feat: 添加 LLM 服务按需启动功能

新增功能:
- 后端新增 /ai/start API，支持启动本地 Ollama
- 后端改进 /ai/status API，返回提供商类型
- 前端看板页面添加启动/停止控制按钮
- 提供商感知 UI：云端 API 无需启动，本地 Ollama 可按需控制

用户体验:
- 云端模式：显示"云端服务已就绪"，直接可分析
- 本地模式：显示"启动 Ollama"按钮，按需启动节省内存
- 分析完成后：仅本地模式自动停止 Ollama

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/app/main.py

@@ -833,13 +833,29 @@ async def get_feedback_stats(db: AsyncSession = Depends(get_db)):
 @app.get("/ai/status")
 async def get_ai_status():
     """
-    Check if Ollama AI service is running and available.
+    检查 AI 服务状态
+    返回当前提供商类型和运行状态
     """
     import subprocess
     import json
+    import os
 
+    provider = os.getenv("LLM_PROVIDER", "ollama").lower()
+
+    # 云端 API 始终就绪
+    if provider in ["deepseek", "openai", "qwen"]:
+        api_key = os.getenv(f"{provider.upper()}_API_KEY")
+        return {
+            "status": "ready",
+            "provider": provider,
+            "provider_type": "cloud",
+            "cloud_service": provider,
+            "api_key_configured": bool(api_key),
+            "service_name": provider.capitalize()
+        }
+
+    # 本地 Ollama 需要检查
     try:
-        # Use curl to check Ollama status
         result = subprocess.run(
             ["curl", "-s", "-o", "/dev/null", "-w", "%{http_code}", "http://127.0.0.1:11434/api/tags"],
             capture_output=True,
@@ -848,7 +864,6 @@ async def get_ai_status():
         )
 
         if result.stdout.strip() == "200":
-            # Try to get model list
             result2 = subprocess.run(
                 ["curl", "-s", "http://127.0.0.1:11434/api/tags"],
                 capture_output=True,
@@ -861,19 +876,25 @@ async def get_ai_status():
                 model_names = [m.get("name", "unknown") for m in models]
                 return {
                     "status": "ready",
+                    "provider": "ollama",
+                    "provider_type": "local",
                     "ollama_running": True,
                     "available_models": model_names,
-                    "model_count": len(model_names)
+                    "model_count": len(model_names),
+                    "service_name": "Ollama"
                 }
     except Exception as e:
         print(f"[AI STATUS] Error: {e}")
 
     return {
         "status": "not_ready",
+        "provider": "ollama",
+        "provider_type": "local",
         "ollama_running": False,
         "available_models": [],
         "model_count": 0,
-        "error": "Ollama service not available. Start with: ollama serve"
+        "service_name": "Ollama",
+        "error": "Ollama 服务未运行，请点击启动"
     }
 
 @app.post("/ai/stop")
@@ -960,6 +981,86 @@ async def stop_ai_service():
         }
 
 
+@app.post("/ai/start")
+async def start_ai_service():
+    """
+    启动本地 Ollama 服务
+    对于云端 API (DeepSeek/OpenAI/Qwen)，返回无需启动
+    """
+    import subprocess
+    import os
+
+    provider = os.getenv("LLM_PROVIDER", "ollama").lower()
+
+    # 云端 API 不需要启动
+    if provider in ["deepseek", "openai", "qwen"]:
+        return {
+            "status": "not_applicable",
+            "message": f"使用 {provider.upper()} 云端服务，无需启动本地服务",
+            "provider": provider,
+            "provider_type": "cloud"
+        }
+
+    # 检查 Ollama 是否已运行
+    try:
+        result = subprocess.run(
+            ["lsof", "-ti", ":11434"],
+            capture_output=True,
+            timeout=2
+        )
+        if result.returncode == 0:
+            return {
+                "status": "already_running",
+                "message": "Ollama 已在运行",
+                "provider": "ollama",
+                "provider_type": "local"
+            }
+    except:
+        pass
+
+    # 启动 Ollama（尝试多种方式）
+    started = False
+    error_msgs = []
+
+    # 方法1: macOS Ollama.app
+    try:
+        subprocess.Popen(
+            ["open", "-a", "Ollama"],
+            capture_output=True
+        )
+        started = True
+    except Exception as e:
+        error_msgs.append(f"Ollama.app: {e}")
+
+    # 方法2: 命令行 ollama serve
+    if not started:
+        try:
+            subprocess.Popen(
+                ["ollama", "serve"],
+                capture_output=True,
+                stdout=subprocess.DEVNULL,
+                stderr=subprocess.DEVNULL
+            )
+            started = True
+        except Exception as e:
+            error_msgs.append(f"ollama serve: {e}")
+
+    if started:
+        return {
+            "status": "starting",
+            "message": "正在启动 Ollama，请稍候...",
+            "provider": "ollama",
+            "provider_type": "local"
+        }
+    else:
+        return {
+            "status": "error",
+            "message": f"启动失败: {'; '.join(error_msgs)}",
+            "provider": "ollama",
+            "provider_type": "local"
+        }
+
+
 # LLM 配置管理
 class LLMConfig(BaseModel):
     provider: str = "deepseek"  # ollama, deepseek, openai, qwen

frontend/src/components/SignalDashboard.vue

@@ -54,11 +54,43 @@
           <n-progress type="line" :percentage="analysisProgress" :indicator-placement="'inside'" processing />
           <n-text depth="3" style="font-size: 11px;">{{ analysisStatus }}</n-text>
         </div>
-        <n-space v-else :size="8">
+        <n-space v-else :size="8" align="center">
+          <!-- 服务状态显示 -->
+          <n-tag v-if="providerType === 'cloud'" type="success" size="small" :bordered="false">
+            🌐 {{ llmServiceName }} 云端服务
+          </n-tag>
+          <n-tag v-else type="default" size="small" :bordered="false">
+            🏠 本地 Ollama
+          </n-tag>
+
+          <!-- 启动分析按钮 -->
           <n-button size="small" :type="ollamaReady ? 'success' : 'warning'" @click="startAnalysis">
             <template #icon>{{ ollamaReady ? '🤖' : '⚠️' }}</template>
-            {{ ollamaReady ? '启动分析' : '启动分析 (未检测到 Ollama)' }}
+            {{ ollamaReady ? '启动分析' : (providerType === 'cloud' ? '启动分析' : '启动分析 (未就绪)') }}
           </n-button>
+
+          <!-- 本地 Ollama 控制按钮 -->
+          <template v-if="providerType === 'local'">
+            <n-button
+              v-if="!ollamaReady"
+              size="small"
+              type="info"
+              @click="startOllama"
+              :loading="startingOllama"
+            >
+              启动 Ollama
+            </n-button>
+            <n-button
+              v-else
+              size="small"
+              type="error"
+              @click="stopOllama"
+            >
+              停止
+            </n-button>
+          </template>
+
+          <!-- 状态检查按钮 -->
           <n-button size="small" @click="checkOllamaStatus" quaternary circle>
             <template #icon>🔄</template>
           </n-button>
@@ -575,6 +607,11 @@ const analysisProgress = ref(0)
 const analysisStatus = ref('')
 const ollamaReady = ref(false)
 
+// AI 服务配置
+const providerType = ref('cloud') // 'cloud' | 'local'
+const llmServiceName = ref('') // 'DeepSeek' | 'OpenAI' | 'Qwen' | 'Ollama'
+const startingOllama = ref(false)
+
 // Typewriter Effect State
 const displayedAnalysis = reactive({
   situation_analysis: '',
@@ -1563,17 +1600,72 @@ const submitSignal = async () => {
   }
 }
 
-// 检查 Ollama 状态
+// 检查 AI 服务状态
 const checkOllamaStatus = async () => {
   try {
     const response = await axios.get('http://127.0.0.1:8000/ai/status', { timeout: 3000 })
-    ollamaReady.value = response.data.status === 'ready'
+
-    const modelCount = response.data.model_count || 0
+    const data = response.data
-    const models = response.data.available_models || []
+    providerType.value = data.provider_type || 'cloud'
-    message.info(ollamaReady.value ? `✅ Ollama 已就绪 (${modelCount} 个模型: ${models.join(', ')})` : '❌ Ollama 未运行')
+    llmServiceName.value = data.service_name || ''
+
+    if (data.provider_type === 'cloud') {
+      // 云端 API
+      ollamaReady.value = true
+      message.success(`✅ 已连接 ${data.service_name} 云端服务`)
+    } else {
+      // 本地 Ollama
+      ollamaReady.value = data.status === 'ready'
+      const modelCount = data.model_count || 0
+      const models = data.available_models || []
+
+      if (ollamaReady.value) {
+        message.success(`✅ Ollama 已就绪 (${modelCount} 个模型)`)
+      } else {
+        message.warning('⚠️ Ollama 未运行，请点击启动')
+      }
+    }
   } catch (error) {
     ollamaReady.value = false
-    message.warning('⚠️ 无法检测 Ollama 状态，请确保后端服务运行中')
+    message.warning('⚠️ 无法检测 AI 服务状态')
+  }
+}
+
+// 启动本地 Ollama
+const startOllama = async () => {
+  startingOllama.value = true
+  try {
+    const response = await axios.post('http://127.0.0.1:8000/ai/start')
+
+    if (response.data.status === 'already_running') {
+      message.info('Ollama 已在运行')
+      ollamaReady.value = true
+    } else if (response.data.status === 'starting') {
+      message.info('正在启动 Ollama，请稍候...')
+      // 等待几秒后检查状态
+      setTimeout(async () => {
+        await checkOllamaStatus()
+      }, 3000)
+    } else if (response.data.status === 'not_applicable') {
+      message.info(response.data.message)
+    } else {
+      message.error(response.data.message)
+    }
+  } catch (error) {
+    message.error('启动失败: ' + (error.response?.data?.detail || error.message))
+  } finally {
+    startingOllama.value = false
+  }
+}
+
+// 停止本地 Ollama
+const stopOllama = async () => {
+  try {
+    await axios.post('http://127.0.0.1:8000/ai/stop')
+    ollamaReady.value = false
+    message.success('✅ Ollama 已停止，内存已释放')
+  } catch (error) {
+    message.error('停止失败')
   }
 }
 
@@ -1614,12 +1706,15 @@ const startAnalysis = async () => {
     // 刷新信号列表
     await fetchSignals()
 
-    // 停止 Ollama 节省资源
+    // 仅本地 Ollama 需要停止节省资源
-    analysisStatus.value = '正在停止 Ollama...'
+    if (providerType.value === 'local') {
-    await axios.post('http://127.0.0.1:8000/ai/stop')
+      analysisStatus.value = '正在停止 Ollama...'
-    ollamaReady.value = false
+      await axios.post('http://127.0.0.1:8000/ai/stop')
-
+      ollamaReady.value = false
-    analysisStatus.value = `完成！Ollama 已停止`
+      analysisStatus.value = `完成！Ollama 已停止`
+    } else {
+      analysisStatus.value = `分析完成！${llmServiceName.value} 云端服务`
+    }
 
     setTimeout(() => {
       analyzing.value = false