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| """Typed models for TorchReview Copilot outputs and examples.""" | |
| from __future__ import annotations | |
| from typing import Dict, List, Literal | |
| from pydantic import BaseModel, Field | |
| IssueLabel = Literal["syntax", "logic", "performance"] | |
| RiskLevel = Literal["low", "medium", "high"] | |
| class TriageSignal(BaseModel): | |
| """One extracted signal used during issue classification.""" | |
| name: str | |
| value: str | |
| impact: Literal["syntax", "logic", "performance", "mixed"] = "mixed" | |
| weight: float = Field(..., ge=0.0, le=1.0) | |
| evidence: str = "" | |
| class PrototypeMatch(BaseModel): | |
| """Nearest known bug pattern from the built-in task catalog.""" | |
| task_id: str | |
| title: str | |
| label: IssueLabel | |
| similarity: float = Field(..., ge=0.0, le=1.0) | |
| summary: str | |
| rationale: str | |
| class TriageExample(BaseModel): | |
| """Example payload exposed in the demo UI.""" | |
| key: str | |
| title: str | |
| label: IssueLabel | |
| summary: str | |
| code: str | |
| traceback_text: str | |
| context_window: str | |
| task_id: str | |
| class TriagePrototype(BaseModel): | |
| """Canonical issue-pattern representation embedded by the triage engine.""" | |
| task_id: str | |
| title: str | |
| label: IssueLabel | |
| summary: str | |
| reference_text: str | |
| starter_code: str | |
| reference_code: str | |
| traceback_text: str | |
| class TriageResult(BaseModel): | |
| """Structured output produced by the triage pipeline.""" | |
| issue_label: IssueLabel | |
| confidence_scores: Dict[str, float] | |
| repair_risk: RiskLevel | |
| ml_quality_score: float = Field(..., ge=0.0, le=1.0) | |
| lint_score: float = Field(..., ge=0.0, le=1.0) | |
| complexity_penalty: float = Field(..., ge=0.0, le=1.0) | |
| reward_score: float = Field(..., ge=0.0, le=1.0) | |
| summary: str | |
| matched_pattern: PrototypeMatch | |
| repair_plan: List[str] | |
| suggested_next_action: str | |
| extracted_signals: List[TriageSignal] = Field(default_factory=list) | |
| model_backend: str | |
| model_id: str | |
| inference_notes: List[str] = Field(default_factory=list) | |
| analysis_time_ms: float = Field(..., ge=0.0) | |