forked from genewildish/Mainline
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.
4.5 KiB
4.5 KiB
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
[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
[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
[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
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
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
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
mainline --graph-config pipeline.toml
Inline Graph Definition
mainline --graph 'source:headlines -> noise:noise:0.5 -> display:terminal'
With Preset Override
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
[connections]
list = ["source -> camera -> display"]
# Effects can be conditionally enabled/disabled
Parameter Binding
[nodes.noise]
type = "effect"
effect = "noise"
intensity = 1.0
# intensity can be bound to sensor values at runtime
Pipeline Inspection
[nodes.inspect]
type = "pipeline-inspect"
# Renders live pipeline visualization
Comparison with Stage-Based Approach
Old (Stage-Based)
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)
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.)