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114 lines
3.7 KiB
Python
114 lines
3.7 KiB
Python
from abc import ABC, abstractmethod
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from typing import Optional
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from pydantic import BaseModel
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from enum import Enum
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from core.extension.extensible import Extensible, ExtensionModule
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class ModerationAction(Enum):
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DIRECT_OUTPUT = 'direct_output'
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OVERRIDED = 'overrided'
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class ModerationInputsResult(BaseModel):
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flagged: bool = False
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action: ModerationAction
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preset_response: str = ""
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inputs: dict = {}
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query: str = ""
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class ModerationOutputsResult(BaseModel):
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flagged: bool = False
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action: ModerationAction
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preset_response: str = ""
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text: str = ""
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class Moderation(Extensible, ABC):
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"""
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The base class of moderation.
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"""
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module: ExtensionModule = ExtensionModule.MODERATION
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def __init__(self, app_id: str, tenant_id: str, config: Optional[dict] = None) -> None:
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super().__init__(tenant_id, config)
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self.app_id = app_id
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@classmethod
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@abstractmethod
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def validate_config(cls, 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 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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raise NotImplementedError
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@abstractmethod
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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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raise NotImplementedError
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@abstractmethod
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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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raise NotImplementedError
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@classmethod
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def _validate_inputs_and_outputs_config(self, config: dict, is_preset_response_required: bool) -> None:
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# inputs_config
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inputs_config = config.get("inputs_config")
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if not isinstance(inputs_config, dict):
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raise ValueError("inputs_config must be a dict")
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# outputs_config
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outputs_config = config.get("outputs_config")
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if not isinstance(outputs_config, dict):
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raise ValueError("outputs_config must be a dict")
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inputs_config_enabled = inputs_config.get("enabled")
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outputs_config_enabled = outputs_config.get("enabled")
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if not inputs_config_enabled and not outputs_config_enabled:
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raise ValueError("At least one of inputs_config or outputs_config must be enabled")
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# preset_response
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if not is_preset_response_required:
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return
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if inputs_config_enabled:
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if not inputs_config.get("preset_response"):
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raise ValueError("inputs_config.preset_response is required")
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if len(inputs_config.get("preset_response")) > 100:
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raise ValueError("inputs_config.preset_response must be less than 100 characters")
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if outputs_config_enabled:
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if not outputs_config.get("preset_response"):
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raise ValueError("outputs_config.preset_response is required")
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if len(outputs_config.get("preset_response")) > 100:
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raise ValueError("outputs_config.preset_response must be less than 100 characters")
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class ModerationException(Exception):
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pass
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