from data_ingestion import data_ingestion
from vectorStore import vectorStore,embedding
from llm_configuration import llm, prompt_helper
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

def main(path,question):
    path = path
    data = data_ingestion(path)
    retriever = vectorStore(data,embedding)
    prompt = prompt_helper()
    chain = (
        {'context': retriever, 'question': RunnablePassthrough()}
        | prompt
        | llm
        | StrOutputParser()
    )
    return chain.invoke(question)

if __name__ == "__main__":
    main()