Merge pull request #5940 from aahill/main

Adding python samples

Author: Stacyrch140

articles/ai-foundry/agents/how-to/tools/bing-custom-search-samples.md

@@ -5,7 +5,7 @@ description: Find samples to ground Azure AI Foundry Agents using Custom Bing Se
 author: aahill
 ms.author: aahi
 manager: nitinme
-ms.date: 04/15/2025
+ms.date: 07/09/2025
 ms.service: azure-ai-agent-service
 ms.topic: how-to
 ms.custom:
@@ -16,6 +16,7 @@ zone_pivot_groups: selection-bing-custom-grounding
 
 # How to use Grounding with Bing Custom Search (preview)
 
+Use this article to find step-by-step instructions and code samples for using the Grounding with Bing Custom Search tool in the Azure AI Foundry Agent Service.
 
 ::: zone pivot="portal"
 
@@ -36,18 +37,43 @@ zone_pivot_groups: selection-bing-custom-grounding
 
 :::zone-end
 
-<!--
 ::: zone pivot="python"
 
+## Prerequisites
+
+* Your Azure AI Foundry Project endpoint.
+
+    [!INCLUDE [endpoint-string-portal](../../includes/endpoint-string-portal.md)]
+
+    Save this endpoint to an environment variable named `PROJECT_ENDPOINT`. 
+
+* The name of your Grounding with Bing Custom Search resource name. You can find it in the Azure AI Foundry portal by selecting **Management center** from the left navigation menu. Then selecting **Connected resources**.
+    
+    :::image type="content" source="../../media/tools/bing/custom-resource-name.png" alt-text="A screenshot showing the Grounding with Bing Custom Search resource name. " lightbox="../../media/tools/bing/custom-resource-name.png":::
+
+    Save this resource name to an environment variable named `BING_CUSTOM_CONNECTION_NAME`. 
+
+* The name of your Grounding with Bing Custom Search configuration, which contains the URLs you want to allow or disallow. You can find it by navigating to the overview page for your resource in the [Azure portal](https://portal.azure.com/). Select **Configurations**, then select your configuration. 
+
+    :::image type="content" source="../../media/tools/bing/custom-connection-name.png" alt-text="A screenshot showing the Grounding with Bing Custom Search connection name. " lightbox="../../media/tools/bing/custom-connection-name.png":::
+
+    Save this connection name to an environment variable named `BING_CUSTOM_CONNECTION_NAME`. 
+
+* The names of your model's deployment name. You can find it in **Models + Endpoints** in the left navigation menu. 
+
+    :::image type="content" source="../../media/tools/model-deployment-portal.png" alt-text="A screenshot showing the model deployment screen the AI Foundry portal." lightbox="../../media/tools/model-deployment-portal.png":::
+    
+    Save the name of your model deployment name as an environment variable named `MODEL_DEPLOYMENT_NAME`. 
+
 ## Create a project client
 
 Create a client object, which will contain the connection string for connecting to your AI project and other resources.
 
 ```python
 import os
 from azure.ai.projects import AIProjectClient
-from azure.ai.projects.models import MessageRole, BingCustomSearchTool
 from azure.identity import DefaultAzureCredential
+from azure.ai.agents.models import BingCustomSearchTool
 
 
 # Create an Azure AI Client from an endpoint, copied from your Azure AI Foundry project.
@@ -57,32 +83,34 @@ project_endpoint = os.environ["PROJECT_ENDPOINT"]  # Ensure the PROJECT_ENDPOINT
 # Create an AIProjectClient instance
 project_client = AIProjectClient(
     endpoint=project_endpoint,
-    credential=DefaultAzureCredential(),  # Use Azure Default Credential for authentication
-    api_version="latest",
+    credential=DefaultAzureCredential(),
 )
 ```
 
 
 ## Create an Agent with the Grounding with Bing Custom Search tool enabled
 
-To make the Grounding with Bing Custom Search tool available to your agent, use a connection to initialize the tool and attach it to the agent. You can find your connection in the **connected resources** section of your project in the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs).
+To make the Grounding with Bing Custom Search tool available to your agent, use a connection to initialize the tool and attach it to the agent.
 
 ```python
-bing_custom_connection = project_client.connections.get(connection_name=os.environ["BING_CUSTOM_CONNECTION_NAME"])
+bing_custom_connection = project_client.connections.get(name=os.environ["BING_CUSTOM_CONNECTION_NAME"])
 conn_id = bing_custom_connection.id
 
 print(conn_id)
 
-# Initialize agent bing custom search tool and add the connection id
-bing_custom_tool = BingCustomSearchTool(connection_id=conn_id, instance_name="<config_instance_name>")
+configuration_name = os.environ["BING_CUSTOM_INSTANCE_NAME"]
+# Initialize Bing Custom Search tool with connection id and configuration name
+bing_custom_tool = BingCustomSearchTool(connection_id=conn_id, instance_name=configuration_name)
 
 # Create agent with the bing custom search tool and process assistant run
 with project_client:
-    agent = project_client.agents.create_agent(
+    agents_client = project_client.agents
+
+    agent = agents_client.create_agent(
         model=os.environ["MODEL_DEPLOYMENT_NAME"],
         name="my-agent",
         instructions="You are a helpful agent",
-        tools=bing_custom_tool.definitions
+        tools=bing_custom_tool.definitions,
     )
     print(f"Created agent, ID: {agent.id}")
 ```
@@ -91,13 +119,13 @@ with project_client:
 
 ```python
 # Create thread for communication
-thread = project_client.agents.create_thread()
+thread = agents_client.threads.create()
 print(f"Created thread, ID: {thread.id}")
 
 # Create message to thread
-message = project_client.agents.create_message(
+message = agents_client.messages.create(
     thread_id=thread.id,
-    role=MessageRole.USER,
+    role="user",
     content="How many medals did the USA win in the 2024 summer olympics?",
 )
 print(f"Created message, ID: {message.id}")
@@ -109,31 +137,29 @@ Create a run and observe that the model uses the Grounding with Bing Search tool
 
 
 ```python
-# Create and process agent run in thread with tools
-run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)
+# Create and process Agent run in thread with tools
+run = agents_client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
 print(f"Run finished with status: {run.status}")
 
 if run.status == "failed":
     print(f"Run failed: {run.last_error}")
 
-# Delete the assistant when done
-project_client.agents.delete_agent(agent.id)
-print("Deleted agent")
-
-# Print the Agent's response message with optional citation
-response_message = project_client.agents.list_messages(thread_id=thread.id).get_last_message_by_role(
-    MessageRole.AGENT
-)
-if response_message:
-    for text_message in response_message.text_messages:
-        print(f"Agent response: {text_message.text.value}")
-    for annotation in response_message.url_citation_annotations:
-        print(f"URL Citation: [{annotation.url_citation.title}]({annotation.url_citation.url})")
+# Uncomment these lines to delete the Agent when done
+#agents_client.delete_agent(agent.id)
+#print("Deleted agent")
+
+# Fetch and log all messages
+messages = agents_client.messages.list(thread_id=thread.id)
+for msg in messages:
+    if msg.text_messages:
+        for text_message in msg.text_messages:
+            print(f"Agent response: {text_message.text.value}")
+        for annotation in msg.url_citation_annotations:
+            print(f"URL Citation: [{annotation.url_citation.title}]({annotation.url_citation.url})")
 ```
 
 
 :::zone-end
--->
 
 <!--
 ::: zone pivot="csharp"

articles/ai-foundry/agents/how-to/tools/deep-research-samples.md

@@ -29,9 +29,8 @@ Use this article to learn how to use the Deep Research tool with the Azure AI Pr
 
 * Your Azure AI Foundry Project endpoint.
 
-    You can find your endpoint in the main project **overview** for your project in the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs), under **Endpoint and keys** > **Libraries** > **Azure AI Foundry**.
-
-    :::image type="content" source="../../media/quickstart/portal-endpoint-string.png" alt-text="A screenshot showing the endpoint in the Azure AI Foundry portal." lightbox="../../media/quickstart/portal-endpoint-string.png":::
+    
+    [!INCLUDE [endpoint-string-portal](../../includes/endpoint-string-portal.md)]
 
     Save this endpoint to an environment variable named `PROJECT_ENDPOINT`. 
 

articles/ai-foundry/agents/how-to/tools/fabric.md

@@ -5,7 +5,7 @@ description: Learn how to perform data analytics in Azure AI Foundry Agents usin
 author: aahill
 ms.author: aahi
 manager: nitinme
-ms.date: 06/17/2025
+ms.date: 07/09/2025
 ms.service: azure-ai-agent-service
 ms.topic: how-to
 ms.custom:
@@ -23,7 +23,7 @@ You need to first build and publish a Fabric data agent and then connect your Fa
 
 |Azure AI foundry support  | Python SDK |	C# SDK | JavaScript SDK | REST API |Basic agent setup | Standard agent setup |
 |---------|---------|---------|---------|---------|---------|---------|
-| ✔️ |  |  |  | ✔️ | ✔️ | ✔️ |
+| ✔️ | ✔️ |  |  | ✔️ | ✔️ | ✔️ |
 
 ## Prerequisites
 * You have created and published a Fabric data agent endpoint
@@ -34,7 +34,29 @@ You need to first build and publish a Fabric data agent and then connect your Fa
 
 * Your Fabric Data Agent and Azure AI Foundry Agent need to be in the same tenant.
 
+
+* Your Azure AI Foundry Project endpoint.
+
+    [!INCLUDE [endpoint-string-portal](../../includes/endpoint-string-portal.md)]
+
+    Save this endpoint to an environment variable named `PROJECT_ENDPOINT`. 
+
+
+* The name of your Microsoft Fabric connection name. You can find it in the Azure AI Foundry portal by selecting **Management center** from the left navigation menu. Then selecting **Connected resources**.
+    
+    :::image type="content" source="../../media/tools/fabric-connection.png" alt-text="A screenshot showing the SharePoint connection name. " lightbox="../../media/tools/fabric-connection.png":::
+
+    Save this endpoint to an environment variable named `FABRIC_CONNECTION_ID`
+
+
+* The names of your model's deployment name. You can find it in **Models + Endpoints** in the left navigation menu. 
+
+    :::image type="content" source="../../media/tools/model-deployment-portal.png" alt-text="A screenshot showing the model deployment screen the AI Foundry portal." lightbox="../../media/tools/model-deployment-portal.png":::
+    
+    Save the name of your model deployment name as an environment variable named `MODEL_DEPLOYMENT_NAME`. 
+
 ## Setup  
+
 > [!NOTE]
 > * The model you selected in Azure AI Foundry Agent setup is only used for agent orchestration and response generation. It doesn't impact which model Fabric data agent uses for NL2SQL operation.
 > * To help your model invoke your Microsoft Fabric tool in the expected way, make sure you update agent instructions with descriptions of your Fabric data agent and what data it can access. An example is "for customer and product sales related data, please use the Fabric tool". We recommend using a smaller AI model such as `gpt-4o-mini`. You can also use `tool_choice` parameter in SDK or API to force Fabric tool to be invoked at each run. 
@@ -65,7 +87,7 @@ You can add the Microsoft Fabric tool to an agent programmatically using the cod
         :::image type="content" source="../../media\tools\fabric-foundry.png" alt-text="A screenshot showing the fabric connection in the Azure AI Foundry portal." lightbox="../../media\tools\fabric-foundry.png":::
 
 :::zone-end
-<!--
+
 :::zone pivot="python"
 
 ## Create a project client
@@ -74,18 +96,17 @@ Create a client object, which will contain the connection string for connecting
 
 ```python
 import os
-from azure.identity import DefaultAzureCredential
 from azure.ai.projects import AIProjectClient
-from azure.ai.agents.models import FabricTool
+from azure.identity import DefaultAzureCredential
+from azure.ai.agents.models import FabricTool, ListSortOrder
 
 # Retrieve the endpoint and credentials
 project_endpoint = os.environ["PROJECT_ENDPOINT"]  # Ensure the PROJECT_ENDPOINT environment variable is set
 
 # Initialize the AIProjectClient
 project_client = AIProjectClient(
     endpoint=project_endpoint,
-    credential=DefaultAzureCredential(exclude_interactive_browser_credential=False),  # Use Azure Default Credential for authentication
-    api_version="latest",
+    credential=DefaultAzureCredential(),
 )
 ``` 
 
@@ -104,13 +125,14 @@ conn_id = os.environ["FABRIC_CONNECTION_ID"]  # Ensure the FABRIC_CONNECTION_ID
 fabric = FabricTool(connection_id=conn_id)
 
 # Create an agent with the Fabric tool
+# Create an Agent with the Fabric tool and process an Agent run
 with project_client:
-    agent = project_client.agents.create_agent(
-        model=os.environ["MODEL_DEPLOYMENT_NAME"],  # Model deployment name
-        name="my-agent",  # Name of the agent
-        instructions="You are a helpful agent",  # Instructions for the agent
-        tools=fabric.definitions,  # Attach the Fabric tool
-        headers={"x-ms-enable-preview": "true"},  # Enable preview features
+    agents_client = project_client.agents
+    agent = agents_client.create_agent(
+        model=os.environ["MODEL_DEPLOYMENT_NAME"],
+        name="my-agent",
+        instructions="You are a helpful agent",
+        tools=fabric.definitions,
     )
     print(f"Created Agent, ID: {agent.id}")
 ```
@@ -134,26 +156,27 @@ print(f"Created message, ID: {message['id']}")
 ## Create a run and check the output
 
 ```python
-# Create and process an agent run in the thread
-run = project_client.agents.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
+# Create and process an Agent run in thread with tools
+run = agents_client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
 print(f"Run finished with status: {run.status}")
 
-# Check if the run failed
 if run.status == "failed":
     print(f"Run failed: {run.last_error}")
 
-# Fetch and log all messages from the thread
-messages = project_client.agents.messages.list(thread_id=thread.id)
-for message in messages.data:
-    print(f"Role: {message.role}, Content: {message.content}")
+# Uncomment the following lines to delete the agent when done
+#agents_client.delete_agent(agent.id)
+#print("Deleted agent")
 
-# Delete the agent after use
-project_client.agents.delete_agent(agent.id)
-print("Deleted agent")
+# Fetch and log all messages
+messages = agents_client.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
+for msg in messages:
+    if msg.text_messages:
+        last_text = msg.text_messages[-1]
+        print(f"{msg.role}: {last_text.text.value}")
 ```
 
 :::zone-end
--->
+
 
 <!--
 :::zone pivot="csharp"