Merge branch 'main' into release-postgres-flexible

Author: v-alje

.openpublishing.redirection.azure-monitor.json

@@ -1185,6 +1185,11 @@
             "redirect_url": "/azure/azure-monitor/agents/agents-overview",
             "redirect_document_id": true
         },
+        {
+            "source_path_from_root": "/articles/azure-monitor/agents/use-azure-monitor-agent-troubleshooter.md",
+            "redirect_url": "/azure/azure-monitor/agents/troubleshooter-ama-windows",
+            "redirect_document_id": true
+        },
         {
             "source_path_from_root": "/azure/azure-monitor/agents/agents-overview.md#log-analytics-agent",
             "redirect_url": "/azure/azure-monitor/agents/log-analytics-agent",

articles/ai-services/cognitive-services-data-loss-prevention.md

@@ -103,6 +103,15 @@ The following services support data loss prevention configuration:
 * Speech Service
 * QnA Maker
 
+
+## Limitations
+
+[Azure OpenAI on your data - text](./openai/concepts/use-your-data.md) features don't support data loss prevention yet. When `restrictOutboundNetworkAccess` is set as true, all requests to the following APIs will fail immediately to prevent potential data loss.
+
+* /extensions/chat/completions (with text models)
+* /extensions/on-your-data/ingestion-jobs
+
+
 ## Next steps
 
 * [Configure Virtual Networks](cognitive-services-virtual-networks.md)

articles/ai-services/openai/how-to/create-resource.md

@@ -46,3 +46,4 @@ In this article, you review examples for creating and deploying resources in the
 
 - Make API calls and generate text with [Azure OpenAI Service quickstarts](../quickstart.md).
 - Learn more about the [Azure OpenAI Service models](../concepts/models.md).
+- For information on pricing visit the [Azure OpenAI pricing page](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/)

articles/ai-services/openai/includes/create-resource-portal.md

@@ -39,7 +39,7 @@ The following steps show how to create an Azure OpenAI resource in the Azure por
    | **Resource group** | The Azure resource group to contain your Azure OpenAI resource. You can create a new group or use a pre-existing group. |
    | **Region** | The location of your instance. Different locations can introduce latency, but they don't affect the runtime availability of your resource. |
    | **Name** | A descriptive name for your Azure OpenAI Service resource, such as _MyOpenAIResource_. |
-   | **Pricing Tier** | The pricing tier for the resource. Currently, only the Standard tier is available for the Azure OpenAI Service. |
+   | **Pricing Tier** | The pricing tier for the resource. Currently, only the Standard tier is available for the Azure OpenAI Service. For more info on pricing visit the [Azure OpenAI pricing page](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) |
 
    :::image type="content" source="../media/create-resource/create-resource-basic-settings.png" alt-text="Screenshot that shows how to configure an Azure OpenAI resource in the Azure portal.":::
 

articles/ai-services/openai/includes/python.md

@@ -113,7 +113,7 @@ deployment_name='REPLACE_WITH_YOUR_DEPLOYMENT_NAME' #This will correspond to the
 print('Sending a test completion job')
 start_phrase = 'Write a tagline for an ice cream shop. '
 response = client.completions.create(model=deployment_name, prompt=start_phrase, max_tokens=10)
-print(response.choices[0].text)
+print(start_phrase+response.choices[0].text)
 ```
 
 ---

articles/ai-studio/how-to/prompt-flow-tools/azure-open-ai-gpt-4v-tool.md

@@ -0,0 +1,49 @@
+---
+title: Azure OpenAI GPT-4 Turbo with Vision tool in Azure AI Studio
+titleSuffix: Azure AI Studio
+description: This article introduces the Azure OpenAI GPT-4 Turbo with Vision tool for flows in Azure AI Studio.
+ms.service: azure-ai-studio
+ms.topic: reference
+author: likebupt
+ms.author: keli19
+ms.reviewer: eur
+ms.date: 01/02/2024
+---
+
+# Azure OpenAI GPT-4 Turbo with Vision tool (preview) in Azure AI studio
+
+Azure OpenAI GPT-4 Turbo with Vision tool enables you to leverage your AzureOpenAI GPT-4 Turbo with Vision model deployment to analyze images and provide textual responses to questions about them.
+
+## Prerequisites
+
+- Create a GPT-4 Turbo with Vision deployment
+
+    In AI studio, select **Deployments** from the left navigation pane and create a deployment by selecting model name: `gpt-4v`.
+
+## Connection
+
+Setup connections to provisioned resources in prompt flow.
+
+| Type        | Name     | API KEY  | API Type | API Version |
+|-------------|----------|----------|----------|-------------|
+| AzureOpenAI | Required | Required | Required | Required    |
+
+## Inputs
+
+| Name                   | Type        | Description                                                                                    | Required |
+|------------------------|-------------|------------------------------------------------------------------------------------------------|----------|
+| connection             | AzureOpenAI | the AzureOpenAI connection to be used in the tool                                              | Yes      |
+| deployment\_name       | string      | the language model to use                                                                      | Yes      |
+| prompt                 | string      | The text prompt that the language model will use to generate its response.                    | Yes      |
+| max\_tokens            | integer     | the maximum number of tokens to generate in the response. Default is 512.                      | No       |
+| temperature            | float       | the randomness of the generated text. Default is 1.                                            | No       |
+| stop                   | list        | the stopping sequence for the generated text. Default is null.                                 | No       |
+| top_p                  | float       | the probability of using the top choice from the generated tokens. Default is 1.               | No       |
+| presence\_penalty      | float       | value that controls the model's behavior with regard to repeating phrases. Default is 0.      | No       |
+| frequency\_penalty     | float       | value that controls the model's behavior with regard to generating rare phrases. Default is 0. | No       |
+
+## Outputs
+
+| Return Type | Description                              |
+|-------------|------------------------------------------|
+| string      | The text of one response of conversation |

articles/ai-studio/how-to/prompt-flow-tools/prompt-flow-tools-overview.md

@@ -22,6 +22,7 @@ The following table provides an index of tools in prompt flow. If existing tools
 | [LLM](./llm-tool.md) | Use Azure Open AI large language models (LLM) for tasks such as text completion or chat. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Prompt](./prompt-tool.md) | Craft a prompt by using Jinja as the templating language. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Python](./python-tool.md) | Run Python code. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
+| [Azure OpenAI GPT-4 Turbo with Vision (preview)](./azure-open-ai-gpt-4v-tool.md) | Use AzureOpenAI GPT-4 Turbo with Vision model deployment to analyze images and provide textual responses to questions about them. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Vector Index Lookup](./vector-index-lookup-tool.md) | Search text or a vector-based query from a vector index. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |
 | [Content Safety (Text)](./content-safety-tool.md) | Use Azure AI Content Safety to detect harmful content. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Faiss Index Lookup](./faiss-index-lookup-tool.md) | Search a vector-based query from the Faiss index file. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |

articles/ai-studio/toc.yml

@@ -109,6 +109,8 @@
         href: how-to/prompt-flow-tools/prompt-tool.md
       - name: Python tool
         href: how-to/prompt-flow-tools/python-tool.md
+      - name: Azure OpenAI GPT-4 Turbo with Vision tool
+        href: how-to/prompt-flow-tools/azure-open-ai-gpt-4v-tool.md
       - name: Vector Index Lookup tool
         href: how-to/prompt-flow-tools/vector-index-lookup-tool.md
       - name: Content Safety tool

articles/aks/kubelogin-authentication.md

@@ -19,7 +19,7 @@ This article provides an overview of the following authentication methods and ex
 * Interactive web browser
 * Service principal
 * Managed identity
-* Workflow identity
+* Workload identity
 
 ## Limitations
 
@@ -328,4 +328,4 @@ The AKS Microsoft Entra ID client application ID used by kubelogin to perform pu
 
 <!-- LINKS - external -->
 [client-go-cred-plugin]: https://kubernetes.io/docs/reference/access-authn-authz/authentication/#client-go-credential-plugins
-[oidc-federation-github]: https://docs.github.com/en/actions/deployment/security-hardening-your-deployments/configuring-openid-connect-in-azure
+[oidc-federation-github]: https://docs.github.com/en/actions/deployment/security-hardening-your-deployments/configuring-openid-connect-in-azure

articles/aks/monitor-aks.md

@@ -61,7 +61,7 @@ The following Azure services and features of Azure Monitor can be used for extra
 
 
 ## Metrics
-Metrics play an important role in cluster monitoring, identifying issues, and optimizing performance in the AKS clusters. Platform metrics are captured using the out of the box metrics server installed in kube-system namespace, which periodically scrapes metrics from all Kubernetes nodes served by Kubelet. T=You should also enable Azure Managed Prometheus metrics to collect container metrics and Kubernetes object metrics, such as object state of Deployments. See [Collect Prometheus metrics from an AKS cluster](../azure-monitor/containers/prometheus-metrics-enable.md) to send data to Azure Managed service for Prometheus.
+Metrics play an important role in cluster monitoring, identifying issues, and optimizing performance in the AKS clusters. Platform metrics are captured using the out of the box metrics server installed in kube-system namespace, which periodically scrapes metrics from all Kubernetes nodes served by Kubelet. You should also enable Azure Managed Prometheus metrics to collect container metrics and Kubernetes object metrics, such as object state of Deployments. See [Collect Prometheus metrics from an AKS cluster](../azure-monitor/containers/prometheus-metrics-enable.md) to send data to Azure Managed service for Prometheus.
 
 :::image type="content" source="media/monitor-aks/prometheus.png" alt-text="Screenshot of enabling Managed Prometheus for existing cluster." lightbox="media/monitor-aks/prometheus.png":::