Merge branch 'main' into rel-container-update

Author: anaharris-ms

.openpublishing.publish.config.json

@@ -1232,6 +1232,7 @@
   ".openpublishing.redirection.government.json",
   ".openpublishing.redirection.guidance.json",
   ".openpublishing.redirection.industry.json",
+  ".openpublishing.redirection.iot-accelerators.json",
   ".openpublishing.redirection.iot-develop.json",
   ".openpublishing.redirection.iot-hub-device-update.json",
   ".openpublishing.redirection.json",
@@ -1297,7 +1298,6 @@
   "articles/hdinsight/.openpublishing.redirection.hdinsight.json",
   "articles/healthcare-apis/.openpublishing.redirection.healthcare-apis.json",
   "articles/internet-peering/.openpublishing.redirection.internet-peering.json",
-  "articles/iot-accelerators/.openpublishing.redirection.iot-accelerators.json",
   "articles/iot-central/.openpublishing.redirection.iot-central.json",
   "articles/iot-dps/.openpublishing.redirection.iot-dps.json",
   "articles/iot-edge/.openpublishing.redirection.iot-edge.json",

articles/iot-accelerators/.openpublishing.redirection.iot-accelerators.json renamed to .openpublishing.redirection.iot-accelerators.json

@@ -440,6 +440,26 @@
       "redirect_url": "/azure/iot-develop/about-iot-develop",
       "redirect_document_id": false
     },
+    {
+      "source_path_from_root": "/articles/iot-develop/quickstart-devkit-mxchip-az3166-iot-hub.md",
+      "redirect_url": "/azure/iot/tutorial-devkit-mxchip-az3166-iot-hub",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/iot-develop/quickstart-devkit-stm-b-l475e-iot-hub.md",
+      "redirect_url": "/azure/iot/tutorial-devkit-stm-b-l475e-iot-hub",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/iot-develop/concepts-azure-rtos-security-practices.md",
+      "redirect_url": "/azure/iot/concepts-eclipse-threadx-security-practices",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/iot-develop/troubleshoot-embedded-device-quickstarts.md",
+      "redirect_url": "/azure/iot/troubleshoot-embedded-device-tutorials",
+      "redirect_document_id": false
+    }, 
     {
       "source_path_from_root": "/articles/iot-develop/how-to-use-reliability-features-in-sdks.md",
       "redirect_url": "/azure/iot-develop/concepts-manage-device-reconnections",

.openpublishing.redirection.iot-develop.json

@@ -60,7 +60,7 @@ There's an [upload limit](../quotas-limits.md), and there are some caveats about
 
 You need to connect to a data source to upload your data. When you want to use your data to chat with an Azure OpenAI model, your data is chunked in a search index so that relevant data can be found based on user queries.
 
-The [Integrated Vector Database in Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb/vcore/vector-search) natively supports integration with Azure OpenAI On Your Data.
+The [Integrated Vector Database in vCore-based Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb/vcore/vector-search) natively supports integration with Azure OpenAI On Your Data.
 
 For some data sources such as uploading files from your local machine (preview) or data contained in a blob storage account (preview), Azure AI Search is used. When you choose the following data sources, your data is ingested into an Azure AI Search index.
 
@@ -139,15 +139,15 @@ If you want to implement additional value-based criteria for query execution, yo
 
 [!INCLUDE [ai-search-ingestion](../includes/ai-search-ingestion.md)]
 
-# [Vector Database in Azure Cosmos DB for MongoDB vCore](#tab/mongo-db)
+# [Vector Database in Azure Cosmos DB for MongoDB](#tab/mongo-db)
 
 ### Prerequisites
-* [Azure Cosmos DB for MongoDB vCore](/azure/cosmos-db/mongodb/vcore/introduction) account
+* [vCore-based Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb/vcore/introduction) account
 * A deployed [embedding model](../concepts/understand-embeddings.md)
 
 ### Limitations
-* Only Azure Cosmos DB for MongoDB vCore is supported.
-* The search type is limited to [Integrated Vector Database in Azure Cosmos DB for MongoDB vCore](/azure/cosmos-db/mongodb/vcore/vector-search) with an Azure OpenAI embedding model.
+* Only vCore-based Azure Cosmos DB for MongoDB is supported.
+* The search type is limited to [Integrated Vector Database in Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb/vcore/vector-search) with an Azure OpenAI embedding model.
 * This implementation works best on unstructured and spatial data.
 
 ### Data preparation
@@ -156,9 +156,9 @@ Use the script provided on [GitHub](https://github.com/microsoft/sample-app-aoai
 
 <!--### Add your data source in Azure OpenAI Studio
 
-To add Azure Cosmos DB for MongoDB vCore as a data source, you will need an existing Azure Cosmos DB for MongoDB vCore index containing your data, and a deployed Azure OpenAI Ada embeddings model that will be used for vector search.
+To add vCore-based Azure Cosmos DB for MongoDB as a data source, you will need an existing Azure Cosmos DB for MongoDB index containing your data, and a deployed Azure OpenAI Ada embeddings model that will be used for vector search.
 
-1. In the [Azure OpenAI portal](https://oai.azure.com/portal) chat playground, select **Add your data**. In the panel that appears, select **Azure Cosmos DB for MongoDB vCore** as the data source. 
+1. In the [Azure OpenAI portal](https://oai.azure.com/portal) chat playground, select **Add your data**. In the panel that appears, select ** vCore-based Azure Cosmos DB for MongoDB** as the data source. 
 1. Select your Azure subscription and database account, then connect to your Azure Cosmos DB account by providing your Azure Cosmos DB account username and password.
     
     :::image type="content" source="../media/use-your-data/add-mongo-data-source.png" alt-text="A screenshot showing the screen for adding Mongo DB as a data source in Azure OpenAI Studio." lightbox="../media/use-your-data/add-mongo-data-source.png":::
@@ -170,7 +170,7 @@ To add Azure Cosmos DB for MongoDB vCore as a data source, you will need an exis
 
 ### Index field mapping
 
-When you add your Azure Cosmos DB for MongoDB vCore data source, you can specify  data fields to properly map your data for retrieval.
+When you add your vCore-based Azure Cosmos DB for MongoDB data source, you can specify data fields to properly map your data for retrieval.
 
 * Content data (required): One or more provided fields that will be used to ground the model on your data. For multiple fields, separate the values with commas, with no spaces.
 * File name/title/URL: Used to display more information when a document is referenced in the chat.

articles/ai-services/openai/concepts/use-your-data.md

@@ -1,7 +1,7 @@
 ---
-title: How to work with the GPT-35-Turbo and GPT-4 models 
+title: Work with the GPT-35-Turbo and GPT-4 models 
 titleSuffix: Azure OpenAI Service
-description: Learn about the options for how to use the GPT-35-Turbo and GPT-4 models 
+description: Learn about the options for how to use the GPT-35-Turbo and GPT-4 models.
 author: mrbullwinkle #dereklegenzoff
 ms.author: mbullwin #delegenz
 ms.service: azure-ai-openai
@@ -12,10 +12,10 @@ manager: nitinme
 keywords: ChatGPT
 ---
 
-# Learn how to work with the GPT-3.5-Turbo and GPT-4 models
+# Work with the GPT-3.5-Turbo and GPT-4 models
 
-The GPT-3.5-Turbo and GPT-4 models are language models that are optimized for conversational interfaces. The models behave differently than the older GPT-3 models. Previous models were text-in and text-out, meaning they accepted a prompt string and returned a completion to append to the prompt. However, the GPT-3.5-Turbo and GPT-4 models are conversation-in and message-out. The models expect input formatted in a specific chat-like transcript format, and return a completion that represents a model-written message in the chat. While this format was designed specifically for multi-turn conversations, you'll find it can also work well for non-chat scenarios too.
+The GPT-3.5-Turbo and GPT-4 models are language models that are optimized for conversational interfaces. The models behave differently than the older GPT-3 models. Previous models were text-in and text-out, which means they accepted a prompt string and returned a completion to append to the prompt. However, the GPT-3.5-Turbo and GPT-4 models are conversation-in and message-out. The models expect input formatted in a specific chat-like transcript format. They return a completion that represents a model-written message in the chat. This format was designed specifically for multi-turn conversations, but it can also work well for nonchat scenarios.
 
-This article walks you through getting started with the GPT-3.5-Turbo and GPT-4 models. It's important to use the techniques described here to get the best results. If you try to interact with the models the same way you did with the older model series, the models will often be verbose and provide less useful responses.
+This article walks you through getting started with the GPT-3.5-Turbo and GPT-4 models. To get the best results, use the techniques described here. Don't try to interact with the models the same way you did with the older model series because the models are often verbose and provide less useful responses.
 
 [!INCLUDE [Chat Completions](../includes/chat-completion.md)]