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import re
from pathlib import Path
class DataExtractor:
def __init__(self):
"""
Initialize DataExtractor with robust logging and error handling
"""
pass
def extract_text_from_pdf(self, pdf_path):
"""
Robust PDF text extraction with enhanced error handling
Args:
pdf_path (str): Path to PDF file
Returns:
str: Extracted text or empty string
"""
try:
import pymupdf
doc = pymupdf.open(pdf_path)
# Extract text with page-level error handling
full_text = []
for page_num, page in enumerate(doc, 1):
try:
page_text = page.get_text()
full_text.append(page_text)
except Exception as page_error:
print(f"Text extraction failed for page {page_num} in {pdf_path}: {page_error}")
return ''.join(full_text)
except Exception as e:
print(f"PDF Extraction failed for {pdf_path}: {e}")
return ""
def extract_text_from_doc(self, doc_path):
"""
Robust DOCX text extraction with comprehensive error handling
Args:
doc_path (str): Path to DOCX file
Returns:
str: Extracted text or empty string
"""
try:
import docx2txt
return docx2txt.process(doc_path)
except Exception as e:
print(f"DOCX Extraction failed for {doc_path}: {e}")
return ""
def extract_text_from_txt(self, txt_path):
"""
Robust TXT file text extraction
Args:
txt_path (str): Path to TXT file
Returns:
str: Extracted text or empty string
"""
try:
with open(txt_path, "r", encoding="utf-8") as f:
return f.read()
except UnicodeDecodeError:
# Try alternative encodings
for encoding in ['latin-1', 'iso-8859-1', 'cp1252']:
try:
with open(txt_path, "r", encoding=encoding) as f:
return f.read()
except:
continue
except Exception as e:
print(f"TXT Extraction failed for {txt_path}: {e}")
return ""
def clean_text(self, text):
"""
Robust cleaner for government FAQ chatbot.
- Removes boilerplate (page numbers, headers/footers)
- Preserves layout for LLM tokenization
- Normalizes structure and character encoding
"""
# Normalize line endings
text = text.replace('\r\n', '\n').replace('\r', '\n')
# Remove page headers/footers (common boilerplate)
text = re.sub(r'\n?Page\s*\d+(\s*of\s*\d+)?\n?', '\n', text, flags=re.IGNORECASE)
text = re.sub(r'\n?Copyright.*?\d{4}.*?\n?', '\n', text, flags=re.IGNORECASE)
text = re.sub(r'\n?All rights reserved.*?\n?', '\n', text, flags=re.IGNORECASE)
# Remove decorative dividers
text = re.sub(r'^\s*[-=_*]{3,}\s*$', '', text, flags=re.MULTILINE)
# Strip non-printable characters but keep structure
text = re.sub(r'[^\x09\x0A\x0D\x20-\x7E]', '', text)
# Normalize whitespace
text = re.sub(r'[ \t]+', ' ', text) # inline
text = re.sub(r'[ \t]+\n', '\n', text) # line ends
text = re.sub(r'\n{3,}', '\n\n', text) # excessive newlines
return text.strip()
def extract_text_from_files(self, doc_paths):
"""
Extract and process text from multiple document types
Returns:
list: Extracted document data with metadata
"""
doc_paths = [
str(Path(path).resolve())
for path in doc_paths
if Path(path).exists()
]
data = []
for path in doc_paths:
try:
ext = Path(path).suffix.lower()
# Text extraction based on file type
if ext == '.pdf':
text = self.extract_text_from_pdf(path)
elif ext == '.docx':
text = self.extract_text_from_doc(path)
elif ext == '.txt':
text = self.extract_text_from_txt(path)
else:
print(f"Unsupported format: {path}")
continue
# Skip empty documents
if not text.strip():
print(f"No text extracted from {path}")
continue
# Clean and structure extracted text
cleaned_text = self.clean_text(text)
# Add document metadata
doc_data = {
"title": Path(path).name,
"path": path,
"text": cleaned_text,
"text_length": len(cleaned_text)
}
data.append(doc_data)
except Exception as e:
print(f"Unexpected error processing {path}: {e}")
return data
def pdf_to_markdown(self, pdf_path, markdown_path):
"""
Convert PDF to Markdown using docling
Args:
pdf_path (Path): Path to PDF file
markdown_path (Path): Output path for markdown file
Returns:
str: Markdown content
"""
try:
from docling.document_converter import DocumentConverter
# Define the source PDF file
source = pdf_path
converter = DocumentConverter()
# Convert the PDF to Markdown
result = converter.convert(source)
markdown = result.document.export_to_markdown()
# Write the Markdown output to a file
with open(markdown_path, "w", encoding="utf-8") as file:
file.write(markdown)
return markdown
except ImportError:
print("Docling is not installed. Please install it with: pip install docling")
return ""
except Exception as e:
print(f"Error converting PDF to Markdown: {e}")
return ""
def load_markdown_file(self, markdown_path):
"""
Load markdown file using langchain_docling
Args:
markdown_path (Path): Path to markdown file
Returns:
list: List of documents
"""
try:
from langchain_docling import DoclingLoader
from langchain_docling.loader import ExportType
loader = DoclingLoader(
file_path=markdown_path,
export_type=ExportType.MARKDOWN
)
data = loader.load()
return data
except ImportError:
print("langchain_docling is not installed. Please install it with: pip install langchain_docling")
return []
except Exception as e:
print(f"Error loading markdown file: {e}")
return []
def spit_markdown(self, markdown):
"""
Split markdown using MarkdownHeaderTextSplitter
Args:
markdown (list): List of markdown documents
Returns:
list: List of split documents
"""
try:
from langchain_text_splitters import MarkdownHeaderTextSplitter
splitter = MarkdownHeaderTextSplitter(
headers_to_split_on=[
("#", "Header_1"),
("##", "Header_2"),
("###", "Header_3"),
],
)
splits = []
for doc in markdown:
for split in splitter.split_text(doc.page_content):
# Include metadata and page_content from the original document
split.metadata.update({
"Header_1": split.metadata.get("Header_1", ""),
"Header_2": split.metadata.get("Header_2", ""),
"Header_3": split.metadata.get("Header_3", "")
})
splits.append(split)
return splits
except ImportError:
print("langchain_text_splitters is not installed or has compatibility issues.")
return []
except Exception as e:
print(f"Error splitting markdown: {e}")
return [] |