"""CareerNexus 全链路数据模型（pydantic v2）。

覆盖：JD 原始记录、结构化抽取结果、岗位定义、图谱观察、
变更事件、人岗匹配结果、简历画像与人机协同审核记录。
"""
from __future__ import annotations

from datetime import date
from typing import Literal, Optional

from pydantic import BaseModel, Field


class JDRecord(BaseModel):
    """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。"""

    jid: str
    job_title: str
    company: str
    city: str = ""
    salary: str = ""
    industry: str = ""            # 招聘平台标注的所属行业
    publish_date: date
    source: str = ""
    url: str = ""
    text: str
    source_id: str = ""           # 形如 batch_2023.jsonl#17
    weight: float = 1.0           # 清洗阶段赋予的时间衰减权重
    is_duplicate: bool = False    # 近重复（抄袭）降权标记
    dup_of: Optional[str] = None  # 指向被保留的最早来源 jid
    is_noise: bool = False        # 噪声（广告/无关条款）标记


class ExtractionResult(BaseModel):
    """从单条 JD 文本抽取出的结构化岗位定义。"""

    jid: str = ""
    job_title: str = ""
    responsibilities: list[str] = Field(default_factory=list)
    required_skills: list[str] = Field(default_factory=list)
    preferred_skills: list[str] = Field(default_factory=list)
    scenarios: list[str] = Field(default_factory=list)
    evidence: dict[str, str] = Field(default_factory=dict)  # 字段 -> 证据片段
    confidence: float = 0.0
    extractor: str = "offline"
    prompt_version: str = "offline-v1"


class JobDefinition(BaseModel):
    """聚合后的岗位定义（图谱中的 Job 节点属性）。"""

    job_id: str
    title: str
    category: str = ""
    industries: list[str] = Field(default_factory=list)  # 归属行业（1-2 个）
    required_skills: list[str] = Field(default_factory=list)
    preferred_skills: list[str] = Field(default_factory=list)
    responsibilities: list[str] = Field(default_factory=list)
    scenarios: list[str] = Field(default_factory=list)
    expected_years: int = 3
    status: Literal["active", "pending", "rejected"] = "active"
    source_count: int = 0    # 支撑 JD 数
    company_count: int = 0   # 独立企业数
    # 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，
    # 审核入库时用于生成真实的 REQUIRES 区间、来源列表与演化事件。
    support_source_ids: list[str] = Field(default_factory=list)
    support_windows: list[str] = Field(default_factory=list)


class GraphObservation(BaseModel):
    """一次岗位-技能关系观察（带时间窗，追加而不覆盖）。"""

    job_id: str
    skill: str
    importance: Literal["required", "preferred"]
    weight: float = 1.0
    window: str = ""                 # 季度窗口，如 2022Q1
    source_ids: list[str] = Field(default_factory=list)


class ChangeEvent(BaseModel):
    """岗位能力演化事件：新增 / 增强 / 减弱 / 退出。"""

    event_id: str
    job_id: str
    skill: str
    change_type: Literal["新增", "增强", "减弱", "退出"]
    window: str
    prev_weight: float = 0.0
    curr_weight: float = 0.0
    source_ids: list[str] = Field(default_factory=list)
    description: str = ""
    created_at: str = ""


class ResumeProfile(BaseModel):
    """脱敏后的简历画像。"""

    name: str = ""
    phone: str = ""
    email: str = ""
    years: float = 0.0
    skills: list[str] = Field(default_factory=list)
    scenarios: list[str] = Field(default_factory=list)
    raw_masked_text: str = ""
    # 结构化信息（规范简历模板用）
    gender: str = ""                     # 性别（不脱敏，人口统计学字段）
    education: str = ""                  # 最高学历（本科/硕士/博士…）
    address: str = ""                    # 家庭住址/现居地（脱敏到城市）
    photo_b64: str = ""                  # 证件照 base64（data URI，无照片为空）
    sections: list[dict] = Field(default_factory=list)  # [{title, text}] 分节正文


class MatchResult(BaseModel):
    """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。"""

    job_id: str
    job_title: str = ""
    total_score: float = 0.0
    dimension_scores: dict[str, float] = Field(default_factory=dict)
    matched_required: list[str] = Field(default_factory=list)
    missing_required: list[str] = Field(default_factory=list)
    matched_preferred: list[str] = Field(default_factory=list)
    missing_preferred: list[str] = Field(default_factory=list)
    transferable_skills: list[str] = Field(default_factory=list)
    learning_path: list[dict] = Field(default_factory=list)
    is_match: bool = False


class ReviewRecord(BaseModel):
    """人机协同审核留痕。"""

    candidate_id: str
    action: Literal["accept", "modify", "reject"]
    operator: str
    reason: str = ""
    before: dict = Field(default_factory=dict)
    after: dict = Field(default_factory=dict)
    time: str = ""