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97 lines
2.5 KiB
Markdown
97 lines
2.5 KiB
Markdown
---
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title: "Docling Integration"
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description: "Native Docling integration for high-fidelity PDF, DOCX, and PPTX parsing with table extraction and OCR."
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icon: "file-lines"
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---
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> Parse complex documents: PDFs, DOCX, PPTX, HTML: with high-fidelity table extraction and built-in OCR.
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## Overview
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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.
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- **Multi-format** — PDF, DOCX, PPTX, HTML, and more.
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- **Table Extraction** — High-fidelity table parsing with header detection.
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- **OCR Support** — Built-in OCR for scanned documents.
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- **Markdown Export** — Clean Markdown output optimized for LLM consumption.
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## Installation
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```bash
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pip install semantica
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# Docling is included as an optional dependency
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# or install separately:
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pip install docling
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```
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## Basic Usage
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```python
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from semantica.parse import DoclingParser
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parser = DoclingParser(enable_ocr=True)
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result = parser.parse("financial_report.pdf")
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print(result["full_text"][:200])
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print(f"Found {len(result['tables'])} tables")
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```
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## Full Example
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```python
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from semantica.parse import DoclingParser
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parser = DoclingParser(
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enable_ocr=True,
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export_format="markdown",
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)
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result = parser.parse("complex_invoice.pdf")
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# Full text content
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print(result["full_text"])
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# Extracted tables
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for i, table in enumerate(result["tables"]):
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print(f"Table {i+1} headers: {table.get('headers', [])}")
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for row in table.get("rows", [])[:3]:
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print(f" Row: {row}")
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# Metadata
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metadata = result["metadata"]
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print(f"Title: {metadata.get('title')}")
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print(f"Pages: {result.get('total_pages')}")
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```
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## DoclingParser Parameters
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| Parameter | Default | Description |
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| :----------- | :--------- | :------------- |
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| `enable_ocr` | `False` | Enable OCR for scanned pages |
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| `export_format` | `"markdown"` | Output format: `"markdown"` or `"text"` |
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## Parsed Result Structure
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```python
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{
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"full_text": str, # Clean document text
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"tables": List[dict], # Extracted tables (headers + rows)
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"metadata": dict, # Title, author, creation date, etc.
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"total_pages": int,
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}
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```
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## See Also
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- [Parse Module](../reference/parse) — Full DocumentParser and DoclingParser reference.
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- [Ingest Module](../reference/ingest) — Loading documents before parsing.
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- [Semantic Extract](../reference/semantic_extract) — NER and relation extraction on parsed text.
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- [Pipeline](../reference/pipeline) — Using DoclingParser in a full pipeline.
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