统一抽象 封装 模型配置配置管理

app/ai/skill_gap_analyzer/analyzer.py

@@ -10,7 +10,7 @@ import time
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 
-from app.ai.models import LLM
+from app.ai.model_config import SkillGapModel
 from app.ai.skill_gap_analyzer.prompts import (
     SKILL_GAP_PROMPT, SUMMARY_OPTIMIZE_PROMPT, EXPERIENCE_OPTIMIZE_PROMPT,
     AGENT_PLAN_PROMPT, AGENT_MODULE_EDIT_PROMPT, AGENT_MODULE_ADD_PROMPT, MODULE_SCHEMAS,
@@ -23,7 +23,7 @@ from app.tool.json_helper import parse_llm_json
 
 _skill_gap_chain = (
     ChatPromptTemplate.from_messages([("system", SKILL_GAP_PROMPT), ("human", "请开始分析。")])
-    | LLM.DOUBAO_LITE_32K.create(temperature=0)
+    | SkillGapModel.ANALYSIS
     | StrOutputParser()
 )
 
@@ -45,7 +45,7 @@ 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.DOUBAO_LITE_32K.create(temperature=0.3)
+    | SkillGapModel.SUMMARY
     | StrOutputParser()
 )
 
@@ -69,7 +69,7 @@ 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.DOUBAO_LITE_32K.create(temperature=0.3)
+    | SkillGapModel.EXPERIENCE
     | StrOutputParser()
 )
 
@@ -94,7 +94,7 @@ async def optimize_module(job_title: str, job_description: str, module_data: str
 
 _plan_chain = (
     ChatPromptTemplate.from_messages([("system", AGENT_PLAN_PROMPT), ("human", "请分析用户指令。")])
-    | LLM.ZM_GPT_5_4.create(temperature=0)
+    | SkillGapModel.AGENT_PLAN
     | StrOutputParser()
 )
 
@@ -119,7 +119,7 @@ async def plan_edit(job_title: str, job_description: str, resume_json: str,
 
 _record_edit_chain = (
     ChatPromptTemplate.from_messages([("system", AGENT_MODULE_EDIT_PROMPT), ("human", "请执行修改。")])
-    | LLM.ZM_GPT_5_4.create(temperature=0.3)
+    | SkillGapModel.AGENT_EDIT
     | StrOutputParser()
 )
 
@@ -144,7 +144,7 @@ async def execute_record_edit(job_title: str, job_description: str, instruction:
 
 _record_add_chain = (
     ChatPromptTemplate.from_messages([("system", AGENT_MODULE_ADD_PROMPT), ("human", "请生成新记录。")])
-    | LLM.ZM_GPT_5_4.create(temperature=0.3)
+    | SkillGapModel.AGENT_ADD
     | StrOutputParser()
 )