@@ -20,31 +20,6 @@ To try out GPT-4 Turbo with Vision, see the [quickstart](/azure/ai-services/open
 
 The GPT-4 Turbo with Vision model answers general questions about what's present in the images or videos you upload.
 
-## Enhancements
-
-Enhancements let you incorporate other Azure AI services (such as Azure AI Vision) to add new functionality to the chat-with-vision experience.
-
-> [!IMPORTANT]
-> To use Vision enhancement, you need a Computer Vision resource. It must be in the paid (S1) tier and in the same Azure region as your GPT-4 Turbo with Vision resource.
-
-> [!IMPORTANT]
-> Vision enhancements are not supported by the GPT-4 Turbo GA model. They are only available with the preview models.
-
-**Object grounding**: Azure AI Vision complements GPT-4 Turbo with Vision’s text response by identifying and locating salient objects in the input images. This lets the chat model give more accurate and detailed responses about the contents of the image.
-
-:::image type="content" source="../media/concepts/gpt-v/object-grounding.png" alt-text="Screenshot of an image with object grounding applied. Objects have bounding boxes with labels.":::
-
-:::image type="content" source="../media/concepts/gpt-v/object-grounding-response.png" alt-text="Screenshot of a chat response to an image prompt about an outfit. The response is an itemized list of clothing items seen in the image.":::
-
-**Optical Character Recognition (OCR)**: Azure AI Vision complements GPT-4 Turbo with Vision by providing high-quality OCR results as supplementary information to the chat model. It allows the model to produce higher quality responses for images with dense text, transformed images, and numbers-heavy financial documents, and increases the variety of languages the model can recognize in text.
-
-:::image type="content" source="../media/concepts/gpt-v/receipts.png" alt-text="Photo of several receipts.":::
-
-:::image type="content" source="../media/concepts/gpt-v/ocr-response.png" alt-text="Screenshot of the JSON response of an OCR call.":::
-
-**Video prompt**: The **video prompt** enhancement lets you use video clips as input for AI chat, enabling the model to generate summaries and answers about video content. It uses Azure AI Vision Video Retrieval to sample a set of frames from a video and create a transcript of the speech in the video.
-
-> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RW1eHRf]
 
 ## Special pricing information
 
@@ -59,15 +34,6 @@ Base Pricing for GPT-4 Turbo with Vision is:
 
 See the [Tokens section of the overview](/azure/ai-services/openai/overview#tokens) for information on how text and images translate to tokens.
 
-If you turn on Enhancements, additional usage applies for using GPT-4 Turbo with Vision with Azure AI Vision functionality.
-
-| Model        | Price        |
-|-----------------|-----------------|
-| + Enhanced add-on features for OCR | $1.5 per 1000 transactions |
-| + Enhanced add-on features for Object Detection | $1.5 per 1000 transactions |
-| + Enhanced add-on feature for “Video Retrieval” integration **<sup>1</sup>** | Ingestion: $0.05 per minute of video <br>Transactions: $0.25 per 1000 queries of the Video Retrieval index |
-
-**<sup>1</sup>** Processing videos involves the use of extra tokens to identify key frames for analysis. The number of these additional tokens will be roughly equivalent to the sum of the tokens in the text input, plus 700 tokens.
 
 ### Example image price calculation
 > [!IMPORTANT]
@@ -108,9 +74,7 @@ This section describes the limitations of GPT-4 Turbo with Vision.
 
 ### Image support
 
-- **Limitation on image enhancements per chat session**: Enhancements cannot be applied to multiple images within a single chat call.
 - **Maximum input image size**: The maximum size for input images is restricted to 20 MB.
-- **Object grounding in enhancement API**: When the enhancement API is used for object grounding, and the model detects duplicates of an object, it will generate one bounding box and label for all the duplicates instead of separate ones for each.
 - **Low resolution accuracy**: When images are analyzed using the "low resolution" setting, it allows for faster responses and uses fewer input tokens for certain use cases. However, this could impact the accuracy of object and text recognition within the image.
 - **Image chat restriction**: When you upload images in Azure OpenAI Studio or the API, there is a limit of 10 images per chat call.
 

@@ -79,7 +79,7 @@ We also recommend that customers using commitments now create their deployments
 See the following links for more information. The guidance for reservations and commitments is the same:
 
 * [Capacity Transparency](#self-service-migration)
-* [Sizing reservations](../how-to/provisioned-throughput-onboarding.md#important-sizing-azure-openai-provisioned-reservations)
+* [Sizing reservations](../how-to/provisioned-throughput-onboarding.md#important-sizing-azure-openai-provisioned--global-provisioned-reservations)
 
 ## New hourly reservation payment model
 

@@ -17,23 +17,23 @@ recommendations: false
  
 The provisioned throughput capability allows you to specify the amount of throughput you require in a deployment. The service then allocates the necessary model processing capacity and ensures it's ready for you. Throughput is defined in terms of provisioned throughput units (PTU) which is a normalized way of representing the throughput for your deployment. Each model-version pair requires different amounts of PTU to deploy and provide different amounts of throughput per PTU. 
 
-## What does the provisioned deployment type provide?
+## What do the provisioned and global provisioned deployment types provide?
 
 - **Predictable performance:** stable max latency and throughput for uniform workloads. 
 - **Reserved processing capacity:** A deployment configures the amount of throughput. Once deployed, the throughput is available whether used or not.
 - **Cost savings:** High throughput workloads might provide cost savings vs token-based consumption.
 
-An Azure OpenAI Deployment is a unit of management for a specific OpenAI Model. A deployment provides customer access to a model for inference and integrates more features like Content Moderation ([See content moderation documentation](content-filter.md)).
+An Azure OpenAI Deployment is a unit of management for a specific OpenAI Model. A deployment provides customer access to a model for inference and integrates more features like Content Moderation ([See content moderation documentation](content-filter.md)). Global deployments are available in the same Azure OpenAI resources as non-global deployment types but allow you to leverage Azure's global infrastructure to dynamically route traffic to the data center with best availability for each request.
 
 ## What do you get?
 
 | Topic | Provisioned|
 |---|---|
 | What is it? | Provides guaranteed throughput at smaller increments than the existing provisioned offer. Deployments have a consistent max latency for a given model-version. |
 | Who is it for? | Customers who want guaranteed throughput with minimal latency variance. |
-| Quota | Provisioned-managed throughput Units for a given model. |
+| Quota | Provisioned Managed Throughput Unit or Global Provisioned Managed Throughput Unit assigned per region. Quota can be used across any available Azure OpenAI model.|
 | Latency | Max latency constrained from the model. Overall latency is a factor of call shape.  |
-| Utilization | Provisioned-managed Utilization measure provided in Azure Monitor. |
+| Utilization | Provisioned-managed Utilization V2 measure provided in Azure Monitor. |
 | Estimating size | Provided calculator in the studio & benchmarking script. |
 
 ## What models and regions are available for provisioned throughput?
@@ -47,9 +47,9 @@ An Azure OpenAI Deployment is a unit of management for a specific OpenAI Model.
 
 ### Deployment types
 
-When creating a provisioned deployment in Azure OpenAI Studio, the deployment type on the Create Deployment dialog is Provisioned-Managed.
+When creating a provisioned deployment in Azure OpenAI Studio, the deployment type on the Create Deployment dialog is Provisioned-Managed. When creating a global provisioned managed deployment in Azure Open Studio, the deployment type on the Create Deployment dialog is Global Provisioned-Managed.
 
-When creating a provisioned deployment in Azure OpenAI via CLI or API, you need to set the `sku-name` to be Provisioned-Managed. The `sku-capacity` specifies the number of PTUs assigned to the deployment. 
+When creating a provisioned deployment in Azure OpenAI via CLI or API, you need to set the `sku-name` to be `ProvisionedManaged`. When creating a global provisioned deployment in Azure OpenAI via CLI or API, you need to set the `sku-name` to be `GlobalProvisionedManaged`. The `sku-capacity` specifies the number of PTUs assigned to the deployment. 
 
 ```azurecli
 az cognitiveservices account deployment create \
@@ -76,13 +76,13 @@ Unlike the Tokens Per Minute (TPM) quota used by other Azure OpenAI offerings, P
 
 :::image type="content" source="../media/provisioned/model-independent-quota.png" alt-text="Diagram of model independent quota with one pool of PTUs available to multiple Azure OpenAI models." lightbox="../media/provisioned/model-independent-quota.png":::
 
-The new quota shows up in Azure OpenAI Studio as a quota item named **Provisioned Managed Throughput Unit**. In the Studio Quota pane, expanding the quota item will show the deployments contributing to usage of the quota.
+For provisioned deployments, the new quota shows up in Azure OpenAI Studio as a quota item named **Provisioned Managed Throughput Unit**. For global provisioned managed deployments, the new quota shows up in the Azure OpenAI Studio as a quota item named **Global Provisioned Managed Throughput Unit**.  In the Studio Quota pane, expanding the quota item will show the deployments contributing to usage of each quota.
 
 :::image type="content" source="../media/provisioned/ptu-quota-page.png" alt-text="Screenshot of quota UI for Azure OpenAI provisioned." lightbox="../media/provisioned/ptu-quota-page.png":::
 
 #### Obtaining PTU Quota 
 
-PTU quota is available by default in many regions. If additional quota is required, customers can request additional quota via the Request Quota link to the right of the Provisioned Managed Throughput Unit quota item in Azure OpenAI Studio. The form allows the customer to request an increase in PTU quota for a specified region. The customer will receive an email at the included address once the request is approved, typically within two business days. 
+PTU quota is available by default in many regions. If additional quota is required, customers can request additional quota via the Request Quota link to the right of the Provisioned Managed Throughput Unit or Global Provisioned Managed Throughput Unit quota items in Azure OpenAI Studio. The form allows the customer to request an increase in the specified PTU quota for a given region. The customer will receive an email at the included address once the request is approved, typically within two business days. 
 
 #### Per-Model PTU Minimums 
 
@@ -123,13 +123,13 @@ A few high-level considerations:
 
 ### How utilization performance works
 
-Provisioned deployments provide you with an allocated amount of model processing capacity to run a given model.
+Provisioned and global provisioned deployments provide you with an allocated amount of model processing capacity to run a given model.
 
-In Provisioned-Managed deployments, when capacity is exceeded, the API will immediately return a 429 HTTP Status Error. This enables the user to make decisions on how to manage their traffic. Users can redirect requests to a separate deployment, to a standard pay-as-you-go instance, or leverage a retry strategy to manage a given request. The service will continue to return the 429 HTTP status code until the utilization drops below 100%.
+In Provisioned-Managed and Global Provisioned-Managed deployments, when capacity is exceeded, the API will immediately return a 429 HTTP Status Error. This enables the user to make decisions on how to manage their traffic. Users can redirect requests to a separate deployment, to a standard pay-as-you-go instance, or leverage a retry strategy to manage a given request. The service will continue to return the 429 HTTP status code until the utilization drops below 100%.
 
 ### How can I monitor capacity?
 
-The [Provisioned-Managed Utilization V2 metric](../how-to/monitoring.md#azure-openai-metrics) in Azure Monitor measures a given deployments utilization on 1-minute increments. Provisioned-Managed deployments are optimized to ensure that accepted calls are processed with a consistent model processing time (actual end-to-end latency is dependent on a call's characteristics).  
+The [Provisioned-Managed Utilization V2 metric](../how-to/monitoring.md#azure-openai-metrics) in Azure Monitor measures a given deployments utilization on 1-minute increments. Provisioned-Managed and Global Provisioned-Managed deployments are optimized to ensure that accepted calls are processed with a consistent model processing time (actual end-to-end latency is dependent on a call's characteristics).  
 
 #### What should  I do when I receive a 429 response?
 The 429 response isn't an error, but instead part of the design for telling users that a given deployment is fully utilized at a point in time. By providing a fast-fail response, you have control over how to handle these situations in a way that best fits your application requirements.
@@ -140,9 +140,10 @@ The  `retry-after-ms` and `retry-after` headers in the response tell you the tim
 
 #### How does the service decide when to send a 429?
 
-In the Provisioned-Managed offering, each request is evaluated individually according to its prompt size, expected generation size, and model to determine its expected utilization. This is in contrast to pay-as-you-go deployments, which have a [custom rate limiting behavior](../how-to/quota.md) based on the estimated traffic load. For pay-as-you-go deployments this can lead to HTTP 429 errors being generated prior to defined quota values being exceeded if traffic is not evenly distributed.
+In the Provisioned-Managed and Global Provisioned-Managed offerings, each request is evaluated individually according to its prompt size, expected generation size, and model to determine its expected utilization. This is in contrast to pay-as-you-go deployments, which have a [custom rate limiting behavior](../how-to/quota.md) based on the estimated traffic load. For pay-as-you-go deployments this can lead to HTTP 429 errors being generated prior to defined quota values being exceeded if traffic is not evenly distributed.
+
+For Provisioned-Managed and Global Provisioned-Managed, we use a variation of the leaky bucket algorithm to maintain utilization below 100% while allowing some burstiness in the traffic. The high-level logic is as follows:
 
-For Provisioned-Managed, we use a variation of the leaky bucket algorithm to maintain utilization below 100% while allowing some burstiness in the traffic. The high-level logic is as follows:
 1.	Each customer has a set amount of capacity they can utilize on a deployment
 2.	When a request is made:
 

@@ -1,7 +1,7 @@
 ---
-title: 'How to generate text with Azure OpenAI Service'
+title: 'How to generate text with the legacy completions API'
 titleSuffix: Azure OpenAI
-description: Learn how to generate or manipulate text, including code by using a completion endpoint in Azure OpenAI Service.
+description: Learn how to generate or manipulate text, including code by using the legacy completion endpoint in Azure OpenAI Service.
 #services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
@@ -13,7 +13,7 @@ recommendations: false
 
 ---
 
-# Learn how to generate or manipulate text
+# Learn how to generate or manipulate text using the legacy completions API
 
 Azure OpenAI Service provides a **completion endpoint** that can be used for a wide variety of tasks. The endpoint supplies a simple yet powerful text-in, text-out interface to any [Azure OpenAI model](../concepts/models.md). To trigger the completion, you input some text as a prompt. The model generates the completion and attempts to match your context or pattern. Suppose you provide the prompt "As Descartes said, I think, therefore" to the API. For this prompt, Azure OpenAI returns the completion endpoint " I am" with high probability.
 

@@ -13,34 +13,34 @@ ms.author: mbullwin
 
 # Azure OpenAI deployment types
 
-Azure OpenAI provides customers with choices on the hosting structure that fits their business and usage patterns. The service offers two main types of deployment: **standard** and **provisioned**. Standard is offered with a global deployment option, routing traffic globally to provide higher throughput. All deployments can perform the exact same inference operations, however the billing, scale and performance are substantially different. As part of your solution design, you will need to make two key decisions:
+Azure OpenAI provides customers with choices on the hosting structure that fits their business and usage patterns. The service offers two main types of deployment: **standard** and **provisioned**. Standard is offered with a global deployment option, routing traffic globally to provide higher throughput. Provisioned is also offered with a global deployment option, allowing customers to purchase and deploy provisioned throughput units across Azure global infrastructure. All deployments can perform the exact same inference operations, however the billing, scale and performance are substantially different. As part of your solution design, you will need to make two key decisions:
 
 - **Data residency needs**: global vs. regional resources  
 - **Call volume**: standard vs. provisioned
 
 ## Global versus regional deployment types
 
-For standard deployments you have an option of two types of configurations within your resource – **global** or **regional**. Global standard is the recommended starting point. 
+For standard and provisioned deployments, you have an option of two types of configurations within your resource – **global** or **regional**. Global standard is the recommended starting point. 
 
-Global deployments leverage Azure's global infrastructure, dynamically route customer traffic to the data center with best availability for the customer’s inference requests. This means you will get the higest initial throughput limits and best model availability with Global while still providing our uptime SLA and low latency.For high voulmne workloads above the specified usage tiers, you may experience increased latency variation. For customers that require the lower latency variance at large workload usage, we recommend purchasing provisioned throughput.
+Global deployments leverage Azure's global infrastructure, dynamically route customer traffic to the data center with best availability for the customer’s inference requests. This means you will get the highest initial throughput limits and best model availability with Global while still providing our uptime SLA and low latency. For high volume workloads above the specified usage tiers on standard and global standard, you may experience increased latency variation. For customers that require the lower latency variance at large workload usage, we recommend purchasing provisioned throughput.
 
 Our global deployments will be the first location for all new models and features. Customers with very large throughput requirements should consider our provisioned deployment offering.
 
 ## Deployment types
 
 Azure OpenAI offers three types of deployments. These provide a varied level of capabilities that provide trade-offs on: throughput, SLAs, and price. Below is a summary of the options followed by a deeper description of each.
 
-| **Offering** | **Global-Batch** | **Global-Standard** | **Standard** | **Provisioned**  |
-|---|:---|:---|:---|:---|
-| **Best suited for**      | Offline scoring <br><br> Workloads that are not latency sensitive and can be completed in hours.<br><br> For use cases that do not have data processing residency requirements.| Recommended starting place for customers. <br><br>Global-Standard will have the higher default quota and larger number of models available than Standard. | For customers with data residency requirements. Optimized for low to medium volume. | Real-time scoring for large consistent volume. Includes the highest commitments and limits.|
-| **How it works**         | Offline processing via files |Traffic may be routed anywhere in the world | | |
-| **Getting started**      | [Global-Batch](./batch.md) | [Model deployment](./create-resource.md) | [Model deployment](./create-resource.md) | [Provisioned onboarding](./provisioned-throughput-onboarding.md) |
-| **Cost**                 | [Least expensive option](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) <br> 50% less cost compared to Global Standard prices. Access to all new models with larger quota allocations.  | [Global deployment pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) |  [Regional pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) | May experience cost savings for consistent usage |
-| **What you get**         |[Significant discount compared to Global Standard](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) | Easy access to all new models with highest default pay-per-call limits.<br><br> Customers with high volume usage may see higher latency variability | Easy access with [SLA on availability](https://azure.microsoft.com/support/legal/sla/). Optimized for low to medium volume workloads with high burstiness. <br><br>Customers with high consistent volume may experience greater latency variability. | Regional access with very high & predictable throughput. Determine throughput per PTU using the provided [capacity calculator](./provisioned-throughput-onboarding.md#estimate-provisioned-throughput-and-cost) |
-| **What you don’t get**   |❌Real-time call performance <br><br>❌Data processing guarantee<br> <br> Data stored at rest remains in the designated Azure geography, while data may be processed for inferencing in any Azure OpenAI location. [Learn more about data residency](https://azure.microsoft.com/explore/global-infrastructure/data-residency/)  |❌Data processing guarantee<br> <br> Data stored at rest remains in the designated Azure geography, while data may be processed for inferencing in any Azure OpenAI location. [Learn more about data residency](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) | ❌High volume w/consistent low latency | ❌Pay-per-call flexibility |
-| **Per-call Latency**     | Not Applicable (file based async process) | Optimized for real-time calling & low to medium volume usage. Customers with high volume usage may see higher latency variability. Threshold set per model | Optimized for real-time calling & low to medium volume usage. Customers with high volume usage may see higher latency variability. Threshold set per model | Optimized for real-time. |
-| **Sku Name in code**     |  `GlobalBatch` |   `GlobalStandard`               | `Standard`   |      `ProvisionedManaged`       |
-| **Billing model**        |  Pay-per-token |Pay-per-token | Pay-per-token | Monthly Commitments |
+| **Offering** | **Global-Batch** | **Global-Standard**|  **Global-Provisioned**  | **Standard** | **Provisioned**  |
+|---|:---|:---| -------- |:---|:---|
+| **Best suited for**      | Offline scoring <br><br> Workloads that are not latency sensitive and can be completed in hours.<br><br> For use cases that do not have data processing residency requirements.|Recommended starting place for customers. <br><br>Global-Standard will have the higher default quota and larger number of models available than Standard. |Real-time scoring for large consistent volume. Includes the highest commitments and limits.                                                                                               For use cases that do not have data residency requirements.| For customers with data residency requirements. Optimized for low to medium volume. |Real-time scoring for large consistent volume. Includes the highest commitments and limits.                                                                                          For use cases with data residency requirements|
+| **How it works**         | Offline processing via files |Traffic may be routed anywhere in the world |Traffic may be routed anywhere in the world| | |
+| **Getting started**      | [Global-Batch](./batch.md) | [Model deployment](./create-resource.md) |[Provisioned onboarding](/azure/ai-services/openai/how-to/provisioned-throughput-onboarding)| [Model deployment](./create-resource.md) | [Provisioned onboarding](./provisioned-throughput-onboarding.md) |
+| **Cost**                 | [Least expensive option](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) <br> 50% less cost compared to Global Standard prices. Access to all new models with larger quota allocations.  | [Global deployment pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) |May experience cost savings for consistent usage|  [Regional pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) |May experience cost savings for consistent usage |
+| **What you get**         |[Significant discount compared to Global Standard](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) | Easy access to all new models with highest default pay-per-call limits.<br><br> Customers with high volume usage may see higher latency variability |Access to high & predictable throughput across Azure global infrastructure. Determine throughput per PTU using the provided [capacity calculator](/azure/ai-services/openai/how-to/provisioned-throughput-onboarding). | Easy access with [SLA on availability](https://azure.microsoft.com/support/legal/sla/). Optimized for low to medium volume workloads with high burstiness. <br><br>Customers with high consistent volume may experience greater latency variability. | Regional access with very high & predictable throughput. Determine throughput per PTU using the provided [capacity calculator](./provisioned-throughput-onboarding.md#estimate-provisioned-throughput-and-cost) |
+| **What you don’t get**   |❌Real-time call performance <br><br>❌Data processing guarantee<br> <br> Data stored at rest remains in the designated Azure geography, while data may be processed for inferencing in any Azure OpenAI location. [Learn more about data residency](https://azure.microsoft.com/explore/global-infrastructure/data-residency/)  |❌Data processing guarantee<br> <br> Data stored at rest remains in the designated Azure geography, while data may be processed for inferencing in any Azure OpenAI location. [Learn more about data residency](https://azure.microsoft.com/explore/global-infrastructure/data-residency/) |❌Pay-per-call flexibility <br> <br>❌Data processing guarantee<br> <br> Data stored at rest remains in the designated Azure geography, while data may be processed for inferencing in any Azure OpenAI location. [Learn more about data residency](https://azure.microsoft.com/explore/global-infrastructure/data-residency/)| ❌High volume w/consistent low latency | ❌Pay-per-call flexibility |
+| **Per-call Latency**     | Not Applicable (file based async process) | Optimized for real-time calling & low to medium volume usage. Customers with high volume usage may see higher latency variability. Threshold set per model |Optimized for real-time calling & high-volume usage. | Optimized for real-time calling & low to medium volume usage. Customers with high volume usage may see higher latency variability. Threshold set per model |Optimized for real-time calling & high-volume usage.|
+| **Sku Name in code**     |  `GlobalBatch` |   `GlobalStandard`               |`GlobalProvisionedManaged`| `Standard`   |      `ProvisionedManaged`       |
+| **Billing model**        |  Pay-per-token |Pay-per-token |Hourly billing with optional purchase of monthly or yearly reservations| Pay-per-token |Hourly billing with optional purchase of monthly or yearly reservations|
 
 ## Provisioned
 
@@ -61,6 +61,13 @@ Global deployments are available in the same Azure OpenAI resources as non-globa
 
 Customers with high consistent volume may experience greater latency variability. The threshold is set per model. See the [quotas page to learn more](./quota.md).  For applications that require the lower latency variance at large workload usage, we recommend purchasing provisioned throughput.
 
+## Global provisioned
+
+> [!IMPORTANT]
+> Data stored at rest remains in the designated Azure geography, while data may be processed for inferencing in any Azure OpenAI location. [Learn more about data residency](https://azure.microsoft.com/explore/global-infrastructure/data-residency/).
+
+Global deployments are available in the same Azure OpenAI resources as non-global deployment types but allow you to leverage Azure's global infrastructure to dynamically route traffic to the data center with best availability for each request. Global provisioned deployments provide reserved model processing capacity for high and predictable throughput using Azure global infrastructure.  
+
 ## Global batch
 
 > [!IMPORTANT]
@@ -88,7 +95,7 @@ Key use cases include:
 
 Azure Policy helps to enforce organizational standards and to assess compliance at-scale. Through its compliance dashboard, it provides an aggregated view to evaluate the overall state of the environment, with the ability to drill down to the per-resource, per-policy granularity. It also helps to bring your resources to compliance through bulk remediation for existing resources and automatic remediation for new resources. [Learn more about Azure Policy and specific built-in controls for AI services](/azure/ai-services/security-controls-policy).
 
-You can use the following policy to disable access to Azure OpenAI global standard deployments.
+You can use the following policy to disable access to Azure OpenAI global standard deployments. To disable access to Azure global provisioned or global batch deployments, replace `GlobalStandard` with `GlobalProvisionedManaged` or `GlobalBatch` for the intended sku name. 
 
 ```json
 {

@@ -15,7 +15,7 @@ manager: nitinme
 
 GPT-4 Turbo with Vision is a large multimodal model (LMM) developed by OpenAI that can analyze images and provide textual responses to questions about them. It incorporates both natural language processing and visual understanding.
 
-The GPT-4 Turbo with Vision model answers general questions about what's present in images. You can also show it video if you use [Vision enhancement](#use-vision-enhancement-with-video).
+The GPT-4 Turbo with Vision model answers general questions about what's present in images.
 
 > [!TIP]
 > To use GPT-4 Turbo with Vision, you call the Chat Completion API on a GPT-4 Turbo with Vision model that you have deployed. If you're not familiar with the Chat Completion API, see the [GPT-4 Turbo & GPT-4 how-to guide](/azure/ai-services/openai/how-to/chatgpt?tabs=python&pivots=programming-language-chat-completions).
@@ -249,143 +249,7 @@ The _detail_ parameter in the model offers three choices: `low`, `high`, or `aut
 
 For details on how the image parameters impact tokens used and pricing please see - [What is OpenAI? Image Tokens with GPT-4 Turbo with Vision](../overview.md#image-tokens-gpt-4-turbo-with-vision-and-gpt-4o)
 
-## Use Vision enhancement with images
 
-GPT-4 Turbo with Vision provides exclusive access to Azure AI Services tailored enhancements. When combined with Azure AI Vision, it enhances your chat experience by providing the chat model with more detailed information about visible text in the image and the locations of objects.
-
-The **Optical character recognition (OCR)** integration allows the model to produce higher quality responses for dense text, transformed images, and number-heavy financial documents. It also covers a wider range of languages.
-
-The **object grounding** integration brings a new layer to data analysis and user interaction, as the feature can visually distinguish and highlight important elements in the images it processes.
-
-> [!IMPORTANT]
-> To use the Vision enhancement with an Azure OpenAI resource, you need to specify a Computer Vision resource. It must be in the paid (S1) tier and in the same Azure region as your GPT-4 Turbo with Vision resource. If you're using an Azure AI Services resource, you don't need an additional Computer Vision resource.
-
-> [!IMPORTANT]
-> Vision enhancements are not supported by the GPT-4 Turbo GA model. They are only available with the preview models.
-
-> [!CAUTION]
-> Azure AI enhancements for GPT-4 Turbo with Vision will be billed separately from the core functionalities. Each specific Azure AI enhancement for GPT-4 Turbo with Vision has its own distinct charges. For details, see the [special pricing information](../concepts/gpt-with-vision.md#special-pricing-information).
-
-#### [REST](#tab/rest)
-
-Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2024-02-15-preview` where 
-
-- RESOURCE_NAME is the name of your Azure OpenAI resource 
-- DEPLOYMENT_NAME is the name of your GPT-4 Turbo with Vision model deployment 
-
-**Required headers**: 
-- `Content-Type`: application/json 
-- `api-key`: {API_KEY} 
-
-**Body**: 
-
-The format is similar to that of the chat completions API for GPT-4, but the message content can be an array containing strings and images (either a valid HTTP or HTTPS URL to an image, or a base-64-encoded image).
-
-You must also include the `enhancements` and `dataSources` objects. `enhancements` represents the specific Vision enhancement features requested in the chat. It has a `grounding` and `ocr` property, which both have a boolean `enabled` property. Use these to request the OCR service and/or the object detection/grounding service. `dataSources` represents the Computer Vision resource data that's needed for Vision enhancement. It has a `type` property which should be `"AzureComputerVision"` and a `parameters` property. Set the `endpoint` and `key` to the endpoint URL and access key of your Computer Vision resource. 
-
-> [!IMPORTANT]
-> Remember to set a `"max_tokens"` value, or the return output will be cut off.
-
-
-```json
-{
-    "enhancements": {
-            "ocr": {
-              "enabled": true
-            },
-            "grounding": {
-              "enabled": true
-            }
-    },
-    "dataSources": [
-    {
-        "type": "AzureComputerVision",
-        "parameters": {
-            "endpoint": "<your_computer_vision_endpoint>",
-            "key": "<your_computer_vision_key>"
-        }
-    }],
-    "messages": [
-        {
-            "role": "system",
-            "content": "You are a helpful assistant."
-        },
-        {
-            "role": "user",
-            "content": [
-	            {
-	                "type": "text",
-	                "text": "Describe this picture:"
-	            },
-	            {
-	                "type": "image_url",
-	                "image_url": {
-                        "url":"<image URL>" 
-                    }
-                }
-           ] 
-        }
-    ],
-    "max_tokens": 100, 
-    "stream": false 
-} 
-```
-
-#### [Python](#tab/python)
-
-You call the same method as in the previous step, but include the new *extra_body* parameter. It contains the `enhancements` and `dataSources` fields. 
-
-`enhancements` represents the specific Vision enhancement features requested in the chat. It has a `grounding` and `ocr` field, which both have a boolean `enabled` property. Use these to request the OCR service and/or the object detection/grounding service. 
-
-`dataSources` represents the Computer Vision resource data that's needed for Vision enhancement. It has a `type` field which should be `"AzureComputerVision"` and a `parameters` field. Set the `endpoint` and `key` to the endpoint URL and access key of your Computer Vision resource. R
-
-> [!IMPORTANT]
-> Remember to set a `"max_tokens"` value, or the return output will be cut off.
-
-
-```python
-response = client.chat.completions.create(
-    model=deployment_name,
-    messages=[
-        { "role": "system", "content": "You are a helpful assistant." },
-        { "role": "user", "content": [  
-            { 
-                "type": "text", 
-                "text": "Describe this picture:" 
-            },
-            { 
-                "type": "image_url",
-                "image_url": {
-                    "url": "<image URL>"
-                }
-            }
-        ] } 
-    ],
-    extra_body={
-        "dataSources": [
-            {
-                "type": "AzureComputerVision",
-                "parameters": {
-                    "endpoint": "<your_computer_vision_endpoint>",
-                    "key": "<your_computer_vision_key>"
-                }
-            }],
-        "enhancements": {
-            "ocr": {
-                "enabled": True
-            },
-            "grounding": {
-                "enabled": True
-            }
-        }
-    },
-    max_tokens=2000
-)
-print(response)
-```
-
-
----
 
 ### Output
 
@@ -409,28 +273,6 @@ The chat responses you receive from the model should now include enhanced inform
             {
                 "role": "assistant",
                 "content": "The image shows a close-up of an individual with dark hair and what appears to be a short haircut. The person has visible ears and a bit of their neckline. The background is a neutral light color, providing a contrast to the dark hair."
-            },
-            "enhancements":
-            {
-                "grounding":
-                {
-                    "lines":
-                    [
-                        {
-                            "text": "The image shows a close-up of an individual with dark hair and what appears to be a short haircut. The person has visible ears and a bit of their neckline. The background is a neutral light color, providing a contrast to the dark hair.",
-                            "spans":
-                            [
-                                {
-                                    "text": "the person",
-                                    "length": 10,
-                                    "offset": 99,
-                                    "polygon": [{"x":0.11950000375509262,"y":0.4124999940395355},{"x":0.8034999370574951,"y":0.4124999940395355},{"x":0.8034999370574951,"y":0.6434999704360962},{"x":0.11950000375509262,"y":0.6434999704360962}]
-                                }
-                            ]
-                        }
-                    ],
-                    "status": "Success"
-                }
             }
         }
     ],
@@ -448,61 +290,7 @@ Every response includes a `"finish_reason"` field. It has the following possible
 - `length`: Incomplete model output due to the `max_tokens` input parameter or model's token limit.
 - `content_filter`: Omitted content due to a flag from our content filters.
 
-## Use Vision enhancement with video
-
-GPT-4 Turbo with Vision provides exclusive access to Azure AI Services tailored enhancements. The **video prompt** integration uses Azure AI Vision video retrieval to sample a set of frames from a video and create a transcript of the speech in the video. It enables the AI model to give summaries and answers about video content.
-
-Follow these steps to set up a video retrieval system and integrate it with your AI chat model.
-
-> [!IMPORTANT]
-> To use the Vision enhancement with an Azure OpenAI resource, you need to specify a Computer Vision resource. It must be in the paid (S1) tier and in the same Azure region as your GPT-4 Turbo with Vision resource. If you're using an Azure AI Services resource, you don't need an additional Computer Vision resource.
-
-> [!IMPORTANT]
-> Vision enhancements are not supported by the GPT-4 Turbo GA model. They are only available with the preview models.
-
-> [!CAUTION]
-> Azure AI enhancements for GPT-4 Turbo with Vision will be billed separately from the core functionalities. Each specific Azure AI enhancement for GPT-4 Turbo with Vision has its own distinct charges. For details, see the [special pricing information](../concepts/gpt-with-vision.md#special-pricing-information).
-
-> [!TIP]
-> If you prefer, you can carry out the below steps using a Jupyter notebook instead: [Video chat completions notebook](https://github.com/Azure-Samples/azureai-samples/blob/main/scenarios/GPT-4V/video/video_chatcompletions_example_restapi.ipynb). 
-
-### Upload videos to Azure Blob Storage
-
-You need to upload your videos to an Azure Blob Storage container. [Create a new storage account](https://ms.portal.azure.com/#create/Microsoft.StorageAccount) if you don't have one already.
 
-Once your videos are uploaded, you can get their SAS URLs, which you use to access them in later steps.
-
-#### Ensure proper read access
-
-Depending on your authentication method, you may need to do some extra steps to grant access to the Azure Blob Storage container. If you're using an Azure AI Services resource instead of an Azure OpenAI resource, you need to use Managed Identities to grant it **read** access to Azure Blob Storage:
-
-#### [using System assigned identities](#tab/system-assigned)
-
-Enable System assigned identities on your Azure AI Services resource by following these steps:
-1. From your AI Services resource in Azure portal select **Resource Management** -> **Identity** and toggle the status to **ON**.
-1. Assign **Storage Blob Data Read** access to the AI Services resource: From the **Identity** page, select **Azure role assignments**, and then **Add role assignment** with the following settings:
-    - scope: storage
-    - subscription: {your subscription}
-    - Resource: {select the Azure Blob Storage resource}
-    - Role: Storage Blob Data Reader
-1. Save your settings.
-
-#### [using User assigned identities](#tab/user-assigned)
-
-To use a User assigned identity on your Azure AI Services resource, follow these steps:
-1. Create a new Managed Identity resource in the Azure portal.
-1. Navigate to the new resource, then to **Azure Role Assignments**.
-1. Add a **New Role Assignment** with the following settings:
-    - scope: storage
-    - subscription: {your subscription}
-    - Resource: {select the Azure Blob Storage resource}
-    - Role: Storage Blob Data Reader
-1. Save your new configuration.
-1. Navigate to your AI Services resource's **Identity** page.
-1. Select the **User Assigned** Tab, then click **+Add** to select the newly created Managed Identity.
-1. Save your configuration.
-
----
 
 ### Create a video retrieval index
 
@@ -579,207 +367,6 @@ To use a User assigned identity on your Azure AI Services resource, follow these
     curl.exe -v -X GET "https://<YOUR_ENDPOINT_URL>/computervision/retrieval/indexes/my-video-index/ingestions?api-version=2023-05-01-preview&$top=20" -H "ocp-apim-subscription-key: <YOUR_SUBSCRIPTION_KEY>"
     ```
 
-### Integrate your video index with GPT-4 Turbo with Vision
-
-#### [REST](#tab/rest)
-
-1. Prepare a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2024-02-15-preview` where 
-
-    - RESOURCE_NAME is the name of your Azure OpenAI resource 
-    - DEPLOYMENT_NAME is the name of your GPT-4 Vision model deployment 
-        
-    **Required headers**: 
-    - `Content-Type`: application/json 
-    - `api-key`: {API_KEY} 
-1. Add the following JSON structure in the request body:
-    ```json
-    {
-        "enhancements": {
-                "video": {
-                  "enabled": true
-                }
-        },
-        "dataSources": [
-        {
-            "type": "AzureComputerVisionVideoIndex",
-            "parameters": {
-                "computerVisionBaseUrl": "<your_computer_vision_endpoint>",
-                "computerVisionApiKey": "<your_computer_vision_key>",
-                "indexName": "<name_of_your_index>",
-                "videoUrls": ["<your_video_SAS_URL>"]
-            }
-        }],
-        "messages": [ 
-            {
-                "role": "system", 
-                "content": "You are a helpful assistant." 
-            },
-            {
-                "role": "user",
-                "content": [
-                        {
-                            "type": "acv_document_id",
-                            "acv_document_id": "<your_video_ID>"
-                        },
-                        {
-                            "type": "text",
-                            "text": "Describe this video:"
-                        }
-                    ]
-            }
-        ],
-        "max_tokens": 100, 
-    } 
-    ```
-
-    The request includes the `enhancements` and `dataSources` objects. `enhancements` represents the specific Vision enhancement features requested in the chat. `dataSources` represents the Computer Vision resource data that's needed for Vision enhancement. It has a `type` property which should be `"AzureComputerVisionVideoIndex"` and a `parameters` property which contains your AI Vision and video information.
-1. Fill in all the `<placeholder>` fields above with your own information: enter the endpoint URLs and keys of your OpenAI and AI Vision resources where appropriate, and retrieve the video index information from the earlier step.
-1. Send the POST request to the API endpoint. It should contain your OpenAI and AI Vision credentials, the name of your video index, and the ID and SAS URL of a single video.
-
-#### [Python](#tab/python)
-
-In your Python script, call the client's **create** method as in the previous sections, but include the *extra_body* parameter. Here, it contains the `enhancements` and `data_sources` fields. `enhancements` represents the specific Vision enhancement features requested in the chat. It has a `video` field, which has a boolean `enabled` property. Use this to request the video retrieval service. 
-
-`data_sources` represents the external resource data that's needed for Vision enhancement. It has a `type` field which should be `"AzureComputerVisionVideoIndex"` and a `parameters` field. 
-
-Set the `computerVisionBaseUrl` and `computerVisionApiKey` to the endpoint URL and access key of your Computer Vision resource. Set `indexName` to the name of your video index. Set `videoUrls` to a list of SAS URLs of your videos. 
-
-> [!IMPORTANT]
-> Remember to set a `"max_tokens"` value, or the return output will be cut off.
-
-```python
-response = client.chat.completions.create(
-    model=deployment_name,
-    messages=[
-        { "role": "system", "content": "You are a helpful assistant." },
-        { "role": "user", "content": [  
-            {
-                "type": "acv_document_id",
-                "acv_document_id": "<your_video_ID>"
-            },
-            { 
-                "type": "text", 
-                "text": "Describe this video:" 
-            }
-        ] } 
-    ],
-    extra_body={
-        "data_sources": [
-            {
-                "type": "AzureComputerVisionVideoIndex",
-                "parameters": {
-                    "computerVisionBaseUrl": "<your_computer_vision_endpoint>", # your endpoint should look like the following https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/computervision
-                    "computerVisionApiKey": "<your_computer_vision_key>",
-                    "indexName": "<name_of_your_index>",
-                    "videoUrls": ["<your_video_SAS_URL>"]
-                }
-            }],
-        "enhancements": {
-            "video": {
-                "enabled": True
-            }
-        }
-    },
-    max_tokens=100
-)
-
-print(response)
-```
----
-
-> [!IMPORTANT]
-> The `"data_sources"` object's content varies depending on which Azure resource type and authentication method you're using. See the following reference:
-> 
-> #### [Azure OpenAI resource](#tab/resource)
-> 
-> ```json
-> "data_sources": [
-> {
->     "type": "AzureComputerVisionVideoIndex",
->     "parameters": {
->     "endpoint": "<your_computer_vision_endpoint>",
->     "computerVisionApiKey": "<your_computer_vision_key>",
->     "indexName": "<name_of_your_index>",
->     "videoUrls": ["<your_video_SAS_URL>"]
->     }
-> }],
-> ```
-> 
-> #### [Azure AIServices resource + SAS authentication](#tab/resource-sas)
-> 
-> ```json
-> "data_sources": [
-> {
->     "type": "AzureComputerVisionVideoIndex",
->     "parameters": {
->     "indexName": "<name_of_your_index>",
->     "videoUrls": ["<your_video_SAS_URL>"]
->     }
-> }],
-> ```	
-> 
-> #### [Azure AIServices resource + Managed Identities](#tab/resource-mi)
-> 
-> ```json
-> "data_sources": [
-> {
->     "type": "AzureComputerVisionVideoIndex",
->     "parameters": {
->         "indexName": "<name_of_your_index>",
->         "documentAuthenticationKind": "managedidentity",
->     }
-> }],
-> ```	
-> ---
-
-### Output
-
-The chat responses you receive from the model should include information about the video. The API response should look like the following.
-
-```json
-{
-    "id": "chatcmpl-8V4J2cFo7TWO7rIfs47XuDzTKvbct",
-    "object": "chat.completion",
-    "created": 1702415412,
-    "model": "gpt-4",
-    "choices":
-    [
-        {
-            "finish_reason":"stop",
-            "index": 0,
-            "message":
-            {
-                "role": "assistant",
-                "content": "The advertisement video opens with a blurred background that suggests a serene and aesthetically pleasing environment, possibly a workspace with a nature view. As the video progresses, a series of frames showcase a digital interface with search bars and prompts like \"Inspire new ideas,\" \"Research a topic,\" and \"Organize my plans,\" suggesting features of a software or application designed to assist with productivity and creativity.\n\nThe color palette is soft and varied, featuring pastel blues, pinks, and purples, creating a calm and inviting atmosphere. The backgrounds of some frames are adorned with abstract, organically shaped elements and animations, adding to the sense of innovation and modernity.\n\nMidway through the video, the focus shifts to what appears to be a browser or software interface with the phrase \"Screens simulated, subject to change; feature availability and timing may vary,\" indicating the product is in development and that the visuals are illustrative of its capabilities.\n\nThe use of text prompts continues with \"Help me relax,\" followed by a demonstration of a 'dark mode' feature, providing a glimpse into the software's versatility and user-friendly design.\n\nThe video concludes by revealing the product name, \"Copilot,\" and positioning it as \"Your everyday AI companion,\" implying the use of artificial intelligence to enhance daily tasks. The final frames feature the Microsoft logo, associating the product with the well-known technology company.\n\nIn summary, the advertisement video is for a Microsoft product named \"Copilot,\" which seems to be an AI-powered software tool aimed at improving productivity, creativity, and organization for its users. The video conveys a message of innovation, ease, and support in daily digital interactions through a visually appealing and calming presentation."
-            }
-        }
-    ],
-    "usage":
-    {
-        "prompt_tokens": 2068,
-        "completion_tokens": 341,
-        "total_tokens": 2409
-    }
-}
-```
-
-Every response includes a `"finish_reason"` field. It has the following possible values:
-- `stop`: API returned complete model output.
-- `length`: Incomplete model output due to the `max_tokens` input parameter or model's token limit.
-- `content_filter`: Omitted content due to a flag from our content filters.
-
-### Pricing example for Video prompts
-The pricing for GPT-4 Turbo with Vision is dynamic and depends on the specific features and inputs used. For a comprehensive view of Azure OpenAI pricing see [Azure OpenAI Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/).
-
-The base charges and additional features are outlined below:
-
-Base Pricing for GPT-4 Turbo with Vision is:
-- Input: $0.01 per 1000 tokens
-- Output: $0.03 per 1000 tokens
-  
-Video prompt integration with Video Retrieval Add-on:
-- Ingestion: $0.05 per minute of video
-- Transactions: $0.25 per 1000 queries of the Video Retrieval
 
 ## Next steps
 

@@ -37,40 +37,40 @@ Creating a new deployment requires available (unused) quota to cover the desired
 
 Then 200 PTUs of quota are considered used, and there are 300 PTUs available for use to create new deployments. 
 
-A default amount of PTU quota is assigned to all subscriptions in several regions. You can view the quota available to you in a region by visiting the Quotas blade in Azure OpenAI Studio and selecting the desired subscription and region. For example, the screenshot below shows a quota limit of 500 PTUs in West US for the selected subscription. Note that you might see lower values of available default quotas. 
- 
+A default amount of provisioned and global provisioned quota is assigned to all subscriptions in several regions. You can view the quota available to you in a region by visiting the Quotas blade in Azure OpenAI Studio and selecting the desired subscription and region. For example, the screenshot below shows a quota limit of 500 PTUs in West US for the selected subscription. Note that you might see lower values of available default quotas. 
+
 :::image type="content" source="../media/provisioned/available-quota.png" alt-text="A screenshot of the available quota in Azure OpenAI studio." lightbox="../media/provisioned/available-quota.png":::
 
 Additional quota can be requested by clicking the Request Quota link to the right of the “Usage/Limit” column.  (This is off-screen in the screenshot above). 
 
 ## Create an Azure OpenAI resource 
 
-Provisioned Throughput deployments are created via Azure OpenAI resource objects within Azure. You must have an Azure OpenAI resource in each region where you intend to create a deployment. Use the Azure portal to [create a resource](./create-resource.md) in a region with available quota, if required.  
+Provisioned and global provisioned deployments are created via Azure OpenAI resource objects within Azure. You must have an Azure OpenAI resource in each region where you intend to create a deployment. Use the Azure portal to [create a resource](./create-resource.md) in a region with available quota, if required.  
 
 > [!NOTE]
-> Azure OpenAI resources can support multiple types of Azure OpenAI deployments at the same time.  It is not necessary to dedicate new resources for your provisioned deployments. 
-
-## Create your provisioned deployment - capacity is available
+> Azure OpenAI resources can support multiple types of Azure OpenAI deployments at the same time.  It is not necessary to dedicate new resources for your provisioned or global provisioned deployments. 
+## Create your provisioned or global provisioned deployment - capacity is available
 
-After you purchase a commitment on your quota, you can create a deployment. To create a provisioned deployment, you can follow these steps; the choices described reflect the entries shown in the screenshot. 
+once you have verified your quota, you can create a deployment. To create a provisioned deployment, you can follow these steps; the choices described reflect the entries shown in the screenshot. 
 
 :::image type="content" source="../media/provisioned/deployment-screen.png" alt-text="Screenshot of the Azure OpenAI Studio deployment page for a provisioned deployment." lightbox="../media/provisioned/deployment-screen.png":::
 
 
 
 1.	Sign into the [Azure OpenAI Studio](https://oai.azure.com)
-2.	Choose the subscription that was enabled for provisioned deployments & select the desired resource in a region where you have the quota.
+1. Choose the subscription that was enabled for provisioned and global provisioned deployments & select the desired resource in a region where you have the quota.
+
 3.	Under **Management** in the left-nav select **Deployments**.
 4.	Select Create new deployment and configure the following fields. Expand the **advanced options** drop-down menu.
 5.	Fill out the values in each field. Here's an example:
 
 | Field | Description |	Example |
-|--|--|--| 
+|--|--|--|
 | Select a model|	Choose the specific model you wish to deploy.	| GPT-4 |
 | Model version |	Choose the version of the model to deploy.	 | 0613 |
 | Deployment Name	 | The deployment name is used in your code to call the model by using the client libraries and the REST APIs.	| gpt-4|
 | Content filter	| Specify the filtering policy to apply to the deployment. Learn more on our [Content Filtering](../concepts/content-filter.md) how-to. | 	Default |
-| Deployment Type	|This impacts the throughput and performance. Choose Provisioned-Managed for your provisioned deployment 	| Provisioned-Managed |
+| Deployment Type	|This impacts the throughput and performance. Choose Provisioned-Managed or Global Provisioned-Managed for your deployment 	| Provisioned-Managed |
 | Provisioned Throughput Units |	Choose the amount of throughput you wish to include in the deployment. |	100 |
 
 Important things to note: 
@@ -87,7 +87,7 @@ The image below shows the pricing confirmation you will see. The price shown is
 
 :::image type="content" source="../media/provisioned/confirm-pricing.png" alt-text="Screenshot showing the pricing confirmation screen." lightbox="../media/provisioned/confirm-pricing.png":::
 
-If you wish to create your deployment programmatically, you can do so with the following Azure CLI command. Update the `sku-capacity` with the desired number of provisioned throughput units.
+If you wish to create your deployment programmatically, you can do so with the following Azure CLI command. To specify the deployment type, modify the `sku-name` to `ProvisionedManaged` or `GlobalProvisionedManaged` based on the intended deployment type. Update the `sku-capacity` with the desired number of provisioned throughput units.
 
 ```cli
 az cognitiveservices account deployment create \
@@ -101,9 +101,9 @@ az cognitiveservices account deployment create \
 --sku-name ProvisionedManaged
 ```
 
-REST, ARM template, Bicep, and Terraform can also be used to create deployments. See the section on automating deployments in the [Managing Quota](quota.md?tabs=rest#automate-deployment) how-to guide and replace the `sku.name` with "ProvisionedManaged" rather than "Standard."
+REST, ARM template, Bicep, and Terraform can also be used to create deployments. See the section on automating deployments in the [Managing Quota](quota.md?tabs=rest#automate-deployment) how-to guide and replace the `sku.name` with "ProvisionedManaged" or "GlobalProvisionedManaged" rather than "Standard."
 
-## Create your provisioned deployment – Capacity is not available 
+## Create your provisioned or global provisioned deployment – Capacity is not available
 
 Due to the dynamic nature of capacity availability, it is possible that the region of your selected resource might not have the service capacity to create the deployment of the specified model, version, and number of PTUs. 
 

@@ -27,7 +27,7 @@ You should consider switching from pay-as-you-go to provisioned throughput when
 > [!NOTE]
 > In function calling and agent use cases, token usage can be variable. You should understand your expected Tokens Per Minute (TPM) usage in detail prior to migrating workloads to PTU.
 
-## Sizing and estimation: provisioned managed only
+## Sizing and estimation: provisioned and global provisioned
 
 Determining the right amount of provisioned throughput, or PTUs, you require for your workload is an essential step to optimizing performance and cost. This section describes how to use the Azure OpenAI capacity planning tool. The tool provides you with an estimate of the required PTU to meet the needs of your workload.
 
@@ -56,38 +56,37 @@ The values in the output column are the estimated value of PTU units required fo
 
 ## Understanding the Provisioned Throughput Purchase Model 
 
-Azure OpenAI Provisioned is purchased on-demand at an hourly basis based on the number of deployed PTUs, with substantial term discount available via the purchase of Azure Reservations.   
+Azure OpenAI Provisioned and Global Provisiones are purchased on-demand at an hourly basis based on the number of deployed PTUs, with substantial term discount available via the purchase of Azure Reservations.   
 
-The hourly model is useful for short-term deployment needs, such as validating new models or acquiring capacity for a hackathon.  However, the discounts provided by the Azure Reservation for Azure OpenAI Provisioned are considerable and most customers with consistent long-term usage will find a reserved model to be a better value proposition. 
+The hourly model is useful for short-term deployment needs, such as validating new models or acquiring capacity for a hackathon.  However, the discounts provided by the Azure Reservation for Azure OpenAI Provisioned and Global Provisioned are considerable and most customers with consistent long-term usage will find a reserved model to be a better value proposition. 
 
 > [!NOTE]
 > Azure OpenAI Provisioned customers onboarded prior to the August self-service update use a purchase model called the Commitment model.  These customers can continue to use this older purchase model alongside the Hourly/reservation purchase model.  The Commitment model is not available for new customers.  For details on the Commitment purchase model and options for coexistence and migration, please see the [Azure OpenAI Provisioned August Update](../concepts/provisioned-migration.md).
-
 ## Hourly Usage  
 
-Provisioned Throughput deployments are charged an hourly rate ($/PTU/hr) on the number of PTUs that have been deployed.  For example, a 300 PTU deployment will be charged the hourly rate times 300.  All Azure OpenAI pricing is available in the Azure Pricing Calculator. 
+Provisioned and Global Provisioned deployments are charged an hourly rate ($/PTU/hr) on the number of PTUs that have been deployed.  For example, a 300 PTU deployment will be charged the hourly rate times 300.  All Azure OpenAI pricing is available in the Azure Pricing Calculator. 
 
 If a deployment exists for a partial hour, it will receive a prorated charge based on the number of minutes it was deployed during the hour.  For example, a deployment that exists for 15 minutes during an hour will receive 1/4th the hourly charge.  
 
 If the deployment size is changed, the costs of the deployment will adjust to match the new number of PTUs.   
 
 :::image type="content" source="../media/provisioned/hourly-billing.png" alt-text="A diagram showing hourly billing." lightbox="../media/provisioned/hourly-billing.png":::
 
-Paying for provisioned deployments on an hourly basis is ideal for short-term deployment scenarios.  For example: Quality and performance benchmarking of new models, or temporarily increasing PTU capacity to cover an event such as a hackathon.  
+Paying for provisioned and global provisioned deployments on an hourly basis is ideal for short-term deployment scenarios.  For example: Quality and performance benchmarking of new models, or temporarily increasing PTU capacity to cover an event such as a hackathon.  
 
-Customers that require long-term usage of provisioned deployments, however, might pay significantly less per month by purchasing a term discount via an Azure Reservation as discussed in the next section. 
+Customers that require long-term usage of provisioned and global provisioned deployments, however, might pay significantly less per month by purchasing a term discount via an Azure Reservation as discussed in the next section. 
 
 > [!NOTE]
 > It is not recommended to scale production deployments according to incoming traffic and pay for them purely on an hourly basis. There are two reasons for this:
 > * The cost savings achieved by purchasing an Azure Reservation for Azure OpenAI Provisioned are significant, and it will be less expensive in many cases to maintain a deployment sized for full production volume paid for via a reservation than it would be to scale the deployment with incoming traffic.
 > * Having unused provisioned quota (PTUs) does not guarentee that capacity will be available to support increasing the size of the deployment when required. Quota limits the maximum number of PTUs that can be deployed, but it is not a capacity guarantee. Provisioned capacity for each region and modal dynamically changes throughout the day and might not be available when required. As a result, it is recommended to maintain a permanant deployment to cover your traffic needs (paid for via a reservation).
 > * Charges for deployments on a deleted resource will continue until the resource is purged.  To prevent this, delete a resource’s deployment before deleting the resource.  For more information, see [Recover or purge deleted Azure AI services resources](../../recover-purge-resources.md). 
 
-## Azure Reservations for Azure OpenAI Provisioned   
+## Azure Reservations for Azure OpenAI Provisioned and Global Provisioned
 
-Discounts on top of the hourly usage price can be obtained by purchasing an Azure Reservation for Azure OpenAI Provisioned. An Azure Reservation is a term-discounting mechanism shared by many Azure products. For example, Compute and Cosmos DB. For Azure OpenAI Provisioned, the reservation provides a discount for committing to payment for fixed number of PTUs for a one-month or one-year period.  
+Discounts on top of the hourly usage price can be obtained by purchasing an Azure Reservation for Azure OpenAI Provisioned and Global Provisioned. An Azure Reservation is a term-discounting mechanism shared by many Azure products. For example, Compute and Cosmos DB. For Azure OpenAI Provisioned and Global Provisioned, the reservation provides a discount for committing to payment for fixed number of PTUs for a one-month or one-year period.  
 
-* Azure Reservations are purchased via the Azure portal, not Azure OpenAI Studio  Link to Azure reservation portal. 
+* Azure Reservations are purchased via the Azure portal, not Azure OpenAI Studio Link to Azure reservation portal. 
 
 * Reservations are purchased regionally and can be flexibly scoped to cover usage from a group of deployments. Reservation scopes include: 
 
@@ -110,7 +109,7 @@ Discounts on top of the hourly usage price can be obtained by purchasing an Azur
 >
 > * The Azure role and tenant policy requirements to purchase a reservation are different than those required to create a deployment or Azure OpenAI resource.  Verify authorization to purchase reservations in advance of needing to do so. See Azure OpenAI [Provisioned reservation documentation](https://aka.ms/oai/docs/ptum-reservations) for more details.
 
-## Important: Sizing Azure OpenAI Provisioned Reservations 
+## Important: Sizing Azure OpenAI Provisioned & Global Provisioned Reservations
 
 The PTU amounts in reservation purchases are independent of PTUs allocated in quota or used in deployments. It is possible to purchase a reservation for more PTUs than you have in quota, or can deploy for the desired region, model, or version.   Credits for over-purchasing a reservation are limited, and customers must take steps to ensure they maintain their reservation sizes in line with their deployed PTUs.  
  

@@ -21,7 +21,6 @@ Use this article to get started using the Azure OpenAI Python SDK to deploy and
 - <a href="https://www.python.org/" target="_blank">Python 3.8 or later version</a>.
 - The following Python libraries: `os`
 - An Azure OpenAI Service resource with a GPT-4 Turbo with Vision model deployed. See [GPT-4 and GPT-4 Turbo Preview model availability](../concepts/models.md#gpt-4-and-gpt-4-turbo-model-availability) for available regions. For more information about resource creation, see the [resource deployment guide](/azure/ai-services/openai/how-to/create-resource).
-- For Vision enhancement (optional): An Azure Computer Vision resource in the same region as your Azure OpenAI resource, in the paid (S1) tier.
 
 ## Set up 
 
@@ -44,7 +43,6 @@ pip install openai
 
 Create a new Python file named _quickstart.py_. Open the new file in your preferred editor or IDE.
 
-#### [Image prompts](#tab/image)
 
 1. Replace the contents of _quickstart.py_ with the following code. 
     
@@ -98,95 +96,6 @@ Create a new Python file named _quickstart.py_. Open the new file in your prefer
     python quickstart.py
     ```
 
-#### [Image prompt enhancements](#tab/enhanced)
-
-GPT-4 Turbo with Vision provides exclusive access to Azure AI Services tailored enhancements. When combined with Azure AI Vision, it enhances your chat experience by providing the chat model with more detailed information about visible text in the image and the locations of objects.
-
-The **Optical Character Recognition (OCR)** integration allows the model to produce higher quality responses for dense text, transformed images, and number-heavy financial documents. It also covers a wider range of languages.
-
-The **object grounding** integration brings a new layer to data analysis and user interaction, as the feature can visually distinguish and highlight important elements in the images it processes.
-
-> [!CAUTION]
-> Azure AI enhancements for GPT-4 Turbo with Vision will be billed separately from the core functionalities. Each specific Azure AI enhancement for GPT-4 Turbo with Vision has its own distinct charges. For details, see the [special pricing information](../concepts/gpt-with-vision.md#special-pricing-information).
-
-> [!IMPORTANT]
-> Vision enhancements are not supported by the GPT-4 Turbo GA model. They are only available with the preview models.
-
-
-1. Replace the contents of _quickstart.py_ with the following code. 
-    
-    ```python
-    from openai import AzureOpenAI
-    
-    api_base = os.getenv("AZURE_OPENAI_ENDPOINT")
-    api_key= os.getenv("AZURE_OPENAI_API_KEY")
-    deployment_name = '<your_deployment_name>'
-    api_version = '2023-12-01-preview' # this might change in the future
-    
-    client = AzureOpenAI(
-        api_key=api_key,  
-        api_version=api_version,
-        base_url=f"{api_base}/openai/deployments/{deployment_name}/extensions",
-    )
-    
-    response = client.chat.completions.create(
-        model=deployment_name,
-        messages=[
-            { "role": "system", "content": "You are a helpful assistant." },
-            { "role": "user", "content": [  
-                { 
-                    "type": "text", 
-                    "text": "Describe this picture:" 
-                },
-                { 
-                    "type": "image_url",
-                    "image_url": {
-                        "url": "<image URL>"
-                    }
-                }
-            ] } 
-        ],
-        extra_body={
-            "dataSources": [
-                {
-                    "type": "AzureComputerVision",
-                    "parameters": {
-                        "endpoint": "<your_computer_vision_endpoint>",
-                        "key": "<your_computer_vision_key>"
-                    }
-                }],
-            "enhancements": {
-                "ocr": {
-                    "enabled": True
-                },
-                "grounding": {
-                    "enabled": True
-                }
-            }
-        },
-        max_tokens=2000
-    )
-    
-    print(response)
-    ```
-
-1. Make the following changes:
-    1. Enter your GPT-4 Turbo with Vision deployment name in the appropriate field. 
-    1. Enter your Computer Vision endpoint URL and key in the appropriate fields.
-    1. Change the value of the `"url"` field to the URL of your image.
-        > [!TIP]
-        > You can also use a base 64 encoded image data instead of a URL. For more information, see the [GPT-4 Turbo with Vision how-to guide](../how-to/gpt-with-vision.md#use-a-local-image).
-1. Run the application with the `python` command:
-
-    ```console
-    python quickstart.py
-    ```
-
-#### [Video prompt enhancements](#tab/video)
-
-Video prompt integration is outside the scope of this quickstart. See the [GPT-4 Turbo with Vision how-to guide](../how-to/gpt-with-vision.md#use-vision-enhancement-with-video) for detailed instructions on setting up video prompts in chat completions programmatically.
-
----
 
 ## Clean up resources
 

@@ -18,7 +18,6 @@ Use this article to get started using the Azure OpenAI REST APIs to deploy and u
 - <a href="https://www.python.org/" target="_blank">Python 3.8 or later version</a>.
 - The following Python libraries: `requests`, `json`.
 - An Azure OpenAI Service resource with a GPT-4 Turbo with Vision model deployed. See [GPT-4 and GPT-4 Turbo Preview model availability](../concepts/models.md#gpt-4-and-gpt-4-turbo-model-availability) for available regions. For more information about resource creation, see the [resource deployment guide](/azure/ai-services/openai/how-to/create-resource).
-- For Vision enhancement (optional): An Azure Computer Vision resource in the same region as your Azure OpenAI resource, in the paid (S1) tier.
 
 > [!NOTE]
 > It is currently not supported to turn off content filtering for the GPT-4 Turbo with Vision model.
@@ -40,7 +39,6 @@ Go to your resource in the Azure portal. On the navigation pane, select **Keys a
 
 Create a new Python file named _quickstart.py_. Open the new file in your preferred editor or IDE.
 
-#### [Image prompts](#tab/image)
 
 1. Replace the contents of _quickstart.py_ with the following code. 
     
@@ -99,101 +97,6 @@ Create a new Python file named _quickstart.py_. Open the new file in your prefer
     python quickstart.py
     ```
 
-#### [Image prompt enhancements](#tab/enhanced)
-
-GPT-4 Turbo with Vision provides exclusive access to Azure AI Services tailored enhancements. When combined with Azure AI Vision, it enhances your chat experience by providing the chat model with more detailed information about visible text in the image and the locations of objects.
-
-The **Optical Character Recognition (OCR)** integration allows the model to produce higher quality responses for dense text, transformed images, and number-heavy financial documents. It also covers a wider range of languages.
-
-The **object grounding** integration brings a new layer to data analysis and user interaction, as the feature can visually distinguish and highlight important elements in the images it processes.
-
-> [!CAUTION]
-> Azure AI enhancements for GPT-4 Turbo with Vision will be billed separately from the core functionalities. Each specific Azure AI enhancement for GPT-4 Turbo with Vision has its own distinct charges. For details, see the [special pricing information](../concepts/gpt-with-vision.md#special-pricing-information).
-
-> [!IMPORTANT]
-> Vision enhancements are not supported by the GPT-4 Turbo GA model. They are only available with the preview models.
-
-
-1. Replace the contents of _quickstart.py_ with the following code. 
-    
-    ```python
-    # Packages required:
-    import requests 
-    import json 
-    
-    api_base = '<your_azure_openai_endpoint>' 
-    deployment_name = '<your_deployment_name>'
-    API_KEY = '<your_azure_openai_key>'
-    
-    base_url = f"{api_base}openai/deployments/{deployment_name}" 
-    headers = {   
-        "Content-Type": "application/json",   
-        "api-key": API_KEY 
-    } 
-    
-    # Prepare endpoint, headers, and request body 
-    endpoint = f"{base_url}/extensions/chat/completions?api-version=2023-12-01-preview" 
-    data = {
-        "model": "gpt-4-vision-preview",
-        "enhancements": {
-            "ocr": {
-              "enabled": True
-            },
-            "grounding": {
-              "enabled": True
-            }
-        },
-        "dataSources": [
-        {
-            "type": "AzureComputerVision",
-            "parameters": {
-                "endpoint": "<your_computer_vision_endpoint>",
-                "key": "<your_computer_vision_key>"
-            }
-        }],
-        "messages": [ 
-            { "role": "system", "content": "You are a helpful assistant." }, 
-            { "role": "user", 
-            "content": [  
-                { 
-                    "type": "text", 
-                    "text": "Describe this picture:" 
-                },
-                { 
-                    "type": "image_url", 
-                    "image_url": {
-                        "url" : "<image URL>"
-                    }
-                }
-            ]} 
-        ], 
-        "max_tokens": 2000 
-    }   
-    
-    # Make the API call   
-    response = requests.post(endpoint, headers=headers, data=json.dumps(data))   
-
-    print(f"Status Code: {response.status_code}")   
-    print(response.text)
-    ```
-
-1. Make the following changes:
-    1. Enter your GPT-4 Turbo with Vision deployment name in the appropriate field. 
-    1. Enter your Computer Vision endpoint URL and key in the appropriate fields.
-    1. Change the value of the `"image"` field to the URL of your image.
-        > [!TIP]
-        > You can also use a base 64 encoded image data instead of a URL. For more information, see the [GPT-4 Turbo with Vision how-to guide](../how-to/gpt-with-vision.md#use-a-local-image).
-1. Run the application with the `python` command:
-
-    ```console
-    python quickstart.py
-    ```
-
-#### [Video prompt enhancements](#tab/video)
-
-Video prompt integration is outside the scope of this quickstart. See the [GPT-4 Turbo with Vision how-to guide](../how-to/gpt-with-vision.md#use-vision-enhancement-with-video) for detailed instructions on setting up video prompts in chat completions programmatically.
-
----
 
 ## Clean up resources
 

@@ -16,7 +16,6 @@ Start exploring GPT-4 Turbo with Vision capabilities with a no-code approach thr
 
 - An Azure subscription. <a href="https://azure.microsoft.com/free/ai-services" target="_blank">Create one for free</a>.
 - An Azure OpenAI Service resource with a GPT-4 Turbo with Vision model deployed. See [GPT-4 and GPT-4 Turbo Preview model availability](../concepts/models.md#gpt-4-and-gpt-4-turbo-model-availability) for available regions. For more information about resource creation, see the [resource deployment guide](/azure/ai-services/openai/how-to/create-resource).
-- For Vision enhancement (optional): An Azure Computer Vision resource in the same region as your Azure OpenAI resource, in the paid (S1) tier.
 
 > [!NOTE]
 > It is currently not supported to turn off content filtering for the GPT-4 Turbo with Vision model.
@@ -53,66 +52,6 @@ In this chat session, you're instructing the assistant to aid in understanding i
 
 :::image type="content" source="../media/quickstarts/studio-vision.png" lightbox="../media/quickstarts/studio-vision.png" alt-text="Screenshot of OpenAI studio chat playground.":::
 
-#### [Image prompt enhancements](#tab/enhanced)
-
-GPT-4 Turbo with Vision provides exclusive access to Azure AI Services tailored enhancements. When combined with Azure AI Vision, it enhances your chat experience by providing the chat model with more detailed information about visible text in the image and the locations of objects.
-
-The **Optical Character Recognition (OCR)** integration allows the model to produce higher quality responses for dense text, transformed images, and number-heavy financial documents. It also covers a wider range of languages.
-
-The **object grounding** integration brings a new layer to data analysis and user interaction, as the feature can visually distinguish and highlight important elements in the images it processes.
-
-> [!CAUTION]
-> Azure AI enhancements for GPT-4 Turbo with Vision will be billed separately from the core functionalities. Each specific Azure AI enhancement for GPT-4 Turbo with Vision has its own distinct charges. For details, see the [special pricing information](../concepts/gpt-with-vision.md#special-pricing-information).
-
-> [!IMPORTANT]
-> Vision enhancements are not supported by the GPT-4 Turbo GA model. They are only available with the preview models.
-
-
-In this chat session, you try out the capabilities of the enhanced Vision model.
-1. To start, select your GPT-4 Turbo with Vision deployment from the dropdown.
-1. In the **Configuration** tab on the right side of the chat experience, turn on the option for **Vision** under the **Enhancements** section.
-1. You're required to select a Computer Vision resource to try the enhanced Vision API. Select your resource, and **Save**. 
-1. In the **Assistant setup** pane, provide a System Message to guide the assistant. The default System Message is: "You are an AI assistant that helps people find information." You can tailor the System Message to the image or scenario that you're uploading.  
-
-    > [!NOTE]
-    > It is recommended to update the System Message to be specific to the task in order to avoid unhelpful responses from the model.
-    
-1. Save your changes, and when prompted to confirm updating the system message, select **Continue**.
-1. In the **Chat session** pane, enter a text prompt like "Describe this image," and upload an image with the attachment button. You can use a different text prompt for your use case. Then select **Send**.  
-1. You should receive a response with more detailed information about visible text in the image and the locations of objects. Consider asking follow-up questions related to the analysis of your image to learn more.
-
-:::image type="content" source="../media/quickstarts/studio-vision-enhanced.png" lightbox="../media/quickstarts/studio-vision-enhanced.png" alt-text="Screenshot of OpenAI studio chat playground with Enhancements turned on and the Computer Vision resource selection box.":::
-
-#### [Video prompt enhancements](#tab/video)
-
-GPT-4 Turbo with Vision provides exclusive access to Azure AI Services tailored enhancements. The **video prompt** integration uses Azure AI Vision video retrieval to sample a set of frames from a video and create a transcript of the speech in the video. It enables the AI model to give summaries and answers about video content.
-
-> [!CAUTION]
-> Azure AI enhancements for GPT-4 Turbo with Vision will be billed separately from the core functionalities. Each specific Azure AI enhancement for GPT-4 Turbo with Vision has its own distinct charges. For details, see the [special pricing information](../concepts/gpt-with-vision.md#special-pricing-information).
-
-> [!IMPORTANT]
-> Vision enhancements are not supported by the GPT-4 Turbo GA model. They are only available with the preview models.
-
-1. To start, select your GPT-4 Turbo with Vision deployment from the dropdown.
-1. In the **Configuration** tab on the right side of the chat experience, turn on the option for **Vision** under the **Enhancements** section.
-1. You're required to select a Computer Vision resource to try the enhanced Vision API. Select your resource, and **Save**. 
-1. In the **Assistant setup** pane, provide a System Message to guide the assistant. The default System Message is: "You are an AI assistant that helps people find information." You can tailor the System Message to the video or scenario that you're uploading.  
-
-    > [!NOTE]
-    > It is recommended to update the System Message to be specific to the task in order to avoid unhelpful responses from the model.
-    
-1. Save your changes, and when prompted to confirm updating the system message, select **Continue**.
-1. In the chat session pane, enter a question about the video like: "Describe this video in detail. Focus on brands, technology and people." You can use a different text prompt for your use case. Upload a video using the attachment button and then select **Send**. 
-
-    > [!NOTE]
-    > Currently the chat playground only supports videos that are less than 3 minutes long.
-
-1. You should receive a response describing the video. Consider asking follow-up questions related to the analysis of your video to learn more.
-
-
-:::image type="content" source="../media/quickstarts/studio-vision-enhanced-video.png" alt-text="Screenshot of OpenAI studio chat playground with Enhancements turned on and the Computer Vision resource selection box. Video-specific text prompt." lightbox="../media/quickstarts/studio-vision-enhanced-video.png":::
-
----
 
 ## Clean up resources
 

@@ -0,0 +1,247 @@
+---
+ms.topic: include
+manager: nitinme
+ms.service: azure-ai-openai
+ms.topic: include
+ms.date: 09/12/2024
+ms.reviewer: v-baolianzou
+ms.author: eur
+author: eric-urban
+recommendations: false
+---
+
+[Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples)
+
+## Prerequisites
+
+#### [JavaScript](#tab/javascript)
+
+- An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
+- [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule)
+- An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
+
+
+#### [TypeScript](#tab/typescript)
+
+- An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
+- [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule)
+- [TypeScript](https://www.typescriptlang.org/download/)
+- An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
+
+
+
+---
+
+## Set up
+
+### Retrieve key and endpoint
+
+To successfully make a call against Azure OpenAI, you need an **endpoint** and a **key**.
+
+|Variable name | Value |
+|--------------------------|-------------|
+| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. Alternatively, you can find the value in the **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://aoai-docs.openai.azure.com/`.|
+| `AZURE_OPENAI_API_KEY` | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. You can use either `KEY1` or `KEY2`.|
+
+Go to your resource in the Azure portal. The **Endpoint and Keys** can be found in the **Resource Management** section. Copy your endpoint and access key as you need both for authenticating your API calls. You can use either `KEY1` or `KEY2`. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.
+
+:::image type="content" source="../media/quickstarts/endpoint.png" alt-text="Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint & access keys location highlighted." lightbox="../media/quickstarts/endpoint.png":::
+
+### Environment variables
+
+Create and assign persistent environment variables for your key and endpoint.
+
+[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
+
+# [Command Line](#tab/command-line)
+
+```CMD
+setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
+```
+
+```CMD
+setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
+```
+
+# [PowerShell](#tab/powershell)
+
+```powershell
+[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
+```
+
+```powershell
+[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
+```
+
+# [Bash](#tab/bash)
+
+```Bash
+echo export AZURE_OPENAI_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
+```
+
+```Bash
+echo export AZURE_OPENAI_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
+```
+---
+
+## Create a Node application
+
+In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it. Then run the `npm init` command to create a node application with a _package.json_ file.
+
+```console
+npm init
+```
+
+## Install the client library
+
+Install the client libraries with:
+
+```console
+npm install openai @azure/identity
+```
+
+Your app's _package.json_ file will be updated with the dependencies.
+
+## Create a speech file
+
+
+
+#### [JavaScript](#tab/javascript)
+
+1. Create a new file named _Text-to-speech.js_ and open it in your preferred code editor. Copy the following code into the _Text-to-speech.js_ file:
+
+    ```javascript
+    require("dotenv/config");
+    const { writeFile } = require("fs/promises");
+    const { AzureOpenAI } = require("openai");
+    require("openai/shims/node");
+    
+    // You will need to set these environment variables or edit the following values
+    const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || "<endpoint>";
+    const apiKey = process.env["AZURE_OPENAI_API_KEY"] || "<api key>";
+    const speechFilePath =
+      process.env["SPEECH_FILE_PATH"] || "<path to save the speech file>";
+    
+    // Required Azure OpenAI deployment name and API version
+    const deploymentName = "tts";
+    const apiVersion = "2024-08-01-preview";
+    
+    function getClient() {
+      return new AzureOpenAI({
+        endpoint,
+        apiKey,
+        apiVersion,
+        deployment: deploymentName,
+      });
+    }
+    
+    async function generateAudioStream(
+      client,
+      params
+    ) {
+      const response = await client.audio.speech.create(params);
+      if (response.ok) return response.body;
+      throw new Error(`Failed to generate audio stream: ${response.statusText}`);
+    }
+    export async function main() {
+      console.log("== Text to Speech Sample ==");
+    
+      const client = getClient();
+      const streamToRead = await generateAudioStream(client, {
+        model: deploymentName,
+        voice: "alloy",
+        input: "the quick brown chicken jumped over the lazy dogs",
+      });
+    
+      console.log(`Streaming response to ${speechFilePath}`);
+      await writeFile(speechFilePath, streamToRead);
+      console.log("Finished streaming");
+    }
+    
+    main().catch((err) => {
+      console.error("The sample encountered an error:", err);
+    });
+    
+    ```
+
+1. Run the script with the following command:
+
+    ```console
+    node Text-to-speech.js
+    ```
+    
+
+#### [TypeScript](#tab/typescript)
+
+1. Create a new file named _Text-to-speech.ts_ and open it in your preferred code editor. Copy the following code into the _Text-to-speech.ts_ file:
+
+    ```typescript
+    import "dotenv/config";
+    import { writeFile } from "fs/promises";
+    import { AzureOpenAI } from "openai";
+    import type { SpeechCreateParams } from "openai/resources/audio/speech";
+    import "openai/shims/node";
+    
+    // You will need to set these environment variables or edit the following values
+    const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || "<endpoint>";
+    const apiKey = process.env["AZURE_OPENAI_API_KEY"] || "<api key>";
+    const speechFilePath =
+      process.env["SPEECH_FILE_PATH"] || "<path to save the speech file>";
+    
+    // Required Azure OpenAI deployment name and API version
+    const deploymentName = "tts";
+    const apiVersion = "2024-08-01-preview";
+    
+    function getClient(): AzureOpenAI {
+      return new AzureOpenAI({
+        endpoint,
+        apiKey,
+        apiVersion,
+        deployment: deploymentName,
+      });
+    }
+    
+    async function generateAudioStream(
+      client: AzureOpenAI,
+      params: SpeechCreateParams
+    ): Promise<NodeJS.ReadableStream> {
+      const response = await client.audio.speech.create(params);
+      if (response.ok) return response.body;
+      throw new Error(`Failed to generate audio stream: ${response.statusText}`);
+    }
+    export async function main() {
+      console.log("== Text to Speech Sample ==");
+    
+      const client = getClient();
+      const streamToRead = await generateAudioStream(client, {
+        model: deploymentName,
+        voice: "alloy",
+        input: "the quick brown chicken jumped over the lazy dogs",
+      });
+    
+      console.log(`Streaming response to ${speechFilePath}`);
+      await writeFile(speechFilePath, streamToRead);
+      console.log("Finished streaming");
+    }
+    
+    main().catch((err) => {
+      console.error("The sample encountered an error:", err);
+    });
+    
+    ```
+    
+   The import of `"openai/shims/node"` is necessary when running the code in a Node.js environment. It ensures that the output type of the `client.audio.speech.create` method is correctly set to `NodeJS.ReadableStream`.
+
+1. Build the application with the following command:
+
+    ```console
+    tsc
+    ```
+
+1. Run the application with the following command:
+
+    ```console
+    node Text-to-speech.js
+    ```
+
+---
\ No newline at end of file

@@ -10,7 +10,65 @@ author: eric-urban
 recommendations: false
 ---
 
-## REST API
+## Prerequisites
+
+- An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true).
+- An Azure OpenAI resource created in the North Central US or Sweden Central regions with the `tts-1` or `tts-1-hd` model deployed. For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
+
+## Set up
+
+### Retrieve key and endpoint
+
+To successfully make a call against Azure OpenAI, you need an **endpoint** and a **key**.
+
+|Variable name | Value |
+|--------------------------|-------------|
+| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. Alternatively, you can find the value in the **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://aoai-docs.openai.azure.com/`.|
+| `AZURE_OPENAI_API_KEY` | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. You can use either `KEY1` or `KEY2`.|
+
+Go to your resource in the Azure portal. The **Endpoint and Keys** can be found in the **Resource Management** section. Copy your endpoint and access key as you need both for authenticating your API calls. You can use either `KEY1` or `KEY2`. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.
+
+:::image type="content" source="../media/quickstarts/endpoint.png" alt-text="Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint & access keys location highlighted." lightbox="../media/quickstarts/endpoint.png":::
+
+### Environment variables
+
+Create and assign persistent environment variables for your key and endpoint.
+
+[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
+
+# [Command Line](#tab/command-line)
+
+```CMD
+setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
+```
+
+```CMD
+setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
+```
+
+# [PowerShell](#tab/powershell)
+
+```powershell
+[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
+```
+
+```powershell
+[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
+```
+
+# [Bash](#tab/bash)
+
+```Bash
+echo export AZURE_OPENAI_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
+```
+
+```Bash
+echo export AZURE_OPENAI_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
+```
+---
+
+
+## Create a REST request and response
 
 In a bash shell, run the following command. You need to replace `YourDeploymentName` with the deployment name you chose when you deployed the text to speech model. The deployment name isn't necessarily the same as the model name. Entering the model name results in an error unless you chose a deployment name that is identical to the underlying model name.
 

@@ -0,0 +1,235 @@
+---
+ms.topic: include
+manager: nitinme
+ms.service: azure-ai-openai
+ms.topic: include
+ms.date: 9/12/2024
+ms.reviewer: v-baolianzou
+ms.author: eur
+author: eric-urban
+---
+
+[Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples)
+
+## Prerequisites
+
+#### [JavaScript](#tab/javascript)
+
+- An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
+- [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule)
+- An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
+
+
+
+#### [TypeScript](#tab/typescript)
+
+- An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
+- [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule)
+- [TypeScript](https://www.typescriptlang.org/download/)
+- An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
+
+
+---
+
+
+## Set up
+
+### Retrieve key and endpoint
+
+To successfully make a call against Azure OpenAI, you need an *endpoint* and a *key*.
+
+|Variable name | Value |
+|--------------------------|-------------|
+| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. Alternatively, you can find the value in the **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://aoai-docs.openai.azure.com/`.|
+| `AZURE_OPENAI_API_KEY` | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. You can use either `KEY1` or `KEY2`.|
+
+Go to your resource in the Azure portal. The **Endpoint and Keys** can be found in the **Resource Management** section. Copy your endpoint and access key as you'll need both for authenticating your API calls. You can use either `KEY1` or `KEY2`. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.
+
+:::image type="content" source="../media/quickstarts/endpoint.png" alt-text="Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint & access keys location circled in red." lightbox="../media/quickstarts/endpoint.png":::
+
+### Environment variables
+
+Create and assign persistent environment variables for your key and endpoint.
+
+[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
+
+# [Command Line](#tab/command-line)
+
+```CMD
+setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
+```
+
+```CMD
+setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
+```
+
+# [PowerShell](#tab/powershell)
+
+```powershell
+[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
+```
+
+```powershell
+[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
+```
+
+# [Bash](#tab/bash)
+
+```Bash
+echo export AZURE_OPENAI_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
+```
+
+```Bash
+echo export AZURE_OPENAI_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
+```
+---
+
+## Passwordless authentication is recommended
+
+For passwordless authentication, you need to 
+
+1. Use the `@azure/identity` package.
+1. Assign the `Cognitive Services User` role to your user account. This can be done in the Azure portal under **Access control (IAM)** > **Add role assignment**.
+1. Sign in with the Azure CLI such as `az login`.
+
+## Create a Node application
+
+In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it. Then run the `npm init` command to create a node application with a _package.json_ file.
+
+```console
+npm init
+```
+
+## Install the client library
+
+Install the client libraries with:
+
+```console
+npm install openai @azure/identity
+```
+
+---
+Your app's _package.json_ file will be updated with the dependencies.
+
+## Create a sample application
+
+
+#### [JavaScript](#tab/javascript)
+
+1. Create a new file named _Whisper.js_ and open it in your preferred code editor. Copy the following code into the _Whisper.js_ file:
+
+    ```javascript
+    require("dotenv/config");
+    const { createReadStream } = require("fs");
+    const { AzureOpenAI } = require("openai");
+    
+    // You will need to set these environment variables or edit the following values
+    const audioFilePath = process.env["AUDIO_FILE_PATH"] || "<audio file path>";
+    const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || "<endpoint>";
+    const apiKey = process.env["AZURE_OPENAI_API_KEY"] || "<api key>";
+    
+    // Required Azure OpenAI deployment name and API version
+    const apiVersion = "2024-08-01-preview";
+    const deploymentName = "whisper";
+    
+    function getClient() {
+      return new AzureOpenAI({
+        endpoint,
+        apiKey,
+        apiVersion,
+        deployment: deploymentName,
+      });
+    }
+    
+    export async function main() {
+      console.log("== Transcribe Audio Sample ==");
+    
+      const client = getClient();
+      const result = await client.audio.transcriptions.create({
+        model: "",
+        file: createReadStream(audioFilePath),
+      });
+    
+      console.log(`Transcription: ${result.text}`);
+    }
+    
+    main().catch((err) => {
+      console.error("The sample encountered an error:", err);
+    });
+    ```
+
+1. Run the script with the following command:
+
+    ```console
+    node Whisper.js
+    ```
+
+
+#### [TypeScript](#tab/typescript)
+
+1. Create a new file named _Whisper.js_ and open it in your preferred code editor. Copy the following code into the _Whisper.js_ file:
+    
+    ```typescript
+    import "dotenv/config";
+    import { createReadStream } from "fs";
+    import { AzureOpenAI } from "openai";
+    
+    // You will need to set these environment variables or edit the following values
+    const audioFilePath = process.env["AUDIO_FILE_PATH"] || "<audio file path>";
+    const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || "<endpoint>";
+    const apiKey = process.env["AZURE_OPENAI_API_KEY"] || "<api key>";
+    
+    // Required Azure OpenAI deployment name and API version
+    const apiVersion = "2024-08-01-preview";
+    const deploymentName = "whisper";
+    
+    function getClient(): AzureOpenAI {
+      return new AzureOpenAI({
+        endpoint,
+        apiKey,
+        apiVersion,
+        deployment: deploymentName,
+      });
+    }
+    
+    export async function main() {
+      console.log("== Transcribe Audio Sample ==");
+    
+      const client = getClient();
+      const result = await client.audio.transcriptions.create({
+        model: "",
+        file: createReadStream(audioFilePath),
+      });
+    
+      console.log(`Transcription: ${result.text}`);
+    }
+    
+    main().catch((err) => {
+      console.error("The sample encountered an error:", err);
+    });
+    ```
+
+1. Build the application with the following command:
+
+    ```console
+    tsc
+    ```
+
+1. Run the application with the following command:
+
+    ```console
+    node Whisper.js
+    ```
+
+---
+
+You can get sample audio files, such as *wikipediaOcelot.wav*, from the [Azure AI Speech SDK repository at GitHub](https://github.com/Azure-Samples/cognitive-services-speech-sdk/tree/master/sampledata/audiofiles).
+
+> [!IMPORTANT]
+> For production, store and access your credentials using a secure method, such as [Azure Key Vault](/azure/key-vault/general/overview). For more information about credential security, see [Azure AI services security](../../security-features.md).
+
+## Output
+
+```json
+{"text":"The ocelot, Lepardus paradalis, is a small wild cat native to the southwestern United States, Mexico, and Central and South America. This medium-sized cat is characterized by solid black spots and streaks on its coat, round ears, and white neck and undersides. It weighs between 8 and 15.5 kilograms, 18 and 34 pounds, and reaches 40 to 50 centimeters 16 to 20 inches at the shoulders. It was first described by Carl Linnaeus in 1758. Two subspecies are recognized, L. p. paradalis and L. p. mitis. Typically active during twilight and at night, the ocelot tends to be solitary and territorial. It is efficient at climbing, leaping, and swimming. It preys on small terrestrial mammals such as armadillo, opossum, and lagomorphs."}
+```

@@ -9,7 +9,67 @@ ms.author: eur
 author: eric-urban
 ---
 
-## PowerShell
+## Prerequisites
+
+
+- An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
+- <a href="https://aka.ms/installpowershell" target="_blank">You can use either the latest version, PowerShell 7, or Windows PowerShell 5.1.</a>
+- An Azure OpenAI Service resource with a model deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md).
+- An Azure OpenAI Service resource with either the `gpt-35-turbo` or the `gpt-4` models deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md).
+
+## Set up
+
+### Retrieve key and endpoint
+
+To successfully make a call against Azure OpenAI, you need an *endpoint* and a *key*.
+
+|Variable name | Value |
+|--------------------------|-------------|
+| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. Alternatively, you can find the value in the **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://aoai-docs.openai.azure.com/`.|
+| `AZURE_OPENAI_API_KEY` | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. You can use either `KEY1` or `KEY2`.|
+
+Go to your resource in the Azure portal. The **Endpoint and Keys** can be found in the **Resource Management** section. Copy your endpoint and access key as you'll need both for authenticating your API calls. You can use either `KEY1` or `KEY2`. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.
+
+:::image type="content" source="../media/quickstarts/endpoint.png" alt-text="Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint & access keys location circled in red." lightbox="../media/quickstarts/endpoint.png":::
+
+### Environment variables
+
+Create and assign persistent environment variables for your key and endpoint.
+
+[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
+
+# [Command Line](#tab/command-line)
+
+```CMD
+setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
+```
+
+```CMD
+setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
+```
+
+# [PowerShell](#tab/powershell)
+
+```powershell
+[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
+```
+
+```powershell
+[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
+```
+
+# [Bash](#tab/bash)
+
+```Bash
+echo export AZURE_OPENAI_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
+```
+
+```Bash
+echo export AZURE_OPENAI_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
+```
+---
+
+## Create a PowerShell app
 
 Run the following command. You need to replace `YourDeploymentName` with the deployment name you chose when you deployed the Whisper model. The deployment name isn't necessarily the same as the model name. Entering the model name results in an error unless you chose a deployment name that is identical to the underlying model name.
 

@@ -7,14 +7,75 @@ ms.topic: include
 ms.date: 3/19/2024
 ---
 
-## Python
-
-### Prerequisites
+## Prerequisites
 
+- An Azure subscription. You can [create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true).
+- An Azure OpenAI resource with a Whisper model deployed in a [supported region](../concepts/models.md#whisper-models). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
 - [Python 3.8 or later](https://www.python.org)
 - The following Python library: os
 
-### Set up
+## Set up
+
+### Retrieve key and endpoint
+
+To successfully make a call against Azure OpenAI, you need an *endpoint* and a *key*.
+
+|Variable name | Value |
+|--------------------------|-------------|
+| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. Alternatively, you can find the value in the **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://aoai-docs.openai.azure.com/`.|
+| `AZURE_OPENAI_API_KEY` | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. You can use either `KEY1` or `KEY2`.|
+
+Go to your resource in the Azure portal. The **Endpoint and Keys** can be found in the **Resource Management** section. Copy your endpoint and access key as you'll need both for authenticating your API calls. You can use either `KEY1` or `KEY2`. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.
+
+:::image type="content" source="../media/quickstarts/endpoint.png" alt-text="Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint & access keys location circled in red." lightbox="../media/quickstarts/endpoint.png":::
+
+### Environment variables
+
+Create and assign persistent environment variables for your key and endpoint.
+
+[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
+
+# [Command Line](#tab/command-line)
+
+```CMD
+setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
+```
+
+```CMD
+setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
+```
+
+# [PowerShell](#tab/powershell)
+
+```powershell
+[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
+```
+
+```powershell
+[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
+```
+
+# [Bash](#tab/bash)
+
+```Bash
+echo export AZURE_OPENAI_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
+```
+
+```Bash
+echo export AZURE_OPENAI_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
+```
+---
+
+## Passwordless authentication is recommended
+
+For passwordless authentication, you need to 
+
+1. Use the `@azure/identity` package.
+1. Assign the `Cognitive Services User` role to your user account. This can be done in the Azure portal under **Access control (IAM)** > **Add role assignment**.
+1. Sign in with the Azure CLI such as `az login`.
+
+
+## Create a Python environment
 
 Install the OpenAI Python client library with:
 
@@ -34,6 +95,8 @@ pip install openai==0.28.1
 
 ---
 
+## Create the Python app
+
 1. Create a new Python file called *quickstart.py*. Then open it up in your preferred editor or IDE.
 
 1. Replace the contents of *quickstart.py* with the following code. Modify the code to add your deployment name:

@@ -9,7 +9,69 @@ ms.author: eur
 author: eric-urban
 ---
 
-## REST API
+## Prerequisites
+
+- An Azure subscription - <a href="https://azure.microsoft.com/free/cognitive-services" target="_blank">Create one for free</a>.
+
+- An Azure OpenAI resource deployed in a [supported region and with a supported model](../concepts/use-your-data.md#regional-availability-and-model-support).
+
+- Be sure that you are assigned at least the [Cognitive Services Contributor](../how-to/role-based-access-control.md#cognitive-services-contributor) role for the Azure OpenAI resource.
+
+- Download the example data from [GitHub](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/openai/contoso_benefits_document_example.pdf) if you don't have your own data.
+
+## Set up
+
+### Retrieve key and endpoint
+
+To successfully make a call against Azure OpenAI, you need an *endpoint* and a *key*.
+
+|Variable name | Value |
+|--------------------------|-------------|
+| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. Alternatively, you can find the value in the **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://aoai-docs.openai.azure.com/`.|
+| `AZURE_OPENAI_API_KEY` | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. You can use either `KEY1` or `KEY2`.|
+
+Go to your resource in the Azure portal. The **Endpoint and Keys** can be found in the **Resource Management** section. Copy your endpoint and access key as you'll need both for authenticating your API calls. You can use either `KEY1` or `KEY2`. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.
+
+:::image type="content" source="../media/quickstarts/endpoint.png" alt-text="Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint & access keys location circled in red." lightbox="../media/quickstarts/endpoint.png":::
+
+### Environment variables
+
+Create and assign persistent environment variables for your key and endpoint.
+
+[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
+
+# [Command Line](#tab/command-line)
+
+```CMD
+setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
+```
+
+```CMD
+setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
+```
+
+# [PowerShell](#tab/powershell)
+
+```powershell
+[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
+```
+
+```powershell
+[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
+```
+
+# [Bash](#tab/bash)
+
+```Bash
+echo export AZURE_OPENAI_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
+```
+
+```Bash
+echo export AZURE_OPENAI_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
+```
+---
+
+## Create a REST API request and response
 
 In a bash shell, run the following command. You need to replace `YourDeploymentName` with the deployment name you chose when you deployed the Whisper model. The deployment name isn't necessarily the same as the model name. Entering the model name results in an error unless you chose a deployment name that is identical to the underlying model name.
 

@@ -1,17 +1,17 @@
 ### YamlMime:Landing
 
 title: Azure OpenAI Service documentation # < 60 chars
-summary: Azure OpenAI Service provides access to OpenAI's models including the GPT-4, GPT-4 Turbo with Vision, GPT-3.5-Turbo, DALLE-3 and Embeddings model series with the security and enterprise capabilities of Azure. 
+summary: Azure OpenAI Service provides access to OpenAI's models including the GPT-4o, GPT-4o mini, GPT-4, GPT-4 Turbo with Vision, GPT-3.5-Turbo, DALLE-3 and Embeddings model series with the security and enterprise capabilities of Azure. 
   
 metadata:
   title: Azure OpenAI Service documentation - Quickstarts, Tutorials, API Reference - Azure AI services | Microsoft Docs
-  description: Learn how to use Azure OpenAI's powerful models including the GPT-4, GPT-4 Turbo with Vision, GPT-3.5-Turbo, DALL-E 3 and Embeddings model series
+  description: Learn how to use Azure OpenAI's powerful models including the GPT-4o, GPT-4o mini, GPT-4, GPT-4 Turbo with Vision, GPT-3.5-Turbo, DALL-E 3 and Embeddings model series
   ms.service: azure-ai-openai
   ms.custom:
   ms.topic: landing-page
   author: mrbullwinkle
   ms.author: mbullwin
-  ms.date: 08/31/2024
+  ms.date: 09/18/2024
 
 # linkListType: architecture | concept | deploy | download | get-started | how-to-guide | learn | overview | quickstart | reference | tutorial | video | whats-new
 # Limits: https://review.learn.microsoft.com/help/contribute/contribute-how-to-write-landing-page?branch=main#limits

@@ -14,7 +14,7 @@ recommendations: false
 
 # What is Azure OpenAI Service?
 
-Azure OpenAI Service provides REST API access to OpenAI's powerful language models including GPT-4o, GPT-4 Turbo with Vision, GPT-4, GPT-3.5-Turbo, and Embeddings model series. These models can be easily adapted to your specific task including but not limited to content generation, summarization, image understanding, semantic search, and natural language to code translation. Users can access the service through REST APIs, Python SDK, or our web-based interface in the Azure OpenAI Studio.
+Azure OpenAI Service provides REST API access to OpenAI's powerful language models including GPT-4o, GPT-4o mini, GPT-4 Turbo with Vision, GPT-4, GPT-3.5-Turbo, and Embeddings model series. These models can be easily adapted to your specific task including but not limited to content generation, summarization, image understanding, semantic search, and natural language to code translation. Users can access the service through REST APIs, Python SDK, or our web-based interface in the Azure OpenAI Studio.
 
 ### Features overview
 

@@ -1,7 +1,7 @@
 ---
-title: 'Quickstart - Deploy a model and generate text using Azure OpenAI Service'
+title: 'Quickstart - Deploy a model and generate text using the legacy completions API'
 titleSuffix: Azure OpenAI
-description: Walkthrough on how to get started with Azure OpenAI and make your first completions call.
+description: Walkthrough on how to get started with Azure OpenAI and make your first legacy completions API call.
 #services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
@@ -14,7 +14,7 @@ zone_pivot_groups: openai-quickstart-new
 recommendations: false
 ---
 
-# Quickstart: Get started generating text using Azure OpenAI Service
+# Quickstart: Get started generating text using the legacy completions API
 
 Use this article to get started making your first calls to Azure OpenAI.
 

@@ -5,11 +5,12 @@ description: Use the Azure OpenAI Service for text to speech with OpenAI voices.
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: quickstart
-ms.date: 2/1/2024
+ms.date: 9/12/2024
 ms.reviewer: v-baolianzou
 ms.author: eur
 author: eric-urban
 recommendations: false
+zone_pivot_groups: programming-languages-rest-js
 ---
 
 # Quickstart: Text to speech with the Azure OpenAI Service
@@ -18,64 +19,18 @@ In this quickstart, you use the Azure OpenAI Service for text to speech with Ope
 
 The available voices are: `alloy`, `echo`, `fable`, `onyx`, `nova`, and `shimmer`. For more information, see [Azure OpenAI Service reference documentation for text to speech](./reference.md#text-to-speech).
 
-## Prerequisites
 
-- An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true).
-- An Azure OpenAI resource created in the North Central US or Sweden Central regions with the `tts-1` or `tts-1-hd` model deployed. For more information, see [Create a resource and deploy a model with Azure OpenAI](how-to/create-resource.md).
+::: zone pivot="rest-api"
 
-## Set up
-
-### Retrieve key and endpoint
-
-To successfully make a call against Azure OpenAI, you need an **endpoint** and a **key**.
-
-|Variable name | Value |
-|--------------------------|-------------|
-| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. Alternatively, you can find the value in the **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://aoai-docs.openai.azure.com/`.|
-| `AZURE_OPENAI_API_KEY` | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. You can use either `KEY1` or `KEY2`.|
-
-Go to your resource in the Azure portal. The **Endpoint and Keys** can be found in the **Resource Management** section. Copy your endpoint and access key as you need both for authenticating your API calls. You can use either `KEY1` or `KEY2`. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.
-
-:::image type="content" source="media/quickstarts/endpoint.png" alt-text="Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint & access keys location highlighted." lightbox="media/quickstarts/endpoint.png":::
-
-### Environment variables
-
-Create and assign persistent environment variables for your key and endpoint.
-
-[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
-
-# [Command Line](#tab/command-line)
-
-```CMD
-setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
-```
-
-```CMD
-setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
-```
-
-# [PowerShell](#tab/powershell)
-
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
-```
-
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
-```
+[!INCLUDE [REST API quickstart](includes/text-to-speech-rest.md)]
 
-# [Bash](#tab/bash)
+::: zone-end
 
-```Bash
-echo export AZURE_OPENAI_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
-```
+::: zone pivot="programming-language-javascript"
 
-```Bash
-echo export AZURE_OPENAI_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
-```
----
+[!INCLUDE [JavaScript quickstart](includes/text-to-speech-javascript.md)]
 
-[!INCLUDE [REST API quickstart](includes/text-to-speech-rest.md)]
+::: zone-end
 
 ## Clean up resources
 

@@ -29,8 +29,6 @@ items:
   items:
     - name: Assistants (preview)
       href: assistants-quickstart.md
-    - name: Completions
-      href: quickstart.md
     - name: Chat
       href: chatgpt-quickstart.md  
       displayName: ChatGPT, chatgpt
@@ -44,6 +42,8 @@ items:
       href: whisper-quickstart.md
     - name: Text to speech (preview)
       href: text-to-speech-quickstart.md
+    - name: Completions (legacy)
+      href: quickstart.md
 - name: Concepts
   items:
     - name: Assistants
@@ -118,7 +118,7 @@ items:
       href: ./how-to/dall-e.md
     - name: Function calling
       href: ./how-to/function-calling.md
-    - name: Completions
+    - name: Completions (legacy)
       href: ./how-to/completions.md
     - name: JSON mode
       href: ./how-to/json-mode.md

@@ -20,6 +20,16 @@ This article provides a summary of the latest releases and major documentation u
 
 ## September 2024
 
+### GPT-4o 2024-08-06 provisioned deployments
+GPT-4o 2024-08-06 is now available for provisioned deployments in East US, East US 2, North Central US, and Sweden Central. It is also available for global provisioned deployments.
+
+For the latest information on model availability, see the [models page](/azure/ai-services/openai/concepts/models#provisioned-deployment-model-availability).
+
+### NEW Global provisioned deployment type
+Global deployments are available in the same Azure OpenAI resources as non-global deployment types but allow you to leverage Azure's global infrastructure to dynamically route traffic to the data center with best availability for each request. Global provisioned deployments provide reserved model processing capacity for high and predictable throughput using Azure global infrastructure. Global provisioned deployments are supported on `gpt-4o-2024-08-06` and `gpt-4o-mini-2024-07-18` models.
+
+For more information, see the [deployment types guide](https://aka.ms/aoai/docs/deployment-types).
+
 ### 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.

@@ -11,7 +11,7 @@ ms.reviewer: v-baolianzou
 ms.author: eur
 author: eric-urban
 recommendations: false
-zone_pivot_groups: openai-whisper
+zone_pivot_groups: programming-languages-rest-ps-py-js
 ---
 
 # Quickstart: Speech to text with the Azure OpenAI Whisper model
@@ -23,67 +23,21 @@ The file size limit for the Whisper model is 25 MB. If you need to transcribe a
 > [!NOTE]
 > The OpenAI Whisper model is currently in Limited Access Public Preview.
 
-## Prerequisites
-
-- An Azure subscription. You can [create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true).
-- An Azure OpenAI resource with a Whisper model deployed in a [supported region](./concepts/models.md#whisper-models). For more information, see [Create a resource and deploy a model with Azure OpenAI](how-to/create-resource.md).
-
-## Set up
-
-### Retrieve key and endpoint
-
-To successfully make a call against Azure OpenAI, you need an *endpoint* and a *key*.
-
-|Variable name | Value |
-|--------------------------|-------------|
-| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. Alternatively, you can find the value in the **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://aoai-docs.openai.azure.com/`.|
-| `AZURE_OPENAI_API_KEY` | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. You can use either `KEY1` or `KEY2`.|
-
-Go to your resource in the Azure portal. The **Endpoint and Keys** can be found in the **Resource Management** section. Copy your endpoint and access key as you'll need both for authenticating your API calls. You can use either `KEY1` or `KEY2`. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.
-
-:::image type="content" source="media/quickstarts/endpoint.png" alt-text="Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint & access keys location circled in red." lightbox="media/quickstarts/endpoint.png":::
-
-### Environment variables
-
-Create and assign persistent environment variables for your key and endpoint.
-
-[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
-
-# [Command Line](#tab/command-line)
-
-```CMD
-setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
-```
-
-```CMD
-setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
-```
-
-# [PowerShell](#tab/powershell)
-
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
-```
+::: zone pivot="rest-api"
 
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
-```
+[!INCLUDE [REST API quickstart](includes/whisper-rest.md)]
 
-# [Bash](#tab/bash)
+::: zone-end
 
-```Bash
-echo export AZURE_OPENAI_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
-```
+::: zone pivot="programming-language-python"
 
-```Bash
-echo export AZURE_OPENAI_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
-```
----
+[!INCLUDE [Python SDK quickstart](includes/whisper-python.md)]
 
+::: zone-end
 
-::: zone pivot="rest-api"
+::: zone pivot="programming-language-javascript"
 
-[!INCLUDE [REST API quickstart](includes/whisper-rest.md)]
+[!INCLUDE [JavaScript quickstart](includes/whisper-javascript.md)]
 
 ::: zone-end
 
@@ -93,12 +47,6 @@ echo export AZURE_OPENAI_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/envi
 
 ::: zone-end
 
-::: zone pivot="programming-language-python"
-
-[!INCLUDE [Python quickstart](includes/whisper-python.md)]
-
-::: zone-end
-
 ## Clean up resources
 
 If you want to clean up and remove an Azure OpenAI resource, you can delete the resource. Before deleting the resource, you must first delete any deployed models.