forked from genewildish/Mainline
David Gwilliam
915598629a
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.
111 lines
3.3 KiB
Python
111 lines
3.3 KiB
Python
#!/usr/bin/env python3
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"""
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Test script to verify graph-based pipeline integration.
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This script tests that the graph DSL can be used to create working pipelines
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that produce output similar to preset-based pipelines.
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"""
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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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from engine.pipeline.params import PipelineParams
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def test_graph_pipeline_execution():
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"""Test that a graph-based pipeline can execute and produce output."""
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print("=== Testing Graph Pipeline Execution ===")
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# Discover plugins
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discover_plugins()
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# Load pipeline from TOML
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pipeline = load_pipeline_from_toml(
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"examples/pipeline_graph.toml", viewport_width=80, viewport_height=24
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)
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print(f"Pipeline loaded with {len(pipeline._stages)} stages")
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print(f"Stages: {list(pipeline._stages.keys())}")
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# Initialize pipeline
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if not pipeline.initialize():
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print("Failed to initialize pipeline")
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return False
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print("Pipeline initialized successfully")
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# Set up context
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ctx = pipeline.context
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params = PipelineParams(viewport_width=80, viewport_height=24)
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ctx.params = params
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# Execute pipeline with empty items (source will provide content)
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result = pipeline.execute([])
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if result.success:
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print(f"Pipeline executed successfully")
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print(f"Output type: {type(result.data)}")
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if isinstance(result.data, list):
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print(f"Output lines: {len(result.data)}")
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if len(result.data) > 0:
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print(f"First line: {result.data[0][:50]}...")
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return True
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else:
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print(f"Pipeline execution failed: {result.error}")
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return False
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def test_graph_vs_preset():
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"""Compare graph-based and preset-based pipelines."""
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print("\n=== Comparing Graph vs Preset ===")
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from engine.pipeline import get_preset
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# Load graph-based pipeline
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graph_pipeline = load_pipeline_from_toml(
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"examples/pipeline_graph.toml", viewport_width=80, viewport_height=24
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)
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# Load preset-based pipeline (using test-basic as a base)
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preset = get_preset("test-basic")
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if not preset:
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print("test-basic preset not found")
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return False
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# Create pipeline from preset config
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from engine.pipeline import Pipeline
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preset_pipeline = Pipeline(config=preset.to_config())
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print(f"Graph pipeline stages: {len(graph_pipeline._stages)}")
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print(f"Preset pipeline stages: {len(preset_pipeline._stages)}")
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# Compare stage types
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graph_stage_types = {
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name: stage.__class__.__name__ for name, stage in graph_pipeline._stages.items()
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}
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preset_stage_types = {
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name: stage.__class__.__name__
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for name, stage in preset_pipeline._stages.items()
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}
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print("\nGraph pipeline stages:")
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for name, stage_type in graph_stage_types.items():
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print(f" - {name}: {stage_type}")
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print("\nPreset pipeline stages:")
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for name, stage_type in preset_stage_types.items():
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print(f" - {name}: {stage_type}")
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return True
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if __name__ == "__main__":
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success1 = test_graph_pipeline_execution()
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success2 = test_graph_vs_preset()
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if success1 and success2:
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print("\n✓ All tests passed!")
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else:
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print("\n✗ Some tests failed")
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exit(1)
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