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import os
import json
from typing import List, Dict, Any, Tuple
from pathlib import Path
import hashlib
# Document parsing imports
try:
import fitz # PyMuPDF
HAS_PYMUPDF = True
except ImportError:
HAS_PYMUPDF = False
try:
from docx import Document
HAS_DOCX = True
except ImportError:
HAS_DOCX = False
# Text processing
import re
from dataclasses import dataclass
@dataclass
class DocumentChunk:
text: str
metadata: Dict[str, Any]
chunk_id: str
def to_dict(self):
return {
'text': self.text,
'metadata': self.metadata,
'chunk_id': self.chunk_id
}
class DocumentProcessor:
def __init__(self, chunk_size: int = 800, chunk_overlap: int = 100):
self.chunk_size = chunk_size
self.chunk_overlap = chunk_overlap
self.supported_extensions = ['.pdf', '.docx', '.txt', '.md']
def process_file(self, file_path: str) -> List[DocumentChunk]:
"""Process a single file and return chunks"""
path = Path(file_path)
if not path.exists():
raise FileNotFoundError(f"File not found: {file_path}")
extension = path.suffix.lower()
if extension not in self.supported_extensions:
raise ValueError(f"Unsupported file type: {extension}")
# Extract text based on file type
if extension == '.pdf':
text = self._extract_pdf_text(file_path)
elif extension == '.docx':
text = self._extract_docx_text(file_path)
elif extension in ['.txt', '.md']:
text = self._extract_text_file(file_path)
else:
raise ValueError(f"Unsupported file type: {extension}")
# Create chunks
chunks = self._create_chunks(text, file_path)
return chunks
def _extract_pdf_text(self, file_path: str) -> str:
"""Extract text from PDF file"""
if not HAS_PYMUPDF:
raise ImportError("PyMuPDF not installed. Install with: pip install PyMuPDF")
text_parts = []
try:
with fitz.open(file_path) as pdf:
for page_num in range(len(pdf)):
page = pdf[page_num]
text = page.get_text()
if text.strip():
text_parts.append(f"[Page {page_num + 1}]\n{text}")
except Exception as e:
raise Exception(f"Error processing PDF: {str(e)}")
return "\n\n".join(text_parts)
def _extract_docx_text(self, file_path: str) -> str:
"""Extract text from DOCX file"""
if not HAS_DOCX:
raise ImportError("python-docx not installed. Install with: pip install python-docx")
text_parts = []
try:
doc = Document(file_path)
for paragraph in doc.paragraphs:
if paragraph.text.strip():
text_parts.append(paragraph.text)
# Also extract text from tables
for table in doc.tables:
for row in table.rows:
row_text = []
for cell in row.cells:
if cell.text.strip():
row_text.append(cell.text.strip())
if row_text:
text_parts.append(" | ".join(row_text))
except Exception as e:
raise Exception(f"Error processing DOCX: {str(e)}")
return "\n\n".join(text_parts)
def _extract_text_file(self, file_path: str) -> str:
"""Extract text from plain text or markdown file"""
try:
with open(file_path, 'r', encoding='utf-8') as f:
return f.read()
except Exception as e:
raise Exception(f"Error reading text file: {str(e)}")
def _create_chunks(self, text: str, file_path: str) -> List[DocumentChunk]:
"""Create overlapping chunks from text"""
chunks = []
# Clean and normalize text
text = re.sub(r'\s+', ' ', text)
text = text.strip()
if not text:
return chunks
# Simple word-based chunking
words = text.split()
for i in range(0, len(words), self.chunk_size - self.chunk_overlap):
chunk_words = words[i:i + self.chunk_size]
chunk_text = ' '.join(chunk_words)
# Create chunk ID
chunk_id = hashlib.md5(f"{file_path}_{i}_{chunk_text[:50]}".encode()).hexdigest()[:8]
# Create metadata
metadata = {
'file_path': file_path,
'file_name': Path(file_path).name,
'chunk_index': len(chunks),
'start_word': i,
'word_count': len(chunk_words)
}
chunk = DocumentChunk(
text=chunk_text,
metadata=metadata,
chunk_id=chunk_id
)
chunks.append(chunk)
return chunks
def process_multiple_files(self, file_paths: List[str]) -> Tuple[List[DocumentChunk], Dict[str, Any]]:
"""Process multiple files and return chunks with summary"""
all_chunks = []
summary = {
'total_files': 0,
'total_chunks': 0,
'files_processed': [],
'errors': []
}
for file_path in file_paths:
try:
chunks = self.process_file(file_path)
all_chunks.extend(chunks)
summary['files_processed'].append({
'path': file_path,
'name': Path(file_path).name,
'chunks': len(chunks)
})
except Exception as e:
summary['errors'].append({
'path': file_path,
'error': str(e)
})
summary['total_files'] = len(summary['files_processed'])
summary['total_chunks'] = len(all_chunks)
return all_chunks, summary
# Utility function for file size validation
def validate_file_size(file_path: str, max_size_mb: float = 10.0) -> bool:
"""Check if file size is within limits"""
size_bytes = os.path.getsize(file_path)
size_mb = size_bytes / (1024 * 1024)
return size_mb <= max_size_mb |