zk
56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
"""求职助手 - 岗位简历优化 AI 引擎
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针对目标岗位并发优化简历（summary + 经历子表）。
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依赖：LLM 枚举、job_agent/prompts、parse_llm_json
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"""
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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.job_agent.prompts import RESUME_SUMMARY_OPTIMIZE_PROMPT, RESUME_EXPERIENCE_OPTIMIZE_PROMPT
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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.tool.json_helper import parse_llm_json
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# ===== summary 优化 =====
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_summary_chain = (
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ChatPromptTemplate.from_messages([("system", RESUME_SUMMARY_OPTIMIZE_PROMPT), ("human", "请开始优化。")])
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| LLM.ZM_GPT_5_4.create(temperature=0.3)
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| StrOutputParser()
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)
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async def optimize_summary(job_title: str, job_description: str, original_summary: str) -> str:
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"""针对岗位优化个人概述"""
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try:
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return await _summary_chain.ainvoke({
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"job_title": job_title, "job_description": job_description or "",
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"original_summary": original_summary or "暂无",
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})
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except Exception as e:
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log.warning(f"岗位简历summary优化失败: {e}")
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return original_summary
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# ===== 经历优化 =====
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_experience_chain = (
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ChatPromptTemplate.from_messages([("system", RESUME_EXPERIENCE_OPTIMIZE_PROMPT), ("human", "请开始优化。")])
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| LLM.ZM_GPT_5_4.create(temperature=0.3)
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| StrOutputParser()
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)
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async def optimize_experience(job_title: str, job_description: str, module_data: str) -> list | dict | None:
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"""针对岗位优化经历模块描述，返回修改后的完整模块数据"""
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try:
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raw = await _experience_chain.ainvoke({
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"job_title": job_title, "job_description": job_description or "",
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"original_module_data": module_data,
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})
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return parse_llm_json(raw)
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except Exception as e:
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log.warning(f"岗位简历经历优化失败: {e}")
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return None
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