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Mohd KaifandClaude Sonnet 4.6
b282487b17
- Rewrote index.md to match README (tagline, badges, Problem/Solution text) - Improved getting-started, concepts, quickstart, installation, faq, use-cases, contributing, glossary, learning-more, examples, modules, architecture, cookbook, deep-dive pages: tighter prose, fixed headings/bullets, removed inconsistencies and duplicate sections - Removed overuse of emojis from headings in integration pages (docling, snowflake) - Fixed change_management reference page: closed unclosed JSON code block that broke the right TOC, demoted noisy sub-headings to bold text - CSS layout: widened content area (max-width 1440px grid, left sidebar 11rem, right TOC narrowed to 11rem for broader content), tightened TOC spacing and font size, fixed word-wrap/overflow on TOC links - Added mkdocs_local.yml for local serving without mkdocs-jupyter plugin Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
108 lines
3.5 KiB
Markdown
108 lines
3.5 KiB
Markdown
# Docling Integration
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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.
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## Overview
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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.
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- 📖 **Semantica Docling Integration Docs**: [Reference Guide](../reference/parse.md)
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- 💻 **Semantica Docling Integration GitHub**: [Source Code](https://github.com/Hawksight-AI/semantica/blob/main/semantica/parse/docling_parser.py)
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- 🧑🏽🍳 **Semantica Docling Integration Example**: [Docling Clear Code Example](../CodeExamples.md#docling-clear-code-example)
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- 📦 **Semantica Docling Integration PyPI**: [Installation Guide](../installation.md)
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---
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## Integration Documentation
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The `DoclingParser` provides a high-level interface for document processing. It supports:
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* **Multi-format support**: PDF, DOCX, PPTX, HTML, and more.
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* **Table Extraction**: High-fidelity table extraction with header detection.
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* **OCR Support**: Built-in Optical Character Recognition for scanned documents.
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* **Markdown Export**: Clean markdown output optimized for LLM consumption.
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### Basic Usage
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```python
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from semantica.parse import DoclingParser
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# Initialize with OCR enabled
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parser = DoclingParser(enable_ocr=True)
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# Parse a complex document
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result = parser.parse("financial_report.pdf")
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# Access the structured data
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print(f"Content: {result['full_text'][:200]}...")
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print(f"Found {len(result['tables'])} tables")
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```
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For more details, see the [Parse Reference](../reference/parse.md).
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---
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## Integration Example
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We provide a detailed cookbook and clear code examples to help you get started quickly.
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### Docling Clear Code Example
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```python
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from semantica.parse import DoclingParser
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import json
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# 1. Initialize the Docling Parser with advanced config
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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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# 2. Parse a complex document (PDF, DOCX, etc.)
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result = parser.parse("complex_invoice.pdf")
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# 3. Access the clean Markdown text
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print(f"--- Document Content ---\n{result['full_text']}")
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# 4. Iterate through extracted tables
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for i, table in enumerate(result['tables']):
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print(f"\nTable {i+1} headers: {table.get('headers', [])}")
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# Access table rows as a list of lists
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for row in table.get('rows', [])[:3]: # Print first 3 rows
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print(f" Row: {row}")
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# 5. Get document metadata
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metadata = result['metadata']
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print(f"\n--- Metadata ---\nTitle: {metadata.get('title')}")
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print(f"Total Pages: {result.get('total_pages')}")
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```
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See more in our [Code Examples](../CodeExamples.md).
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---
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## GitHub Source
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The integration is open-source and available on GitHub. You can explore the implementation, contribute improvements, or report issues.
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- [docling_parser.py](https://github.com/Hawksight-AI/semantica/blob/main/semantica/parse/docling_parser.py) - The core implementation of the Docling integration.
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---
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## PyPI & Installation
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Docling is an optional but highly recommended dependency for Semantica. You can install it along with Semantica or as a separate requirement.
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### Install via Semantica
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```bash
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pip install semantica
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```
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### Install Docling manually
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If you are working in a custom environment:
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```bash
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pip install docling
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```
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For full installation details, see the [Installation Guide](../installation.md).
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