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

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# Graph-Based Pipeline DSL
This document describes the new graph-based DSL for defining pipelines in Mainline.
## Overview
The graph DSL represents pipelines as nodes and connections, replacing the verbose `XYZStage` naming convention with a more intuitive graph abstraction.
## TOML Syntax
### Basic Pipeline
```toml
[nodes.source]
type = "source"
source = "headlines"
[nodes.camera]
type = "camera"
mode = "scroll"
[nodes.display]
type = "display"
backend = "terminal"
[connections]
list = ["source -> camera -> display"]
```
### With Effects
```toml
[nodes.source]
type = "source"
source = "headlines"
[nodes.noise]
type = "effect"
effect = "noise"
intensity = 0.5
[nodes.fade]
type = "effect"
effect = "fade"
intensity = 0.8
[nodes.display]
type = "display"
backend = "terminal"
[connections]
list = ["source -> noise -> fade -> display"]
```
### With Positioning
```toml
[nodes.source]
type = "source"
source = "headlines"
[nodes.position]
type = "position"
mode = "mixed"
[nodes.display]
type = "display"
backend = "terminal"
[connections]
list = ["source -> position -> display"]
```
## Python API
### Basic Construction
```python
from engine.pipeline.graph import Graph, NodeType
graph = Graph()
graph.node("source", NodeType.SOURCE, source="headlines")
graph.node("camera", NodeType.CAMERA, mode="scroll")
graph.node("display", NodeType.DISPLAY, backend="terminal")
graph.chain("source", "camera", "display")
pipeline = graph_to_pipeline(graph)
```
### With Effects
```python
from engine.pipeline.graph import Graph, NodeType
graph = Graph()
graph.node("source", NodeType.SOURCE, source="headlines")
graph.node("noise", NodeType.EFFECT, effect="noise", intensity=0.5)
graph.node("fade", NodeType.EFFECT, effect="fade", intensity=0.8)
graph.node("display", NodeType.DISPLAY, backend="terminal")
graph.chain("source", "noise", "fade", "display")
pipeline = graph_to_pipeline(graph)
```
### Dictionary/JSON Input
```python
from engine.pipeline.graph_adapter import dict_to_pipeline
data = {
"nodes": {
"source": "headlines",
"noise": {"type": "effect", "effect": "noise", "intensity": 0.5},
"display": {"type": "display", "backend": "terminal"}
},
"connections": ["source -> noise -> display"]
}
pipeline = dict_to_pipeline(data)
```
## CLI Usage
### Using Graph Config File
```bash
mainline --graph-config pipeline.toml
```
### Inline Graph Definition
```bash
mainline --graph 'source:headlines -> noise:noise:0.5 -> display:terminal'
```
### With Preset Override
```bash
mainline --preset demo --graph-modify 'add:noise:0.5 after:source'
```
## Node Types
| Type | Description | Config Options |
|------|-------------|----------------|
| `source` | Data source | `source`: "headlines", "poetry", "empty", etc. |
| `camera` | Viewport camera | `mode`: "scroll", "feed", "horizontal", etc. `speed`: float |
| `effect` | Visual effect | `effect`: effect name, `intensity`: 0.0-1.0 |
| `position` | Positioning mode | `mode`: "absolute", "relative", "mixed" |
| `display` | Output backend | `backend`: "terminal", "null", "websocket", etc. |
| `render` | Text rendering | (auto-injected) |
| `overlay` | Message overlay | (auto-injected) |
## Advanced Features
### Conditional Connections
```toml
[connections]
list = ["source -> camera -> display"]
# Effects can be conditionally enabled/disabled
```
### Parameter Binding
```toml
[nodes.noise]
type = "effect"
effect = "noise"
intensity = 1.0
# intensity can be bound to sensor values at runtime
```
### Pipeline Inspection
```toml
[nodes.inspect]
type = "pipeline-inspect"
# Renders live pipeline visualization
```
## Comparison with Stage-Based Approach
### Old (Stage-Based)
```python
pipeline = Pipeline()
pipeline.add_stage("source", DataSourceStage(HeadlinesDataSource()))
pipeline.add_stage("camera", CameraStage(Camera.scroll()))
pipeline.add_stage("render", FontStage())
pipeline.add_stage("noise", EffectPluginStage(noise_effect))
pipeline.add_stage("display", DisplayStage(terminal_display))
```
### New (Graph-Based)
```python
graph = Graph()
graph.node("source", NodeType.SOURCE, source="headlines")
graph.node("camera", NodeType.CAMERA, mode="scroll")
graph.node("noise", NodeType.EFFECT, effect="noise")
graph.node("display", NodeType.DISPLAY, backend="terminal")
graph.chain("source", "camera", "noise", "display")
pipeline = graph_to_pipeline(graph)
```
The graph system automatically:
- Inserts the render stage between camera and effects
- Handles capability-based dependency resolution
- Auto-injects required stages (camera_update, render, etc.)

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# Presets Usage Guide
## Overview
The sideline branch introduces a new preset system that allows you to easily configure different pipeline behaviors. This guide explains the available presets and how to use them.
## Available Presets
### 1. upstream-default
**Purpose:** Matches the default upstream Mainline operation for comparison.
**Configuration:**
- **Display:** Terminal (not pygame)
- **Camera:** Scroll mode
- **Effects:** noise, fade, glitch, firehose (classic four effects)
- **Positioning:** Mixed mode
- **Message Overlay:** Disabled (matches upstream)
**Usage:**
```bash
python -m mainline --preset upstream-default --display terminal
```
**Best for:**
- Comparing sideline vs upstream behavior
- Legacy terminal-based operation
- Baseline performance testing
### 2. demo
**Purpose:** Showcases sideline features including hotswappable effects and sensors.
**Configuration:**
- **Display:** Pygame (graphical display)
- **Camera:** Scroll mode
- **Effects:** noise, fade, glitch, firehose, hud (with visual feedback)
- **Positioning:** Mixed mode
- **Message Overlay:** Enabled (with ntfy integration)
**Features:**
- **Hotswappable Effects:** Effects can be toggled and modified at runtime
- **LFO Sensor Modulation:** Oscillator sensor provides smooth intensity modulation
- **Visual Feedback:** HUD effect shows current effect state and pipeline info
- **Mixed Positioning:** Optimal balance of performance and control
**Usage:**
```bash
python -m mainline --preset demo --display pygame
```
**Best for:**
- Exploring sideline capabilities
- Testing effect hotswapping
- Demonstrating sensor integration
### 3. demo-base / demo-pygame
**Purpose:** Base presets for custom effect hotswapping experiments.
**Configuration:**
- **Display:** Terminal (base) or Pygame (pygame variant)
- **Camera:** Feed mode
- **Effects:** Empty (add your own)
- **Positioning:** Mixed mode
**Usage:**
```bash
python -m mainline --preset demo-pygame --display pygame
```
### 4. Other Presets
- `poetry`: Poetry feed with subtle effects
- `firehose`: High-speed firehose mode
- `ui`: Interactive UI mode with control panel
- `fixture`: Uses cached headline fixtures
- `websocket`: WebSocket display mode
## Positioning Modes
The `--positioning` flag controls how text is positioned in the terminal:
```bash
# Relative positioning (newlines, good for scrolling)
python -m mainline --positioning relative --preset demo
# Absolute positioning (cursor codes, good for overlays)
python -m mainline --positioning absolute --preset demo
# Mixed positioning (default, optimal balance)
python -m mainline --positioning mixed --preset demo
```
## Pipeline Stages
### Upstream-Default Pipeline
1. **Source Stage:** Headlines data source
2. **Viewport Filter:** Filters items to viewport height
3. **Font Stage:** Renders headlines as block characters
4. **Camera Stages:** Scrolling animation
5. **Effect Stages:** noise, fade, glitch, firehose
6. **Display Stage:** Terminal output
### Demo Pipeline
1. **Source Stage:** Headlines data source
2. **Viewport Filter:** Filters items to viewport height
3. **Font Stage:** Renders headlines as block characters
4. **Camera Stages:** Scrolling animation
5. **Effect Stages:** noise, fade, glitch, firehose, hud
6. **Message Overlay:** Ntfy message integration
7. **Display Stage:** Pygame output
## Command-Line Examples
### Basic Usage
```bash
# Run upstream-default preset
python -m mainline --preset upstream-default --display terminal
# Run demo preset
python -m mainline --preset demo --display pygame
# Run with custom positioning
python -m mainline --preset demo --display pygame --positioning absolute
```
### Comparison Testing
```bash
# Capture upstream output
python -m mainline --preset upstream-default --display null --viewport 80x24
# Capture sideline output
python -m mainline --preset demo --display null --viewport 80x24
```
### Hotswapping Effects
The demo preset supports hotswapping effects at runtime:
- Use the WebSocket display to send commands
- Toggle effects on/off
- Adjust intensity values in real-time
## Configuration Files
### Built-in Presets
Location: `engine/pipeline/presets.py` (Python code)
### User Presets
Location: `~/.config/mainline/presets.toml` or `./presets.toml`
Example user preset:
```toml
[presets.my-custom-preset]
description = "My custom configuration"
source = "headlines"
display = "terminal"
camera = "scroll"
effects = ["noise", "fade"]
positioning = "mixed"
viewport_width = 100
viewport_height = 30
```
## Sensor Configuration
### Oscillator Sensor (LFO)
The oscillator sensor provides Low Frequency Oscillator modulation:
```toml
[sensors.oscillator]
enabled = true
waveform = "sine" # sine, square, triangle, sawtooth
frequency = 0.05 # 20 second cycle (gentle)
amplitude = 0.5 # 50% modulation
```
### Effect Configuration
Effect intensities can be configured with initial values:
```toml
[effect_configs.noise]
enabled = true
intensity = 1.0
[effect_configs.fade]
enabled = true
intensity = 1.0
[effect_configs.glitch]
enabled = true
intensity = 0.5
```
## Troubleshooting
### No Display Output
- Check if display backend is available (pygame, terminal, etc.)
- Use `--display null` for headless testing
### Effects Not Modulating
- Ensure sensor is enabled in presets.toml
- Check effect intensity values in configuration
### Performance Issues
- Use `--positioning relative` for large buffers
- Reduce viewport height for better performance
- Use null display for testing without rendering

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#!/usr/bin/env python3
"""
Demo script showing the new graph-based DSL for pipeline configuration.
This demonstrates how to define pipelines using the graph abstraction,
which is more intuitive than the verbose XYZStage naming convention.
"""
from engine.effects.plugins import discover_plugins
from engine.pipeline.graph import Graph, NodeType
from engine.pipeline.graph_adapter import graph_to_pipeline, dict_to_pipeline
def demo_imperative_api():
"""Demo: Imperative Python API for building graphs."""
print("=== Imperative Python API ===")
graph = Graph()
graph.node("source", NodeType.SOURCE, source="headlines")
graph.node("camera", NodeType.CAMERA, mode="scroll", speed=1.0)
graph.node("noise", NodeType.EFFECT, effect="noise", intensity=0.3)
graph.node("display", NodeType.DISPLAY, backend="null")
# Connect nodes in a chain
graph.chain("source", "camera", "noise", "display")
# Validate the graph
errors = graph.validate()
if errors:
print(f"Validation errors: {errors}")
return
# Convert to pipeline
pipeline = graph_to_pipeline(graph, viewport_width=80, viewport_height=24)
print(f"Pipeline created with {len(pipeline._stages)} stages:")
for name, stage in pipeline._stages.items():
print(f" - {name}: {stage.__class__.__name__}")
return pipeline
def demo_dict_api():
"""Demo: Dictionary-based API for building graphs."""
print("\n=== Dictionary API ===")
data = {
"nodes": {
"source": "headlines",
"camera": {"type": "camera", "mode": "scroll", "speed": 1.0},
"noise": {"type": "effect", "effect": "noise", "intensity": 0.5},
"fade": {"type": "effect", "effect": "fade", "intensity": 0.8},
"display": {"type": "display", "backend": "null"},
},
"connections": ["source -> camera -> noise -> fade -> display"],
}
pipeline = dict_to_pipeline(data, viewport_width=80, viewport_height=24)
print(f"Pipeline created with {len(pipeline._stages)} stages:")
for name, stage in pipeline._stages.items():
print(f" - {name}: {stage.__class__.__name__}")
return pipeline
def demo_graph_validation():
"""Demo: Graph validation."""
print("\n=== Graph Validation ===")
# Create a graph with a cycle
graph = Graph()
graph.node("a", NodeType.SOURCE)
graph.node("b", NodeType.CAMERA)
graph.node("c", NodeType.DISPLAY)
graph.connect("a", "b")
graph.connect("b", "c")
graph.connect("c", "a") # Creates cycle
errors = graph.validate()
print(f"Cycle detection errors: {errors}")
# Create a valid graph
graph2 = Graph()
graph2.node("source", NodeType.SOURCE, source="headlines")
graph2.node("display", NodeType.DISPLAY, backend="null")
graph2.connect("source", "display")
errors2 = graph2.validate()
print(f"Valid graph errors: {errors2}")
def demo_node_types():
"""Demo: Different node types."""
print("\n=== Node Types ===")
graph = Graph()
# Source node
graph.node("headlines", NodeType.SOURCE, source="headlines")
print("✓ Source node created")
# Camera node with different modes
graph.node("camera_scroll", NodeType.CAMERA, mode="scroll", speed=1.0)
graph.node("camera_feed", NodeType.CAMERA, mode="feed", speed=0.5)
graph.node("camera_horizontal", NodeType.CAMERA, mode="horizontal", speed=1.0)
print("✓ Camera nodes created (scroll, feed, horizontal)")
# Effect nodes
graph.node("noise", NodeType.EFFECT, effect="noise", intensity=0.3)
graph.node("fade", NodeType.EFFECT, effect="fade", intensity=0.8)
print("✓ Effect nodes created (noise, fade)")
# Positioning node
graph.node("position", NodeType.POSITION, mode="mixed")
print("✓ Positioning node created")
# Display nodes
graph.node("terminal", NodeType.DISPLAY, backend="terminal")
graph.node("null", NodeType.DISPLAY, backend="null")
print("✓ Display nodes created")
print(f"\nTotal nodes: {len(graph.nodes)}")
if __name__ == "__main__":
# Discover effect plugins first
discover_plugins()
# Run demos
demo_imperative_api()
demo_dict_api()
demo_graph_validation()
demo_node_types()
print("\n=== Demo Complete ===")

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#!/usr/bin/env python3
"""
Test script to verify graph-based pipeline integration.
This script tests that the graph DSL can be used to create working pipelines
that produce output similar to preset-based pipelines.
"""
from engine.effects.plugins import discover_plugins
from engine.pipeline.graph_toml import load_pipeline_from_toml
from engine.pipeline.params import PipelineParams
def test_graph_pipeline_execution():
"""Test that a graph-based pipeline can execute and produce output."""
print("=== Testing Graph Pipeline Execution ===")
# Discover plugins
discover_plugins()
# Load pipeline from TOML
pipeline = load_pipeline_from_toml(
"examples/pipeline_graph.toml", viewport_width=80, viewport_height=24
)
print(f"Pipeline loaded with {len(pipeline._stages)} stages")
print(f"Stages: {list(pipeline._stages.keys())}")
# Initialize pipeline
if not pipeline.initialize():
print("Failed to initialize pipeline")
return False
print("Pipeline initialized successfully")
# Set up context
ctx = pipeline.context
params = PipelineParams(viewport_width=80, viewport_height=24)
ctx.params = params
# Execute pipeline with empty items (source will provide content)
result = pipeline.execute([])
if result.success:
print(f"Pipeline executed successfully")
print(f"Output type: {type(result.data)}")
if isinstance(result.data, list):
print(f"Output lines: {len(result.data)}")
if len(result.data) > 0:
print(f"First line: {result.data[0][:50]}...")
return True
else:
print(f"Pipeline execution failed: {result.error}")
return False
def test_graph_vs_preset():
"""Compare graph-based and preset-based pipelines."""
print("\n=== Comparing Graph vs Preset ===")
from engine.pipeline import get_preset
# Load graph-based pipeline
graph_pipeline = load_pipeline_from_toml(
"examples/pipeline_graph.toml", viewport_width=80, viewport_height=24
)
# Load preset-based pipeline (using test-basic as a base)
preset = get_preset("test-basic")
if not preset:
print("test-basic preset not found")
return False
# Create pipeline from preset config
from engine.pipeline import Pipeline
preset_pipeline = Pipeline(config=preset.to_config())
print(f"Graph pipeline stages: {len(graph_pipeline._stages)}")
print(f"Preset pipeline stages: {len(preset_pipeline._stages)}")
# Compare stage types
graph_stage_types = {
name: stage.__class__.__name__ for name, stage in graph_pipeline._stages.items()
}
preset_stage_types = {
name: stage.__class__.__name__
for name, stage in preset_pipeline._stages.items()
}
print("\nGraph pipeline stages:")
for name, stage_type in graph_stage_types.items():
print(f" - {name}: {stage_type}")
print("\nPreset pipeline stages:")
for name, stage_type in preset_stage_types.items():
print(f" - {name}: {stage_type}")
return True
if __name__ == "__main__":
success1 = test_graph_pipeline_execution()
success2 = test_graph_vs_preset()
if success1 and success2:
print("\n✓ All tests passed!")
else:
print("\n✗ Some tests failed")
exit(1)