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articles/ai-services/openai/concepts/models.md

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| `gpt-4` (0125-Preview)*<br>**GPT-4 Turbo Preview** | **Preview Model** <br> -Replaces 1106-Preview <br>- Better code generation performance <br> - Reduces cases where the model doesn't complete a task <br> - JSON Mode <br> - parallel function calling <br> - reproducible output (preview) | Input: 128,000 <br> Output: 4,096 | Dec 2023 |
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| `gpt-4` (vision-preview)<br>**GPT-4 Turbo with Vision Preview** | **Preview model** <br> - Accepts text and image input. <br> - Supports enhancements <br> - JSON Mode <br> - parallel function calling <br> - reproducible output (preview) | Input: 128,000 <br> Output: 4,096 | Apr 2023 |
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| `gpt-4` (1106-Preview)<br>**GPT-4 Turbo Preview** | **Preview Model** <br> - JSON Mode <br> - parallel function calling <br> - reproducible output (preview) | Input: 128,000 <br> Output: 4,096 | Apr 2023 |
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| `gpt-4-32k` (0613) | **Older GA model** <br> - Basic function calling with tools | 32,768 | Sep 2021 |
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| `gpt-4-32k` (0613) | **Older GA model** <br> - Basic function calling with tools | 32,768 | Sep 2021 |
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| `gpt-4` (0613) | **Older GA model** <br> - Basic function calling with tools | 8,192 | Sep 2021 |
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| `gpt-4-32k`(0314) | **Older GA model** <br> - Deprecated function calling | 32,768 | Sep 2021 |
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| `gpt-4` (0314) | **Older GA model** <br> - Deprecated function calling | 8,192 | Sep 2021 |
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| `gpt-4-32k`(0314) | **Older GA model** <br> - [Retirement information](./model-retirements.md#current-models) | 32,768 | Sep 2021 |
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| `gpt-4` (0314) | **Older GA model** <br> - [Retirement information](./model-retirements.md#current-models) | 8,192 | Sep 2021 |
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> [!CAUTION]
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> We don't recommend using preview models in production. We will upgrade all deployments of preview models to either future preview versions or to the latest stable/GA version. Models designated preview do not follow the standard Azure OpenAI model lifecycle.
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GPT-3.5 models can understand and generate natural language or code. The most capable and cost effective model in the GPT-3.5 family is GPT-3.5 Turbo, which has been optimized for chat and works well for traditional completions tasks as well. GPT-3.5 Turbo is available for use with the Chat Completions API. GPT-3.5 Turbo Instruct has similar capabilities to `text-davinci-003` using the Completions API instead of the Chat Completions API. We recommend using GPT-3.5 Turbo and GPT-3.5 Turbo Instruct over [legacy GPT-3.5 and GPT-3 models](./legacy-models.md).
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- `gpt-35-turbo`
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- `gpt-35-turbo-16k`
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- `gpt-35-turbo-instruct`
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You can see the token context length supported by each model in the [model summary table](#model-summary-table-and-region-availability).
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| Model ID | Description | Max Request (tokens) | Training Data (up to) |
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| --------- |:---|:------:|:----:|
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| `gpt-35-turbo` (0125) **NEW** | **Latest GA Model** <br> - JSON Mode <br> - parallel function calling <br> - reproducible output (preview) <br> - Higher accuracy at responding in requested formats. <br> - Fix for a bug which caused a text encoding issue for non-English language function calls. | Input: 16,385<br> Output: 4,096 | Sep 2021 |
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| `gpt-35-turbo` (1106) | **Older GA Model** <br> - JSON Mode <br> - parallel function calling <br> - reproducible output (preview) | Input: 16,385<br> Output: 4,096 | Sep 2021|
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| `gpt-35-turbo-instruct` (0914) | **Completions endpoint only** | 4,097 |Sep 2021 |
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| `gpt-35-turbo-16k` (0613) | **Older GA Model** <br> - Basic function calling with tools | 16,384 | Sep 2021 |
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| `gpt-35-turbo` (0613) | **Older GA Model** <br> - Basic function calling with tools | 4,096 | Sep 2021 |
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| `gpt-35-turbo`**<sup>1</sup>** (0301) | **Older GA Model** <br> - [Retirement information](./model-retirements.md#current-models) | 4,096 | Sep 2021 |
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To learn more about how to interact with GPT-3.5 Turbo and the Chat Completions API check out our [in-depth how-to](../how-to/chatgpt.md).
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**<sup>1</sup>** This model will accept requests > 4,096 tokens. It is not recommended to exceed the 4,096 input token limit as the newer version of the model are capped at 4,096 tokens. If you encounter issues when exceeding 4,096 input tokens with this model this configuration is not officially supported.
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## Embeddings
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`text-embedding-3-large` is the latest and most capable embedding model. Upgrading between embeddings models is not possible. In order to move from using `text-embedding-ada-002` to `text-embedding-3-large` you would need to generate new embeddings.
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> [!NOTE]
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> Version `0613` of `gpt-35-turbo` and `gpt-35-turbo-16k` will be retired no earlier than August 1, 2024. Version `0301` of `gpt-35-turbo` will be retired no earlier than August 1, 2024. See [model updates](../how-to/working-with-models.md#model-updates) for model upgrade behavior.
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| Model ID | Max Request (tokens) | Training Data (up to) |
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| --------- |:------:|:----:|
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| `gpt-35-turbo`**<sup>1</sup>** (0301) | 4,096 | Sep 2021 |
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| `gpt-35-turbo` (0613) | 4,096 | Sep 2021 |
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| `gpt-35-turbo-16k` (0613) | 16,384 | Sep 2021 |
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| `gpt-35-turbo-instruct` (0914) | 4,097 |Sep 2021 |
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| `gpt-35-turbo` (1106) | Input: 16,385<br> Output: 4,096 | Sep 2021|
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| `gpt-35-turbo` (0125) **NEW** | Input: 16,385<br> Output: 4,096 | Sep 2021 |
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### GPT-3.5-Turbo model availability
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#### Public cloud regions
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[!INCLUDE [GPT-35-Turbo](../includes/model-matrix/standard-gpt-35-turbo.md)]
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**<sup>1</sup>** This model will accept requests > 4,096 tokens. It is not recommended to exceed the 4,096 input token limit as the newer version of the model are capped at 4,096 tokens. If you encounter issues when exceeding 4,096 input tokens with this model this configuration is not officially supported.
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#### Azure Government regions
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The following GPT-3.5 turbo models are available with [Azure Government](/azure/azure-government/documentation-government-welcome):
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articles/azure-resource-manager/bicep/private-module-registry.md

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description: Learn how to set up an Azure container registry for private Bicep modules
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ms.topic: conceptual
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ms.custom: devx-track-bicep
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ms.date: 04/18/2023
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ms.date: 05/10/2024
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---
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# Create private registry for Bicep modules
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---
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With Bicep CLI version 0.27.1 or newer, you can publish a module with the Bicep source code in addition to the compiled JSON template. If a module is published with the Bicep source code to a registry, you can press `F12` ([Go to Definition](./visual-studio-code.md#go-to-definition)) from Visual Studio Code to see the Bicep Code. The Bicep extension version 0.27 or new is required to see the Bicep file.
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# [PowerShell](#tab/azure-powershell)
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```azurepowershell
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Publish-AzBicepModule -FilePath ./storage.bicep -Target br:exampleregistry.azurecr.io/bicep/modules/storage:v1 -DocumentationUri https://www.contoso.com/exampleregistry.html -WithSource
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```
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# [Azure CLI](#tab/azure-cli)
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To run this deployment command, you must have the [latest version](/cli/azure/install-azure-cli) of Azure CLI.
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```azurecli
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az bicep publish --file storage.bicep --target br:exampleregistry.azurecr.io/bicep/modules/storage:v1 --documentationUri https://www.contoso.com/exampleregistry.html --with-source
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```
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---
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With the with source switch, you see an additional layer in the manifest:
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:::image type="content" source="./media/private-module-registry/bicep-module-with-source-manifest.png" lightbox="./media/private-module-registry/bicep-module-with-source-manifest.png" alt-text="Screenshot of bicep module registry with source.":::
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Note that if the Bicep module references a module in a Private Registry, the ACR endpoint will be visible. To hide the full endpoint, you can configure an alias for the private registry.
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## View files in registry
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To see the published module in the portal:
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1. Sign in to the [Azure portal](https://portal.azure.com).
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1. Search for **container registries**.
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1. Select your registry.
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1. Select **Repositories** from the left menu.
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1. Select **Services** -> **Repositories** from the left menu.
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1. Select the module path (repository). In the preceding example, the module path name is **bicep/modules/storage**.
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1. Select the tag. In the preceding example, the tag is **v1**.
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1. The **Artifact reference** matches the reference you'll use in the Bicep file.

articles/azure-resource-manager/bicep/visual-studio-code.md

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description: Describes how to create Bicep files by using Visual Studio Code
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ms.topic: conceptual
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ms.date: 05/10/2024
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# Create Bicep files by using Visual Studio Code
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## View type document
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From Visual Studio Code, you can easily open the template reference for the resource type you're working on. To do so, hover your cursor over the resource symbolic name, and then select **View type document**.
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From Visual Studio Code, you can open the template reference for the resource type you're working on. To do so, hover your cursor over the resource symbolic name, and then select **View type document**.
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:::image type="content" source="./media/visual-studio-code/visual-studio-code-bicep-view-type-document.png" alt-text="Screenshot of Visual Studio Code Bicep view type document.":::
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## Go to definition
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When defining a [module](./modules.md), regardless of the types of the referenced file - whether it is a local file, module registry file, template spec, you can open the referenced file by selecting or highlighting the module path and then press **[F12]**. If the referenced file is an [Azure Verified Modules(AVM)](https://aka.ms/avm), you can toggle between compiled JSON or Bicep file. To be able to open the Bicep file of a private registry module, ensure that the module is published to the registry with the `WithSource` switch enabled. For more information, see [Publish files to registry](./private-module-registry.md#publish-files-to-registry). The Visual Studio Code Bicep extension version 0.27.1 or newer is required for opening Bicep file from private module registry.
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## Paste as Bicep
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You can paste a JSON snippet from an ARM template to Bicep file. Visual Studio Code automatically decompiles the JSON to Bicep. This feature is only available with the Bicep extension version 0.14.0 or newer. This feature is enabled by default. To disable the feature, see [VS Code and Bicep extension](./install.md#visual-studio-code-and-bicep-extension).

articles/container-apps/TOC.yml

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- name: Build environment variables
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href: java-build-environment-variables.md
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displayName: java
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- name: Metrics
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href: java-metrics.md
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displayName: java
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- name: Use Eureka Server for Spring
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- name: Use Config Server for Spring
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displayName: java
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- name: Set dynamic logger level
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href: java-dynamic-log-level.md
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displayName: java
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- name: Tutorials
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- name: Connect to Eureka Server for Spring
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- name: Connect to Config Server for Spring
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- name: Query managed component logs
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displayName: java
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- name: Billing & quotas
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- name: Billing
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---
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title: Observability of managed Java components in Azure Container Apps
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description: Learn how to retrieve logs of managed Java components in Azure Container Apps.
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services: container-apps
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author: craigshoemaker
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ms.service: container-apps
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ms.custom: devx-track-extended-java
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ms.topic: how-to
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ms.date: 05/01/2024
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ms.author: cshoe
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zone_pivot_groups: container-apps-portal-or-cli
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---
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# Tutorial: Observability of managed Java components in Azure Container Apps
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Java components include built-in observability features that can give you a holistic view of Java component health throughout its lifecycle. In this tutorial, you learn how to query logs messages generated by a Java component.
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## Prerequisites
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The following prerequisites are required for this tutorial.
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| Resource | Description |
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|---|---|
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| Azure Log Analytics | To use the built-in observability features of managed Java components, ensure you set up Azure Log Analytics to use Log Analytics or *Azure Monitor*. For more information, see [Log storage and monitoring options in Azure Container Apps](log-options.md). |
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| Java component | Make sure to create at least one Java component in your environment, such as [Eureka Server](java-eureka-server.md) or [Config Server](java-config-server.md). |
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## Query log data
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Log Analytics is a tool that helps you view and analyze log data. Using Log Analytics, you can write Kusto queries to retrieve, sort, filter, and visualize log data. These visualizations help you spot trends and identify issues with your application. You can work interactively with the query results or use them with other features such as alerts, dashboards, and workbooks.
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::: zone pivot="azure-portal"
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1. Open the Azure portal and go to your Azure Log Analytics workspace.
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```kusto
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| project Time=TimeGenerated, Type=ComponentType_s, Component=ComponentName_s, Message=Log_s
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:::image type="content" source="media/java-components-logs/java-component-logs.png" alt-text="Screenshot of the Log Analytics Java component logs." lightbox="media/java-components-logs/java-component-logs.png":::
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::: zone-end
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You query the component logs via the Azure CLI [log analytics](/cli/azure/monitor/log-analytics) extension.
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1. Run the following command to create a variable for your Log Analytics workspace ID.
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Make sure to replace `<WORKSPACE_ID>` with your Log Analytics workspace ID before running the query.
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# [Bash](#tab/bash)
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```azurecli
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SET $WORKSPACE_ID=<WORKSPACE_ID>
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```
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# [PowerShell](#tab/powershell)
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```powershell
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$WORKSPACE_ID = "<WORKSPACE_ID>"
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```
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---
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# [Bash](#tab/bash)
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```azurecli
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az monitor log-analytics query \
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--workspace $WORKSPACE_ID \
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--analytics-query "ContainerAppSystemLogs_CL | where ComponentType_s == 'SpringCloudEureka' | project Time=TimeGenerated, Type=ComponentType_s, Component=ComponentName_s, Message=Log_s | take 5" --out table
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```
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# [PowerShell](#tab/powershell)
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```powershell
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$queryResults = Invoke-AzOperationalInsightsQuery -WorkspaceId $WORKSPACE_ID -Query "ContainerAppSystemLogs_CL | where ComponentType_s == 'SpringCloudEureka' | project Time=TimeGenerated, Type=ComponentType_s, Component=ComponentName_s, Message=Log_s | take 5"
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$queryResults.Results
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```
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---
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The `project` operator's parameters specify the table columns.
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::: zone-end
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## Query Java Component Log with Azure monitor
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You can query Azure Monitor for monitoring data for your Java component logs.
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::: zone pivot="azure-portal"
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1. From the sidebar, under the *Monitoring* section, select **Logs**.
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```kusto
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```
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1. Select the **Run** button to run the query.
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::: zone-end
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::: zone pivot="azure-cli"
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You query the component logs via the Azure CLI [log analytics](/cli/azure/monitor/log-analytics) extension.
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1. Run the following command to create a variable for your Log Analytics workspace ID.
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Make sure to replace `<WORKSPACE_ID>` with your Log Analytics workspace ID before running the query.
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# [Bash](#tab/bash)
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```azurecli
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SET $WORKSPACE_ID=<WORKSPACE_ID>
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```
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# [PowerShell](#tab/powershell)
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```powershell
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$WORKSPACE_ID = "<WORKSPACE_ID>"
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```
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---
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1. Run the following command to query the logs table.
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# [Bash](#tab/bash)
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```azurecli
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az monitor log-analytics query --workspace $WORKSPACE_CUSTOMER_ID --analytics-query "ContainerAppSystemLogs | where ComponentType == 'SpringCloudEureka' | project Time=TimeGenerated, Type=ComponentType, Component=ComponentName, Message=Log | take 5" --out table
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```
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# [PowerShell](#tab/powershell)
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```powershell
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$queryResults = Invoke-AzOperationalInsightsQuery -WorkspaceId $WORKSPACE_ID -Query "ContainerAppSystemLogs | where ComponentType == 'SpringCloudEureka' | project Time=TimeGenerated, Type=ComponentType, Component=ComponentName, Message=Log | take 5"
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$queryResults.Results
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```
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---
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The `project` operator's parameters specify the table columns.
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::: zone-end
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## Next steps
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> [!div class="nextstepaction"]
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> [Log storage and monitoring options in Azure Container Apps](log-options.md)

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