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