Merge pull request #267866 from MicrosoftDocs/main

3/1/2024 PM Publish

Author: Taojunshen

.openpublishing.publish.config.json

@@ -1200,6 +1200,7 @@
   ".openpublishing.redirection.azure-arc-data.json",
   ".openpublishing.redirection.azure-attestation.json",
   ".openpublishing.redirection.azure-app-configuration.json",
+  ".openpublishing.redirection.azure-datalake-storage-gen1.json",
   ".openpublishing.redirection.azure-australia.json",
   ".openpublishing.redirection.azure-databricks.json",
   ".openpublishing.redirection.azure-hpc.json",

.openpublishing.redirection.azure-datalake-storage-gen1.json

@@ -1,6 +1,11 @@
 {
     "redirections": [
     {
+      "source_path": "articles/batch/batch-pool-no-public-ip-address.md",
+      "redirect_url": "/previous-versions/azure/batch/articles/batch/batch-pool-no-public-ip-address",
+      "redirect_document_id": false
+    }, 
+    {  
       "source_path": "articles/batch/batch-rendering-applications.md",
       "redirect_url": "/previous-versions/azure/batch/articles/batch/batch-rendering-applications",
       "redirect_document_id": false

.openpublishing.redirection.json

@@ -18,12 +18,14 @@ ms.custom:
 
 [!INCLUDE [availability](../includes/regional-availability.md)]
 
-## Conversation summarization types
+## Conversation summarization aspects
 
-- Chapter title and narrative (general conversation) are designed to summarize a conversation into chapter titles, and a summarization of the conversation's contents. This summarization type works on conversations with any number of parties. 
+- Chapter title and narrative (general conversation) are designed to summarize a conversation into chapter titles, and a summarization of the conversation's contents. This summarization aspect works on conversations with any number of parties. 
 
 - Issue and resolution (call center focused) is designed to summarize text chat logs between customers and customer-service agents. This feature is capable of providing both issues and resolutions present in these logs, which occur between two parties.
 
+- Narrative is designed to summarize the narrative of a conversation.
+
 - Recap is designed to condense lengthy meetings or conversations into a concise one-paragraph summary to provide a quick overview.
 
 - Follow-up tasks is designed to summarize action items and tasks that arise during a meeting.
@@ -32,6 +34,15 @@ ms.custom:
 
 The AI models used by the API are provided by the service, you just have to send content for analysis.
 
+For easier navigation, here are links to the corresponding sections for each service:
+
+|Aspect                 |Section                                               |
+|-----------------------|------------------------------------------------------|
+|Issue and Resolution   |[Issue and Resolution](#get-summaries-from-text-chats)|
+|Chapter Title          |[Chapter Title](#get-chapter-titles)                  |
+|Narrative              |[Narrative](#get-narrative-summarization)             |
+|Recap and Follow-up    |[Recap and follow-up](#get-narrative-summarization)   |
+
 ## Features
 
 The conversation summarization API uses natural language processing techniques to summarize conversations into shorter summaries per request. Conversation summarization can summarize for issues and resolutions discussed in a two-party conversation or summarize a long conversation into chapters and a short narrative for each chapter.

articles/ai-services/language-service/summarization/how-to/conversation-summarization.md

@@ -22,10 +22,21 @@ Document summarization is designed to shorten content that users consider too lo
 
 **Abstractive summarization**: Produces a summary by generating summarized sentences from the document that capture the main idea.
 
-Both of these capabilities are able to summarize around specific items of interest when specified.
+**Query-focused summarization**: Allows you to use a query when summarizing.
+
+Each of these capabilities are able to summarize around specific items of interest when specified.
 
 The AI models used by the API are provided by the service, you just have to send content for analysis.
 
+For easier navigation, here are links to the corresponding sections for each service:
+
+|Aspect       |Section                                                            |
+|-------------|-------------------------------------------------------------------|
+|Extractive   |[Extractive Summarization](#try-document-extractive-summarization) |
+|Abstractive  |[Abstrctive Summarization](#try-document-abstractive-summarization)|
+|Query-focused|[Query-focused Summarization](#query-based-summarization)          |
+
+
 ## Features
 
 > [!TIP]

articles/ai-services/language-service/summarization/how-to/document-summarization.md

@@ -24,6 +24,7 @@ To use summarization, you submit for analysis and handle the API output in your
 |---------|---------|---------|
 | REST API     | Integrate conversation summarization into your applications using the REST API. | [Quickstart: Use conversation summarization](../quickstart.md?tabs=conversation-summarization&pivots=rest-api) |
 
+Custom Summarization enables users to build custom AI models to summarize unstructured text, such as contracts or novels. By creating a Custom Summarization project, developers can iteratively label data, train, evaluate, and improve model performance before making it available for consumption. The quality of the labeled data greatly impacts model performance. To simplify building and customizing your model, the service offers a custom web portal that can be accessed through the [Language studio](https://aka.ms/languageStudio). You can easily get started with the service by following the steps in this [quickstart](../custom/quickstart.md).
 ---
 
 

articles/ai-services/language-service/summarization/includes/development-options.md

@@ -20,16 +20,14 @@ Summarization is one of the features offered by [Azure AI Language](../overview.
 
 Though the services are labeled document and conversation summarization, document summarization only accepts plain text blocks, and conversation summarization accept various speech artifacts in order for the model to learn more. If you want to process a conversation but only care about text, you can use document summarization for that scenario.
 
-Custom Summarization enables users to build custom AI models to summarize unstructured text, such as contracts or novels. By creating a Custom Summarization project, developers can iteratively label data, train, evaluate, and improve model performance before making it available for consumption. The quality of the labeled data greatly impacts model performance. To simplify building and customizing your model, the service offers a custom web portal that can be accessed through the [Language studio](https://aka.ms/languageStudio). You can easily get started with the service by following the steps in this [quickstart](custom/quickstart.md).
-
 # [Document summarization](#tab/document-summarization)
 
 This documentation contains the following article types:
 
 * **[Quickstarts](quickstart.md?pivots=rest-api&tabs=document-summarization)** are getting-started instructions to guide you through making requests to the service.
 * **[How-to guides](how-to/document-summarization.md)** contain instructions for using the service in more specific or customized ways.
 
-Document summarization uses natural language processing techniques to generate a summary for documents. There are two supported API approaches to automatic summarization: extractive and abstractive. 
+Document summarization uses natural language processing techniques to generate a summary for documents. There are three supported API approaches to automatic summarization: extractive, abstractive and query-focused. 
 
 Extractive summarization extracts sentences that collectively represent the most important or relevant information within the original content. Abstractive summarization generates a summary with concise, coherent sentences or words that aren't verbatim extract sentences from the original document. These features are designed to shorten content that could be considered too long to read.
 
@@ -49,19 +47,21 @@ For more information, *see* [**Use native documents for language processing**](.
 
 ## Key features
 
-There are two types of document summarization this API provides:
+There are the aspects of document summarization this API provides:
 
-* **Extractive summarization**: Produces a summary by extracting salient sentences within the document.
+* [**Extractive summarization**](how-to/document-summarization.md#try-document-extractive-summarization): Produces a summary by extracting salient sentences within the document.
 
   * Multiple extracted sentences: These sentences collectively convey the main idea of the document. They're original sentences extracted from the input document's content.
   * Rank score: The rank score indicates how relevant a sentence is to a document's main topic. Document summarization ranks extracted sentences, and you can determine whether they're returned in the order they appear, or according to their rank.
   * Multiple returned sentences: Determine the maximum number of sentences to be returned. For example, if you request a three-sentence summary extractive summarization returns the three highest scored sentences.
   * Positional information: The start position and length of extracted sentences.
 
-* **Abstractive summarization**: Generates a summary that doesn't use the same words as in the document, but captures the main idea.
+* [**Abstractive summarization**](how-to/document-summarization.md#try-document-abstractive-summarization): Generates a summary that doesn't use the same words as in the document, but captures the main idea.
   * Summary texts: Abstractive summarization returns a summary for each contextual input range within the document. A long document can be segmented so multiple groups of summary texts can be returned with their contextual input range.
   * Contextual input range: The range within the input document that was used to generate the summary text.
 
+* [**Query-focused summarization**](how-to/document-summarization.md#query-based-summarization): Generates a summary based on a query
+
 As an example, consider the following paragraph of text:
 
 *"At Microsoft, we are on a quest to advance AI beyond existing techniques, by taking a more holistic, human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI services, I have been working with a team of amazing scientists and engineers to turn this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship among three attributes of human cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z). At the intersection of all three, there's magic—what we call XYZ-code as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear, see, and understand humans better. We believe XYZ-code enables us to fulfill our long-term vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have pretrained models that can jointly learn representations to support a broad range of downstream AI tasks, much in the way humans do today. Over the past five years, we achieve human performance on benchmarks in conversational speech recognition, machine translation, conversational question answering, machine reading comprehension, and image captioning. These five breakthroughs provided us with strong signals toward our more ambitious aspiration to produce a leap in AI capabilities, achieving multi-sensory and multilingual learning that is closer in line with how humans learn and understand. I believe the joint XYZ-code is a foundational component of this aspiration, if grounded with external knowledge sources in the downstream AI tasks."*
@@ -92,11 +92,11 @@ This documentation contains the following article types:
 
 Conversation summarization supports the following features:
 
-* **Issue/resolution summarization**: A call center specific feature that gives a summary of issues and resolutions in conversations between customer-service agents and your customers.
-* **Chapter title summarization**: Segments a conversation into chapters based on the topics discussed in the conversation, and gives suggested chapter titles of the input conversation.
-* **Recap**: Summarizes a conversation into a brief paragraph.
-* **Narrative summarization**: Generates detail call notes, meeting notes or chat summaries of the input conversation.
-* **Follow-up tasks**: Gives a list of follow-up tasks discussed in the input conversation.
+* [**Issue/resolution summarization**](how-to/conversation-summarization.md#get-summaries-from-text-chats): A call center specific feature that gives a summary of issues and resolutions in conversations between customer-service agents and your customers.
+* [**Chapter title summarization**](how-to/conversation-summarization.md#get-chapter-titles): Segments a conversation into chapters based on the topics discussed in the conversation, and gives suggested chapter titles of the input conversation.
+* [**Recap**](how-to/conversation-summarization.md#get-narrative-summarization): Summarizes a conversation into a brief paragraph.
+* [**Narrative summarization**](how-to/conversation-summarization.md#get-narrative-summarization): Generates detail call notes, meeting notes or chat summaries of the input conversation.
+* [**Follow-up tasks**](how-to/conversation-summarization.md#get-narrative-summarization): Gives a list of follow-up tasks discussed in the input conversation.
 
 ## When to use issue and resolution summarization
 

articles/ai-services/language-service/summarization/overview.md

@@ -29,7 +29,6 @@ This version contains support for all the latest Azure OpenAI features including
 - [Fine-tuning](./how-to/fine-tuning.md) `gpt-35-turbo`, `babbage-002`, and `davinci-002` models.[**Added in 2023-10-01-preview**]
 - [Whisper](./whisper-quickstart.md). [**Added in 2023-09-01-preview**]
 - [Function calling](./how-to/function-calling.md)  [**Added in 2023-07-01-preview**]
-- [DALL-E](./dall-e-quickstart.md)  [**Added in 2023-06-01-preview**]
 - [Retrieval augmented generation with the on your data feature](./use-your-data-quickstart.md).  [**Added in 2023-06-01-preview**]
 
 ## Retiring soon
@@ -40,6 +39,7 @@ On April 2, 2024 the following API preview releases will be retired and will sto
 - 2023-07-01-preview
 - 2023-08-01-preview
 - 2023-09-01-preview
+- 2023-10-01-preview
 - 2023-12-01-preview
 
 To avoid service disruptions, you must update to use the latest preview version before the retirement date.

articles/ai-services/openai/api-version-deprecation.md

@@ -495,6 +495,11 @@ curl https://YOUR_RESOURCE_NAME.openai.azure.com/openai/assistants/{assistant_id
 
 ---
 
+## File upload API reference
+
+Assistants use the [same API for file upload as fine-tuning](/rest/api/azureopenai/files/upload?view=rest-azureopenai-2024-02-15-preview&tabs=HTTP). When uploading a file you have to specify an appropriate value for the [purpose parameter](/rest/api/azureopenai/files/upload?view=rest-azureopenai-2024-02-15-preview&tabs=HTTP#purpose).
+
+
 ## Assistant object
 
 | Field  | Type  | Description   |

articles/ai-services/openai/assistants-reference.md

@@ -43,11 +43,19 @@ We provide a walkthrough of the Assistants playground in our [quickstart guide](
 |**Run** | Activation of an Assistant to begin running based on the contents of the Thread. The Assistant uses its configuration and the Thread’s Messages to perform tasks by calling models and tools. As part of a Run, the Assistant appends Messages to the Thread.|
 |**Run Step** | A detailed list of steps the Assistant took as part of a Run. An Assistant can call tools or create Messages during it’s run. Examining Run Steps allows you to understand how the Assistant is getting to its final results. |
 
+## Assistants data access
+
+Currently, assistants, threads, messages, and files created for Assistants are scoped at the Azure OpenAI resource level. Therefore, anyone with access to the Azure OpenAI resource or API key access is able to read/write assistants, threads, messages, and files.
+
+We strongly recommend the following data access controls:
+
+- Implement authorization. Before performing reads or writes on assistants, threads, messages, and files, ensure that the end-user is authorized to do so.
+- Restrict Azure OpenAI resource and API key access. Carefully consider who has access to Azure OpenAI resources where assistants are being used and associated API keys.
+- Routinely audit which accounts/individuals have access to the Azure OpenAI resource. API keys and resource level access enable a wide range of operations including reading and modifying messages and files.
+- Enable [diagnostic settings](../how-to/monitoring.md#configure-diagnostic-settings) to allow long-term tracking of certain aspects of the Azure OpenAI resource's activity log.
+
 ## See also
 
 * Learn more about Assistants and [Code Interpreter](../how-to/code-interpreter.md)
 * Learn more about Assistants and [function calling](../how-to/assistant-functions.md)
 * [Azure OpenAI Assistants API samples](https://github.com/Azure-Samples/azureai-samples/tree/main/scenarios/Assistants)
-
-
-