Merge pull request #266589 from aahill/aoai-ga

Feedback updates

Author: American-Dipper

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

@@ -15,11 +15,11 @@ ms.custom: references_regions
 
 # Azure OpenAI On Your Data 
 
-Use this article to learn about Azure OpenAI On Your Data, which makes it easier for developers to connect, ingest and ground their enterprise data to create personalized copilots rapidly. It enhances user comprehension, expedites task completion, improves operational efficiency, and aids decision-making.
+Use this article to learn about Azure OpenAI On Your Data, which makes it easier for developers to connect, ingest and ground their enterprise data to create personalized copilots (preview) rapidly. It enhances user comprehension, expedites task completion, improves operational efficiency, and aids decision-making.
 
 ## What is Azure OpenAI On Your Data
 
-Azure OpenAI On Your Data enables you to run advanced AI models such as GPT-35-Turbo and GPT-4 on your own enterprise data without needing to train or fine-tune models. You can chat on top of and analyze your data with greater accuracy. You can specify sources to support the responses based on the latest information available in your designated data sources. You can access Azure OpenAI On Your Data using a REST API, via the SDK or the web-based interface in the [Azure OpenAI Studio](https://oai.azure.com/). You can also create a web app that connects to your data to enable an enhanced chat solution or deploy it directly as a copilot in the Microsoft Copilot Studio (preview).
+Azure OpenAI On Your Data enables you to run advanced AI models such as GPT-35-Turbo and GPT-4 on your own enterprise data without needing to train or fine-tune models. You can chat on top of and analyze your data with greater accuracy. You can specify sources to support the responses based on the latest information available in your designated data sources. You can access Azure OpenAI On Your Data using a REST API, via the SDK or the web-based interface in the [Azure OpenAI Studio](https://oai.azure.com/). You can also create a web app that connects to your data to enable an enhanced chat solution or deploy it directly as a copilot in the Copilot Studio (preview).
 
 ## Get started
 
@@ -58,22 +58,17 @@ There's an [upload limit](../quotas-limits.md), and there are some caveats about
 
 ## Supported data sources
 
-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. For some data sources (such as uploading files from your local machine or data contained in a blob storage account), Azure AI Search is used. 
+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. 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.
 
 |Data source  | Description  |
 |---------|---------|
 | [Azure AI Search](/azure/search/search-what-is-azure-search)  | Use an existing Azure AI Search index with Azure OpenAI On Your Data.      |
-| [Azure Cosmos DB for MongoDB vCore](/azure/search/search-what-is-azure-search)  | Use an existing Azure Cosmos DB for MongoDB vCore database with Azure OpenAI On Your Data.      |
 |Upload files (preview)      | Upload files from your local machine to be stored in an Azure Blob Storage database, and ingested into Azure AI Search.         |
 |URL/Web address (preview)        | Web content from the URLs is stored in Azure Blob Storage.         |
 |Azure Blob Storage (preview) | Upload files from Azure Blob Storage to be ingested into an Azure AI Search index.         |
 
-
-* [Azure Cosmos DB for MongoDB vCore](/azure/cosmos-db/mongodb/vcore/introduction) account
-
-
 # [Azure AI Search](#tab/ai-search)
 
 You might want to consider using an Azure AI Search index when you either want to:
@@ -99,14 +94,14 @@ If you're using your own index, you can customize the [field mapping](#index-fie
 
 
 > [!IMPORTANT]
-> * [Semantic search](/azure/search/semantic-search-overview#availability-and-pricing) and [vector search](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) are subject to additional pricing. You need to choose **Basic or higher SKU** to enable semantic search or vector search. See [pricing tier difference](/azure/search/search-sku-tier) and [service limits](/azure/search/search-limits-quotas-capacity) for more information.
-> * To help improve the quality of the information retrieval and model response, we recommend enabling [semantic search](/azure/search/semantic-search-overview) for the following search languages: English, French, Spanish, Portuguese, Italian, Germany, Chinese(Zh), Japanese, Korean, Russian, and Arabic.
+> * [Semantic search](/azure/search/semantic-search-overview#availability-and-pricing) is subject to additional pricing. You need to choose **Basic or higher SKU** to enable semantic search or vector search. See [pricing tier difference](/azure/search/search-sku-tier) and [service limits](/azure/search/search-limits-quotas-capacity) for more information.
+> * To help improve the quality of the information retrieval and model response, we recommend enabling [semantic search](/azure/search/semantic-search-overview) for the following data source languages: English, French, Spanish, Portuguese, Italian, Germany, Chinese(Zh), Japanese, Korean, Russian, and Arabic.
 
 | Search option       | Retrieval type | Additional pricing? |Benefits|
 |---------------------|------------------------|---------------------| -------- |
 | *keyword*            | Keyword search                       | No additional pricing.                    |Performs fast and flexible query parsing and matching over searchable fields, using terms or phrases in any supported language, with or without operators.|
 | *semantic*          |  Semantic search  |  Additional pricing for [semantic search](/azure/search/semantic-search-overview#availability-and-pricing) usage.                  |Improves the precision and relevance of search results by using a reranker (with AI models) to understand the semantic meaning of query terms and documents returned by the initial search ranker|
-| *vector*            | Vector search       | [Additional pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) on your Azure OpenAI account from calling the embedding model.                    |Enables you to find documents that are similar to a given query input based on the vector embeddings of the content. |
+| *vector*            | Vector search       | No additional pricing |Enables you to find documents that are similar to a given query input based on the vector embeddings of the content. |
 | *hybrid (vector + keyword)*   | A hybrid of vector search and keyword search | [Additional pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) on your Azure OpenAI account from calling the embedding model.            |Performs similarity search over vector fields using vector embeddings, while also supporting flexible query parsing and full text search over alphanumeric fields using term queries.|
 | *hybrid (vector + keyword) + semantic* | A hybrid of vector search, semantic search, and keyword search.     | [Additional pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) on your Azure OpenAI account from calling the embedding model, and additional pricing for [semantic search](/azure/search/semantic-search-overview#availability-and-pricing) usage.                    |Uses vector embeddings, language understanding, and flexible query parsing to create rich search experiences and generative AI apps that can handle complex and diverse information retrieval scenarios. |
 
@@ -214,6 +209,9 @@ To modify the schedule, you can use the [Azure portal](https://portal.azure.com/
 
 Using Azure OpenAI Studio, you can upload files from your machine to try Azure OpenAI On Your Data, and optionally creating a new Azure Blob Storage account and Azure AI Search resource. The service then stores the files to an Azure storage container and performs ingestion from the container. You can use the [quickstart](../use-your-data-quickstart.md) article to learn how to use this data source option.
 
+:::image type="content" source="../media/quickstarts/add-your-data-source.png" alt-text="A screenshot showing options for selecting a data source in Azure OpenAI Studio." lightbox="../media/quickstarts/add-your-data-source.png":::
+
+
 # [URL/Web address (preview)](#tab/web-pages)
 
 You can paste URLs and the service will store the webpage content, using it when generating responses from the model. The content in URLs/web addresses that you use need to have the following characteristics to be properly ingested:
@@ -269,7 +267,7 @@ You can modify the following additional settings in the **Data parameters** sect
 |Parameter name  | Description  |
 |---------|---------|
 | **Limit responses to your data** | This flag configures the chatbot's approach to handling queries unrelated to the data source or when search documents are insufficient for a complete answer. When this setting is disabled, the model supplements its responses with its own knowledge in addition to your documents. When this setting is enabled, the model attempts to only rely on your documents for responses. This is the `inScope` parameter in the API. |
-|**Top K Documents**     |  This parameter is an integer that can be set to 3, 5, 10, or 20, and controls the number of document chunks provided to the large language model for formulating the final response. By default, this is set to 5. The search process can be noisy and sometimes, due to chunking, relevant information might be spread across multiple chunks in the search index. Selecting a top-K number, like 5, ensures that the model can extract relevant information, despite the inherent limitations of search and chunking. However, increasing the number too high can potentially distract the model. Additionally, the maximum number of documents that can be effectively used depends on the version of the model, as each has a different context size and capacity for handling documents. If you find that responses are missing important context, try increasing this parameter. This is the `topNDocuments` parameter in the API. |
+|**Retrieved documents**     |  This parameter is an integer that can be set to 3, 5, 10, or 20, and controls the number of document chunks provided to the large language model for formulating the final response. By default, this is set to 5. The search process can be noisy and sometimes, due to chunking, relevant information might be spread across multiple chunks in the search index. Selecting a top-K number, like 5, ensures that the model can extract relevant information, despite the inherent limitations of search and chunking. However, increasing the number too high can potentially distract the model. Additionally, the maximum number of documents that can be effectively used depends on the version of the model, as each has a different context size and capacity for handling documents. If you find that responses are missing important context, try increasing this parameter. This is the `topNDocuments` parameter in the API. |
 | **Strictness**     | Determines the system's aggressiveness in filtering search documents based on their similarity scores. The system queries Azure Search or other document stores, then decides which documents to provide to large language models like ChatGPT. Filtering out irrelevant documents can significantly enhance the performance of the end-to-end chatbot. Some documents are excluded from the top-K results if they have low similarity scores before forwarding them to the model. This is controlled by an integer value ranging from 1 to 5. Setting this value to 1 means that the system will minimally filter documents based on search similarity to the user query. Conversely, a setting of 5 indicates that the system will aggressively filter out documents, applying a very high similarity threshold. If you find that the chatbot omits relevant information, lower the filter's strictness (set the value closer to 1) to include more documents. Conversely, if irrelevant documents distract the responses, increase the threshold (set the value closer to 5). This is the `strictness` parameter in the API. |
 
 ### System message

articles/ai-services/openai/media/quickstarts/add-your-data-source.png

@@ -912,7 +912,7 @@ curl -i -X PUT https://YOUR_RESOURCE_NAME.openai.azure.com/openai/extensions/on-
 | `chunkSize` | int | Optional |1024 |This number defines the maximum number of tokens in each chunk produced by the ingestion flow. |
 
 
-### List ingestion jobs
+### List ingestion jobs (preview)
 
 ```console
 curl -i -X GET https://YOUR_RESOURCE_NAME.openai.azure.com/openai/extensions/on-your-data/ingestion-jobs?api-version=2023-10-01-preview \ 
@@ -946,7 +946,7 @@ curl -i -X GET https://YOUR_RESOURCE_NAME.openai.azure.com/openai/extensions/on-
 } 
 ```
 
-### Get the status of an ingestion job 
+### Get the status of an ingestion job (preview)
 
 ```console
 curl -i -X GET https://YOUR_RESOURCE_NAME.openai.azure.com/openai/extensions/on-your-data/ingestion-jobs/YOUR_JOB_NAME?api-version=2023-10-01-preview \