fix: review

Author: santiagxf

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

@@ -29,4 +29,4 @@ To use structured outputs with chat completions models in your application, you
 
     * You can check which models support structured outputs by checking the column **Response format** in the [Models](../../concepts/models.md) article.
 
-    * This article uses `Cohere-command-r-plus-08-2024`.
+    * This article uses `gpt-4o`.

articles/ai-foundry/model-inference/includes/use-structured-outputs/intro.md

@@ -171,7 +171,7 @@ You can use structured outputs with multi-modal models to extract information fr
 
 Let's consider the following chart:
 
-:::image type="content" source="../../media/use-structured-outputs/example_graph_treecover.png" alt-text="An example image showing a chart with the annual loss in thousand square kilometers of global tree cover across different climate zones." lightbox="../../media/use-structured-outputs/example_graph_treecover.png":::
+:::image type="content" source="../../media/use-structured-outputs/example-graph-treecover.png" alt-text="An example image showing a chart with the annual loss in thousand square kilometers of global tree cover across different climate zones." lightbox="../../media/use-structured-outputs/example-graph-treecover.png":::
 
 We can define a generic schema that can be used to encode the information contained in the chart and then use it for further analysis.
 
@@ -297,7 +297,7 @@ var messages = [
             content: {
               type: "image_url",
               image_url: {
-                url: getImageDataUrl("example_graph_treecover.png", "png"),
+                url: getImageDataUrl("example-graph-treecover.png", "png"),
               },
             }
         },
@@ -366,6 +366,6 @@ console.log(JSON.stringify(jsonResponseMessage, null, 4));
 
 To see how much information the model was able to capture, we can try to plot the data:
 
-:::image type="content" source="../../media/use-structured-outputs/graph_treecover_plot.png" alt-text="The resulting plot of the data contained in the structured output generated by the model." lightbox="../../media/use-structured-outputs/graph_treecover_plot.png":::
+:::image type="content" source="../../media/use-structured-outputs/graph-treecover-plot.png" alt-text="The resulting plot of the data contained in the structured output generated by the model." lightbox="../../media/use-structured-outputs/graph-treecover-plot.png":::
 
 While the information isn't perfect, we can see the model was able to capture a good amount of information from the original chart.

articles/ai-foundry/model-inference/includes/use-structured-outputs/javascript.md

@@ -30,7 +30,7 @@ zone_pivot_groups: azure-ai-inference-samples
         endpoint="https://aiservices-demo-wus2.services.ai.azure.com/models",
         credential=AzureKeyCredential(os.environ["AZURE_INFERENCE_CREDENTIAL"]),
         model="gpt-4o",
-        api-version="2024-05-01-preview"
+        api_version="2024-12-01-preview"
     )
     ```
 
@@ -303,7 +303,7 @@ You can use structured outputs with multi-modal models to extract information fr
 
 Let's consider the following chart:
 
-:::image type="content" source="../../media/use-structured-outputs/example_graph_treecover.png" alt-text="An example image showing a chart with the annual loss in thousand square kilometers of global tree cover across different climate zones." lightbox="../../media/use-structured-outputs/example_graph_treecover.png":::
+:::image type="content" source="../../media/use-structured-outputs/example-graph-treecover.png" alt-text="An example image showing a chart with the annual loss in thousand square kilometers of global tree cover across different climate zones." lightbox="../../media/use-structured-outputs/example-graph-treecover.png":::
 
 We can define a generic schema that can be used to encode the information contained in the chart and then use it for further analysis. We use [Pyndatic objects](#use-pydantic-objects) as described before.
 
@@ -330,7 +330,7 @@ We can load the image as follows to pass it to the model:
 from azure.ai.inference.models import ImageContentItem, ImageUrl
 
 image_graph = ImageUrl.load(
-    image_file="example_graph_treecover.png",
+    image_file="example-graph-treecover.png",
     image_format="png"
 )
 ```
@@ -430,6 +430,6 @@ plt.tight_layout()
 plt.show()
 ```
 
-:::image type="content" source="../../media/use-structured-outputs/graph_treecover_plot.png" alt-text="The resulting plot of the data contained in the structured output generated by the model." lightbox="../../media/use-structured-outputs/graph_treecover_plot.png":::
+:::image type="content" source="../../media/use-structured-outputs/graph-treecover-plot.png" alt-text="The resulting plot of the data contained in the structured output generated by the model." lightbox="../../media/use-structured-outputs/graph-treecover-plot.png":::
 
 While the information isn't perfect, we can see the model was able to capture a good amount of information from the original chart.

articles/ai-foundry/model-inference/includes/use-structured-outputs/python.md

@@ -68,7 +68,7 @@ We can use structure outputs with the defined schema as follows:
 __Request__
 
 ```http
-POST https://<resource>.services.ai.azure.com/models/chat/completions?api-version=2024-05-01-preview
+POST https://<resource>.services.ai.azure.com/models/chat/completions?api-version=2024-12-01-preview
 Content-Type: application/json
 api-key: <key>
 ```
@@ -125,7 +125,7 @@ __Body__
             "strict": true
         }
     },
-    "model": "Cohere-command-r-plus-08-2024"
+    "model": "gpt-4o"
 }
 ```
 
@@ -138,7 +138,7 @@ __Response__
     "id": "0a1234b5de6789f01gh2i345j6789klm",
     "object": "chat.completion",
     "created": 1718726686,
-    "model": "Cohere-command-r-plus-08-2024",
+    "model": "gpt-4o",
     "choices": [
         {
             "index": 0,
@@ -170,7 +170,7 @@ You can use structured outputs with multi-modal models to extract information fr
 
 Let's consider the following chart:
 
-:::image type="content" source="../../media/use-structured-outputs/example_graph_treecover.png" alt-text="An example image showing a chart with the annual loss in thousand square kilometers of global tree cover across different climate zones." lightbox="../../media/use-structured-outputs/example_graph_treecover.png":::
+:::image type="content" source="../../media/use-structured-outputs/example-graph-treecover.png" alt-text="An example image showing a chart with the annual loss in thousand square kilometers of global tree cover across different climate zones." lightbox="../../media/use-structured-outputs/example-graph-treecover.png":::
 
 We can define a generic schema that can be used to encode the information contained in the chart and then use it for further analysis.
 
@@ -261,7 +261,7 @@ We can use structure outputs with the defined schema as follows:
 __Request__
 
 ```http
-POST https://<resource>.services.ai.azure.com/models/chat/completions?api-version=2024-05-01-preview
+POST https://<resource>.services.ai.azure.com/models/chat/completions?api-version=2024-12-01-preview
 Content-Type: application/json
 api-key: <key>
 ```
@@ -366,7 +366,7 @@ __Body__
             "strict": true
         }
     },
-    "model": "Cohere-command-r-plus-08-2024"
+    "model": "gpt-4o"
 }
 ```
 
@@ -379,7 +379,7 @@ __Response__
     "id": "0a1234b5de6789f01gh2i345j6789klm",
     "object": "chat.completion",
     "created": 1718726686,
-    "model": "Cohere-command-r-plus-08-2024",
+    "model": "gpt-4o",
     "choices": [
         {
             "index": 0,