zk
72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
"""简历并行提取：将完整简历文本拆分为5个AI任务并行提取"""
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import asyncio
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import re
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from json_repair import repair_json
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from app.ai.models import LLM
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from app.ai.resume_extractor.prompts import (
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PROFILE_PROMPT, EDUCATION_PROMPT, WORK_PROMPT,
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PROJECT_PROMPT, COMPETITION_PROMPT,
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)
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from app.core.logger import log
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def _parse_json(text: str) -> dict:
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"""解析 AI 输出的 JSON，自动去除 markdown 代码块包裹，容错处理"""
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cleaned = re.sub(r"^```(?:json)?\s*\n?", "", text.strip())
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cleaned = re.sub(r"\n?```\s*$", "", cleaned)
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return repair_json(cleaned, return_objects=True)
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def _build_chain(prompt: str):
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"""构建单个提取链：prompt → LLM → 文本输出"""
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return (
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ChatPromptTemplate.from_messages([("system", prompt), ("human", "{text}")])
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| LLM.JIAYU_CLAUDE_SONNET_4_5.create(temperature=0)
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| StrOutputParser()
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)
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# 5 条独立的提取链
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_profile_chain = _build_chain(PROFILE_PROMPT)
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_education_chain = _build_chain(EDUCATION_PROMPT)
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_work_chain = _build_chain(WORK_PROMPT)
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_project_chain = _build_chain(PROJECT_PROMPT)
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_competition_chain = _build_chain(COMPETITION_PROMPT)
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async def extract_all(text: str) -> dict:
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"""asyncio.gather 并行提取简历所有模块，返回合并后的结构化数据"""
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log.info("开始5路并行AI提取")
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inp = {"text": text}
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profile, education, work_intern, project, competition = await asyncio.gather(
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_safe_invoke(_profile_chain, inp, "个人信息"),
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_safe_invoke(_education_chain, inp, "教育经历"),
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_safe_invoke(_work_chain, inp, "工作+实习经历"),
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_safe_invoke(_project_chain, inp, "项目经历"),
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_safe_invoke(_competition_chain, inp, "竞赛经历"),
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)
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result = profile if isinstance(profile, dict) else {}
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result["education"] = education if isinstance(education, list) else []
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result["work"] = work_intern.get("work", []) if isinstance(work_intern, dict) else []
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result["internship"] = work_intern.get("internship", []) if isinstance(work_intern, dict) else []
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result["project"] = project if isinstance(project, list) else []
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result["competition"] = competition if isinstance(competition, list) else []
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return result
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async def _safe_invoke(chain, inp: dict, label: str):
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"""单个链调用，失败返回空"""
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try:
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raw = await chain.ainvoke(inp)
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return _parse_json(raw)
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except Exception as e:
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log.warning(f"AI提取[{label}]失败: {e}")
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return {} if "个人信息" in label else []
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