9c781f5f2a
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Add complete Pipeline DSL system including:
- Rust backend (zclaw-pipeline crate) with parser, executor, and state management
- Frontend components: PipelinesPanel, PipelineResultPreview, ClassroomPreviewer
- Pipeline recommender for Agent conversation integration
- 5 pipeline templates: education, marketing, legal, research, productivity
- Documentation for Pipeline DSL architecture
Pipeline DSL enables declarative workflow definitions with:
- YAML-based configuration
- Expression resolution (${inputs.topic}, ${steps.step1.output})
- LLM integration, parallel execution, file export
- Agent smart recommendations in conversations
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
34 lines
791 B
Rust
34 lines
791 B
Rust
//! Parallel execution action
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use futures::stream::{self, StreamExt};
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use serde_json::Value;
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use super::ActionError;
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/// Execute steps in parallel
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pub async fn execute_parallel<F, Fut>(
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items: &[Value],
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max_workers: usize,
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executor: F,
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) -> Result<Vec<Value>, ActionError>
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where
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F: Fn(Value, usize) -> Fut,
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Fut: std::future::Future<Output = Result<Value, ActionError>>,
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{
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let results: Vec<Result<Value, ActionError>> = stream::iter(items.iter().enumerate())
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.map(|(index, item)| {
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let item = item.clone();
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executor(item, index)
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})
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.buffer_unordered(max_workers)
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.collect()
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.await;
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let mut outputs = Vec::new();
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for result in results {
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outputs.push(result?);
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}
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Ok(outputs)
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}
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