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import pdfplumber
import re
from pathlib import Path
from typing import Dict, Any, Optional, List, Tuple
from dataclasses import dataclass
from loguru import logger
@dataclass
class DocumentChunk:
"""chunk of text from document"""
chunk_id: str
text: str
page_num: int
start_char: int
end_char: int
metadata: Dict[str, Any]
@dataclass
class ParsedDocument:
"""parsed document data"""
file_name: str
total_pages: int
text_content: str
pages: List[Dict[str, Any]]
tables: List[Dict[str, Any]]
chunks: List[DocumentChunk]
metadata: Dict[str, Any]
class DocumentParser:
# PDF parser with chunking for RAG
def __init__(self, chunk_size=1000, chunk_overlap=200):
self.chunk_size = chunk_size
self.chunk_overlap = chunk_overlap
logger.info(f"Parser initialized - chunk_size={chunk_size}, overlap={chunk_overlap}")
def parse_pdf(self, pdf_path):
"""
parse PDF and extract content
"""
logger.info(f"Parsing: {Path(pdf_path).name}")
try:
with pdfplumber.open(pdf_path) as pdf:
all_text = []
pages_data = []
tables_data = []
# go through each page
for page_num, page in enumerate(pdf.pages, start=1):
try:
page_result = self._parse_page(page, page_num)
all_text.append(page_result["text"])
pages_data.append(page_result["page_data"])
tables_data.extend(page_result["tables"])
logger.debug(f"Page {page_num}: {len(page_result['text'])} chars, {len(page_result['tables'])} tables")
except Exception as e:
logger.error(f"Error on page {page_num}: {str(e)}")
continue # skip problematic pages
full_text = "\n\n".join(all_text)
# create chunks for embeddings
chunks = self._create_chunks(full_text, Path(pdf_path).name)
metadata = {
"file_path": pdf_path,
"file_name": Path(pdf_path).name,
"total_pages": len(pdf.pages),
"total_tables": len(tables_data),
"total_chunks": len(chunks),
"text_length": len(full_text)
}
parsed_doc = ParsedDocument(
file_name=Path(pdf_path).name,
total_pages=len(pdf.pages),
text_content=full_text,
pages=pages_data,
tables=tables_data,
chunks=chunks,
metadata=metadata
)
logger.success(f"Parsed {len(pdf.pages)} pages, {len(tables_data)} tables, {len(chunks)} chunks")
return parsed_doc
except FileNotFoundError:
logger.error(f"File not found: {pdf_path}")
return None
except Exception as e:
logger.error(f"Failed to parse {pdf_path}: {str(e)}")
return None
def _parse_page(self, page, page_num):
"""parse single page"""
try:
# grab text
page_text = page.extract_text()
if page_text is None:
page_text = ""
# extract tables
tables = []
raw_tables = page.extract_tables()
for table_idx, table in enumerate(raw_tables):
if table and len(table) > 0:
try:
table_data = {
"page": page_num,
"table_id": f"p{page_num}_t{table_idx + 1}",
"headers": table[0] if table else [],
"rows": table[1:] if len(table) > 1 else [],
"raw_data": table
}
tables.append(table_data)
except Exception as e:
logger.warning(f"Table {table_idx} error on page {page_num}: {str(e)}")
page_data = {
"page_num": page_num,
"text": page_text,
"text_length": len(page_text),
"tables_count": len(tables),
"width": page.width,
"height": page.height
}
return {
"text": page_text,
"tables": tables,
"page_data": page_data
}
except Exception as e:
logger.error(f"_parse_page error for page {page_num}: {str(e)}")
return {
"text": "",
"tables": [],
"page_data": {
"page_num": page_num,
"text": "",
"text_length": 0,
"tables_count": 0
}
}
def _create_chunks(self, text, file_name):
"""
break text into chunks with overlap
TODO: maybe improve the chunking logic later
"""
try:
chunks = []
if not text:
logger.warning("Empty text for chunking")
return chunks
# split by paragraphs
paragraphs = text.split('\n\n')
current_chunk = ""
current_start = 0
chunk_id = 0
for para in paragraphs:
para = para.strip()
if not para:
continue
# check if adding para exceeds size
if len(current_chunk) + len(para) > self.chunk_size and current_chunk:
# save chunk
chunk = DocumentChunk(
chunk_id=f"chunk_{chunk_id}",
text=current_chunk.strip(),
page_num=0, # not tracking page num for now
start_char=current_start,
end_char=current_start + len(current_chunk),
metadata={
"source_file": file_name,
"chunk_length": len(current_chunk)
}
)
chunks.append(chunk)
chunk_id += 1
# start new chunk with overlap
if len(current_chunk) > self.chunk_overlap:
overlap_text = current_chunk[-self.chunk_overlap:]
else:
overlap_text = current_chunk
current_start = current_start + len(current_chunk) - len(overlap_text)
current_chunk = overlap_text + "\n\n" + para
else:
# add to current chunk
if current_chunk:
current_chunk += "\n\n" + para
else:
current_chunk = para
# add final chunk
if current_chunk:
chunk = DocumentChunk(
chunk_id=f"chunk_{chunk_id}",
text=current_chunk.strip(),
page_num=0,
start_char=current_start,
end_char=current_start + len(current_chunk),
metadata={
"source_file": file_name,
"chunk_length": len(current_chunk)
}
)
chunks.append(chunk)
logger.info(f"Created {len(chunks)} chunks")
return chunks
except Exception as e:
logger.error(f"Chunking error: {str(e)}")
return []
def extract_bureau_score(self, parsed_doc):
"""
grab CIBIL score from CRIF report
looks for pattern like "PERFORM CONSUMER 2.2 300-900 627"
"""
try:
text = parsed_doc.text_content
# main pattern - score after range
pattern = r'PERFORM\s+CONSUMER.*?300-900\s+(\d{3})'
match = re.search(pattern, text, re.IGNORECASE)
if match:
score = int(match.group(1))
if 300 <= score <= 900:
logger.info(f"Found bureau score: {score}")
return {
"value": score,
"source": "CRIF Report – Score Section"
}
# fallback - check first couple pages
for page in parsed_doc.pages[:2]:
page_text = page["text"]
numbers = re.findall(r'\b(\d{3})\b', page_text)
for num_str in numbers:
num = int(num_str)
if 300 <= num <= 900:
# check if its actually a score
idx = page_text.find(num_str)
context = page_text[max(0, idx-100):idx+100]
keywords = ['score', 'cibil', 'credit', 'bureau']
if any(kw in context.lower() for kw in keywords):
logger.info(f"Found score (fallback): {num}")
return {
"value": num,
"source": f"CRIF Report – Page {page['page_num']}"
}
logger.warning("Bureau score not found")
return None
except Exception as e:
logger.error(f"Error extracting bureau score: {str(e)}")
return None
def extract_gst_sales(self, parsed_doc):
"""extract sales from GSTR-3B table"""
try:
text = parsed_doc.text_content
filename = parsed_doc.file_name
# get month from document
month_match = re.search(r'Period\s+(\w+)', text)
month_name = month_match.group(1) if month_match else "Unknown"
# extract year from filename (GSTR3B_..._012025.pdf format)
filename_year_match = re.search(r'_(\d{2})(\d{4})\.pdf', filename)
if filename_year_match:
year = filename_year_match.group(2)
else:
# fallback
year_match = re.search(r'Year\s+(\d{4})', text)
year = year_match.group(1) if year_match else "2025"
formatted_month = f"{month_name} {year}"
# search tables for sales
for table in parsed_doc.tables:
rows = table.get("rows", [])
for row in rows:
if row and len(row) > 1:
first_cell = str(row[0]).replace('\n', ' ')
# find row (a) with outward supplies
if "(a)" in first_cell and "Outward taxable supplies" in first_cell:
if len(row) > 1 and row[1]:
value_str = str(row[1])
clean_value = re.sub(r'[^\d.]', '', value_str)
if clean_value:
try:
sales = float(clean_value)
logger.info(f"GST sales: {sales} for {formatted_month}")
return {
"month": formatted_month,
"sales": sales,
"source": "GSTR-3B Table 3.1(a)"
}
except ValueError as e:
logger.warning(f"Couldn't parse sales value '{clean_value}': {str(e)}")
logger.warning(f"Sales data not found for {formatted_month}")
return None
except Exception as e:
logger.error(f"Error extracting GST sales: {str(e)}")
return None
def get_chunks_text(self, chunks):
"""get text from chunks for embedding"""
try:
return [chunk.text for chunk in chunks]
except Exception as e:
logger.error(f"Error getting chunks text: {str(e)}")
return []
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