RAG preprocessing
convert your PDF library into clean Markdown for embedding
// official site: 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.
// What it is
A closer look.
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.
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.
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.
AI engineers
building RAG pipelines over real-world PDF corpora
Knowledge management teams
digitizing legacy document archives
Legal & compliance
converting contract PDFs into searchable Markdown
Researchers
extracting structured data from scientific papers
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 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)
5
// min disk (GB)
5001
// access port
http
// protocol
pro
// bluixapps tier
// Alternatives in AI / LLM
Compare with
Project resources
Official sitedocling-project.github.io ↗