sayou-document 0.2.4

Document components for the Sayou Data Platform

sayou-document

The Universal Document Parsing Gateway for Sayou Fabric.

sayou-document is a high-fidelity parsing engine that converts diverse document formats (PDF, DOCX, PPTX, XLSX, Images) into a unified, structured Document Object Model (DOM).

Unlike simple text extractors, it preserves the semantic structure of documents—headers, tables, charts, and layout coordinates—making it ideal for RAG (Retrieval-Augmented Generation) and Layout-aware LLM applications.

💡 Core Philosophy

"One Interface, High Fidelity."

We abstract away the complexity of proprietary file formats. Whether it's a slide deck or a spreadsheet, sayou-document normalizes it into a consistent Document > Page > Element hierarchy.

1. Smart Routing: Automatically detects file types (and converts images to PDF if needed) to select the optimal parser.
2. Hybrid Extraction: Combines native text extraction with OCR fallback for scanned pages or images.
3. Strict Schema: Outputs data strictly adhering to Pydantic models, ready for the next pipeline stage (Refinery).

flowchart LR
    File[Raw File] --> Pipeline[Document Pipeline]
    Pipeline -->|PDF/Image| OCR[PDF Parser + OCR]
    Pipeline -->|Office| Office[DOCX/PPTX/XLSX Parser]
    OCR --> DOM[Structured Document Model]
    Office --> DOM

📦 Installation

pip install sayou-document

# For OCR support (requires Tesseract installed on OS)
pip install "sayou-document[ocr]"

⚡ Quick Start

The DocumentPipeline handles file detection, conversion, and parsing automatically.

import os
from sayou.document.pipeline import DocumentPipeline

def run_demo():
    # 1. Initialize Pipeline (with optional OCR)
    pipeline = DocumentPipeline(use_default_ocr=True)
    pipeline.initialize()

    # 2. Parse a file (PDF, Word, Excel, PPT, or Image)
    file_path = "quarterly_report.pdf"
    # file_path = "scan_image.png"  # Images are auto-converted to PDF & OCR'd
    
    with open(file_path, "rb") as f:
        file_bytes = f.read()
        
    doc = pipeline.run(file_bytes, os.path.basename(file_path))
    
    if doc:
        print(f"File: {doc.file_name} ({doc.doc_type})")
        print(f"Pages: {doc.page_count}")
        
        # 3. Access Structured Data
        first_page = doc.pages[0]
        if first_page.elements:
            print(f"Content Preview: {first_page.elements[0].text[:100]}...")
        
        # Export to JSON
        print(doc.model_dump_json(indent=2))

if __name__ == "__main__":
    run_demo()

🔑 Key Components

Parsers

• PdfParser: Extracts text, images, and TOC from PDFs using PyMuPDF. Supports full-page OCR for scanned documents.
• DocxParser: Parses Word documents, preserving heading levels and table structures.
• PptxParser: Extracts text frames, notes, and tables from slides.
• ExcelParser: Converts sheets into table elements and extracts embedded images.

Converters & OCR

• ImageToPdfConverter: Automatically converts JPG/PNG images to PDF to leverage the robust PDF parsing pipeline.
• TesseractOCR: (Optional) Provides OCR capabilities for handling scanned content and embedded images.

🤝 Contributing

We welcome contributions for new Parsers (e.g., HwpParser for Korean documents, HtmlParser) or Enhanced OCR integrations (e.g., Google Vision API).

📜 License

Apache 2.0 License © 2025 Sayouzone