llm模型接入调整

backend/agentscope_integration/factory.py

@@ -3,6 +3,7 @@
 提供统一的智能体创建接口，根据用户类型（员工/管理者/任务/文档）创建对应的 AI 智能体实例。
 支持智能体缓存以减少重复创建的开销。
 """
+from sqlalchemy import select
 from agentscope.agent import AgentBase
 from agentscope.agent._react_agent import ReActAgent
 from agentscope.model import OpenAIChatModel
@@ -10,6 +11,8 @@ from agentscope.formatter import OpenAIChatFormatter
 from agentscope.tool import Toolkit
 from agentscope.message import Msg
 from config import settings
+from models import ModelInstance, ModelProvider
+from database import get_db
 from .memory.user_memory import UserIsolatedMemory
 from .hooks.rbac_hook import register_rbac_hooks_for_user
 
@@ -26,13 +29,120 @@ class AgentFactory:
     _MAX_CACHE_SIZE = 50  # 智能体缓存上限
 
     @classmethod
-    def _get_model(cls) -> OpenAIChatModel:
+    async def _get_llm_config(cls, db, model_instance_id=None):
+        """从数据库获取 LLM 配置信息。
+
+        根据提供的 model_instance_id 查询对应的模型实例和供应商配置，
+        如果未提供或未找到，则回退到默认模型（is_default=True）。
+        最终如果都未找到，则使用 settings 中的默认配置。
+
+        Args:
+            db: 数据库会话对象。
+            model_instance_id: 可选的模型实例 ID，用于指定特定模型。
+
+        Returns:
+            dict: 包含 LLM 配置的字典：
+                - model_name (str): 模型名称
+                - api_key (str): API 密钥
+                - base_url (str): API 基础地址
+                - default_params (dict): 默认参数（temperature, max_tokens 等）
+        """
+        try:
+            if model_instance_id:
+                stmt = select(ModelInstance).where(
+                    ModelInstance.id == model_instance_id,
+                    ModelInstance.is_active == True
+                )
+                result = await db.execute(stmt)
+                model_instance = result.scalar_one_or_none()
+
+                if model_instance:
+                    stmt_provider = select(ModelProvider).where(
+                        ModelProvider.id == model_instance.provider_id,
+                        ModelProvider.is_active == True
+                    )
+                    result_provider = await db.execute(stmt_provider)
+                    provider = result_provider.scalar_one_or_none()
+
+                    if provider:
+                        return {
+                            "model_name": model_instance.model_name,
+                            "api_key": provider.api_key or settings.LLM_API_KEY,
+                            "base_url": provider.base_url or settings.LLM_API_BASE,
+                            "default_params": model_instance.default_params or {},
+                        }
+
+            stmt_default = select(ModelInstance).where(
+                ModelInstance.is_default == True,
+                ModelInstance.is_active == True,
+                ModelInstance.model_type == "llm"
+            ).limit(1)
+            result_default = await db.execute(stmt_default)
+            default_instance = result_default.scalar_one_or_none()
+
+            if default_instance:
+                stmt_provider = select(ModelProvider).where(
+                    ModelProvider.id == default_instance.provider_id,
+                    ModelProvider.is_active == True
+                )
+                result_provider = await db.execute(stmt_provider)
+                provider = result_provider.scalar_one_or_none()
+
+                if provider:
+                    return {
+                        "model_name": default_instance.model_name,
+                        "api_key": provider.api_key or settings.LLM_API_KEY,
+                        "base_url": provider.base_url or settings.LLM_API_BASE,
+                        "default_params": default_instance.default_params or {},
+                    }
+        except Exception as e:
+            pass
+
+        return {
+            "model_name": settings.LLM_MODEL,
+            "api_key": settings.LLM_API_KEY,
+            "base_url": settings.LLM_API_BASE,
+            "default_params": {},
+        }
+
+    @classmethod
+    def _get_model(cls, llm_config: dict = None) -> OpenAIChatModel:
         """获取或创建全局共享的大语言模型实例。
 
+        支持通过 llm_config 参数动态指定模型配置，如果未提供则使用默认配置。
+
+        Args:
+            llm_config: 可选的 LLM 配置字典，包含：
+                - model_name (str): 模型名称
+                - api_key (str): API 密钥
+                - base_url (str): API 基础地址
+                - default_params (dict): 默认参数（temperature, max_tokens 等）
+
         Returns:
             OpenAIChatModel: 配置好的大语言模型实例。
         """
         if cls._model is None:
+            if llm_config:
+                model_name = llm_config.get("model_name", settings.LLM_MODEL)
+                api_key = llm_config.get("api_key", settings.LLM_API_KEY)
+                base_url = llm_config.get("base_url", settings.LLM_API_BASE)
+                default_params = llm_config.get("default_params", {})
+
+                model_kwargs = {
+                    "config_name": "enterprise_model",
+                    "model_name": model_name,
+                    "api_key": api_key,
+                    "api_base": base_url,
+                }
+
+                if default_params:
+                    if "temperature" in default_params:
+                        model_kwargs["temperature"] = default_params["temperature"]
+                    if "max_tokens" in default_params:
+                        model_kwargs["max_tokens"] = default_params["max_tokens"]
+
+                cls._model = OpenAIChatModel(**model_kwargs)
+            else:
                 cls._model = OpenAIChatModel(
                     config_name="enterprise_model",
                     model_name=settings.LLM_MODEL,

backend/modules/memory/manager.py

@@ -18,9 +18,10 @@ from datetime import datetime, timezone
 from typing import Callable
 
 from redis.asyncio import Redis
-from sqlalchemy import text
+from sqlalchemy import text, select
 from sqlalchemy.ext.asyncio import AsyncSession
 from config import settings
+from models import ModelInstance, ModelProvider
 
 logger = logging.getLogger(__name__)
 
@@ -65,6 +66,62 @@ class MemoryManager:
         self.db_factory = db_factory
         self.redis = redis
         self._extract_tasks: dict[str, asyncio.Task] = {}  # 后台提取任务追踪
+        self._llm_config_cache: dict | None = None  # LLM 配置缓存
+
+    async def _get_llm_config(self) -> dict:
+        """从 model_instances 表获取 LLM 配置，带缓存。
+
+        优先级：数据库默认 LLM → 环境变量回退。
+        缓存有效期：5分钟（避免每次调用都查库）。
+
+        Returns:
+            dict: {model_name, api_key, base_url}
+        """
+        if self._llm_config_cache is not None:
+            return self._llm_config_cache
+        try:
+            db = self.db_factory()
+            result = await db.execute(
+                select(ModelInstance, ModelProvider)
+                .join(ModelProvider, ModelInstance.provider_id == ModelProvider.id)
+                .where(ModelInstance.model_type == 'llm')
+                .where(ModelInstance.is_default == True)
+                .where(ModelInstance.is_active == True)
+                .limit(1)
+            )
+            row = result.first()
+            if not row:
+                result2 = await db.execute(
+                    select(ModelInstance, ModelProvider)
+                    .join(ModelProvider, ModelInstance.provider_id == ModelProvider.id)
+                    .where(ModelInstance.model_type == 'llm')
+                    .where(ModelInstance.is_active == True)
+                    .limit(1)
+                )
+                row = result2.first()
+            if row:
+                instance, provider = row
+                config = {
+                    "api_base": (provider.base_url or settings.LLM_API_BASE).rstrip("/"),
+                    "model": instance.model_name,
+                    "api_key": provider.api_key or settings.LLM_API_KEY,
+                }
+            else:
+                config = {
+                    "api_base": settings.LLM_API_BASE.rstrip("/"),
+                    "model": settings.LLM_MODEL,
+                    "api_key": settings.LLM_API_KEY,
+                }
+            self._llm_config_cache = config
+            # 5分钟后自动清除缓存
+            asyncio.get_event_loop().call_later(300, lambda: setattr(self, '_llm_config_cache', None))
+            return config
+        except Exception:
+            return {
+                "api_base": settings.LLM_API_BASE.rstrip("/"),
+                "model": settings.LLM_MODEL,
+                "api_key": settings.LLM_API_KEY,
+            }
 
     async def inject_memory(
         self,
@@ -394,19 +451,19 @@ class MemoryManager:
             )
 
             import httpx
-            api_base = settings.LLM_API_BASE.rstrip("/")
+            llm = await self._get_llm_config()
             async with httpx.AsyncClient(timeout=30) as client:
                 resp = await client.post(
-                    f"{api_base}/chat/completions",
+                    f"{llm['api_base']}/chat/completions",
                     json={
-                        "model": settings.LLM_MODEL,
+                        "model": llm["model"],
                         "messages": [{
                             "role": "user",
                             "content": f"请用一段话简要总结以下对话的关键内容。保留人名、任务、决策、时间等关键信息。\n\n{dialogue}"
                         }],
                         "max_tokens": 200,
                     },
-                    headers={"Authorization": f"Bearer {settings.LLM_API_KEY}"},
+                    headers={"Authorization": f"Bearer {llm['api_key']}"},
                 )
                 data = resp.json()
                 summary = data.get("choices", [{}])[0].get("message", {}).get("content", "")
@@ -515,17 +572,17 @@ class MemoryManager:
 只返回JSON数组，不要其他内容。如果没有可提取的信息返回空数组[]。"""
 
             import httpx
-            api_base = settings.LLM_API_BASE.rstrip("/")
+            llm = await self._get_llm_config()
             async with httpx.AsyncClient(timeout=60) as client:
                 resp = await client.post(
-                    f"{api_base}/chat/completions",
+                    f"{llm['api_base']}/chat/completions",
                     json={
-                        "model": settings.LLM_MODEL,
+                        "model": llm["model"],
                         "messages": [{"role": "user", "content": prompt}],
                         "max_tokens": 800,
                         "temperature": 0.3,
                     },
-                    headers={"Authorization": f"Bearer {settings.LLM_API_KEY}"},
+                    headers={"Authorization": f"Bearer {llm['api_key']}"},
                 )
                 data = resp.json()
                 result_text = data.get("choices", [{}])[0].get("message", {}).get("content", "[]")
@@ -704,17 +761,17 @@ class MemoryManager:
 只返回JSON数组，不要其他内容。"""
 
             import httpx
-            api_base = settings.LLM_API_BASE.rstrip("/")
+            llm = await self._get_llm_config()
             async with httpx.AsyncClient(timeout=60) as client:
                 resp = await client.post(
-                    f"{api_base}/chat/completions",
+                    f"{llm['api_base']}/chat/completions",
                     json={
-                        "model": settings.LLM_MODEL,
+                        "model": llm["model"],
                         "messages": [{"role": "user", "content": prompt}],
                         "max_tokens": 500,
                         "temperature": 0.3,
                     },
-                    headers={"Authorization": f"Bearer {settings.LLM_API_KEY}"},
+                    headers={"Authorization": f"Bearer {llm['api_key']}"},
                 )
                 data = resp.json()
                 result_text = data.get("choices", [{}])[0].get("message", {}).get("content", "[]")
@@ -817,17 +874,17 @@ class MemoryManager:
 只返回JSON对象，不要其他内容。"""
 
             import httpx
-            api_base = settings.LLM_API_BASE.rstrip("/")
+            llm = await self._get_llm_config()
             async with httpx.AsyncClient(timeout=60) as client:
                 resp = await client.post(
-                    f"{api_base}/chat/completions",
+                    f"{llm['api_base']}/chat/completions",
                     json={
-                        "model": settings.LLM_MODEL,
+                        "model": llm["model"],
                         "messages": [{"role": "user", "content": prompt}],
                         "max_tokens": 500,
                         "temperature": 0.3,
                     },
-                    headers={"Authorization": f"Bearer {settings.LLM_API_KEY}"},
+                    headers={"Authorization": f"Bearer {llm['api_key']}"},
                 )
                 data = resp.json()
                 result_text = data.get("choices", [{}])[0].get("message", {}).get("content", "{}")

backend/modules/model_provider/router.py

@@ -295,3 +295,138 @@ async def delete_model(provider_id: str, model_id: str, db: AsyncSession = Depen
     await db.delete(m)
     await db.commit()
     return {"code": 200, "message": "已删除"}
+
+
+@router.get("/default-llm")
+async def get_default_llm(db: AsyncSession = Depends(get_db)):
+    """获取系统默认 LLM 模型配置。
+
+    从 model_instances 表中查找 is_default=True 且 model_type='llm' 的模型实例，
+    返回包含供应商连接信息的完整配置，供系统中各模块统一使用。
+
+    Returns:
+        dict: 默认 LLM 配置（model_name, api_key, base_url, default_params 等），
+              若未配置则返回 null。
+    """
+    result = await db.execute(
+        select(ModelInstance, ModelProvider)
+        .join(ModelProvider, ModelInstance.provider_id == ModelProvider.id)
+        .where(ModelInstance.model_type == 'llm')
+        .where(ModelInstance.is_default == True)
+        .where(ModelInstance.is_active == True)
+        .limit(1)
+    )
+    row = result.first()
+    if not row:
+        # 回退：取第一个启用的 LLM
+        result2 = await db.execute(
+            select(ModelInstance, ModelProvider)
+            .join(ModelProvider, ModelInstance.provider_id == ModelProvider.id)
+            .where(ModelInstance.model_type == 'llm')
+            .where(ModelInstance.is_active == True)
+            .order_by(ModelInstance.created_at)
+            .limit(1)
+        )
+        row = result2.first()
+    if not row:
+        return {"code": 200, "data": None}
+
+    instance, provider = row
+    return {
+        "code": 200,
+        "data": {
+            "id": str(instance.id),
+            "model_name": instance.model_name,
+            "display_name": instance.display_name or instance.model_name,
+            "model_type": instance.model_type,
+            "api_key": provider.api_key or "",
+            "base_url": provider.base_url or settings.LLM_API_BASE,
+            "default_params": instance.default_params or {},
+            "capabilities": instance.capabilities or {},
+            "is_default": instance.is_default,
+            "is_active": instance.is_active,
+            "provider_id": str(instance.provider_id),
+            "provider_name": provider.name,
+        },
+    }
+
+
+async def resolve_model_config(db: AsyncSession, model_instance_id: str = None) -> dict:
+    """根据模型实例 ID 解析完整的模型调用配置。
+
+    这是系统的核心模型解析函数。各模块（Agent工厂、流程引擎、记忆管理器等）
+    应通过此函数获取模型配置，实现统一的模型管理。
+
+    解析优先级：
+    1. 如果提供了 model_instance_id → 从数据库读取该模型的完整配置
+    2. 否则 → 获取默认 LLM 配置（is_default 的 LLM 实例）
+    3. 都没有 → 回退到环境变量 settings.LLM_*
+
+    Args:
+        db: 异步数据库会话。
+        model_instance_id: 可选的模型实例 UUID 字符串。
+
+    Returns:
+        dict: 包含 model_name, api_key, base_url, default_params 等的配置字典。
+    """
+    if model_instance_id:
+        try:
+            uid = uuid.UUID(model_instance_id)
+            result = await db.execute(
+                select(ModelInstance, ModelProvider)
+                .join(ModelProvider, ModelInstance.provider_id == ModelProvider.id)
+                .where(ModelInstance.id == uid)
+                .limit(1)
+            )
+            row = result.first()
+            if row:
+                instance, provider = row
+                return {
+                    "model_name": instance.model_name,
+                    "api_key": provider.api_key or settings.LLM_API_KEY,
+                    "base_url": provider.base_url or settings.LLM_API_BASE,
+                    "default_params": instance.default_params or {},
+                    "capabilities": instance.capabilities or {},
+                }
+        except (ValueError, Exception):
+            pass
+
+    # 无指定实例或未找到：使用默认 LLM
+    result = await db.execute(
+        select(ModelInstance, ModelProvider)
+        .join(ModelProvider, ModelInstance.provider_id == ModelProvider.id)
+        .where(ModelInstance.model_type == 'llm')
+        .where(ModelInstance.is_default == True)
+        .where(ModelInstance.is_active == True)
+        .limit(1)
+    )
+    row = result.first()
+    if not row:
+        result = await db.execute(
+            select(ModelInstance, ModelProvider)
+            .join(ModelProvider, ModelInstance.provider_id == ModelProvider.id)
+            .where(ModelInstance.model_type == 'llm')
+            .where(ModelInstance.is_active == True)
+            .order_by(ModelInstance.created_at)
+            .limit(1)
+        )
+        row = result.first()
+
+    if row:
+        instance, provider = row
+        return {
+            "model_name": instance.model_name,
+            "api_key": provider.api_key or settings.LLM_API_KEY,
+            "base_url": provider.base_url or settings.LLM_API_BASE,
+            "default_params": instance.default_params or {},
+            "capabilities": instance.capabilities or {},
+        }
+
+    # 最终回退到环境变量
+    return {
+        "model_name": settings.LLM_MODEL,
+        "api_key": settings.LLM_API_KEY,
+        "base_url": settings.LLM_API_BASE,
+        "default_params": {},
+        "capabilities": {},
+    }

backend/modules/monitor/router.py

@@ -12,6 +12,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from database import get_db
 from models import User, ChatSession, ChatMessage
 from modules.org.router import _get_subordinate_ids, _user_to_out
+from modules.model_provider.router import resolve_model_config
 from schemas import EmployeeAnalysis, UserOut
 
 router = APIRouter(prefix="/api/monitor", tags=["monitor"])  # 监控模块路由前缀
@@ -204,12 +205,13 @@ async def get_employee_analysis(
         f"[{m.role}] {m.content[:300]}" for m in messages
     ])
 
-    # 初始化 LLM 模型
+    # 初始化 LLM 模型（从模型管理表读取配置）
+    llm_cfg = await resolve_model_config(db)
     model = OpenAIChatModel(
         config_name="analysis_model",
-        model_name=settings.LLM_MODEL,
-        api_key=settings.LLM_API_KEY,
-        api_base=settings.LLM_API_BASE,
+        model_name=llm_cfg["model_name"],
+        api_key=llm_cfg["api_key"],
+        api_base=llm_cfg["base_url"],
     )
     formatter = OpenAIChatFormatter()
 

frontend/src/views/flow/node-configs/LlmConfig.vue

@@ -109,17 +109,21 @@ function getProviderName(providerId: string): string {
 }
 
 function onModelSelect(val: string) {
-  update('model', val)
-  // 如果选择了已管理的模型，自动填入其默认参数
-  if (!val) return
-  const model = llmModels.value.find((m: any) => m.model_name === val)
+  // 如果选择了已管理的模型，同时保存 model_name 和 model_instance_id
+  const model = val ? llmModels.value.find((m: any) => m.model_name === val) : null
   if (model) {
+    // 从模型管理选择：保存 model + model_instance_id
+    const updated = { ...props.modelValue, model: val, model_instance_id: model.id }
+    // 自动填入默认参数
     const params = model.default_params || {}
-    const updated = { ...props.modelValue }
     if (params.temperature !== undefined && !updated.temperature) updated.temperature = params.temperature
     if (params.max_tokens && !updated.max_tokens) updated.max_tokens = params.max_tokens
     if ((model.capabilities || {}).function_calling && updated.tool_call === false) updated.tool_call = true
     emit('update:modelValue', updated)
+  } else {
+    // 手动输入或清空：只保存 model，清除 model_instance_id
+    const updated = { ...props.modelValue, model: val, model_instance_id: undefined }
+    emit('update:modelValue', updated)
   }
 }
 </script>

frontend/src/views/settings/Settings.vue

@@ -2,46 +2,49 @@
   <div class="settings-page">
     <el-page-header @back="$router.back()" content="系统配置" />
 
+    <!-- LLM 模型配置 - 引导到模型管理 -->
     <el-card style="margin-top: 20px">
-      <template #header>LLM 模型配置</template>
-      <el-form :model="form" label-width="140px" style="max-width: 600px">
-        <el-form-item label="API 地址">
-          <el-input v-model="form.api_base" placeholder="https://api.openai.com/v1" />
-        </el-form-item>
-        <el-form-item label="API Key">
-          <el-input v-model="form.api_key" type="password" show-password placeholder="sk-..." />
-        </el-form-item>
-        <el-form-item label="默认模型">
-          <el-select v-model="form.model" style="width: 100%">
-            <el-option label="GPT-4o-mini" value="gpt-4o-mini" />
-            <el-option label="GPT-4o" value="gpt-4o" />
-            <el-option label="GPT-4-turbo" value="gpt-4-turbo" />
-            <el-option label="GPT-3.5-turbo" value="gpt-3.5-turbo" />
-            <el-option label="DeepSeek-V3" value="deepseek-chat" />
-            <el-option label="DeepSeek-R1" value="deepseek-reasoner" />
-            <el-option label="Qwen-Max" value="qwen-max" />
-            <el-option label="自定义" value="__custom__" />
-          </el-select>
-        </el-form-item>
-        <el-form-item v-if="form.model === '__custom__'" label="自定义模型名">
-          <el-input v-model="form.custom_model" placeholder="请输入模型名称" />
-        </el-form-item>
-        <el-form-item label="Max Tokens">
-          <el-input-number v-model="form.max_tokens" :min="100" :max="128000" :step="100" />
-        </el-form-item>
-        <el-form-item label="温度 (创建任务)">
-          <el-slider v-model="form.temperature_task" :min="0" :max="2" :step="0.1" />
-        </el-form-item>
-        <el-form-item label="温度 (企业分析)">
-          <el-slider v-model="form.temperature_analysis" :min="0" :max="2" :step="0.1" />
-        </el-form-item>
-        <el-form-item>
-          <el-button type="primary" @click="saveConfig" :loading="saving">保存配置</el-button>
-          <el-button @click="testConnection" :loading="testing">测试连接</el-button>
-        </el-form-item>
-      </el-form>
+      <template #header>
+        <div style="display: flex; justify-content: space-between; align-items: center;">
+          <span>LLM 模型配置</span>
+          <el-button size="small" type="primary" @click="$router.push('/admin/model/providers')">
+            前往模型管理 →
+          </el-button>
+        </div>
+      </template>
+      <el-alert
+        title="模型配置已迁移至「模型管理」页面"
+        type="info"
+        :closable="false"
+        show-icon
+        style="margin-bottom: 16px;"
+      >
+        <template #default>
+          <p style="margin: 0;">在「模型管理」中配置 LLM/Embedding/Rerank 模型的供应商、API Key、参数等。系统各模块（AI 助手、流程引擎、知识库、监控分析）将统一使用模型管理中的配置。</p>
+          <el-button type="primary" link @click="$router.push('/admin/model/providers')" style="margin-top: 8px;">
+            前往管理模型 →
+          </el-button>
+        </template>
+      </el-alert>
+
+      <!-- 当前默认模型展示（只读） -->
+      <div v-if="defaultModel" style="padding: 12px; background: #f5f7fa; border-radius: 8px;">
+        <div style="display: flex; gap: 24px; font-size: 13px;">
+          <div><strong>默认 LLM：</strong>{{ defaultModel.display_name || defaultModel.model_name || '未配置' }}</div>
+          <div><strong>供应商：</strong>{{ defaultModel.provider_name || '-' }}</div>
+          <div><strong>状态：</strong>
+            <el-tag :type="defaultModel.is_active ? 'success' : 'danger'" size="small">
+              {{ defaultModel.is_active ? '启用' : '禁用' }}
+            </el-tag>
+          </div>
+        </div>
+      </div>
+      <div v-else style="padding: 12px; background: #fef0f0; border-radius: 8px; color: #f56c6c; font-size: 13px;">
+        ⚠️ 尚未配置默认 LLM 模型，请前往「模型管理」添加并设为默认
+      </div>
     </el-card>
 
+    <!-- 企微配置 -->
     <el-card style="margin-top: 20px">
       <template #header>企微配置</template>
       <el-form :model="form" label-width="140px" style="max-width: 600px">
@@ -63,6 +66,7 @@
       </el-form>
     </el-card>
 
+    <!-- RAG 知识库 -->
     <el-card style="margin-top: 20px">
       <template #header>RAG 知识库</template>
       <div style="display: flex; gap: 16px; align-items: flex-start">
@@ -92,7 +96,7 @@
 </template>
 
 <script setup lang="ts">
-import { ref, reactive } from 'vue'
+import { ref, reactive, onMounted } from 'vue'
 import { ElMessage } from 'element-plus'
 import { systemApi, ragApi } from '@/api'
 import api from '@/api'
@@ -102,15 +106,9 @@ const testing = ref(false)
 const ragQuery = ref('')
 const ragSearching = ref(false)
 const ragResults = ref<any[]>([])
+const defaultModel = ref<any>(null)
 
 const form = reactive({
-  api_base: '',
-  api_key: '',
-  model: 'gpt-4o-mini',
-  custom_model: '',
-  max_tokens: 4096,
-  temperature_task: 0.7,
-  temperature_analysis: 0.5,
   wecom_corp_id: '',
   wecom_app_secret: '',
   wecom_agent_id: 0,
@@ -118,23 +116,18 @@ const form = reactive({
   wecom_encoding_aes: '',
 })
 
-async function saveConfig() {
-  saving.value = true
+async function loadDefaultModel() {
   try {
-    const finalModel = form.model === '__custom__' ? form.custom_model : form.model
-    await systemApi.health()
-    ElMessage.success('配置保存成功')
-    localStorage.setItem('llm_config', JSON.stringify({
-      api_base: form.api_base,
-      api_key: form.api_key,
-      model: finalModel,
-      max_tokens: form.max_tokens,
-      temperature_task: form.temperature_task,
-      temperature_analysis: form.temperature_analysis,
-    }))
-  } catch { ElMessage.warning('保存失败') }
-  finally { saving.value = false }
+    const res: any = await api.get('/model-providers/default-llm')
+    defaultModel.value = res?.data || res || null
+  } catch {
+    defaultModel.value = null
   }
+}
+
+onMounted(() => {
+  loadDefaultModel()
+})
 
 async function testConnection() {
   testing.value = true