# Copyright (c) Microsoft. All rights reserved. from pydantic import ValidationError from samples.service_settings import ServiceSettings from semantic_kernel import Kernel from semantic_kernel.connectors.ai.open_ai import ( AzureChatCompletion, AzureTextCompletion, OpenAIChatCompletion, OpenAITextCompletion, ) from semantic_kernel.exceptions.service_exceptions import ServiceInitializationError def add_service( kernel: Kernel, use_chat: bool = True, env_file_path: str | None = None, env_file_encoding: str | None = None ) -> Kernel: """ Configure the AI service for the kernel Args: kernel (Kernel): The kernel to configure use_chat (bool): Whether to use the chat completion model, or the text completion model env_file_path (str | None): The absolute or relative file path to the .env file. env_file_encoding (str | None): The desired type of encoding. Defaults to utf-8. Returns: Kernel: The configured kernel """ try: settings = ServiceSettings.create( env_file_path=env_file_path, env_file_encoding=env_file_encoding, ) except ValidationError as ex: raise ServiceInitializationError("Unable to configure learn resources settings.", ex) from ex if not settings.global_llm_service: print("GLOBAL_LLM_SERVICE not set, trying to use Azure OpenAI.") settings.global_llm_service = "AzureOpenAI" # The service_id is used to identify the service in the kernel. # This can be updated to a custom value if needed. # It should match the execution setting's key in a config.json file. service_id = "default" # Configure AI service used by the kernel. Load settings from the .env file. if settings.global_llm_service == "OpenAI": if use_chat: # <OpenAIKernelCreation> kernel.add_service(OpenAIChatCompletion(service_id=service_id)) # </OpenAIKernelCreation> else: # <OpenAITextCompletionKernelCreation> kernel.add_service(OpenAITextCompletion(service_id=service_id)) # </OpenAITextCompletionKernelCreation> else: if use_chat: # <TypicalKernelCreation> kernel.add_service(AzureChatCompletion(service_id=service_id)) # </TypicalKernelCreation> else: # <TextCompletionKernelCreation> kernel.add_service(AzureTextCompletion(service_id=service_id)) # </TextCompletionKernelCreation> return kernel