Merge pull request #2530 from aahill/foundry-docs-2

Foundry docs update

Author: AnnaMHuff

articles/ai-services/agents/concepts/model-region-support.md

@@ -7,7 +7,7 @@ author: aahill
 ms.author: aahi
 ms.service: azure-ai-agent-service
 ms.topic: conceptual
-ms.date: 01/07/2025
+ms.date: 01/27/2025
 ms.custom: azure-ai-agents
 ---
 
@@ -33,48 +33,19 @@ Azure AI Agent Service supports the same models as the chat completions API in A
 | westus3    | ✅ | ✅ | ✅ | - | ✅ | - | ✅ | - | - | - | ✅ | - |
 
 
-## More models
+## Non-Microsoft models
 
 The Azure AI Agent Service also supports the following models from the Azure AI Foundry model catalog.
 
 * Llama 3.1-70B-instruct
 * Mistral-large-2407
 * Cohere command R+
 
-To use these models, you can use Azure AI Foundry portal to make a deployment, and then reference it in your agent. 
-
-1. Go to the [Azure AI Foundry portal](https://ai.azure.com/) and select **Model catalog** in the left navigation menu, and scroll down to **Meta-Llama-3-70B-Instruct**. You can also find and use one of the models listed previously.  
-
-1. Select **Deploy**. 
-
-1. In the Deployment options screen that appears, select **Serverless API** with Azure AI Content Safety. 
-
-    :::image type="content" source="../media/llama/llama-deployment.png" alt-text="An image of the llama model project selection screen.":::
- 
-1. Select your project and then select **Subscribe and deploy**. 
-
-    :::image type="content" source="../media/llama/llama-deployment-2.png" alt-text="An image of the llama model deployment screen.":::
-
-1. Add the serverless connection to your hub/project. The deployment name you choose is the one that you reference in your code.  
-
-1. When calling agent creation API, set the `models` parameter to your deployment name. For example:
-
-    # [Python](#tab/python)
-
-    ```python
-    agent = project_client.agents.create_agent( model="llama-3", name="my-agent", instructions="You are a helpful agent" ) 
-    ```
-
-    # [C#](#tab/csharp)
-
-    ```csharp
-    Response<Agent> agentResponse = await client.CreateAgentAsync(
-            model: "llama-3",
-            name: "My agent",
-            instructions: "You are a helpful agent"
-    ```
-    ---
+To use these models, you can use Azure AI Foundry portal to make a deployment, and then reference the deployment name in your agent. For example:
 
+```python
+agent = project_client.agents.create_agent( model="llama-3", name="my-agent", instructions="You are a helpful agent" ) 
+```
 
 ## Next steps
 

articles/ai-services/agents/how-to/tools/openapi-spec.md

@@ -35,7 +35,7 @@ work together, generate client code, create tests, apply design standards, and m
 
 ## Authenticating with API Key
 
-1. Verify that the OpenAPI spec supports API keys: it has `securitySchemes` section and has one scheme of type `apiKey". An example would be:
+1. Verify that the OpenAPI spec supports API keys: it has `securitySchemes` section and has one scheme of type `apiKey`. For example:
    ```json
        "securitySchemes": {
           "apiKeyHeader": {
@@ -56,7 +56,7 @@ work together, generate client code, create tests, apply design standards, and m
 
     1. Select **+ new connection** in the settings page. 
         >[!NOTE]
-        > If you re-generate the API key at a later date, you need to update the connection with the new key.
+        > If you regenerate the API key at a later date, you need to update the connection with the new key.
         
        :::image type="content" source="../../media/tools/bing/project-connections.png" alt-text="A screenshot of the connections screen for the AI project." lightbox="../../media/tools/bing/project-connections.png":::
 
@@ -138,7 +138,7 @@ public partial class Sample_Agent_OpenAPI : SamplesBase<AIProjectsTestEnvironmen
 ---
 
 ## Step 2: Enable the OpenAPI Spec tool
-You might want to store the OpenAPI specification in another file and import the content to initialize the tool. Please note the sample code is using `anonymous` as authentication type.
+You might want to store the OpenAPI specification in another file and import the content to initialize the tool. Note the sample code is using `anonymous` as authentication type.
 
 # [Python](#tab/python)
 
@@ -187,7 +187,7 @@ An example of the audience would be ```https://cognitiveservices.azure.com/```.
 # Create agent with OpenAPI tool and process assistant run
 with project_client:
     agent = project_client.agents.create_agent(
-        model="gpt-4o-mini",
+        model="gpt-4o",
         name="my-assistant",
         instructions="You are a helpful assistant",
         tools=openapi.definitions
@@ -201,7 +201,7 @@ with project_client:
 # [C#](#tab/csharp)
 ```csharp
 Response<Agent> agentResponse = await client.CreateAgentAsync(
-            model: "gpt-4",
+            model: "gpt-4o",
             name: "azure-function-agent-foo",
             instructions: "You are a helpful assistant.",
             tools: new List<ToolDefinition> { openapiTool }

articles/ai-services/agents/media/llama/llama-deployment-2.png

@@ -22,7 +22,7 @@ This article provides a summary of the latest releases and major documentation u
 Azure AI Service is now available in preview. The service builds off of the [Assistants API](../openai/how-to/assistant.md) in Azure OpenAI, and offers several additional features, such as:
 
 * Several [additional tools](./how-to/tools/overview.md) to enhance your AI agents' functionality, such as the ability to use Bing and as a knowledge source and call functions. 
-* The ability to use non Azure OpenAI [models](./concepts/model-region-support.md#more-models): 
+* The ability to use non Azure OpenAI [models](./concepts/model-region-support.md): 
     * Llama 3.1-70B-instruct
     * Mistral-large-2407    
     * Cohere command R+