20250408 增加向量数据库milvus配置，将知识库内容向量化后保存到milvus中

pom.xml

@@ -56,6 +56,17 @@
             <version>1.0.0-beta1</version>
         </dependency>
 
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-milvus</artifactId>
+            <version>1.0.0-beta1</version>
+        </dependency>
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-embeddings-all-minilm-l6-v2</artifactId>
+            <version>1.0.0-beta1</version>
+        </dependency>
+
         <dependency>
             <groupId>dev.langchain4j</groupId>
             <artifactId>langchain4j-community-dashscope-spring-boot-starter</artifactId>

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

@@ -39,15 +39,10 @@ public class AssistantInit {
         ChatLanguageModel qwenModel = QwenChatModel.builder()
                 .apiKey(apiKey)
                 .modelName(model)
-                .build();
+                .build();// for simplicity, we will use an in-memory one:
-        List<Document> documents = FileSystemDocumentLoader.loadDocuments(ragPath);
-        // for simplicity, we will use an in-memory one:
-        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
-        EmbeddingStoreIngestor.ingest(documents, embeddingStore);
         return AiServices.builder(Assist.class)
                 .chatMemoryProvider(memoryId -> MessageWindowChatMemory.withMaxMessages(10))
                 .chatLanguageModel(qwenModel)
-                .contentRetriever(EmbeddingStoreContentRetriever.from(embeddingStore))
 //                .tools(new MyCalculator())
                 .build();
     }

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

@@ -13,7 +13,11 @@ import dev.langchain4j.model.chat.ChatLanguageModel;
 import dev.langchain4j.rag.content.retriever.ContentRetriever;
 import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
 import dev.langchain4j.service.AiServices;
-import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
+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 lombok.RequiredArgsConstructor;
 import org.springframework.beans.factory.annotation.Value;
 import org.springframework.context.annotation.Bean;
@@ -41,7 +45,21 @@ public class SegmentConfig {
                 .modelName(model)
                 .build();
         QwenEmbeddingModel embeddingModel = QwenEmbeddingModel.builder().apiKey(apiKey).build();
-        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
+//        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);

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

@@ -95,7 +95,7 @@ public class LangChainController {
     @GetMapping("/embedd/chat")
     public String embeddChat(@RequestParam("input") String input) {
         System.out.println("start embedd chat...");
-        embeddingService.embedding(input);
+//        embeddingService.embedding(input);
         return segmentAssist.chat(input);
     }