Merge pull request #262513 from MicrosoftDocs/main

1/5 11:00 AM IST Publish

Author: Saisang

articles/ai-services/openai/reference.md

@@ -486,7 +486,6 @@ The following parameters can be used inside of the `parameters` field inside of
 |--|--|--|--|--|
 | `type` | string | Required | null | The data source to be used for the Azure OpenAI on your data feature. For Azure AI Search the value is `AzureCognitiveSearch`. For Azure Cosmos DB for MongoDB vCore, the value is `AzureCosmosDB`. |
 | `indexName` | string | Required | null | The search index to be used. |
-| `fieldsMapping` | dictionary | Optional for Azure AI Search. Required for Azure Cosmos DB for MongoDB vCore.  | null | Index data column mapping. When using Azure Cosmos DB for MongoDB vCore, the value `vectorFields` is required, which indicates the fields that store vectors.  |
 | `inScope` | boolean | Optional | true | If set, this value will limit responses specific to the grounding data content.  |
 | `topNDocuments` | number | Optional | 5 | Specifies the number of top-scoring documents from your data index used to generate responses. You might want to increase the value when you have short documents or want to provide more context. This is the *retrieved documents* parameter in Azure OpenAI studio.   |
 | `semanticConfiguration` | string | Optional | null |  The semantic search configuration. Only required when `queryType` is set to `semantic` or  `vectorSemanticHybrid`.  |
@@ -498,13 +497,36 @@ The following parameters can be used inside of the `parameters` field inside of
 | `strictness` | number | Optional | 3 | Sets the threshold to categorize documents as relevant to your queries. Raising the value means a higher threshold for relevance and filters out more less-relevant documents for responses. Setting this value too high might cause the model to fail to generate responses due to limited available documents. |
 
 
-**The following parameters are used for Azure AI Search only**
+**The following parameters are used for Azure AI Search**
 
 | Parameters | Type | Required? | Default | Description |
 |--|--|--|--|--|
 | `endpoint` | string | Required | null | Azure AI Search only. The data source endpoint. |
 | `key` | string | Required | null | Azure AI Search only. One of the Azure AI Search admin keys for your service. |
 | `queryType` | string | Optional | simple |  Indicates which query option will be used for Azure AI Search. Available types: `simple`, `semantic`, `vector`, `vectorSimpleHybrid`, `vectorSemanticHybrid`. |
+| `fieldsMapping` | dictionary | Optional for Azure AI Search.  | null | defines which [fields](./concepts/use-your-data.md?tabs=ai-search#index-field-mapping) you want to map when you add your data source. |
+
+The following parameters are used inside of the `fieldsMapping` field.
+
+| Parameters | Type | Required? | Default | Description |
+|--|--|--|--|--|
+| `titleField` | string | Optional  | null | The field in your index that contains the original title of each document. |
+| `urlField` | string | Optional  | null | The field in your index that contains the original URL of each document. |
+| `filepathField` | string | Optional  | null | The field in your index that contains the original file name of each document. |
+| `contentFields` | dictionary | Optional  | null | The fields in your index that contain the main text content of each document. |
+| `contentFieldsSeparator` | string | Optional  | null | The separator for the your content fields. Use `\n` by default.  |
+
+```json
+"fieldsMapping": {
+  "titleField": "myTitleField",
+  "urlField": "myUrlField",
+  "filepathField": "myFilePathField",
+  "contentFields": [
+    "myContentField"
+  ],
+  "contentFieldsSeparator": "\n"
+}
+```
 
 **The following parameters are used for Azure Cosmos DB for MongoDB vCore**
 
@@ -516,6 +538,7 @@ The following parameters can be used inside of the `parameters` field inside of
 | `containerName` | string | Required | null | Azure Cosmos DB for MongoDB vCore only. The Azure Cosmos Mongo vCore container name in the database. |
 | `type` (found inside of`embeddingDependencyType`) | string | Required | null | Indicates the embedding model dependency. |
 | `deploymentName` (found inside of`embeddingDependencyType`) | string | Required | null | The embedding model deployment name. |
+| `fieldsMapping` | dictionary | Required for Azure Cosmos DB for MongoDB vCore.  | null | Index data column mapping. When using Azure Cosmos DB for MongoDB vCore, the value `vectorFields` is required, which indicates the fields that store vectors.  |
 
 ### Start an ingestion job 
 

articles/ai-studio/how-to/data-add.md

@@ -114,7 +114,7 @@ These steps explain how to create a File typed data in the Azure AI Studio:
 To create a data that is a File type, use the following code and update the `<>` placeholders with your information.
 
 ```python
-from azure.ai.generative import AIClient
+from azure.ai.resources.client import AIClient
 from azure.ai.generative.entities import Data
 from azure.ai.generative.constants import AssetTypes
 from azure.identity import DefaultAzureCredential
@@ -174,7 +174,7 @@ Use these steps to create a Folder typed data in the Azure AI Studio:
 To create a data that is a Folder type use the following code and update the `<>` placeholders with your information.
 
 ```python
-from azure.ai.generative import AIClient
+from azure.ai.resources.client import AIClient
 from azure.ai.generative.entities import Data
 from azure.ai.generative.constants import AssetTypes
 from azure.identity import DefaultAzureCredential
@@ -240,7 +240,7 @@ To archive *all versions* of the data under a given name, use:
 # [Python SDK](#tab/python)
 
 ```python
-from azure.ai.generative import AIClient
+from azure.ai.resources.client import AIClient
 from azure.ai.generative.entities import Data
 from azure.ai.generative.constants import AssetTypes
 from azure.identity import DefaultAzureCredential
@@ -265,7 +265,7 @@ To archive a specific data version, use:
 # [Python SDK](#tab/python)
 
 ```python
-from azure.ai.generative import AIClient
+from azure.ai.resources.client import AIClient
 from azure.ai.generative.entities import Data
 from azure.ai.generative.constants import AssetTypes
 from azure.identity import DefaultAzureCredential
@@ -294,7 +294,7 @@ To restore *all versions* of the data under a given name, use:
 # [Python SDK](#tab/python)
 
 ```python
-from azure.ai.generative import AIClient
+from azure.ai.resources.client import AIClient
 from azure.ai.generative.entities import Data
 from azure.ai.generative.constants import AssetTypes
 from azure.identity import DefaultAzureCredential
@@ -321,7 +321,7 @@ To restore a specific data version, use:
 # [Python SDK](#tab/python)
 
 ```python
-from azure.ai.generative import AIClient
+from azure.ai.resources.client import AIClient
 from azure.ai.generative.entities import Data
 from azure.ai.generative.constants import AssetTypes
 from azure.identity import DefaultAzureCredential

articles/ai-studio/how-to/flow-deploy.md

@@ -83,7 +83,7 @@ You can use the Azure AI Generative SDK to deploy a prompt flow as an online end
 
 ```python
 # Import required dependencies 
-from azure.ai.generative import AIClient 
+from azure.ai.resources.client import AIClient 
 from azure.ai.generative.entities.deployment import Deployment 
 from azure.ai.generative.entities.models import PromptflowModel 
 from azure.identity import InteractiveBrowserCredential as Credential 

articles/ai-studio/how-to/simulator-interaction-data.md

@@ -37,7 +37,7 @@ First we set up the system large language model, which acts as the "agent" simul
 
 ```python
 from azure.identity import DefaultAzureCredential
-from azure.ai.generative import AIClient
+from azure.ai.resources.client import AIClient
 from azure.ai.generative.entities import AzureOpenAIModelConfiguration
 
 credential = DefaultAzureCredential()

articles/ai-studio/includes/evaluations/from-data/python.md

@@ -134,7 +134,7 @@ Before you call the `evaluate()` function, your environment needs to set up your
 
 ```python
 from azure.identity import DefaultAzureCredential
-from azure.ai.generative import AIClient
+from azure.ai.resources.client import AIClient
 
 client = AIClient.from_config(DefaultAzureCredential())
 ```

articles/azure-netapp-files/snapshots-restore-new-volume.md

@@ -24,7 +24,9 @@ ms.author: anfdocs
 
 * Currently, you can [restore a snapshot only to a new volume](snapshots-introduction.md#restoring-cloning-an-online-snapshot-to-a-new-volume). 
 
-* If you use the standard storage with cool access feature, see [Manage Azure NetApp Files standard storage with cool access](manage-cool-access.md#considerations) for more considerations. 
+* If you use the standard storage with cool access feature, see [Manage Azure NetApp Files standard storage with cool access](manage-cool-access.md#considerations) for more considerations.
+  
+* Cross-region replication and cross-zone replication operations are suspended and cannot be added while restoring a snapshot to a new volume.
 
 ## Steps
 

articles/azure-relay/includes/relay-create-hybrid-connection-portal.md

@@ -2,7 +2,7 @@
 author: spelluru
 ms.service: service-bus-relay
 ms.topic: include
-ms.date: 08/10/2023
+ms.date: 01/04/2024
 ms.author: spelluru
 ---
 On the **Relay** page for your namespace, follow these steps to create a hybrid connection.

articles/azure-relay/includes/relay-create-namespace-portal.md

@@ -2,7 +2,7 @@
 author: spelluru
 ms.service: service-bus-relay
 ms.topic: include
-ms.date: 08/10/2023
+ms.date: 01/04/2024
 ms.author: spelluru
 ---
 1. Sign in to the [Azure portal].

articles/azure-relay/includes/relay-hybrid-connections-http-requests-java-get-started-client.md

@@ -25,11 +25,11 @@ a Hybrid Connections URL with client authorization utilizing the Azure Relay lib
 
 Modify your pom.xml file in your maven application package to include the following: 
   ```bash
-   	    <dependency>
-		    <groupId>com.microsoft.azure</groupId>
-		    <artifactId>azure-relay</artifactId>
-		    <version>0.0.6</version>
-	          </dependency>
+	<dependency>
+    	<groupId>com.microsoft.azure</groupId>
+    	<artifactId>azure-relay</artifactId>
+    	<version>0.0.6</version>
+	</dependency>
    ```
 Run `mvn dependency:copy-dependencies -DoutputDirectory=lib` in your mvn project to add the 
 dependency jar file in the lib directory of your project. This will also import all dependencies
@@ -44,36 +44,36 @@ of the `azure-relay` mvn package. This package provides functions to construct R
 	```
 2. Import the dependencies into your `Sender.java` class.
     ```java
-	import java.io.BufferedReader;
-	import java.io.IOException;
-	import java.io.InputStream;
-	import java.io.InputStreamReader;
-	import java.io.OutputStreamWriter;
-	import java.net.HttpURLConnection;
-	import java.net.MalformedURLException;
-	import java.net.URL;
-	import java.time.Duration;
-	import java.util.Scanner;
-	import com.microsoft.azure.relay.RelayConnectionStringBuilder;
-	import com.microsoft.azure.relay.TokenProvider;
+    import java.io.BufferedReader;
+    import java.io.IOException;
+    import java.io.InputStream;
+    import java.io.InputStreamReader;
+    import java.io.OutputStreamWriter;
+    import java.net.HttpURLConnection;
+    import java.net.MalformedURLException;
+    import java.net.URL;
+    import java.time.Duration;
+    import java.util.Scanner;
+    import com.microsoft.azure.relay.RelayConnectionStringBuilder;
+    import com.microsoft.azure.relay.TokenProvider;
 	```
 
 4. Add the following `constants` to the top of the `Sender.java` file to a `createConnectionString` 
    java function for the hybrid connection details.
 
     ```java
-    public static String createConnectionString(){
-        StringBuilder connectionString = new StringBuilder();
-        connectionString.append("Endpoint=sb://");
-        connectionString.append("{namespace}");
-        connectionString.append(".servicebus.windows.net/;SharedAccessKeyName=");
-        connectionString.append("{keyrule}");
-        connectionString.append(";SharedAccessKey=");
-        connectionString.append("{key}");
-        connectionString.append(";EntityPath=");
-        connectionString.append("{path}");
-        return connectionString.toString();
-		}
+	public static String createConnectionString(){
+	    StringBuilder connectionString = new StringBuilder();
+	    connectionString.append("Endpoint=sb://");
+	    connectionString.append("{namespace}");
+	    connectionString.append(".servicebus.windows.net/;SharedAccessKeyName=");
+	    connectionString.append("{keyrule}");
+	    connectionString.append(";SharedAccessKey=");
+	    connectionString.append("{key}");
+	    connectionString.append(";EntityPath=");
+	    connectionString.append("{path}");
+	    return connectionString.toString();
+	}
     ```
 	Replace the placeholders in brackets with the values you obtained when you created the hybrid connection.
     1. `namespace` - The Relay namespace. Be sure to use the fully qualified namespace name; for example, `{namespace}.servicebus.windows.net`.