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from abc import ABC, abstractmethod
from typing import Any, Dict, Type
from langchain.output_parsers import PydanticOutputParser
from langchain.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, SecretStr
from langchain_core.runnables import Runnable
from langchain_openai import ChatOpenAI
class ChainGenerator(ABC):
def __init__(
self,
openai_api_key: SecretStr,
chat_temperature: float = 0.3,
chat_model: str = "gpt-3.5-turbo",
) -> None:
"""
Initializes the class with the given parameters.
Args:
openai_api_key (SecretStr): The API key for OpenAI.
chat_temperature (float, optional): The temperature to use for chat. Defaults to 0.3.
chat_model (str, optional): The model to use for chat. Defaults to "gpt-3.5-turbo".
Returns:
None
"""
self.openai_api_key = openai_api_key
self.chat_temperature = chat_temperature
self._initialize_chat_llm(chat_model)
@abstractmethod
def _get_system_prompt(self) -> ChatPromptTemplate:
"""Returns the system prompt for the exam.
Returns:
ChatPromptTemplate: System prompt.
"""
@abstractmethod
def _create_chain(self) -> Runnable[Any, Any]:
"""Creates the chain.
Returns:
RunnableSequence: Chain.
"""
def _initialize_chat_llm(self, chat_model: str) -> None:
"""Initializes the ChatOpenAI language model."""
self.chat_llm = ChatOpenAI(
api_key=self.openai_api_key,
temperature=self.chat_temperature,
model=chat_model,
)
class EvaluationChatModel(ChainGenerator):
"""Abstract base class for evaluation chat models."""
def __init__(self, level: str, openai_api_key: SecretStr, chat_temperature: float) -> None:
"""Initialize the evaluation chat model.
Args:
level (str): Level of the exam.
openai_api_key (SecretStr): OpenAI API key.
chat_temperature (float): Temperature for the chat model.
Returns:
None
"""
super().__init__(openai_api_key=openai_api_key, chat_temperature=chat_temperature)
self.level = level
@abstractmethod
def _get_output_parser(self, pydantic_schema: Type[BaseModel]) -> PydanticOutputParser[Any]:
"""Get the output parser for the model.
Args:
pydantic_schema (BaseModel): The output schema of the model.
Returns:
PydanticOutputParser: The output parser for the model.
"""
@abstractmethod
def predict(self, *args: Any, **kwargs: Any) -> Dict[str, str]:
"""
Defines how the chain should be called in the predict method.
Returns:
Dict: The return value of the predict method.
"""
class ContentGenerator(ChainGenerator):
"""Abstract base class for generating content"""
@abstractmethod
def generate(self) -> str:
"""
Defines how the chain should be called in the generate method.
Returns:
str: The return value of the generate method.
"""
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