contextifier 0.1.0

Convert raw documents into AI-understandable context with intelligent text extraction, table detection, and semantic chunking

Contextify

Contextify is a document processing library that converts raw documents into AI-understandable context. It analyzes, restructures, and normalizes content so that language models can reason over documents with higher accuracy and consistency.

Features

• Multi-format Support: Process a wide variety of document formats including:

  • PDF (with table detection, OCR fallback, and complex layout handling)
  • Microsoft Office: DOCX, DOC, PPTX, PPT, XLSX, XLS
  • Korean documents: HWP, HWPX (Hangul Word Processor)
  • Text formats: TXT, MD, RTF, CSV, HTML
  • Code files: Python, JavaScript, TypeScript, and 20+ languages

• Intelligent Text Extraction:

  • Preserves document structure (headings, paragraphs, lists)
  • Extracts tables as HTML with proper rowspan/colspan handling
  • Handles merged cells and complex table layouts
  • Extracts and processes inline images

• OCR Integration:

  • Pluggable OCR engine architecture
  • Supports OpenAI, Anthropic, Google Gemini, and vLLM backends
  • Automatic OCR fallback for scanned documents or image-based PDFs

• Smart Chunking:

  • Semantic text chunking with configurable size and overlap
  • Table-aware chunking that preserves table integrity
  • Protected regions for code blocks and special content

• Metadata Extraction:

  • Extracts document metadata (title, author, creation date, etc.)
  • Formats metadata in a structured, parseable format

Installation

pip install contextify

Or using uv:

uv add contextify

Quick Start

Basic Usage

from libs.core.document_processor import DocumentProcessor

# Create processor instance
processor = DocumentProcessor()

# Extract text from a document
text = processor.extract_text("document.pdf")
print(text)

# Chunk the extracted text
chunks = processor.chunk_text(text, chunk_size=1000, chunk_overlap=200)
for chunk in chunks:
    print(chunk)

With OCR Processing

from libs.core.document_processor import DocumentProcessor
from libs.ocr.ocr_engine import OpenAIOCR

# Initialize OCR engine
ocr_engine = OpenAIOCR(api_key="sk-...", model="gpt-4o")

# Create processor with OCR
processor = DocumentProcessor(ocr_engine=ocr_engine)

# Extract text with OCR processing enabled
text = processor.extract_text(
    "scanned_document.pdf",
    ocr_processing=True
)

Supported Formats

Category	Extensions
Documents	.pdf, .docx, .doc, .pptx, .ppt, .hwp, .hwpx
Spreadsheets	.xlsx, .xls, .csv, .tsv
Text	.txt, .md, .rtf
Web	.html, .htm, .xml
Code	.py, .js, .ts, .java, .cpp, .c, .go, .rs, and more
Config	.json, .yaml, .yml, .toml, .ini, .env

Architecture

libs/
├── core/
│   ├── document_processor.py    # Main entry point
│   ├── processor/               # Format-specific handlers
│   │   ├── pdf_handler.py       # PDF processing with V4 engine
│   │   ├── docx_handler.py      # DOCX processing
│   │   ├── ppt_handler.py       # PowerPoint processing
│   │   ├── excel_handler.py     # Excel processing
│   │   ├── hwp_processor.py     # HWP 5.0 OLE processing
│   │   ├── hwpx_processor.py    # HWPX (ZIP/XML) processing
│   │   └── ...
│   └── functions/
│       └── img_processor.py     # Image handling utilities
├── chunking/
│   ├── chunking.py              # Main chunking interface
│   ├── text_chunker.py          # Text-based chunking
│   ├── table_chunker.py         # Table-aware chunking
│   └── page_chunker.py          # Page-based chunking
└── ocr/
    ├── base.py                  # OCR base class
    ├── ocr_processor.py         # OCR processing utilities
    └── ocr_engine/              # OCR engine implementations
        ├── openai_ocr.py
        ├── anthropic_ocr.py
        ├── gemini_ocr.py
        └── vllm_ocr.py

Requirements

• Python 3.14+
• Required dependencies are automatically installed (see pyproject.toml)

System Dependencies

For full functionality, you may need:

• Tesseract OCR: For local OCR fallback
• LibreOffice: For DOC/RTF conversion (optional)
• Poppler: For PDF image extraction

Configuration

# Custom configuration
config = {
    "pdf": {
        "extract_images": True,
        "ocr_fallback": True,
    },
    "chunking": {
        "default_size": 1000,
        "default_overlap": 200,
    }
}

processor = DocumentProcessor(config=config)

License

MIT License - see LICENSE for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.