结合mcp工具返回值流式输出

llm-back/ruoyi-agent/src/main/java/com/ruoyi/agent/service/impl/McpServiceImpl.java

 package com.ruoyi.agent.service.impl;
 
 import com.ruoyi.agent.service.IMcpService;
+import org.apache.poi.xwpf.usermodel.XWPFDocument;
+import org.apache.poi.xwpf.usermodel.XWPFParagraph;
 import org.noear.solon.ai.annotation.PromptMapping;
 import org.noear.solon.ai.annotation.ResourceMapping;
 import org.noear.solon.ai.annotation.ToolMapping;
 import org.noear.solon.annotation.Param;
 import org.springframework.stereotype.Service;
 
+import java.io.FileInputStream;
+import java.io.IOException;
 import java.util.Arrays;
 import java.util.Collection;
 
         return "晴，14度";
     }
 
+    @ToolMapping(description = "查看word文档")
+    public String openDocument() throws IOException {
+        String filePath = "C:\\Users\\lamphua\\Desktop\\智慧水利\\李家岩水库数字孪生招标文件.DOCX";
+        try (XWPFDocument doc = new XWPFDocument(new FileInputStream(filePath))) {
+            StringBuilder content = new StringBuilder();
+            for (XWPFParagraph paragraph : doc.getParagraphs()) {
+                content.append(paragraph.getText()).append("\n");
+            }
+            return content.toString();
+        }
+    }
+
     @ResourceMapping(uri = "config://app-version", description = "获取应用版本号")
     public String getAppVersion() {
         return "v3.2.0";

llm-back/ruoyi-llm/pom.xml

             <groupId>org.noear</groupId>
             <artifactId>solon-ai-mcp</artifactId>
             <version>3.3.1</version>
-        </dependency>
-
-        <dependency>
-            <groupId>com.squareup.okhttp3</groupId>
-            <artifactId>okhttp</artifactId>
+            <exclusions>
+                <exclusion>
+                    <groupId>org.slf4j</groupId>
+                    <artifactId>*</artifactId>
+                </exclusion>
+            </exclusions>
         </dependency>
 
     </dependencies>

llm-back/ruoyi-llm/src/main/java/com/ruoyi/web/llm/controller/McpController.java

 package com.ruoyi.web.llm.controller;
 
 import com.ruoyi.common.core.controller.BaseController;
-import com.ruoyi.common.core.domain.AjaxResult;
+import com.ruoyi.web.llm.service.ILangChainMilvusService;
+import org.noear.solon.ai.chat.ChatModel;
+import org.noear.solon.ai.chat.ChatResponse;
+import org.noear.solon.ai.chat.message.AssistantMessage;
 import org.noear.solon.ai.mcp.client.McpClientProvider;
+import org.reactivestreams.Publisher;
+import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.web.bind.annotation.GetMapping;
 import org.springframework.web.bind.annotation.RequestMapping;
 import org.springframework.web.bind.annotation.RestController;
+import reactor.core.publisher.Flux;
 
-import java.util.Collections;
-import java.util.Map;
 
 /**
  * mcp模型上下文协议Controller
 @RequestMapping("/llm/mcp")
 public class McpController extends BaseController
 {
+    @Autowired
+    private ILangChainMilvusService langChainMilvusService;
+
     /**
-     * 生成回答
+     * 打开word文档
+     * @return
      */
     @GetMapping("/answer")
-    public AjaxResult answer() {
+    public Flux<AssistantMessage> answer(String question)
+    {
         McpClientProvider clientProvider = McpClientProvider.builder()
-                .apiUrl("http://localhost:52548/llm/mcp/sse")
+                    .apiUrl("http://localhost:8080/llm/mcp/sse")
+                    .build();
+
+        ChatModel chatModel = ChatModel.of("http://192.168.28.188:8000/v1/chat/completions")
+                .provider("openai")
+                .model("DeepSeek-R1-Distill-Qwen-1.5B")
+                .apiKey("1")
+//                .defaultToolsAdd(clientProvider)
                 .build();
+        Publisher<ChatResponse> publisher = chatModel.prompt(question).stream();
 
-        Map<String, Object> map = Collections.singletonMap("location", "杭州");
-        String rst = clientProvider.callToolAsText("getWeather", map).getContent();
-        // 1. 调用本地LLM或HTTP服务
-        return success(rst);
+        return Flux.from(publisher)
+                .map(response -> {
+                    return response.getChoices().get(0).getMessage();
+                });
     }
 
-
 }

llm-back/ruoyi-llm/src/main/java/com/ruoyi/web/llm/controller/RagController.java

 import dev.langchain4j.model.embedding.onnx.bgesmallzhv15.BgeSmallZhV15EmbeddingModel;
 import io.milvus.client.MilvusServiceClient;
 import io.milvus.param.ConnectParam;
+import org.noear.solon.ai.chat.message.AssistantMessage;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.web.bind.annotation.GetMapping;
 import org.springframework.web.bind.annotation.RequestMapping;
 import org.springframework.web.bind.annotation.RestController;
+import reactor.core.publisher.Flux;
 
 import java.io.IOException;
 import java.util.List;
      * 增强检索生成回答
      */
     @GetMapping("/answer")
-    public AjaxResult answer(String question, String collectionName) throws IOException {
+    public Flux<AssistantMessage> answer(String question, String collectionName) throws IOException {
 
         List<String> contexts = langChainMilvusService.retrieveFromMilvus(milvusClient, embeddingModel, collectionName, question, 1);
-        String result = langChainMilvusService.generateAnswerWithRag(question, contexts, "http://192.168.28.188:8000/generate");
-        return success(result);
+        return langChainMilvusService.generateAnswerWithRag(question, contexts, "http://192.168.28.188:8000/v1/chat/completions");
     }
 
 }

llm-back/ruoyi-llm/src/main/java/com/ruoyi/web/llm/controller/SessionController.java

 import com.ruoyi.common.core.controller.BaseController;
 import com.ruoyi.common.core.domain.AjaxResult;
 import com.ruoyi.web.llm.service.ILangChainMilvusService;
+import org.noear.solon.ai.chat.message.AssistantMessage;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.web.bind.annotation.GetMapping;
 import org.springframework.web.bind.annotation.RequestMapping;
 import org.springframework.web.bind.annotation.RestController;
+import reactor.core.publisher.Flux;
 
 import java.io.IOException;
 
      * 生成回答
      */
     @GetMapping("/answer")
-    public AjaxResult answer(String question) throws IOException {
-        return success(langChainMilvusService.generateAnswer(question, "http://192.168.28.188:8000/generate"));
+    public Flux<AssistantMessage> answer(String question) {
+        return langChainMilvusService.generateAnswer(question, "http://192.168.28.188:8000/v1/chat/completions");
     }
 
 }

llm-back/ruoyi-llm/src/main/java/com/ruoyi/web/llm/service/ILangChainMilvusService.java

 import io.milvus.client.MilvusClient;
 import io.milvus.grpc.MutationResult;
 import io.milvus.param.R;
+import org.noear.solon.ai.chat.message.AssistantMessage;
 import org.springframework.web.multipart.MultipartFile;
+import reactor.core.publisher.Flux;
 
 import java.io.IOException;
 import java.util.List;
 
     /**
      * 调用LLM+RAG生成回答
+     * @return
      */
-    public String generateAnswerWithRag(String question, List<String> contexts, String llmServiceUrl) throws IOException;
+    public Flux<AssistantMessage> generateAnswerWithRag(String question, List<String> contexts, String llmServiceUrl);
 
     /**
      * 调用LLM生成回答
+     * @return
      */
-    public String generateAnswer(String question, String llmServiceUrl) throws IOException;
+    public Flux<AssistantMessage> generateAnswer(String question, String llmServiceUrl);
 }

llm-back/ruoyi-llm/src/main/java/com/ruoyi/web/llm/service/impl/LangChainMilvusServiceImpl.java

  */
 package com.ruoyi.web.llm.service.impl;
 
-import com.alibaba.fastjson2.JSONObject;
 import com.ruoyi.common.config.RuoYiConfig;
 import com.ruoyi.web.llm.service.ILangChainMilvusService;
 import dev.langchain4j.data.document.Document;
 import io.milvus.param.dml.InsertParam;
 import io.milvus.param.dml.SearchParam;
 import io.milvus.response.SearchResultsWrapper;
-import okhttp3.*;
 import org.apache.poi.xwpf.extractor.XWPFWordExtractor;
 import org.apache.poi.xwpf.usermodel.XWPFDocument;
+import org.noear.solon.ai.chat.ChatModel;
+import org.noear.solon.ai.chat.ChatResponse;
+import org.noear.solon.ai.chat.message.AssistantMessage;
+import org.reactivestreams.Publisher;
 import org.springframework.stereotype.Service;
 import org.springframework.web.multipart.MultipartFile;
+import reactor.core.publisher.Flux;
 
 import java.io.File;
 import java.io.FileInputStream;
 import java.io.IOException;
 import java.io.InputStream;
 import java.util.*;
-import java.util.concurrent.TimeUnit;
 import java.util.stream.Collectors;
 
 @Service
 
     /**
      * 调用LLM+RAG生成回答
+     * @return
      */
     @Override
-    public String generateAnswerWithRag(String question, List<String> contexts, String llmServiceUrl) throws IOException {
+    public Flux<AssistantMessage> generateAnswerWithRag(String question, List<String> contexts, String llmServiceUrl) {
         StringBuilder sb = new StringBuilder();
         sb.append("根据以下上下文回答问题：\n\n");
         for (int i = 0; i < contexts.size(); i++) {
 
     /**
      * 调用LLM生成回答
+     * @return
      */
     @Override
-    public String generateAnswer(String prompt, String llmServiceUrl) throws IOException {
-        HttpUrl url = HttpUrl.parse(llmServiceUrl)
-                .newBuilder()
-                .addQueryParameter("prompt", prompt)
-//                .addQueryParameter("max_token", String.valueOf(1024))
+    public Flux<AssistantMessage> generateAnswer(String prompt, String llmServiceUrl) {
+        ChatModel chatModel = ChatModel.of(llmServiceUrl)
+                .provider("openai")
+                .model("DeepSeek-R1-Distill-Qwen-1.5B")
+                .apiKey("1")
                 .build();
+        Publisher<ChatResponse> publisher = chatModel.prompt(prompt).stream();
 
-        Request request = new Request.Builder()
-                .url(url)
-                .build();
-
-        OkHttpClient client = new OkHttpClient.Builder()
-                .readTimeout(60, TimeUnit.SECONDS)      // 读取响应超时
-                .build();
-
-        try (Response response = client.newCall(request).execute()) {
-            if (response.body() != null) {
-                String responseBody = response.body().string();
-                return JSONObject.parseObject(responseBody).getString("generated_text");
-            }
-            else return null;
-        }
+        return Flux.from(publisher)
+                .map(response -> {
+                    return response.getChoices().get(0).getMessage();
+                });
     }
 
 }

llm-ui/src/views/llm/chat/index.vue

       // 使用Vue.set或直接赋值来确保响应式更新
       chatMessages.value[messageIndex] = {
         ...chatMessages.value[messageIndex],
-        output: answer.msg,
+        output: answer.resultContent,
         outputTime: proxy.parseTime(new Date(), '{y}-{m}-{d}')
       };
     }

llm-ui/src/views/llm/knowledge/index.vue

       console.log(response);
       const aiMessage = {
         type: 'ai',
-        content: response.msg,
+        content: response.resultContent,
         time: new Date()
       };
       chatMessages.value.push(aiMessage);