Python: Improve agent getting started samples (#10667)

### Motivation and Context

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We are improving developer experience by updating the agent getting
started samples.

### Description

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
Simplify the agent getting started samples by removing unnecessary code
and add excessive comments to make sure we are only doing what we need
to get started.

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄

Author: TaoChenOSU

python/samples/getting_started_with_agents/README.md

@@ -20,12 +20,11 @@ The getting started with agents examples include:
 
 Example|Description
 ---|---
-[step1_agent](../getting_started_with_agents/chat_completion/step1_agent.py)|How to create and use an agent.
-[step2_plugins](../getting_started_with_agents/chat_completion/step2_plugins.py)|How to associate plugins with an agent.
-[step3_chat](../getting_started_with_agents/chat_completion/step3_chat.py)|How to create a conversation between agents.
-[step4_kernel_function_strategies](../getting_started_with_agents/chat_completion/step4_kernel_function_strategies.py)|How to utilize a `KernelFunction` as a chat strategy.
-[step5_json_result](../getting_started_with_agents/chat_completion/step5_json_result.py)|How to have an agent produce JSON.
-[step6_logging](../getting_started_with_agents/chat_completion/step6_logging.py)|How to enable logging for agents.
+[step1_chat_completion_agent](../getting_started_with_agents/chat_completion/step1_chat_completion_agent.py)|How to create and use an agent.
+[step2_chat_completion_agent_chat](../getting_started_with_agents/chat_completion/step2_chat_completion_agent_chat.py)|How to create a conversation between agents.
+[step3_kernel_function_strategies](../getting_started_with_agents/chat_completion/step3_kernel_function_strategies.py)|How to utilize a `KernelFunction` as a chat strategy.
+[step4_chat_completion_agent_json_result](../getting_started_with_agents/chat_completion/step4_chat_completion_agent_json_result.py)|How to have an agent produce JSON.
+[step5_chat_completion_agent_logging](../getting_started_with_agents/chat_completion/step5_chat_completion_agent_logging.py)|How to enable logging for agents.
 
 ## OpenAI Assistant Agent
 

python/samples/getting_started_with_agents/azure_ai_agent/step1_azure_ai_agent.py

@@ -6,15 +6,13 @@
 from azure.identity.aio import DefaultAzureCredential
 
 from semantic_kernel.agents.azure_ai import AzureAIAgent, AzureAIAgentSettings
-from semantic_kernel.contents.chat_message_content import ChatMessageContent
-from semantic_kernel.contents.utils.author_role import AuthorRole
-from semantic_kernel.functions.kernel_function_decorator import kernel_function
+from semantic_kernel.contents import AuthorRole
+from semantic_kernel.functions import kernel_function
 
-###################################################################
-# The following sample demonstrates how to create a simple,       #
-# Azure AI agent that answers questions about a sample menu       #
-# using a Semantic Kernel Plugin.                                 #
-###################################################################
+"""
+The following sample demonstrates how to create an Azure AI agent that answers
+questions about a sample menu using a Semantic Kernel Plugin.
+"""
 
 
 # Define a sample plugin for the sample
@@ -36,65 +34,68 @@ def get_item_price(
         return "$9.99"
 
 
+# Simulate a conversation with the agent
+USER_INPUTS = [
+    "Hello",
+    "What is the special soup?",
+    "How much does that cost?",
+    "Thank you",
+]
+
+
 async def main() -> None:
     ai_agent_settings = AzureAIAgentSettings.create()
 
     async with (
         DefaultAzureCredential() as creds,
-        AzureAIAgent.create_client(
-            credential=creds,
-            conn_str=ai_agent_settings.project_connection_string.get_secret_value(),
-        ) as client,
+        AzureAIAgent.create_client(credential=creds) as client,
     ):
-        AGENT_NAME = "Host"
-        AGENT_INSTRUCTIONS = "Answer questions about the menu."
-
-        # Create agent definition
+        # 1. Create an agent on the Azure AI agent service
         agent_definition = await client.agents.create_agent(
             model=ai_agent_settings.model_deployment_name,
-            name=AGENT_NAME,
-            instructions=AGENT_INSTRUCTIONS,
+            name="Host",
+            instructions="Answer questions about the menu.",
         )
 
-        # Create the AzureAI Agent
+        # 2. Create a Semantic Kernel agent for the Azure AI agent
         agent = AzureAIAgent(
             client=client,
             definition=agent_definition,
             # Optionally configure polling options
             # polling_options=RunPollingOptions(run_polling_interval=timedelta(seconds=1)),
         )
 
-        # Add the sample plugin to the kernel
+        # 3. Add a plugin to the agent via the kernel
         agent.kernel.add_plugin(MenuPlugin(), plugin_name="menu")
 
-        # Create a new thread
+        # 4. Create a new thread on the Azure AI agent service
         thread = await client.agents.create_thread()
 
-        user_inputs = [
-            "Hello",
-            "What is the special soup?",
-            "How much does that cost?",
-            "Thank you",
-        ]
-
         try:
-            for user_input in user_inputs:
-                # Add the user input as a chat message
-                await agent.add_chat_message(
-                    thread_id=thread.id, message=ChatMessageContent(role=AuthorRole.USER, content=user_input)
-                )
-                print(f"# User: '{user_input}'")
-                # Invoke the agent for the specified thread
+            for user_input in USER_INPUTS:
+                # 5. Add the user input as a chat message
+                await agent.add_chat_message(thread_id=thread.id, message=user_input)
+                print(f"# User: {user_input}")
+                # 6. Invoke the agent for the specified thread for response
                 async for content in agent.invoke(
                     thread_id=thread.id,
                     temperature=0.2,  # override the agent-level temperature setting with a run-time value
                 ):
                     if content.role != AuthorRole.TOOL:
                         print(f"# Agent: {content.content}")
         finally:
+            # 7. Cleanup: Delete the thread and agent
             await client.agents.delete_thread(thread.id)
             await client.agents.delete_agent(agent.id)
 
+        """
+        Sample Output:
+        # User: Hello
+        # Agent: Hello! How can I assist you today?
+        # User: What is the special soup?
+        # ...
+        """
+
 
 if __name__ == "__main__":
     asyncio.run(main())

python/samples/getting_started_with_agents/azure_ai_agent/step2_azure_ai_agent_chat.py

@@ -6,16 +6,14 @@
 
 from semantic_kernel.agents import AgentGroupChat
 from semantic_kernel.agents.azure_ai import AzureAIAgent, AzureAIAgentSettings
-from semantic_kernel.agents.strategies.termination.termination_strategy import TerminationStrategy
-from semantic_kernel.contents.chat_message_content import ChatMessageContent
-from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.agents.strategies import TerminationStrategy
+from semantic_kernel.contents import AuthorRole
 
-#####################################################################
-# The following sample demonstrates how to create an OpenAI         #
-# assistant using either Azure OpenAI or OpenAI, a chat completion  #
-# agent and have them participate in a group chat to work towards   #
-# the user's requirement.                                           #
-#####################################################################
+"""
+The following sample demonstrates how to create an OpenAI assistant using either
+Azure OpenAI or OpenAI, a chat completion agent and have them participate in a
+group chat to work towards the user's requirement.
+"""
 
 
 class ApprovalTerminationStrategy(TerminationStrategy):
@@ -44,66 +42,70 @@ async def should_agent_terminate(self, agent, history):
 Consider suggestions when refining an idea.
 """
 
+TASK = "a slogan for a new line of electric cars."
+
 
 async def main():
     ai_agent_settings = AzureAIAgentSettings.create()
 
     async with (
         DefaultAzureCredential() as creds,
-        AzureAIAgent.create_client(
-            credential=creds,
-            conn_str=ai_agent_settings.project_connection_string.get_secret_value(),
-        ) as client,
+        AzureAIAgent.create_client(credential=creds) as client,
     ):
-        # Create the reviewer agent definition
+        # 1. Create the reviewer agent on the Azure AI agent service
         reviewer_agent_definition = await client.agents.create_agent(
             model=ai_agent_settings.model_deployment_name,
             name=REVIEWER_NAME,
             instructions=REVIEWER_INSTRUCTIONS,
         )
-        # Create the reviewer Azure AI Agent
+
+        # 2. Create a Semantic Kernel agent for the reviewer Azure AI agent
         agent_reviewer = AzureAIAgent(
             client=client,
             definition=reviewer_agent_definition,
         )
 
-        # Create the copy writer agent definition
+        # 3. Create the copy writer agent on the Azure AI agent service
         copy_writer_agent_definition = await client.agents.create_agent(
             model=ai_agent_settings.model_deployment_name,
             name=COPYWRITER_NAME,
             instructions=COPYWRITER_INSTRUCTIONS,
         )
-        # Create the copy writer Azure AI Agent
+
+        # 4. Create a Semantic Kernel agent for the copy writer Azure AI agent
         agent_writer = AzureAIAgent(
             client=client,
             definition=copy_writer_agent_definition,
         )
 
+        # 5. Place the agents in a group chat with a custom termination strategy
         chat = AgentGroupChat(
             agents=[agent_writer, agent_reviewer],
             termination_strategy=ApprovalTerminationStrategy(agents=[agent_reviewer], maximum_iterations=10),
         )
 
-        input = "a slogan for a new line of electric cars."
-
         try:
-            await chat.add_chat_message(ChatMessageContent(role=AuthorRole.USER, content=input))
-            print(f"# {AuthorRole.USER}: '{input}'")
-
+            # 6. Add the task as a message to the group chat
+            await chat.add_chat_message(message=TASK)
+            print(f"# {AuthorRole.USER}: '{TASK}'")
+            # 7. Invoke the chat
             async for content in chat.invoke():
                 print(f"# {content.role} - {content.name or '*'}: '{content.content}'")
-
-            print(f"# IS COMPLETE: {chat.is_complete}")
-
-            print("*" * 60)
-            print("Chat History (In Descending Order):\n")
-            async for message in chat.get_chat_messages(agent=agent_reviewer):
-                print(f"# {message.role} - {message.name or '*'}: '{message.content}'")
         finally:
+            # 8. Cleanup: Delete the agents
             await chat.reset()
             await client.agents.delete_agent(agent_reviewer.id)
             await client.agents.delete_agent(agent_writer.id)
 
+        """
+        Sample Output:
+        # AuthorRole.USER: 'a slogan for a new line of electric cars.'
+        # AuthorRole.ASSISTANT - CopyWriter: '"Charge Ahead: Drive the Future."'
+        # AuthorRole.ASSISTANT - ArtDirector: 'This slogan has a nice ring to it and captures the ...'
+        # AuthorRole.ASSISTANT - CopyWriter: '"Plug In. Drive Green."'
+        ...
+        """
+
 
 if __name__ == "__main__":
     asyncio.run(main())

python/samples/getting_started_with_agents/azure_ai_agent/step3_azure_ai_agent_code_interpreter.py

@@ -6,63 +6,83 @@
 from azure.identity.aio import DefaultAzureCredential
 
 from semantic_kernel.agents.azure_ai import AzureAIAgent, AzureAIAgentSettings
-from semantic_kernel.contents.chat_message_content import ChatMessageContent
-from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.contents import AuthorRole
 
-###################################################################
-# The following sample demonstrates how to create a simple,       #
-# Azure AI agent that uses the code interpreter tool to answer    #
-# a coding question.                                              #
-###################################################################
+"""
+The following sample demonstrates how to create a simple, Azure AI agent that
+uses the code interpreter tool to answer a coding question.
+"""
+
+TASK = "Use code to determine the values in the Fibonacci sequence that that are less then the value of 101."
 
 
 async def main() -> None:
     ai_agent_settings = AzureAIAgentSettings.create()
 
     async with (
         DefaultAzureCredential() as creds,
-        AzureAIAgent.create_client(
-            credential=creds,
-            conn_str=ai_agent_settings.project_connection_string.get_secret_value(),
-        ) as client,
+        AzureAIAgent.create_client(credential=creds) as client,
     ):
+        # 1. Create an agent with a code interpreter on the Azure AI agent service
         code_interpreter = CodeInterpreterTool()
-
-        # Create agent definition
         agent_definition = await client.agents.create_agent(
             model=ai_agent_settings.model_deployment_name,
             tools=code_interpreter.definitions,
             tool_resources=code_interpreter.resources,
         )
 
-        # Create the AzureAI Agent
+        # 2. Create a Semantic Kernel agent for the Azure AI agent
         agent = AzureAIAgent(
             client=client,
             definition=agent_definition,
         )
 
-        # Create a new thread
+        # 3. Create a new thread on the Azure AI agent service
         thread = await client.agents.create_thread()
 
-        user_inputs = [
-            "Use code to determine the values in the Fibonacci sequence that that are less then the value of 101."
-        ]
-
         try:
-            for user_input in user_inputs:
-                # Add the user input as a chat message
-                await agent.add_chat_message(
-                    thread_id=thread.id, message=ChatMessageContent(role=AuthorRole.USER, content=user_input)
-                )
-                print(f"# User: '{user_input}'")
-                # Invoke the agent for the specified thread
-                async for content in agent.invoke(thread_id=thread.id):
-                    if content.role != AuthorRole.TOOL:
-                        print(f"# Agent: {content.content}")
+            # 4. Add the task as a chat message
+            await agent.add_chat_message(thread_id=thread.id, message=TASK)
+            print(f"# User: '{TASK}'")
+            # 5. Invoke the agent for the specified thread for response
+            async for content in agent.invoke(thread_id=thread.id):
+                if content.role != AuthorRole.TOOL:
+                    print(f"# Agent: {content.content}")
         finally:
+            # 6. Cleanup: Delete the thread and agent
             await client.agents.delete_thread(thread.id)
             await client.agents.delete_agent(agent.id)
 
+        """
+        Sample Output:
+        # User: 'Use code to determine the values in the Fibonacci sequence that that are less then the value of 101.'
+        # Agent: # Function to generate Fibonacci sequence values less than a given limit
+        def fibonacci_less_than(limit):
+            fib_sequence = []
+            a, b = 0, 1
+            while a < limit:
+                fib_sequence.append(a)
+                a, b = b, a + b
+            a, b = 0, 1
+            while a < limit:
+                fib_sequence.append(a)
+            a, b = 0, 1
+            while a < limit:
+            a, b = 0, 1
+            a, b = 0, 1
+            while a < limit:
+                fib_sequence.append(a)
+                a, b = b, a + b
+            return fib_sequence
+        
+        Generate Fibonacci sequence values less than 101
+        fibonacci_values = fibonacci_less_than(101)
+        fibonacci_values
+        # Agent: The values in the Fibonacci sequence that are less than 101 are:
+        
+        [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
+        """
+
 
 if __name__ == "__main__":
     asyncio.run(main())