aa7c539cc3
Reflect the two Docker targets across README, getting-started, contributing guides, and .env.example with new configuration variables (CONVERSION_ENGINE, DOCLING_SERVE_URL, DOCLING_SERVE_API_KEY).
124 lines
3.8 KiB
Markdown
124 lines
3.8 KiB
Markdown
# Getting Started
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Docling Studio ships two Docker image variants:
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| Variant | Image tag | Size | Description |
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|---------|-----------|------|-------------|
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| **remote** | `latest-remote` | ~300 MB | Lightweight — delegates to an external [Docling Serve](https://github.com/DS4SD/docling-serve) instance |
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| **local** | `latest-local` | ~2–3 GB | Full — runs Docling in-process (downloads ML models on first run) |
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## Docker — remote mode (fastest)
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```bash
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docker run -p 3000:3000 \
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-e DOCLING_SERVE_URL=http://your-docling-serve:5001 \
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ghcr.io/scub-france/docling-studio:latest-remote
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```
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## Docker — local mode (self-contained)
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```bash
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docker run -p 3000:3000 ghcr.io/scub-france/docling-studio:latest-local
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```
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> **Note:** The first analysis takes longer as Docling downloads its ML models (~400 MB). Subsequent runs are fast.
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Open [http://localhost:3000](http://localhost:3000).
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## Docker Compose (recommended for development)
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```bash
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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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# Local mode (default)
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docker compose up --build
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# Remote mode
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CONVERSION_MODE=remote DOCLING_SERVE_URL=http://your-docling-serve:5001 docker compose up --build
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```
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## Local Development
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=== "Backend (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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# Remote mode (lightweight)
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pip install -r requirements.txt
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# Local mode (with Docling)
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pip install -r requirements-local.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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The frontend runs on `http://localhost:3000` and proxies API calls to `http://localhost:8000`.
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## Running Tests
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=== "Backend"
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```bash
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cd document-parser
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pip install pytest pytest-asyncio httpx
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pytest tests/ -v
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```
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=== "Frontend"
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```bash
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cd frontend
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npm run test:run
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```
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## Pipeline Options
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These options map directly to Docling's [`PdfPipelineOptions`](https://docling-project.github.io/docling/usage/).
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| Option | Default | Description |
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|--------|---------|-------------|
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| `do_ocr` | `true` | OCR for scanned pages and embedded images |
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| `do_table_structure` | `true` | Table detection and row/column reconstruction |
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| `table_mode` | `accurate` | `accurate` (TableFormer) or `fast` |
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| `do_code_enrichment` | `false` | Specialized OCR for code blocks |
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| `do_formula_enrichment` | `false` | Math formula recognition (LaTeX output) |
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| `do_picture_classification` | `false` | Classify images by type |
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| `do_picture_description` | `false` | Generate image descriptions via VLM |
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| `generate_picture_images` | `false` | Extract detected images as separate files |
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| `generate_page_images` | `false` | Rasterize each page as an image |
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| `images_scale` | `1.0` | Scale factor for generated images (0.1–10) |
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## Configuration
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All configuration is done via environment variables:
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| Variable | Default | Description |
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|----------|---------|-------------|
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| `CONVERSION_ENGINE` | `local` | `local` (in-process Docling) or `remote` (Docling Serve) |
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| `DOCLING_SERVE_URL` | `http://localhost:5001` | Docling Serve endpoint (remote mode only) |
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| `DOCLING_SERVE_API_KEY` | — | API key for Docling Serve (optional) |
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| `CORS_ORIGINS` | `http://localhost:3000,...` | CORS allowed origins |
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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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| `CONVERSION_TIMEOUT` | `600` | Max seconds per Docling conversion |
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## System Requirements
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| | Remote image | Local image |
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|---|---|---|
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| **Image size** | ~300 MB | ~2–3 GB |
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| **Memory** | 2 GB | 6 GB (recommended 8 GB+) |
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| **CPUs** | 2 | 4 (recommended 8+) |
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All Docker images are multi-arch (`linux/amd64` + `linux/arm64`). No GPU required.
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