优化 agent 定制简历生成速度

app/services/job_agent_chat_service.py

@@ -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__}")