114 lines
5.4 KiB
Python
114 lines
5.4 KiB
Python
"""简历 Service
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上传简历文件 → 解析为纯文本 → AI 两阶段并行结构化 → 写入数据库。
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依赖：resume_text_extractor（文件文本提取）、resume_extractor（AI两阶段并行提取）
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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 shortuuid
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.ai.resume_extractor_v2.extractor import extract_all
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from app.ai.resume_polisher.polisher import polish_paragraphs
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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.resume_text_extractor import extract_text
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from app.tool.snowflake import next_id
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class ResumeService:
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async def parse_and_extract(self, filename: str, content: bytes) -> dict:
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"""文件解析 + AI 两阶段并行结构化，不涉及数据库操作"""
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log.info(f"开始解析简历文件: {filename}")
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text = await extract_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: {text}")
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log.info(f"文件解析完成，文本字符数: {len(text)}")
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log.info("开始AI两阶段并行结构化提取")
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parsed = await extract_all(text)
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log.info("AI两阶段并行结构化提取完成")
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return parsed
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async def polish_paragraphs(self, content: list[str]) -> list[str]:
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"""对简历段落做表达层面的润色（格式/错字/表达），不涉及数据库操作"""
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log.info(f"开始简历段落润色, 段落数={len(content)}")
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result = await polish_paragraphs(content)
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log.info("简历段落润色完成")
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return result
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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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session.add(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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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_paragraphs(edu.get("description")), sort_order=i,
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))
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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_paragraphs(work.get("description")), sort_order=i,
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))
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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_paragraphs(intern.get("description")), sort_order=i,
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))
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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_paragraphs(proj.get("description")), sort_order=i,
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))
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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_paragraphs(comp.get("description")), 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_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]
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