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microsoft--semantic-kernel/python/samples/sk_service_configurator.py
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Python

# Copyright (c) Microsoft. All rights reserved.
from azure.identity import AzureCliCredential
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(
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 "global_llm_service" not in settings.model_fields_set:
print("GLOBAL_LLM_SERVICE not set, trying to use Azure OpenAI.")
# 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:
credential = AzureCliCredential()
if use_chat:
# <TypicalKernelCreation>
kernel.add_service(AzureChatCompletion(service_id=service_id, credential=credential))
# </TypicalKernelCreation>
else:
# <TextCompletionKernelCreation>
kernel.add_service(AzureTextCompletion(service_id=service_id, credential=credential))
# </TextCompletionKernelCreation>
return kernel