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Python: updated reasoning samples (#10563)
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### Motivation and Context

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Small update to the reasoning samples, that leverages the latest updates
to those api's.

Fixed typing issue in filters.

### Description

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

### Contribution Checklist

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- [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)
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raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄
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@eavanvalkenburg
eavanvalkenburg authored Feb 19, 2025
1 parent 374d8f9 commit b8835d2
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41 changes: 18 additions & 23 deletions python/samples/concepts/reasoning/simple_reasoning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,11 +2,8 @@

import asyncio

from samples.concepts.setup.chat_completion_services import (
Services,
get_chat_completion_service_and_request_settings,
)
from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.open_ai_prompt_execution_settings import (
from semantic_kernel.connectors.ai.open_ai import (
OpenAIChatCompletion,
OpenAIChatPromptExecutionSettings,
)
from semantic_kernel.contents import ChatHistory
Expand Down Expand Up @@ -59,17 +56,25 @@
Note: Unsupported features may be added in future updates.
"""

chat_completion_service, request_settings = get_chat_completion_service_and_request_settings(Services.OPENAI)
chat_service = OpenAIChatCompletion(service_id="reasoning", instruction_role="developer")
# Set the reasoning effort to "medium" and the maximum completion tokens to 5000.
request_settings = OpenAIChatPromptExecutionSettings(
service_id="reasoning", max_completion_tokens=2000, reasoning_effort="medium"
)


# Create a ChatHistory object
chat_history = ChatHistory()

# This is the system message that gives the chatbot its personality.
developer_message = """
As an assistant supporting the user,
you recognize all user input
as questions or consultations and answer them.
you recognize all user input
as questions or consultations and answer them.
"""

# Create a ChatHistory object
chat_history = ChatHistory()
# The developer message was newly introduced for reasoning models such as OpenAI’s o1 and o1-mini.
# `system message` cannot be used with reasoning models.
chat_history.add_developer_message(developer_message)


async def chat() -> bool:
Expand All @@ -86,25 +91,15 @@ async def chat() -> bool:
print("\n\nExiting chat...")
return False

# The developer message was newly introduced for reasoning models such as OpenAI’s o1 and o1-mini.
# `system message` cannot be used with reasoning models.
chat_history.add_developer_message(developer_message)
chat_history.add_user_message(user_input)

if not isinstance(request_settings, OpenAIChatPromptExecutionSettings):
raise ValueError("The OpenAI prompt execution settings are not supported for this sample.")

# Set the reasoning effort to "medium" and the maximum completion tokens to 5000.
request_settings.max_completion_tokens = 5000
request_settings.reasoning_effort = "medium"

# Get the chat message content from the chat completion service.
response = await chat_completion_service.get_chat_message_content(
response = await chat_service.get_chat_message_content(
chat_history=chat_history,
settings=request_settings,
)
if response:
print(f"Mosscap:> {response}")
print(f"Reasoning model:> {response}")

# Add the chat message to the chat history to keep track of the conversation.
chat_history.add_message(response)
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116 changes: 33 additions & 83 deletions python/samples/concepts/reasoning/simple_reasoning_function_calling.py
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Original file line number Diff line number Diff line change
@@ -1,22 +1,14 @@
# Copyright (c) Microsoft. All rights reserved.

import asyncio
from collections.abc import Awaitable, Callable

from samples.concepts.setup.chat_completion_services import (
Services,
get_chat_completion_service_and_request_settings,
)
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.function_calling_utils import (
kernel_function_metadata_to_function_call_format,
)
from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.open_ai_prompt_execution_settings import (
OpenAIChatPromptExecutionSettings,
)
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.contents.function_call_content import FunctionCallContent
from semantic_kernel.contents.function_result_content import FunctionResultContent
from semantic_kernel.core_plugins.time_plugin import TimePlugin
from semantic_kernel.filters import AutoFunctionInvocationContext, FilterTypes

"""
# Reasoning Models Sample
Expand Down Expand Up @@ -70,25 +62,44 @@
Note: Unsupported features may be added in future updates.
"""

chat_completion_service, request_settings = get_chat_completion_service_and_request_settings(
Services.OPENAI, instruction_role="developer"

chat_service = OpenAIChatCompletion(service_id="reasoning", instruction_role="developer")
# 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.
request_settings = OpenAIChatPromptExecutionSettings(
service_id="reasoning",
max_completion_tokens=5000,
reasoning_effort="medium",
function_choice_behavior=FunctionChoiceBehavior.Auto(),
)

# This is the system message that gives the chatbot its personality.
developer_message = """
As an assistant supporting the user,
you recognize all user input
as questions or consultations and answer them.
"""

# Create a ChatHistory object
chat_history = ChatHistory()
# 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.
"""
)

# Create a kernel and register plugin.
kernel = Kernel()
kernel.add_plugin(TimePlugin(), "time")


# add a simple filter to track the function call result
@kernel.filter(filter_type=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:> ")
Expand All @@ -103,78 +114,17 @@ async def chat() -> bool:
print("\n\nExiting chat...")
return False

# The developer message was newly introduced for reasoning models such as OpenAI’s o1 and o1-mini.
# `system message` cannot be used with reasoning models.
chat_history.add_developer_message(developer_message)
chat_history.add_user_message(user_input)

if not isinstance(request_settings, OpenAIChatPromptExecutionSettings):
raise ValueError(f"{type(request_settings).__name__} settings are not supported for this sample.")

# Set the reasoning effort to "medium" and the maximum completion tokens to 5000.
request_settings.max_completion_tokens = 5000
request_settings.reasoning_effort = "medium"

# enable the function calling and disable parallel tool calls for reasoning models.
request_settings.parallel_tool_calls = None
request_settings.tool_choice = None
request_settings.tools = [
kernel_function_metadata_to_function_call_format(f) for f in kernel.get_full_list_of_function_metadata()
]

# Get the chat message content from the chat completion service.
response = await chat_completion_service.get_chat_message_content(
response = await chat_service.get_chat_message_content(
chat_history=chat_history,
settings=request_settings,
kernel=kernel,
)

if not response:
return True

function_calls = [item for item in response.items if isinstance(item, FunctionCallContent)]
if len(function_calls) == 0:
print(f"Mosscap:> {response}")
chat_history.add_message(response)
return True

# Invoke the function calls and update the chat history with the results.
print(f"processing {len(function_calls)} tool calls")
await asyncio.gather(
*[
kernel.invoke_function_call(
function_call=function_call,
chat_history=chat_history,
function_call_count=len(function_calls),
request_index=0,
)
for function_call in function_calls
],
)

# Convert the last tool message to a user message.
fc_results = [item for item in chat_history.messages[-1].items if isinstance(item, FunctionResultContent)]

result_prompt: list[str] = ["FUNCTION CALL RESULT"]
for fc_result in fc_results:
result_prompt.append(f"- {fc_result.plugin_name}: {fc_result.result}")

chat_history.remove_message(chat_history.messages[-1])
chat_history.add_user_message("\n".join(result_prompt))
print("Tools:> ", "\n".join(result_prompt))

# Get the chat message content from the chat completion service.
request_settings.tools = None
response = await chat_completion_service.get_chat_message_content(
chat_history=chat_history,
settings=request_settings,
)

# Add the chat message to the chat history to keep track of the conversation.
if response:
print(f"Mosscap:> {response}")
chat_history.add_message(response)

return True


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