Merge branch 'main' of https://github.com/microsoftdocs/azure-ai-docs-pr into components

Author: v-thepet

articles/ai-services/document-intelligence/toc.yml

@@ -295,10 +295,8 @@ items:
   items:
   - name: REST APIs
     items:
-    - name: 2024-02-29-preview (v4.0)
-      href: /rest/api/aiservices/document-models/build-model?view=rest-aiservices-2024-02-29-preview&preserve-view=true&branch=docintelligence&tabs=HTTP
-    - name: 2023-10-31-preview (v4.0)
-      href: /rest/api/aiservices/operation-groups?view=rest-aiservices-2023-10-31-preview&preserve-view=true&tabs=HTTP
+    - name: 2024-07-31-preview (v4.0)
+      href: /rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-07-31-preview)&preserve-view=true
     - name: 2023-07-31 (v3.1)
       href: /rest/api/aiservices/document-models/analyze-document?view=rest-aiservices-2023-07-31&preserve-view=true&tabs=HTTP
     - name: 2022-08-31 (v3.0)
@@ -307,7 +305,7 @@ items:
       href: v3-error-guide.md
     - name: v2.1
       href: /rest/api/aiservices/analyzer?view=rest-aiservices-v2.1&preserve-view=true
-  - name: Client libraries (2023-10-31-preview)
+  - name: Client libraries (2024-07-31-preview)
     items:
     - name: C# / .NET
       href: /dotnet/api/overview/azure/ai.documentintelligence-readme?view=azure-dotnet-preview&preserve-view=true

articles/ai-services/openai/index.yml

@@ -56,7 +56,7 @@ landingContent:
         links:
          - text: Embeddings
            url: ./tutorials/embeddings.md
-         - text: Fine-tuning GPT-3.5 Turbo
+         - text: Fine-tuning GPT-4o mini
            url: ./tutorials/fine-tune.md
 
 #Card 2

articles/ai-studio/how-to/deploy-models-serverless-connect.md

@@ -202,7 +202,7 @@ Follow these steps to create a connection:
     # [Python SDK](#tab/python)
 
     ```python
-    client.connections.create(ServerlessConnection(
+    client.connections.create_or_update(ServerlessConnection(
         name="meta-llama3-8b-connection",
         endpoint="https://meta-llama3-8b-qwerty-serverless.inference.ai.azure.com",
         api_key="1234567890qwertyuiop"

articles/ai-studio/tutorials/copilot-sdk-build-rag.md

@@ -9,6 +9,7 @@ ms.date: 08/29/2024
 ms.reviewer: lebaro
 ms.author: sgilley
 author: sdgilley
+ms.custom: [copilot-learning-hub]
 #customer intent: As a developer, I want to learn how to use the prompt flow SDK so that I can build a RAG-based chat app.
 ---
 
@@ -29,7 +30,7 @@ This tutorial is part two of a three-part tutorial.
 
 * Complete [Tutorial:  Part 1 - Create resources for building a custom chat application with the prompt flow SDK](copilot-sdk-create-resources.md).
 
-* You need a local copy of product data. The [Azure-Samples/rag-data-openai-python-promptflow repository on GitHub](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/) contains sample retail product information that's relevant for this tutorial scenario. [Download the example Contoso Trek retail product data in a ZIP file](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/tree/main/tutorial/data) to your local machine.
+* You need a local copy of product data. The [Azure-Samples/rag-data-openai-python-promptflow repository on GitHub](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/) contains sample retail product information that's relevant for this tutorial scenario. [Download the example Contoso Trek retail product data in a ZIP file](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/blob/main/tutorial/data/product-info.zip) to your local machine.
 
 ## Application code structure
 
@@ -114,7 +115,7 @@ These steps deploy a model to a real-time endpoint from the AI Studio [model cat
 When you deploy the `gpt-3.5-turbo` model, find the following values in the **View Code** section, and add them to your **.env** file:
 
 ```env
-AZURE_OPENAI_ENDPOINT=<chat_model_endpoint_value>
+AZURE_OPENAI_ENDPOINT=<endpoint_value>
 AZURE_OPENAI_CHAT_DEPLOYMENT=<chat_model_deployment_name>
 AZURE_OPENAI_API_VERSION=<api_version>
 ```
@@ -155,7 +156,7 @@ The goal with this RAG-based application is to ground the model responses in you
 
 If you don't have an Azure AI Search index already created, we walk through how to create one. If you already have an index to use, you can skip to the [set the search environment variable](#set-search-index) section. The search index is created on the Azure AI Search service that was either created or referenced in the previous step.
 
-1. Use your own data or [download the example Contoso Trek retail product data in a ZIP file](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/tree/main/tutorial/data) to your local machine. Unzip the file into your **rag-tutorial** folder. This data is a collection of markdown files that represent product information. The data is structured in a way that is easy to ingest into a search index. You build a search index from this data.
+1. Use your own data or [download the example Contoso Trek retail product data in a ZIP file](https://github.com/Azure-Samples/rag-data-openai-python-promptflow/blob/main/tutorial/data/product-info.zip) to your local machine. Unzip the file into your **rag-tutorial/data** folder. This data is a collection of markdown files that represent product information. The data is structured in a way that is easy to ingest into a search index. You build a search index from this data.
 
 1. The prompt flow RAG package allows you to ingest the markdown files, locally create a search index, and register it in the cloud project. Install the prompt flow RAG package: