- Updated the validate_graph_structure method to handle empty graph cases gracefully.
- Introduced a variable for workflow_id to ensure consistent handling of unknown workflow IDs.
- Enhanced code readability and maintainability by refining the method's structure.
- Added a new validation class to ensure that trigger nodes do not coexist with UserInput (start) nodes in the workflow graph.
- Implemented a method in WorkflowService to validate the graph structure before persisting workflows, leveraging the new validation logic.
- Updated unit tests to cover the new validation scenarios and ensure proper error propagation.
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.
- Updated references from `PluginTriggerNode` to `TriggerEventNode` across multiple files to reflect the new naming convention.
- Modified `PluginTriggerNodeData` to `TriggerEventNodeData`, including changes to event parameters for better clarity and consistency in data handling.
- Removed the deprecated `trigger_plugin_node.py` file as part of the refactor.
- Updated `DraftWorkflowTriggerNodeApi` to utilize the new `TriggerDebugEvent` and `TriggerDebugEventPoller` for improved event polling.
- Removed deprecated `poll_debug_event` methods from `TriggerService`, `ScheduleService`, and `WebhookService`, consolidating functionality into the new event structure.
- Enhanced error handling in `invoke_trigger_event` to utilize `TriggerPluginInvokeError` for better clarity on invocation issues.
- Updated frontend API routes to reflect changes in trigger event handling, ensuring consistency across the application.
- Added validation for `PluginTriggerData` and `ScheduleTriggerData` in the `WorkflowService` to support new trigger types.
- Updated debug event return strings in `PluginTriggerDebugEvent` and `WebhookDebugEvent` for clarity and consistency.
- Enhanced logging in `dispatch_triggered_workflows_async` to include subscription and provider IDs, improving traceability during trigger dispatching.
- Refactored DraftWorkflowTriggerNodeApi and DraftWorkflowTriggerRunApi to implement polling for trigger events instead of listening, improving responsiveness and reliability.
- Introduced TriggerSubscriptionBuilderGetApi to retrieve subscription instances for trigger providers, enhancing the API's capabilities.
- Removed deprecated trigger event classes and streamlined event handling in TriggerDebugService, ensuring a cleaner architecture.
- Updated Queue and Stream entities to reflect the changes in trigger event handling, improving overall clarity and maintainability.
These enhancements significantly improve the trigger debugging experience and API usability.
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.
Harry