Merge branch 'MicrosoftDocs:main' into master

Author: neilghuman

.openpublishing.publish.config.json

@@ -1290,6 +1290,7 @@
   "articles/synapse-analytics/.openpublishing.redirection.synapse-analytics.json",
   "articles/virtual-machine-scale-sets/.openpublishing.redirection.virtual-machine-scale-sets.json",
   "articles/virtual-machines/.openpublishing.redirection.virtual-machines.json",
+  "articles/virtual-network/.openpublishing.redirection.virtual-network.json",
   "articles/operator-nexus/.openpublishing.redirection.operator-nexus.json"
 ]
 }

.openpublishing.redirection.app-service.json

@@ -2639,6 +2639,11 @@
             "source_path_from_root": "/articles/app-service-web/web-sites-dotnet-deploy-aspnet-mvc-app-membership-oauth-sql-database.md",
             "redirect_url": "/aspnet/core/security/authorization/secure-data",
             "redirect_document_id": false
+        },
+        {
+            "source_path": "articles/app-service/quickstart-dotnet-framework.md",
+            "redirect_url": "/azure/app-service/quickstart-dotnetcore?tabs=netframework48",
+            "redirect_document_id": false
         }
     ]
 }

.openpublishing.redirection.json

@@ -298,7 +298,7 @@ Query fields are an add-on capability to extend the schema extracted from any pr
 
 > [!NOTE]
 >
-> Document Intelligence Studio query field extraction is currently available with the Layout and Prebuilt models starting with the `2023-10-31-preview` API and later releases.
+> Document Intelligence Studio query field extraction is currently available with the Layout and Prebuilt models starting with the `2023-10-31-preview` API and later releases except for the ```us.tax.*``` models (W2, 1098s and 1099s models).
 
 ### Query field extraction
 

articles/ai-services/document-intelligence/concept-add-on-capabilities.md

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: include
-ms.date: 12/18/2023
+ms.date: 01/29/2024
 ms.author: lajanuar
 ---
 <!-- markdownlint-disable MD025 -->
@@ -135,11 +135,30 @@ Extract text, selection marks, text styles, table structures, and bounding regio
 import os
 from azure.core.credentials import AzureKeyCredential
 from azure.ai.documentintelligence import DocumentIntelligenceClient
+from azure.ai.documentintelligence.models import AnalyzeResult
 
 # set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
 endpoint = "<your-endpoint>"
 key = "<your-key>"
 
+# helper functions
+
+def get_words(page, line):
+    result = []
+    for word in page.words:
+        if _in_span(word, line.spans):
+            result.append(word)
+    return result
+
+
+def _in_span(word, spans):
+    for span in spans:
+        if word.span.offset >= span.offset and (
+            word.span.offset + word.span.length
+        ) <= (span.offset + span.length):
+            return True
+    return False
+
 
 def analyze_layout():
     # sample document
@@ -152,9 +171,10 @@ def analyze_layout():
     poller = document_intelligence_client.begin_analyze_document_from_url(
         "prebuilt-layout", formUrl
     )
-    result = poller.result()
 
-    if any([style.is_handwritten for style in result.styles]):
+    result: AnalyzeResult = poller.result()
+
+    if result.styles and any([style.is_handwritten for style in result.styles]):
         print("Document contains handwritten content")
     else:
         print("Document does not contain handwritten content")
@@ -165,49 +185,53 @@ def analyze_layout():
             f"Page has width: {page.width} and height: {page.height}, measured with unit: {page.unit}"
         )
 
-        for line_idx, line in enumerate(page.lines):
-            words = get_words(page, line)
-            print(
-                f"...Line # {line_idx} has word count {len(words)} and text '{line.content}' "
-                f"within bounding polygon '{line.polygon}'"
-            )
-
-            for word in words:
+        if page.lines:
+            for line_idx, line in enumerate(page.lines):
+                words = get_words(page, line)
                 print(
-                    f"......Word '{word.content}' has a confidence of {word.confidence}"
+                    f"...Line # {line_idx} has word count {len(words)} and text '{line.content}' "
+                    f"within bounding polygon '{line.polygon}'"
                 )
 
-        for selection_mark in page.selection_marks:
-            print(
-                f"Selection mark is '{selection_mark.state}' within bounding polygon "
-                f"'{selection_mark.polygon}' and has a confidence of {selection_mark.confidence}"
-            )
+                for word in words:
+                    print(
+                        f"......Word '{word.content}' has a confidence of {word.confidence}"
+                    )
 
-    for table_idx, table in enumerate(result.tables):
-        print(
-            f"Table # {table_idx} has {table.row_count} rows and "
-            f"{table.column_count} columns"
-        )
-        for region in table.bounding_regions:
-            print(
-                f"Table # {table_idx} location on page: {region.page_number} is {region.polygon}"
-            )
-        for cell in table.cells:
+        if page.selection_marks:
+            for selection_mark in page.selection_marks:
+                print(
+                    f"Selection mark is '{selection_mark.state}' within bounding polygon "
+                    f"'{selection_mark.polygon}' and has a confidence of {selection_mark.confidence}"
+                )
+
+    if result.tables:
+        for table_idx, table in enumerate(result.tables):
             print(
-                f"...Cell[{cell.row_index}][{cell.column_index}] has text '{cell.content}'"
+                f"Table # {table_idx} has {table.row_count} rows and "
+                f"{table.column_count} columns"
             )
-            for region in cell.bounding_regions:
+            if table.bounding_regions:
+                for region in table.bounding_regions:
+                    print(
+                        f"Table # {table_idx} location on page: {region.page_number} is {region.polygon}"
+                    )
+            for cell in table.cells:
                 print(
-                    f"...content on page {region.page_number} is within bounding polygon '{region.polygon}'"
+                    f"...Cell[{cell.row_index}][{cell.column_index}] has text '{cell.content}'"
                 )
+                if cell.bounding_regions:
+                    for region in cell.bounding_regions:
+                        print(
+                            f"...content on page {region.page_number} is within bounding polygon '{region.polygon}'"
+                        )
 
     print("----------------------------------------")
 
 
 if __name__ == "__main__":
     analyze_layout()
 
-
 ```
 
 **Run the application**

articles/ai-services/document-intelligence/quickstarts/includes/python-sdk.md

@@ -80,7 +80,7 @@ When opening the support request to increase the total compute limit, provide th
 
 Azure AI Studio provides a pool of shared quota that is available for different users across various regions to use concurrently. Depending upon availability, users can temporarily access quota from the shared pool, and use the quota to perform testing for a limited amount of time. The specific time duration depends on the use case. By temporarily using quota from the quota pool, you no longer need to file a support ticket for a short-term quota increase or wait for your quota request to be approved before you can proceed with your workload. 
 
-Use of the shared quota pool is available for testing inferencing for Llama models from the Model Catalog. You should use the shared quota only for creating temporary test endpoints, not production endpoints. For endpoints in production, you should [request dedicated quota](#view-and-request-quotas-in-the-studio). Billing for shared quota is usage-based, just like billing for dedicated virtual machine families. 
+Use of the shared quota pool is available for testing inferencing for Llama-2, Phi, Nemotron, Mistral, Dolly and Deci-DeciLM models from the Model Catalog. You should use the shared quota only for creating temporary test endpoints, not production endpoints. For endpoints in production, you should [request dedicated quota](#view-and-request-quotas-in-the-studio). Billing for shared quota is usage-based, just like billing for dedicated virtual machine families. 
 
 ## Container Instances 
 

articles/ai-studio/how-to/quota.md

@@ -529,7 +529,9 @@
               items:
                 - name: Application routing add-on overview
                   href: app-routing.md
-                - name: Advanced application routing add-on configurations
+                - name: Advanced application routing add-on configurations (NGINX)
+                  href: app-routing-nginx-configuration.md
+                - name: Custom domain name and SSL certificate configuration
                   href: app-routing-dns-ssl.md
                 - name: Monitor using Prometheus and Grafana
                   href: app-routing-nginx-prometheus.md

articles/aks/TOC.yml

@@ -7,7 +7,7 @@ ms.topic: how-to
 ms.date: 12/04/2023
 ---
 
-#  Set up advanced Ingress configurations with the application routing add-on
+#  Set up a custom domain name and SSL certificate with the application routing add-on
 
 An Ingress is an API object that defines rules, which allow external access to services in an Azure Kubernetes Service (AKS) cluster. When you create an Ingress object that uses the application routing add-on nginx Ingress classes, the add-on creates, configures, and manages one or more Ingress controllers in your AKS cluster.
 
@@ -18,14 +18,14 @@ This article shows you how to set up an advanced Ingress configuration to encryp
 The application routing add-on with nginx delivers the following:
 
 * Easy configuration of managed nginx Ingress controllers.
-* Integration with an external DNS such as [Azure DNS][azure-dns-overview] for public and private zone management
+* Integration with an external DNS such as [Azure DNS][azure-dns-overview] for global and private zone management
 * SSL termination with certificates stored in a key vault, such as [Azure Key Vault][azure-key-vault-overview].
 
 ## Prerequisites
 
 - An AKS cluster with the [application routing add-on][app-routing-add-on-basic-configuration].
 - Azure Key Vault if you want to configure SSL termination and store certificates in the vault hosted in Azure.
-- Azure DNS if you want to configure public and private zone management and host them in Azure.
+- Azure DNS if you want to configure global and private zone management and host them in Azure.
 - To attach an Azure Key Vault or Azure DNS Zone, you need the [Owner][rbac-owner], [Azure account administrator][rbac-classic], or [Azure co-administrator][rbac-classic] role on your Azure subscription.
 
 ## Connect to your AKS cluster
@@ -108,7 +108,8 @@ az aks approuting update -g <ResourceGroupName> -n <ClusterName> --enable-kv --a
 
 To enable support for DNS zones, review the following prerequisite:
 
-* The app routing add-on can be configured to automatically create records on one or more Azure public and private DNS zones for hosts defined on Ingress resources. All public Azure DNS zones need to be in the same resource group, and all private Azure DNS zones need to be in the same resource group. If you don't have an Azure DNS zone, you can [create one][create-an-azure-dns-zone].
+* The app routing add-on can be configured to automatically create records on one or more Azure global and private DNS zones for hosts defined on Ingress resources. All global Azure DNS zones need to be in the same resource group, and all private Azure DNS zones need to be in the same resource group. If you don't have an Azure DNS zone, you can [create one][create-an-azure-dns-zone].
+
 
 ### Create a public Azure DNS zone