Merge pull request #2508 from santiagxf/santiagxf-patch-1

Update langchain.md

Author: prmerger-automator[bot]

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

@@ -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.