48b7d5d3e8
Install torch and torchvision from the CPU-only index before docling to avoid pulling CUDA/nvidia/triton dependencies. Update documentation with measured image sizes (270MB remote, 1.9GB local).
124 lines
3.8 KiB
Markdown
124 lines
3.8 KiB
Markdown
# Getting Started
|
||
|
||
Docling Studio ships two Docker image variants:
|
||
|
||
| Variant | Image tag | Size | Description |
|
||
|---------|-----------|------|-------------|
|
||
| **remote** | `latest-remote` | ~270 MB | Lightweight — delegates to an external [Docling Serve](https://github.com/DS4SD/docling-serve) instance |
|
||
| **local** | `latest-local` | ~1.9 GB | Full — runs Docling in-process, CPU-only (downloads ML models on first run) |
|
||
|
||
## Docker — remote mode (fastest)
|
||
|
||
```bash
|
||
docker run -p 3000:3000 \
|
||
-e DOCLING_SERVE_URL=http://your-docling-serve:5001 \
|
||
ghcr.io/scub-france/docling-studio:latest-remote
|
||
```
|
||
|
||
## Docker — local mode (self-contained)
|
||
|
||
```bash
|
||
docker run -p 3000:3000 ghcr.io/scub-france/docling-studio:latest-local
|
||
```
|
||
|
||
> **Note:** The first analysis takes longer as Docling downloads its ML models (~400 MB). Subsequent runs are fast.
|
||
|
||
Open [http://localhost:3000](http://localhost:3000).
|
||
|
||
## Docker Compose (recommended for development)
|
||
|
||
```bash
|
||
git clone https://github.com/scub-france/Docling-Studio.git
|
||
cd Docling-Studio
|
||
|
||
# Local mode (default)
|
||
docker compose up --build
|
||
|
||
# Remote mode
|
||
CONVERSION_MODE=remote DOCLING_SERVE_URL=http://your-docling-serve:5001 docker compose up --build
|
||
```
|
||
|
||
## Local Development
|
||
|
||
=== "Backend (Python 3.12+)"
|
||
|
||
```bash
|
||
cd document-parser
|
||
python -m venv .venv && source .venv/bin/activate
|
||
|
||
# Remote mode (lightweight)
|
||
pip install -r requirements.txt
|
||
|
||
# Local mode (with Docling)
|
||
pip install -r requirements-local.txt
|
||
|
||
uvicorn main:app --reload --port 8000
|
||
```
|
||
|
||
=== "Frontend (Node 20+)"
|
||
|
||
```bash
|
||
cd frontend
|
||
npm install
|
||
npm run dev
|
||
```
|
||
|
||
The frontend runs on `http://localhost:3000` and proxies API calls to `http://localhost:8000`.
|
||
|
||
## Running Tests
|
||
|
||
=== "Backend"
|
||
|
||
```bash
|
||
cd document-parser
|
||
pip install pytest pytest-asyncio httpx
|
||
pytest tests/ -v
|
||
```
|
||
|
||
=== "Frontend"
|
||
|
||
```bash
|
||
cd frontend
|
||
npm run test:run
|
||
```
|
||
|
||
## Pipeline Options
|
||
|
||
These options map directly to Docling's [`PdfPipelineOptions`](https://docling-project.github.io/docling/usage/).
|
||
|
||
| 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 |
|
||
| `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:
|
||
|
||
| Variable | Default | Description |
|
||
|----------|---------|-------------|
|
||
| `CONVERSION_ENGINE` | `local` | `local` (in-process Docling) or `remote` (Docling Serve) |
|
||
| `DOCLING_SERVE_URL` | `http://localhost:5001` | Docling Serve endpoint (remote mode only) |
|
||
| `DOCLING_SERVE_API_KEY` | — | API key for Docling Serve (optional) |
|
||
| `CORS_ORIGINS` | `http://localhost:3000,...` | CORS allowed origins |
|
||
| `UPLOAD_DIR` | `./uploads` | File storage directory |
|
||
| `DB_PATH` | `./data/docling_studio.db` | SQLite database path |
|
||
| `CONVERSION_TIMEOUT` | `600` | Max seconds per Docling conversion |
|
||
|
||
## System Requirements
|
||
|
||
| | Remote image | Local image |
|
||
|---|---|---|
|
||
| **Image size** | ~270 MB | ~1.9 GB |
|
||
| **Memory** | 2 GB | 6 GB (recommended 8 GB+) |
|
||
| **CPUs** | 2 | 4 (recommended 8+) |
|
||
|
||
All Docker images are multi-arch (`linux/amd64` + `linux/arm64`). No GPU required.
|