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49 lines
2.0 KiB
Python
49 lines
2.0 KiB
Python
from core.extension.extensible import ExtensionModule
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from core.moderation.base import Moderation, ModerationInputsResult, ModerationOutputsResult
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from extensions.ext_code_based_extension import code_based_extension
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class ModerationFactory:
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__extension_instance: Moderation
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def __init__(self, name: str, app_id: str, tenant_id: str, config: dict) -> None:
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extension_class = code_based_extension.extension_class(ExtensionModule.MODERATION, name)
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self.__extension_instance = extension_class(app_id, tenant_id, config)
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@classmethod
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def validate_config(cls, name: str, tenant_id: str, config: dict) -> None:
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"""
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Validate the incoming form config data.
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:param name: the name of extension
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:param tenant_id: the id of workspace
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:param config: the form config data
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:return:
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"""
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code_based_extension.validate_form_schema(ExtensionModule.MODERATION, name, config)
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extension_class = code_based_extension.extension_class(ExtensionModule.MODERATION, name)
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extension_class.validate_config(tenant_id, config)
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def moderation_for_inputs(self, inputs: dict, query: str = "") -> ModerationInputsResult:
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"""
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Moderation for inputs.
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After the user inputs, this method will be called to perform sensitive content review
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on the user inputs and return the processed results.
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:param inputs: user inputs
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:param query: query string (required in chat app)
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:return:
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"""
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return self.__extension_instance.moderation_for_inputs(inputs, query)
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def moderation_for_outputs(self, text: str) -> ModerationOutputsResult:
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"""
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Moderation for outputs.
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When LLM outputs content, the front end will pass the output content (may be segmented)
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to this method for sensitive content review, and the output content will be shielded if the review fails.
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:param text: LLM output content
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:return:
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"""
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return self.__extension_instance.moderation_for_outputs(text)
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