from helpers.utils import create_or_load_vectore_store
from helpers.import_envs import openai_api_key
from langchain_openai import ChatOpenAI
from langchain.schema import StrOutputParser
from langchain_core.runnables.passthrough import RunnablePassthrough
from langchain.prompts import ChatPromptTemplate
from helpers.model_utils import set_question_answer_llm

def answer_question(question, transcript_file_name, llm_choice=None):
    question_answer_llm = set_question_answer_llm(llm_choice)

    # Specify the path to the file you want to check
    vector_store = create_or_load_vectore_store(transcript_file_name=transcript_file_name)

    # create a prompt template to send to our LLM that will incorporate the documents from our retriever with the
    # question we ask the chat model
    prompt_template = ChatPromptTemplate.from_template(
        "Answer the {question} based on the following {context}."
    )

    # create a retriever for our documents
    retriever = vector_store.as_retriever()

    # create a parser to parse the output of our LLM
    parser = StrOutputParser()

    # 💻 Create the sequence (recipe)
    runnable_chain = (
        # TODO: How do we chain the output of our retriever, prompt, model and model output parser so that we can get a good answer to our query?
        {"context": retriever, "question": RunnablePassthrough()}
        | prompt_template
        | question_answer_llm
        | StrOutputParser()
    )

    answer = runnable_chain.invoke(question)
    print(answer)
    return answer