@@ -55,9 +55,9 @@ Document Intelligence supports more sophisticated and modular analysis capabilit
 
 > [!NOTE]
 >
-> * Not all add-on capabilities are supported by all models. For more information, *see* [model data extraction](../model-overview.md#model-analysis-features).
+> Not all models or Microsoft Office file types support add-on capabilities. For more information, *see* [model data extraction](../model-overview.md#model-analysis-features).
 >
-> * Add-on capabilities are currently not supported for Microsoft Office file types.
+
 :::moniker-end
 
 ## Version availability
@@ -73,7 +73,7 @@ Document Intelligence supports more sophisticated and modular analysis capabilit
 |High resolution extraction|**Add-On**| ✔️| ✔️| n/a| n/a|
 |Query fields|**Add-On**| ✔️|n/a|n/a| n/a|
 
-✱ Add-On - Query fields are priced differently than the other add-on features. See [pricing](https://azure.microsoft.com/pricing/details/ai-document-intelligence/) for details. </br>
+✱ Add-On - Query fields are priced differently than the other add-on features. See [pricing](https://azure.microsoft.com/pricing/details/ai-document-intelligence/) for details.</br>
 ** Add-On - Searchable pdf is available only with Read model as an add-on feature. 
 
 ## Supported file formats
@@ -759,7 +759,7 @@ The `ocr.barcode` capability extracts all identified barcodes in the `barcodes`
 | `Databar` |:::image type="content" source="../media/barcodes/databar.png" alt-text="Screenshot of the Data bar.":::|
 | `Databar` Expanded |:::image type="content" source="../media/barcodes/databar-expanded.gif" alt-text="Screenshot of the Data bar Expanded.":::|
 | `ITF` |:::image type="content" source="../media/barcodes/interleaved-two-five.png" alt-text="Screenshot of the interleaved-two-of-five barcode (ITF).":::|
-| `Data Matrix` |:::image type="content" source="../media/barcodes/datamatrix.gif" alt-text="Screenshot of the Data Matrix.":::|
+| `Data Matrix` |:::image type="content" source="../media/barcodes/data-matrix.png" alt-text="Screenshot of the Data Matrix.":::|
 
 ::: moniker range="doc-intel-4.0.0"
 
@@ -983,8 +983,8 @@ The searchable PDF capability enables you to convert an analog PDF, such as scan
 
   > [!IMPORTANT]
   >
-  > * Currently, the searchable PDF capability is only supported by Read OCR model `prebuilt-read`. When using this feature, please specify the `modelId` as `prebuilt-read`.
-  > * Searchable PDF is included with the 2024-11-30 (GA) `prebuilt-read` model with no usage cost for general PDF consumption.
+  > * Currently, only the Read model `prebuilt-read` supports the searchable PDF capability. When using this feature, specify the `modelId` as `prebuilt-read`.
+  > * Searchable PDF is included with the `2024-11-30` (GA) `prebuilt-read` model with no usage cost for general PDF consumption.
 
 ### Use searchable PDF
 
@@ -1044,7 +1044,7 @@ Query fields are an add-on capability to extend the schema extracted from any pr
 
 > [!NOTE]
 >
-> Document Intelligence Studio query field extraction is currently available with the Layout and Prebuilt models `2024-11-30 (GA) API with the exception of the `US tax` models (W2, 1098s, and 1099s models).
+> Document Intelligence Studio query field extraction is currently available with the Layout and Prebuilt models `2024-11-30` (GA) API except for US tax models W2, 1098, and 1099.
 
 ### Query field extraction
 

@@ -383,7 +383,8 @@ sections:
         answer: |
           **Yes,** briefly.
 
-          For all features, Document Intelligence temporarily stores data and results in Azure Storage in the same region as the request. Your data is then deleted within 24 hours from the time that you submit an analyze request.
+          For all features, Document Intelligence temporarily stores data and results in Azure Storage in the same region as the request. Your data is then deleted 24 hours from the time that you submit an analyze request. If you would like the data deleted sooner, you can call the [delete analyze response](https://learn.microsoft.com/rest/api/aiservices/document-models/delete-analyze-result?view=rest-aiservices-v4.0%20(2024-11-30)&tabs=HTTP). This API marks the results for deletion and is available in the v4.0 API.
+    
 
           Learn more about [data, privacy, and security for Document Intelligence](/legal/cognitive-services/document-intelligence/data-privacy-security?toc=/azure/ai-services/document-intelligence/toc.json&bc=/azure/ai-services/document-intelligence/breadcrumb/toc.json).
 

@@ -76,6 +76,9 @@ For more information, *see* client libraries for the following supported program
 *  [🆕 US Tax model](prebuilt/tax-document.md)
    *  New prebuilt tax models added for 1095A, 1095C, 1099SSA, and W4.
 
+* [Delete analyze response](https://learn.microsoft.com/rest/api/aiservices/document-models/delete-analyze-result?view=rest-aiservices-v4.0%20(2024-11-30)&tabs=HTTP)
+  * Analyze response is stored for 24 hours from when the operation completes for retrieval. For scenarios where you want to delete the response sooner, use the delete analyze response API to delete the response.  
+
 * The v4.0 API includes cumulative updates from preview releases as listed:
   * [August 2024](#august-2024)
   * [May 2024](#may-2024)

@@ -30,11 +30,7 @@ In this tutorial, you learn how to use the packages `langchain-azure-ai` to buil
 To run this tutorial, you need:
 
 * An [Azure subscription](https://azure.microsoft.com).
-* An Azure AI project as explained at [Create a project in Azure AI Foundry portal](../create-projects.md).
-* A model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference) deployed. In this example, we use a `Mistral-Large` deployment, but use any model of your preference. 
-
-    * You can follow the instructions at [Deploy models as serverless APIs](../deploy-models-serverless.md).
-
+* A model deployment supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference) deployed. In this example, we use a `Mistral-Large-2407` deployment in the [Azure AI model inference](../../../ai-foundry/model-inference/overview.md).
 * Python 3.9 or later installed, including pip.
 * LangChain installed. You can do it with:
 
@@ -78,25 +74,13 @@ from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
 model = AzureAIChatCompletionsModel(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
     credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
+    model="mistral-large-2407",
 )
 ```
 
 > [!TIP]
 > For Azure OpenAI models, configure the client as indicated at [Using Azure OpenAI models](#using-azure-openai-models).
 
-If your endpoint is serving more than one model, like with the [Azure AI model inference service](../../ai-services/model-inference.md) or [GitHub Models](https://github.com/marketplace/models), you have to indicate `model_name` parameter:
-
-```python
-import os
-from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
-
-model = AzureAIChatCompletionsModel(
-    endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
-    credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
-    model_name="mistral-large-2407",
-)
-```
-
 You can use the following code to create the client if your endpoint supports Microsoft Entra ID:
 
 ```python
@@ -129,6 +113,18 @@ model = AzureAIChatCompletionsModel(
 )
 ```
 
+If your endpoint is serving one model, like with the Serverless API Endpoints, you don't have to indicate `model_name` parameter:
+
+```python
+import os
+from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
+
+model = AzureAIChatCompletionsModel(
+    endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
+    credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
+)
+```
+
 ## Use chat completions models
 
 Let's first use the model directly. `ChatModels` are instances of LangChain `Runnable`, which means they expose a standard interface for interacting with them. To simply call the model, we can pass in a list of messages to the `invoke` method.

@@ -147,6 +147,9 @@ Learn more about data processing for MaaS in the [article about data privacy](co
 
 :::image type="content" source="../media/explore/model-publisher-cycle.png" alt-text="Diagram that shows the model publisher service cycle." lightbox="../media/explore/model-publisher-cycle.png":::
 
+> [!NOTE]
+> Cloud Solution Provider (CSP) subscriptions do not have the ability to purchase serverless API deployments (MaaS) models.
+
 ### Billing
 
 The discovery, subscription, and consumption experience for models deployed via MaaS is in Azure AI Foundry portal and Azure Machine Learning studio. Users accept license terms for use of the models. Pricing information for consumption is provided during deployment.