Merge pull request #259 from MicrosoftDocs/main

[Publishing] [Scheduled Publish] 9/12/2024 14:10 PM Publish

Author: Albertyang0

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

@@ -4,7 +4,7 @@ titleSuffix: Azure OpenAI
 description: Learn about the different model capabilities that are available with Azure OpenAI.
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 09/09/2024
+ms.date: 09/12/2024
 ms.custom: references_regions, build-2023, build-2023-dataai, refefences_regions
 manager: nitinme
 author: mrbullwinkle #ChrisHMSFT
@@ -26,6 +26,23 @@ Azure OpenAI Service is powered by a diverse set of models with different capabi
 | [Whisper](#whisper-models) | A series of models in preview that can transcribe and translate speech to text. |
 | [Text to speech](#text-to-speech-models-preview) (Preview) | A series of models in preview that can synthesize text to speech. |
 
+## o1-preview and o1-mini models limited access
+
+The Azure OpenAI `o1-preview` and `o1-mini` models are specifically designed to tackle reasoning and problem-solving tasks with increased focus and capability. These models spend more time processing and understanding the user's request, making them exceptionally strong in areas like science, coding, and math compared to previous iterations.
+
+### Availability
+
+The `o1-preview` and `o1-mini` models are available in the East US2 region for limited access through the [AI Studio](https://ai.azure.com) early access playground. Data processing for the `o1` models may occur in a different region than where they are available for use.
+
+To try the `o1-preview` and `o1-mini` models in the early access playground, **registration is required, and access will be granted based on Microsoft’s eligibility criteria**.
+
+Request access: [limited access model application](https://aka.ms/oai/modelaccess)
+
+Once access has been granted, you will need to:
+
+1. Navigate to https://ai.azure.com/resources and select a resource in the `eastus2` region. If you do not have an Azure OpenAI resource in this region you will need to [create one](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI).  
+2. Once the `eastus2` Azure OpenAI resource is selected, in the upper left-hand panel under **Playgrounds** select **Early access playground (preview)**.
+
 ## GPT-4o and GPT-4 Turbo
 
 GPT-4o integrates text and images in a single model, enabling it to handle multiple data types simultaneously. This multimodal approach enhances accuracy and responsiveness in human-computer interactions. GPT-4o matches GPT-4 Turbo in English text and coding tasks while offering superior performance in non-English languages and vision tasks, setting new benchmarks for AI capabilities.

articles/ai-services/openai/whats-new.md

@@ -10,14 +10,57 @@ ms.custom:
   - ignite-2023
   - references_regions
 ms.topic: whats-new
-ms.date: 9/03/2024
+ms.date: 9/12/2024
 recommendations: false
 ---
 
 # What's new in Azure OpenAI Service
 
 This article provides a summary of the latest releases and major documentation updates for Azure OpenAI.
 
+## September 2024
+
+### NEW o1-preview and o1-mini models available for limited access
+
+The Azure OpenAI `o1-preview` and `o1-mini` models are specifically designed to tackle reasoning and problem-solving tasks with increased focus and capability. These models spend more time processing and understanding the user's request, making them exceptionally strong in areas like science, coding, and math compared to previous iterations.
+
+### Key capabilities of the o1 series
+
+- Complex Code Generation: Capable of generating algorithms and handling advanced coding tasks to support developers.
+- Advanced Problem Solving: Ideal for comprehensive brainstorming sessions and addressing multifaceted challenges.
+- Complex Document Comparison: Perfect for analyzing contracts, case files, or legal documents to identify subtle differences.
+- Instruction Following and Workflow Management: Particularly effective for managing workflows requiring shorter contexts.
+
+### Model variants
+
+- `o1-preview`: `o1-preview` is the more capable of the `o1` series models.  
+- `o1-mini`: `o1-mini` is the faster and cheaper of the `o1` series models.
+
+Model version: `2024-09-12`
+
+Request access: [limited access model application](https://aka.ms/oai/modelaccess)
+
+### Limitations
+
+The `o1-preview` and `o1-mini` models are currently in preview and do not include some features available in other models, such as image understanding and structured outputs found in the GPT-4o and GPT-4o-mini models. For many tasks, the generally available GPT-4o models may still be more suitable.
+
+### Safety
+
+OpenAI has incorporated additional safety measures into the `o1` models, including new techniques to help the models refuse unsafe requests. These advancements make the `o1` series some of the most robust models available.
+
+### Availability
+
+The `o1-preview` and `o1-mini` are available in the East US2 region for limited access through the [AI Studio](https://ai.azure.com) early access playground. Data processing for the `o1` models may occur in a different region than where they are available for use.
+
+To try the `o1-preview` and `o1-mini` models in the early access playground **registration is required, and access will be granted based on Microsoft’s eligibility criteria.**
+
+Request access: [limited access model application](https://aka.ms/oai/modelaccess)
+
+Once access has been granted, you will need to:
+
+1. Navigate to https://ai.azure.com/resources and select a resource in the `eastus2` region. If you do not have an Azure OpenAI resource in this region you will need to [create one](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI).  
+2. Once the `eastus2` Azure OpenAI resource is selected, in the upper left-hand panel under **Playgrounds** select **Early access playground (preview)**.
+ 
 ## August 2024
 
 ### GPT-4o 2024-08-06 structured outputs

articles/ai-studio/concepts/rbac-ai-studio.md

@@ -8,7 +8,7 @@ ms.custom:
   - ignite-2023
   - build-2024
 ms.topic: conceptual
-ms.date: 5/21/2024
+ms.date: 9/12/2024
 ms.reviewer: deeikele
 ms.author: larryfr
 author: Blackmist
@@ -220,20 +220,20 @@ When you create a connection that uses Microsoft Entra ID authentication, you mu
 
 | Resource connection | Role | Description |
 |----------|------|-------------|
-| Azure AI Search | Contributor | List API-Keys to list indexes from Azure OpenAI Studio. |
+| Azure AI Search | Contributor | List API-Keys to list indexes from Azure AI Studio. |
 | Azure AI Search | Search Index Data Contributor | Required for indexing scenarios |
-| Azure AI services/Azure OpenAI | Cognitive Services OpenAI Contributor | Call public ingestion API from Azure OpenAI Studio. |
-| Azure AI services/OpenAI | Cognitive Services User | List API-Keys from Azure OpenAI Studio. |
-| Azure AI services/OpenAI | Contributor | Allows for calls to the control plane. |
+| Azure AI services / Azure OpenAI | Cognitive Services OpenAI Contributor | Call public ingestion API from Azure AI Studio. |
+| Azure AI services / Azure OpenAI | Cognitive Services User | List API-Keys from Azure AI Studio. |
+| Azure AI services / Azure OpenAI | Contributor | Allows for calls to the control plane. |
 
 When using Microsoft Entra ID authenticated connections in the chat playground, the services need to authorize each other to access the required resources. The admin performing the configuration needs to have the __Owner__ role on these resources to add role assignments. The following table lists the required role assignments for each resource. The __Assignee__ column refers to the system-assigned managed identity of the listed resource. The __Resource__ column refers to the resource that the assignee needs to access. For example, Azure OpenAI has a system-assigned managed identity that needs to be assigned the __Search Index Data Reader__ role for the Azure AI Search resource.
 
 | Role | Assignee | Resource | Description |
 |------|----------|----------|-------------|
-| Search Index Data Reader | Azure AI services/OpenAI | Azure AI Search | Inference service queries the data from the index. Only used for inference scenarios. |
-| Search Index Data Contributor | Azure AI services/OpenAI | Azure AI Search | Read-write access to content in indexes. Import, refresh, or query the documents collection of an index. Only used for ingestion and inference scenarios. |
-| Search Service Contributor | Azure AI services/OpenAI | Azure AI Search | Read-write access to object definitions (indexes, aliases, synonym maps, indexers, data sources, and skillsets). Inference service queries the index schema for auto fields mapping. Data ingestion service creates index, data sources, skill set, indexer, and queries the indexer status. |
-| Cognitive Services OpenAI Contributor | Azure AI Search | Azure AI services/OpenAI | Custom skill |
+| Search Index Data Reader | Azure AI services / Azure OpenAI | Azure AI Search | Inference service queries the data from the index. Only used for inference scenarios. |
+| Search Index Data Contributor | Azure AI services / Azure OpenAI | Azure AI Search | Read-write access to content in indexes. Import, refresh, or query the documents collection of an index. Only used for ingestion and inference scenarios. |
+| Search Service Contributor | Azure AI services / Azure OpenAI | Azure AI Search | Read-write access to object definitions (indexes, aliases, synonym maps, indexers, data sources, and skillsets). Inference service queries the index schema for auto fields mapping. Data ingestion service creates index, data sources, skill set, indexer, and queries the indexer status. |
+| Cognitive Services OpenAI Contributor | Azure AI Search | Azure AI services / Azure OpenAI | Custom skill |
 | Cognitive Services OpenAI User | Azure OpenAI Resource for chat model | Azure OpenAI resource for embedding model | Required only if using two Azure OpenAI resources to communicate. |
 
 > [!NOTE]
@@ -316,8 +316,8 @@ The following example defines a role for a developer using [Azure OpenAI Assista
 {
     "id": "",
     "properties": {
-        "roleName": "CognitiveServices OpenAI Assistants API Developer",
-        "description": "Custom role to work with AOAI Assistants API",
+        "roleName": "Azure OpenAI Assistants API Developer",
+        "description": "Custom role to work with Azure OpenAI Assistants API",
         "assignableScopes": [
             "<your-scope>"
         ],

articles/ai-studio/how-to/deploy-models-jais.md

@@ -201,7 +201,7 @@ print_stream(result)
 Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference).
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormatText
+from azure.ai.inference.models import ChatCompletionsResponseFormat
 
 response = client.complete(
     messages=[
@@ -214,12 +214,12 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format=ChatCompletionsResponseFormatText(),
+    response_format={ "type": ChatCompletionsResponseFormat.TEXT },
 )
 ```
 
 > [!WARNING]
-> Jais doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Jais models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -482,7 +482,7 @@ var response = await client.path("/chat/completions").post({
 ```
 
 > [!WARNING]
-> Jais doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Jais models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -580,7 +580,7 @@ Deployment to a serverless API endpoint doesn't require quota from your subscrip
 
 ### The inference package installed
 
-You can consume predictions from this model by using the `Azure.AI.Inference` package from [Nuget](https://www.nuget.org/). To install this package, you need the following prerequisites:
+You can consume predictions from this model by using the `Azure.AI.Inference` package from [NuGet](https://www.nuget.org/). To install this package, you need the following prerequisites:
 
 * The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2).
 * Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string.
@@ -606,7 +606,7 @@ using Azure.Identity;
 using Azure.AI.Inference;
 ```
 
-This example also use the following namespaces but you may not always need them:
+This example also uses the following namespaces but you may not always need them:
 
 
 ```csharp
@@ -775,7 +775,7 @@ Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}");
 ```
 
 > [!WARNING]
-> Jais doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Jais models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -1088,7 +1088,7 @@ Explore other parameters that you can specify in the inference client. For a ful
 ```
 
 > [!WARNING]
-> Jais doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Jais models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -1165,14 +1165,14 @@ The following example shows how to handle events when the model detects harmful
 
 ## More inference examples
 
-For more examples of how to use Jais, see the following examples and tutorials:
+For more examples of how to use Jais models, see the following examples and tutorials:
 
 | Description                               | Language          | Sample                                                          |
 |-------------------------------------------|-------------------|-----------------------------------------------------------------|
 | Azure AI Inference package for JavaScript | JavaScript        | [Link](https://aka.ms/azsdk/azure-ai-inference/javascript/samples)  |
 | Azure AI Inference package for Python     | Python            | [Link](https://aka.ms/azsdk/azure-ai-inference/python/samples)  |
 
-## Cost and quota considerations for Jais family of models deployed as serverless API endpoints
+## Cost and quota considerations for Jais models deployed as serverless API endpoints
 
 Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits aren't sufficient for your scenarios.
 
@@ -1189,4 +1189,4 @@ For more information on how to track costs, see [Monitor costs for models offere
 * [Deploy models as serverless APIs](deploy-models-serverless.md)
 * [Consume serverless API endpoints from a different Azure AI Studio project or hub](deploy-models-serverless-connect.md)
 * [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md)
-* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace)
+* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace)