Merge pull request #1233 from MicrosoftDocs/main

11/4 11:00 AM IST Publish

Author: PhilKang0704

articles/ai-studio/how-to/develop/llama-index.md

@@ -45,7 +45,10 @@ To run this tutorial, you need:
     ```bash
     pip install -U llama-index-llms-azure-inference
     pip install -U llama-index-embeddings-azure-inference
-    ``` 
+    ```
+
+    > [!IMPORTANT]
+    > Using the [Azure AI model inference service](https://aka.ms/aiservices/inference) requires version `0.2.4` for `llama-index-llms-azure-inference` or `llama-index-embeddings-azure-inference`.
 
 ## Configure the environment
 
@@ -67,7 +70,7 @@ export AZURE_INFERENCE_ENDPOINT="<your-model-endpoint-goes-here>"
 export AZURE_INFERENCE_CREDENTIAL="<your-key-goes-here>"
 ```
 
-Once configured, create a client to connect to the endpoint. The parameter `model_name` in the constructor is not required for endpoints serving a single model, like serverless endpoints.
+Once configured, create a client to connect to the endpoint.
 
 ```python
 import os
@@ -80,7 +83,20 @@ llm = AzureAICompletionsModel(
 ```
 
 > [!TIP]
-> If your model is an OpenAI model deployed to Azure OpenAI service or AI services resource, configure the client as indicated at [Azure OpenAI models](#azure-openai-models).
+> If your model is an OpenAI model deployed to Azure OpenAI service or AI services resource, configure the client as indicated at [Azure OpenAI models and Azure AI model inference service](#azure-openai-models-and-azure-ai-model-infernece-service).
+
+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 llama_index.llms.azure_inference import AzureAICompletionsModel
+
+llm = AzureAICompletionsModel(
+    endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
+    credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
+    model_name="mistral-large-2407",
+)
+```
 
 Alternatively, if your endpoint support Microsoft Entra ID, you can use the following code to create the client:
 
@@ -112,22 +128,23 @@ llm = AzureAICompletionsModel(
 )
 ```
 
-### Azure OpenAI models
+### Azure OpenAI models and Azure AI model infernece service
 
-If you are using Azure OpenAI models with key-based authentication, you need to pass the authentication key in the header `api-key`, which is the one expected in the Azure OpenAI service and in Azure AI Services. This configuration is not required if you are using Microsoft Entra ID (formerly known as Azure AD). The following example shows how to configure the client:
+If you are using Azure OpenAI models or [Azure AI model inference service](../../ai-services/model-inference.md), ensure you have at least version `0.2.4` of the LlamaIndex integration. Use `api_version` parameter in case you need to select a specific `api_version`. For the [Azure AI model inference service](../../ai-services/model-inference.md), you need to pass `model_name` parameter:
 
 ```python
-import os
 from llama_index.llms.azure_inference import AzureAICompletionsModel
 
 llm = AzureAICompletionsModel(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
-    credential="",
-    client_kwargs={"headers" : { "api-key": os.environ["AZURE_INFERENCE_CREDENTIAL"] } }
+    credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
+    model_name="gpt-4o",
+    api_version="2024-05-01-preview",
 )
 ```
 
-Notice that `credentials` is still being passed with an empty value since it's a required parameter.
+> [!TIP]
+> Using a wrong `api_version` or one not supported by the model results in a `ResourceNotFound` exception.
 
 ### Inference parameters
 

articles/machine-learning/concept-customer-managed-keys.md

@@ -124,20 +124,17 @@ Data that previously was stored in Azure Cosmos DB in your subscription, is stor
 
 Pipelines metadata that previously was stored in a storage account in a managed resource group, is now stored on the storage account in your subscription that is associated to the Azure Machine Learning workspace. Since this Azure Storage resource is managed separately in your subscription, you're responsible to configure encryption settings on it.
 
-To opt in for this preview, set the `enableServiceSideCMKEncryption` on a REST API or in your Bicep or Resource Manager template. You can also use Azure portal. Preview availability varies by [workspace kind](concept-workspace.md):
-
-| Kind | Supported |
-| ----- | ----- |
-| Default | Yes |
-| Hub | No |
-| Project | No |
+To opt in for this preview, set the `enableServiceSideCMKEncryption` on a REST API or in your Bicep or Resource Manager template. You can also use Azure portal.
 
 :::image type="content" source="./media/concept-customer-managed-keys/cmk-service-side-encryption.png" alt-text="Screenshot of the encryption tab with the option for server side encryption selected." lightbox="./media/concept-customer-managed-keys/cmk-service-side-encryption.png":::
 
 > [!NOTE]
 > During this preview key rotation and data labeling capabilities are not supported. Server-side encryption is currently not supported in reference to an Azure Key Vault for storing your encryption key that has public network access disabled.
 
-For a template that creates a workspace with service-side encryption of metadata, see [https://github.com/azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.machinelearningservices/machine-learning-workspace-cmk-service-side-encryption](https://github.com/azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.machinelearningservices/machine-learning-workspace-cmk-service-side-encryption).
+For templates that create a workspace with service-side encryption of metadata, see 
+
+- [Bicep template for creating default workspace](https://github.com/azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.machinelearningservices/machine-learning-workspace-cmk-service-side-encryption).
+- [Bicep template for creating hub workspace](https://github.com/Azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.machinelearningservices/aistudio-cmk-service-side-encryption).
 
 ## hbi_workspace flag
 

articles/search/includes/quickstarts/java.md

@@ -5,7 +5,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: include
-ms.date: 10/07/2024
+ms.date: 11/01/2024
 ---
 
 Build a Java console application using the [Azure.Search.Documents](/java/api/overview/azure/search) library to create, load, and query a search index. 
@@ -65,12 +65,12 @@ Use the following tools to create this quickstart.
         <dependency>
           <groupId>com.azure</groupId>
           <artifactId>azure-search-documents</artifactId>
-          <version>11.5.2</version>
+          <version>11.7.3</version>
         </dependency>
         <dependency>
           <groupId>com.azure</groupId>
           <artifactId>azure-core</artifactId>
-          <version>1.34.0</version>
+          <version>1.53.0</version>
         </dependency>
         <dependency>
           <groupId>junit</groupId>

articles/search/search-get-started-text.md

@@ -14,7 +14,7 @@ ms.custom:
   - devx-track-python
   - ignite-2023
 ms.topic: quickstart
-ms.date: 10/07/2024
+ms.date: 11/01/2024
 ---
 
 # Quickstart: Full text search using the Azure SDKs

articles/search/search-synonyms.md

@@ -155,7 +155,7 @@ If the synonym map exists on the search service, it's used on the next query, wi
 Use the [Create or Update Index (REST API)](/rest/api/searchservice/indexes/create-or-update) to modify a field definition.
 
 ```http
-POST /indexes?api-version=2024-07-01
+PUT /indexes?api-version=2024-07-01
 {
     "name":"hotels-sample-index",
     "fields":[