Python: Agent reasoning sample

python/samples/concepts/README.md

@@ -174,6 +174,7 @@
 
 - [Simple Chatbot](./reasoning/simple_reasoning.py)
 - [Simple Function Calling](./reasoning/simple_reasoning_function_calling.py)
+- [Chat Completion Agent with Reasoning Model](./reasoning/agent_reasoning.py)
 
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python/samples/concepts/reasoning/agent_reasoning.py

@@ -0,0 +1,141 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+from collections.abc import Awaitable, Callable
+
+from semantic_kernel.agents.chat_completion.chat_completion_agent import ChatCompletionAgent
+from semantic_kernel.connectors.ai import FunctionChoiceBehavior
+from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAIChatPromptExecutionSettings
+from semantic_kernel.contents import ChatHistory
+from semantic_kernel.core_plugins.time_plugin import TimePlugin
+from semantic_kernel.filters import AutoFunctionInvocationContext, FilterTypes
+from semantic_kernel.functions.kernel_arguments import KernelArguments
+
+"""
+# Reasoning Agents Sample
+
+This sample demonstrates an example of how to use reasoning models such as OpenAI’s o1 and o1-mini for Agents.
+Reasoning models currently have certain limitations, which are outlined below.
+
+1. Requires API version `2024-09-01-preview` or later.
+  - `reasoning_effort` and `developer_message` are only supported in API version `2024-12-01-preview` or later.
+  - o1-mini is not supported property `developer_message` `reasoning_effort` now.
+2. Developer message must be used instead of system message
+3. Parallel tool invocation is currently not supported
+4. Token limit settings need to consider both reasoning and completion tokens
+
+# Unsupported Properties ⛔
+
+The following parameters are currently not supported:
+- temperature
+- top_p
+- presence_penalty
+- frequency_penalty
+- logprobs
+- top_logprobs
+- logit_bias
+- max_tokens
+- stream
+- tool_choice
+
+# Unsupported Roles ⛔
+- system
+- tool
+
+# .env examples
+
+OpenAI: semantic_kernel/connectors/ai/open_ai/settings/open_ai_settings.py
+
+```.env
+OPENAI_API_KEY=*******************
+OPENAI_CHAT_MODEL_ID=o1-2024-12-17
+```
+
+Azure OpenAI: semantic_kernel/connectors/ai/open_ai/settings/azure_open_ai_settings.py
+
+```.env
+AZURE_OPENAI_API_KEY=*******************
+AZURE_OPENAI_ENDPOINT=https://*********.openai.azure.com
+AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=o1-2024-12-17
+AZURE_OPENAI_API_VERSION="2024-12-01-preview"
+```
+
+Note: Unsupported features may be added in future updates.
+"""
+
+agent = ChatCompletionAgent(
+    service=OpenAIChatCompletion(service_id="reasoning", instruction_role="developer"),
+    arguments=KernelArguments(
+        settings=OpenAIChatPromptExecutionSettings(
+            service_id="reasoning",
+            max_completion_tokens=5000,
+            reasoning_effort="medium",
+            function_choice_behavior=FunctionChoiceBehavior.Auto(),
+        )
+    ),
+    plugins=[TimePlugin()],
+)
+# Set the reasoning effort to "medium" and the maximum completion tokens to 5000.
+# also set the function_choice_behavior to auto and that includes auto invoking the functions.
+
+
+# Create a ChatHistory object
+# The reasoning models use developer instead of system, but because we set the instruction_role to developer,
+# we can use the system message as the developer message.
+chat_history = ChatHistory(
+    system_message="""
+As an assistant supporting the user,
+you recognize all user input
+as questions or consultations and answer them.
+"""
+)
+
+
+@agent.kernel.filter(FilterTypes.AUTO_FUNCTION_INVOCATION)
+async def auto_function_invocation_filter(
+    context: AutoFunctionInvocationContext, next: Callable[[AutoFunctionInvocationContext], Awaitable[None]]
+) -> None:
+    await next(context)
+    print("Tools:>  FUNCTION CALL RESULT")
+    print(f"       - time: {context.function_result}")
+
+
+async def chat() -> bool:
+    try:
+        user_input = input("User:> ")
+    except KeyboardInterrupt:
+        print("\n\nExiting chat...")
+        return False
+    except EOFError:
+        print("\n\nExiting chat...")
+        return False
+
+    if user_input == "exit":
+        print("\n\nExiting chat...")
+        return False
+
+    chat_history.add_user_message(user_input)
+
+    # Get the chat message content from the chat completion service.
+    response = await agent.get_response(history=chat_history)
+    if response:
+        print(f"Mosscap:> {response}")
+        chat_history.add_message(response)
+    return True
+
+
+async def main() -> None:
+    # Start the chat loop. The chat loop will continue until the user types "exit".
+    chatting = True
+    while chatting:
+        chatting = await chat()
+
+    # Sample output:
+    # User:> What time is it?
+    # Tools:>  FUNCTION CALL RESULT
+    #          - time: Thursday, January 09, 2025 05:41 AM
+    # Mosscap:> The current time is 05:41 AM.
+
+
+if __name__ == "__main__":
+    asyncio.run(main())