context:通义万相文生图节点功能以及发送邮箱和HTTP请求节点调研

2026-04-09 01:37:32 +00:00 · 2026-02-24 10:34:26 +08:00
parent b40805cb57
commit 0a115f289e
14 changed files with 476 additions and 57 deletions
--- a/ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/enums/ImageModeType.java
+++ b/ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/enums/ImageModeType.java
@@ -0,0 +1,23 @@
+package org.ruoyi.enums;
+
+import lombok.Getter;
+
+/**
+ * 文生图模型分类
+ *
+ * @author Zengxb
+ * @date 2026-02-14
+ */
+@Getter
+public enum ImageModeType {
+
+    TONGYI_WANX("Tongyiwanx", "万相");
+
+    private final String code;
+    private final String description;
+
+    ImageModeType(String code, String description) {
+        this.code = code;
+        this.description = description;
+    }
+}
--- a/ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/impl/provider/QianWenChatServiceImpl.java
+++ b/ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/impl/provider/QianWenChatServiceImpl.java
@@ -1,6 +1,5 @@
 package org.ruoyi.service.chat.impl.provider;

-import dev.langchain4j.agent.tool.ToolSpecification;
 import dev.langchain4j.agentic.AgenticServices;
 import dev.langchain4j.agentic.supervisor.SupervisorAgent;
 import dev.langchain4j.agentic.supervisor.SupervisorResponseStrategy;
@@ -30,6 +29,8 @@ import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;

 import java.util.ArrayList;
 import java.util.List;
+import java.util.concurrent.ConcurrentHashMap;
+import java.util.concurrent.locks.ReentrantLock;

 /**
 * qianWenAI服务调用
@@ -44,14 +45,20 @@ public class QianWenChatServiceImpl extends AbstractStreamingChatService {
    @Autowired
    private McpSseConfig mcpSseConfig;

-    /**
-     * 千问开发者默认地址
-     */
-    private static final String QWEN_API_HOST = "https://dashscope.aliyuncs.com/api/v1";
-
    // 添加文档解析的前缀字段
    private static final String UPLOAD_FILE_API_PREFIX = "fileid";

+    // 缓存不同API Key和模型的MCP智能体实例
+    private final ConcurrentHashMap<String, SupervisorAgent> supervisorCache = new ConcurrentHashMap<>();
+
+    // 缓存不同API Key和模型的MCP客户端实例
+    private final ConcurrentHashMap<String, McpClient> mcpClientCache = new ConcurrentHashMap<>();
+
+    // 缓存不同API Key和模型的MCP工具提供者实例
+    private final ConcurrentHashMap<String, ToolProvider> toolProviderCache = new ConcurrentHashMap<>();
+    // 用于线程安全的锁
+    private final ReentrantLock cacheLock = new ReentrantLock();
+
    @Override
    protected StreamingChatModel buildStreamingChatModel(ChatModelVo chatModelVo,ChatRequest chatRequest) {
        return QwenStreamingChatModel.builder()
@@ -102,17 +109,16 @@ public class QianWenChatServiceImpl extends AbstractStreamingChatService {
    }

    /**
-     * 调用MCP服务（智能体）
-     * @param userMessage 用户信息
-     * @param chatModelVo 模型信息
-     * @return 返回LLM信息
+     * 获取缓存键
     */
-    protected String doAgent(String userMessage,ChatModelVo chatModelVo) {
-        // 判断是否开启MCP服务
-        if (!mcpSseConfig.isEnabled()) {
-            return "";
-        }
+    private String getCacheKey(ChatModelVo chatModelVo) {
+        return chatModelVo.getApiKey() + ":" + chatModelVo.getModelName();
+    }

+    /**
+     * 初始化MCP客户端连接
+     */
+    private McpClient initializeMcpClient() {
        // 步骤1：根据SSE对外暴露端点连接
        McpTransport httpMcpTransport = new StreamableHttpMcpTransport.Builder().
            url(mcpSseConfig.getUrl()).
@@ -120,42 +126,74 @@ public class QianWenChatServiceImpl extends AbstractStreamingChatService {
            build();

        // 步骤2：开启客户端连接
-        McpClient mcpClient = new DefaultMcpClient.Builder()
+        return new DefaultMcpClient.Builder()
            .transport(httpMcpTransport)
            .build();
+    }

-        // 获取所有mcp工具
-        List<ToolSpecification> toolSpecifications = mcpClient.listTools();
-        System.out.println(toolSpecifications);
+    /**
+     * 调用MCP服务（智能体）
+     * @param userMessage 用户信息
+     * @param chatModelVo 模型信息
+     * @return 返回LLM信息
+     */
+    protected String doAgent(String userMessage, ChatModelVo chatModelVo) {
+        // 判断是否开启MCP服务
+        if (!mcpSseConfig.isEnabled()) {
+            return "";
+        }
+        // 生成缓存键
+        String cacheKey = getCacheKey(chatModelVo);
+        // 尝试从缓存获取监督智能体
+        SupervisorAgent cachedSupervisor = supervisorCache.get(cacheKey);
+        if (cachedSupervisor != null) {
+            // 如果已存在缓存的监督智能体，直接使用
+            return cachedSupervisor.invoke(userMessage);
+        }
+        cacheLock.lock();
+        try {
+            // 双重检查，防止并发情况下的重复初始化
+            cachedSupervisor = supervisorCache.get(cacheKey);
+            if (cachedSupervisor != null) {
+                return cachedSupervisor.invoke(userMessage);
+            }

-        // 步骤3：将mcp对象包装
-        ToolProvider toolProvider = McpToolProvider.builder()
-            .mcpClients(List.of(mcpClient))
-            .build();
+            // 获取或初始化MCP客户端
+            McpClient mcpClient = mcpClientCache.computeIfAbsent(cacheKey, k -> initializeMcpClient());

-        // 步骤4：加载LLM模型对话
-        QwenChatModel qwenChatModel = QwenChatModel.builder()
-            .baseUrl(QWEN_API_HOST)
-            .apiKey(chatModelVo.getApiKey())
-            .modelName(chatModelVo.getModelName())
-            .build();
+            // 步骤3：将mcp对象包装
+            ToolProvider toolProvider = toolProviderCache.computeIfAbsent(cacheKey, k -> McpToolProvider.builder()
+                .mcpClients(List.of(mcpClient))
+                .build());

-        // 步骤5：将MCP对象由智能体Agent管控
-        McpAgent mcpAgent = AgenticServices.agentBuilder(McpAgent.class)
-            .chatModel(qwenChatModel)
-            .toolProvider(toolProvider)
-            .build();
+            // 步骤4：加载LLM模型对话
+            QwenChatModel qwenChatModel = QwenChatModel.builder()
+                .apiKey(chatModelVo.getApiKey())
+                .modelName(chatModelVo.getModelName())
+                .build();

-        // 步骤6：将所有MCP对象由超级智能体管控
-        SupervisorAgent supervisor = AgenticServices
-            .supervisorBuilder()
-            .chatModel(qwenChatModel)
-            .subAgents(mcpAgent)
-            .responseStrategy(SupervisorResponseStrategy.LAST)
-            .build();
+            // 步骤5：将MCP对象由智能体Agent管控
+            McpAgent mcpAgent = AgenticServices.agentBuilder(McpAgent.class)
+                .chatModel(qwenChatModel)
+                .toolProvider(toolProvider)
+                .build();

-        // 步骤7：调用大模型LLM
-        return supervisor.invoke(userMessage);
+            // 步骤6：将所有MCP对象由超级智能体管控
+            SupervisorAgent supervisor = AgenticServices
+                .supervisorBuilder()
+                .chatModel(qwenChatModel)
+                .subAgents(mcpAgent)
+                .responseStrategy(SupervisorResponseStrategy.LAST)
+                .build();
+
+            // 缓存监督智能体
+            supervisorCache.put(cacheKey, supervisor);
+
+            // 步骤7：调用大模型LLM
+            return supervisor.invoke(userMessage);
+        } finally {
+            cacheLock.unlock();
+        }
    }

    @Override
--- a/ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/image/AbstractImageGenerationService.java
+++ b/ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/image/AbstractImageGenerationService.java
@@ -0,0 +1,43 @@
+package org.ruoyi.service.image;
+
+import lombok.extern.slf4j.Slf4j;
+import org.ruoyi.common.chat.Service.IImageGenerationService;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.common.chat.domain.entity.image.ImageContext;
+import org.springframework.validation.annotation.Validated;
+
+@Slf4j
+@Validated
+public abstract class AbstractImageGenerationService implements IImageGenerationService {
+
+    /**
+     * 根据文字生成图片
+     * @param imageContext 文生图上下文对象
+     * @return 生成的图片URL
+     */
+    @Override
+    public String generateImage(ImageContext imageContext){
+        // 获取模型管理视图对象
+        ChatModelVo chatModelVo = imageContext.getChatModelVo();
+        // 获取提示词
+        String prompt = imageContext.getPrompt();
+        // 获取图片尺寸大小
+        String size = imageContext.getSize();
+        // 获取随机数种子
+        Integer seed = imageContext.getSeed();
+        return doGenerateImage(chatModelVo, prompt, size, seed);
+    }
+
+    /**
+     * 执行生成图片（钩子方法 - 子类必须实现）
+     *
+     * @param prompt 提示词
+     */
+    protected abstract String doGenerateImage(ChatModelVo chatModelVo, String prompt, String size, Integer seed);
+
+    /**
+     * 构建具体厂商的 ImageModel（原生SDK 非langchain4j-dashscope版）
+     * 子类必须实现此方法，返回对应厂商的模型实例
+     */
+    protected abstract Object buildImageModel(ChatModelVo chatModelVo);
+}
--- a/ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/image/provider/TongYiWanxImageServiceImpl.java
+++ b/ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/image/provider/TongYiWanxImageServiceImpl.java
@@ -0,0 +1,73 @@
+package org.ruoyi.service.image.provider;
+
+import com.alibaba.dashscope.aigc.imagesynthesis.ImageSynthesis;
+import com.alibaba.dashscope.aigc.imagesynthesis.ImageSynthesisParam;
+import com.alibaba.dashscope.aigc.imagesynthesis.ImageSynthesisResult;
+import com.alibaba.dashscope.exception.ApiException;
+import com.alibaba.dashscope.exception.NoApiKeyException;
+import lombok.extern.slf4j.Slf4j;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.common.core.utils.StringUtils;
+import org.ruoyi.enums.ImageModeType;
+import org.ruoyi.service.image.AbstractImageGenerationService;
+import org.springframework.stereotype.Service;
+
+/**
+ * 万相文生图AI调用
+ *
+ * @author Zengxb
+ * @date 2026/02/14
+ */
+@Service
+@Slf4j
+public class TongYiWanxImageServiceImpl extends AbstractImageGenerationService {
+
+    /**
+     * 默认图片数量（1张）
+     */
+    private final static int IMAGE_DEFAULT_SIZE = 1;
+
+    /**
+     * 默认图片分辨率（1280*1280）
+     */
+    private final static String IMAGE_DEFAULT_RESOLUTION = "1280*1280";
+
+    @Override
+    protected String doGenerateImage(ChatModelVo chatModelVo, String prompt, String size, Integer seed) {
+        // 构建万相模型对象
+        var param = (ImageSynthesisParam) buildImageModel(chatModelVo);
+        // 设置图片大小和提示词以及随机数种子
+        param.setSize(StringUtils.isEmpty(size) ? IMAGE_DEFAULT_RESOLUTION : size);
+        param.setPrompt(prompt);
+        param.setSeed(seed);
+        // 同步调用 AI 大模型，生成图片
+        var imageSynthesis = new ImageSynthesis();
+        ImageSynthesisResult result;
+        try {
+            log.info("同步调用通义万相文生图接口中....");
+            result = imageSynthesis.call(param);
+        } catch (ApiException | NoApiKeyException e) {
+            log.error("同步调用通义万相文生图接口失败", e);
+            return "";
+        }
+        // 直接提取图片URL
+        var output = result.getOutput();
+        var results = output.getResults();
+        return results.isEmpty() ? "" : results.get(0).get("url");
+    }
+
+    @Override
+    protected Object buildImageModel(ChatModelVo chatModelVo) {
+        return ImageSynthesisParam.builder()
+            .prompt("")
+            .apiKey(chatModelVo.getApiKey())
+            .model(chatModelVo.getModelName())
+            .n(IMAGE_DEFAULT_SIZE)
+            .build();
+    }
+
+    @Override
+    public String getProviderName() {
+        return ImageModeType.TONGYI_WANX.getCode();
+    }
+}