# Docling

> Open-source document parsing framework from IBM with 97.9% accuracy in complex table extraction and excellent text fidelity. Self-hostable solution for converting PDFs, spreadsheets, and scanned images into structured data for RAG pipelines.

- **URL:** https://github.com/DS4SD/docling
- **Category:** LLM Tools
- **Tags:** document-parsing, Open Source, Rag
- **Updated:** 2026-03-11 13:24
- **Canonical page:** https://vectordb.ever.works/nl/items/docling

## Details

## Overview

Docling is an open-source framework for parsing documents into structured data, developed by IBM. It excels at complex table extraction and provides self-hosting capabilities for privacy-sensitive applications.

## Features

- **High Accuracy**: 97.9% accuracy in complex table extraction
- **Excellent Text Fidelity**: Preserves document structure and formatting
- **Self-Hostable**: Run entirely on-premises without API charges
- **Multiple Formats**: Handles PDFs, spreadsheets, scanned images
- **Complex Layouts**: Handles multi-column and nested structures
- **Multimodal**: Processes both text and images
- **Open Source**: Free to use and modify

## Performance

Comprehensive evaluation reveals Docling as the superior framework for extracting structured data from sustainability reports. Achieves 100% accuracy on simple tables and 97.9% on complex structures. Processing time is 17+ seconds for complex documents but provides better accuracy than alternatives.

## Use Cases

- Enterprise RAG pipelines requiring privacy
- Complex document processing (financial reports, research papers)
- Applications with sensitive data
- On-premises AI deployments
- Organizations wanting to avoid per-page API charges

## Comparison

- **vs LlamaParse**: Slower but more accurate, self-hostable
- **vs Unstructured**: Better on complex tables, slower processing

## Integration

Works with LangChain, LlamaIndex, and can be integrated with IBM watsonx.data and Granite models.

## Pricing

Free and open-source. No per-page charges.

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