update

backend/app/agent/smart_agent.py

@@ -37,11 +37,14 @@ class SmartStockAgent:
         # 注册技能
         self._register_skills()
 
+        # 获取配置的模型
+        self.model_override = getattr(self.settings, 'smart_agent_model', None)
+
         # 检查LLM是否可用
         self.use_llm = bool(self.settings.zhipuai_api_key) and llm_service.client is not None
 
         if self.use_llm:
-            logger.info("Smart Agent初始化完成（智能模式 + LLM深度集成 + Tushare Pro高级数据）")
+            logger.info(f"Smart Agent初始化完成（智能模式 + LLM深度集成 + Tushare Pro高级数据，模型: {self.model_override or '默认'}）")
         else:
             logger.warning("Smart Agent初始化完成（规则模式，建议配置LLM）")
 
@@ -50,7 +53,7 @@ class SmartStockAgent:
         loop = asyncio.get_event_loop()
         return await loop.run_in_executor(
             None,
-            lambda: llm_service.chat(messages, temperature, max_tokens)
+            lambda: llm_service.chat(messages, temperature, max_tokens, model_override=self.model_override)
         )
 
     def _register_skills(self):
@@ -2088,7 +2091,8 @@ RSI：{technical.get('rsi', 0):.2f if technical.get('rsi') else '计算中'}
                 stream = llm_service.chat_stream(
                     messages=[{"role": "user", "content": prompt}],
                     temperature=0.7,
-                    max_tokens=2500  # 增加 token 数量以容纳新闻分析
+                    max_tokens=2500,  # 增加 token 数量以容纳新闻分析
+                    model_override=self.model_override
                 )
 
                 for chunk in stream:
@@ -2163,7 +2167,8 @@ RSI：{technical.get('rsi', 0):.2f if technical.get('rsi') else '计算中'}
                 stream = llm_service.chat_stream(
                     messages=[{"role": "user", "content": prompt}],
                     temperature=0.7,
-                    max_tokens=2500
+                    max_tokens=2500,
+                    model_override=self.model_override
                 )
 
                 for chunk in stream:
@@ -2480,7 +2485,8 @@ RSI：{technical.get('rsi', 0):.2f if technical.get('rsi') else '计算中'}
         stream = llm_service.chat_stream(
             messages=[{"role": "user", "content": prompt}],
             temperature=0.7,
-            max_tokens=2500
+            max_tokens=2500,
+            model_override=self.model_override
         )
 
         # 在线程中迭代同步生成器，避免阻塞事件循环
@@ -2581,7 +2587,8 @@ MACD：{f"{technical.get('macd'):.4f}" if technical.get('macd') else '计算中'
         stream = llm_service.chat_stream(
             messages=[{"role": "user", "content": prompt}],
             temperature=0.7,
-            max_tokens=2500  # 增加 token 数量以容纳新闻分析
+            max_tokens=2500,  # 增加 token 数量以容纳新闻分析
+            model_override=self.model_override
         )
 
         # 在线程中迭代同步生成器，避免阻塞事件循环
@@ -2984,7 +2991,8 @@ MACD：{f"{technical.get('macd'):.4f}" if technical.get('macd') else '计算中'
             stream = llm_service.chat_stream(
                 messages=[{"role": "user", "content": prompt}],
                 temperature=0.7,
-                max_tokens=self._calculate_max_tokens(intent)
+                max_tokens=self._calculate_max_tokens(intent),
+                model_override=self.model_override
             )
 
             for chunk in stream:

backend/app/config.py

@@ -116,6 +116,10 @@ class Settings(BaseSettings):
     paper_trading_position_b: float = 500   # B级信号仓位 (USDT)
     paper_trading_position_c: float = 200   # C级信号仓位 (USDT)
 
+    # Agent 模型配置 (可选值: zhipu, deepseek)
+    smart_agent_model: str = "deepseek"   # SmartAgent 使用的模型
+    crypto_agent_model: str = "deepseek"  # CryptoAgent 使用的模型
+
     class Config:
         env_file = find_env_file()
         case_sensitive = False

backend/app/crypto_agent/llm_signal_analyzer.py

@@ -143,8 +143,11 @@ class LLMSignalAnalyzer:
 
     def __init__(self):
         """初始化分析器"""
+        from app.config import get_settings
         self.news_service = get_news_service()
-        logger.info("LLM 信号分析器初始化完成（含新闻舆情）")
+        settings = get_settings()
+        self.model_override = getattr(settings, 'crypto_agent_model', None)
+        logger.info(f"LLM 信号分析器初始化完成（含新闻舆情，模型: {self.model_override or '默认'}）")
 
     async def analyze(self, symbol: str, data: Dict[str, pd.DataFrame],
                        symbols: List[str] = None) -> Dict[str, Any]:
@@ -170,7 +173,7 @@ class LLMSignalAnalyzer:
             response = llm_service.chat([
                 {"role": "system", "content": self.SYSTEM_PROMPT},
                 {"role": "user", "content": data_prompt}
-            ])
+            ], model_override=self.model_override)
 
             if not response:
                 logger.warning(f"{symbol} LLM 分析无响应")

backend/app/services/llm_service.py

@@ -19,16 +19,18 @@ class LLMService:
         messages: List[Dict[str, str]],
         model: str = None,
         temperature: float = 0.7,
-        max_tokens: int = 2000
+        max_tokens: int = 2000,
+        model_override: str = None
     ) -> Optional[str]:
         """
         调用LLM进行对话
 
         Args:
             messages: 消息列表
-            model: 模型名称（忽略，使用当前选择的模型）
+            model: 模型名称（已废弃，使用 model_override）
             temperature: 温度参数
             max_tokens: 最大token数
+            model_override: 指定使用的模型 (zhipu/deepseek)
 
         Returns:
             LLM响应文本
@@ -36,7 +38,8 @@ class LLMService:
         return self.multi_service.chat(
             messages=messages,
             temperature=temperature,
-            max_tokens=max_tokens
+            max_tokens=max_tokens,
+            model_override=model_override
         )
 
     def analyze_intent(self, user_message: str) -> Dict[str, Any]:
@@ -47,7 +50,8 @@ class LLMService:
         self,
         messages: List[Dict[str, str]],
         temperature: float = 0.7,
-        max_tokens: int = 2000
+        max_tokens: int = 2000,
+        model_override: str = None
     ):
         """
         流式调用LLM进行对话
@@ -56,6 +60,7 @@ class LLMService:
             messages: 消息列表
             temperature: 温度参数
             max_tokens: 最大token数
+            model_override: 指定使用的模型 (zhipu/deepseek)
 
         Yields:
             LLM响应的文本片段
@@ -63,7 +68,8 @@ class LLMService:
         return self.multi_service.chat_stream(
             messages=messages,
             temperature=temperature,
-            max_tokens=max_tokens
+            max_tokens=max_tokens,
+            model_override=model_override
         )
 
 

backend/app/services/multi_llm_service.py

@@ -66,8 +66,12 @@ class MultiLLMService:
             except Exception as e:
                 logger.error(f"DeepSeek初始化失败: {e}")
 
-        # 设置默认模型（优先DeepSeek，因为更便宜）
+        # 设置默认模型（优先使用配置文件中的设置）
-        if 'deepseek' in self.clients:
+        preferred_model = getattr(settings, 'smart_agent_model', None)
+        if preferred_model and preferred_model in self.clients:
+            self.current_model = preferred_model
+            logger.info(f"使用配置的模型: {preferred_model}")
+        elif 'deepseek' in self.clients:
             self.current_model = 'deepseek'
         elif 'zhipu' in self.clients:
             self.current_model = 'zhipu'
@@ -138,10 +142,12 @@ class MultiLLMService:
 
             if provider == 'zhipu':
                 # 智谱AI调用
+                # Zhipu对参数更严格，temperature范围是0.0-1.0
+                safe_temperature = max(0.0, min(1.0, temperature))
                 response = client.chat.completions.create(
                     model=model_id,
                     messages=messages,
-                    temperature=temperature,
+                    temperature=safe_temperature,
                     max_tokens=max_tokens
                 )
             elif provider == 'deepseek':
@@ -205,10 +211,12 @@ class MultiLLMService:
 
             if provider == 'zhipu':
                 # 智谱AI流式调用
+                # Zhipu对参数更严格，temperature范围是0.0-1.0
+                safe_temperature = max(0.0, min(1.0, temperature))
                 response = client.chat.completions.create(
                     model=model_id,
                     messages=messages,
-                    temperature=temperature,
+                    temperature=safe_temperature,
                     max_tokens=max_tokens,
                     stream=True
                 )

frontend/css/style.css

@@ -135,6 +135,12 @@ html, body {
     color: var(--text-primary);
 }
 
+.model-name {
+    color: var(--text-primary);
+    font-size: 13px;
+    font-family: inherit;
+}
+
 .status {
     display: flex;
     align-items: center;

frontend/index.html

@@ -29,17 +29,13 @@
                     <span>Tradus｜AI金融智能体</span>
                 </div>
                 <div class="header-right">
-                    <!-- Model Selector -->
+                    <!-- Model Display (只显示，不可切换) -->
                     <div class="model-selector" v-if="currentModel">
                         <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
                             <circle cx="12" cy="12" r="3"/>
                             <path d="M12 1v6m0 6v6M5.64 5.64l4.24 4.24m4.24 4.24l4.24 4.24M1 12h6m6 0h6M5.64 18.36l4.24-4.24m4.24-4.24l4.24-4.24"/>
                         </svg>
-                        <select v-model="selectedModel" @change="switchModel" class="model-select">
+                        <span class="model-name">{{ currentModel.name }}</span>
-                            <option v-for="model in availableModels" :key="model.provider" :value="model.provider">
-                                {{ model.name }}
-                            </option>
-                        </select>
                     </div>
                     <!-- Logout Button -->
                     <button class="logout-btn" @click="logout" title="登出">

frontend/js/app.js

@@ -12,9 +12,7 @@ createApp({
             showImageModal: false,
             modalImageUrl: '',
             showContactModal: false,
-            availableModels: [],
+            currentModel: null
-            currentModel: null,
-            selectedModel: null
         };
     },
     mounted() {
@@ -469,40 +467,10 @@ createApp({
                 const data = await response.json();
 
                 if (data.success) {
-                    this.availableModels = data.models;
                     this.currentModel = data.current;
-                    this.selectedModel = data.current ? data.current.provider : null;
                 }
             } catch (error) {
-                
+                // 忽略错误
-            }
-        },
-
-        async switchModel() {
-            if (!this.selectedModel) return;
-
-            try {
-                const response = await fetch('/api/llm/switch', {
-                    method: 'POST',
-                    headers: {
-                        'Content-Type': 'application/json'
-                    },
-                    body: JSON.stringify({
-                        provider: this.selectedModel
-                    })
-                });
-
-                const data = await response.json();
-
-                if (data.success) {
-                    this.currentModel = data.current;
-                    this.showNotification(`已切换到 ${data.current.name}`);
-                } else {
-                    this.showNotification('切换失败');
-                }
-            } catch (error) {
-                
-                this.showNotification('切换失败');
             }
         }
     },