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

• Home page with quick upload and recent documents
• PDF viewer with page navigation, bounding box overlay, and resizable results panel
• Configurable Docling pipeline — OCR, table extraction, code/formula enrichment, picture classification & description, image generation
• Bounding box visualization — color-coded element overlay directly on the PDF
• Per-page results — right panel syncs with the current PDF page
• Markdown & HTML export of extracted content
• Document management — upload, list, delete
• Analysis history — re-visit and open past analyses
• Dark / Light theme and FR / EN localization

Architecture

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

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

Backend structure (clean architecture)

document-parser/
├── main.py                   # FastAPI app, CORS, lifespan
├── domain/                   # Pure domain — no HTTP, no DB
│   ├── models.py             # Document, AnalysisJob dataclasses
│   ├── parsing.py            # Docling conversion & page extraction
│   └── bbox.py               # Bounding box coordinate normalization
├── 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 via aiosqlite)
│   ├── 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
└── tests/                    # 99 tests (pytest)

Frontend structure (feature-based)

frontend/src/
├── app/                      # App shell, router, global styles
├── pages/                    # Route-level pages
│   ├── HomePage.vue          # Landing page with upload & stats
│   ├── StudioPage.vue        # PDF viewer + config + results
│   ├── DocumentsPage.vue     # Document management
│   ├── HistoryPage.vue       # Past analyses
│   └── SettingsPage.vue      # Theme, language, API URL
├── features/                 # Feature modules
│   ├── analysis/             # Analysis store, API, bbox, UI components
│   ├── document/             # Document store, API, upload, list
│   ├── history/              # History store, API, navigation
│   └── settings/             # Settings store
└── shared/                   # Shared utilities (types, i18n, http, format)

Quick Start

Docker (fastest)

docker run -p 3000:3000 ghcr.io/scub-france/docling-studio:latest

Open http://localhost:3000

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

Docker Compose (for development)

git clone https://github.com/scub-france/Docling-Studio.git
cd Docling-Studio
docker compose up --build

Local Development

Backend (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

Running Tests

# Backend (99 tests)
cd document-parser
pip install pytest pytest-asyncio httpx
pytest tests/ -v

# Frontend (81 tests)
cd frontend
npm run test:run

Pipeline Options

These options map directly to Docling's PdfPipelineOptions. See the Docling documentation for details on each feature.

Option	Default	Description
do_ocr	true	OCR for scanned pages and embedded images
do_table_structure	true	Table detection and row/column reconstruction
table_mode	accurate	accurate (TableFormer) or fast
do_code_enrichment	false	Specialized OCR for code blocks
do_formula_enrichment	false	Math formula recognition (LaTeX output)
do_picture_classification	false	Classify images by type (chart, photo, diagram…)
do_picture_description	false	Generate image descriptions via VLM
generate_picture_images	false	Extract detected images as separate files
generate_page_images	false	Rasterize each page as an image
images_scale	1.0	Scale factor for generated images (0.1–10)

Configuration

All configuration is done via environment variables. See .env.example.

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
CONVERSION_TIMEOUT	600	Max seconds for a single Docling conversion

CI / Release

GitHub Actions pipelines (see .github/workflows/):

Workflow	Trigger	What it does
CI	push to main, pull requests	Lint + type check + Backend tests (99) + Frontend tests (81) + build
Release	push tag v*	Build & push multi-arch Docker image to ghcr.io

To publish a new version:

git tag v0.2.0
git push origin v0.2.0

Performance & System Requirements

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:

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

Platform support

All Docker images are multi-arch (linux/amd64 + linux/arm64). No GPU required.

Tech Stack

• Frontend: Vue 3, TypeScript, Vite, Pinia, DOMPurify
• Backend: FastAPI, Docling 2.x, SQLite (aiosqlite), pdf2image
• CI: GitHub Actions
• 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