Docling Studio

A visual document analysis studio powered by Docling. Upload a PDF, configure the extraction pipeline, and visualize the results — text, tables, images, formulas, bounding boxes — all from your browser.

Features

• PDF viewer with page navigation and visual overlay toggle
• Configurable Docling pipeline — OCR on/off, table extraction mode (fast/accurate)
• Bounding box visualization — overlay extracted elements directly on the PDF with color-coded types
• Per-page results — right panel syncs with the current PDF page
• Document hierarchy — heading levels and structure preserved from Docling's iterate_items() API
• Markdown & HTML export of extracted content
• Analysis history — re-visit past analyses

More screenshots

Import	Configure	Results

Architecture

┌────────────┐         ┌───────────────────────┐
│  Frontend   │────────▶│   Document Parser      │
│  Vue 3      │  /api/* │ FastAPI + Docling       │
│  port 3000  │         │ SQLite + file storage   │
└────────────┘         │   port 8000             │
                        └───────────────────────┘

Service	Stack	Role
frontend	Vue 3, Vite, Pinia	UI, PDF viewer, results display
document-parser	FastAPI, Docling, SQLite, pdf2image	REST API, document parsing, storage, persistence

Python project structure (clean architecture)

document-parser/
├── main.py                   # FastAPI app, CORS, lifespan
├── domain/                   # Pure domain models & Docling logic
│   ├── models.py             # Document, AnalysisJob dataclasses
│   └── parsing.py            # Docling conversion & page extraction
├── api/                      # HTTP layer (FastAPI routers)
│   ├── schemas.py            # Pydantic DTOs (camelCase serialization)
│   ├── documents.py          # /api/documents endpoints
│   └── analyses.py           # /api/analyses endpoints
├── persistence/              # Data layer (SQLite)
│   ├── database.py           # Connection management, schema init
│   ├── document_repo.py      # Document CRUD
│   └── analysis_repo.py      # AnalysisJob CRUD
└── services/                 # Use case orchestration
    ├── document_service.py   # Upload, delete, preview
    └── analysis_service.py   # Async Docling processing

Quick Start

Docker Compose (recommended)

# Clone the repo
git clone https://github.com/scub-france/docling-studio.git
cd docling-studio

# (Optional) customize settings
cp .env.example .env

# Start all services
docker compose up --build

Open http://localhost:3000

Note: The first analysis takes a bit longer as Docling downloads and caches its ML models (~400 MB). Subsequent runs are fast.

Local Development

Document Parser (Python 3.12+):

cd document-parser
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --reload --port 8000

Frontend (Node 20+):

cd frontend
npm install
npm run dev

Docling Integration

The document parser wraps Docling with configurable pipeline options exposed as query parameters on the /parse endpoint:

Parameter	Default	Description
do_ocr	true	Enable OCR for scanned documents
do_table_structure	true	Enable table structure extraction
table_mode	accurate	Table extraction mode: accurate or fast

Element types are detected using isinstance() checks against Docling's type hierarchy (TextItem, TableItem, PictureItem, SectionHeaderItem, etc.) and the document tree depth from iterate_items() is preserved for heading-level reconstruction.

Configuration

All configuration is done via environment variables. See .env.example for available options.

Variable	Default	Description
CORS_ORIGINS	http://localhost:3000,...	CORS allowed origins (comma-separated)
UPLOAD_DIR	./uploads	File storage directory
DB_PATH	./data/docling_studio.db	SQLite database path

Performance & System Requirements

Docling leverages optimized ML models (layout analysis, OCR, table structure) that run efficiently on CPU. The first analysis takes slightly longer as models are downloaded and cached (~400 MB). Subsequent runs are fast, even on large documents.

Document type	Pages	Approx. time (CPU)
Simple report	5-10	~30s-1 min
Research paper	10-30	~1-2 min
Large document	100+	~2-5 min

Docker Desktop settings

The document parser needs at least 4 GB of RAM. Recommended Docker Desktop allocation:

Resource	Minimum	Recommended
Memory	6 GB	8 GB+
CPUs	4	8+

On macOS: Docker Desktop > Settings > Resources On Windows: Docker Desktop > Settings > Resources > WSL 2

Platform support

All Docker images are multi-arch (linux/amd64 + linux/arm64). All processing runs on CPU — no GPU required.

Platform	Architecture
macOS Apple Silicon (M1/M2/M3/M4)	arm64
macOS Intel	amd64
Linux x86_64	amd64
Linux ARM (Raspberry Pi 5, Ampere)	arm64
Windows + WSL2	amd64

Tech Stack

• Frontend: Vue 3 + Vite + Pinia
• Backend: FastAPI + Docling 2.x + SQLite + pdf2image
• Infra: Docker Compose + Nginx

Contributing

Contributions are welcome! Please open an issue first to discuss what you'd like to change.

License

MIT — Pier-Jean Malandrino