chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) Microsoft. All rights reserved.
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from opentelemetry import trace
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from samples.demos.telemetry.demo_plugins import LocationPlugin, WeatherPlugin
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from samples.demos.telemetry.repo_utils import get_sample_plugin_path
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from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
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from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
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from semantic_kernel.connectors.ai.open_ai.services.open_ai_chat_completion import OpenAIChatCompletion
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from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
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from semantic_kernel.connectors.ai.text_completion_client_base import TextCompletionClientBase
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from semantic_kernel.contents.chat_history import ChatHistory
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from semantic_kernel.functions.kernel_arguments import KernelArguments
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from semantic_kernel.kernel import Kernel
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from semantic_kernel.services.ai_service_client_base import AIServiceClientBase
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def set_up_kernel() -> Kernel:
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# Create a kernel and add services and plugins
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kernel = Kernel()
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# All built-in AI services are instrumented with telemetry.
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# Select any AI service to see the telemetry in action.
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kernel.add_service(OpenAIChatCompletion(service_id="open_ai"))
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# kernel.add_service(
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# AzureAIInferenceChatCompletion(
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# ai_model_id="serverless-deployment",
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# service_id="azure-ai-inference",
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# )
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# )
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# kernel.add_service(GoogleAIChatCompletion(service_id="google_ai"))
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if (sample_plugin_path := get_sample_plugin_path()) is None:
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raise FileNotFoundError("Sample plugin path not found.")
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kernel.add_plugin(
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plugin_name="WriterPlugin",
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parent_directory=sample_plugin_path,
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)
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kernel.add_plugin(WeatherPlugin(), "WeatherPlugin")
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kernel.add_plugin(LocationPlugin(), "LocationPlugin")
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return kernel
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#############################################################
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# Below are scenarios that are instrumented with telemetry. #
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#############################################################
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async def run_ai_service(stream: bool = False) -> None:
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"""Run an AI service.
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This function runs an AI service and prints the output.
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Telemetry will be collected for the service execution behind the scenes,
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and the traces will be sent to the configured telemetry backend.
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The telemetry will include information about the AI service execution.
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Args:
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stream (bool): Whether to use streaming for the plugin
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"""
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kernel = set_up_kernel()
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ai_service: AIServiceClientBase = kernel.get_service()
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tracer = trace.get_tracer(__name__)
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with tracer.start_as_current_span("Scenario: AI Service") as current_span:
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print("Running scenario: AI Service")
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try:
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if isinstance(ai_service, ChatCompletionClientBase):
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chat_history = ChatHistory()
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chat_history.add_user_message("Why is the sky blue in one sentence?")
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if not stream:
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responses = await ai_service.get_chat_message_contents(chat_history, PromptExecutionSettings())
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print(responses[0].content)
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else:
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async for update in ai_service.get_streaming_chat_message_contents(
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chat_history, PromptExecutionSettings()
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):
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print(update[0].content, end="")
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print()
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elif isinstance(ai_service, TextCompletionClientBase):
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if not stream:
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completion = await ai_service.get_text_contents(
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"Why is the sky blue in one sentence?", PromptExecutionSettings()
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)
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print(completion)
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else:
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async for update in ai_service.get_streaming_text_contents(
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"Why is the sky blue?", PromptExecutionSettings()
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):
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print(update[0].content, end="")
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print()
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else:
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raise ValueError("AI service not recognized.")
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except Exception as e:
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current_span.record_exception(e)
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print(f"Error running AI service: {e}")
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async def run_kernel_function(stream: bool = False) -> None:
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"""Run a kernel function.
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This function runs a kernel function and prints the output.
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Telemetry will be collected for the function execution behind the scenes,
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and the traces will be sent to the configured telemetry backend.
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The telemetry will include information about the kernel function execution
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and the AI service execution.
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Args:
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stream (bool): Whether to use streaming for the plugin invocation.
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"""
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kernel = set_up_kernel()
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tracer = trace.get_tracer(__name__)
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with tracer.start_as_current_span("Scenario: Kernel Plugin") as current_span:
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print("Running scenario: Kernel Plugin")
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try:
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plugin = kernel.get_plugin("WriterPlugin")
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if not stream:
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poem = await kernel.invoke(
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function=plugin["ShortPoem"],
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arguments=KernelArguments(
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input="Write a poem about John Doe.",
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),
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)
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print(f"Poem:\n{poem}")
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else:
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print("Poem:")
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async for update in kernel.invoke_stream(
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function=plugin["ShortPoem"],
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arguments=KernelArguments(
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input="Write a poem about John Doe.",
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),
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):
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print(update[0].content, end="")
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print()
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except Exception as e:
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current_span.record_exception(e)
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print(f"Error running kernel plugin: {e}")
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async def run_auto_function_invocation(stream: bool = False) -> None:
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"""Run a task with auto function invocation.
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This function runs a task with auto function invocation and prints the output.
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Telemetry will be collected for the task execution behind the scenes,
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and the traces will be sent to the configured telemetry backend.
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The telemetry will include information about the auto function invocation loop,
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the AI service execution, and the kernel function execution.
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Args:
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stream (bool): Whether to use streaming for the prompt.
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"""
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kernel = set_up_kernel()
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tracer = trace.get_tracer(__name__)
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with tracer.start_as_current_span("Scenario: Auto Function Invocation") as current_span:
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print("Running scenario: Auto Function Invocation")
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try:
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if not stream:
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result = await kernel.invoke_prompt(
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"What is the weather like in my location?",
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arguments=KernelArguments(
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settings=PromptExecutionSettings(
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function_choice_behavior=FunctionChoiceBehavior.Auto(
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filters={"excluded_plugins": ["WriterPlugin"]}
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),
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),
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),
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)
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print(result)
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else:
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async for update in kernel.invoke_prompt_stream(
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"What is the weather like in my location?",
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arguments=KernelArguments(
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settings=PromptExecutionSettings(
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function_choice_behavior=FunctionChoiceBehavior.Auto(
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filters={"excluded_plugins": ["WriterPlugin"]}
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),
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),
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),
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):
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print(update[0].content, end="")
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print()
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except Exception as e:
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current_span.record_exception(e)
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print(f"Error running auto function invocation: {e}")
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