File size: 4,374 Bytes
4e9b744 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | """Pydantic schemas for interview feedback output."""
from typing import List, Optional
from pydantic import BaseModel, Field
class CandidatFeedback(BaseModel):
"""Feedback section for the candidate."""
points_forts: List[str] = Field(..., description="Liste des points forts démontrés")
points_a_ameliorer: List[str] = Field(..., description="Liste des axes d'amélioration identifiés")
conseils_apprentissage: List[str] = Field(..., description="Ressources ou actions concrètes pour progresser")
score_global: int = Field(..., description="Note globale sur 100")
feedback_constructif: str = Field(..., description="Message bienveillant et constructif adressé au candidat")
class MetricsBreakdown(BaseModel):
"""Detailed breakdown of scores."""
communication: float = Field(..., description="Score 0-10: Orthographe, grammaire, clarté")
technique: float = Field(..., description="Score 0-10: Compétence technique (SOAR)")
comportemental: float = Field(..., description="Score 0-10: Soft skills (STAR)")
fidelite_cv: float = Field(..., description="Score 0-10: Cohérence avec le CV")
class FraudDetectionMetrics(BaseModel):
"""Detailed fraud detection metrics (0-100)."""
vocab_score: int = Field(..., description="Usage de mots 'GPT' (delve, tapestry...)")
structure_score: int = Field(..., description="Patterns structurels (listes, longueur...)")
paste_score: int = Field(..., description="Basé sur l'événement copier-coller")
pattern_score: int = Field(..., description="Répétitions, questions en retour, proximité CV")
class FraudDetection(BaseModel):
"""Complete fraud analysis."""
score_global: int = Field(..., description="Probabilité globale d'usage IA (0-100)")
red_flag: bool = Field(False, description="Vrai si score > 70")
detected_keywords: List[str] = Field(..., description="Mots suspects identifiés")
resume: str = Field(..., description="Eplication courte")
details: FraudDetectionMetrics
class DashboardCompetences(BaseModel):
"""Score de similarité basé sur la triade."""
technique: float = Field(..., description="Adéquation Hard Skills (0-100)")
cognitive: float = Field(..., description="Raisonnement et structure (0-100)")
comportementale: float = Field(..., description="Soft Skills & Culture Fit (0-100)")
average_score: float = Field(..., description="Moyenne pondérée")
class DecisionStrategique(BaseModel):
"""Pilier décisionnel du rapport."""
recommendation: str = Field(..., description="RECRUTER, APPROFONDIR ou REJETER")
action_plan: str = Field(..., description="Recommandation d'action immédiate (ex: Tester sur SQL)")
so_what: str = Field(..., description="Impact business direct du candidat (Le 'Pourquoi' stratégique)")
class RolsPcdAnalysis(BaseModel):
"""Analyse structurée ROLS et PCD."""
rols_summary: str = Field(..., description="Résumé ROLS (Résumé, Objectifs, Localisation, Stratégie)")
pcd_analysis: str = Field(..., description="Analyse PCD (Produits, Clients, Distribution)")
class EntrepriseInsights(BaseModel):
"""Insights section for the recruiter/company."""
correspondance_profil_offre: str = Field(..., description="Analyse de l'adéquation CV/Poste")
# Dashboard
dashboard: DashboardCompetences = Field(..., description="Tableau de bord des compétences")
# Qualitative Analysis
qualitative_analysis: RolsPcdAnalysis = Field(..., description="Analyse structurée ROLS/PCD")
# Strategic Decision
decision: DecisionStrategique = Field(..., description="Aide à la décision")
# Fraud & Cheat Detection
fraud_detection: FraudDetection = Field(..., description="Analyse complète anti-triche")
# Legacy Metrics (kept for backward compatibility if needed, or mapped from dashboard)
metrics: MetricsBreakdown = Field(..., description="Détail des notes par catégorie")
# Global verification
red_flag_detected: bool = Field(False, description="True si un comportement inacceptable est détecté")
red_flag_reason: Optional[str] = Field(None, description="Raison du Veto si applicable")
class FeedbackOutput(BaseModel):
"""Complete feedback output with candidate and company sections."""
candidat: CandidatFeedback
entreprise: EntrepriseInsights
|