Import L1 (celery) as l1/
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l1/README.md
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l1/README.md
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# Art DAG L1 Server
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L1 rendering server for the Art DAG system. Manages distributed rendering jobs via Celery workers with content-addressable caching and optional IPFS integration.
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## Features
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- **3-Phase Execution**: Analyze → Plan → Execute pipeline for recipe-based rendering
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- **Content-Addressable Caching**: IPFS CIDs with deduplication
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- **IPFS Integration**: Optional IPFS-primary mode for distributed storage
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- **Storage Providers**: S3, IPFS, and local storage backends
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- **DAG Visualization**: Interactive graph visualization of execution plans
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- **SPA-Style Navigation**: Smooth URL-based navigation without full page reloads
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- **L2 Federation**: Publish outputs to ActivityPub registry
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## Dependencies
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- **artdag** (GitHub): Core DAG execution engine
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- **artdag-effects** (rose-ash): Effect implementations
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- **artdag-common**: Shared templates and middleware
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- **Redis**: Message broker, result backend, and run persistence
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- **PostgreSQL**: Metadata storage
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- **IPFS** (optional): Distributed content storage
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## Quick Start
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```bash
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# Install dependencies
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pip install -r requirements.txt
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# Start Redis
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redis-server
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# Start a worker
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celery -A celery_app worker --loglevel=info -E
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# Start the L1 server
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python server.py
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```
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## Docker Swarm Deployment
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```bash
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docker stack deploy -c docker-compose.yml artdag
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```
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The stack includes:
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- **redis**: Message broker (Redis 7)
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- **postgres**: Metadata database (PostgreSQL 16)
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- **ipfs**: IPFS node (Kubo)
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- **l1-server**: FastAPI web server
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- **l1-worker**: Celery workers (2 replicas)
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- **flower**: Celery task monitoring
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## Configuration
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### Environment Variables
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| Variable | Default | Description |
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|----------|---------|-------------|
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| `HOST` | `0.0.0.0` | Server bind address |
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| `PORT` | `8000` | Server port |
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| `REDIS_URL` | `redis://localhost:6379/5` | Redis connection |
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| `DATABASE_URL` | **(required)** | PostgreSQL connection |
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| `CACHE_DIR` | `~/.artdag/cache` | Local cache directory |
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| `IPFS_API` | `/dns/localhost/tcp/5001` | IPFS API multiaddr |
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| `IPFS_GATEWAY_URL` | `https://ipfs.io/ipfs` | Public IPFS gateway |
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| `IPFS_PRIMARY` | `false` | Enable IPFS-primary mode |
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| `L1_PUBLIC_URL` | `http://localhost:8100` | Public URL for redirects |
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| `L2_SERVER` | - | L2 ActivityPub server URL |
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| `L2_DOMAIN` | - | L2 domain for federation |
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| `ARTDAG_CLUSTER_KEY` | - | Cluster key for trust domains |
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### IPFS-Primary Mode
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When `IPFS_PRIMARY=true`, all content is stored on IPFS:
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- Input files are added to IPFS on upload
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- Analysis results stored as JSON on IPFS
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- Execution plans stored on IPFS
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- Step outputs pinned to IPFS
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- Local cache becomes a read-through cache
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This enables distributed execution across multiple L1 nodes sharing the same IPFS network.
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## Web UI
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| Path | Description |
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|------|-------------|
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| `/` | Home page with server info |
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| `/runs` | View and manage rendering runs |
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| `/run/{id}` | Run detail with tabs: Plan, Analysis, Artifacts |
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| `/run/{id}/plan` | Interactive DAG visualization |
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| `/run/{id}/analysis` | Audio/video analysis data |
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| `/run/{id}/artifacts` | Cached step outputs |
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| `/recipes` | Browse and run available recipes |
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| `/recipe/{id}` | Recipe detail page |
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| `/recipe/{id}/dag` | Recipe DAG visualization |
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| `/media` | Browse cached media files |
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| `/storage` | Manage storage providers |
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| `/auth` | Receive auth token from L2 |
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| `/logout` | Log out |
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| `/download/client` | Download CLI client |
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## API Reference
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Interactive docs: http://localhost:8100/docs
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### Runs
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| Method | Path | Description |
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|--------|------|-------------|
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| POST | `/runs` | Start a rendering run |
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| GET | `/runs` | List all runs (paginated) |
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| GET | `/runs/{run_id}` | Get run status |
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| DELETE | `/runs/{run_id}` | Delete a run |
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| GET | `/api/run/{run_id}` | Get run as JSON |
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| GET | `/api/run/{run_id}/plan` | Get execution plan JSON |
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| GET | `/api/run/{run_id}/analysis` | Get analysis data JSON |
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### Recipes
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| Method | Path | Description |
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|--------|------|-------------|
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| POST | `/recipes/upload` | Upload recipe YAML |
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| GET | `/recipes` | List recipes (paginated) |
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| GET | `/recipes/{recipe_id}` | Get recipe details |
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| DELETE | `/recipes/{recipe_id}` | Delete recipe |
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| POST | `/recipes/{recipe_id}/run` | Execute recipe |
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### Cache
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| Method | Path | Description |
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|--------|------|-------------|
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| GET | `/cache/{cid}` | Get cached content (with preview) |
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| GET | `/cache/{cid}/raw` | Download raw content |
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| GET | `/cache/{cid}/mp4` | Get MP4 video |
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| GET | `/cache/{cid}/meta` | Get content metadata |
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| PATCH | `/cache/{cid}/meta` | Update metadata |
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| POST | `/cache/{cid}/publish` | Publish to L2 |
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| DELETE | `/cache/{cid}` | Delete from cache |
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| POST | `/cache/import?path=` | Import local file |
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| POST | `/cache/upload` | Upload file |
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| GET | `/media` | Browse media gallery |
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### IPFS
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| Method | Path | Description |
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|--------|------|-------------|
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| GET | `/ipfs/{cid}` | Redirect to IPFS gateway |
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| GET | `/ipfs/{cid}/raw` | Fetch raw content from IPFS |
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### Storage Providers
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| Method | Path | Description |
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|--------|------|-------------|
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| GET | `/storage` | List storage providers |
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| POST | `/storage` | Add provider (form) |
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| POST | `/storage/add` | Add provider (JSON) |
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| GET | `/storage/{id}` | Get provider details |
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| PATCH | `/storage/{id}` | Update provider |
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| DELETE | `/storage/{id}` | Delete provider |
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| POST | `/storage/{id}/test` | Test connection |
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| GET | `/storage/type/{type}` | Get form for provider type |
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### 3-Phase API
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| Method | Path | Description |
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|--------|------|-------------|
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| POST | `/api/plan` | Generate execution plan |
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| POST | `/api/execute` | Execute a plan |
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| POST | `/api/run-recipe` | Full pipeline (analyze+plan+execute) |
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### Authentication
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| Method | Path | Description |
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|--------|------|-------------|
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| GET | `/auth` | Receive auth token from L2 |
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| GET | `/logout` | Log out |
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| POST | `/auth/revoke` | Revoke a specific token |
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| POST | `/auth/revoke-user` | Revoke all user tokens |
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## 3-Phase Execution
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Recipes are executed in three phases:
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### Phase 1: Analyze
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Extract features from input files:
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- **Audio/Video**: Tempo, beat times, energy levels
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- Results cached by CID
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### Phase 2: Plan
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Generate an execution plan:
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- Parse recipe YAML
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- Resolve dependencies between steps
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- Compute cache IDs for each step
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- Skip already-cached steps
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### Phase 3: Execute
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Run the plan level by level:
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- Steps at each level run in parallel
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- Results cached with content-addressable hashes
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- Progress tracked in Redis
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## Recipe Format
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Recipes define reusable DAG pipelines:
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```yaml
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name: beat-sync
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version: "1.0"
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description: "Synchronize video to audio beats"
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inputs:
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video:
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type: video
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description: "Source video"
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audio:
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type: audio
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description: "Audio track"
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steps:
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- id: analyze_audio
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type: ANALYZE
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inputs: [audio]
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config:
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features: [beats, energy]
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- id: sync_video
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type: BEAT_SYNC
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inputs: [video, analyze_audio]
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config:
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mode: stretch
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output: sync_video
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```
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## Storage
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### Local Cache
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- Location: `~/.artdag/cache/` (or `CACHE_DIR`)
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- Content-addressed by IPFS CID
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- Subdirectories: `plans/`, `analysis/`
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### Redis
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- Database 5 (configurable via `REDIS_URL`)
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- Keys:
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- `artdag:run:*` - Run state
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- `artdag:recipe:*` - Recipe definitions
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- `artdag:revoked:*` - Token revocation
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- `artdag:user_tokens:*` - User token tracking
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### PostgreSQL
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- Content metadata
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- Storage provider configurations
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- Provenance records
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## Authentication
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L1 servers authenticate via L2 (ActivityPub registry). No shared secrets required.
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### Flow
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1. User clicks "Attach" on L2's Renderers page
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2. L2 creates a scoped token bound to this L1
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3. User redirected to L1's `/auth?auth_token=...`
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4. L1 calls L2's `/auth/verify` to validate
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5. L1 sets local cookie and records token
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### Token Revocation
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- Tokens tracked per-user in Redis
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- L2 calls `/auth/revoke-user` on logout
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- Revoked hashes stored with 30-day expiry
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- Every request checks revocation list
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## CLI Usage
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```bash
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# Quick render (effect mode)
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python render.py dog cat --sync
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# Submit async
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python render.py dog cat
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# Run a recipe
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curl -X POST http://localhost:8100/recipes/beat-sync/run \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer <token>" \
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-d '{"inputs": {"video": "abc123...", "audio": "def456..."}}'
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```
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## Architecture
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```
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L1 Server (FastAPI)
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│
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├── Web UI (Jinja2 + HTMX + Tailwind)
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│
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├── POST /runs → Celery tasks
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│ │
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│ └── celery_app.py
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│ ├── tasks/analyze.py (Phase 1)
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│ ├── tasks/execute.py (Phase 3 steps)
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│ └── tasks/orchestrate.py (Full pipeline)
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│
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├── cache_manager.py
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│ │
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│ ├── Local filesystem (CACHE_DIR)
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│ ├── IPFS (ipfs_client.py)
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│ └── S3/Storage providers
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│
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└── database.py (PostgreSQL metadata)
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```
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## Provenance
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Every render produces a provenance record:
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```json
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{
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"task_id": "celery-task-uuid",
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"rendered_at": "2026-01-07T...",
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"rendered_by": "@giles@artdag.rose-ash.com",
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"output": {"name": "...", "cid": "Qm..."},
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"inputs": [...],
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"effects": [...],
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"infrastructure": {
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"software": {"name": "infra:artdag", "cid": "Qm..."},
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"hardware": {"name": "infra:giles-hp", "cid": "Qm..."}
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}
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}
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```
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