chore: import upstream snapshot with attribution
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wehub-resource-sync
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# OpenAI Structured Outputs
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## Supported Models
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### Azure OpenAI:
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- Access to `gpt-4o-2024-08-06` or later
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- The `2024-08-01-preview` API version
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- If using a token instead of an API key, you must have the `Cognitive Services OpenAI Contributor` role assigned to your Azure AD user.
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- See more information [here](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/structured-outputs?tabs=python-secure)
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### OpenAI:
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- The OpenAI models supported are:
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- `gpt-4o-mini-2024-07-18` and later
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- `gpt-4o-2024-08-06` and later
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import json
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from pydantic import BaseModel, ConfigDict
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from samples.concepts.setup.chat_completion_services import Services, get_chat_completion_service_and_request_settings
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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.contents import ChatHistory, StreamingChatMessageContent
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"""
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The following sample demonstrates how to create a chat
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completion call that assists users in solving math problems.
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The bot guides the user step-by-step through the solution
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process using a structured output format based on either a
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Pydantic model or a non-Pydantic model.
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NOTE: If using Azure OpenAI the the following is required:
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- access to gpt-4o-2024-08-06
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- the 2024-08-01-preview API version
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- if using a token instead of an API KEY, you must have the
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`Cognitive Services OpenAI Contributor` role assigned to your
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Azure AD user.
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- flip the `use_azure_openai` flag to `True`
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"""
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system_message = """
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You are a helpful math tutor. Guide the user through the solution step by step.
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"""
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"""
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Define the Pydantic model that represents the
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structured output from the OpenAI service. This model will be
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used to parse the structured output from the OpenAI service,
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and ensure that the model correctly outputs the schema based
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on the Pydantic model.
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"""
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# Note: The `extra=forbid` means to forbid extra fields during model initialization
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# It is required to ensure that the model is strict and does not
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# accept any extra fields that are not defined in the model.
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class Step(BaseModel):
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model_config = ConfigDict(extra="forbid")
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explanation: str
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output: str
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class Reasoning(BaseModel):
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model_config = ConfigDict(extra="forbid")
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steps: list[Step]
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final_answer: str
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kernel = Kernel()
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# You can select from the following chat completion services:
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# Note: the model must allow for structured outputs.
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# - Services.OPENAI
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# - Services.AZURE_OPENAI
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# - Services.AZURE_AI_INFERENCE
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# - Services.ANTHROPIC
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# - Services.BEDROCK
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# - Services.GOOGLE_AI
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# - Services.MISTRAL_AI
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# - Services.OLLAMA
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# - Services.ONNX
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# - Services.VERTEX_AI
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# - Services.DEEPSEEK
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# Please make sure you have configured your environment correctly for the selected chat completion service.
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chat_completion_service, request_settings = get_chat_completion_service_and_request_settings(Services.AZURE_OPENAI)
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kernel.add_service(chat_completion_service)
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request_settings.max_tokens = 2000
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request_settings.temperature = 0.7
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request_settings.top_p = 0.8
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request_settings.function_choice_behavior = FunctionChoiceBehavior.Auto(filters={"excluded_plugins": ["chat"]})
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# NOTE: This is the key setting in this example that tells the OpenAI service
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# to return structured output based on the Pydantic model Reasoning.
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request_settings.response_format = Reasoning
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chat_function = kernel.add_function(
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prompt=system_message + """{{$chat_history}}""",
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function_name="chat",
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plugin_name="chat",
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prompt_execution_settings=request_settings,
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)
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history = ChatHistory()
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history.add_user_message("how can I solve 8x + 7y = -23, and 4x=12?")
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async def main():
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stream = False
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if stream:
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answer = kernel.invoke_stream(
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chat_function,
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chat_history=history,
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)
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print("Mosscap:> ", end="")
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result_content: list[StreamingChatMessageContent] = []
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async for message in answer:
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result_content.append(message[0])
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print(str(message[0]), end="", flush=True)
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if result_content:
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result = "".join([str(content) for content in result_content])
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else:
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result = await kernel.invoke(
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chat_function,
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chat_history=history,
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)
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reasoned_result = Reasoning.model_validate(json.loads(result.value[0].content))
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print(f"{reasoned_result.model_dump_json(indent=4)}")
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history.add_assistant_message(str(result))
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"""
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Sample Output:
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{
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"steps": [
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{
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"explanation": "User requested the current weather condition in Paris, so I utilized the 'weather-get_weather_for_city' function to retrieve the data.",
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"output": "The weather in Paris is 60 degrees Fahrenheit and rainy."
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}
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],
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"final_answer": "The current weather in Paris is 60 degrees Fahrenheit and rainy."
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}
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""" # noqa: E501
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if __name__ == "__main__":
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asyncio.run(main())
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+165
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import json
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from functools import reduce
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from typing import Annotated
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from samples.concepts.setup.chat_completion_services import Services, get_chat_completion_service_and_request_settings
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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.contents import ChatHistory, FunctionResultContent, StreamingChatMessageContent
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from semantic_kernel.functions import kernel_function
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"""
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The following sample demonstrates how to create a chat
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completion call that assists users in solving a question
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using a Semantic Kernel Plugin and function calling problems.
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The chat plugin guides the user step-by-step through the
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solution process using a structured output format based on
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a Pydantic model.
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NOTE: If using Azure OpenAI the the following is required:
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- access to gpt-4o-2024-08-06
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- the 2024-08-01-preview API version
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- if using a token instead of an API KEY, you must have the
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`Cognitive Services OpenAI Contributor` role assigned to your
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Azure AD user.
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- flip the `use_azure_openai` flag to `True`
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"""
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system_message = """
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You are a helpful assistant who provides answers to the user's questions in structured JSON format.
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"""
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# Define a sample plugin to use for function calling
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class WeatherPlugin:
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"""A sample plugin that provides weather information for cities."""
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@kernel_function(name="get_weather_for_city", description="Get the weather for a city")
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def get_weather_for_city(self, city: Annotated[str, "The input city"]) -> Annotated[str, "The output is a string"]:
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if city == "Boston":
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return "61 and rainy"
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if city == "London":
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return "55 and cloudy"
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if city == "Miami":
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return "80 and sunny"
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if city == "Paris":
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return "60 and rainy"
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if city == "Tokyo":
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return "50 and sunny"
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if city == "Sydney":
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return "75 and sunny"
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if city == "Tel Aviv":
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return "80 and sunny"
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return "31 and snowing"
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"""
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Define the Pydantic model that represents the
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structured output from the OpenAI service. This model will be
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used to parse the structured output from the OpenAI service,
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and ensure that the model correctly outputs the schema based
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on the Pydantic model.
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"""
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from pydantic import BaseModel, ConfigDict # noqa: E402
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# Note: The `extra=forbid` means to forbid extra fields during model initialization
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# It is required to ensure that the model is strict and does not
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# accept any extra fields that are not defined in the model.
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class Step(BaseModel):
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model_config = ConfigDict(extra="forbid")
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explanation: str
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output: str
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class Reasoning(BaseModel):
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model_config = ConfigDict(extra="forbid")
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steps: list[Step]
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final_answer: str
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kernel = Kernel()
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# You can select from the following chat completion services:
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# Note: the model must allow for structured outputs.
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# - Services.OPENAI
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# - Services.AZURE_OPENAI
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# - Services.AZURE_AI_INFERENCE
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# - Services.ANTHROPIC
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# - Services.BEDROCK
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# - Services.GOOGLE_AI
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# - Services.MISTRAL_AI
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# - Services.OLLAMA
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# - Services.ONNX
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# - Services.VERTEX_AI
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# - Services.DEEPSEEK
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# Please make sure you have configured your environment correctly for the selected chat completion service.
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chat_completion_service, request_settings = get_chat_completion_service_and_request_settings(Services.AZURE_OPENAI)
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kernel.add_service(chat_completion_service)
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kernel.add_plugin(WeatherPlugin(), plugin_name="weather")
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request_settings.response_format = Reasoning
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request_settings.function_choice_behavior = FunctionChoiceBehavior.Auto(filters={"excluded_plugins": ["chat"]})
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chat_function = kernel.add_function(
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prompt=system_message + """{{$chat_history}}""",
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function_name="chat",
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plugin_name="chat",
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prompt_execution_settings=request_settings,
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)
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history = ChatHistory()
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history.add_user_message("What is the weather in Paris?")
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async def main():
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stream = False
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if stream:
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answer = kernel.invoke_stream(
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chat_function,
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chat_history=history,
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)
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print("Mosscap:> ", end="")
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result_content: list[StreamingChatMessageContent] = []
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async for message in answer:
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if not any(isinstance(item, FunctionResultContent | FunctionResultContent) for item in message[0].items):
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print(str(message[0]), end="", flush=True)
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result_content.append(message[0])
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print()
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if result_content:
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full_response: StreamingChatMessageContent = reduce(lambda x, y: x + y, result_content)
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reasoned_result = Reasoning.model_validate(json.loads(full_response.content))
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print("Result formatted in JSON:")
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print(f"{reasoned_result.model_dump_json(indent=4)}")
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history.add_assistant_message(str(full_response))
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else:
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result = await kernel.invoke(
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chat_function,
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chat_history=history,
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)
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reasoned_result = Reasoning.model_validate(json.loads(result.value[0].content))
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print(f"{reasoned_result.model_dump_json(indent=4)}")
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history.add_assistant_message(str(result))
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"""
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Sample Output:
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{
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"steps": [
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{
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"explanation": "User requested the current weather condition in Paris, so I utilized the 'weather-get_weather_for_city' function to retrieve the data.",
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"output": "The weather in Paris is 60 degrees Fahrenheit and rainy."
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}
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],
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"final_answer": "The current weather in Paris is 60 degrees Fahrenheit and rainy."
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}
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""" # noqa: E501
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if __name__ == "__main__":
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asyncio.run(main())
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