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| """ | |
| Typed Pydantic v2 models for action, observation, and state schemas. | |
| All models follow the OpenEnv specification for RL environment interfaces. | |
| """ | |
| from __future__ import annotations | |
| from enum import Enum | |
| from typing import Any, Dict, List, Literal, Optional | |
| from pydantic import BaseModel, Field | |
| # -- Domain enumerations -- | |
| class BugType(str, Enum): | |
| """Supported bug categories for classification.""" | |
| CRASH = "crash" | |
| UI = "ui" | |
| PERFORMANCE = "performance" | |
| SECURITY = "security" | |
| DATA_LOSS = "data_loss" | |
| COMPATIBILITY = "compatibility" | |
| class Priority(str, Enum): | |
| """Severity levels ordered low to critical.""" | |
| LOW = "low" | |
| MEDIUM = "medium" | |
| HIGH = "high" | |
| CRITICAL = "critical" | |
| class SuggestedAction(str, Enum): | |
| """Recommended triage actions.""" | |
| FIX_IMMEDIATELY = "fix_immediately" | |
| SCHEDULE_SPRINT = "schedule_sprint" | |
| NEEDS_MORE_INFO = "needs_more_info" | |
| WONTFIX = "wontfix" | |
| DUPLICATE = "duplicate" | |
| # -- Domain constants -- | |
| DEVELOPERS: list[str] = ["Alice", "Bob", "Carol", "David", "Eve"] | |
| DEVELOPER_SPECIALIZATIONS: Dict[str, List[str]] = { | |
| "Alice": ["crash", "performance", "data_loss"], | |
| "Bob": ["crash", "security"], | |
| "Carol": ["ui", "compatibility"], | |
| "David": ["security", "data_loss"], | |
| "Eve": ["ui", "performance", "compatibility"], | |
| } | |
| PRIORITY_ORDER: Dict[str, int] = { | |
| "low": 0, | |
| "medium": 1, | |
| "high": 2, | |
| "critical": 3, | |
| } | |
| # -- Action / Observation / State models -- | |
| class BugTriageAction(BaseModel): | |
| """Agent action submitted via the /step endpoint.""" | |
| task_id: Literal["task_1", "task_2", "task_3"] | |
| bug_type: Optional[BugType] = None | |
| priority: Optional[Priority] = None | |
| assigned_developer: Optional[str] = None | |
| suggested_action: Optional[SuggestedAction] = None | |
| reasoning: Optional[str] = None | |
| confidence: Optional[float] = Field( | |
| default=None, | |
| ge=0.0, | |
| le=1.0, | |
| description=( | |
| "Optional agent confidence in [0.0, 1.0]. " | |
| "Enables confidence-calibration bonus/penalty in reward." | |
| ), | |
| ) | |
| metadata: Dict[str, Any] = Field(default_factory=dict) | |
| model_config = {"use_enum_values": True} | |
| class BugReport(BaseModel): | |
| """Structured representation of a software bug report.""" | |
| bug_id: str | |
| title: str | |
| description: str | |
| logs: Optional[str] = None | |
| environment: Optional[str] = None | |
| reporter: Optional[str] = None | |
| created_at: Optional[str] = None | |
| metadata: Dict[str, Any] = Field(default_factory=dict) | |
| class BugTriageObservation(BaseModel): | |
| """Observation returned by reset() and step().""" | |
| done: bool = False | |
| reward: float = 0.0 | |
| task_id: str = "" | |
| bug_report: Optional[BugReport] = None | |
| available_developers: List[str] = Field( | |
| default_factory=lambda: list(DEVELOPERS) | |
| ) | |
| step_number: int = 0 | |
| feedback: str = "" | |
| grader_score: Optional[float] = None | |
| episode_id: Optional[str] = None | |
| metadata: Dict[str, Any] = Field(default_factory=dict) | |
| class BugTriageState(BaseModel): | |
| """Episode-level metadata returned by the /state endpoint.""" | |
| episode_id: Optional[str] = None | |
| step_count: int = 0 | |
| task_id: str = "" | |
| bug_id: Optional[str] = None | |
| cumulative_reward: float = 0.0 | |
| # -- Request / Response models for FastAPI endpoints -- | |
| class ResetRequest(BaseModel): | |
| task_id: Literal["task_1", "task_2", "task_3"] = "task_1" | |
| class StepRequest(BaseModel): | |
| episode_id: str | |
| action: BugTriageAction | |
| class GraderRequest(BaseModel): | |
| episode_id: str | |
| task_id: Literal["task_1", "task_2", "task_3"] | |
| class GraderResponse(BaseModel): | |
| task_id: str | |
| episode_id: str | |
| score: float | |
| breakdown: Dict[str, float] | |
| passed: bool | |
| class BaselineResponse(BaseModel): | |
| baseline_model: str | |
| results: Dict[str, Dict[str, Any]] | |
| mean_score: float | |