refactor: 抽离特殊聊天模式处理逻辑

- 将工作流、人机交互恢复、思考模式处理逻辑抽离为独立方法
- 新增 handleSpecialChatModes 方法统一处理特殊模式
- 新增 handleThinkingMode 方法专门处理思考模式
- 简化 sseChat 方法结构，提高代码可读性

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

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/agent/GetNameInfo.java

@@ -1,38 +0,0 @@
-package org.ruoyi.agent;
-
-import dev.langchain4j.agentic.Agent;
-import dev.langchain4j.service.SystemMessage;
-import dev.langchain4j.service.UserMessage;
-import dev.langchain4j.service.V;
-
-/**
- * User Name Retrieval Agent
- * A simple assistant that retrieves user names using the get_name tool.
- */
-public interface GetNameInfo {
-
-    @SystemMessage("""
-        You are a user identity assistant. You MUST always use tools to get information.
-
-        MANDATORY REQUIREMENTS:
-        - You MUST call the get_user_name_by_id tool for ANY question about names or identity
-        - NEVER respond without calling the get_user_name_by_id tool first
-        - Return ONLY the exact string returned by the get_user_name_by_id tool
-        - Do not make up names like "John Doe" or any other default names
-        - Do not use your knowledge to answer - ALWAYS use the tool
-
-        Your workflow:
-        1. Extract userId from the query (if mentioned), or use "1" as default
-        2. ALWAYS call the get_user_name_by_id tool with the userId parameter
-        3. Return the exact result as plain text with no additions
-
-        CRITICAL: If you don't call the get_user_name_by_id tool, your response is wrong.
-        """)
-    @UserMessage("""
-        Get the user name using the get_user_name_by_id tool. Query: {{query}}
-
-        IMPORTANT: Return only the exact result from the tool.
-        """)
-    @Agent("User identity assistant that returns user name from get_name tool")
-    String search(@V("query") String query);
-}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/agent/McpAgent.java

@@ -1,42 +0,0 @@
-package org.ruoyi.agent;
-
-import dev.langchain4j.agentic.Agent;
-import dev.langchain4j.service.SystemMessage;
-import dev.langchain4j.service.UserMessage;
-import dev.langchain4j.service.V;
-
-public interface McpAgent extends Agent {
-
-    /**
-     * 系统提示词：通用工具调用智能体
-     * 不限定具体工具类型，让 LangChain4j 自动传递工具描述给 LLM
-     */
-    @SystemMessage("""
-        你是一个AI助手，可以通过调用各种工具来帮助用户完成不同的任务。
-
-        【工具使用规则】
-        1. 根据用户的请求，判断需要使用哪些工具
-        2. 仔细阅读每个工具的描述，确保理解工具的功能和参数要求
-        3. 使用正确的参数调用工具
-        4. 如果工具执行失败，向用户友好地说明错误原因，并尝试提供替代方案
-        5. 对于复杂任务，可以分步骤使用多个工具完成
-        6. 将工具执行结果以清晰易懂的方式呈现给用户
-
-        【响应格式】
-        - 直接回答用户的问题
-        - 如果使用了工具，说明使用了什么工具以及结果
-        - 如果遇到错误，提供友好的错误信息和解决建议
-        """)
-
-    @UserMessage("""
-        {{query}}
-        """)
-
-    @Agent("通用工具调用智能体")
-    /**
-     * 智能体对外调用入口
-     * @param query 用户的自然语言请求
-     * @return 处理结果
-     */
-    String callMcpTool(@V("query") String query);
-}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/handler/AgentChatHandler.java

@@ -1,132 +0,0 @@
-//package org.ruoyi.service.chat.handler;
-//
-//import dev.langchain4j.agentic.AgenticServices;
-//import dev.langchain4j.community.model.dashscope.QwenChatModel;
-//import dev.langchain4j.service.tool.ToolProvider;
-//import lombok.RequiredArgsConstructor;
-//import lombok.extern.slf4j.Slf4j;
-//import org.ruoyi.agent.McpAgent;
-//import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
-//import org.ruoyi.common.chat.entity.chat.ChatContext;
-//import org.ruoyi.common.chat.service.chatMessage.IChatMessageService;
-//import org.ruoyi.common.sse.utils.SseMessageUtils;
-//import org.ruoyi.mcp.service.core.ToolProviderFactory;
-//import org.springframework.core.annotation.Order;
-//import org.springframework.stereotype.Component;
-//import org.springframework.web.servlet.mvc.method.annotation.SseEmitter;
-//
-//import java.util.ArrayList;
-//import java.util.List;
-//
-///**
-// * Agent 深度思考处理器
-// * <p>
-// * 处理 enableThinking=true 的场景，使用 Agent 进行深度思考和工具调用
-// *
-// * @author ageerle@163.com
-// * @date 2025/12/13
-// */
-//@Slf4j
-//@Component
-//@Order(3)
-//@RequiredArgsConstructor
-//public class AgentChatHandler implements ChatHandler {
-//
-//    private final ToolProviderFactory toolProviderFactory;
-//
-//    @Override
-//    public boolean supports(ChatContext context) {
-//        Boolean enableThinking = context.getChatRequest().getEnableThinking();
-//        return enableThinking != null && enableThinking;
-//    }
-//
-//    @Override
-//    public SseEmitter handle(ChatContext context) {
-//        log.info("处理 Agent 深度思考，用户: {}", context.getUserId());
-//
-//        Long userId = context.getUserId();
-//        String tokenValue = context.getTokenValue();
-//        ChatModelVo chatModelVo = context.getChatModelVo();
-//
-//        try {
-//            // 1. 保存用户消息
-//            String content = extractUserContent(context);
-////            saveChatMessage(context.getChatRequest(), userId, content,
-////                RoleType.USER.getName(), chatModelVo);
-//
-//            // 2. 执行 Agent 任务
-//            String result = doAgent(content, chatModelVo);
-//
-//            // 3. 发送结果并保存
-//            SseMessageUtils.sendMessage(userId, result);
-//            SseMessageUtils.completeConnection(userId, tokenValue);
-//
-////            saveChatMessage(context.getChatRequest(), userId, result,
-////                RoleType.ASSISTANT.getName(), chatModelVo);
-//            // todo 保存消息
-//        } catch (Exception e) {
-//            log.error("Agent 执行失败: {}", e.getMessage(), e);
-//            SseMessageUtils.sendMessage(userId, "Agent 执行失败：" + e.getMessage());
-//            SseMessageUtils.completeConnection(userId, tokenValue);
-//        }
-//
-//        return context.getEmitter();
-//    }
-//
-//    /**
-//     * 执行 Agent 任务
-//     */
-//    private String doAgent(String userMessage, ChatModelVo chatModelVo) {
-//        log.info("执行 Agent 任务，消息: {}", userMessage);
-//
-//        try {
-//            // 1. 加载 LLM 模型
-//            QwenChatModel qwenChatModel = QwenChatModel.builder()
-//                .apiKey(chatModelVo.getApiKey())
-//                .modelName(chatModelVo.getModelName())
-//                .build();
-//
-//            // 2. 获取内置工具
-//            List<Object> builtinTools = toolProviderFactory.getAllBuiltinToolObjects();
-//            List<Object> allTools = new ArrayList<>(builtinTools);
-//            log.debug("加载 {} 个内置工具", builtinTools.size());
-//
-//            // 3. 获取 MCP 工具提供者
-//            ToolProvider mcpToolProvider = toolProviderFactory.getAllEnabledMcpToolsProvider();
-//
-//            // 4. 创建 MCP Agent
-//            var agentBuilder = AgenticServices.agentBuilder(McpAgent.class)
-//                .chatModel(qwenChatModel);
-//
-//            if (!allTools.isEmpty()) {
-//                agentBuilder.tools(allTools.toArray(new Object[0]));
-//            }
-//            if (mcpToolProvider != null) {
-//                agentBuilder.toolProvider(mcpToolProvider);
-//            }
-//
-//            McpAgent mcpAgent = agentBuilder.build();
-//
-//            // 5. 调用 Agent
-//            String result = mcpAgent.callMcpTool(userMessage);
-//            log.info("Agent 执行完成，结果长度: {}", result.length());
-//            return result;
-//
-//        } catch (Exception e) {
-//            log.error("Agent 模式执行失败: {}", e.getMessage(), e);
-//            return "Agent 执行失败: " + e.getMessage();
-//        }
-//    }
-//
-//    /**
-//     * 提取用户消息内容
-//     */
-//    private String extractUserContent(ChatContext context) {
-//        var messages = context.getChatRequest().getMessages();
-//        if (messages != null && !messages.isEmpty()) {
-//            return messages.get(0).getContent();
-//        }
-//        return "";
-//    }
-//
-//}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/handler/ResumeChatHandler.java

@@ -1,53 +0,0 @@
-//package org.ruoyi.service.chat.handler;
-//
-//import lombok.RequiredArgsConstructor;
-//import lombok.extern.slf4j.Slf4j;
-//import org.ruoyi.common.chat.domain.dto.request.ReSumeRunner;
-//import org.ruoyi.common.chat.entity.chat.ChatContext;
-//import org.ruoyi.common.chat.service.workFlow.IWorkFlowStarterService;
-//import org.ruoyi.common.core.utils.ObjectUtils;
-//import org.springframework.core.annotation.Order;
-//import org.springframework.stereotype.Component;
-//import org.springframework.web.servlet.mvc.method.annotation.SseEmitter;
-//
-///**
-// * 人机交互恢复处理器
-// * <p>
-// * 处理 isResume=true 的场景，恢复工作流的人机交互
-// *
-// * @author ageerle@163.com
-// * @date 2025/12/13
-// */
-//@Slf4j
-//@Component
-//@Order(1)
-//@RequiredArgsConstructor
-//public class ResumeChatHandler implements ChatHandler {
-//
-//    private final IWorkFlowStarterService workFlowStarterService;
-//
-//    @Override
-//    public boolean supports(ChatContext context) {
-//        Boolean isResume = context.getChatRequest().getIsResume();
-//        return isResume != null && isResume;
-//    }
-//
-//    @Override
-//    public SseEmitter handle(ChatContext context) {
-//        log.info("处理人机交互恢复，用户: {}", context.getUserId());
-//
-//        ReSumeRunner reSumeRunner = context.getChatRequest().getReSumeRunner();
-//        if (ObjectUtils.isEmpty(reSumeRunner)) {
-//            log.warn("人机交互恢复参数为空");
-//            return context.getEmitter();
-//        }
-//
-//        workFlowStarterService.resumeFlow(
-//            reSumeRunner.getRuntimeUuid(),
-//            reSumeRunner.getFeedbackContent(),
-//            context.getEmitter()
-//        );
-//
-//        return context.getEmitter();
-//    }
-//}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/handler/WorkflowChatHandler.java

@@ -1,54 +0,0 @@
-//package org.ruoyi.service.chat.handler;
-//
-//import lombok.RequiredArgsConstructor;
-//import lombok.extern.slf4j.Slf4j;
-//import org.ruoyi.common.chat.base.ThreadContext;
-//import org.ruoyi.common.chat.domain.dto.request.WorkFlowRunner;
-//import org.ruoyi.common.chat.entity.chat.ChatContext;
-//import org.ruoyi.common.chat.service.workFlow.IWorkFlowStarterService;
-//import org.ruoyi.common.core.utils.ObjectUtils;
-//import org.springframework.core.annotation.Order;
-//import org.springframework.stereotype.Component;
-//import org.springframework.web.servlet.mvc.method.annotation.SseEmitter;
-//
-///**
-// * 工作流对话处理器
-// * <p>
-// * 处理 enableWorkFlow=true 的场景，启动工作流对话
-// *
-// * @author ageerle@163.com
-// * @date 2025/12/13
-// */
-//@Slf4j
-//@Component
-//@Order(2)
-//@RequiredArgsConstructor
-//public class WorkflowChatHandler implements ChatHandler {
-//
-//    private final IWorkFlowStarterService workFlowStarterService;
-//
-//    @Override
-//    public boolean supports(ChatContext context) {
-//        Boolean enableWorkFlow = context.getChatRequest().getEnableWorkFlow();
-//        return enableWorkFlow != null && enableWorkFlow;
-//    }
-//
-//    @Override
-//    public SseEmitter handle(ChatContext context) {
-//        log.info("处理工作流对话，用户: {}, 会话: {}",
-//            context.getUserId(), context.getChatRequest().getSessionId());
-//
-//        WorkFlowRunner runner = context.getChatRequest().getWorkFlowRunner();
-//        if (ObjectUtils.isEmpty(runner)) {
-//            log.warn("工作流参数为空");
-//            return context.getEmitter();
-//        }
-//
-//        return workFlowStarterService.streaming(
-//            ThreadContext.getCurrentUser(),
-//            runner.getUuid(),
-//            runner.getInputs(),
-//            context.getChatRequest().getSessionId()
-//        );
-//    }
-//}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/impl/ChatServiceFacade.java

@@ -1,25 +1,48 @@
 package org.ruoyi.service.chat.impl;
 
 import cn.dev33.satoken.stp.StpUtil;
+import dev.langchain4j.agentic.AgenticServices;
+import dev.langchain4j.agentic.supervisor.SupervisorAgent;
+import dev.langchain4j.agentic.supervisor.SupervisorResponseStrategy;
+import dev.langchain4j.community.model.dashscope.QwenChatModel;
 import dev.langchain4j.data.message.*;
+import dev.langchain4j.mcp.McpToolProvider;
+import dev.langchain4j.mcp.client.DefaultMcpClient;
+import dev.langchain4j.mcp.client.McpClient;
+import dev.langchain4j.mcp.client.transport.McpTransport;
+import dev.langchain4j.mcp.client.transport.stdio.StdioMcpTransport;
 import dev.langchain4j.memory.chat.MessageWindowChatMemory;
 import dev.langchain4j.model.chat.StreamingChatModel;
 import dev.langchain4j.model.chat.response.ChatResponse;
 import dev.langchain4j.model.chat.response.StreamingChatResponseHandler;
+import dev.langchain4j.model.openai.OpenAiChatModel;
+import dev.langchain4j.service.tool.ToolProvider;
 import lombok.RequiredArgsConstructor;
 import lombok.SneakyThrows;
 import lombok.extern.slf4j.Slf4j;
+import org.ruoyi.agent.ChartGenerationAgent;
+import org.ruoyi.agent.SqlAgent;
+import org.ruoyi.agent.WebSearchAgent;
+import org.ruoyi.agent.tool.ExecuteSqlQueryTool;
+import org.ruoyi.agent.tool.QueryAllTablesTool;
+import org.ruoyi.agent.tool.QueryTableSchemaTool;
+import org.ruoyi.common.chat.base.ThreadContext;
 import org.ruoyi.common.chat.domain.dto.request.ChatRequest;
+import org.ruoyi.common.chat.domain.dto.request.ReSumeRunner;
+import org.ruoyi.common.chat.domain.dto.request.WorkFlowRunner;
 import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
 import org.ruoyi.common.chat.enums.RoleType;
 import org.ruoyi.common.chat.service.chat.IChatModelService;
 import org.ruoyi.common.chat.service.chat.IChatService;
+import org.ruoyi.common.chat.service.workFlow.IWorkFlowStarterService;
+import org.ruoyi.common.core.utils.ObjectUtils;
 import org.ruoyi.common.satoken.utils.LoginHelper;
 import org.ruoyi.common.sse.core.SseEmitterManager;
 import org.ruoyi.common.sse.utils.SseMessageUtils;
 import org.ruoyi.domain.bo.vector.QueryVectorBo;
 import org.ruoyi.domain.vo.knowledge.KnowledgeInfoVo;
 import org.ruoyi.factory.ChatServiceFactory;
+import org.ruoyi.mcp.service.core.ToolProviderFactory;
 import org.ruoyi.service.chat.AbstractChatService;
 import org.ruoyi.service.chat.IChatMessageService;
 import org.ruoyi.service.chat.impl.memory.PersistentChatMemoryStore;
@@ -62,12 +85,18 @@ public class ChatServiceFacade implements IChatService {
 
     private final IChatMessageService chatMessageService;
 
+    private final IWorkFlowStarterService workFlowStarterService;
+
+    private final ToolProviderFactory toolProviderFactory;
+
     /**
      * 内存实例缓存，避免同一会话重复创建
      * Key: sessionId, Value: MessageWindowChatMemory实例
      */
     private static final Map<Object, MessageWindowChatMemory> memoryCache = new ConcurrentHashMap<>();
 
+
+
     /**
      * 统一聊天入口 - SSE流式响应
      *
@@ -76,6 +105,11 @@ public class ChatServiceFacade implements IChatService {
      */
     public SseEmitter sseChat(ChatRequest chatRequest) {
 
+        // 4. 具体的服务实现
+        Long userId = LoginHelper.getUserId();
+        String tokenValue = StpUtil.getTokenValue();
+        SseEmitter emitter = sseEmitterManager.connect(userId, tokenValue);
+
         // 1. 根据模型名称查询完整配置
         ChatModelVo chatModelVo = chatModelService.selectModelByName(chatRequest.getModel());
         if (chatModelVo == null) {
@@ -85,14 +119,17 @@ public class ChatServiceFacade implements IChatService {
         // 2. 构建上下文消息列表
         List<ChatMessage> contextMessages = buildContextMessages(chatRequest);
 
-        // 3. 路由服务提供商
+        // 3. 处理特殊聊天模式（工作流、人机交互恢复、思考模式）
+        SseEmitter specialResult = handleSpecialChatModes(chatRequest, contextMessages, chatModelVo, emitter);
+        if (specialResult != null) {
+            return specialResult;
+        }
+
+        // 4. 路由服务提供商
         String providerCode = chatModelVo.getProviderCode();
         log.info("路由到服务提供商: {}, 模型: {}", providerCode, chatRequest.getModel());
         AbstractChatService chatService = chatServiceFactory.getOriginalService(providerCode);
-        // 4. 具体的服务实现
+
-        Long userId = LoginHelper.getUserId();
-        String tokenValue = StpUtil.getTokenValue();
-        SseEmitter emitter = sseEmitterManager.connect(userId, tokenValue);
 
         StreamingChatResponseHandler handler = createResponseHandler(userId, tokenValue,chatRequest);
 
@@ -105,6 +142,131 @@ public class ChatServiceFacade implements IChatService {
         return emitter;
     }
 
+    /**
+     * 处理特殊聊天模式（工作流、人机交互恢复、思考模式）
+     *
+     * @param chatRequest      聊天请求
+     * @param contextMessages  上下文消息列表（可能被修改）
+     * @param chatModelVo      聊天模型配置
+     * @param emitter          SSE发射器
+     * @return 如果需要提前返回则返回SseEmitter，否则返回null
+     */
+    private SseEmitter handleSpecialChatModes(ChatRequest chatRequest, List<ChatMessage> contextMessages,
+                                              ChatModelVo chatModelVo, SseEmitter emitter) {
+        // 处理工作流对话
+        if (chatRequest.getEnableWorkFlow()) {
+            log.info("处理工作流对话,会话: {}", chatRequest.getSessionId());
+
+            WorkFlowRunner runner = chatRequest.getWorkFlowRunner();
+            if (ObjectUtils.isEmpty(runner)) {
+                log.warn("工作流参数为空");
+            }
+
+            return workFlowStarterService.streaming(
+                ThreadContext.getCurrentUser(),
+                runner.getUuid(),
+                runner.getInputs(),
+                chatRequest.getSessionId()
+            );
+        }
+
+        // 处理人机交互恢复
+        if (chatRequest.getIsResume()) {
+            log.info("处理人机交互恢复");
+
+            ReSumeRunner reSumeRunner = chatRequest.getReSumeRunner();
+            if (ObjectUtils.isEmpty(reSumeRunner)) {
+                log.warn("人机交互恢复参数为空");
+                return emitter;
+            }
+
+            workFlowStarterService.resumeFlow(
+                reSumeRunner.getRuntimeUuid(),
+                reSumeRunner.getFeedbackContent(),
+                emitter
+            );
+
+            return emitter;
+        }
+
+        // 处理思考模式
+        if (chatRequest.getEnableThinking()) {
+            handleThinkingMode(chatRequest, contextMessages, chatModelVo);
+        }
+
+        return null;
+    }
+
+    /**
+     * 处理思考模式
+     *
+     * @param chatRequest      聊天请求
+     * @param contextMessages  上下文消息列表
+     * @param chatModelVo      聊天模型配置
+     */
+    private void handleThinkingMode(ChatRequest chatRequest, List<ChatMessage> contextMessages, ChatModelVo chatModelVo) {
+        // 步骤1: 配置MCP传输层 - 连接到bing-cn-mcp服务器
+        McpTransport transport = new StdioMcpTransport.Builder()
+            .command(List.of("C:\\Program Files\\nodejs\\npx.cmd", "-y", "bing-cn-mcp"))
+            .logEvents(true)
+            .build();
+
+        McpClient mcpClient = new DefaultMcpClient.Builder()
+            .transport(transport)
+            .build();
+
+        ToolProvider toolProvider = McpToolProvider.builder()
+            .mcpClients(List.of(mcpClient))
+            .build();
+
+        // 配置echarts MCP
+        McpTransport transport1 = new StdioMcpTransport.Builder()
+            .command(List.of("C:\\Program Files\\nodejs\\npx.cmd", "-y", "mcp-echarts"))
+            .logEvents(true)
+            .build();
+
+        McpClient mcpClient1 = new DefaultMcpClient.Builder()
+            .transport(transport1)
+            .build();
+
+        ToolProvider toolProvider1 = McpToolProvider.builder()
+            .mcpClients(List.of(mcpClient1))
+            .build();
+
+        // 配置模型
+        OpenAiChatModel plannerModel = OpenAiChatModel.builder()
+            .baseUrl(chatModelVo.getApiHost())
+            .apiKey(chatModelVo.getApiKey())
+            .modelName(chatModelVo.getModelName())
+            .build();
+
+        // 构建各Agent
+        SqlAgent sqlAgent = AgenticServices.agentBuilder(SqlAgent.class)
+            .chatModel(plannerModel)
+            .tools(new QueryAllTablesTool(), new QueryTableSchemaTool(), new ExecuteSqlQueryTool())
+            .build();
+
+        WebSearchAgent searchAgent = AgenticServices.agentBuilder(WebSearchAgent.class)
+            .chatModel(plannerModel)
+            .toolProvider(toolProvider)
+            .build();
+
+        ChartGenerationAgent chartGenerationAgent = AgenticServices.agentBuilder(ChartGenerationAgent.class)
+            .chatModel(plannerModel)
+            .toolProvider(toolProvider1)
+            .build();
+
+        // 构建监督者Agent
+        SupervisorAgent supervisor = AgenticServices.supervisorBuilder()
+            .chatModel(plannerModel)
+            .subAgents(sqlAgent, chartGenerationAgent)
+            .responseStrategy(SupervisorResponseStrategy.LAST)
+            .build();
+
+        String invoke = supervisor.invoke(chatRequest.getContent());
+        contextMessages.add(AiMessage.from(invoke));
+    }
+
     /**
      * 支持外部 handler 的对话接口（跨模块调用）
      * 同时发送到 SSE 和外部 handler