semantica/docs/integrations/docling.md

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Docling Integration	Native Docling integration for high-fidelity PDF, DOCX, and PPTX parsing with table extraction and OCR.	file-lines

Parse complex documents — PDFs, DOCX, PPTX, HTML — with high-fidelity table extraction and built-in OCR.

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Overview

Docling is integrated into Semantica's parse module via the DoclingParser. Documents pass through Docling's layout engine, then feed directly into Semantica's extraction and KG pipeline.

PDF, DOCX, PPTX, HTML, and more. High-fidelity table parsing with header detection. Built-in OCR for scanned documents. Clean Markdown output optimized for LLM consumption.

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Installation

pip install semantica
# Docling is included as an optional dependency
# or install separately:
pip install docling

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Basic Usage

from semantica.parse import DoclingParser

parser = DoclingParser(enable_ocr=True)
result = parser.parse("financial_report.pdf")

print(result["full_text"][:200])
print(f"Found {len(result['tables'])} tables")

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Full Example

from semantica.parse import DoclingParser

parser = DoclingParser(
    enable_ocr=True,
    export_format="markdown",
)

result = parser.parse("complex_invoice.pdf")

# Full text content
print(result["full_text"])

# Extracted tables
for i, table in enumerate(result["tables"]):
    print(f"Table {i+1} headers: {table.get('headers', [])}")
    for row in table.get("rows", [])[:3]:
        print(f"  Row: {row}")

# Metadata
metadata = result["metadata"]
print(f"Title: {metadata.get('title')}")
print(f"Pages: {result.get('total_pages')}")

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DoclingParser Parameters

Parameter	Default	Description
enable_ocr	False	Enable OCR for scanned pages
export_format	"markdown"	Output format: "markdown" or "text"

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Parsed Result Structure

{
    "full_text":    str,         # Clean document text
    "tables":       list[dict],  # Extracted tables (headers + rows)
    "metadata":     dict,        # Title, author, creation date, etc.
    "total_pages":  int,
}

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See Also

Full DocumentParser and DoclingParser reference. Loading documents before parsing. NER and relation extraction on parsed text. Using DoclingParser in a full pipeline.