PDF_Reader.py

import os 
from langchain_experimental.text_splitter import SemanticChunker
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_chroma import Chroma
from langchain_community.document_loaders import PyPDFLoader
from langchain.embeddings import HuggingFaceEmbeddings
from PyPDF2 import PdfReader

embedding_modelPath = "sentence-transformers/all-MiniLM-l6-v2"
embeddings = HuggingFaceEmbeddings(model_name=embedding_modelPath,model_kwargs = {'device':'cpu'},encode_kwargs = {'normalize_embeddings': False})

def replace_t_with_space(list_of_documents):
    """
    Replaces all tab characters ('\t') with spaces in the page content of each document.

    Args:
        list_of_documents: A list of document objects, each with a 'page_content' attribute.

    Returns:
        The modified list of documents with tab characters replaced by spaces.
    """

    for doc in list_of_documents:
        doc.page_content = doc.page_content.replace('\t', ' ')  # Replace tabs with spaces
    return list_of_documents

def read_pdf_text(pdf_path):
    text = ""
    pdf_reader = PdfReader(pdf_path)
        for page in pdf_reader.pages:
            text += page.extract_text()
    
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
    text_chunks = text_splitter.split_text(text)
    return text_chunks

def read_pdf(pdf_path):
    loader = PyPDFLoader(pdf_path)
    docs = loader.load()
    print("Total Documents :",len(docs))
    return docs

def Chunks(docs):
    text_splitter = SemanticChunker(embeddings,breakpoint_threshold_type='interquartile')
    docs = text_splitter.split_documents(docs)
    cleaned_docs = replace_t_with_space(docs)
    return cleaned_docs
    
def PDF_4_QA(file_path):
    #docs = read_pdf(file_path)
    #cleaned_docs = Chunks(docs)
    read_pdf_text(file_path)
    vectordb = Chroma.from_documents(
        documents=cleaned_docs,
        embedding=embeddings,
        persist_directory="Chroma/docs"
    )
    return vectordb,docs