Merge pull request #5169 from santiagxf/santiagxf-patch-1

Update langchain.md

Author: prmerger-automator[bot]

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

@@ -126,7 +126,7 @@ model = AzureAIChatCompletionsModel(
 )
 ```
 
-If your endpoint is serving one model, like with the standard deployment, you don't have to indicate `model_name` parameter:
+If your endpoint is serving one model, like with the standard deployment, you don't have to indicate `model` parameter:
 
 ```python
 import os
@@ -191,7 +191,7 @@ chain.invoke({"language": "italian", "text": "hi"})
 
 Models deployed to Azure AI Foundry support the Foundry Models API, which is standard across all the models. Chain multiple LLM operations based on the capabilities of each model so you can optimize for the right model based on capabilities. 
 
-In the following example, we create two model clients. One is a producer and another one is a verifier. To make the distinction clear, we're using a multi-model endpoint like the [Foundry Models API](../../model-inference/overview.md) and hence we're passing the parameter `model_name` to use a `Mistral-Large` and a `Mistral-Small` model, quoting the fact that **producing content is more complex than verifying it**.
+In the following example, we create two model clients. One is a producer and another one is a verifier. To make the distinction clear, we're using a multi-model endpoint like the [Foundry Models API](../../model-inference/overview.md) and hence we're passing the parameter `model` to use a `Mistral-Large` and a `Mistral-Small` model, quoting the fact that **producing content is more complex than verifying it**.
 
 ```python
 from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel