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优化 agent 定制简历生成速度

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zk
2026-05-13 15:07:41 +08:00
parent 8c3f3b4f58
commit ed8663cc42
2 changed files with 76 additions and 44 deletions
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app/ai/job_agent/resume_optimizer.py
+19 -11
View File
@@ -1,9 +1,11 @@
"""求职助手 - 岗位简历优化 AI 引擎
针对目标岗位并发优化简历（summary + 经历子表）。
针对目标岗位并发优化简历（summary + 经历子表，按单条记录粒度并发）。
依赖：LLM 枚举、job_agent/prompts、parse_llm_json
"""
import time
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
@@ -16,40 +18,46 @@ from app.tool.json_helper import parse_llm_json
_summary_chain = (
ChatPromptTemplate.from_messages([("system", RESUME_SUMMARY_OPTIMIZE_PROMPT), ("human", "请开始优化。")])
| LLM.ZM_GPT_5_4.create(temperature=0.3)
| LLM.ZM_GPT_5_4_MINI.create(temperature=0.3)
| StrOutputParser()
)
async def optimize_summary(job_title: str, job_description: str, original_summary: str) -> str:
"""针对岗位优化个人概述"""
t0 = time.monotonic()
try:
return await _summary_chain.ainvoke({
result = await _summary_chain.ainvoke({
"job_title": job_title, "job_description": job_description or "",
"original_summary": original_summary or "暂无",
})
log.info(f"岗位简历summary优化完成 ({round(time.monotonic() - t0, 2)}s)")
return result
except Exception as e:
log.warning(f"岗位简历summary优化失败: {e}")
log.warning(f"岗位简历summary优化失败: {e} ({round(time.monotonic() - t0, 2)}s)")
return original_summary
# ===== 经历优化 =====
# ===== 单条经历优化 =====
_experience_chain = (
ChatPromptTemplate.from_messages([("system", RESUME_EXPERIENCE_OPTIMIZE_PROMPT), ("human", "请开始优化。")])
| LLM.ZM_GPT_5_4.create(temperature=0.3)
| LLM.ZM_GPT_5_4_MINI.create(temperature=0.3)
| StrOutputParser()
)
async def optimize_experience(job_title: str, job_description: str, module_data: str) -> list | dict | None:
"""针对岗位优化经历模块描述，返回修改后的完整模块数据"""
async def optimize_experience_record(job_title: str, job_description: str, record_json: str) -> dict | None:
"""针对岗位优化单条经历记录，返回修改后的记录数据"""
t0 = time.monotonic()
try:
raw = await _experience_chain.ainvoke({
"job_title": job_title, "job_description": job_description or "",
"original_module_data": module_data,
"original_module_data": record_json,
})
return parse_llm_json(raw)
result = parse_llm_json(raw)
log.info(f"岗位简历经历记录优化完成 ({round(time.monotonic() - t0, 2)}s)")
return result
except Exception as e:
log.warning(f"岗位简历经历优化失败: {e}")
log.warning(f"岗位简历经历记录优化失败: {e} ({round(time.monotonic() - t0, 2)}s)")
return None
app/services/job_agent_chat_service.py
+57 -33
View File
@@ -7,12 +7,13 @@
import asyncio
import json
import time
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from app.ai.job_agent.chat import agent_chat
from app.ai.job_agent.resume_optimizer import optimize_summary, optimize_experience
from app.ai.job_agent.resume_optimizer import optimize_summary, optimize_experience_record
from app.core.logger import log
from app.models.job import Job
from app.schemas.customize_resume import CustomizeResume, Education, Work, Internship, Project, Competition
@@ -71,35 +72,47 @@ class JobAgentChatService:
return "\n".join(parts) if parts else "暂无简历信息"
async def optimize_resume(self, user_id: int, resume_id: int, job_id: int) -> dict:
"""针对岗位优化简历：查简历+岗位 → 构建定制简历 → 并发AI优化 → 存Redis → 返回"""
"""针对岗位优化简历：查简历+岗位 → 构建定制简历 → 按单条记录并发AI优化 → 存数据库 → 返回"""
# 1. 查简历 + 岗位
detail = await load_resume_detail(self.session, resume_id, user_id)
job = await self._get_job(job_id)
# 2. 构建定制简历
cr = customize_resume_store.build_from_detail(detail)
# 3. 并发 AI 优化（summary + 5张子表经历）
tasks = []
# 3. 构建并发任务（按单条记录粒度）
tasks: list[tuple[str, int, object]] = []
job_desc = f"{job.description or ''}\n{job.requirement or ''}"
if cr.resume.summary:
tasks.append(("summary", optimize_summary(job.title or "", job_desc, cr.resume.summary)))
tasks.append(("summary", 0, optimize_summary(job.title or "", job_desc, cr.resume.summary)))
for mod_name, idx, record_json in self._experience_tasks(cr):
tasks.append((mod_name, idx, optimize_experience_record(job.title or "", job_desc, record_json)))
log.info(f"岗位简历优化开始: {len(tasks)}个并发任务 [job={job_id}, resume={resume_id}]")
# 4. 并发执行
if tasks:
t0 = time.monotonic()
results = await asyncio.gather(*[t[2] for t in tasks], return_exceptions=True)
log.info(f"岗位简历优化全部完成, 总耗时={round(time.monotonic() - t0, 2)}s")
for (mod_name, idx, _), result in zip(tasks, results):
if isinstance(result, Exception):
log.warning(f"岗位简历优化[{mod_name}[{idx}]]失败: {result}")
continue
if result is None:
continue
self._apply_optimize_result(cr, mod_name, idx, result)
# 5. 存数据库
await customize_resume_store.save(user_id, job_id, cr)
log.info(f"岗位简历优化已保存 [user={user_id}, job={job_id}]")
# 6. 返回
return cr.model_dump(by_alias=True)
@staticmethod
def _experience_tasks(cr: CustomizeResume) -> list[tuple[str, int, str]]:
"""构建各子表的 AI 优化任务列表，按单条记录拆分"""
result: list[tuple[str, int, str]] = []
for name, items in [("education", cr.education), ("work", cr.work), ("internship", cr.internship),
("project", cr.project), ("competition", cr.competition)]:
if items:
rows_json = json.dumps([item.model_dump(by_alias=True) for item in items], ensure_ascii=False)
tasks.append((name, optimize_experience(job.title or "", job_desc, rows_json)))
# 执行并发
if tasks:
keys = [t[0] for t in tasks]
results = await asyncio.gather(*[t[1] for t in tasks], return_exceptions=True)
for key, result in zip(keys, results):
if isinstance(result, Exception):
log.warning(f"岗位简历优化[{key}]失败: {result}")
continue
self._apply_optimize_result(cr, key, result)
# 4. 存数据库
await customize_resume_store.save(user_id, job_id, cr)
# 5. 返回
return cr.model_dump(by_alias=True)
for idx, item in enumerate(items or []):
result.append((name, idx, json.dumps(item.model_dump(by_alias=True), ensure_ascii=False)))
return result
async def _get_job(self, job_id: int) -> Job:
"""查岗位"""
@@ -110,17 +123,28 @@ class JobAgentChatService:
return job
@staticmethod
def _apply_optimize_result(cr: CustomizeResume, key: str, result) -> None:
"""将 AI 优化结果应用到定制简历"""
def _apply_optimize_result(cr: CustomizeResume, key: str, idx: int, result) -> None:
"""将 AI 优化结果应用到定制简历（单条记录粒度）"""
if key == "summary" and isinstance(result, str):
cr.resume.summary = result
elif key == "education" and isinstance(result, list):
cr.education = [Education.model_validate(item) for item in result]
elif key == "work" and isinstance(result, list):
cr.work = [Work.model_validate(item) for item in result]
elif key == "internship" and isinstance(result, list):
cr.internship = [Internship.model_validate(item) for item in result]
elif key == "project" and isinstance(result, list):
cr.project = [Project.model_validate(item) for item in result]
elif key == "competition" and isinstance(result, list):
cr.competition = [Competition.model_validate(item) for item in result]
return
model_map = {"education": Education, "work": Work, "internship": Internship, "project": Project, "competition": Competition}
list_map = {"education": cr.education, "work": cr.work, "internship": cr.internship, "project": cr.project, "competition": cr.competition}
model_cls = model_map.get(key)
items = list_map.get(key)
if model_cls is None or items is None:
log.warning(f"未知优化模块: {key}")
return
if isinstance(result, dict):
try:
items[idx] = model_cls.model_validate(result)
except (IndexError, Exception) as e:
log.warning(f"应用优化结果[{key}[{idx}]]失败: {e}")
elif isinstance(result, list) and len(result) == 1 and isinstance(result[0], dict):
# 兼容 LLM 偶尔返回单元素数组的情况
try:
items[idx] = model_cls.model_validate(result[0])
except (IndexError, Exception) as e:
log.warning(f"应用优化结果[{key}[{idx}]]失败(数组兼容): {e}")
else:
log.warning(f"优化结果格式异常[{key}[{idx}]]: type={type(result).__name__}")