6.5 KiB
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 |
|---|---|---|
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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
Note: First analysis may take a few minutes as Docling downloads its ML models (~40 MB) on first run.
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 runs ML models (layout analysis, OCR, table structure) on CPU by default. Processing time depends on document size and complexity.
| Document type | Pages | Approx. time (CPU) |
|---|---|---|
| Simple report | 5-10 | 1-3 min |
| Research paper | 15-30 | 5-10 min |
| Dense PDF with tables | 30+ | 10-20 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). Works natively on:
| Platform | Architecture | GPU acceleration |
|---|---|---|
| macOS Apple Silicon (M1/M2/M3) | arm64 | Not in Docker (MPS unavailable). Run parser locally for GPU. |
| macOS Intel | amd64 | N/A |
| Linux x86_64 | amd64 | NVIDIA GPU via docker compose --profile gpu (coming soon) |
| Linux ARM (Raspberry Pi 5, Ampere) | arm64 | CPU only |
| Windows + WSL2 | amd64 | NVIDIA GPU passthrough supported |
Tip for Mac users: For faster processing, run the document parser locally (outside Docker) to leverage Apple Silicon's MPS acceleration when supported by PyTorch/Docling.
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