237 lines
9.6 KiB
Python
237 lines
9.6 KiB
Python
from __future__ import annotations
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from datetime import datetime, timedelta, timezone
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from enum import Enum
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from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
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class SalaryType(str, Enum):
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daily = "daily"
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hourly = "hourly"
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monthly = "monthly"
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task = "task"
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class SourceType(str, Enum):
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job_to_worker = "job_to_worker"
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worker_to_job = "worker_to_job"
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class Salary(BaseModel):
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type: SalaryType = Field(default=SalaryType.daily, description="薪资类型：daily/hourly/monthly/task")
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amount: float = Field(default=0, description="薪资金额")
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currency: str = Field(default="CNY", description="货币类型，默认 CNY")
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class SkillScore(BaseModel):
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name: str = Field(description="技能名称")
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score: float = Field(ge=0, le=1, description="技能熟练度，范围 0~1")
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class JobCard(BaseModel):
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job_id: str = Field(description="岗位唯一 ID")
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title: str = Field(description="岗位标题")
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category: str = Field(description="岗位类别")
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description: str = Field(description="岗位描述")
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skills: list[str] = Field(default_factory=list, description="岗位技能要求列表")
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city: str = Field(description="城市")
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region: str = Field(description="区域")
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location_detail: str = Field(description="详细地点描述")
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start_time: datetime = Field(description="岗位开始时间，ISO-8601")
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duration_hours: float = Field(gt=0, description="工时（小时），必须大于 0")
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headcount: int = Field(gt=0, description="招聘人数，必须大于 0")
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salary: Salary = Field(description="薪资信息")
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work_mode: str = Field(description="工作模式，如兼职、全职、活动")
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tags: list[str] = Field(default_factory=list, description="业务标签列表")
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confidence: float = Field(ge=0, le=1, description="数据置信度，范围 0~1")
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@field_validator("start_time", mode="after")
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@classmethod
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def normalize_start_time(cls, value: datetime) -> datetime:
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shanghai_tz = timezone(timedelta(hours=8))
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if value.tzinfo is None:
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value = value.replace(tzinfo=shanghai_tz)
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else:
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value = value.astimezone(shanghai_tz)
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return value.replace(second=0, microsecond=0)
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class WorkerCard(BaseModel):
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worker_id: str = Field(description="工人唯一 ID")
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name: str = Field(description="工人姓名或昵称")
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description: str = Field(description="工人自我描述")
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skills: list[SkillScore] = Field(default_factory=list, description="技能及熟练度列表")
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cities: list[str] = Field(default_factory=list, description="可接单城市列表")
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regions: list[str] = Field(default_factory=list, description="可接单区域列表")
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availability: list[str] = Field(default_factory=list, description="可上岗时间描述")
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experience_tags: list[str] = Field(default_factory=list, description="经验标签列表")
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reliability_score: float = Field(ge=0, le=1, description="履约可靠性分，范围 0~1")
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profile_completion: float = Field(ge=0, le=1, description="档案完善度，范围 0~1")
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confidence: float = Field(ge=0, le=1, description="数据置信度，范围 0~1")
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class MatchBreakdown(BaseModel):
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skill_score: float = Field(ge=0, le=1, description="技能匹配分，范围 0~1")
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region_score: float = Field(ge=0, le=1, description="地域匹配分，范围 0~1")
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time_score: float = Field(ge=0, le=1, description="时间匹配分，范围 0~1")
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experience_score: float = Field(ge=0, le=1, description="经验匹配分，范围 0~1")
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reliability_score: float = Field(ge=0, le=1, description="可靠性匹配分，范围 0~1")
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class MatchResult(BaseModel):
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match_id: str = Field(description="匹配记录 ID")
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source_type: SourceType = Field(description="匹配方向：job_to_worker 或 worker_to_job")
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source_id: str = Field(description="源实体 ID")
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target_id: str = Field(description="目标实体 ID")
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match_score: float = Field(ge=0, le=1, description="综合匹配分，范围 0~1")
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breakdown: MatchBreakdown = Field(description="多维打分拆解")
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reasons: list[str] = Field(default_factory=list, min_length=3, description="匹配理由，至少 3 条")
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class ExtractTextRequest(BaseModel):
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text: str = Field(min_length=5, description="待抽取的自然语言文本，最少 5 个字符")
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model_config = ConfigDict(
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json_schema_extra={
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"example": {
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"text": "明天下午南山会展中心需要2个签到协助，5小时，150/人，女生优先",
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}
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}
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)
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class IngestJobRequest(BaseModel):
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job: JobCard = Field(description="岗位卡片对象")
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class IngestWorkerRequest(BaseModel):
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worker: WorkerCard = Field(description="工人卡片对象")
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class MatchWorkersRequest(BaseModel):
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job_id: str | None = Field(default=None, description="岗位 ID（与 job 二选一）")
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job: JobCard | None = Field(default=None, description="内联岗位对象（与 job_id 二选一）")
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top_n: int = Field(default=10, ge=1, le=50, description="返回条数，范围 1~50")
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@model_validator(mode="after")
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def validate_source(self) -> "MatchWorkersRequest":
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if not self.job_id and not self.job:
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raise ValueError("job_id 或 job 至少需要提供一个")
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return self
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class MatchJobsRequest(BaseModel):
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worker_id: str | None = Field(default=None, description="工人 ID（与 worker 二选一）")
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worker: WorkerCard | None = Field(default=None, description="内联工人对象（与 worker_id 二选一）")
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top_n: int = Field(default=10, ge=1, le=50, description="返回条数，范围 1~50")
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@model_validator(mode="after")
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def validate_source(self) -> "MatchJobsRequest":
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if not self.worker_id and not self.worker:
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raise ValueError("worker_id 或 worker 至少需要提供一个")
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return self
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class ExtractResponse(BaseModel):
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success: bool = Field(description="抽取是否成功")
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data: JobCard | WorkerCard | None = Field(default=None, description="抽取结果对象，可能为空")
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errors: list[str] = Field(default_factory=list, description="错误信息列表")
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missing_fields: list[str] = Field(default_factory=list, description="缺失字段列表")
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class BootstrapResponse(BaseModel):
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jobs: int = Field(description="导入岗位数量")
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workers: int = Field(description="导入工人数量")
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skills: int = Field(description="技能词条数量")
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categories: int = Field(description="类目数量")
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regions: int = Field(description="区域数量")
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class HealthStatus(BaseModel):
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service: str = Field(description="服务状态，通常为 ok")
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database: str = Field(description="数据库状态：ok 或 error")
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rag: str = Field(description="RAG 组件状态：ok 或 error")
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timestamp: datetime = Field(description="服务端当前时间")
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class ListResponse(BaseModel):
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items: list[dict] = Field(description="列表项")
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total: int = Field(description="总数")
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class MatchResponse(BaseModel):
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items: list[MatchResult] = Field(description="匹配结果列表")
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class ExplainResponse(BaseModel):
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match: MatchResult = Field(description="单条匹配结果详情")
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class MatchFeedbackRequest(BaseModel):
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match_id: str = Field(description="匹配记录 ID")
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accepted: bool = Field(description="反馈是否接受该推荐")
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class MatchWeightResponse(BaseModel):
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weights: dict[str, float] = Field(description="当前生效的排序权重")
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learning_enabled: bool = Field(description="是否开启在线学习")
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class AIObservabilityResponse(BaseModel):
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metrics: dict[str, float | int] = Field(description="AI 调用观测指标")
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class IngestAsyncResponse(BaseModel):
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task_id: str = Field(description="异步任务 ID")
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status: str = Field(description="任务状态")
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class QueueStatusResponse(BaseModel):
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queued: int = Field(description="当前队列中任务数量")
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processed: int = Field(description="历史处理成功数量")
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failed: int = Field(description="历史处理失败数量")
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class MatchAsyncWorkersRequest(BaseModel):
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job_id: str = Field(description="岗位 ID")
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top_n: int = Field(default=10, ge=1, le=50, description="返回条数，范围 1~50")
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class MatchAsyncJobsRequest(BaseModel):
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worker_id: str = Field(description="工人 ID")
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top_n: int = Field(default=10, ge=1, le=50, description="返回条数，范围 1~50")
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class MatchAsyncResponse(BaseModel):
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task_id: str = Field(description="异步任务 ID")
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status: str = Field(description="任务状态")
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items: list[MatchResult] | None = Field(default=None, description="任务完成后返回的匹配结果")
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class SystemOpsResponse(BaseModel):
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traffic: dict[str, float | int] = Field(description="全局流量护栏与错误窗口指标")
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cache: dict[str, float | int | str] = Field(description="缓存命中与大小")
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ingest_queue: QueueStatusResponse = Field(description="异步入库队列状态")
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match_queue: QueueStatusResponse = Field(description="异步匹配队列状态")
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class PromptOutput(BaseModel):
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content: dict
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raw_text: str
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class QueryFilters(BaseModel):
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entity_type: str
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city: str | None = None
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region: str | None = None
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categories: list[str] = Field(default_factory=list)
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tags: list[str] = Field(default_factory=list)
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skills: list[str] = Field(default_factory=list)
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@field_validator("entity_type")
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@classmethod
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def validate_entity_type(cls, value: str) -> str:
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if value not in {"job", "worker"}:
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raise ValueError("entity_type must be job or worker")
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return value
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