| from pydantic import BaseModel, validator |
| from typing import List, Optional, Dict, Literal |
|
|
| |
| class Observation(BaseModel): |
| code: str |
| intent: str |
| constraints: List[str] |
| task_id: str |
| difficulty: Literal["easy", "medium", "hard"] |
| step_index: int |
|
|
| |
| class Action(BaseModel): |
| action_type: Literal["detect", "risk", "fix"] |
| detected_issues: Optional[List[str]] = None |
| risk_level: Optional[Literal["low", "medium", "high"]] = None |
| suggested_fix: Optional[str] = None |
|
|
| |
| class AgentResponse(BaseModel): |
| """Structured response from LLM agent with confidence and reasoning""" |
| issues_detected: List[str] = [] |
| risk_level: Literal["low", "medium", "high"] = "low" |
| fix: str = "" |
| confidence: float = 0.5 |
| reasoning: str = "" |
| reward_breakdown: Dict[str, float] = {} |
| |
| @validator('confidence') |
| def clamp_confidence(cls, v): |
| return max(0.0, min(1.0, v)) |
|
|
| |
| class Reward(BaseModel): |
| value: float |
| feedback: str |
| partial_scores: Dict[str, float] |
| breakdown: Dict[str, float] = {} |
| |
| @validator('value') |
| def clamp_value(cls, v): |
| return max(0.0, min(1.0, v)) |
|
|
| |
| class AgentState(BaseModel): |
| """State tracking for RL agent learning""" |
| episode: int = 0 |
| total_reward: float = 0.0 |
| best_score: float = 0.0 |
| prompt_strategy: str = "default" |
| past_failures: List[str] = [] |
| learning_rate: float = 0.1 |
| improvement_history: List[float] = [] |
