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| from pydantic import BaseModel, Field | |
| from typing import List, Annotated, Optional | |
| from langgraph.graph.message import add_messages | |
| from langchain_core.messages import BaseMessage | |
| class Skill(BaseModel): | |
| score: int = Field(..., ge=0, le=10, description="Score between 0 and 10") | |
| feedback: str = Field(..., description="Short justification/feedback for this score") | |
| class SkillsBreakdown(BaseModel): | |
| technicalKnowledge: Skill = Field(..., description="Technical knowledge evaluation") | |
| communicationSkills: Skill = Field(..., description="Communication skills evaluation") | |
| problemSolvingAbility: Skill = Field(..., description="Problem solving ability evaluation") | |
| confidence: Skill = Field(..., description="Confidence evaluation") | |
| clarityOfExplanation: Skill = Field(..., description="Clarity of explanation evaluation") | |
| class Performance(BaseModel): | |
| overallScore: int = Field(..., ge=0, le=10, description="Overall performance score") | |
| verdict: str = Field(..., description="Hire decision", pattern="^(hire|maybe|reject)$") | |
| summaryFeedback: str | |
| skills: SkillsBreakdown = Field(..., description="Detailed breakdown of candidate skills") | |
| strengths: List[str] = Field(default_factory=list) | |
| weaknesses: List[str] = Field(default_factory=list) | |
| practiceRecommendations: List[str] = Field(default_factory=list) | |
| studyRecommendations: List[str] = Field(default_factory=list) | |
| lowPriorityOrAvoid: List[str] = Field(default_factory=list) | |
| confidenceLevel: int = Field(..., ge=0, le=10, description="Overall confidence level") | |
| class InterviewState(BaseModel): | |
| messages: Annotated[List[BaseMessage], add_messages] | |
| topic: Optional[str] = None | |
| time_remaining: int # in seconds | |
| questions_generated: bool = False | |
| questions: Optional[str] = None | |
| performance: Optional[Performance] = None |