229 lines
8.4 KiB
Python
229 lines
8.4 KiB
Python
from datetime import datetime
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from enum import Enum
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from typing import Any, Optional
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from sqlalchemy import JSON, Text
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from sqlmodel import Column, Field, Relationship, SQLModel
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class RecommendationType(str, Enum):
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STRONGLY_RECOMMEND = "strongly_recommend"
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RECOMMEND = "recommend"
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CONSIDER = "consider"
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REJECT = "reject"
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def __str__(self):
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return self.value
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class InterviewReportBase(SQLModel):
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"""Базовая модель отчета по интервью"""
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interview_session_id: int = Field(foreign_key="interview_sessions.id", unique=True)
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# Основные критерии оценки (0-100)
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technical_skills_score: int = Field(ge=0, le=100)
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technical_skills_justification: str | None = Field(default=None, max_length=1000)
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technical_skills_concerns: str | None = Field(default=None, max_length=500)
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experience_relevance_score: int = Field(ge=0, le=100)
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experience_relevance_justification: str | None = Field(
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default=None, max_length=1000
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)
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experience_relevance_concerns: str | None = Field(default=None, max_length=500)
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communication_score: int = Field(ge=0, le=100)
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communication_justification: str | None = Field(default=None, max_length=1000)
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communication_concerns: str | None = Field(default=None, max_length=500)
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problem_solving_score: int = Field(ge=0, le=100)
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problem_solving_justification: str | None = Field(default=None, max_length=1000)
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problem_solving_concerns: str | None = Field(default=None, max_length=500)
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cultural_fit_score: int = Field(ge=0, le=100)
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cultural_fit_justification: str | None = Field(default=None, max_length=1000)
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cultural_fit_concerns: str | None = Field(default=None, max_length=500)
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# Агрегированные поля
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overall_score: int = Field(ge=0, le=100)
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recommendation: RecommendationType
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# Дополнительные поля для анализа
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strengths: list[str] | None = Field(default=None, sa_column=Column(JSON))
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weaknesses: list[str] | None = Field(default=None, sa_column=Column(JSON))
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red_flags: list[str] | None = Field(default=None, sa_column=Column(JSON))
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# Метрики интервью
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questions_quality_score: float | None = Field(
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default=None, ge=0, le=10
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) # Средняя оценка ответов
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interview_duration_minutes: int | None = Field(default=None, ge=0)
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response_count: int | None = Field(default=None, ge=0)
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dialogue_messages_count: int | None = Field(default=None, ge=0)
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# Дополнительная информация
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next_steps: str | None = Field(default=None, max_length=1000)
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interviewer_notes: str | None = Field(default=None, sa_column=Column(Text))
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# Детальный анализ вопросов (JSON)
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questions_analysis: list[dict[str, Any]] | None = Field(
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default=None, sa_column=Column(JSON)
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)
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# Метаданные анализа
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analysis_method: str | None = Field(
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default="openai_gpt4", max_length=50
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) # openai_gpt4, fallback_heuristic
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llm_model_used: str | None = Field(default=None, max_length=100)
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analysis_duration_seconds: int | None = Field(default=None, ge=0)
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# PDF отчет
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pdf_report_url: str | None = Field(default=None, max_length=500)
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class InterviewReport(InterviewReportBase, table=True):
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"""Полный отчет по интервью с ID и временными метками"""
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__tablename__ = "interview_reports"
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id: int | None = Field(default=None, primary_key=True)
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created_at: datetime = Field(default_factory=datetime.utcnow)
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updated_at: datetime = Field(default_factory=datetime.utcnow)
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# Связь с сессией интервью
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interview_session: Optional["InterviewSession"] = Relationship(
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back_populates="report"
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)
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class InterviewReportCreate(SQLModel):
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"""Модель для создания отчета"""
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interview_session_id: int
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technical_skills_score: int = Field(ge=0, le=100)
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technical_skills_justification: str | None = None
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technical_skills_concerns: str | None = None
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experience_relevance_score: int = Field(ge=0, le=100)
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experience_relevance_justification: str | None = None
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experience_relevance_concerns: str | None = None
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communication_score: int = Field(ge=0, le=100)
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communication_justification: str | None = None
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communication_concerns: str | None = None
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problem_solving_score: int = Field(ge=0, le=100)
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problem_solving_justification: str | None = None
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problem_solving_concerns: str | None = None
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cultural_fit_score: int = Field(ge=0, le=100)
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cultural_fit_justification: str | None = None
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cultural_fit_concerns: str | None = None
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overall_score: int = Field(ge=0, le=100)
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recommendation: RecommendationType
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strengths: list[str] | None = None
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weaknesses: list[str] | None = None
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red_flags: list[str] | None = None
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questions_quality_score: float | None = None
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interview_duration_minutes: int | None = None
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response_count: int | None = None
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dialogue_messages_count: int | None = None
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next_steps: str | None = None
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interviewer_notes: str | None = None
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questions_analysis: list[dict[str, Any]] | None = None
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analysis_method: str | None = "openai_gpt4"
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llm_model_used: str | None = None
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analysis_duration_seconds: int | None = None
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pdf_report_url: str | None = None
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class InterviewReportUpdate(SQLModel):
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"""Модель для обновления отчета"""
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technical_skills_score: int | None = Field(default=None, ge=0, le=100)
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technical_skills_justification: str | None = None
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technical_skills_concerns: str | None = None
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experience_relevance_score: int | None = Field(default=None, ge=0, le=100)
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experience_relevance_justification: str | None = None
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experience_relevance_concerns: str | None = None
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communication_score: int | None = Field(default=None, ge=0, le=100)
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communication_justification: str | None = None
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communication_concerns: str | None = None
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problem_solving_score: int | None = Field(default=None, ge=0, le=100)
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problem_solving_justification: str | None = None
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problem_solving_concerns: str | None = None
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cultural_fit_score: int | None = Field(default=None, ge=0, le=100)
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cultural_fit_justification: str | None = None
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cultural_fit_concerns: str | None = None
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overall_score: int | None = Field(default=None, ge=0, le=100)
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recommendation: RecommendationType | None = None
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strengths: list[str] | None = None
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weaknesses: list[str] | None = None
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red_flags: list[str] | None = None
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questions_quality_score: float | None = None
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interview_duration_minutes: int | None = None
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response_count: int | None = None
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dialogue_messages_count: int | None = None
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next_steps: str | None = None
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interviewer_notes: str | None = None
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questions_analysis: list[dict[str, Any]] | None = None
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analysis_method: str | None = None
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llm_model_used: str | None = None
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analysis_duration_seconds: int | None = None
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pdf_report_url: str | None = None
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class InterviewReportRead(InterviewReportBase):
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"""Модель для чтения отчета с ID и временными метками"""
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id: int
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created_at: datetime
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updated_at: datetime
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class InterviewReportSummary(SQLModel):
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"""Краткая сводка отчета для списков"""
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id: int
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interview_session_id: int
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overall_score: int
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recommendation: RecommendationType
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created_at: datetime
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# Основные баллы
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technical_skills_score: int
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experience_relevance_score: int
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communication_score: int
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problem_solving_score: int
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cultural_fit_score: int
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# Краткие выводы
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strengths: list[str] | None = None
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red_flags: list[str] | None = None
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# Индексы для эффективных запросов по скорингу
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"""
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Полезные SQL индексы:
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CREATE INDEX idx_interview_reports_overall_score ON interview_reports (overall_score DESC);
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CREATE INDEX idx_interview_reports_recommendation ON interview_reports (recommendation);
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CREATE INDEX idx_interview_reports_technical_skills ON interview_reports (technical_skills_score DESC);
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CREATE INDEX idx_interview_reports_communication ON interview_reports (communication_score DESC);
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CREATE INDEX idx_interview_reports_session_id ON interview_reports (interview_session_id);
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"""
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