wehub-resource-sync
128 lines
5.2 KiB
Python
128 lines
5.2 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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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.contents import ChatHistory
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from semantic_kernel.functions import KernelArguments
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from semantic_kernel.prompt_template import PromptTemplateConfig
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# This sample shows how to create a chatbot using a kernel function.
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# This sample uses the following two main components:
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# - a ChatCompletionService: This component is responsible for generating responses to user messages.
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# - a ChatHistory: This component is responsible for keeping track of the chat history.
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# - a KernelFunction: This function will be a prompt function, meaning the function is composed of
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# a prompt and will be invoked by Semantic Kernel.
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# The chatbot in this sample is called Mosscap, who responds to user messages with long flowery prose.
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# [NOTE]
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# The purpose of this sample is to demonstrate how to use a kernel function.
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# To build a basic chatbot, it is sufficient to use a ChatCompletionService with a chat history directly.
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# You can select from the following chat completion services:
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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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# This is the system message that gives the chatbot its personality.
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system_message = """
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You are a chat bot. Your name is Mosscap and
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you have one goal: figure out what people need.
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Your full name, should you need to know it, is
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Splendid Speckled Mosscap. You communicate
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effectively, but you tend to answer with long
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flowery prose.
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"""
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# Create a chat history object with the system message.
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chat_history = ChatHistory(system_message=system_message)
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# Create a kernel and register a prompt function.
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# The prompt here contains two variables: chat_history and user_input.
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# They will be replaced by the kernel with the actual values when the function is invoked.
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# [NOTE]
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# The chat_history, which is a ChatHistory object, will be serialized to a string internally
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# to create/render the final prompt.
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# Since this sample uses a chat completion service, the prompt will be deserialized back to
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# a ChatHistory object that gets passed to the chat completion service. This new chat history
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# object will contain the original messages and the user input.
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kernel = Kernel()
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chat_function = kernel.add_function(
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plugin_name="ChatBot",
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function_name="Chat",
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prompt_template_config=PromptTemplateConfig(
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template="{{$chat_history}}{{$user_input}}", allow_dangerously_set_content=True
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),
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# You can attach the request settings to the function or
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# pass the settings to the kernel.invoke method via the kernel arguments.
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# If you specify the settings in both places, the settings in the kernel arguments will
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# take precedence given the same service id.
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# prompt_execution_settings=request_settings,
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)
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# Invoking a kernel function requires a service, so we add the chat completion service to the kernel.
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kernel.add_service(chat_completion_service)
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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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# Get the chat message content from the chat completion service.
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kernel_arguments = KernelArguments(
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settings=request_settings,
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# Use keyword arguments to pass the chat history and user input to the kernel function.
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chat_history=chat_history,
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user_input=user_input,
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)
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answer = await kernel.invoke(plugin_name="ChatBot", function_name="Chat", arguments=kernel_arguments)
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# Alternatively, you can invoke the function directly with the kernel as an argument:
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# answer = await chat_function.invoke(kernel, kernel_arguments)
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if answer:
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print(f"Mosscap:> {answer}")
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# Since the user_input is rendered by the template, it is not yet part of the chat history, so we add it here.
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chat_history.add_user_message(user_input)
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# Add the chat message to the chat history to keep track of the conversation.
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chat_history.add_message(answer.value[0])
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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:> Why is the sky blue in one sentence?
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# Mosscap:> The sky is blue due to the scattering of sunlight by the molecules in the Earth's atmosphere,
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# a phenomenon known as Rayleigh scattering, which causes shorter blue wavelengths to become more
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# prominent in our visual perception.
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if __name__ == "__main__":
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asyncio.run(main())
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