7e0579f65d
- Added 5 new capabilities: feature-engineering, annotation-ingestion, ml-training, ml-inference, prediction-ui - Updated 2 existing capabilities: backend-api, span-annotation - All specs synced from openspec/changes/candle-backend/specs/
2.7 KiB
ADDED Requirements
Requirement: Span annotation JSON export for ML pipeline
The system SHALL provide a GET /api/span-annotations/export endpoint that exports all span annotations for a given chart as JSON in the format expected by the ML pipeline. The output SHALL be a JSON object with an annotations array where each entry has: id, start_time (Unix timestamp), end_time (Unix timestamp), label, confidence (nullable), outcome (nullable), and sub_spans (nullable). The endpoint SHALL accept an optional chartId query parameter.
Scenario: Export span annotations as JSON
- WHEN GET /api/span-annotations/export?chartId=3 is called
- THEN the system returns a JSON object with all span annotations for chart 3 in the ML pipeline format
Scenario: Export without chartId
- WHEN GET /api/span-annotations/export is called without chartId
- THEN the system exports span annotations for the most recently created chart
Requirement: Prediction-sourced span annotation creation
The system SHALL support creating span annotations with a source field indicating whether the annotation was created by a human ("human"), confirmed from a model prediction ("model_confirmed"), or corrected from a model prediction ("model_corrected"). The existing POST endpoint for span annotations SHALL accept an optional source field (default: "human") and optional model_prediction field (object with label and confidence from the original prediction).
Scenario: Create human annotation
- WHEN a span annotation is created without a source field
- THEN the source defaults to "human"
Scenario: Confirm model prediction
- WHEN a user confirms a model prediction as an annotation
- THEN the span annotation is created with source "model_confirmed" and model_prediction containing the original predicted label and confidence
Scenario: Correct model prediction
- WHEN a user changes the label of a model prediction before saving
- THEN the span annotation is created with source "model_corrected" and model_prediction containing the original predicted label and confidence
Requirement: Negative annotation for dismissed predictions
The system SHALL support saving negative annotations when a user dismisses a model prediction as "not a pattern". A negative annotation SHALL have label "O", source "human_correction", and a model_prediction field recording what the model originally predicted.
Scenario: Save negative annotation
- WHEN user dismisses a "bull_flag" prediction with confidence 0.72
- THEN the system creates a span annotation with label "O", source "human_correction", and model_prediction
{ "label": "bull_flag", "confidence": 0.72 }