- Updated the naming convention from 'trigger_name' to 'event_name' across various models and services to align with the new event-driven architecture.
- Refactored methods in PluginTriggerManager and PluginTriggerProviderController to use 'invoke_trigger_event' instead of 'invoke_trigger'.
- Adjusted database migration scripts to reflect changes in the schema, including the addition of 'event_name' and 'subscription_id' fields in the workflow_plugin_triggers table.
- Removed deprecated trigger-related methods in WorkflowPluginTriggerService to streamline the codebase.
- Added a new event handler for syncing plugin trigger relationships when a draft workflow is synced, ensuring that the database reflects the current state of plugin triggers.
- Introduced subscription management features in the frontend, allowing users to select, add, and remove subscriptions for trigger plugins.
- Updated various components to support subscription handling, including the addition of new UI elements for subscription selection and removal.
- Enhanced internationalization support by adding new translation keys related to subscription management.
These changes improve the overall functionality and user experience of trigger plugins within workflows.
- Remove triggered_by field from WorkflowWebhookTrigger model
- Replace manual webhook creation/deletion APIs with automatic sync via WebhookService
- Keep only GET API for retrieving webhook information
- Use same webhook ID for both debug and production environments (differentiated by endpoint)
- Add sync_webhook_relationships to automatically manage webhook lifecycle
- Update tests to remove triggered_by references
- Clean up unused imports and fix type checking issues
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Co-Authored-By: Claude <noreply@anthropic.com>
- Modified `PluginTriggerApi` to accept `trigger_name` as a JSON argument and return encoded plugin triggers.
- Updated `WorkflowPluginTrigger` model to replace `trigger_id` with `trigger_name` for better clarity.
- Adjusted `WorkflowPluginTriggerService` to handle the new `trigger_name` field and ensure proper error handling for subscriptions.
- Enhanced `workflow_trigger_fields` to include `trigger_name` in the plugin trigger schema.
This change improves the API's clarity and aligns the model with the updated naming conventions.
- Renamed `TriggerSubscriptionBuilderRequestLogsApi` to `TriggerSubscriptionBuilderLogsApi` for clarity.
- Updated the API endpoint to retrieve logs for subscription builders.
- Enhanced logging functionality in `TriggerSubscriptionBuilderService` to append and list logs more effectively.
- Refactored trigger processing tasks to improve naming consistency and clarity in logging.
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- Add new workflow plugin trigger service for managing plugin-based triggers
- Implement trigger provider encryption utilities for secure credential storage
- Add custom trigger errors module for better error handling
- Refactor trigger provider and manager classes for improved plugin integration
- Update API endpoints to support plugin trigger workflows
- Add database migration for plugin trigger workflow support
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Remove the debug endpoint for cleaner API structure
- Add support for TRIGGER_PLUGIN in NodeType enumeration
- Implement WorkflowPluginTrigger model to map plugin triggers to workflow nodes
- Enhance TriggerService to process plugin triggers and store trigger data in Redis
- Update node mapping to include TriggerPluginNode for workflow execution
Co-authored-by: Claude <noreply@anthropic.com>
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.
- Add `node_execution_id` column to `WorkflowDraftVariable`, allowing efficient implementation of
the "Reset to last run value" feature.
- Add additional index for `WorkflowNodeExecutionModel` to improve the performance of last run lookup.
Closes#20745.
Currently, `WorkflowNodeExecution.execution_metadata_dict` returns `None` when metadata is absent in the database. This requires all callers to perform `None` checks when processing metadata, leading to more complex caller-side logic.
This pull request updates the `execution_metadata_dict` method to return an empty dictionary instead of `None` when metadata is absent. This change would simplify the caller logic, as it removes the need for explicit `None` checks and provides a more consistent data structure to work with.
