多轮对话，根据上传文件回答

llm-back/ruoyi-llm/pom.xml

@@ -55,6 +55,13 @@
             <version>0.35.0</version>
         </dependency>
 
+        <!-- LangChain4j rag 集成 -->
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-easy-rag</artifactId>
+            <version>0.35.0</version>
+        </dependency>
+
         <!-- LangChain4j pdfParser 集成 -->
         <dependency>
             <groupId>dev.langchain4j</groupId>

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

@@ -21,7 +21,6 @@ import org.springframework.web.multipart.MultipartFile;
 import java.io.File;
 import java.io.IOException;
 import java.util.*;
-import java.util.stream.Collectors;
 
 
 /**

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

@@ -38,17 +38,17 @@ public class RagController extends BaseController
                     .build());
 
     /**
-     * 增强检索生成回答
+     * 调用LLM+RAG（知识库）生成回答
      */
     @GetMapping("/answer")
-    public Flux<AssistantMessage> answer(String question, String collectionName)
+    public Flux<AssistantMessage> answerWithCollection(String collectionName, String topicId, String question)
     {
         List<JSONObject> contexts = langChainMilvusService.retrieveFromMilvus(milvusClient, embeddingModel, collectionName, question, 10);
-        return langChainMilvusService.generateAnswerWithRag(question, contexts, "http://192.168.28.188:8000/v1/chat/completions");
+        return langChainMilvusService.generateAnswerWithCollection(topicId, question, contexts, "http://192.168.28.188:8000/v1/chat/completions");
     }
 
     /**
-     * 增强检索生成上下文
+     * RAG上下文列表
      */
     @GetMapping("/context")
     public List<JSONObject> context(String question, String collectionName)

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

@@ -1,14 +1,23 @@
 package com.ruoyi.web.llm.controller;
 
+import com.ruoyi.common.config.RuoYiConfig;
 import com.ruoyi.common.core.controller.BaseController;
+import com.ruoyi.llm.domain.CmcDocument;
+import com.ruoyi.llm.service.ICmcDocumentService;
 import com.ruoyi.web.llm.service.ILangChainMilvusService;
+import dev.langchain4j.model.embedding.EmbeddingModel;
+import dev.langchain4j.model.embedding.onnx.bgesmallzhv15.BgeSmallZhV15EmbeddingModel;
 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 org.springframework.web.multipart.MultipartFile;
 import reactor.core.publisher.Flux;
 
+import java.io.File;
+import java.io.IOException;
+
 /**
  * session对话Controller
  * 
@@ -22,12 +31,46 @@ public class SessionController extends BaseController
     @Autowired
     private ILangChainMilvusService langChainMilvusService;
 
+    @Autowired
+    private ICmcDocumentService cmcDocumentService;
+
+    private static final EmbeddingModel embeddingModel = new BgeSmallZhV15EmbeddingModel();
+
     /**
      * 生成回答
      */
     @GetMapping("/answer")
-    public Flux<AssistantMessage> answer(String question) {
-        return langChainMilvusService.generateAnswer(question, "http://192.168.28.188:8000/v1/chat/completions");
+    public Flux<AssistantMessage> answer(String topicId, String question) {
+        return langChainMilvusService.generateAnswer(topicId, question, "http://192.168.28.188:8000/v1/chat/completions");
+    }
+
+    /**
+     * 上传外部文件
+     * @return
+     */
+    @GetMapping("/upload")
+    public String upload(MultipartFile file) throws IOException {
+        File profilePath = new File( RuoYiConfig.getProfile() + "/upload/rag/document" );
+        if (!profilePath.exists())
+            profilePath.mkdirs();
+        File transferFile = new File( profilePath + File.separator + file.getOriginalFilename());
+        if (!transferFile.exists()) {
+            file.transferTo(transferFile);
+        }
+        return file.getOriginalFilename();
+    }
+
+    /**
+     * 调用LLM+RAG（外部文件）生成回答
+     */
+    @GetMapping("/answerWithDocument")
+    public Flux<AssistantMessage> answerWithDocument(String filename, String chatId, String topicId, String question) throws IOException
+    {
+        CmcDocument cmcDocument = new CmcDocument();
+        cmcDocument.setChatId(chatId);
+        cmcDocument.setPath(filename);
+        cmcDocumentService.insertCmcDocument(cmcDocument);
+        return langChainMilvusService.generateAnswerWithDocument(embeddingModel, filename, topicId, question, "http://192.168.28.188:8000/v1/chat/completions");
     }
 
 }

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

@@ -32,14 +32,21 @@ public interface ILangChainMilvusService {
     public List<JSONObject> similarityFromMilvus(MilvusClient milvusClient, EmbeddingModel embeddingModel, String collectionName, String query, int topK);
 
     /**
-     * 调用LLM+RAG生成回答
+     * 调用LLM生成回答
      * @return
      */
-    public Flux<AssistantMessage> generateAnswerWithRag(String question, List<JSONObject> contexts, String llmServiceUrl);
+    public Flux<AssistantMessage> generateAnswer(String topicId, String question, String llmServiceUrl);
 
     /**
-     * 调用LLM生成回答
+     * 调用LLM+RAG（知识库）生成回答
      * @return
      */
-    public Flux<AssistantMessage> generateAnswer(String question, String llmServiceUrl);
+    public Flux<AssistantMessage> generateAnswerWithCollection(String topicId, String question, List<JSONObject> contexts, String llmServiceUrl);
+
+    /**
+     * 调用LLM+RAG（外部文件）生成回答
+     * @return
+     */
+    public Flux<AssistantMessage> generateAnswerWithDocument(EmbeddingModel embeddingModel, String filename, String topicId, String question, String llmServiceUrl) throws IOException;
+
 }

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

@@ -1,20 +1,18 @@
-/*
- * @Author: ysh
- * @Date: 2025-07-08 15:10:42
- * @LastEditors: Please set LastEditors
- * @LastEditTime: 2025-07-21 15:42:26
- */
 package com.ruoyi.web.llm.service.impl;
 
 import com.alibaba.fastjson2.JSONObject;
 import com.ruoyi.common.config.RuoYiConfig;
+import com.ruoyi.llm.domain.CmcChat;
+import com.ruoyi.llm.service.ICmcChatService;
 import com.ruoyi.web.llm.service.ILangChainMilvusService;
 import dev.langchain4j.data.document.Document;
 import dev.langchain4j.data.document.parser.apache.pdfbox.ApachePdfBoxDocumentParser;
 import dev.langchain4j.data.document.splitter.DocumentByParagraphSplitter;
+import dev.langchain4j.data.embedding.Embedding;
 import dev.langchain4j.data.segment.TextSegment;
 import dev.langchain4j.model.embedding.EmbeddingModel;
-
+import dev.langchain4j.store.embedding.EmbeddingSearchRequest;
+import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
 import io.milvus.client.MilvusClient;
 import io.milvus.grpc.MutationResult;
 import io.milvus.grpc.SearchResults;
@@ -30,53 +28,40 @@ 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.ChatSession;
+import org.noear.solon.ai.chat.ChatSessionDefault;
 import org.noear.solon.ai.chat.message.AssistantMessage;
+import org.noear.solon.ai.chat.message.ChatMessage;
 import org.reactivestreams.Publisher;
+import org.springframework.beans.factory.annotation.Autowired;
 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.io.*;
 import java.util.*;
 import java.util.stream.Collectors;
 
 @Service
 public class LangChainMilvusServiceImpl implements ILangChainMilvusService
 {
+    @Autowired
+    private ICmcChatService cmcChatService;
+
     /**
      * 导入知识库文件
      */
     @Override
     public R<MutationResult> insertLangchainEmbeddingDocument(MilvusClient milvusClient, MultipartFile file, String collectionName, EmbeddingModel embeddingModel) throws IOException
     {
-        File profilePath = new File( RuoYiConfig.getProfile() + "/upload/knowledge/" + collectionName);
+        File profilePath = new File( RuoYiConfig.getProfile() + "/upload/rag/knowledge/" + collectionName);
         if (!profilePath.exists())
             profilePath.mkdirs();
         File transferFile = new File( profilePath + File.separator + file.getOriginalFilename());
         if (!transferFile.exists()) {
             file.transferTo(transferFile);
         }
-        // 加载文档
-        Document document;
-        InputStream fileInputStream = new FileInputStream(transferFile);
-        String filename = file.getOriginalFilename().toLowerCase();
-        if (filename.endsWith(".docx")) {
-            XWPFDocument docx = new XWPFDocument(fileInputStream);
-            XWPFWordExtractor extractor = new XWPFWordExtractor(docx);
-            String text = extractor.getText();
-            document = Document.from(text);
-        }
-        else if (filename.endsWith(".pdf")) {
-            document = new ApachePdfBoxDocumentParser().parse(fileInputStream);
-        }
-        else {
-            throw new UnsupportedOperationException("不支持文件类型: " + filename);
-        }
-        DocumentByParagraphSplitter splitter = new DocumentByParagraphSplitter(1000,200);;
-        List<TextSegment> segments = splitter.split(document);
+        List<TextSegment> segments = splitDocument(file.getOriginalFilename(), transferFile);
 
         // 提取文本和生成嵌入
         List<String> fileNames = new ArrayList<>();
@@ -144,16 +129,45 @@ public class LangChainMilvusServiceImpl implements ILangChainMilvusService
                     return result;
                 })
                 .collect(Collectors.toList());
-        wrapperList.removeIf(jsonObject -> jsonObject.getDouble("distance") < 0.75);
+        wrapperList.removeIf(jsonObject -> jsonObject.getDouble("distance") < 0.7);
         return wrapperList;
     }
 
+    /**
+     * 调用LLM生成回答
+     * @return
+     */
+    @Override
+    public Flux<AssistantMessage> generateAnswer(String topicId, String prompt, String llmServiceUrl) {
+        ChatSession chatSession = new ChatSessionDefault(topicId);
+        ChatModel chatModel = ChatModel.of(llmServiceUrl)
+                .provider("openai")
+                .model("Qwen2.5-1.5B-Instruct")
+                .apiKey("1")
+                .build();
+
+        CmcChat cmcChat = new CmcChat();
+        cmcChat.setTopicId(topicId);
+        List<CmcChat> cmcChatList = cmcChatService.selectCmcChatList(cmcChat);
+        for (CmcChat chat : cmcChatList) {
+            chatSession.addMessage(ChatMessage.ofUser(chat.getInput()));
+            chatSession.addMessage(ChatMessage.ofAssistant(chat.getOutput()));
+        }
+        chatSession.addMessage(ChatMessage.ofUser(prompt));
+
+        Publisher<ChatResponse> publisher = chatModel.prompt(chatSession).stream();
+        return Flux.from(publisher)
+                .map(response -> {
+                    return response.lastChoice().getMessage();
+                });
+    }
+
     /**
      * 调用LLM+RAG生成回答
      * @return
      */
     @Override
-    public Flux<AssistantMessage> generateAnswerWithRag(String question, List<JSONObject> contexts, String llmServiceUrl) {
+    public Flux<AssistantMessage> generateAnswerWithCollection(String topicId, String question, List<JSONObject> contexts, String llmServiceUrl) {
         StringBuilder sb = new StringBuilder();
         sb.append("问题: ").append(question).append("\n\n");
         sb.append("根据以下上下文回答问题：\n\n");
@@ -163,29 +177,38 @@ public class LangChainMilvusServiceImpl implements ILangChainMilvusService
                     .append("上下文").append(": ")
                     .append(contexts.get(i).getString("content")).append("\n\n");
         }
-        // 构建带动态参数的URL
-        return generateAnswer(sb.toString(), llmServiceUrl);
+        return generateAnswer(topicId, sb.toString(), llmServiceUrl);
     }
 
     /**
      * 调用LLM生成回答
-     * @return
      */
     @Override
-    public Flux<AssistantMessage> generateAnswer(String prompt, String llmServiceUrl) {
-        ChatModel chatModel = ChatModel.of(llmServiceUrl)
-                .provider("openai")
-                .model("Qwen2.5-1.5B-Instruct")
-                .apiKey("1")
-                .build();
-        Publisher<ChatResponse> publisher = chatModel.prompt(prompt).stream();
+    public Flux<AssistantMessage> generateAnswerWithDocument(EmbeddingModel embeddingModel, String filename, String topicId, String question, String llmServiceUrl) throws IOException {
 
-        return Flux.from(publisher)
-                .map(response -> {
-                    return response.lastChoice().getMessage();
-                });
+        File profilePath = new File( RuoYiConfig.getProfile() + "/upload/rag/document/" + filename);
+        List<TextSegment> segments = splitDocument(filename, profilePath);
+        List<Embedding> embeddings = embeddingModel.embedAll(segments).content();
+        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
+        embeddingStore.addAll(embeddings, segments);
+        Embedding queryEmbedding = embeddingModel.embed(question).content();
+        EmbeddingSearchRequest embeddingSearchRequest = EmbeddingSearchRequest.builder()
+                .queryEmbedding(queryEmbedding)
+                .maxResults(1)
+                .build();
+        String contexts = embeddingStore.search(embeddingSearchRequest).matches().get(0).embedded().text();
+        String sb = "问题: " + question + "\n\n" +
+                "根据以下上下文回答问题：\n\n" +
+                "文件" + ": " +
+                filename + "\n\n" +
+                "上下文" + ": " +
+                contexts + "\n\n";
+        return generateAnswer(topicId, sb, llmServiceUrl);
     }
 
+    /**
+     * 检索知识库
+     */
     private SearchResultsWrapper retrieve(MilvusClient milvusClient, EmbeddingModel embeddingModel, String collectionName, String query, int topK) {
         List<List<Float>> queryVector = Collections.singletonList(embeddingModel.embed(query).content().vectorAsList());
 
@@ -222,4 +245,33 @@ public class LangChainMilvusServiceImpl implements ILangChainMilvusService
 
         return wrapper;
     }
+
+
+    /**
+     * 检索知识库
+     */
+    private List<TextSegment> splitDocument(String filename, File profilePath) throws IOException {
+        // 加载文档
+        Document document;
+        InputStream fileInputStream = new FileInputStream(profilePath);
+        filename = filename.toLowerCase();
+        if (filename.endsWith(".docx")) {
+            XWPFDocument docx = new XWPFDocument(fileInputStream);
+            XWPFWordExtractor extractor = new XWPFWordExtractor(docx);
+            String text = extractor.getText();
+            document = Document.from(text);
+        }
+        else if (filename.endsWith(".pdf")) {
+            document = new ApachePdfBoxDocumentParser().parse(fileInputStream);
+        }
+        else {
+            throw new UnsupportedOperationException("不支持文件类型: " + filename);
+        }
+        DocumentByParagraphSplitter splitter = new DocumentByParagraphSplitter(1000,200);
+        return splitter.split(document);
+    }
+    interface Assistant {
+
+        String chat(String message);
+    }
 }

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

@@ -1,8 +1,8 @@
 <!--
  * @Author: wrh
  * @Date: 2025-04-07 14:14:05
- * @LastEditors: Please set LastEditors
- * @LastEditTime: 2025-06-26 11:44:43
+ * @LastEditors: wrh
+ * @LastEditTime: 2025-07-21 17:06:36
 -->
 <template>
   <div class="app-container">
@@ -418,7 +418,7 @@ const sendMessage = async () => {
   // 发送消息到后端，得到回答，并更新到数据库
   try {
     isLoading.value = true;
-    const answer = await getAnswer({ question: userMessage.input });
+    const answer = await getAnswer({ topicId: currentTopicId.value, question: userMessage.input });
     
     // 使用Vue的响应式更新方法
     const messageIndex = chatMessages.value.length - 1;
@@ -426,7 +426,7 @@ const sendMessage = async () => {
       // 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.resultContent,
+        output: answer[0].content,
         outputTime: proxy.parseTime(new Date(), '{y}-{m}-{d}')
       };
     }