优化简历定制简历生产

app/ai/skill_gap_analyzer/analyzer.py

@@ -5,6 +5,7 @@
 """
 
 import asyncio
+import time
 
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
@@ -44,20 +45,23 @@ async def analyze_skill_gap(skill_tags: list[str], resume_json: str) -> list[str
 
 _summary_optimize_chain = (
     ChatPromptTemplate.from_messages([("system", SUMMARY_OPTIMIZE_PROMPT), ("human", "请开始优化。")])
-    | LLM.JIAYU_CLAUDE_SONNET_4_5.create(temperature=0.3)
+    | LLM.DOUBAO_PRO_32K.create(temperature=0.3)
     | StrOutputParser()
 )
 
 
 async def optimize_summary(job_title: str, add_skills: list[str], original_summary: str) -> str:
     """优化个人概述，融入技能关键词"""
+    t0 = time.monotonic()
     try:
-        return await _summary_optimize_chain.ainvoke({
+        result = await _summary_optimize_chain.ainvoke({
             "job_title": job_title, "add_skills": "、".join(add_skills) if add_skills else "无",
             "original_summary": original_summary or "暂无",
         })
+        log.info(f"AI优化summary完成 ({round(time.monotonic() - t0, 2)}s)")
+        return result
     except Exception as e:
-        log.warning(f"AI优化summary失败: {e}")
+        log.warning(f"AI优化summary失败: {e} ({round(time.monotonic() - t0, 2)}s)")
         return original_summary
 
 
@@ -65,21 +69,24 @@ async def optimize_summary(job_title: str, add_skills: list[str], original_summa
 
 _experience_optimize_chain = (
     ChatPromptTemplate.from_messages([("system", EXPERIENCE_OPTIMIZE_PROMPT), ("human", "请开始优化。")])
-    | LLM.JIAYU_CLAUDE_SONNET_4_5.create(temperature=0.3)
+    | LLM.DOUBAO_PRO_32K.create(temperature=0.3)
     | StrOutputParser()
 )
 
 
 async def optimize_module(job_title: str, job_description: str, module_data: str) -> list | dict | None:
-    """优化子表模块经历描述，返回修改后的完整模块数据"""
+    """优化单条经历描述，返回修改后的记录数据"""
+    t0 = time.monotonic()
     try:
         raw = await _experience_optimize_chain.ainvoke({
             "job_title": job_title, "job_description": job_description or "",
             "original_module_data": module_data,
         })
-        return parse_llm_json(raw)
+        result = parse_llm_json(raw)
+        log.info(f"AI优化经历模块完成 ({round(time.monotonic() - t0, 2)}s)")
+        return result
     except Exception as e:
-        log.warning(f"AI优化经历模块失败: {e}")
+        log.warning(f"AI优化经历模块失败: {e} ({round(time.monotonic() - t0, 2)}s)")
         return None