Merge branch 'main' into 5091-5095-5100-kate-jp-joe

Author: laujan

.openpublishing.redirection.json

@@ -294,6 +294,21 @@
       "source_path_from_root": "/articles/ai-services/speech-service/text-to-speech-avatar/custom-avatar-endpoint.md",
       "redirect_url": "/azure/ai-services/speech-service/custom-avatar-create",
       "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/speech-service/migration-overview-neural-voice.md",
+      "redirect_url": "/azure/ai-services/speech-service/custom-neural-voice",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/speech-service/how-to-migrate-to-custom-neural-voice.md",
+      "redirect_url": "/azure/ai-services/speech-service/custom-neural-voice",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/speech-service/how-to-migrate-to-prebuilt-neural-voice.md",
+      "redirect_url": "/azure/ai-services/speech-service/custom-neural-voice",
+      "redirect_document_id": false
     }
   ]
 }

articles/ai-foundry/how-to/connections-add.md

@@ -53,7 +53,7 @@ Here's a table of some of the available connection types in Azure AI Foundry por
 |-------------------------------|:-------:|:--------------------------------------:|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | Azure AI Search               |         | ✓                                      | Azure AI Search is an Azure resource that supports information retrieval over your vector and textual data stored in search indexes.                                                   |
 | Azure Storage                 |         | ✓                                      | Azure Storage is a cloud storage solution for storing unstructured data like documents, images, videos, and application installers.                                                   |
-| Azure Cosmos DB                |         | ✓                                      | Azure Cosmos DB is a globally distributed, multi-model database service that offers low latency, high availability, and scalability across multiple geographical regions.                |
+| Azure Cosmos DB               | ✓       | ✓                                      | Azure Cosmos DB is a globally distributed, multi-model database service that offers low latency, high availability, and scalability across multiple geographical regions.                |
 | Azure OpenAI                  |         |                                        | Azure OpenAI is a service that provides access to OpenAI's models including the GPT-4o, GPT-4o mini, GPT-4, GPT-4 Turbo with Vision, GPT-3.5-Turbo, DALLE-3, and Embeddings model series with the security and enterprise capabilities of Azure. |
 | Application Insights          |         |                                        | Azure Application Insights is a service within Azure Monitor that enables developers and DevOps teams to automatically detect performance anomalies, diagnose issues, and gain deep insights into application usage and behavior through powerful telemetry and analytics tools. |
 | API key                       |         |                                        | API Key connections handle authentication to your specified target on an individual basis. |

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

@@ -9,7 +9,7 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: how-to
-ms.date: 02/11/2025
+ms.date: 05/21/2025
 ms.author: franksolomon
 author: fbsolo-ms1
 ---
@@ -29,12 +29,10 @@ Data can help when you need these capabilities:
 > - **Lineage:** For any given data, you can view which jobs or prompt flow pipelines consume the data.
 > - **Ease-of-use:** An Azure AI Foundry data resembles web browser bookmarks (favorites). Instead of remembering long storage paths that *reference* your frequently-used data on Azure Storage, you can create a data *version* and then access that version of the asset with a friendly name.
 
-## Prerequisites
 
-To create and work with data, you need:
+## Prerequisites
 
-- An Azure subscription. If you don't have one, create a [free account](https://azure.microsoft.com/free/).
-- An [Azure AI Foundry project](../how-to/create-projects.md).
+[!INCLUDE [hub-only-prereq](../includes/hub-only-prereq.md)]
 
 ## Create data
 

articles/ai-foundry/how-to/develop/langchain.md

@@ -51,8 +51,11 @@ To use LLMs deployed in Azure AI Foundry portal, you need the endpoint and crede
 [!INCLUDE [tip-left-pane](../../includes/tip-left-pane.md)]
 
 1. Go to the [Azure AI Foundry](https://ai.azure.com/).
+
 1. Open the project where the model is deployed, if it isn't already open.
+
 1. Go to **Models + endpoints** and select the model you deployed as indicated in the prerequisites.
+
 1. Copy the endpoint URL and the key.
 
     :::image type="content" source="../../media/how-to/inference/serverless-endpoint-url-keys.png" alt-text="Screenshot of the option to copy endpoint URI and keys from an endpoint." lightbox="../../media/how-to/inference/serverless-endpoint-url-keys.png":::
@@ -63,11 +66,19 @@ To use LLMs deployed in Azure AI Foundry portal, you need the endpoint and crede
 In this scenario, we placed both the endpoint URL and key in the following environment variables:
 
 ```bash
-export AZURE_INFERENCE_ENDPOINT="<your-model-endpoint-goes-here>"
+export AZURE_INFERENCE_ENDPOINT="https://<resource>.services.ai.azure.com/models"
 export AZURE_INFERENCE_CREDENTIAL="<your-key-goes-here>"
 ```
 
-Once configured, create a client to connect to the endpoint. In this case, we're working with a chat completions model hence we import the class `AzureAIChatCompletionsModel`.
+Once configured, create a client to connect with the chat model by using the `init_chat_model`. For Azure OpenAI models, configure the client as indicated at [Using Azure OpenAI models](#using-azure-openai-models).
+
+```python
+from langchain.chat_models import init_chat_model
+
+llm = init_chat_model(model="mistral-large-2407", model_provider="azure_ai")
+```
+
+You can also use the class `AzureAIChatCompletionsModel` directly.
 
 ```python
 import os
@@ -80,8 +91,8 @@ model = AzureAIChatCompletionsModel(
 )
 ```
 
-> [!TIP]
-> For Azure OpenAI models, configure the client as indicated at [Using Azure OpenAI models](#using-azure-openai-models).
+> [!CAUTION]
+> **Breaking change:** Parameter `model_name` was renamed `model` in version `0.1.3`.
 
 You can use the following code to create the client if your endpoint supports Microsoft Entra ID:
 
@@ -93,7 +104,7 @@ from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
 model = AzureAIChatCompletionsModel(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
     credential=DefaultAzureCredential(),
-    model_name="mistral-large-2407",
+    model="mistral-large-2407",
 )
 ```
 
@@ -111,7 +122,7 @@ from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
 model = AzureAIChatCompletionsModel(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
     credential=DefaultAzureCredentialAsync(),
-    model_name="mistral-large-2407",
+    model="mistral-large-2407",
 )
 ```
 
@@ -188,13 +199,13 @@ from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
 producer = AzureAIChatCompletionsModel(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
     credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
-    model_name="mistral-large-2407",
+    model="mistral-large-2407",
 )
 
 verifier = AzureAIChatCompletionsModel(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
     credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
-    model_name="mistral-small",
+    model="mistral-small",
 )
 ```
 
@@ -271,7 +282,7 @@ from langchain_azure_ai.embeddings import AzureAIEmbeddingsModel
 embed_model = AzureAIEmbeddingsModel(
     endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"],
     credential=os.environ['AZURE_INFERENCE_CREDENTIAL'],
-    model_name="text-embedding-3-large",
+    model="text-embedding-3-large",
 )
 ```
 
@@ -305,31 +316,15 @@ for doc in results:
 
 ## Using Azure OpenAI models
 
-If you're using Azure OpenAI in Foundry Models or Foundry Models service with OpenAI models with `langchain-azure-ai` package, you might need to use `api_version` parameter to select a specific API version. The following example shows how to connect to an Azure OpenAI in Foundry Models deployment:
-
-```python
-from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
-
-llm = AzureAIChatCompletionsModel(
-    endpoint="https://<resource>.openai.azure.com/openai/deployments/<deployment-name>",
-    credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
-    api_version="2024-05-01-preview",
-)
-```
-
-> [!IMPORTANT]
-> Check which is the API version that your deployment is using. Using a wrong `api_version` or one not supported by the model results in a `ResourceNotFound` exception.
-
-If the deployment is hosted in Azure AI Services, you can use the Foundry Models service:
+If you're using Azure OpenAI models with `langchain-azure-ai` package, use the following URL:
 
 ```python
 from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
 
 llm = AzureAIChatCompletionsModel(
-    endpoint="https://<resource>.services.ai.azure.com/models",
+    endpoint="https://<resource>.openai.azure.com/openai/v1",
     credential=os.environ["AZURE_INFERENCE_CREDENTIAL"],
-    model_name="<model-name>",
-    api_version="2024-05-01-preview",
+    model="gpt-4o"
 )
 ```
 
@@ -370,7 +365,7 @@ 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",
+    model="mistral-large-2407",
     client_kwargs={"logging_enable": True},
 )
 ```

articles/ai-foundry/model-inference/concepts/models.md

@@ -55,9 +55,9 @@ DeepSeek family of models includes DeepSeek-R1, which excels at reasoning tasks
 
 | Model  | Type | Tier | Capabilities |
 | ------ | ---- | ---- | ------------ |
-| [DeekSeek-V3-0324](https://ai.azure.com/explore/models/deepseek-v3-0324/version/1/registry/azureml-deepseek) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:**  (131,072 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON |
-| [DeekSeek-R1](https://ai.azure.com/explore/models/deepseek-r1/version/1/registry/azureml-deepseek) | chat-completion <br /> [(with reasoning content)](../how-to/use-chat-reasoning.md) | Global standard | - **Input:** text (163,840 tokens) <br /> - **Output:**  (163,840 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** No <br /> - **Response formats:** Text. |
-| [DeekSeek-V3](https://ai.azure.com/explore/models/deepseek-v3/version/1/registry/azureml-deepseek) <br />(Legacy) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:**  (131,072 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** No <br /> - **Response formats:** Text, JSON |
+| [DeepSeek-V3-0324](https://ai.azure.com/explore/models/deepseek-v3-0324/version/1/registry/azureml-deepseek) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:**  (131,072 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** Yes <br /> - **Response formats:** Text, JSON |
+| [DeepSeek-R1](https://ai.azure.com/explore/models/deepseek-r1/version/1/registry/azureml-deepseek) | chat-completion <br /> [(with reasoning content)](../how-to/use-chat-reasoning.md) | Global standard | - **Input:** text (163,840 tokens) <br /> - **Output:**  (163,840 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** No <br /> - **Response formats:** Text. |
+| [DeepSeek-V3](https://ai.azure.com/explore/models/deepseek-v3/version/1/registry/azureml-deepseek) <br />(Legacy) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:**  (131,072 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** No <br /> - **Response formats:** Text, JSON |
 
 For a tutorial on DeepSeek-R1, see [Tutorial: Get started with DeepSeek-R1 reasoning model in Azure AI Foundry Models](../tutorials/get-started-deepseek-r1.md).
 

articles/ai-foundry/model-inference/includes/how-to-prerequisites-openai-python.md

@@ -0,0 +1,22 @@
+---
+manager: nitinme
+ms.service: azure-ai-model-inference
+ms.topic: include
+ms.date: 1/21/2025
+ms.author: fasantia
+author: santiagxf
+---
+
+* Install the SDK with the following command:
+
+    # [OpenAI API](#tab/openai)
+    
+    ```bash
+    pip install -U openai
+    ```
+    
+    # [Model Inference API (preview)](#tab/inference)
+    
+    ```bash
+    pip install -U azure-ai-inference
+    ```