wehub-resource-sync
126 lines
4.4 KiB
Python
126 lines
4.4 KiB
Python
"""Pydantic models for multi-pass resume refinement."""
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from pydantic import BaseModel, Field
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class RefinementConfig(BaseModel):
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"""Configuration for refinement passes."""
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enable_keyword_injection: bool = True
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enable_ai_phrase_removal: bool = True
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enable_master_alignment_check: bool = True
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max_refinement_passes: int = Field(default=2, ge=1, le=5)
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class KeywordGapAnalysis(BaseModel):
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"""Result of keyword gap analysis."""
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missing_keywords: list[str] = Field(default_factory=list)
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injectable_keywords: list[str] = Field(
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default_factory=list,
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description="Missing keywords that exist in master resume (safe to add)",
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)
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non_injectable_keywords: list[str] = Field(
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default_factory=list,
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description="Missing keywords not in master resume (cannot add truthfully)",
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)
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current_match_percentage: float = Field(
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default=0.0, ge=0.0, le=100.0, description="Current keyword match percentage"
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)
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potential_match_percentage: float = Field(
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default=0.0,
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ge=0.0,
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le=100.0,
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description="Potential match if injectable keywords are added",
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)
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class AlignmentViolation(BaseModel):
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"""Single alignment violation between tailored and master resume."""
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field_path: str = Field(description="Path to the violated field in resume data")
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violation_type: str = Field(
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description="Type: fabricated_skill, skill_variant, fabricated_cert, fabricated_company, invented_content"
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)
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value: str = Field(description="The violating value")
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severity: str = Field(
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default="warning", description="Severity: critical, warning, or info"
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)
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class AlignmentReport(BaseModel):
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"""Master resume alignment validation result."""
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is_aligned: bool = Field(
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default=True, description="True if no critical violations found"
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)
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violations: list[AlignmentViolation] = Field(default_factory=list)
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confidence_score: float = Field(
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default=1.0,
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ge=0.0,
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le=1.0,
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description="Alignment confidence (1.0 = perfect alignment)",
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)
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class RefinementStats(BaseModel):
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"""Statistics from the refinement process for API responses."""
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passes_completed: int = Field(default=0, ge=0, description="Number of passes run")
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keywords_injected: int = Field(
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default=0, ge=0, description="Number of keywords injected"
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)
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ai_phrases_removed: list[str] = Field(
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default_factory=list, description="List of AI phrases that were removed"
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)
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alignment_violations_fixed: int = Field(
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default=0, ge=0, description="Number of alignment violations corrected"
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)
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initial_match_percentage: float = Field(
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default=0.0,
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ge=0.0,
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le=100.0,
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description="Keyword match before refinement",
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)
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final_match_percentage: float = Field(
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default=0.0, ge=0.0, le=100.0, description="Keyword match after refinement"
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)
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class RefinementResult(BaseModel):
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"""Complete result from the refinement process."""
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refined_data: dict = Field(
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default_factory=dict, description="The refined resume data"
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)
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passes_completed: int = Field(default=0, ge=0)
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keyword_analysis: KeywordGapAnalysis | None = None
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alignment_report: AlignmentReport | None = None
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ai_phrases_removed: list[str] = Field(default_factory=list)
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final_match_percentage: float = Field(default=0.0, ge=0.0, le=100.0)
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def to_stats(self, initial_match: float = 0.0) -> RefinementStats:
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"""Convert to RefinementStats for API response."""
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return RefinementStats(
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passes_completed=self.passes_completed,
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keywords_injected=(
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len(self.keyword_analysis.injectable_keywords)
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if self.keyword_analysis and self.keyword_analysis.injectable_keywords
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else 0
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),
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ai_phrases_removed=self.ai_phrases_removed,
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alignment_violations_fixed=(
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len(
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[
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v
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for v in self.alignment_report.violations
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if v.severity == "critical"
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]
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)
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if self.alignment_report and self.alignment_report.violations
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else 0
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),
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initial_match_percentage=initial_match,
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final_match_percentage=self.final_match_percentage,
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)
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