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DocuMind AI — PDF Processing Service
─────────────────────────────────────
Business Logic:
1. Extract raw text from PDF using PyMuPDF (fastest Python PDF lib)
2. Clean and normalize the text
3. Split into overlapping chunks using LangChain's RecursiveCharacterTextSplitter
4. Return structured Chunk objects ready for embedding
Why RecursiveCharacterTextSplitter?
- Tries to split on paragraphs → sentences → words (in that order)
- Preserves semantic meaning better than fixed-size splits
- Overlap ensures context isn't lost at chunk boundaries
"""
import fitz # PyMuPDF
import re
import hashlib
import tiktoken
from pathlib import Path
from dataclasses import dataclass, field
from typing import List
from langchain_text_splitters import RecursiveCharacterTextSplitter
from backend.core import settings, get_logger
logger = get_logger(__name__)
@dataclass
class DocumentChunk:
"""Represents a single text chunk from a PDF document."""
chunk_id: str # unique hash ID
doc_id: str # parent document ID
doc_name: str # original filename
text: str # chunk text content
page_number: int # source page number
chunk_index: int # position within document
char_count: int # character length
metadata: dict = field(default_factory=dict)
@dataclass
class ProcessedDocument:
"""Represents a fully processed PDF document."""
doc_id: str
filename: str
total_pages: int
total_chunks: int
chunks: List[DocumentChunk]
file_size_kb: float
class PDFProcessor:
"""
Handles all PDF ingestion:
- Text extraction page by page
- Text cleaning (remove noise, fix spacing)
- Recursive chunking with overlap
"""
def __init__(self):
self.encoding = self._load_encoding()
if settings.chunk_unit == "chars":
length_fn = len
elif self.encoding is not None:
length_fn = lambda value: len(self.encoding.encode(value)) # noqa: E731
else:
# Offline fallback: ~4 characters per token.
length_fn = lambda value: max(1, len(value) // 4) # noqa: E731
self.splitter = RecursiveCharacterTextSplitter(
chunk_size=settings.chunk_size,
chunk_overlap=settings.chunk_overlap,
length_function=length_fn,
# Try splitting on paragraphs → newlines → sentences → words
separators=["\n\n", "\n", ". ", "? ", "! ", " ", ""],
)
logger.info(
f"PDFProcessor initialized | chunk_size={settings.chunk_size} "
f"overlap={settings.chunk_overlap}"
)
@staticmethod
def _load_encoding():
"""tiktoken downloads its vocabulary on first use; fall back if that is impossible."""
try:
return tiktoken.get_encoding("cl100k_base")
except Exception as exc: # network blocked, cache not writable, ...
logger.warning(
f"tiktoken encoding unavailable ({exc}); using ~4 chars/token estimate"
)
return None
@property
def _overlap_chars(self) -> int:
"""Cross-page overlap expressed in characters (tokens are ~4 chars)."""
factor = 1 if settings.chunk_unit == "chars" else 4
return settings.chunk_overlap * factor
def _generate_doc_id(self, path: Path) -> str:
"""Hash incrementally to avoid reading the complete PDF into memory."""
digest = hashlib.sha256()
with path.open("rb") as source:
for block in iter(lambda: source.read(1024 * 1024), b""):
digest.update(block)
return digest.hexdigest()[:16]
def _clean_text(self, text: str) -> str:
"""
Clean extracted PDF text:
- Remove excessive whitespace and blank lines
- Fix broken hyphenated words (e.g. 'impor-\ntant' → 'important')
- Normalize unicode characters
"""
# Fix hyphenated line breaks
text = re.sub(r"(\w)-\n(\w)", r"\1\2", text)
# Collapse multiple spaces
text = re.sub(r" {2,}", " ", text)
# Collapse more than 2 newlines into 2
text = re.sub(r"\n{3,}", "\n\n", text)
# Remove page header/footer noise (short lines with only numbers)
text = re.sub(r"^\s*\d+\s*$", "", text, flags=re.MULTILINE)
return text.strip()
def _extract_text_by_page(self, pdf_path: str):
"""
Extract text from each page individually.
Returns (pages, total_pages) where pages is a list of {page_number, text}
dicts (empty pages skipped) and total_pages is the real PDF page count.
"""
pages = []
try:
doc = fitz.open(pdf_path)
except Exception as exc:
raise ValueError("Invalid or unreadable PDF file.") from exc
ocr_enabled = True # switched off after the first OCR failure (e.g. no Tesseract)
try:
total_pages = len(doc)
for page_num in range(total_pages):
page = doc[page_num]
cleaned = self._clean_text(page.get_text("text", sort=True))
if not cleaned and ocr_enabled:
try:
cleaned = self._ocr_page(page)
except Exception as exc: # ImportError, TesseractNotFoundError, ...
ocr_enabled = False
logger.warning(f"OCR unavailable, skipping scanned pages: {exc}")
if cleaned: # skip empty pages
pages.append({
"page_number": page_num + 1, # 1-indexed
"text": cleaned
})
finally:
doc.close()
logger.info(f"Extracted {len(pages)} text pages from {Path(pdf_path).name}")
return pages, total_pages
def _ocr_page(self, page) -> str:
"""OCR one page (needs the pytesseract package and the Tesseract binary)."""
import io
import pytesseract
from PIL import Image
pix = page.get_pixmap(matrix=fitz.Matrix(2, 2), alpha=False)
text = pytesseract.image_to_string(Image.open(io.BytesIO(pix.tobytes("png"))))
return self._clean_text(text)
def process(self, pdf_path: str) -> ProcessedDocument:
"""
Main entry point: process a PDF file end-to-end.
Args:
pdf_path: Absolute path to the PDF file
Returns:
ProcessedDocument with all chunks ready for embedding
"""
path = Path(pdf_path)
filename = path.name
file_size_kb = path.stat().st_size / 1024
doc_id = self._generate_doc_id(path)
logger.info(f"Processing PDF: {filename} ({file_size_kb:.1f} KB)")
# Step 1: Extract text page by page
pages, total_pages = self._extract_text_by_page(pdf_path)
if not pages:
raise ValueError(f"No extractable text found in {filename}. "
"File may be scanned/image-only (OCR needs Tesseract installed).")
# Step 2: Chunk each page separately (preserves page metadata)
all_chunks: List[DocumentChunk] = []
chunk_index = 0
previous_tail = ""
for page_data in pages:
overlap = min(self._overlap_chars, len(previous_tail))
page_text = (previous_tail[-overlap:] + "\n" if overlap else "") + page_data["text"]
page_chunks = self.splitter.split_text(page_text)
previous_tail = page_data["text"][-self._overlap_chars:]
for chunk_text in page_chunks:
if len(chunk_text.strip()) < 20: # skip trivially short chunks
continue
chunk_id = hashlib.md5(
f"{doc_id}_{chunk_index}".encode()
).hexdigest()[:16]
all_chunks.append(DocumentChunk(
chunk_id=chunk_id,
doc_id=doc_id,
doc_name=filename,
text=chunk_text.strip(),
page_number=page_data["page_number"],
chunk_index=chunk_index,
char_count=len(chunk_text),
metadata={
"source": filename,
"page": page_data["page_number"],
"doc_id": doc_id,
}
))
chunk_index += 1
logger.info(
f"Chunking complete | {filename} → {len(all_chunks)} chunks "
f"across {total_pages} pages"
)
return ProcessedDocument(
doc_id=doc_id,
filename=filename,
total_pages=total_pages,
total_chunks=len(all_chunks),
chunks=all_chunks,
file_size_kb=round(file_size_kb, 2),
)
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