20250407 rag知识库地址在applicaiton中配置

src/main/java/com/ai/config/AssistantInit.java

@@ -31,13 +31,16 @@ public class AssistantInit {
     @Value("${langchain4j.model}")
     private String model;
 
+    @Value("${rag.path}")
+    private String ragPath;
+
     @Bean
     public Assist init() {
         ChatLanguageModel qwenModel = QwenChatModel.builder()
                 .apiKey(apiKey)
                 .modelName(model)
                 .build();
-        List<Document> documents = FileSystemDocumentLoader.loadDocuments("E:\\ideaProject\\liang-ai");
+        List<Document> documents = FileSystemDocumentLoader.loadDocuments(ragPath);
         // for simplicity, we will use an in-memory one:
         InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
         EmbeddingStoreIngestor.ingest(documents, embeddingStore);

src/main/java/com/ai/config/SegmentConfig.java

@@ -31,6 +31,9 @@ public class SegmentConfig {
     @Value("${langchain4j.model}")
     private String model;
 
+    @Value("${rag.path}")
+    private String ragPath;
+
     @Bean
     public SegmentAssist segmentAssist() {
         ChatLanguageModel qwenModel = QwenChatModel.builder()
@@ -39,7 +42,7 @@ public class SegmentConfig {
                 .build();
         QwenEmbeddingModel embeddingModel = QwenEmbeddingModel.builder().apiKey(apiKey).build();
         InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
-        List<Document> documents = FileSystemDocumentLoader.loadDocuments("E:\\ideaProject\\liang-ai\\rag");
+        List<Document> documents = FileSystemDocumentLoader.loadDocuments(ragPath);
         for (Document document : documents) {
             DocumentByLineSplitter splitter = new DocumentByLineSplitter(300,30);
             List<TextSegment> segments = splitter.split(document);

src/main/java/com/ai/http/chat.http

@@ -2,7 +2,7 @@
 GET http://localhost:8080/langchain/chat?input=今天天气如何
 
 ### 测试 LangChainController 的 highlevel chat 接口
-GET http://localhost:8080/langchain/high/chat?input=请推荐3件DWALK商城的商品
+GET http://localhost:8080/langchain/high/chat?input=梁靖林的个人博客地址是什么
 
 ### 测试 LangChainController 的 highlevel memory chat 接口
 GET http://localhost:8080/langchain/high/memory-chat?memoryId=1&input=你好,我想要买电脑笔记本

src/main/java/com/ai/service/EmbeddingService.java

@@ -22,10 +22,13 @@ public class EmbeddingService {
     @Value("${langchain4j.api-key}")
     private String apiKey;
 
+    @Value("${rag.path}")
+    private String ragPath;
+
     public void embedding(String input) {
         QwenEmbeddingModel embeddingModel = QwenEmbeddingModel.builder().apiKey(apiKey).build();
         InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
-        List<Document> documents = FileSystemDocumentLoader.loadDocuments("E:\\ideaProject\\liang-ai\\rag");
+        List<Document> documents = FileSystemDocumentLoader.loadDocuments(ragPath);
         for (Document document : documents) {
             DocumentByLineSplitter splitter = new DocumentByLineSplitter(200,30);
             List<TextSegment> segments = splitter.split(document);

src/main/resources/application.properties

@@ -5,4 +5,6 @@ langchain4j.community.dashscope.chat-model.model-name=deepseek-v3
 
 
 langchain4j.api-key=sk-2f703a41fff0488e9b6888013d2ee58a
-langchain4j.model=deepseek-v3
+langchain4j.model=deepseek-v3
+
+rag.path=D:/IdeaProjects/liang-ai/rag