20250412 调整服务启动时embedding加载策略，改成通过调用接口(后续可能是个定时任务)的方式去插入向量数据而不是每次启动都插入

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

@@ -0,0 +1,42 @@
+package com.ai.config;
+
+import dev.langchain4j.community.model.dashscope.QwenEmbeddingModel;
+import dev.langchain4j.data.segment.TextSegment;
+import dev.langchain4j.store.embedding.EmbeddingStore;
+import dev.langchain4j.store.embedding.milvus.MilvusEmbeddingStore;
+import io.milvus.common.clientenum.ConsistencyLevelEnum;
+import io.milvus.param.IndexType;
+import io.milvus.param.MetricType;
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+
+@Configuration
+public class EmbeddingConfig {
+
+    @Value("${langchain4j.api-key}")
+    private String apiKey;
+
+    @Bean
+    public QwenEmbeddingModel embeddingModel() {
+        return QwenEmbeddingModel.builder().apiKey(apiKey).build();
+    }
+
+    @Bean
+    public EmbeddingStore<TextSegment> embeddingStore() {
+        return MilvusEmbeddingStore.builder()
+                .host("localhost")
+                .port(19530)
+                .collectionName("langchain4j_collection")
+                .dimension(1536)
+                .indexType(IndexType.FLAT)
+                .metricType(MetricType.COSINE)
+                .consistencyLevel(ConsistencyLevelEnum.EVENTUALLY)
+                .autoFlushOnInsert(true)
+                .idFieldName("id")
+                .textFieldName("text")
+                .metadataFieldName("metadata")
+                .vectorFieldName("vector")
+                .build();
+    }
+}

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

@@ -18,6 +18,7 @@ import dev.langchain4j.store.embedding.milvus.MilvusEmbeddingStore;
 import io.milvus.common.clientenum.ConsistencyLevelEnum;
 import io.milvus.param.IndexType;
 import io.milvus.param.MetricType;
+import jakarta.annotation.Resource;
 import lombok.RequiredArgsConstructor;
 import org.springframework.beans.factory.annotation.Value;
 import org.springframework.context.annotation.Bean;
@@ -38,35 +39,19 @@ public class SegmentConfig {
     @Value("${rag.path}")
     private String ragPath;
 
+    @Resource
+    private QwenEmbeddingModel embeddingModel;
+
+    @Resource
+    private EmbeddingStore<TextSegment> embeddingStore;
+
     @Bean
     public SegmentAssist segmentAssist() {
         ChatLanguageModel qwenModel = QwenChatModel.builder()
                 .apiKey(apiKey)
                 .modelName(model)
                 .build();
-        QwenEmbeddingModel embeddingModel = QwenEmbeddingModel.builder().apiKey(apiKey).build();
 //        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
-        EmbeddingStore<TextSegment> embeddingStore = MilvusEmbeddingStore.builder()
-                .host("localhost")
-                .port(19530)
-                .collectionName("langchain4j_collection")
-                .dimension(1536)
-                .indexType(IndexType.FLAT)
-                .metricType(MetricType.COSINE)
-                .consistencyLevel(ConsistencyLevelEnum.EVENTUALLY)
-                .autoFlushOnInsert(true)
-                .idFieldName("id")
-                .textFieldName("text")
-                .metadataFieldName("metadata")
-                .vectorFieldName("vector")
-                .build();
-        List<Document> documents = FileSystemDocumentLoader.loadDocuments(ragPath);
-        for (Document document : documents) {
-            DocumentByLineSplitter splitter = new DocumentByLineSplitter(300,30);
-            List<TextSegment> segments = splitter.split(document);
-            List<Embedding> embeddings = embeddingModel.embedAll(segments).content();
-            embeddingStore.addAll(embeddings, segments);
-        }
 
         ContentRetriever contentRetriever = EmbeddingStoreContentRetriever.builder()
                 .embeddingStore(embeddingStore)

src/main/java/com/ai/controller/MilvusController.java

@@ -1,10 +1,21 @@
 package com.ai.controller;
 
 import com.ai.service.SimilarService;
+import dev.langchain4j.community.model.dashscope.QwenEmbeddingModel;
+import dev.langchain4j.data.document.Document;
+import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
+import dev.langchain4j.data.document.splitter.DocumentByLineSplitter;
+import dev.langchain4j.data.embedding.Embedding;
+import dev.langchain4j.data.segment.TextSegment;
+import dev.langchain4j.store.embedding.EmbeddingStore;
+import jakarta.annotation.Resource;
 import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.beans.factory.annotation.Value;
 import org.springframework.web.bind.annotation.RequestMapping;
 import org.springframework.web.bind.annotation.RestController;
 
+import java.util.List;
+
 @RestController
 @RequestMapping("/milvus")
 public class MilvusController {
@@ -12,6 +23,15 @@ public class MilvusController {
     @Autowired
     private SimilarService similarService;
 
+    @Resource
+    private QwenEmbeddingModel embeddingModel;
+
+    @Resource
+    private EmbeddingStore<TextSegment> embeddingStore;
+
+    @Value("${rag.path}")
+    private String ragPath;
+
     @RequestMapping("/create")
     public String create() {
         similarService.createCollection();
@@ -23,4 +43,15 @@ public class MilvusController {
         similarService.insertProducts();
         return "vector create";
     }
+
+    @RequestMapping("/import")
+    public void ImportShopData() {
+        List<Document> documents = FileSystemDocumentLoader.loadDocuments(ragPath);
+        for (Document document : documents) {
+            DocumentByLineSplitter splitter = new DocumentByLineSplitter(150,30);
+            List<TextSegment> segments = splitter.split(document);
+            List<Embedding> embeddings = embeddingModel.embedAll(segments).content();
+            embeddingStore.addAll(embeddings, segments);
+        }
+    }
 }