Merge pull request #623 from MicrosoftDocs/main

10/2/2024 PM Publish

Author: PhilKang0704

articles/ai-services/openai/concepts/content-filter.md

@@ -83,27 +83,6 @@ Detecting indirect attacks requires using document delimiters when constructing
 
 [!INCLUDE [content-filter-configurability](../includes/content-filter-configurability.md)]
 
-
-<sup>1</sup> For Azure OpenAI models, only customers who have been approved for modified content filtering have full content filtering control and can turn off content filters. Apply for modified content filters via this form: [Azure OpenAI Limited Access Review: Modified Content Filters](https://ncv.microsoft.com/uEfCgnITdR) For Azure Government customers, please apply for modified content filters via this form: [Azure Government - Request Modified Content Filtering for Azure OpenAI Service](https://aka.ms/AOAIGovModifyContentFilter).
-
-Configurable content filters for inputs (prompts) and outputs (completions) are available for the following Azure OpenAI models:
-
-* GPT model series
-* GPT-4 Turbo Vision GA<sup>*</sup> (turbo-2024-04-09)
-* GPT-4o
-* GPT-4o mini
-* DALL-E 2 and 3
-
-Configurable content filters are currently not available for 
-
-`o1-preview` and `o1-mini`
-
-<sup>*</sup>Only available for GPT-4 Turbo Vision GA, does not apply to GPT-4 Turbo Vision preview 
-
-Content filtering configurations are created within a Resource in Azure AI Studio, and can be associated with Deployments. [Learn more about configurability here](../how-to/content-filters.md).  
-
-Customers are responsible for ensuring that applications integrating Azure OpenAI comply with the [Code of Conduct](/legal/cognitive-services/openai/code-of-conduct?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext). 
-
 ## Scenario details
 
 When the content filtering system detects harmful content, you receive either an error on the API call if the prompt was deemed inappropriate, or the `finish_reason` on the response will be `content_filter` to signify that some of the completion was filtered. When building your application or system, you'll want to account for these scenarios where the content returned by the Completions API is filtered, which might result in content that is incomplete. How you act on this information will be application specific. The behavior can be summarized in the following points:

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

@@ -18,6 +18,7 @@ Azure OpenAI Service is powered by a diverse set of models with different capabi
 
 | Models | Description |
 |--|--|
+| [o1-preview and o1-mini](#o1-preview-and-o1-mini-models-limited-access) | Limited access models, specifically designed to tackle reasoning and problem-solving tasks with increased focus and capability.  |
 | [GPT-4o & GPT-4o mini & GPT-4 Turbo](#gpt-4o-and-gpt-4-turbo) | The latest most capable Azure OpenAI models with multimodal versions, which can accept both text and images as input. |
 | [GPT-4o audio](#gpt-4o-audio) | A GPT-4o model that supports low-latency, "speech in, speech out" conversational interactions. |
 | [GPT-4](#gpt-4) | A set of models that improve on GPT-3.5 and can understand and generate natural language and code. |
@@ -31,6 +32,11 @@ Azure OpenAI Service is powered by a diverse set of models with different capabi
 
 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.
 
+|  Model ID  | Description | Max Request (tokens) | Training Data (up to)  |
+|  --- |  :--- |:--- |:---: |
+|`o1-preview` (2024-09-12) | The most capable model in the o1 series, offering enhanced reasoning abilities.| Input: 128,000  <br> Output: 32,768 | Oct 2023 |
+| `o1-mini` (2024-09-12) | A faster and more cost-efficient option in the o1 series, ideal for coding tasks requiring speed and lower resource consumption.| Input: 128,000  <br> Output: 65,536 | Oct 2023 |
+
 ### Availability
 
 The `o1-preview` and `o1-mini` models are now available for API access and model deployment. **Registration is required, and access will be granted based on Microsoft's eligibility criteria**.
@@ -43,7 +49,11 @@ Once access has been granted, you will need to create a deployment for each mode
 
 Support for the **o1 series** models was added in API version `2024-09-01-preview`.
 
-The `max_tokens` parameter has been deprecated and replaced with the new `max_completion_tokens` parameter. **o1 series** models will only work with the `max_completions_tokens` parameter.
+The `max_tokens` parameter has been deprecated and replaced with the new `max_completion_tokens` parameter. **o1 series** models will only work with the `max_completions_tokens` parameter. 
+
+### Region availability
+
+Available for standard and global standard deployment in East US2 and Sweden Central for approved customers.
 
 ## GPT-4o audio
 
@@ -99,8 +109,6 @@ See [model versions](../concepts/model-versions.md) to learn about how Azure Ope
 
 |  Model ID  | Description | Max Request (tokens) | Training Data (up to)  |
 |  --- |  :--- |:--- |:---: |
-|`o1-preview` (2024-09-12) | The most capable model in the o1 series, offering enhanced reasoning abilities.| Input: 128,000  <br> Output: 32,768 | Oct 2023 |
-| `o1-mini` (2024-09-12) | A faster and more cost-efficient option in the o1 series, ideal for coding tasks requiring speed and lower resource consumption.| Input: 128,000  <br> Output: 65,536 | Oct 2023 |
 |`gpt-4o` (2024-08-06) <br> **GPT-4o (Omni)** | **Latest large GA model** <br> - Structured outputs<br> - Text, image processing <br> - JSON Mode <br> - parallel function calling <br> - Enhanced accuracy and responsiveness <br> - Parity with English text and coding tasks compared to GPT-4 Turbo with Vision <br> - Superior performance in non-English languages and in vision tasks |Input: 128,000  <br> Output: 16,384 | Oct 2023 |
 |`gpt-4o-mini` (2024-07-18) <br> **GPT-4o mini** | **Latest small GA model** <br> - Fast, inexpensive, capable model ideal for replacing GPT-3.5 Turbo series models. <br> - Text, image processing <br>- JSON Mode <br> - parallel function calling | Input: 128,000 <br> Output: 16,384  | Oct 2023 |
 |`gpt-4o` (2024-05-13) <br> **GPT-4o (Omni)** | Text, image processing <br> - JSON Mode <br> - parallel function calling <br> - Enhanced accuracy and responsiveness <br> - Parity with English text and coding tasks compared to GPT-4 Turbo with Vision <br> - Superior performance in non-English languages and in vision tasks |Input: 128,000  <br> Output: 4,096| Oct 2023 |

articles/ai-services/openai/includes/content-filter-configurability.md

@@ -24,16 +24,19 @@ All customers can also configure content filters and create custom safety polici
 | No filters | If approved<sup>1</sup>| If approved<sup>1</sup>| No content is filtered regardless of severity level detected. Requires approval<sup>1</sup>.|
 |Annotate only | If approved<sup>1</sup>| If approved<sup>1</sup>| Disables the filter functionality, so content will not be blocked, but annotations are returned via API response. Requires approval<sup>1</sup>.|
 
-<sup>1</sup> For Azure OpenAI models, only customers who have been approved for modified content filtering have full content filtering control and can turn off content filters. Apply for modified content filters via this form: [Azure OpenAI Limited Access Review: Modified Content Filters](https://ncv.microsoft.com/uEfCgnITdR) For Azure Government customers, apply for modified content filters via this form: [Azure Government - Request Modified Content Filtering for Azure OpenAI Service](https://aka.ms/AOAIGovModifyContentFilter).
+<sup>1</sup> For Azure OpenAI models, only customers who have been approved for modified content filtering have full content filtering control and can turn off content filters. Apply for modified content filters via this form: [Azure OpenAI Limited Access Review: Modified Content Filters](https://ncv.microsoft.com/uEfCgnITdR). For Azure Government customers, apply for modified content filters via this form: [Azure Government - Request Modified Content Filtering for Azure OpenAI Service](https://aka.ms/AOAIGovModifyContentFilter).
 
 Configurable content filters for inputs (prompts) and outputs (completions) are available for the following Azure OpenAI models:
-
 * GPT model series
 * GPT-4 Turbo Vision GA<sup>*</sup> (`turbo-2024-04-09`)
 * GPT-4o
 * GPT-4o mini
 * DALL-E 2 and 3
 
+Configurable content filters are not available for 
+- o1-preview
+- o1-mini 
+
 <sup>*</sup>Only available for GPT-4 Turbo Vision GA, does not apply to GPT-4 Turbo Vision preview 
 
 Content filtering configurations are created within a Resource in Azure AI Studio, and can be associated with Deployments. [Learn more about configurability here](../how-to/content-filters.md).  

articles/search/search-howto-schedule-indexers.md

@@ -9,7 +9,7 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 01/17/2024
+ms.date: 10/02/2024
 ---
 
 # Schedule an indexer in Azure AI Search
@@ -105,22 +105,25 @@ await indexerClient.CreateOrUpdateIndexerAsync(indexer);
 
 ## Scheduling behavior
 
-For text-based indexing, the scheduler can kick off as many indexer jobs as the search service supports, which is determined by the number of search units. For example, if the service has three replicas and four partitions, you can have 12 indexer jobs in active execution, whether initiated on demand or on a schedule.
++ For text-based indexing, the scheduler can kick off as many indexer jobs as the search service supports, which is determined by the number of search units. For example, if the service has three replicas and four partitions, you can have 12 indexer jobs in active execution, whether initiated on demand or on a schedule.
 
-Skills-based indexers run in a different [execution environment](search-howto-run-reset-indexers.md#indexer-execution). For this reason, the number of service units has no bearing on the number of skills-based indexer jobs you can run. Multiple skills-based indexers can run in parallel, but doing so depends on node availability within the execution environment.
++ Skills-based indexers run in a different [execution environment](search-howto-run-reset-indexers.md#indexer-execution). For this reason, the number of service units has no bearing on the number of skills-based indexer jobs you can run. Multiple skills-based indexers can run in parallel, but doing so depends on node availability within the execution environment.
 
-Although multiple indexers can run simultaneously, a given indexer is single instance. You can't run two copies of the same indexer concurrently. If an indexer happens to still be running when its next scheduled execution is set to start, the pending execution is postponed until the next scheduled occurrence, allowing the current job to finish.
++ Although multiple indexers can run simultaneously, a given indexer is single instance. You can't run two copies of the same indexer concurrently. If an indexer happens to still be running when its next scheduled execution is set to start, the pending execution is postponed until the next scheduled occurrence, allowing the current job to finish.
+
++ If an indexer is set to a certain schedule but repeatedly fails on the same document each time, the indexer will begin running on a less frequent interval (up to the maximum interval of at least once every 2 hours or 24 hours, depending on different implementation factors) until it successfully makes progress again. If you believe you have fixed whatever the underlying issue, you can [run the indexer manually](search-howto-run-reset-indexers.md), and if indexing succeeds, the indexer will return to its regular schedule.
+
++ Indexer processes can be queued up and may not start exactly at the time posted, depending on the processing workload and other factors. Based on this, If there is a strict business need tied to the exact time indexing is performed, you should consider using the [Push model](search-what-is-data-import.md#pushing-data-to-an-index) so you can control the indexing pipeline directly.
 
 Let’s consider an example to make this more concrete. Suppose we configure an indexer schedule with an interval of hourly and a start time of January 1, 2024 at 8:00:00 AM UTC. Here's what could happen when an indexer run takes longer than an hour:
 
-+ The first indexer execution starts at or around January 1, 2024 at 8:00 AM UTC. Assume this execution takes 20 minutes (or any amount of time that's less than 1 hour).
+1. The first indexer execution starts at or around January 1, 2024 at 8:00 AM UTC. Assume this execution takes 20 minutes (or any amount of time that's less than 1 hour).
 
-+ The second execution starts at or around January 1, 2022 9:00 AM UTC. Suppose that this execution takes 70 minutes - more than an hour – and it will not complete until 10:10 AM UTC.
+1. The second execution starts at or around January 1, 2022 9:00 AM UTC. Suppose that this execution takes 70 minutes - more than an hour – and it will not complete until 10:10 AM UTC.
 
-+ The third execution is scheduled to start at 10:00 AM UTC, but at that time the previous execution is still running. This scheduled execution is then skipped. The next execution of the indexer won't start until 11:00 AM UTC.
+1. The third execution is scheduled to start at 10:00 AM UTC, but at that time the previous execution is still running. This scheduled execution is then skipped. The next execution of the indexer won't start until 11:00 AM UTC.
+  
 
-> [!NOTE]
-> If an indexer is set to a certain schedule but repeatedly fails on the same document each time, the indexer will begin running on a less frequent interval (up to the maximum interval of at least once every 2 hours or 24 hours, depending on different implementation factors) until it successfully makes progress again. If you believe you have fixed whatever the underlying issue, you can [run the indexer manually](search-howto-run-reset-indexers.md), and if indexing succeeds, the indexer will return to its regular schedule.
 
 ## Next steps