zk
208 lines
8.0 KiB
Python
208 lines
8.0 KiB
Python
"""简历解析 Service
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上传简历文件 → 解析为纯文本 → AI 结构化 → 写入数据库。
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依赖：file_parser（文件解析工具）、LLM（AI模型）
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使用表：bg_user_resume（主表）、bg_user_resume_education/work/internship/project/competition（5张子表）
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"""
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import asyncio
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import json
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import shortuuid
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from langchain_core.messages import SystemMessage, HumanMessage
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.ai.models import LLM
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from app.core.logger import log
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from app.models.user_resume import UserResume
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from app.models.user_resume_competition import UserResumeCompetition
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from app.models.user_resume_education import UserResumeEducation
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from app.models.user_resume_internship import UserResumeInternship
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from app.models.user_resume_project import UserResumeProject
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from app.models.user_resume_work import UserResumeWork
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from app.tool.file_parser import parse_to_text
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from app.tool.snowflake import next_id
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_SYSTEM_PROMPT = """你是一个专业的简历解析助手。请将用户提供的简历纯文本解析为结构化JSON。
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输出格式要求（严格按此JSON结构输出，不要输出任何其他内容）：
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```json
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{
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"name": "姓名",
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"email": "邮箱",
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"mobileNumber": "手机号",
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"city": "所在城市",
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"wechatNumber": "微信号（如有）",
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"portfolioUrl": "作品集链接（如有）",
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"skills": ["技能1", "技能2"],
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"certificates": ["证书1", "证书2"],
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"summary": "个人概述/自我评价",
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"education": [
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{
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"school": "学校名称",
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"major": "专业",
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"degree": "学历（大专/本科/硕士/博士）",
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"studyType": "学习形式（全日制/非全日制）",
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"startDate": "2020.09",
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"endDate": "2024.06",
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"description": ["描述段落1", "描述段落2"]
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}
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],
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"work": [
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{
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"companyName": "公司名称",
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"position": "职位",
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"startDate": "2024.07",
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"endDate": "2025.03",
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"description": ["工作描述段落1", "工作描述段落2"]
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}
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],
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"internship": [
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{
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"companyName": "公司名称",
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"position": "实习职位",
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"startDate": "2023.06",
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"endDate": "2023.09",
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"description": ["实习描述段落1"]
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}
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],
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"project": [
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{
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"companyName": "所属公司（如有）",
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"projectName": "项目名称",
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"role": "担任角色",
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"startDate": "2023.03",
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"endDate": "2023.12",
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"description": ["项目描述段落1"]
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}
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],
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"competition": [
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{
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"competitionName": "竞赛名称",
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"award": "获奖情况",
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"awardDate": "2023.07",
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"description": ["竞赛描述段落1"]
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}
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]
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}
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```
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规则：
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1. 时间格式统一为 YYYY.MM（如 2023.09），如果只有年份则写 YYYY.01
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2. 没有的字段填 null，没有的数组填 []
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3. description 是字符串数组，每个元素是一个描述段落
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4. 区分工作经历和实习经历：明确标注"实习"的归入 internship，其余归入 work
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5. 只输出 JSON，不要输出任何解释文字"""
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class ResumeParseService:
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async def parse_and_extract(self, filename: str, content: bytes) -> dict:
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"""文件解析 + AI 结构化，不涉及数据库操作"""
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# 1. 文件解析为纯文本（同步操作丢线程池）
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log.info(f"开始解析简历文件: {filename}")
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text = await asyncio.to_thread(parse_to_text, filename, content)
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if not text or not text.strip():
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raise ValueError("文件内容为空，无法解析")
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log.info(f"文件解析完成，文本长度: {len(text)}")
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# 2. AI 结构化解析
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log.info("开始AI结构化解析")
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parsed = await self._ai_parse(text)
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log.info("AI结构化解析完成")
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return parsed
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async def _ai_parse(self, text: str) -> dict:
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"""调用 AI 将纯文本解析为结构化 JSON"""
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llm = LLM.DOUBAO_SEED_PRO.create(temperature=0)
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messages = [SystemMessage(content=_SYSTEM_PROMPT), HumanMessage(content=text)]
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response = await llm.ainvoke(messages)
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# 提取 JSON（兼容 markdown 代码块包裹）
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raw = response.content.strip()
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if raw.startswith("```"):
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raw = raw.split("\n", 1)[1] if "\n" in raw else raw[3:]
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raw = raw.rsplit("```", 1)[0]
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return json.loads(raw)
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async def save_resume(self, session: AsyncSession, user_id: int, filename: str, parsed: dict) -> int:
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"""将解析结果写入主表 + 5张子表，返回简历ID"""
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resume_id = next_id()
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# 主表
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resume = UserResume(
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id=resume_id, user_id=user_id,
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resume_name=filename.rsplit(".", 1)[0],
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target_position=None, is_default=0, sort_order=0,
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name=parsed.get("name"), email=parsed.get("email"),
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mobile_number=parsed.get("mobileNumber"), city=parsed.get("city"),
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wechat_number=parsed.get("wechatNumber"), portfolio_url=parsed.get("portfolioUrl"),
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skills=parsed.get("skills") or [], certificates=parsed.get("certificates") or [],
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summary=parsed.get("summary"),
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)
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session.add(resume)
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# 教育经历
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for i, edu in enumerate(parsed.get("education") or []):
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session.add(UserResumeEducation(
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id=next_id(), resume_id=resume_id, user_id=user_id,
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school=edu.get("school"), major=edu.get("major"),
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degree=edu.get("degree"), study_type=edu.get("studyType"),
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start_date=edu.get("startDate"), end_date=edu.get("endDate"),
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description=_to_description_paragraphs(edu.get("description")),
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sort_order=i,
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))
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# 工作经历
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for i, work in enumerate(parsed.get("work") or []):
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session.add(UserResumeWork(
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id=next_id(), resume_id=resume_id, user_id=user_id,
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company_name=work.get("companyName"), position=work.get("position"),
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start_date=work.get("startDate"), end_date=work.get("endDate"),
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description=_to_description_paragraphs(work.get("description")),
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sort_order=i,
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))
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# 实习经历
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for i, intern in enumerate(parsed.get("internship") or []):
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session.add(UserResumeInternship(
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id=next_id(), resume_id=resume_id, user_id=user_id,
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company_name=intern.get("companyName"), position=intern.get("position"),
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start_date=intern.get("startDate"), end_date=intern.get("endDate"),
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description=_to_description_paragraphs(intern.get("description")),
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sort_order=i,
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))
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# 项目经历
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for i, proj in enumerate(parsed.get("project") or []):
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session.add(UserResumeProject(
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id=next_id(), resume_id=resume_id, user_id=user_id,
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company_name=proj.get("companyName"), project_name=proj.get("projectName"),
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role=proj.get("role"),
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start_date=proj.get("startDate"), end_date=proj.get("endDate"),
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description=_to_description_paragraphs(proj.get("description")),
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sort_order=i,
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))
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# 竞赛经历
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for i, comp in enumerate(parsed.get("competition") or []):
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session.add(UserResumeCompetition(
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id=next_id(), resume_id=resume_id, user_id=user_id,
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competition_name=comp.get("competitionName"), award=comp.get("award"),
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award_date=comp.get("awardDate"),
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description=_to_description_paragraphs(comp.get("description")),
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sort_order=i,
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))
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await session.flush()
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log.info(f"简历保存完成，resumeId: {resume_id}")
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return resume_id
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def _to_description_paragraphs(texts: list[str] | None) -> list[dict] | None:
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"""将字符串数组转为 [{id, text}] 格式的描述段落"""
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if not texts:
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return None
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return [{"id": shortuuid.ShortUUID().random(length=8), "text": t} for t in texts if t] |