search-how-to-semantic-chunking.md). This skill calls Document Intelligence and uses the Document Intelligence layout model. Available in selected regions through the [Create or Update Skillset (2024-11-01-preview)](/rest/api/searchservice/skillsets/create-or-update?view=rest-searchservice-2024-11-01-preview&preserve-view=true), the Azure portal, and in the Azure SDK beta packages that provide this feature.|