Add StreamProcessor.visualize() method #300
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Add visualization support that displays the workflow graph with node classification styling to help understand the streaming data flow: - Static nodes (gray): Pre-computed once, cached - Context keys/nodes (blue): Input entry points and cached dependents - Dynamic keys/nodes (green): Chunk inputs and per-chunk computations - Accumulator keys (orange cylinder): Aggregation points - Finalize nodes (lavender): Computed from accumulators - Target keys (double border): Final outputs The method stores the base workflow during __init__ to show the full graph including pre-computed static subgraphs. Dynamic and context keys are styled with black borders to override sciline's red "unsatisfied" styling since these are intentional stream inputs. --- Prompt: develop an understanding of StreamProcessor, then think about how we can make an equivalent to sciline.Pipeline.visualize, highlight specifics of the wrapping StreamProcessor - where are accumulators, which are dynamic inputs, which nodes are cached and which are downstream of dynamic and thus recomputed?
SimonHeybrock
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- Unify _find_descendants and _find_graph_descendants into a single function that accepts either Pipeline or DiGraph - Convert _classify_nodes_for_visualization to a method _classify_nodes on StreamProcessor for cleaner access to instance attributes - Add comprehensive tests for visualization in streaming_visualize_test.py Prompt: Please carefully think through and review the implementation of StreamProcessor.visualize. Could it be written in a better way, e.g., by relying more on what StreamProcessor already does in __init__ (node classification, ...)? Co-Authored-By: Claude Opus 4.5 <[email protected]>
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- Exclude nodes unreachable from target_keys (unrelated branches) - Show full static subgraph including nodes upstream of input keys - Build visualization workflow once in constructor for consistency - Classify nodes based on actual visualized graph to ensure all visible nodes are properly styled Prompt: StreamProcessor.visualize currently seems to show the full graph, even parts/nodes unreachable/unused by computing the given targets. Also fixes: visualization shows nodes upstream of input keys (dynamic_keys, context_keys). Also fixes: some root nodes appearing unstyled/white. Co-Authored-By: Claude Opus 4.5 <[email protected]>
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Summary
StreamProcessor.visualize()method that post-processes sciline's visualization to highlight streaming-specific node classificationsTest plan
tests/streaming_visualize_test.pycovering:_classify_nodes)visualize()🤖 Generated with Claude Code