from langchain.llms import BaseLLM
from langchain.base_language import BaseLanguageModel
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate


class QuestionCreationChain(LLMChain):
    """Chain to generates subsequent questions."""
    # Check what the below code line means and what it in practice does
    @classmethod
    def from_llm(cls, llm: BaseLLM, verbose: bool = True) -> LLMChain:
        questions_creation_template = (
            "You are a part of a team. The ultimate goal of your team is to"
            " answer the following Question: '{question}'.\n"
            "Your team has discovered some new text (delimited by ```) that may be relevant to your ultimate goal."
            " text: \n ``` {context} ``` \n"
            "Your task is to ask new questions that may help your team achieve the ultimate goal."
            " If you think that the text is relevant to your ultimate goal, then ask new questions."
            " New questions should be based only on the text and the goal Question and no other previous knowledge."
            " The new questions should have no semantic overlap with questions in the following list:\n"
            " {previous_questions}\n"
            "You can ask up to {num_questions} new questions."
            " Return the questions as a comma separated list. "
            " Format your response as a numbered list of questions, like:\n"
            "n. First question\n"
            "n. Second question\n"
            "Start the list with number {start_id}"
        )

        prompt = PromptTemplate(
            template=questions_creation_template,
            input_variables=[
                "question",
                "context",
                "previous_questions",
                "num_questions",
                "start_id",
            ],
        )
        return cls(prompt=prompt, llm=llm, verbose=verbose)