Python: updated declarative samples and handling of non-pydantic response formats (#5022)

* updated declarative samples and handling of non-pydantic response formats

* fixed from comments

* update docstring

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

@@ -18,7 +18,7 @@ Prerequisites:
 - `pip install agent-framework-foundry agent-framework-declarative --pre`
 - Set the following environment variables in a .env file or your environment:
     - FOUNDRY_PROJECT_ENDPOINT
-    - AZURE_OPENAI_MODEL
+    - FOUNDRY_MODEL
 """
 
 
@@ -31,7 +31,7 @@ instructions: Specialized diagnostic and issue detection agent for systems with
 description: A agent that performs diagnostics on systems and can escalate issues when critical errors are detected.
 
 model:
-  id: =Env.AZURE_OPENAI_MODEL
+  id: =Env.FOUNDRY_MODEL
 """
     # create the agent from the yaml
     async with (

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

@@ -9,6 +9,20 @@ 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."""

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

@@ -14,11 +14,8 @@ async def main():
     # get the path
     current_path = Path(__file__).parent
     yaml_path = current_path.parent.parent.parent.parent / "agent-samples" / "openai" / "OpenAIResponses.yaml"
-    # load the yaml from the path
-    with yaml_path.open("r") as f:
-        yaml_str = f.read()
     # create the agent from the yaml
-    agent = AgentFactory(safe_mode=False).create_agent_from_yaml(yaml_str)
+    agent = AgentFactory(safe_mode=False).create_agent_from_yaml_path(yaml_path)
     # use the agent
     response = await agent.run("Why is the sky blue, answer in Dutch?")
     # Use response.value with try/except for safe parsing