auditqa/doc_process.py

import glob
import os
#from langchain_text_splitters import MarkdownHeaderTextSplitter
#from langchain_community.document_loaders import UnstructuredMarkdownLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter, SentenceTransformersTokenTextSplitter
from transformers import AutoTokenizer
from langchain_community.document_loaders import PyMuPDFLoader
path_to_data = "./data/"

def process_markdown():
    headers_to_split_on = [
        ("#", "Header 1"),
        ("##", "Header 2"),
        ("###", "Header 3"),
        ("####", "Header 4"),
        ("#####", "Header 5")
    ]
    markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on)
    
    files = glob.glob(path_to_data+"*.md")
    print(files)
    docs = []
    for file in files:
      try:
          with open(file) as f:
            docs.append(f.read())
      except Exception as e:
        print("Exception: ", e)
    docs_processed = [markdown_splitter.split_text(doc) for doc in docs]
    print(len(docs_processed))
    print(docs_processed[0])

def process_pdf():
    files = glob.glob(path_to_data+"*.pdf")
    docs = []
    for file in files:
        try:
            docs.append(PyMuPDFLoader(file).load())
        except Exception as e:
            print("Exception: ", e)
    

    chunk_size = 256
    text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
            AutoTokenizer.from_pretrained("BAAI/bge-small-en-v1.5"),
            chunk_size=chunk_size,
            chunk_overlap=int(chunk_size / 10),
            add_start_index=True,
            strip_whitespace=True,
            separators=["\n\n", "\n", ".", " ", ""],
    )
    docs_processed = [text_splitter.split_documents(doc) for doc in docs]
    docs_processed = [item for sublist in docs_processed for item in sublist]
    print("length of text chunks:",len(docs_processed))
    return docs_processed