统一抽象封装模型配置配置管理

2026-05-26 15:10:21 +08:00
parent b558863207
commit 18589adf8c
10 changed files with 131 additions and 31 deletions
@@ -5,7 +5,7 @@
 """

 from app.ai.browser_plug.prompts import FORM_FILL_SYSTEM_PROMPT
-from app.ai.models import LLM
+from app.ai.model_config import BrowserPlugModel
 from app.core.logger import log


@@ -27,8 +27,7 @@ async def generate_form_answer(resume_text: str, job_text: str, agent_config_tex
    messages = [("system", system_content), ("human", user_message)]

    try:
-        llm = LLM.DOUBAO_PRO_32K.create(temperature=0.3)
-        result = await llm.ainvoke(messages)
+        result = await BrowserPlugModel.FORM_FILL.ainvoke(messages)
        return result.content.strip()
    except Exception as e:
        log.error(f"表单填写AI调用失败: {e}")
@@ -5,7 +5,7 @@
 """

 from app.ai.job_agent.prompts import SYSTEM_PROMPT
-from app.ai.models import LLM
+from app.ai.model_config import JobAgentModel
 from app.core.logger import log
 from app.tool.json_helper import parse_llm_json

@@ -32,8 +32,7 @@ async def agent_chat(resume_text: str, message: str, history: list[dict],

    # 3. 调 LLM
    try:
-        llm = LLM.ZM_GPT_5_4.create(temperature=0.7)
-        result = await llm.ainvoke(messages)
+        result = await JobAgentModel.CHAT.ainvoke(messages)
        raw = result.content
    except Exception as e:
        log.error(f"求职助手AI调用失败: {e}")
@@ -10,7 +10,7 @@ from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate

 from app.ai.job_agent.prompts import RESUME_SUMMARY_OPTIMIZE_PROMPT, RESUME_EXPERIENCE_OPTIMIZE_PROMPT
-from app.ai.models import LLM
+from app.ai.model_config import JobAgentModel
 from app.core.logger import log
 from app.tool.json_helper import parse_llm_json

@@ -18,7 +18,7 @@ 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_MINI.create(temperature=0.3)
+    | JobAgentModel.SUMMARY
    | StrOutputParser()
 )

@@ -42,7 +42,7 @@ async def optimize_summary(job_title: str, job_description: str, original_summar

 _experience_chain = (
    ChatPromptTemplate.from_messages([("system", RESUME_EXPERIENCE_OPTIMIZE_PROMPT), ("human", "请开始优化。")])
-    | LLM.ZM_GPT_5_4_MINI.create(temperature=0.3)
+    | JobAgentModel.EXPERIENCE
    | StrOutputParser()
 )

@@ -0,0 +1,60 @@
+"""AI 模型场景配置
+
+集中管理各业务模块的模型选择与参数，修改模型只需改此文件。
+"""
+
+from app.ai.models import LLM
+
+
+class SkillGapModel:
+    """技能差距分析模块"""
+    # 技能差距识别：对比简历与岗位技能标签，输出缺失技能列表
+    ANALYSIS   = LLM.DOUBAO_LITE_32K.create(temperature=0)
+    # 个人概述优化：将缺失技能关键词融入 summary
+    SUMMARY    = LLM.DOUBAO_LITE_32K.create(temperature=0.3)
+    # 经历描述优化：针对目标岗位优化单条经历的 description
+    EXPERIENCE = LLM.DOUBAO_LITE_32K.create(temperature=0.3)
+    # Agent规划：解析用户自然语言指令，拆解为原子编辑操作列表
+    AGENT_PLAN = LLM.ZM_GPT_5_4.create(temperature=0)
+    # Agent执行-修改：按指令修改简历中的单条记录
+    AGENT_EDIT = LLM.ZM_GPT_5_4.create(temperature=0.3)
+    # Agent执行-新增：按指令生成一条新的简历记录
+    AGENT_ADD  = LLM.ZM_GPT_5_4.create(temperature=0.3)
+
+
+class JobAgentModel:
+    """求职助手Agent模块"""
+    # 多轮对话：理解用户求职意图，返回结构化回复(message+tool调用)
+    CHAT       = LLM.ZM_GPT_5_4.create(temperature=0.7)
+    # 岗位简历-summary优化：针对具体岗位JD优化个人概述
+    SUMMARY    = LLM.ZM_GPT_5_4_MINI.create(temperature=0.3)
+    # 岗位简历-经历优化：针对具体岗位JD优化单条经历描述
+    EXPERIENCE = LLM.ZM_GPT_5_4_MINI.create(temperature=0.3)
+
+
+class NovaChatModel:
+    """Nova智能聊天模块"""
+    # 通用对话：基于简历和岗位上下文的自由问答，返回Markdown文本
+    CHAT = LLM.ZM_GPT_5_2.create(temperature=0.7)
+
+
+class ResumeExtractorModel:
+    """简历解析模块"""
+    # 简历结构化提取：两阶段并行提取简历文本为JSON结构
+    PARSE = LLM.DOUBAO_LITE_32K.create(temperature=0)
+
+
+class DiagnoserModel:
+    """简历诊断模块"""
+    # 模块诊断：逐条分析经历记录的问题(错别字/无量化/弱相关等)
+    MODULE  = LLM.ZM_GPT_5_4_MINI.create(temperature=0)
+    # 整体评价：汇总所有诊断结果生成总结性评语
+    SUMMARY = LLM.ZM_GPT_5_4_MINI.create(temperature=0.3)
+    # 内容润色：用户编辑后的文本做专业润色
+    POLISH  = LLM.JIAYU_CLAUDE_SONNET_4_5.create(temperature=0.3)
+
+
+class BrowserPlugModel:
+    """浏览器插件模块"""
+    # 表单自动填写：根据简历+岗位信息生成招聘网站表单字段的回答
+    FORM_FILL = LLM.DOUBAO_PRO_32K.create(temperature=0.3)
@@ -4,7 +4,7 @@
 依赖：LLM 枚举、nova_chat/prompts
 """

-from app.ai.models import LLM
+from app.ai.model_config import NovaChatModel
 from app.ai.nova_chat.prompts import SYSTEM_PROMPT
 from app.core.logger import log

@@ -24,8 +24,7 @@ async def nova_chat(resume_text: str, message: str, history: list[dict],
    messages.append(("human", message))

    try:
-        llm = LLM.ZM_GPT_5_2.create(temperature=0.7)
-        result = await llm.ainvoke(messages)
+        result = await NovaChatModel.CHAT.ainvoke(messages)
        return result.content.strip()
    except Exception as e:
        log.error(f"Nova Chat AI 调用失败: {e}")
@@ -5,7 +5,7 @@ import asyncio
 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 DiagnoserModel
 from app.ai.resume_diagnoser.prompts import DIAGNOSE_MODULE_PROMPT, SUMMARY_PROMPT, POLISH_PROMPT
 from app.core.logger import log
 from app.tool.json_helper import parse_llm_json
@@ -14,14 +14,14 @@ from app.tool.json_helper import parse_llm_json
 # 诊断链（StrOutputParser 拿原始文本，再手动解析 JSON，避免 markdown 代码块导致解析失败）
 _diagnose_chain = (
    ChatPromptTemplate.from_messages([("system", DIAGNOSE_MODULE_PROMPT), ("human", "请开始诊断。")])
-    | LLM.ZM_GPT_5_4_MINI.create(temperature=0)
+    | DiagnoserModel.MODULE
    | StrOutputParser()
 )

 # 汇总评价链（纯文本输出）
 _summary_chain = (
    ChatPromptTemplate.from_messages([("system", SUMMARY_PROMPT), ("human", "请生成整体评价。")])
-    | LLM.ZM_GPT_5_4_MINI.create(temperature=0.3)
+    | DiagnoserModel.SUMMARY
    | StrOutputParser()
 )

@@ -55,7 +55,7 @@ async def generate_summary(grade: str, urgent_total: int, important_total: int,

 _polish_chain = (
    ChatPromptTemplate.from_messages([("system", POLISH_PROMPT), ("human", "请开始优化。")])
-    | LLM.JIAYU_CLAUDE_SONNET_4_5.create(temperature=0.3)
+    | DiagnoserModel.POLISH
    | StrOutputParser()
 )

@@ -11,7 +11,7 @@ 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 ResumeExtractorModel
 from app.ai.resume_extractor.prompts import (
    OVERVIEW_PROFILE_PROMPT, OVERVIEW_EDUCATION_PROMPT, OVERVIEW_WORK_PROMPT,
    OVERVIEW_PROJECT_PROMPT, OVERVIEW_COMPETITION_PROMPT,
@@ -21,14 +21,12 @@ from app.ai.resume_extractor.prompts import (
 from app.core.logger import log
 from app.tool.json_helper import parse_llm_json

-_LLM_MODEL = LLM.DOUBAO_LITE_32K
-

 # ==================== LLM 调用工具 ====================

 def _build_chain(prompt: str):
    """构建提取链：prompt → LLM → 文本输出"""
-    return ChatPromptTemplate.from_messages([("system", prompt), ("human", "{text}")]) | _LLM_MODEL.create(temperature=0) | StrOutputParser()
+    return ChatPromptTemplate.from_messages([("system", prompt), ("human", "{text}")]) | ResumeExtractorModel.PARSE | StrOutputParser()


 async def _safe_invoke(chain, inp: dict, label: str):
@@ -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()
 )