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### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Small update to test coverage workflow, to retrigger based on workflow run. Small updates to samples notebooks, to use GLOBAL_LLM_SERVICE allowing control of where they run through env. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [x] I didn't break anyone 😄
21 lines
813 B
Python
21 lines
813 B
Python
# Copyright (c) Microsoft. All rights reserved.
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from semantic_kernel.kernel_pydantic import KernelBaseSettings
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class ServiceSettings(KernelBaseSettings):
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"""The Learn Resources Service Settings.
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The settings are first loaded from environment variables. If the
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environment variables are not found, the settings can be loaded from a .env file with the
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encoding 'utf-8' as default or the specific encoding. If the settings are not found in the
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.env file, the settings are ignored; however, validation will fail alerting that the settings
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are missing.
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Args:
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global_llm_service (str | None): The LLM service to use for the samples, either "OpenAI" or "AzureOpenAI"
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If not provided, defaults to "AzureOpenAI".
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"""
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global_llm_service: str | None = None
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