Python: Added Samples for Bing Grounding and Custom Search (#2200)

* bing grounding and custom search samples

* readme

python/samples/getting_started/agents/azure_ai/README.md

@@ -9,6 +9,8 @@ This folder contains examples demonstrating different ways to create and use age
 | [`azure_ai_basic.py`](azure_ai_basic.py) | The simplest way to create an agent using `AzureAIClient`. Demonstrates both streaming and non-streaming responses with function tools. Shows automatic agent creation and basic weather functionality. |
 | [`azure_ai_use_latest_version.py`](azure_ai_use_latest_version.py) | Demonstrates how to reuse the latest version of an existing agent instead of creating a new agent version on each instantiation using the `use_latest_version=True` parameter. |
 | [`azure_ai_with_azure_ai_search.py`](azure_ai_with_azure_ai_search.py) | Shows how to use Azure AI Search with Azure AI agents to search through indexed data and answer user questions with proper citations. Requires an Azure AI Search connection and index configured in your Azure AI project. |
+| [`azure_ai_with_bing_grounding.py`](azure_ai_with_bing_grounding.py) | Shows how to use Bing Grounding search with Azure AI agents to search the web for current information and provide grounded responses with citations. Requires a Bing connection configured in your Azure AI project. |
+| [`azure_ai_with_bing_custom_search.py`](azure_ai_with_bing_custom_search.py) | Shows how to use Bing Custom Search with Azure AI agents to search custom search instances and provide responses with relevant results. Requires a Bing Custom Search connection and instance configured in your Azure AI project. |
 | [`azure_ai_with_code_interpreter.py`](azure_ai_with_code_interpreter.py) | Shows how to use the `HostedCodeInterpreterTool` with Azure AI agents to write and execute Python code for mathematical problem solving and data analysis. |
 | [`azure_ai_with_existing_agent.py`](azure_ai_with_existing_agent.py) | Shows how to work with a pre-existing agent by providing the agent name and version to the Azure AI client. Demonstrates agent reuse patterns for production scenarios. |
 | [`azure_ai_with_existing_conversation.py`](azure_ai_with_existing_conversation.py) | Demonstrates how to use an existing conversation created on the service side with Azure AI agents. Shows two approaches: specifying conversation ID at the client level and using AgentThread with an existing conversation ID. |

python/samples/getting_started/agents/azure_ai/azure_ai_with_bing_custom_search.py

@@ -0,0 +1,48 @@
+# Copyright (c) Microsoft. All rights reserved.
+import asyncio
+import os
+
+from agent_framework.azure import AzureAIClient
+from azure.identity.aio import AzureCliCredential
+
+"""
+Azure AI Agent with Bing Custom Search Example
+
+This sample demonstrates usage of AzureAIClient with Bing Custom Search
+to search custom search instances and provide responses with relevant results.
+
+Prerequisites:
+1. Set AZURE_AI_PROJECT_ENDPOINT and AZURE_AI_MODEL_DEPLOYMENT_NAME environment variables.
+2. Ensure you have a Bing Custom Search connection configured in your Azure AI project
+   and set BING_CUSTOM_SEARCH_PROJECT_CONNECTION_ID and BING_CUSTOM_SEARCH_INSTANCE_NAME environment variables.
+"""
+
+
+async def main() -> None:
+    async with (
+        AzureCliCredential() as credential,
+        AzureAIClient(async_credential=credential).create_agent(
+            name="MyCustomSearchAgent",
+            instructions="""You are a helpful agent that can use Bing Custom Search tools to assist users.
+            Use the available Bing Custom Search tools to answer questions and perform tasks.""",
+            tools={
+                "type": "bing_custom_search",
+                "bing_custom_search": {
+                    "search_configurations": [
+                        {
+                            "project_connection_id": os.environ["BING_CUSTOM_SEARCH_PROJECT_CONNECTION_ID"],
+                            "instance_name": os.environ["BING_CUSTOM_SEARCH_INSTANCE_NAME"],
+                        }
+                    ]
+                },
+            },
+        ) as agent,
+    ):
+        query = "Tell me more about foundry agent service"
+        print(f"User: {query}")
+        result = await agent.run(query)
+        print(f"Result: {result}\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/getting_started/agents/azure_ai/azure_ai_with_bing_grounding.py

@@ -0,0 +1,54 @@
+# Copyright (c) Microsoft. All rights reserved.
+import asyncio
+import os
+
+from agent_framework.azure import AzureAIClient
+from azure.identity.aio import AzureCliCredential
+
+"""
+Azure AI Agent with Bing Grounding Example
+
+This sample demonstrates usage of AzureAIClient with Bing Grounding
+to search the web for current information and provide grounded responses.
+
+Prerequisites:
+1. Set AZURE_AI_PROJECT_ENDPOINT and AZURE_AI_MODEL_DEPLOYMENT_NAME environment variables.
+2. Ensure you have a Bing connection configured in your Azure AI project
+   and set BING_PROJECT_CONNECTION_ID environment variable.
+
+To get your Bing connection ID:
+- Go to Azure AI Foundry portal (https://ai.azure.com)
+- Navigate to your project's "Connected resources" section
+- Add a new connection for "Grounding with Bing Search"
+- Copy the connection ID and set it as the BING_PROJECT_CONNECTION_ID environment variable
+"""
+
+
+async def main() -> None:
+    async with (
+        AzureCliCredential() as credential,
+        AzureAIClient(async_credential=credential).create_agent(
+            name="MyBingGroundingAgent",
+            instructions="""You are a helpful assistant that can search the web for current information.
+            Use the Bing search tool to find up-to-date information and provide accurate, well-sourced answers.
+            Always cite your sources when possible.""",
+            tools={
+                "type": "bing_grounding",
+                "bing_grounding": {
+                    "search_configurations": [
+                        {
+                            "project_connection_id": os.environ["BING_PROJECT_CONNECTION_ID"],
+                        }
+                    ]
+                },
+            },
+        ) as agent,
+    ):
+        query = "What is today's date and weather in Seattle?"
+        print(f"User: {query}")
+        result = await agent.run(query)
+        print(f"Result: {result}\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())