// screenshot of docling-project.github.io ↗

AI / LLM · PRO TIER

Doclingpro

Docling is IBM's document conversion library that transforms PDFs, DOCX, PPTX, HTML into structured Markdown or JSON. Layout-aware OCR, table detection, image extraction, formula recognition — built specifically for RAG preprocessing where document structure matters.

Install via WHMCS → Visit docling-project.github.io ↗

🤖 AI / LLM Min 2048 MB RAM Port 5001 (http) Tier pro

// What it is

A closer look.

The MIT-licensed open-source release is the same engine IBM uses in its enterprise AI offerings — high-quality output that captures semantic structure, not just plain text.

// Use cases

What it's for.

Concrete scenarios where teams pick Docling over the SaaS alternative.

◆

RAG preprocessing

convert your PDF library into clean Markdown for embedding

◈

Document digitization

OCR scanned documents with layout preserved

◇

Knowledge base ingestion

extract structured content from messy enterprise docs

▣

Compliance archival

convert physical documents to searchable format

▦

Content migration

DOCX → Markdown for static site generators

// Who it's for

Built for these teams.

If your team profile matches one of these, Docling is a strong fit out of the box.

Profile A

AI engineers

building RAG pipelines over real-world PDF corpora

Profile B

Knowledge management teams

digitizing legacy document archives

Profile C

Legal & compliance

converting contract PDFs into searchable Markdown

Profile D

Researchers

extracting structured data from scientific papers

Profile E

Tech writers

migrating documentation from Word/PDF to Markdown

// Differentiators

Why teams pick Docling.

When evaluating self-hosted options for this category, here are the dimensions on which Docling consistently lands above the alternatives.

✓Layout-aware — preserves table structure, headers, lists (vs simple text extraction)
✓OCR built-in — handles scanned PDFs with Tesseract integration
✓Formula recognition — STEM papers with equations stay intact
✓Apache 2.0 — IBM-backed but fully open
✓Python-first — clean API, easy to integrate
✓Output flexibility — Markdown, JSON, with optional structured metadata

// Integrations

Connects to.

The stack you'll plug Docling into — services, protocols, and adjacent apps in the BluixApps catalog.

◇

Python API

primary interface; pip install and go

◈

HTTP API mode

Docling-Serve wrapper exposes REST endpoint

◆

OCR engines

Tesseract, EasyOCR pluggable

▣

PDF parsers

pdfium, PyMuPDF backends

▦

LLM frameworks

LangChain document loader available

▩

Output formats

Markdown, JSON, DocLayNet structured format

▼

Embedded image handling

extract or inline as base64

// Adoption & deployment

Notable users & community

20k+ GitHub stars
Backed by IBM Research with active engineering team
Featured in IBM's enterprise AI stack
Strong adoption in research / academic RAG pipelines
Growing community around document AI use cases

What we ship

Docker compose: Docling-Serve HTTP wrapper
Pinned quay.io/ds4sd/docling-serve:latest (release-tagged)
HTTPS via Let's Encrypt; API key auth enabled
OCR enabled by default with Tesseract
Persistent model cache volume to avoid re-download on restart
API rate limiting configured for fair use
Backup not needed (stateless service)

// Tips & operations

Run it properly.

Operational guidance from running this in production — what to do before you scale, what to lock down, what surprises people.

// PERFORMANCE

Use HTTP mode for multi-language stacks

embedded Python only for Python apps; REST works for any client

// SECURITY

Pre-warm models

first request downloads several hundred MB of model weights; bake into image

// OPERATIONS

OCR vs text extraction

disable OCR for born-digital PDFs; saves 10× processing time

// RELIABILITY

Batch processing

Docling can handle multiple docs per request; batch when possible

// DEPLOYMENT

GPU acceleration

optional but significantly speeds OCR on scanned doc archives

// SCALING

Output cleanup

Docling Markdown can need light post-processing for LLM ingestion

2048

// min ram (MB)

// min disk (GB)

5001

// access port

http

// protocol

pro

// bluixapps tier

5001:5001 · quay.io/docling-project/docling-serve:latest

// docker image

Project resources

Official sitedocling-project.github.io ↗

// Alternatives in AI / LLM

Compare with