financial_qa_rag / utils /data_preprocessing.py
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Update utils/data_preprocessing.py
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import os
import pdfplumber
import re
from pathlib import Path
from utils.chunking import smart_chunk_text
RAW_DIR = "data/raw"
PROCESSED_DIR = "data/processed"
CHUNKS_DIR = "data/chunks"
Path(CHUNKS_DIR).mkdir(parents=True, exist_ok=True)
Path(PROCESSED_DIR).mkdir(parents=True, exist_ok=True)
def extract_text_from_pdf(pdf_path):
with pdfplumber.open(pdf_path) as pdf:
text = ""
for page in pdf.pages:
page_text = page.extract_text()
if page_text: # skip empty pages
text += page_text + "\n"
return text
def clean_text(text: str) -> str:
# Remove common headers/footers
text = re.sub(r'Allstate.*?\n', '', text, flags=re.IGNORECASE)
text = re.sub(r'Page \d+ of \d+', '', text)
# Fix broken numbers: "57 , 094" β†’ "57,094"
text = re.sub(r'(\d)\s*,\s*(\d)', r'\1,\2', text)
# Fix broken words like "T o t a l" β†’ "Total" (only when letters are isolated)
text = re.sub(r'(?<=\b\w) (?=\w\b)', '', text)
# Normalize spaces/newlines
text = re.sub(r'\s+', ' ', text)
text = re.sub(r'\n+', '\n', text)
# Remove stray lines: pure digits, year-only, or too short
lines = []
for line in text.splitlines():
line = line.strip()
if len(line) <= 5:
continue
if re.fullmatch(r"\d{4}", line): # year like 2023
continue
if re.fullmatch(r"[\d,\. ]+", line): # only numbers
continue
lines.append(line)
return "\n".join(lines).strip()
# Process all PDFs
for fname in os.listdir(RAW_DIR):
if fname.endswith(".pdf"):
raw_text = extract_text_from_pdf(os.path.join(RAW_DIR, fname))
clean = clean_text(raw_text)
# Save cleaned text
with open(os.path.join(PROCESSED_DIR, fname.replace(".pdf", ".txt")), "w", encoding="utf-8") as f:
f.write(clean)
# Chunk and save
chunks = smart_chunk_text([clean], chunk_size=300, overlap=50)
with open(os.path.join(CHUNKS_DIR, fname.replace(".pdf", "_chunks.txt")), "w", encoding="utf-8") as f:
for chunk in chunks:
f.write(chunk + "\n---\n")