Fastapi refactor update
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@@ -1,38 +1,38 @@
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from abc import ABC, abstractmethod
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from typing import List, Optional, TypeVar, Callable
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from openai.types.chat import ChatCompletionMessageParam
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from pydantic import BaseModel
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T = TypeVar('T', bound=BaseModel)
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class ILLMService(ABC):
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@abstractmethod
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async def prediction(
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self,
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model: str,
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messages: List,
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fields_to_check: Optional[List[str]],
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temperature: float,
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check_blacklisted: bool = True,
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token_count: int = -1
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):
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pass
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@abstractmethod
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async def prediction_override(self, **kwargs):
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pass
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@abstractmethod
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async def pydantic_prediction(
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self,
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messages: List[ChatCompletionMessageParam],
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map_to_model: Callable,
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json_scheme: str,
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*,
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model: Optional[str] = None,
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temperature: Optional[float] = None,
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max_retries: int = 3
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) -> List[T] | T | None:
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pass
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from abc import ABC, abstractmethod
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from typing import List, Optional, TypeVar, Callable
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from openai.types.chat import ChatCompletionMessageParam
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from pydantic import BaseModel
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T = TypeVar('T', bound=BaseModel)
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class ILLMService(ABC):
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@abstractmethod
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async def prediction(
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self,
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model: str,
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messages: List,
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fields_to_check: Optional[List[str]],
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temperature: float,
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check_blacklisted: bool = True,
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token_count: int = -1
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):
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pass
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@abstractmethod
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async def prediction_override(self, **kwargs):
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pass
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@abstractmethod
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async def pydantic_prediction(
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self,
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messages: List[ChatCompletionMessageParam],
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map_to_model: Callable,
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json_scheme: str,
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*,
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model: Optional[str] = None,
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temperature: Optional[float] = None,
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max_retries: int = 3
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) -> List[T] | T | None:
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pass
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