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Copy file name to clipboardExpand all lines: articles/ai-services/openai/api-version-deprecation.md
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@@ -5,7 +5,7 @@ services: cognitive-services
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manager: nitinme
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ms.service: azure-ai-openai
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ms.topic: conceptual
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ms.date: 02/29/2024
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ms.date: 03/07/2024
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author: mrbullwinkle
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ms.author: mbullwin
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recommendations: false
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## Latest preview API release
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Azure OpenAI API version [2024-02-15-preview](https://github.com/Azure/azure-rest-api-specs/blob/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview/2024-02-15-preview/inference.json)
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Azure OpenAI API version [2024-03-01-preview](https://github.com/Azure/azure-rest-api-specs/blob/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview/2024-03-01-preview/inference.json)
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is currently the latest preview release.
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This version contains support for all the latest Azure OpenAI features including:
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-[Embeddings `encoding_format` and `dimensions` parameters][**Added in 2024-03-01-preview**]
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-[Assistants API](./assistants-reference.md). [**Added in 2024-02-15-preview**]
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-[DALL-E 3](./dall-e-quickstart.md). [**Added in 2023-12-01-preview**]
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-[Text to speech](./text-to-speech-quickstart.md). [**Added in 2024-02-15-preview**]
Copy file name to clipboardExpand all lines: articles/ai-services/openai/concepts/models.md
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@@ -102,7 +102,7 @@ GPT-4 version 0125-preview is an updated version of the GPT-4 Turbo preview prev
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> [!IMPORTANT]
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>
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> -`gpt-4`version 0125-preview replaces version 1106-preview. Deployments of `gpt-4`version 1106-preview set to "Auto-update to default" and "Upgrade when expired" will start to be upgraded on March 8th, 2024 and will complete upgrades within 2 weeks. Deployments of `gpt-4`version 1106-preview set to "No autoupgrade" will stop working starting March 8th, 2024. If you have a deployment of `gpt-4` version 1106-preview, you can test version `0125-preview`in the available regions below.
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> -`gpt-4`versions 1106-Preview and 0125-Preview will be upgraded with a stable version of `gpt-4` in the future. The deployment upgrade of `gpt-4`1106-Preview to `gpt-4` 0125-Preview scheduled for March 8, 2024 is no longer taking place. Deployments of `gpt-4`versions 1106-Preview and 0125-Preview set to "Auto-update to default" and "Upgrade when expired" will start to be upgraded after the stable version is released. For each deployment, a model version upgrade takes place with no interruption in service for API calls. Upgrades are staged by region and the full upgrade process is expected to take 2 weeks. Deployments of `gpt-4`versions 1106-Preview and 0125-Preview set to "No autoupgrade" will not be upgraded and will stop operating when the previewversion is upgraded in the region.
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| Model ID | Max Request (tokens) | Training Data (up to) |
|```input```| string or array | Yes | N/A | Input text to get embeddings for, encoded as an array or string. The number of input tokens varies depending on what [model you're using](./concepts/models.md). Only `text-embedding-ada-002 (Version 2)` supports array input.|
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|```user```| string | No | Null | A unique identifier representing your end-user. This will help Azure OpenAI monitor and detect abuse. **Do not pass PII identifiers instead use pseudoanonymized values such as GUIDs**|
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|```encoding_format```| string | No |`float`| The format to return the embeddings in. Can be either `float` or `base64`. Defaults to `float`. <br><br>[Added in `2024-03-01-preview`].|
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|```dimensions```| integer | No || The number of dimensions the resulting output embeddings should have. Only supported in `text-embedding-3` and later models. <br><br>[Added in `2024-03-01-preview`]|
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#### Example request
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-`2023-09-01-preview` (retiring April 2, 2024) [Swagger spec](https://github.com/Azure/azure-rest-api-specs/blob/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview/2023-09-01-preview/inference.json)
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-`2023-12-01-preview` (retiring April 2, 2024) (This version or greater required for Vision scenarios) [Swagger spec](https://github.com/Azure/azure-rest-api-specs/tree/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview/2023-12-01-preview)
Copy file name to clipboardExpand all lines: articles/ai-services/openai/whats-new.md
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- ignite-2023
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- references_regions
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ms.topic: whats-new
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ms.date: 02/21/2024
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ms.date: 03/07/2024
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recommendations: false
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---
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# What's new in Azure OpenAI Service
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## March 2024
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### 2024-03-01-preview API released
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`2024-03-01-preview` has all the same functionality as `2024-02-15-preview` and adds two new parameters for embeddings:
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-`encoding_format` allows you to specify the format to generate embeddings in `float`, or `base64`. The default is `float`.
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-`dimensions` allows you set the number of output embeddings. This parameter is only supported with the new third generation embeddings models: `text-embedding-3-large`, `text-embedding-3-small`. Typically larger embeddings are more expensive from a compute, memory, and storage perspective. Being able to adjust the number of dimensions allows more control over overall cost and performance. The `dimensions` parameter is not supported in all versions of the OpenAI 1.x Python library, to take advantage of this parameter we recommend upgrading to the latest version: `pip install openai --upgrade`.
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If you are currently using a preview API version to take advantage of the latest features, we recommend consulting the [API version lifecycle](./api-version-deprecation.md) article to track how long your current API version will be supported.
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## February 2024
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### Update to GPT-4-1106-Preview upgrade plans
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The deployment upgrade of `gpt-4` 1106-Preview to `gpt-4` 0125-Preview scheduled for March 8, 2024 is no longer taking place. Deployments of `gpt-4` versions 1106-Preview and 0125-Preview set to "Auto-update to default" and "Upgrade when expired" will start to be upgraded after a stable version of the model is released.
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For more information on the upgrade process refer to the [models page](./concepts/models.md).
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### GPT-3.5-turbo-0125 model available
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This model has various improvements, including higher accuracy at responding in requested formats and a fix for a bug which caused a text encoding issue for non-English language function calls.
Copy file name to clipboardExpand all lines: articles/aks/concepts-identity.md
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title: Concepts - Access and identity in Azure Kubernetes Services (AKS)
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description: Learn about access and identity in Azure Kubernetes Service (AKS), including Microsoft Entra integration, Kubernetes role-based access control (Kubernetes RBAC), and roles and bindings.
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ms.topic: conceptual
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ms.date: 04/28/2023
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ms.date: 03/07/2024
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author: palma21
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Kubernetes RBAC provides granular filtering of user actions. With this control mechanism:
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* You assign users or user groups permission to create and modify resources or view logs from running application workloads.
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* You assign users or user groups permission to create and modify resources or view logs from running application workloads.
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* You can scope permissions to a single namespace or across the entire AKS cluster.
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* You create *roles* to define permissions, and then assign those roles to users with *role bindings*.
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#### RoleBindings
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Assign roles to users for a given namespace using RoleBindings. With RoleBindings, you can logically segregate a single AKS cluster, only enabling users to access the application resources in their assigned namespace.
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Assign roles to users for a given namespace using RoleBindings. With RoleBindings, you can logically segregate a single AKS cluster, only enabling users to access the application resources in their assigned namespace.
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To bind roles across the entire cluster, or to cluster resources outside a given namespace, you instead use *ClusterRoleBindings*.
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With a ClusterRoleBinding, you bind roles to users and apply to resources across the entire cluster, not a specific namespace. This approach lets you grant administrators or support engineers access to all resources in the AKS cluster.
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> [!NOTE]
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> Microsoft/AKS performs any cluster actions with user consent under a built-in Kubernetes role `aks-service` and built-in role binding `aks-service-rolebinding`.
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> Microsoft/AKS performs any cluster actions with user consent under a built-in Kubernetes role `aks-service` and built-in role binding `aks-service-rolebinding`.
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> This role enables AKS to troubleshoot and diagnose cluster issues, but can't modify permissions nor create roles or role bindings, or other high privilege actions. Role access is only enabled under active support tickets with just-in-time (JIT) access. Read more about [AKS support policies](support-policies.md).
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| Description | Role grant required| Cluster admin Microsoft Entra group(s) | When to use |
| Legacy admin login using client certificate|**Azure Kubernetes Admin Role**. This role allows `az aks get-credentials` to be used with the `--admin` flag, which downloads a [legacy (non-Microsoft Entra) cluster admin certificate](control-kubeconfig-access.md) into the user's `.kube/config`. This is the only purpose of "Azure Kubernetes Admin Role".|n/a|If you're permanently blocked by not having access to a valid Microsoft Entra group with access to your cluster.|
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| Microsoft Entra ID with manual (Cluster)RoleBindings| **Azure Kubernetes User Role**. The "User" role allows `az aks get-credentials` to be used without the `--admin` flag. (This is the only purpose of "Azure Kubernetes User Role".) The result, on a Microsoft Entra ID-enabled cluster, is the download of [an empty entry](control-kubeconfig-access.md) into `.kube/config`, which triggers browser-based authentication when it's first used by `kubectl`.| User is not in any of these groups. Because the user is not in any Cluster Admin groups, their rights will be controlled entirely by any RoleBindings or ClusterRoleBindings that have been set up by cluster admins. The (Cluster)RoleBindings [nominate Microsoft Entra users or Microsoft Entra groups](azure-ad-rbac.md) as their `subjects`. If no such bindings have been set up, the user will not be able to excute any `kubectl` commands.|If you want fine-grained access control, and you're not using Azure RBAC for Kubernetes Authorization. Note that the user who sets up the bindings must log in by one of the other methods listed in this table.|
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| Legacy admin login using client certificate|**Azure Kubernetes Service Admin Role**. This role allows `az aks get-credentials` to be used with the `--admin` flag, which downloads a [legacy (non-Microsoft Entra) cluster admin certificate](control-kubeconfig-access.md) into the user's `.kube/config`. This is the only purpose of "Azure Kubernetes Admin Role".|n/a|If you're permanently blocked by not having access to a valid Microsoft Entra group with access to your cluster.|
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| Microsoft Entra ID with manual (Cluster)RoleBindings| **Azure Kubernetes Service Cluster User Role**. The "User" role allows `az aks get-credentials` to be used without the `--admin` flag. (This is the only purpose of "Azure Kubernetes Service Cluster User Role".) The result, on a Microsoft Entra ID-enabled cluster, is the download of [an empty entry](control-kubeconfig-access.md) into `.kube/config`, which triggers browser-based authentication when it's first used by `kubectl`.| User is not in any of these groups. Because the user is not in any Cluster Admin groups, their rights will be controlled entirely by any RoleBindings or ClusterRoleBindings that have been set up by cluster admins. The (Cluster)RoleBindings [nominate Microsoft Entra users or Microsoft Entra groups](azure-ad-rbac.md) as their `subjects`. If no such bindings have been set up, the user will not be able to excute any `kubectl` commands.|If you want fine-grained access control, and you're not using Azure RBAC for Kubernetes Authorization. Note that the user who sets up the bindings must log in by one of the other methods listed in this table.|
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| Microsoft Entra ID by member of admin group| Same as above|User is a member of one of the groups listed here. AKS automatically generates a ClusterRoleBinding that binds all of the listed groups to the `cluster-admin` Kubernetes role. So users in these groups can run all `kubectl` commands as `cluster-admin`.|If you want to conveniently grant users full admin rights, and are _not_ using Azure RBAC for Kubernetes authorization.|
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| Microsoft Entra ID with Azure RBAC for Kubernetes Authorization|Two roles: <br> First, **Azure Kubernetes User Role** (as above). <br> Second, one of the "Azure Kubernetes Service **RBAC**..." roles listed above, or your own custom alternative.|The admin roles field on the Configuration tab is irrelevant when Azure RBAC for Kubernetes Authorization is enabled.|You are using Azure RBAC for Kubernetes authorization. This approach gives you fine-grained control, without the need to set up RoleBindings or ClusterRoleBindings.|
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| Microsoft Entra ID with Azure RBAC for Kubernetes Authorization|Two roles: <br> First, **Azure Kubernetes Service Cluster User Role** (as above). <br> Second, one of the "Azure Kubernetes Service **RBAC**..." roles listed above, or your own custom alternative.|The admin roles field on the Configuration tab is irrelevant when Azure RBAC for Kubernetes Authorization is enabled.|You are using Azure RBAC for Kubernetes authorization. This approach gives you fine-grained control, without the need to set up RoleBindings or ClusterRoleBindings.|
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