from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import UnstructuredFileLoader
from langchain.vectorstores.faiss import FAISS
from langchain.embeddings import OpenAIEmbeddings
import pickle
import os
import nltk
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
def ingest_data(vector_file_path):
   # Load Data
    loader = UnstructuredFileLoader("cleaned_site_contents2023-02-24.txt")
    raw_documents = loader.load()
    
    # Split text
    text_splitter = RecursiveCharacterTextSplitter()
    documents = text_splitter.split_documents(raw_documents)
    
    
    # Load Data to vectorstore
    embeddings = OpenAIEmbeddings()
    vectorstore = FAISS.from_documents(documents, embeddings)
    
    
    # Save vectorstore
    with open(vector_file_path, "wb") as f:
        pickle.dump(vectorstore, f)
    return vector_file_path

def get_vectorstore(vector_file_path):
    if os.path.isfile(vector_file_path):
        return vector_file_path
    else:
        return ingest_data(vector_file_path)