RoyirinRosmontis
a10442d78c
- 全链路：多源JD接入/清洗交叉验证/结构化抽取(星火+离线双通道)/时序知识图谱/新岗位发现/演化分析/人岗匹配/学习路径 - WebUI 五视图：驾驶舱/图谱工作台/岗位演化/匹配(单人+批量)/学习路径 - 设置中心(config.json)/Token鉴权/SQLite状态库/语义向量通道(BGE-M3本地或联网API) - 71 个 pytest 用例；数据目录仅保留结构(.gitkeep)，数据由 scripts/generate_dataset.py 复现
140 lines
5.0 KiB
Python
140 lines
5.0 KiB
Python
"""CareerNexus 全链路数据模型（pydantic v2）。
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覆盖：JD 原始记录、结构化抽取结果、岗位定义、图谱观察、
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变更事件、人岗匹配结果、简历画像与人机协同审核记录。
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"""
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from __future__ import annotations
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from datetime import date
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from typing import Literal, Optional
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from pydantic import BaseModel, Field
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class JDRecord(BaseModel):
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"""一条招聘 JD 原始记录。source_id 由接入层分配（文件名#行号），不可变。"""
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jid: str
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job_title: str
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company: str
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city: str = ""
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salary: str = ""
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industry: str = "" # 招聘平台标注的所属行业
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publish_date: date
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source: str = ""
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url: str = ""
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text: str
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source_id: str = "" # 形如 batch_2023.jsonl#17
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weight: float = 1.0 # 清洗阶段赋予的时间衰减权重
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is_duplicate: bool = False # 近重复（抄袭）降权标记
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dup_of: Optional[str] = None # 指向被保留的最早来源 jid
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is_noise: bool = False # 噪声（广告/无关条款）标记
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class ExtractionResult(BaseModel):
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"""从单条 JD 文本抽取出的结构化岗位定义。"""
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jid: str = ""
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job_title: str = ""
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responsibilities: list[str] = Field(default_factory=list)
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required_skills: list[str] = Field(default_factory=list)
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preferred_skills: list[str] = Field(default_factory=list)
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scenarios: list[str] = Field(default_factory=list)
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evidence: dict[str, str] = Field(default_factory=dict) # 字段 -> 证据片段
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confidence: float = 0.0
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extractor: str = "offline"
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prompt_version: str = "offline-v1"
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class JobDefinition(BaseModel):
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"""聚合后的岗位定义（图谱中的 Job 节点属性）。"""
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job_id: str
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title: str
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category: str = ""
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industries: list[str] = Field(default_factory=list) # 归属行业（1-2 个）
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required_skills: list[str] = Field(default_factory=list)
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preferred_skills: list[str] = Field(default_factory=list)
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responsibilities: list[str] = Field(default_factory=list)
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scenarios: list[str] = Field(default_factory=list)
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expected_years: int = 3
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status: Literal["active", "pending", "rejected"] = "active"
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source_count: int = 0 # 支撑 JD 数
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company_count: int = 0 # 独立企业数
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# 候选新岗位的支撑证据（新岗位发现时填写）：支撑 JD 的 source_id 与出现季度窗口，
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# 审核入库时用于生成真实的 REQUIRES 区间、来源列表与演化事件。
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support_source_ids: list[str] = Field(default_factory=list)
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support_windows: list[str] = Field(default_factory=list)
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class GraphObservation(BaseModel):
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"""一次岗位-技能关系观察（带时间窗，追加而不覆盖）。"""
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job_id: str
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skill: str
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importance: Literal["required", "preferred"]
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weight: float = 1.0
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window: str = "" # 季度窗口，如 2022Q1
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source_ids: list[str] = Field(default_factory=list)
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class ChangeEvent(BaseModel):
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"""岗位能力演化事件：新增 / 增强 / 减弱 / 退出。"""
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event_id: str
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job_id: str
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skill: str
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change_type: Literal["新增", "增强", "减弱", "退出"]
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window: str
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prev_weight: float = 0.0
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curr_weight: float = 0.0
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source_ids: list[str] = Field(default_factory=list)
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description: str = ""
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created_at: str = ""
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class ResumeProfile(BaseModel):
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"""脱敏后的简历画像。"""
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name: str = ""
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phone: str = ""
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email: str = ""
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years: float = 0.0
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skills: list[str] = Field(default_factory=list)
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scenarios: list[str] = Field(default_factory=list)
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raw_masked_text: str = ""
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# 结构化信息（规范简历模板用）
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gender: str = "" # 性别（不脱敏，人口统计学字段）
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education: str = "" # 最高学历（本科/硕士/博士…）
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address: str = "" # 家庭住址/现居地（脱敏到城市）
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photo_b64: str = "" # 证件照 base64（data URI，无照片为空）
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sections: list[dict] = Field(default_factory=list) # [{title, text}] 分节正文
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class MatchResult(BaseModel):
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"""人岗匹配结果：总分、四维分、命中/缺失/可迁移技能与学习路径。"""
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job_id: str
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job_title: str = ""
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total_score: float = 0.0
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dimension_scores: dict[str, float] = Field(default_factory=dict)
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matched_required: list[str] = Field(default_factory=list)
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missing_required: list[str] = Field(default_factory=list)
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matched_preferred: list[str] = Field(default_factory=list)
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missing_preferred: list[str] = Field(default_factory=list)
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transferable_skills: list[str] = Field(default_factory=list)
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learning_path: list[dict] = Field(default_factory=list)
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is_match: bool = False
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class ReviewRecord(BaseModel):
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"""人机协同审核留痕。"""
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candidate_id: str
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action: Literal["accept", "modify", "reject"]
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operator: str
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reason: str = ""
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before: dict = Field(default_factory=dict)
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after: dict = Field(default_factory=dict)
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time: str = ""
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