Added a timestamp field to the SystemVariable model and updated the WorkflowAppRunner to include the current timestamp during execution. Enhanced node type checks to recognize trigger nodes in various services, ensuring proper handling of system variables and node outputs in TriggerEventNode and TriggerScheduleNode. This improves the overall workflow execution context and maintains consistency across node types.
- add suspend, timeslice, and trigger post engine layers
- introduce CFS workflow scheduler tasks and supporting entities
- update async workflow, trigger, and webhook services to wire in the new scheduling flow
- Replaced direct imports of `TriggerProviderID` and `ToolProviderID` from `core.plugin.entities.plugin` with imports from `models.provider_ids` for better organization.
- Refactored workflow node classes to inherit from a unified `Node` class, improving consistency and maintainability.
- Removed unused code and comments to clean up the implementation, particularly in the `workflow_trigger.py` and `builtin_tools_manage_service.py` files.
These changes enhance the clarity and structure of the codebase, facilitating easier future modifications.
refactor(api): Separate SegmentType for Integer/Float to Enable Pydantic Serialization (#22025)
This PR addresses serialization issues in the VariablePool model by separating the `value_type` tags for `IntegerSegment`/`FloatSegment` and `IntegerVariable`/`FloatVariable`. Previously, both Integer and Float types shared the same `SegmentType.NUMBER` tag, causing conflicts during serialization.
Key changes:
- Introduce distinct `value_type` tags for Integer and Float segments/variables
- Add `VariableUnion` and `SegmentUnion` types for proper type discrimination
- Leverage Pydantic's discriminated union feature for seamless serialization/deserialization
- Enable accurate serialization of data structures containing these types
Closes#22024.
This pull request introduces a feature aimed at improving the debugging experience during workflow editing. With the addition of variable persistence, the system will automatically retain the output variables from previously executed nodes. These persisted variables can then be reused when debugging subsequent nodes, eliminating the need for repetitive manual input.
By streamlining this aspect of the workflow, the feature minimizes user errors and significantly reduces debugging effort, offering a smoother and more efficient experience.
Key highlights of this change:
- Automatic persistence of output variables for executed nodes.
- Reuse of persisted variables to simplify input steps for nodes requiring them (e.g., `code`, `template`, `variable_assigner`).
- Enhanced debugging experience with reduced friction.
Closes#19735.
