PDF_Reader.py

import PyPDF2
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.vectorstores import FAISS
import streamlit as st

@st.cache_data
def read_pdf(uploaded_file):
    pdf_reader = PyPDF2.PdfReader(uploaded_file)
    text = ""
    for page in pdf_reader.pages:
        text += page.extract_text()
    return text

@st.cache_data
def Chunks(docs):
    text_splitter = RecursiveCharacterTextSplitter(
    # Set a really small chunk size, just to show.
    chunk_size = 1000,
    chunk_overlap  = 100,
)
    doc = text_splitter.split_text(docs)
    return doc

@st.cache_resource
def PDF_4_QA(file):
    content = read_pdf(file)
    pdf_chunks = Chunks(docs=content)

    embeddings = HuggingFaceBgeEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2',
                                          model_kwargs={'device': 'cpu'})
    vectorstore_openai = FAISS.from_texts(pdf_chunks, embeddings)

    return vectorstore_openai