import os
from src.processor.processor import Processor
from src.db.db import DB

from langchain.memory import ConversationBufferMemory
from langchain.chains.conversational_retrieval.base import ConversationalRetrievalChain
from src.llm.llm import OpenAILLM
from langchain_openai import OpenAIEmbeddings
from src.chatbot import Chatbot

def create_chain(llm, db_retriever):
    memory = ConversationBufferMemory(
        memory_key="chat_history", return_messages=True, output_key="answer"
    )

    return ConversationalRetrievalChain.from_llm(
        llm=llm, retriever=db_retriever, memory=memory, return_source_documents=True
    )


######################### CHATBOT #############################
MODEL_NAME = "gpt-3.5-turbo"
INDEX_NAME = "test"

cb = Chatbot()
llm = OpenAILLM(model_name=MODEL_NAME).oai_llm
embeddings = OpenAIEmbeddings()
doc_processor = Processor()
db = DB(index_name=INDEX_NAME, embeddings=embeddings)
db_retreiver = db.get_reriever()
conversation_chain = cb.create_conversational_chain(llm, db_retreiver)


def get_response(query:str):
    return conversation_chain({"question": query})

def add_file(filename:str):
    
    docs = doc_processor.process(filename)
    db.insert(docs, file_name_without_extension=os.path.splitext(os.path.basename(filename))[0])