chore: import upstream snapshot with attribution

python/samples/02-agents/declarative/microsoft_learn_agent.py

@@ -0,0 +1,50 @@
+# Copyright (c) Microsoft. All rights reserved.
+import asyncio
+from pathlib import Path
+
+from agent_framework.declarative import AgentFactory
+from azure.identity.aio import AzureCliCredential
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+"""
+This sample demonstrates creating an agent from a declarative YAML file specification.
+
+It uses a MCP server to connect to the Microsoft Learn content and a FoundryChatClient.
+
+The yaml also has some chat options set, such as temperature and topP.
+These options do not work with newer OpenAI models, so ensure to use a compatible model such as gpt-4o-mini.
+
+Environment variables:
+- FOUNDRY_PROJECT_ENDPOINT: The endpoint URL for the Foundry project.
+- FOUNDRY_MODEL: The model ID to use for the agent, make sure it is compatible with the chat options specified in
+    the yaml, or remove the options.
+"""
+
+
+async def main():
+    """Create an agent from a declarative yaml specification and run it."""
+    # get the path
+    current_path = Path(__file__).parent
+    yaml_path = (
+        current_path.parent.parent.parent.parent
+        / "declarative-agents"
+        / "agent-samples"
+        / "foundry"
+        / "MicrosoftLearnAgent.yaml"
+    )
+    # create the agent from the yaml
+    async with (
+        AzureCliCredential() as credential,
+        AgentFactory(client_kwargs={"credential": credential}, safe_mode=False).create_agent_from_yaml_path(
+            yaml_path
+        ) as agent,
+    ):
+        response = await agent.run("How do I create a storage account with private endpoint using bicep?")
+        print("Agent response:", response.text)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())