semantica/docs/integrations/docling.md

Docling Integration

Semantica features a native integration with Docling, the powerful document parsing library that excels at extracting structured data from complex documents like PDFs, DOCX, and PPTX.

Overview

Docling is integrated into Semantica's parse module via the DoclingParser. This allows you to seamlessly convert unstructured documents into semantic structures that can be indexed, searched, and analyzed within the Semantica framework.

• 📖 Semantica Docling Integration Docs: Reference Guide
• 💻 Semantica Docling Integration GitHub: Source Code
• 🧑🏽‍🍳 Semantica Docling Integration Example: Docling Clear Code Example
• 📦 Semantica Docling Integration PyPI: Installation Guide

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📖 Integration Documentation

The DoclingParser provides a high-level interface for document processing. It supports:

• Multi-format support: PDF, DOCX, PPTX, HTML, and more.
• Table Extraction: High-fidelity table extraction with header detection.
• OCR Support: Built-in Optical Character Recognition for scanned documents.
• Markdown Export: Clean markdown output optimized for LLM consumption.

Basic Usage

from semantica.parse import DoclingParser

# Initialize with OCR enabled
parser = DoclingParser(enable_ocr=True)

# Parse a complex document
result = parser.parse("financial_report.pdf")

# Access the structured data
print(f"Content: {result['full_text'][:200]}...")
print(f"Found {len(result['tables'])} tables")

For more details, see the Parse Reference.

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🧑🏽‍🍳 Integration Example

We provide a detailed cookbook and clear code examples to help you get started quickly.

Docling Clear Code Example

from semantica.parse import DoclingParser
import json

# 1. Initialize the Docling Parser with advanced config
parser = DoclingParser(
    enable_ocr=True, 
    export_format="markdown"
)

# 2. Parse a complex document (PDF, DOCX, etc.)
result = parser.parse("complex_invoice.pdf")

# 3. Access the clean Markdown text
print(f"--- Document Content ---\n{result['full_text']}")

# 4. Iterate through extracted tables
for i, table in enumerate(result['tables']):
    print(f"\nTable {i+1} headers: {table.get('headers', [])}")
    # Access table rows as a list of lists
    for row in table.get('rows', [])[:3]:  # Print first 3 rows
        print(f"  Row: {row}")

# 5. Get document metadata
metadata = result['metadata']
print(f"\n--- Metadata ---\nTitle: {metadata.get('title')}")
print(f"Total Pages: {result.get('total_pages')}")

See more in our Code Examples.

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💻 GitHub Source

The integration is open-source and available on GitHub. You can explore the implementation, contribute improvements, or report issues.

• docling_parser.py - The core implementation of the Docling integration.

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📦 PyPI & Installation

Docling is an optional but highly recommended dependency for Semantica. You can install it along with Semantica or as a separate requirement.

Install via Semantica

pip install semantica

Install Docling manually

If you are working in a custom environment:

pip install docling

For full installation details, see the Installation Guide.