review

Author: santiagxf

articles/ai-foundry/model-inference/includes/use-chat-completions/javascript.md

@@ -24,13 +24,9 @@ To use chat completion models in your application, you need:
 
 [!INCLUDE [how-to-prerequisites](../how-to-prerequisites.md)]
 
-* A chat completions model deployment. If you don't have one read [Add and configure models to Azure AI services](../../how-to/create-model-deployments.md) to add a chat completions model to your resource.
-
-* Install the [Azure Inference library for JavaScript](https://aka.ms/azsdk/azure-ai-inference/javascript/reference) with the following command:
+[!INCLUDE [how-to-prerequisites-javascript](../how-to-prerequisites-javascript.md)]
 
-  ```bash
-  npm install @azure-rest/ai-inference
-  ```
+* A chat completions model deployment. If you don't have one read [Add and configure models to Azure AI services](../../how-to/create-model-deployments.md) to add a chat completions model to your resource.
 
 ## Use chat completions
 

articles/ai-foundry/model-inference/includes/use-chat-completions/python.md

@@ -174,15 +174,13 @@ Some models can create JSON outputs. Set `response_format` to `json_object` to e
 
 
 ```python
-from azure.ai.inference.models import ChatCompletionsResponseFormatJSON
-
 response = client.complete(
     messages=[
         SystemMessage(content="You are a helpful assistant that always generate responses in JSON format, using."
                       " the following format: { ""answer"": ""response"" }."),
         UserMessage(content="How many languages are in the world?"),
     ],
-    response_format={ "type": ChatCompletionsResponseFormatJSON() }
+    response_format="json_object"
 )
 ```
 
@@ -213,9 +211,9 @@ The following code example creates a tool definition that is able to look from f
 
 
 ```python
-from azure.ai.inference.models import FunctionDefinition, ChatCompletionsFunctionToolDefinition
+from azure.ai.inference.models import FunctionDefinition, ChatCompletionsToolDefinition
 
-flight_info = ChatCompletionsFunctionToolDefinition(
+flight_info = ChatCompletionsToolDefinition(
     function=FunctionDefinition(
         name="get_flight_info",
         description="Returns information about the next flight between two cities. This includes the name of the airline, flight number and the date and time of the next flight",

articles/ai-foundry/model-inference/includes/use-chat-completions/rest.md

@@ -553,7 +553,7 @@ Some models can reason across text and images and generate text completions base
 To see this capability, download an image and encode the information as `base64` string. The resulting data should be inside of a [data URL](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/Data_URLs):
 
 > [!TIP]
-> You will need to construct the data URL using a scripting or programming language. This tutorial use [this sample image](../../../../ai-foundry/media/how-to/sdks/small-language-models-chart-example.jpg) in JPEG format. A data URL has a format as follows: `data:image/jpg;base64,0xABCDFGHIJKLMNOPQRSTUVWXYZ...`.
+> You will need to construct the data URL using a scripting or programming language. This tutorial uses [this sample image](../../../../ai-foundry/media/how-to/sdks/small-language-models-chart-example.jpg) in JPEG format. A data URL has a format as follows: `data:image/jpg;base64,0xABCDFGHIJKLMNOPQRSTUVWXYZ...`.
 
 Visualize the image:
 

articles/ai-foundry/model-inference/includes/use-chat-reasoning/python.md

@@ -23,7 +23,7 @@ To complete this tutorial, you need:
 
 * A model with reasoning capabilities model deployment. If you don't have one read [Add and configure models to Azure AI services](../../how-to/create-model-deployments.md) to add a reasoning model. 
 
-  * This examples use `DeepSeek-R1`.
+  * This example use `DeepSeek-R1`.
 
 ## Use reasoning capabilities with chat
 

articles/ai-foundry/model-inference/includes/use-embeddings/java.md

@@ -24,30 +24,7 @@ To use embedding models in your application, you need:
 
 [!INCLUDE [how-to-prerequisites](../how-to-prerequisites.md)]
 
-* An embeddings model deployment. If you don't have one read [Add and configure models to Azure AI services](../../how-to/create-model-deployments.md) to add an embeddings model to your resource.
-
-* Add the Azure AI inference package to your project:
-
-  ```xml
-  <dependency>
-      <groupId>com.azure</groupId>
-      <artifactId>azure-ai-inference</artifactId>
-      <version>1.0.0-beta.1</version>
-  </dependency>
-  ```
-  
-  > [!TIP]
-  > Read more about the [Azure AI inference package and reference](https://aka.ms/azsdk/azure-ai-inference/java/reference).
-
-* If you are using Entra ID, you also need the following package:
-
-  ```xml
-  <dependency>
-      <groupId>com.azure</groupId>
-      <artifactId>azure-identity</artifactId>
-      <version>1.13.3</version>
-  </dependency>
-  ```
+[!INCLUDE [how-to-prerequisites-java](../how-to-prerequisites-java.md)]
 
 * Import the following namespace:
 
@@ -65,6 +42,8 @@ To use embedding models in your application, you need:
   import java.util.List;
   ```
 
+* An embeddings model deployment. If you don't have one read [Add and configure models to Azure AI services](../../how-to/create-model-deployments.md) to add an embeddings model to your resource.
+
 ## Use embeddings
 
 First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables.