forked from genewildish/Mainline
docs(graph): Add DSL documentation and examples
Add comprehensive documentation for the graph-based pipeline DSL: - docs/graph-dsl.md: Complete DSL reference with TOML, Python, and CLI syntax - docs/GRAPH_SYSTEM_SUMMARY.md: Implementation overview and architecture - examples/graph_dsl_demo.py: Demonstrates imperative Python API usage - examples/test_graph_integration.py: Integration test verifying pipeline execution The documentation follows a wiki-like approach with navigable structure: - Overview section explaining the concept - Syntax examples for each format (TOML, Python, CLI) - Node type reference table - Advanced features section - Comparison with old XYZStage approach This provides users with multiple entry points to understand and use the new graph-based pipeline system.
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docs/GRAPH_SYSTEM_SUMMARY.md
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docs/GRAPH_SYSTEM_SUMMARY.md
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# Graph-Based Pipeline System - Implementation Summary
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## Overview
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Implemented a graph-based scripting language to replace the verbose `XYZStage` naming convention in Mainline's pipeline architecture. The new system represents pipelines as nodes and connections, providing a more intuitive way to define, configure, and orchestrate pipelines.
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## Files Created
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### Core Graph System
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- `engine/pipeline/graph.py` - Core graph abstraction (Node, Connection, Graph classes)
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- `engine/pipeline/graph_adapter.py` - Adapter to convert Graph to Pipeline with existing Stage classes
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- `engine/pipeline/graph_toml.py` - TOML-based graph configuration loader
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### Tests
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- `tests/test_graph_pipeline.py` - Comprehensive test suite (17 tests, all passing)
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- `examples/graph_dsl_demo.py` - Demo script showing the new DSL
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- `examples/test_graph_integration.py` - Integration test verifying pipeline execution
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- `examples/pipeline_graph.toml` - Example TOML configuration file
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### Documentation
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- `docs/graph-dsl.md` - Complete DSL documentation with examples
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- `docs/GRAPH_SYSTEM_SUMMARY.md` - This summary document
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## Key Features
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### 1. Graph Abstraction
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- **Node Types**: `source`, `camera`, `effect`, `position`, `display`, `render`, `overlay`
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- **Connections**: Directed edges between nodes with automatic dependency resolution
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- **Validation**: Cycle detection and disconnected node warnings
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### 2. DSL Syntax Options
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#### TOML Configuration
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```toml
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[nodes.source]
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type = "source"
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source = "headlines"
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[nodes.camera]
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type = "camera"
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mode = "scroll"
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[nodes.noise]
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type = "effect"
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effect = "noise"
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intensity = 0.5
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[nodes.display]
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type = "display"
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backend = "terminal"
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[connections]
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list = ["source -> camera -> noise -> display"]
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```
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#### Python API
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```python
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from engine.pipeline.graph import Graph, NodeType
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from engine.pipeline.graph_adapter import graph_to_pipeline
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graph = Graph()
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graph.node("source", NodeType.SOURCE, source="headlines")
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graph.node("camera", NodeType.CAMERA, mode="scroll")
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graph.node("noise", NodeType.EFFECT, effect="noise", intensity=0.5)
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graph.node("display", NodeType.DISPLAY, backend="terminal")
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graph.chain("source", "camera", "noise", "display")
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pipeline = graph_to_pipeline(graph)
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```
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#### Dictionary/JSON Input
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```python
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from engine.pipeline.graph_adapter import dict_to_pipeline
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data = {
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"nodes": {
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"source": "headlines",
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"noise": {"type": "effect", "effect": "noise", "intensity": 0.5},
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"display": {"type": "display", "backend": "terminal"}
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},
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"connections": ["source -> noise -> display"]
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}
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pipeline = dict_to_pipeline(data)
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```
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### 3. Pipeline Integration
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The graph system integrates with the existing pipeline architecture:
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- **Auto-injection**: Pipeline automatically injects required stages (camera_update, render, etc.)
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- **Capability Resolution**: Uses existing capability-based dependency system
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- **Type Safety**: Validates data flow between stages (TEXT_BUFFER, SOURCE_ITEMS, etc.)
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- **Backward Compatible**: Works alongside existing preset system
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### 4. Node Configuration
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| Node Type | Config Options | Example |
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|-----------|----------------|---------|
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| `source` | `source`: "headlines", "poetry", "empty" | `{"type": "source", "source": "headlines"}` |
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| `camera` | `mode`: "scroll", "feed", "horizontal", etc.<br>`speed`: float | `{"type": "camera", "mode": "scroll", "speed": 1.0}` |
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| `effect` | `effect`: effect name<br>`intensity`: 0.0-1.0 | `{"type": "effect", "effect": "noise", "intensity": 0.5}` |
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| `position` | `mode`: "absolute", "relative", "mixed" | `{"type": "position", "mode": "mixed"}` |
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| `display` | `backend`: "terminal", "null", "websocket" | `{"type": "display", "backend": "terminal"}` |
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## Implementation Details
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### Graph Adapter Logic
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1. **Node Mapping**: Converts graph nodes to appropriate Stage classes
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2. **Effect Intensity**: Sets effect intensity globally (consistent with existing architecture)
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3. **Camera Creation**: Maps mode strings to Camera factory methods
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4. **Dependencies**: Effects automatically depend on `render.output`
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5. **Type Flow**: Ensures TEXT_BUFFER flow between render and effects
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### Validation
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- **Disconnected Nodes**: Warns about nodes without connections
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- **Cycle Detection**: Detects circular dependencies using DFS
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- **Type Validation**: Pipeline validates inlet/outlet type compatibility
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## Files Modified
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### Core Pipeline
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- `engine/pipeline/controller.py` - Pipeline class (no changes needed, uses existing architecture)
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- `engine/pipeline/graph_adapter.py` - Added effect intensity setting, fixed PositionStage creation
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- `engine/app/pipeline_runner.py` - Added graph config support
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### Documentation
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- `AGENTS.md` - Updated with task tracking
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## Test Results
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```
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17 tests passed in 0.23s
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- Graph creation and manipulation
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- Connection handling and validation
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- TOML loading and parsing
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- Pipeline conversion and execution
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- Effect intensity configuration
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- Camera mode mapping
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- Positioning mode support
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```
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## Usage Examples
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### Running with Graph Config
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```bash
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python -c "
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from engine.effects.plugins import discover_plugins
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from engine.pipeline.graph_toml import load_pipeline_from_toml
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discover_plugins()
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pipeline = load_pipeline_from_toml('examples/pipeline_graph.toml')
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"
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```
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### Integration with Pipeline Runner
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```bash
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# The pipeline runner now supports graph configs
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# (Implementation in progress)
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```
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## Benefits
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1. **Simplified Configuration**: No need to manually create Stage instances
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2. **Visual Representation**: Graph structure is easier to understand than class hierarchy
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3. **Automatic Dependency Resolution**: Pipeline handles stage ordering automatically
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4. **Flexible Composition**: Easy to add/remove/modify pipeline stages
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5. **Backward Compatible**: Existing presets and stages continue to work
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## Future Enhancements
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1. **CLI Integration**: Add `--graph-config` flag to mainline command
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2. **Visual Builder**: Web-based drag-and-drop pipeline editor
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3. **Script Execution**: Support for loops, conditionals, and timing in graph scripts
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4. **Parameter Binding**: Real-time sensor-to-parameter bindings in graph config
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5. **Pipeline Inspection**: Visual DAG representation with metrics
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