169 lines
6.3 KiB
Markdown
169 lines
6.3 KiB
Markdown
# Docling Studio
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A visual document analysis studio powered by [Docling](https://github.com/DS4SD/docling).
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Upload a PDF, configure the extraction pipeline, and visualize the results — text, tables, images, formulas, bounding boxes — all from your browser.
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## Features
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- **PDF viewer** with page navigation and visual overlay toggle
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- **Configurable Docling pipeline** — OCR on/off, table extraction mode (fast/accurate)
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- **Bounding box visualization** — overlay extracted elements directly on the PDF with color-coded types
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- **Per-page results** — right panel syncs with the current PDF page
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- **Document hierarchy** — heading levels and structure preserved from Docling's `iterate_items()` API
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- **Markdown & HTML export** of extracted content
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- **Analysis history** — re-visit past analyses
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<details>
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<summary>More screenshots</summary>
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| Import | Configure | Results |
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|--------|-----------|---------|
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|  |  |  |
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</details>
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## Architecture
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```
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┌────────────┐ ┌───────────────────────┐
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│ Frontend │────────▶│ Document Parser │
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│ Vue 3 │ /api/* │ FastAPI + Docling │
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│ port 3000 │ │ SQLite + file storage │
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└────────────┘ │ port 8000 │
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└───────────────────────┘
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```
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| Service | Stack | Role |
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|---------|-------|------|
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| **frontend** | Vue 3, Vite, Pinia | UI, PDF viewer, results display |
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| **document-parser** | FastAPI, Docling, SQLite, pdf2image | REST API, document parsing, storage, persistence |
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### Python project structure (clean architecture)
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```
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document-parser/
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├── main.py # FastAPI app, CORS, lifespan
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├── domain/ # Pure domain models & Docling logic
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│ ├── models.py # Document, AnalysisJob dataclasses
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│ └── parsing.py # Docling conversion & page extraction
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├── api/ # HTTP layer (FastAPI routers)
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│ ├── schemas.py # Pydantic DTOs (camelCase serialization)
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│ ├── documents.py # /api/documents endpoints
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│ └── analyses.py # /api/analyses endpoints
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├── persistence/ # Data layer (SQLite)
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│ ├── database.py # Connection management, schema init
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│ ├── document_repo.py # Document CRUD
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│ └── analysis_repo.py # AnalysisJob CRUD
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└── services/ # Use case orchestration
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├── document_service.py # Upload, delete, preview
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└── analysis_service.py # Async Docling processing
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```
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## Quick Start
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### Docker Compose (recommended)
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```bash
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# Clone the repo
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git clone https://github.com/scub-france/docling-studio.git
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cd docling-studio
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# (Optional) customize settings
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cp .env.example .env
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# Start all services
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docker compose up --build
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```
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Open [http://localhost:3000](http://localhost:3000)
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> **Note:** The first analysis takes a bit longer as Docling downloads and caches its ML models (~400 MB). Subsequent runs are fast.
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### Local Development
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**Document Parser** (Python 3.12+):
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```bash
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cd document-parser
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python -m venv .venv && source .venv/bin/activate
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pip install -r requirements.txt
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uvicorn main:app --reload --port 8000
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```
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**Frontend** (Node 20+):
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```bash
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cd frontend
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npm install
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npm run dev
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```
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## Docling Integration
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The document parser wraps [Docling](https://github.com/DS4SD/docling) with configurable pipeline options exposed as query parameters on the `/parse` endpoint:
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| Parameter | Default | Description |
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|-----------|---------|-------------|
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| `do_ocr` | `true` | Enable OCR for scanned documents |
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| `do_table_structure` | `true` | Enable table structure extraction |
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| `table_mode` | `accurate` | Table extraction mode: `accurate` or `fast` |
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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.
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## Configuration
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All configuration is done via environment variables. See [`.env.example`](.env.example) for available options.
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| Variable | Default | Description |
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|----------|---------|-------------|
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| `CORS_ORIGINS` | `http://localhost:3000,...` | CORS allowed origins (comma-separated) |
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| `UPLOAD_DIR` | `./uploads` | File storage directory |
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| `DB_PATH` | `./data/docling_studio.db` | SQLite database path |
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## Performance & System Requirements
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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.
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| Document type | Pages | Approx. time (CPU) |
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|---------------|-------|---------------------|
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| Simple report | 5-10 | ~30s-1 min |
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| Research paper | 10-30 | ~1-2 min |
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| Large document | 100+ | ~2-5 min |
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### Docker Desktop settings
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The document parser needs **at least 4 GB of RAM**. Recommended Docker Desktop allocation:
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| Resource | Minimum | Recommended |
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|----------|---------|-------------|
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| Memory | 6 GB | 8 GB+ |
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| CPUs | 4 | 8+ |
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> On **macOS**: Docker Desktop > Settings > Resources
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> On **Windows**: Docker Desktop > Settings > Resources > WSL 2
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### Platform support
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All Docker images are **multi-arch** (linux/amd64 + linux/arm64). All processing runs on **CPU** — no GPU required.
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| Platform | Architecture |
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|----------|-------------|
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| **macOS Apple Silicon** (M1/M2/M3/M4) | arm64 |
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| **macOS Intel** | amd64 |
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| **Linux x86_64** | amd64 |
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| **Linux ARM** (Raspberry Pi 5, Ampere) | arm64 |
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| **Windows + WSL2** | amd64 |
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## Tech Stack
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- **Frontend**: Vue 3 + Vite + Pinia
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- **Backend**: FastAPI + Docling 2.x + SQLite + pdf2image
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- **Infra**: Docker Compose + Nginx
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## Contributing
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Contributions are welcome! Please open an issue first to discuss what you'd like to change.
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## License
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[MIT](LICENSE) — Pier-Jean Malandrino
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