'''
    This module contains all the loaders
'''

import os
from langchain.document_loaders import PyPDFLoader, Docx2txtLoader, TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from constants import TEMPERATURE, MODEL_NAME

openai_api_key=os.environ['OPENAI_API_KEY']

def load_pdf(path: str = "resume.pdf"):
    '''
    Load a pdf file from a stringio object
    '''
    pdf_loader = PyPDFLoader(path)
    documents = pdf_loader.load()
    return documents

def load_multiple_documents(path: str = "documents"):
    '''
        Load multiple documents from a folder
    '''
    documents = []
    for file in os.listdir(path):
        if file.endswith('.pdf'):
            pdf_path = './documents/' + file
            loader = PyPDFLoader(pdf_path)
            documents.extend(loader.load())
        elif file.endswith('.docx') or file.endswith('.doc'):
            doc_path = './documents/' + file
            loader = Docx2txtLoader(doc_path)
            documents.extend(loader.load())
        elif file.endswith('.txt'):
            text_path = './documents/' + file
            loader = TextLoader(text_path)
            documents.extend(loader.load())
    return documents

    
def get_embeddings(documents):
    '''
    Get embeddings from a list of documents
    '''
    splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=10)
    texts = splitter.split_documents(documents)
    embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
    return texts, embeddings

def get_db(texts, embeddings):
    '''
    Get a vectorstore from a list of texts and embeddings
    '''
    db = Chroma.from_documents(texts, embeddings)
    return db

def get_retriever(db):
    '''
    Get a retriever from a vectorstore
    '''
    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k":1})
    return retriever

def get_chain_for_pdf(path):
    '''
    Get a conversation chain from a path
    '''
    documents = load_multiple_documents(path)
    texts, embeddings = get_embeddings(documents)
    db = get_db(texts, embeddings)
    retriever = get_retriever(db)
    chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(temperature=TEMPERATURE, openai_api_key=openai_api_key, model=MODEL_NAME),
        chain_type="stuff",
        retriever=retriever,
        return_source_documents=True)
    return chain