The backend part of the human in the loop (HITL) feature and relevant architecture / workflow engine changes.
Signed-off-by: yihong0618 <zouzou0208@gmail.com>
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Co-authored-by: 盐粒 Yanli <yanli@dify.ai>
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- Ensure `EventManager._notify_layers` logs exceptions instead of silently swallowing them
so GraphEngine layer failures surface for debugging
- Introduce unit tests to assert the logger captures the runtime error when collecting events
- Enable the `S110` lint rule to catch `try-except-pass` patterns
- Add proper error logging for existing `try-except-pass` blocks.
This commit:
1. Convert `pause_reason` to `pause_reasons` in `GraphExecution` and relevant classes. Change the field from a scalar value to a list that can contain multiple `PauseReason` objects, ensuring all pause events are properly captured.
2. Introduce a new `WorkflowPauseReason` model to record reasons associated with a specific `WorkflowPause`.
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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 PR fix the issue that `ObjectSegment` are not recursively added to the draft variable pool while loading draft variables from database. It also fixes an issue about loading variables with more than two elements in the its selector.
Enhances #19735.
Closes#21477.
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.