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 enum import Enum
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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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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class Services(str, Enum):
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"""Enum for supported text completion services.
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For service specific settings, refer to this documentation:
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https://github.com/microsoft/semantic-kernel/blob/main/python/samples/concepts/setup/ALL_SETTINGS.md
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"""
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OPENAI = "openai"
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BEDROCK = "bedrock"
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GOOGLE_AI = "google_ai"
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HUGGING_FACE = "huggingface"
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OLLAMA = "ollama"
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ONNX = "onnx"
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VERTEX_AI = "vertex_ai"
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def get_text_completion_service_and_request_settings(
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service_name: Services,
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) -> tuple["TextCompletionClientBase", "PromptExecutionSettings"]:
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"""Return service and request settings.
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Args:
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service_name (Services): The service name.
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"""
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# Use lambdas or functions to delay instantiation
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text_services = {
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Services.OPENAI: lambda: get_openai_text_completion_service_and_request_settings(),
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Services.BEDROCK: lambda: get_bedrock_text_completion_service_and_request_settings(),
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Services.GOOGLE_AI: lambda: get_google_ai_text_completion_service_and_request_settings(),
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Services.HUGGING_FACE: lambda: get_hugging_face_text_completion_service_and_request_settings(),
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Services.OLLAMA: lambda: get_ollama_text_completion_service_and_request_settings(),
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Services.ONNX: lambda: get_onnx_text_completion_service_and_request_settings(),
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Services.VERTEX_AI: lambda: get_vertex_ai_text_completion_service_and_request_settings(),
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}
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# Call the appropriate lambda or function based on the service name
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if service_name not in text_services:
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raise ValueError(f"Unsupported service name: {service_name}")
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return text_services[service_name]()
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def get_openai_text_completion_service_and_request_settings() -> tuple[
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"TextCompletionClientBase", "PromptExecutionSettings"
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]:
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"""Return OpenAI text completion service and request settings.
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The service credentials can be read by 3 ways:
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1. Via the constructor
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2. Via the environment variables
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3. Via an environment file
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The request settings control the behavior of the service. The default settings are sufficient to get started.
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However, you can adjust the settings to suit your needs.
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Note: Some of the settings are NOT meant to be set by the user.
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Please refer to the Semantic Kernel Python documentation for more information:
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https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel-python
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"""
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from semantic_kernel.connectors.ai.open_ai import OpenAITextCompletion, OpenAITextPromptExecutionSettings
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text_service = OpenAITextCompletion()
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request_settings = OpenAITextPromptExecutionSettings(max_tokens=20, temperature=0.7, top_p=0.8)
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return text_service, request_settings
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def get_bedrock_text_completion_service_and_request_settings() -> tuple[
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"TextCompletionClientBase", "PromptExecutionSettings"
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]:
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"""Return Bedrock text completion service and request settings.
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The service credentials can be read by 3 ways:
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1. Via the constructor
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2. Via the environment variables
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3. Via an environment file
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The request settings control the behavior of the service. The default settings are sufficient to get started.
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However, you can adjust the settings to suit your needs.
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Note: Some of the settings are NOT meant to be set by the user.
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Please refer to the Semantic Kernel Python documentation for more information:
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https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel
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"""
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from semantic_kernel.connectors.ai.bedrock import BedrockTextCompletion, BedrockTextPromptExecutionSettings
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text_service = BedrockTextCompletion(model_id="amazon.titan-text-premier-v1:0")
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request_settings = BedrockTextPromptExecutionSettings(
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# For model specific settings, specify them in the extension_data dictionary.
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# For example, for Cohere Command specific settings, refer to:
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# https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-text.html
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extension_data={
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"temperature": 0.8,
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"maxTokenCount": 20,
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},
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)
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return text_service, request_settings
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def get_google_ai_text_completion_service_and_request_settings() -> tuple[
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"TextCompletionClientBase", "PromptExecutionSettings"
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]:
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"""Return Google AI text completion service and request settings.
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The service credentials can be read by 3 ways:
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1. Via the constructor
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2. Via the environment variables
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3. Via an environment file
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The request settings control the behavior of the service. The default settings are sufficient to get started.
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However, you can adjust the settings to suit your needs.
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Note: Some of the settings are NOT meant to be set by the user.
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Please refer to the Semantic Kernel Python documentation for more information:
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https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel
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"""
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from semantic_kernel.connectors.ai.google.google_ai import (
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GoogleAITextCompletion,
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GoogleAITextPromptExecutionSettings,
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)
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text_service = GoogleAITextCompletion()
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request_settings = GoogleAITextPromptExecutionSettings()
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return text_service, request_settings
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def get_hugging_face_text_completion_service_and_request_settings() -> tuple[
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"TextCompletionClientBase", "PromptExecutionSettings"
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]:
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"""Return HuggingFace text completion service and request settings.
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The service credentials can be read by 3 ways:
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1. Via the constructor
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2. Via the environment variables
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3. Via an environment file
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The request settings control the behavior of the service. The default settings are sufficient to get started.
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However, you can adjust the settings to suit your needs.
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Note: Some of the settings are NOT meant to be set by the user.
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Please refer to the Semantic Kernel Python documentation for more information:
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https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel
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"""
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from semantic_kernel.connectors.ai.hugging_face import HuggingFacePromptExecutionSettings, HuggingFaceTextCompletion
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# Note this model is a demonstration model that outputs random text.
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text_service = HuggingFaceTextCompletion(ai_model_id="HuggingFaceM4/tiny-random-LlamaForCausalLM")
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request_settings = HuggingFacePromptExecutionSettings()
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return text_service, request_settings
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def get_ollama_text_completion_service_and_request_settings() -> tuple[
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"TextCompletionClientBase", "PromptExecutionSettings"
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]:
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"""Return Ollama text completion service and request settings.
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The service credentials can be read by 3 ways:
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1. Via the constructor
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2. Via the environment variables
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3. Via an environment file
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The request settings control the behavior of the service. The default settings are sufficient to get started.
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However, you can adjust the settings to suit your needs.
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Note: Some of the settings are NOT meant to be set by the user.
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Please refer to the Semantic Kernel Python documentation for more information:
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https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel
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"""
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from semantic_kernel.connectors.ai.ollama import OllamaTextCompletion, OllamaTextPromptExecutionSettings
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text_service = OllamaTextCompletion()
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request_settings = OllamaTextPromptExecutionSettings(
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# For model specific settings, specify them in the options dictionary.
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# For more information on the available options, refer to the Ollama API documentation:
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# https://github.com/ollama/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values
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options={
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"temperature": 0.8,
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},
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)
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return text_service, request_settings
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def get_onnx_text_completion_service_and_request_settings() -> tuple[
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"TextCompletionClientBase", "PromptExecutionSettings"
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]:
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"""Return Onnx text completion service and request settings.
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The service credentials can be read by 3 ways:
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1. Via the constructor
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2. Via the environment variables
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3. Via an environment file
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The request settings control the behavior of the service. The default settings are sufficient to get started.
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However, you can adjust the settings to suit your needs.
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Note: Some of the settings are NOT meant to be set by the user.
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Please refer to the Semantic Kernel Python documentation for more information:
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https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel
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"""
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from semantic_kernel.connectors.ai.onnx import (
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OnnxGenAIPromptExecutionSettings,
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OnnxGenAITextCompletion,
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ONNXTemplate,
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)
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text_service = OnnxGenAITextCompletion(
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ONNXTemplate.PHI3,
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)
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request_settings = OnnxGenAIPromptExecutionSettings()
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return text_service, request_settings
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def get_vertex_ai_text_completion_service_and_request_settings() -> tuple[
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"TextCompletionClientBase", "PromptExecutionSettings"
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]:
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"""Return Vertex AI text completion service and request settings.
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The service credentials can be read by 3 ways:
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1. Via the constructor
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2. Via the environment variables
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3. Via an environment file
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The request settings control the behavior of the service. The default settings are sufficient to get started.
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However, you can adjust the settings to suit your needs.
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Note: Some of the settings are NOT meant to be set by the user.
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Please refer to the Semantic Kernel Python documentation for more information:
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https://learn.microsoft.com/en-us/python/api/semantic-kernel/semantic_kernel?view=semantic-kernel
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"""
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from semantic_kernel.connectors.ai.google import GoogleAITextCompletion, GoogleAITextPromptExecutionSettings
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text_service = GoogleAITextCompletion()
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request_settings = GoogleAITextPromptExecutionSettings()
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return text_service, request_settings
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