generated from kgod/ai-review-template
hypandClaude
ae2d9b128d
自内部仓库剥离深度优化与 RAG 知识库后的交付版本： - Builder 对话式简历生成（FSM + 意图路由 LLM 兜底增强） - 条目级轻度优化：事实覆盖门禁 + STAR/bullet 修复链，功能/简介/成果与技术栈同级保护 - 简历导入：DOCX/PDF 解析、结构归一、手机号脱敏 - PostgreSQL 运行时 + Alembic 迁移链 Co-Authored-By: Claude <noreply@anthropic.com>
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
"""Contracts shared by the light and deep experience optimization flows."""
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from __future__ import annotations
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from typing import Any, Literal
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from pydantic import BaseModel, ConfigDict, Field
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class OptimizationStartRequest(BaseModel):
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model_config = ConfigDict(extra="forbid")
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entry_id: str = Field(min_length=1, max_length=64)
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instruction: str | None = Field(default=None, max_length=200)
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source: str | None = Field(default=None, max_length=32)
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class OptimizationAnswerRequest(BaseModel):
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model_config = ConfigDict(extra="forbid")
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answer: str = Field(min_length=1, max_length=1000)
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class TargetPositionRequest(BaseModel):
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model_config = ConfigDict(extra="forbid")
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target_position: str = Field(min_length=1, max_length=32)
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class OptimizationRunView(BaseModel):
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id: str
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mode: Literal["light", "deep"]
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status: str
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entry_id: str
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question_count: int = 0
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question: dict[str, Any] | None = None
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proposal: dict[str, Any] | None = None
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covered_dimensions: list[str] = Field(default_factory=list)
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remaining_high_priority_gaps: list[str] = Field(default_factory=list)
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decision_source: str | None = None
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error_code: str | None = None
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action: Any | None = None
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gap_report: list[dict[str, Any]] | None = None
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tier: str | None = None
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