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

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

---

## 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.

<CardGroup cols={2}>
  <Card title="Multi-format" icon="file">
    PDF, DOCX, PPTX, HTML, and more.
  </Card>
  <Card title="Table Extraction" icon="table">
    High-fidelity table parsing with header detection.
  </Card>
  <Card title="OCR Support" icon="eye">
    Built-in OCR for scanned documents.
  </Card>
  <Card title="Markdown Export" icon="markdown">
    Clean Markdown output optimized for LLM consumption.
  </Card>
</CardGroup>

---

## Installation

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

---

## Basic Usage

```python
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")
```

---

## Full Example

```python
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')}")
```

---

## DoclingParser Parameters

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

---

## Parsed Result Structure

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

---

## See Also

<CardGroup cols={2}>
  <Card title="Parse Module" icon="file-lines" href="../reference/parse">
    Full DocumentParser and DoclingParser reference.
  </Card>
  <Card title="Ingest Module" icon="file-import" href="../reference/ingest">
    Loading documents before parsing.
  </Card>
  <Card title="Semantic Extract" icon="magnifying-glass" href="../reference/semantic_extract">
    NER and relation extraction on parsed text.
  </Card>
  <Card title="Pipeline" icon="gear" href="../reference/pipeline">
    Using DoclingParser in a full pipeline.
  </Card>
</CardGroup>