feat: UI重构 - 历史对话列表页 + 新建按钮顶部右侧 + 删除功能

用户指出SiliconFlow平台确实支持标准tool消息类型，之前的修改是错误的

版本: v3.0.7

main_v2.py

@@ -1,6 +1,6 @@
 """
-AI对话系统 v2.0.0 - 主应用
+AI对话系统 v3.0.0 - 主应用
-支持：大模型池、Agent管理、渠道独立绑定、思考功能开关
+支持：大模型池、Agent管理、渠道独立绑定、思考功能开关、Function Calling（LLM自主调用工具）
 """
 from fastapi import FastAPI, WebSocket, WebSocketDisconnect, Depends, HTTPException, Request
 from fastapi.responses import HTMLResponse, JSONResponse
@@ -122,6 +122,7 @@ async def get_providers(db: Session = Depends(get_db)):
                 "thinking_model": p.thinking_model,
                 "supports_vision": p.supports_vision,
                 "vision_model": p.vision_model,
+                "supports_function_calling": p.supports_function_calling,
                 "max_tokens": p.max_tokens,
                 "temperature": p.temperature,
                 "is_active": p.is_active,
@@ -646,17 +647,19 @@ async def get_conversations(db: Session = Depends(get_db)):
     user = conv_service.get_or_create_user(MAIN_USER_ID, display_name="主用户", user_type='web')
     conversations = conv_service.get_user_conversations(user.id)
     
-    return {
+    # 为每个对话计算消息数量
-        "conversations": [
+    result = []
-            {
+    for c in conversations:
+        msg_count = db.query(Message).filter(Message.conversation_id == c.id).count()
+        result.append({
             "id": c.conversation_id,
             "title": c.title or "新对话",
             "created_at": c.created_at.isoformat(),
-                "updated_at": c.updated_at.isoformat()
+            "updated_at": c.updated_at.isoformat(),
-            }
+            "message_count": msg_count
-            for c in conversations
+        })
-        ]
+    
-    }
+    return {"conversations": result}
 
 
 @app.get("/api/conversations/latest")
@@ -692,6 +695,26 @@ async def create_conversation(db: Session = Depends(get_db)):
     }
 
 
+@app.get("/api/conversations/{conversation_id}")
+async def get_conversation(conversation_id: str, db: Session = Depends(get_db)):
+    """获取单个对话详情"""
+    conv_service = ConversationService(db)
+    conversation = conv_service.get_conversation(conversation_id)
+    
+    if not conversation:
+        raise HTTPException(status_code=404, detail="会话不存在")
+    
+    msg_count = db.query(Message).filter(Message.conversation_id == conversation.id).count()
+    
+    return {
+        "id": conversation.conversation_id,
+        "title": conversation.title or "新对话",
+        "created_at": conversation.created_at.isoformat(),
+        "updated_at": conversation.updated_at.isoformat(),
+        "message_count": msg_count
+    }
+
+
 @app.get("/api/conversations/{conversation_id}/messages")
 async def get_messages(conversation_id: str, db: Session = Depends(get_db)):
     """获取会话消息"""
@@ -832,7 +855,7 @@ async def websocket_endpoint(websocket: WebSocket, user_id: str):
                     conversation_id = data.get("conversation_id")
                     enable_thinking = data.get("enable_thinking", True)
                     agent_id_override = data.get("agent_id")
-                    disabled_tools = data.get("disabled_tools", [])  # 禁用的工具列表
+                    # v3.0: 移除 disabled_tools，由LLM自主决定
                     
                     if agent_id_override:
                         agent = agent_service.get_agent(agent_id_override)
@@ -846,48 +869,41 @@ async def websocket_endpoint(websocket: WebSocket, user_id: str):
                     if not message.strip() and not files:
                         continue
                     
-                    # 处理文件内容，添加到消息
+                    # 处理文件内容
-                    image_contents = []  # 图片内容（用于视觉模型）
+                    image_contents = []
-                    text_contents = []  # 文本文件内容
+                    text_contents = []
-                    image_paths = []  # 图片服务器路径（用于历史记录显示）
+                    image_paths = []
                     if files:
                         for f in files:
                             if f.get('type') and f['type'].startswith('image/'):
-                                # 图片：记录 base64 数据，用于视觉模型
                                 image_contents.append({
                                     'name': f['name'],
                                     'type': f['type'],
-                                    'data': f.get('content', '')  # base64 数据
+                                    'data': f.get('content', '')
                                 })
-                                # 记录服务器路径（用于历史记录）
                                 if f.get('serverPath'):
                                     image_paths.append({
                                         'name': f['name'],
                                         'type': f['type'],
-                                        'url': f['serverPath']  # 服务器文件路径
+                                        'url': f['serverPath']
                                     })
-                                # 不添加文件名文本，图片信息保存在 extra_data 中
                             elif f.get('content'):
-                                # 文本文件：直接添加内容，不带文件名前缀
                                 text_contents.append(f['content'][:3000])
                                 if len(f['content']) > 3000:
                                     text_contents[-1] += "...(内容过长已截断)"
                     
-                    # 如果有文本文件内容，追加到消息后面
                     if text_contents:
                         for content in text_contents:
                             message += f"\n\n{content}"
                     
-                    # 保存图片和文件信息到 extra_data（用于历史记录）
+                    # 保存文件信息到 extra_data
                     extra_data_for_msg = None
                     if image_paths:
-                        # 图片保存服务器路径URL，历史记录可以显示
                         extra_data_for_msg = {
                             'images': image_paths,
                             'files': [{'name': f['name'], 'type': f['type']} for f in files if not f['type'].startswith('image/')]
                         }
                     elif image_contents:
-                        # 没有服务器路径但有问题（可能上传失败）
                         extra_data_for_msg = {
                             'images': [{'name': i['name'], 'type': i['type']} for i in image_contents],
                             'files': [{'name': f['name'], 'type': f['type']} for f in files if not f['type'].startswith('image/')]
@@ -896,8 +912,9 @@ async def websocket_endpoint(websocket: WebSocket, user_id: str):
                     # 1. 获取Agent配置
                     agent_config = agent_service.get_agent_config(current_agent_id)
                     agent_tools = agent_config.get('agent', {}).get('tools', [])
+                    supports_function_calling = agent_config.get('provider', {}).get('supports_function_calling', False)
                     
-                    # 2. 获取或创建会话（先有 conversation_id）
+                    # 2. 获取或创建会话
                     if conversation_id:
                         conversation = conv_service.get_conversation(conversation_id)
                     else:
@@ -908,12 +925,12 @@ async def websocket_endpoint(websocket: WebSocket, user_id: str):
                             "conversation_id": conversation_id
                         })
                     
-                    # 3. 广播用户消息（前端立即看到）
+                    # 3. 广播用户消息
                     await manager.send_to_user(MAIN_USER_ID, {
                         "type": "user_message",
                         "conversation_id": conversation_id,
                         "message": {
-                            "id": None,  # 临时，后面会保存
+                            "id": None,
                             "role": "user",
                             "content": message,
                             "source": "web",
@@ -921,29 +938,102 @@ async def websocket_endpoint(websocket: WebSocket, user_id: str):
                         }
                     })
                     
-                    # 4. 执行搜索并发送搜索结果
+                    # 4. 保存用户消息
-                    search_context = None
+                    user_msg = conv_service.add_message(
-                    search_results_for_client = None  # 用于发送给前端和保存
+                        conversation_id=conversation.id,
-                    logger.info(f"检查搜索条件: agent_tools={agent_tools}, disabled_tools={disabled_tools}")
+                        role='user',
+                        content=message,
+                        source='web',
+                        extra_data=extra_data_for_msg
+                    )
                     
-                    if 'search' in agent_tools and 'search' not in disabled_tools:
+                    # 5. 获取对话历史
-                        logger.info("搜索条件满足，开始执行搜索")
+                    history = conv_service.get_conversation_history(conversation_id, limit=agent_config['agent'].get('max_history', 20))
                     
+                    # 6. 构建工具 schema（Function Calling）
+                    tools_schema = []
+                    if supports_function_calling and agent_tools:
+                        # 搜索工具
+                        if 'search' in agent_tools:
                             tool_service = ToolService(db)
                             search_tool = tool_service.get_default_tool('search')
-                        logger.info(f"获取到搜索工具: {search_tool.name if search_tool else 'None'}")
-                        
                             if search_tool and search_tool.config.get('api_key'):
+                                tools_schema.append({
+                                    "type": "function",
+                                    "function": {
+                                        "name": "web_search",
+                                        "description": "搜索互联网获取实时信息、新闻、数据等。当用户询问需要最新信息的问题时使用此工具。",
+                                        "parameters": {
+                                            "type": "object",
+                                            "properties": {
+                                                "query": {
+                                                    "type": "string",
+                                                    "description": "搜索关键词或问题"
+                                                }
+                                            },
+                                            "required": ["query"]
+                                        }
+                                    }
+                                })
+                    
+                    # 7. 调用LLM（Function Calling模式）
+                    if not agent_config or not agent_config.get('provider'):
+                        await websocket.send_json({
+                            "type": "error",
+                            "message": "Agent配置不完整"
+                        })
+                        continue
+                    
+                    try:
+                        response = None
+                        thinking_content = None
+                        tool_calls_record = []
+                        
+                        # 第一阶段：让LLM决定是否调用工具
+                        if tools_schema:
+                            response, thinking_content, tool_calls = await llm_service.chat_with_tools(
+                                messages=history,
+                                provider_config=agent_config['provider'],
+                                agent_config=agent_config['agent'],
+                                tools=tools_schema,
+                                enable_thinking=enable_thinking,
+                                images=image_contents
+                            )
+                            
+                            # 如果LLM请求调用工具
+                            if tool_calls:
+                                logger.info(f"LLM请求调用工具: {tool_calls}")
+                                
+                                # 发送工具调用通知给前端
+                                await websocket.send_json({
+                                    "type": "tool_calls",
+                                    "conversation_id": conversation_id,
+                                    "tool_calls": [
+                                        {"name": tc['name'], "arguments": tc['arguments']}
+                                        for tc in tool_calls
+                                    ]
+                                })
+                                
+                                # 执行工具调用
+                                tool_results = []
+                                tool_service = ToolService(db)
+                                search_tool = tool_service.get_default_tool('search')
+                                
+                                for tc in tool_calls:
+                                    if tc['name'] == 'web_search':
+                                        query = tc['arguments'].get('query', message)
+                                        logger.info(f"执行搜索: query={query}")
+                                        
                                         import httpx
                                         import time
                                         start_time = time.time()
+                                        
                                         try:
-                                logger.info(f"执行搜索: query={message}")
                                             tavily_url = "https://api.tavily.com/search"
                                             config = search_tool.config
                                             payload = {
                                                 "api_key": config.get('api_key'),
-                                    "query": message,
+                                                "query": query,
                                                 "max_results": config.get('max_results', 5),
                                                 "search_depth": config.get('search_depth', 'basic')
                                             }
@@ -955,103 +1045,124 @@ async def websocket_endpoint(websocket: WebSocket, user_id: str):
                                             duration_ms = int((time.time() - start_time) * 1000)
                                             
                                             if search_result.get("results"):
-                                    # 构建搜索上下文（给LLM）
+                                                # 构建搜索结果
-                                    max_for_llm = config.get('max_results', 5)
+                                                search_content = []
-                                    search_context = "\n\n【搜索结果】\n"
+                                                for i, r in enumerate(search_result["results"][:5], 1):
-                                    for i, r in enumerate(search_result["results"][:max_for_llm], 1):
+                                                    search_content.append({
-                                        search_context += f"{i}. {r.get('title', 'N/A')}\n   {r.get('content', r.get('snippet', 'N/A'))[:200]}\n   来源: {r.get('url', 'N/A')}\n"
-                                    logger.info(f"搜索完成: {len(search_result['results'])} 条结果，使用 {min(len(search_result['results']), max_for_llm)} 条")
-                                    
-                                    # 发送搜索结果给前端（按配置的数量）
-                                    max_display = config.get('max_results', 5)
-                                    search_results_for_client = [
-                                        {
                                                         "title": r.get('title', 'N/A'),
-                                            "snippet": r.get('content', r.get('snippet', ''))[:150],
+                                                        "content": r.get('content', r.get('snippet', ''))[:300],
                                                         "url": r.get('url', 'N/A')
-                                        }
+                                                    })
-                                        for r in search_result["results"][:max_display]
+                                                
-                                    ]
+                                                tool_results.append({
+                                                    "tool_call_id": tc['id'],
+                                                    "content": json.dumps(search_content)
+                                                })
+                                                
+                                                # 发送搜索结果给前端
                                                 await websocket.send_json({
                                                     "type": "search_results",
                                                     "conversation_id": conversation_id,
-                                        "results": search_results_for_client,
+                                                    "results": [
-                                        "query": message
+                                                        {"title": r.get('title'), "snippet": r.get('content', '')[:150], "url": r.get('url')}
+                                                        for r in search_result["results"][:5]
+                                                    ],
+                                                    "query": query
                                                 })
                                                 
-                                    # 更新统计和日志
+                                                # 记录日志
                                                 tool_service.increment_stats(search_tool.id, True)
                                                 tool_service.log_usage({
                                                     'tool_id': search_tool.id,
                                                     'tool_type': 'search',
-                                        'query': message,
+                                                    'query': query,
                                                     'success': True,
                                                     'result_summary': f'{len(search_result["results"])} results',
                                                     'conversation_id': conversation_id,
                                                     'agent_id': current_agent_id,
                                                     'duration_ms': duration_ms
                                                 })
+                                                
+                                                tool_calls_record.append({
+                                                    "name": "web_search",
+                                                    "query": query,
+                                                    "results_count": len(search_result["results"])
+                                                })
+                                                
                                         except Exception as e:
-                                duration_ms = int((time.time() - start_time) * 1000)
                                             logger.error(f"搜索失败: {e}")
+                                            duration_ms = int((time.time() - start_time) * 1000)
                                             tool_service.increment_stats(search_tool.id, False)
                                             tool_service.log_usage({
                                                 'tool_id': search_tool.id,
                                                 'tool_type': 'search',
-                                    'query': message,
+                                                'query': query,
                                                 'success': False,
                                                 'error_message': str(e),
                                                 'conversation_id': conversation_id,
                                                 'duration_ms': duration_ms
                                             })
+                                            tool_results.append({
+                                                "tool_call_id": tc['id'],
+                                                "content": json.dumps({"error": str(e)})
+                                            })
                                 
-                    # 5. 保存用户消息到数据库
+                                # 将工具调用消息添加到历史
-                    extra_data_to_save = None
+                                # 注意：这里需要将 assistant 的 tool_calls 消息添加到历史
-                    if search_results_for_client:
+                                # 但我们用的是简化的历史格式，需要重新构建
-                        extra_data_to_save = {'search_results': search_results_for_client, 'search_query': message}
-                    if extra_data_for_msg:
-                        if extra_data_to_save:
-                            extra_data_to_save.update(extra_data_for_msg)
-                        else:
-                            extra_data_to_save = extra_data_for_msg
                                 
-                    user_msg = conv_service.add_message(
+                                # 第二阶段：将工具结果返回给LLM
-                        conversation_id=conversation.id,
+                                if tool_results:
-                        role='user',
+                                    # 重新获取完整历史（包含工具调用）
-                        content=message,
+                                    history_with_tools = history.copy()
-                        source='web',
+                                    # 添加 assistant 的 tool_calls 消息
-                        extra_data=extra_data_to_save
+                                    history_with_tools.append({
+                                        "role": "assistant",
+                                        "content": None,
+                                        "tool_calls": [
+                                            {
+                                                "id": tc['id'],
+                                                "type": "function",
+                                                "function": {
+                                                    "name": tc['name'],
+                                                    "arguments": json.dumps(tc['arguments'])
+                                                }
+                                            }
+                                            for tc in tool_calls
+                                        ]
+                                    })
+                                    # 添加工具结果
+                                    for tr in tool_results:
+                                        history_with_tools.append({
+                                            "role": "tool",
+                                            "tool_call_id": tr['tool_call_id'],
+                                            "content": tr['content']
+                                        })
+                                    
+                                    response, thinking_content = await llm_service.chat_with_tool_results(
+                                        messages=history_with_tools,
+                                        provider_config=agent_config['provider'],
+                                        agent_config=agent_config['agent'],
+                                        enable_thinking=enable_thinking
                                     )
                         
-                    # 6. 获取对话历史（包含刚保存的用户消息）
+                        # 如果不支持 Function Calling 或没有工具，直接调用普通 chat
-                    history = conv_service.get_conversation_history(conversation_id, limit=agent_config['agent'].get('max_history', 20))
+                        if response is None:
-                    
-                    # 7. 如果有搜索结果，添加到消息中
-                    if search_context:
-                        modified_system_prompt = agent_config['agent'].get('system_prompt', '') + "\n\n如果提供了搜索结果，请基于搜索结果回答用户问题，并注明信息来源。"
-                        agent_config['agent']['system_prompt'] = modified_system_prompt
-                        history.append({"role": "system", "content": f"以下是搜索到的相关信息，请参考这些内容回答用户问题：{search_context}"})
-                    
-                    # 8. 调用LLM返回回复
-                    if not agent_config or not agent_config.get('provider'):
-                        await websocket.send_json({
-                            "type": "error",
-                            "message": "Agent配置不完整"
-                        })
-                        continue
-                    
-                    try:
                             response, thinking_content = await llm_service.chat(
                                 messages=history,
                                 provider_config=agent_config['provider'],
                                 agent_config=agent_config['agent'],
                                 enable_thinking=enable_thinking,
-                            images=image_contents  # 传递图片数据给多模态模型
+                                images=image_contents
                             )
                         
                         logger.info(f"LLM响应: response长度={len(response)}, thinking长度={len(thinking_content) if thinking_content else 0}")
                         
                         # 保存AI回复
+                        extra_data_to_save = None
+                        if tool_calls_record:
+                            extra_data_to_save = {'tool_calls': tool_calls_record}
+                        
                         assistant_msg = conv_service.add_message(
                             conversation_id=conversation.id,
                             role='assistant',
@@ -1059,7 +1170,8 @@ async def websocket_endpoint(websocket: WebSocket, user_id: str):
                             source='web',
                             thinking_content=thinking_content if thinking_content else None,
                             agent_id=current_agent_id,
-                            model_used=agent_config['provider'].get('default_model')
+                            model_used=agent_config['provider'].get('default_model'),
+                            extra_data=extra_data_to_save
                         )
                         
                         # 发送AI回复
@@ -1074,6 +1186,7 @@ async def websocket_endpoint(websocket: WebSocket, user_id: str):
                                 "source": "web",
                                 "agent_id": current_agent_id,
                                 "agent_name": agent_config['agent'].get('display_name'),
+                                "tool_calls": tool_calls_record,  # v3.0: 返回工具调用记录
                                 "created_at": assistant_msg.created_at.isoformat()
                             }
                         })

models_v2.py

@@ -36,6 +36,9 @@ class LLMProvider(Base):
     supports_vision = Column(Boolean, default=False)  # 是否支持图片理解（多模态）
     vision_model = Column(String(100), nullable=True)  # 视觉模型名（如与默认模型不同）
     
+    # Function Calling 支持
+    supports_function_calling = Column(Boolean, default=False)  # 是否支持函数调用（工具自主调用）
+    
     # 配额和限制
     max_tokens = Column(Integer, default=4096)
     temperature = Column(Float, default=0.7)

services/agent_service.py

@@ -137,6 +137,9 @@ class AgentService:
                 'api_key': provider.api_key if provider else None,
                 'supports_thinking': provider.supports_thinking if provider else False,
                 'thinking_model': provider.thinking_model if provider else None,
+                'supports_vision': provider.supports_vision if provider else False,
+                'vision_model': provider.vision_model if provider else None,
+                'supports_function_calling': provider.supports_function_calling if provider else False,
                 'default_model': provider.default_model if provider else 'auto',
                 'max_tokens': provider.max_tokens if provider else 4096,
                 'temperature': provider.temperature if provider else 0.7,

services/llm_service.py

@@ -382,5 +382,192 @@ class LLMService:
             yield {"type": "content", "text": buffer}
 
 
+    async def chat_with_tools(
+        self,
+        messages: List[Dict],
+        provider_config: dict,
+        agent_config: dict,
+        tools: List[Dict] = None,
+        enable_thinking: bool = True,
+        images: List[Dict] = None
+    ) -> Tuple[str, Optional[str], Optional[List[Dict]]]:
+        """
+        支持Function Calling的对话
+        
+        Args:
+            messages: 对话历史
+            provider_config: LLM Provider配置
+            agent_config: Agent配置
+            tools: 工具定义列表（OpenAI Function Calling格式）
+            enable_thinking: 是否启用思考
+            images: 图片数据列表
+        
+        Returns:
+            Tuple[str, Optional[str], Optional[List[Dict]]]: (回复内容, 思考过程, 工具调用记录)
+        """
+        api_base = provider_config.get('api_base')
+        api_key = provider_config.get('api_key')
+        model = agent_config.get('model_override') or provider_config.get('default_model', 'auto')
+        supports_function_calling = provider_config.get('supports_function_calling', False)
+        
+        max_tokens = provider_config.get('max_tokens', 4096)
+        temperature = agent_config.get('temperature_override') or provider_config.get('temperature', 0.7)
+        
+        # 如果不支持Function Calling，直接调用普通chat
+        if not supports_function_calling or not tools:
+            response, thinking = await self.chat(messages, provider_config, agent_config, enable_thinking, images)
+            return response, thinking, None
+        
+        # 构建消息
+        final_messages = messages.copy()
+        system_prompt = agent_config.get('system_prompt', '你是一个有用的AI助手。')
+        if final_messages and final_messages[0]['role'] != 'system':
+            final_messages.insert(0, {"role": "system", "content": system_prompt})
+        
+        # 处理图片（多模态）
+        if images and len(images) > 0:
+            for i in range(len(final_messages) - 1, -1, -1):
+                if final_messages[i]['role'] == 'user':
+                    original_text = final_messages[i]['content']
+                    multimodal_content = [{"type": "text", "text": original_text if original_text else "请描述这张图片"}]
+                    for img in images:
+                        multimodal_content.append({
+                            "type": "image_url",
+                            "image_url": {"url": img['data']}
+                        })
+                    final_messages[i]['content'] = multimodal_content
+                    break
+        
+        # 第一次调用：让LLM决定是否调用工具
+        url = f"{api_base.rstrip('/')}/chat/completions"
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json"
+        }
+        payload = {
+            "model": model,
+            "messages": final_messages,
+            "temperature": temperature,
+            "max_tokens": max_tokens,
+            "tools": tools  # 传入工具定义
+        }
+        
+        logger.info(f"Function Calling调用: url={url}, model={model}, tools={len(tools)}")
+        
+        tool_calls_record = []  # 记录工具调用
+        
+        try:
+            async with httpx.AsyncClient(timeout=60.0) as client:
+                response = await client.post(url, headers=headers, json=payload)
+                
+                if response.status_code != 200:
+                    logger.error(f"API返回错误: status={response.status_code}, body={response.text[:500]}")
+                    response.raise_for_status()
+                
+                data = response.json()
+                
+                if 'choices' not in data or len(data['choices']) == 0:
+                    raise ValueError("API响应格式错误：缺少choices")
+                
+                message = data['choices'][0]['message']
+                
+                # 检查是否有工具调用
+                if 'tool_calls' in message and message['tool_calls']:
+                    logger.info(f"LLM请求调用工具: {len(message['tool_calls'])} 个")
+                    
+                    # 将LLM的工具调用消息添加到历史
+                    final_messages.append({
+                        "role": "assistant",
+                        "content": None,
+                        "tool_calls": message['tool_calls']
+                    })
+                    
+                    # 记录工具调用
+                    for tc in message['tool_calls']:
+                        tool_calls_record.append({
+                            "id": tc['id'],
+                            "name": tc['function']['name'],
+                            "arguments": json.loads(tc['function']['arguments'])
+                        })
+                    
+                    # 返回工具调用记录，由调用方执行工具
+                    return None, None, tool_calls_record
+                
+                # 没有工具调用，直接返回内容
+                content = message.get('content', '')
+                
+                # 处理思考内容（如果有）
+                thinking_content = None
+                # 这里可以添加思考内容提取逻辑
+                
+                return content, thinking_content, None
+                
+        except httpx.HTTPStatusError as e:
+            logger.error(f"HTTP错误: {e.response.status_code}, {e.response.text}")
+            raise
+        except Exception as e:
+            logger.error(f"Function Calling调用异常: {type(e).__name__}: {e}")
+            raise
+    
+    async def chat_with_tool_results(
+        self,
+        messages: List[Dict],
+        provider_config: dict,
+        agent_config: dict,
+        enable_thinking: bool = True
+    ) -> Tuple[str, Optional[str]]:
+        """
+        第二阶段调用：使用包含工具调用和结果的完整消息历史
+        
+        Args:
+            messages: 已包含assistant tool_calls和tool结果的完整消息历史
+            provider_config: LLM Provider配置
+            agent_config: Agent配置
+        
+        Returns:
+            Tuple[str, Optional[str]]: (回复内容, 思考过程)
+        """
+        api_base = provider_config.get('api_base')
+        api_key = provider_config.get('api_key')
+        model = agent_config.get('model_override') or provider_config.get('default_model', 'auto')
+        max_tokens = provider_config.get('max_tokens', 4096)
+        temperature = agent_config.get('temperature_override') or provider_config.get('temperature', 0.7)
+        
+        # 消息历史已经包含了assistant的tool_calls和tool结果，直接使用
+        final_messages = messages.copy()
+        
+        # 调用LLM生成最终回复
+        url = f"{api_base.rstrip('/')}/chat/completions"
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json"
+        }
+        payload = {
+            "model": model,
+            "messages": final_messages,
+            "temperature": temperature,
+            "max_tokens": max_tokens
+        }
+        
+        logger.info(f"工具结果返回LLM: url={url}, model={model}, 消息数={len(final_messages)}")
+        
+        try:
+            async with httpx.AsyncClient(timeout=60.0) as client:
+                response = await client.post(url, headers=headers, json=payload)
+                
+                if response.status_code != 200:
+                    logger.error(f"API返回错误: status={response.status_code}, body={response.text[:500]}")
+                    response.raise_for_status()
+                
+                data = response.json()
+                content = data['choices'][0]['message']['content']
+                
+                return content, None
+                
+        except Exception as e:
+            logger.error(f"工具结果调用异常: {e}")
+            raise
+
+
 # 全局实例
 llm_service = LLMService()

templates/admin_v2/index.html

@@ -58,8 +58,8 @@
                 </div>
                 <div class="card-body">
                     <table class="table">
-                        <thead><tr><th>名称</th><th>API地址</th><th>默认模型</th><th>思考</th><th>视觉</th><th>状态</th><th>操作</th></tr></thead>
+                        <thead><tr><th>名称</th><th>API地址</th><th>默认模型</th><th>思考</th><th>视觉</th><th>FC</th><th>状态</th><th>操作</th></tr></thead>
-                        <tbody id="providers-list"><tr><td colspan="7" class="text-center">加载中...</td></tr></tbody>
+                        <tbody id="providers-list"><tr><td colspan="8" class="text-center">加载中...</td></tr></tbody>
                     </table>
                 </div>
             </div>
@@ -164,6 +164,8 @@
             <div class="thinking-config"><div class="row"><div class="col-md-6 form-check"><input type="checkbox" class="form-check-input" id="provider-supports-thinking"><label class="form-check-label">支持原生思考</label></div><div class="col-md-6"><label class="form-label">思考模型名</label><input type="text" class="form-control" id="provider-thinking-model"></div></div></div>
             <hr><h6>视觉能力</h6>
             <div class="thinking-config"><div class="row"><div class="col-md-6 form-check"><input type="checkbox" class="form-check-input" id="provider-supports-vision"><label class="form-check-label">支持图片理解</label></div><div class="col-md-6"><label class="form-label">视觉模型名</label><input type="text" class="form-control" id="provider-vision-model" placeholder="留空则使用默认模型"></div></div><small class="text-muted mt-2 d-block">启用后可上传图片让AI识别分析内容</small></div>
+            <hr><h6>Function Calling</h6>
+            <div class="thinking-config"><div class="form-check"><input type="checkbox" class="form-check-input" id="provider-supports-function-calling"><label class="form-check-label">支持函数调用</label></div><small class="text-muted mt-2 d-block">启用后LLM可自主决定何时调用工具（更智能）</small></div>
             <div class="mt-3"><button type="button" class="btn btn-outline-primary" onclick="fetchProviderModels()"><i class="ri-refresh-line"></i> 获取模型</button><button type="button" class="btn btn-outline-secondary" onclick="testProviderConnection()"><i class="ri-link"></i> 测试连接</button></div>
             <div class="mt-2" id="provider-models-preview"></div><div class="mt-2" id="provider-test-result"></div>
         </form></div>
@@ -183,7 +185,10 @@
             <hr><h6>思考功能</h6>
             <div class="thinking-config"><div class="form-check"><input type="checkbox" class="form-check-input" id="agent-enable-thinking" checked><label class="form-check-label">启用思考</label></div><div class="mt-2"><label class="form-label">思考提示词</label><textarea class="form-control" id="agent-thinking-prompt" rows="2"></textarea></div><div class="row mt-2"><div class="col-md-6"><label class="form-label">前缀</label><input type="text" class="form-control" id="agent-thinking-prefix"></div><div class="col-md-6"><label class="form-label">后缀</label><input type="text" class="form-control" id="agent-thinking-suffix"></div></div></div>
             <hr><h6>工具配置</h6>
-            <div class="thinking-config"><div class="form-check"><input type="checkbox" class="form-check-input" id="agent-tool-search"><label class="form-check-label">启用搜索工具</label></div><small class="text-muted">启用后 Agent 可以使用搜索功能获取实时信息</small></div>
+            <div class="thinking-config">
+                <div id="agent-tools-list">加载中...</div>
+                <small class="text-muted">从系统工具列表中选择Agent可使用的工具</small>
+            </div>
         </form></div>
         <div class="modal-footer"><button type="button" class="btn btn-secondary" data-bs-dismiss="modal">取消</button><button type="button" class="btn btn-primary" onclick="saveAgent()">保存</button></div>
     </div></div></div>
@@ -252,7 +257,7 @@
     <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.bundle.min.js"></script>
     <script>
         // 全局数据
-        let providersData = [], agentsData = [], channelsData = [];
+        let providersData = [], agentsData = [], channelsData = [], toolsData = [];
 
         // 页面切换函数
         function loadPageData(page) {
@@ -263,6 +268,57 @@
             if (page === 'stats') loadStats();
         }
         
+        // 加载工具列表（用于Agent配置）
+        async function loadToolsList() {
+            try {
+                const res = await fetch('/api/v2/tools');
+                const data = await res.json();
+                toolsData = data.tools || [];
+                renderAgentToolsList();
+            } catch (e) { console.error('加载工具列表失败:', e); }
+        }
+        
+        // 渲染Agent工具选择列表
+        function renderAgentToolsList(selectedTools = []) {
+            const container = document.getElementById('agent-tools-list');
+            if (!container) return;
+            
+            if (toolsData.length === 0) {
+                container.innerHTML = '<div class="text-muted">暂无可用工具，请先在「工具管理」中添加</div>';
+                return;
+            }
+            
+            container.innerHTML = toolsData.filter(t => t.is_active).map(t => {
+                const toolType = t.tool_type || 'unknown';
+                const isSelected = selectedTools.includes(toolType);
+                const icon = getToolIcon(toolType);
+                return `<div class="form-check">
+                    <input type="checkbox" class="form-check-input agent-tool-checkbox" id="agent-tool-${t.id}" value="${toolType}" ${isSelected ? 'checked' : ''}>
+                    <label class="form-check-label" for="agent-tool-${t.id}">
+                        <i class="${icon}"></i> ${t.name} <small class="text-muted">(${toolType})</small>
+                    </label>
+                </div>`;
+            }).join('');
+        }
+        
+        // 工具图标
+        function getToolIcon(toolType) {
+            const icons = {
+                'search': 'ri-search-line',
+                'calculator': 'ri-calculator-line',
+                'code': 'ri-code-line',
+                'image': 'ri-image-line',
+                'web': 'ri-global-line'
+            };
+            return icons[toolType] || 'ri-tools-line';
+        }
+        
+        // 获取Agent选中的工具列表
+        function getSelectedAgentTools() {
+            const checkboxes = document.querySelectorAll('.agent-tool-checkbox:checked');
+            return Array.from(checkboxes).map(cb => cb.value);
+        }
+
         // ===== 大模型池 =====
         async function loadProviders() {
             const res = await fetch('/api/v2/providers');
@@ -277,6 +333,7 @@
                 <td><strong>${p.name}</strong></td><td><small>${p.api_base||'-'}</small></td><td>${p.default_model||'auto'}</td>
                 <td>${p.supports_thinking?'<span class="badge bg-success">支持</span>':'<span class="badge bg-secondary">不支持</span>'}</td>
                 <td>${p.supports_vision?'<span class="badge bg-info">支持</span>':'<span class="badge bg-secondary">不支持</span>'}</td>
+                <td>${p.supports_function_calling?'<span class="badge bg-primary">支持</span>':'<span class="badge bg-secondary">不支持</span>'}</td>
                 <td>${p.is_active?'<span class="badge bg-success">启用</span>':'<span class="badge bg-secondary">禁用</span>'}</td>
                 <td><button class="btn btn-sm btn-outline-primary" onclick="editProvider(${p.id})"><i class="ri-edit-line"></i></button>
                     <button class="btn btn-sm btn-outline-danger" onclick="deleteProvider(${p.id},'${p.name}')"><i class="ri-delete-bin-line"></i></button></td>
@@ -295,6 +352,7 @@
             document.getElementById('provider-active').checked = true;
             document.getElementById('provider-supports-thinking').checked = false;
             document.getElementById('provider-supports-vision').checked = false;
+            document.getElementById('provider-supports-function-calling').checked = false;
             document.getElementById('provider-models-preview').innerHTML = '';
             document.getElementById('provider-test-result').innerHTML = '';
             new bootstrap.Modal(document.getElementById('providerModal')).show();
@@ -317,6 +375,7 @@
             document.getElementById('provider-thinking-model').value = p.thinking_model || '';
             document.getElementById('provider-supports-vision').checked = p.supports_vision;
             document.getElementById('provider-vision-model').value = p.vision_model || '';
+            document.getElementById('provider-supports-function-calling').checked = p.supports_function_calling;
             new bootstrap.Modal(document.getElementById('providerModal')).show();
         }
 
@@ -335,7 +394,8 @@
                 supports_thinking: document.getElementById('provider-supports-thinking').checked,
                 thinking_model: document.getElementById('provider-thinking-model').value,
                 supports_vision: document.getElementById('provider-supports-vision').checked,
-                vision_model: document.getElementById('provider-vision-model').value
+                vision_model: document.getElementById('provider-vision-model').value,
+                supports_function_calling: document.getElementById('provider-supports-function-calling').checked
             };
             const res = await fetch(id ? `/api/v2/providers/${id}` : '/api/v2/providers', { method: id ? 'PUT' : 'POST', headers: {'Content-Type':'application/json'}, body: JSON.stringify(data) });
             const result = await res.json();
@@ -403,6 +463,7 @@
             document.getElementById('agent-enable-thinking').checked = true;
             document.getElementById('agent-system-prompt').value = '你是一个有用的AI助手。';
             updateProviderSelect();
+            loadToolsList();  // 加载工具列表
             new bootstrap.Modal(document.getElementById('agentModal')).show();
         }
 
@@ -425,17 +486,16 @@
             document.getElementById('agent-thinking-prompt').value = a.thinking_prompt || '';
             document.getElementById('agent-thinking-prefix').value = a.thinking_prefix || '';
             document.getElementById('agent-thinking-suffix').value = a.thinking_suffix || '';
-            // 工具配置
+            // 工具配置 - 从工具列表渲染并勾选Agent已有的工具
-            const tools = a.tools || [];
+            loadToolsList();
-            document.getElementById('agent-tool-search').checked = tools.includes('search');
+            setTimeout(() => renderAgentToolsList(a.tools || []), 100);  // 等工具列表加载完成
             new bootstrap.Modal(document.getElementById('agentModal')).show();
         }
 
         async function saveAgent() {
             const id = document.getElementById('agent-id').value;
-            // 处理工具列表
+            // 获取选中的工具列表
-            const tools = [];
+            const tools = getSelectedAgentTools();
-            if (document.getElementById('agent-tool-search').checked) tools.push('search');
             
             const data = {
                 name: document.getElementById('agent-name').value,
@@ -452,7 +512,7 @@
                 thinking_prompt: document.getElementById('agent-thinking-prompt').value,
                 thinking_prefix: document.getElementById('agent-thinking-prefix').value,
                 thinking_suffix: document.getElementById('agent-thinking-suffix').value,
-                tools: tools  // 工具列表
+                tools: tools  // 工具列表（从系统工具中选择）
             };
             const res = await fetch(id ? `/api/v2/agents/${id}` : '/api/v2/agents', { method: id ? 'PUT' : 'POST', headers:{'Content-Type':'application/json'}, body: JSON.stringify(data) });
             const result = await res.json();