refactor(milvus): 重构Milvus向量存储策略使用LangChain4j

将原有的直接Milvus客户端调用重构为使用LangChain4j的MilvusEmbeddingStore
简化了集合创建、数据存储和查询的实现逻辑
更新了相关依赖

ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/AbstractVectorStoreStrategy.java

@@ -14,7 +14,7 @@ import org.ruoyi.common.core.utils.StringUtils;
  * 向量库策略抽象基类
  * 提供公共的方法实现，如embedding模型获取等
  *
- * @author ageer
+ * @author Yzm
  */
 @Slf4j
 @RequiredArgsConstructor

ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/VectorStoreStrategy.java

@@ -6,7 +6,7 @@ import org.ruoyi.service.VectorStoreService;
  * 向量库策略接口
  * 继承VectorStoreService以避免重复定义相同的方法
  * 
- * @author ageer
+ * @author Yzm
  */
 public interface VectorStoreStrategy extends VectorStoreService {
     

ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/VectorStoreStrategyFactory.java

@@ -15,7 +15,7 @@ import java.util.Map;
  * 向量库策略工厂
  * 根据配置动态选择向量库实现
  *
- * @author ageer
+ * @author Yzm
  */
 @Slf4j
 @Component

ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/impl/MilvusVectorStoreStrategy.java

@@ -1,20 +1,17 @@
 package org.ruoyi.service.strategy.impl;
 
-import org.ruoyi.common.core.exception.ServiceException;
 import dev.langchain4j.data.embedding.Embedding;
+import dev.langchain4j.data.segment.TextSegment;
+import dev.langchain4j.data.document.Metadata;
 import dev.langchain4j.model.embedding.EmbeddingModel;
-import io.milvus.client.MilvusServiceClient;
-import io.milvus.common.clientenum.ConsistencyLevelEnum;
-import io.milvus.grpc.*;
-import io.milvus.param.*;
-import io.milvus.param.collection.*;
-import io.milvus.param.dml.DeleteParam;
-import io.milvus.param.dml.InsertParam;
-import io.milvus.param.dml.SearchParam;
-import io.milvus.param.index.CreateIndexParam;
-import io.milvus.param.index.DescribeIndexParam;
-import io.milvus.response.DescCollResponseWrapper;
-import io.milvus.response.SearchResultsWrapper;
+import dev.langchain4j.store.embedding.EmbeddingMatch;
+import dev.langchain4j.store.embedding.EmbeddingSearchRequest;
+import dev.langchain4j.store.embedding.EmbeddingStore;
+import dev.langchain4j.store.embedding.filter.Filter;
+import dev.langchain4j.store.embedding.filter.MetadataFilterBuilder;
+import dev.langchain4j.store.embedding.milvus.MilvusEmbeddingStore;
+import io.milvus.param.IndexType;
+import io.milvus.param.MetricType;
 import lombok.SneakyThrows;
 import lombok.extern.slf4j.Slf4j;
 import org.ruoyi.common.core.config.VectorStoreProperties;
@@ -23,19 +20,19 @@ import org.ruoyi.domain.bo.StoreEmbeddingBo;
 import org.ruoyi.service.strategy.AbstractVectorStoreStrategy;
 import org.springframework.stereotype.Component;
 
-import java.util.*;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.stream.IntStream;
 
 /**
  * Milvus向量库策略实现
  *
- * @author ageer
+ * @author Yzm
  */
 @Slf4j
 @Component
 public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
-    private MilvusServiceClient milvusClient;
-
     public MilvusVectorStoreStrategy(VectorStoreProperties vectorStoreProperties) {
         super(vectorStoreProperties);
     }
@@ -49,290 +46,164 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
     public void createSchema(String vectorModelName, String kid) {
         String url = vectorStoreProperties.getMilvus().getUrl();
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-        
-        // 创建Milvus客户端连接
-        ConnectParam connectParam = ConnectParam.newBuilder()
-                .withUri(url)
+        // 使用 LangChain4j 的 MilvusEmbeddingStore 来确保集合存在（按需创建）
+        MilvusEmbeddingStore store = MilvusEmbeddingStore.builder()
+                .uri(url)
+                .collectionName(collectionName)
+                .dimension(2048)
+                .indexType(IndexType.IVF_FLAT)
+                .metricType(MetricType.L2)
+                .autoFlushOnInsert(true)
+                .idFieldName("id")
+                .textFieldName("text")
+                .metadataFieldName("metadata")
+                .vectorFieldName("vector")
                 .build();
-        milvusClient = new MilvusServiceClient(connectParam);
-
-        // 检查集合是否存在
-        HasCollectionParam hasCollectionParam = HasCollectionParam.newBuilder()
-                .withCollectionName(collectionName)
-                .build();
-        
-        R<Boolean> hasCollectionResponse = milvusClient.hasCollection(hasCollectionParam);
-        if (hasCollectionResponse.getStatus() != R.Status.Success.getCode()) {
-            log.error("检查集合是否存在失败: {}", hasCollectionResponse.getMessage());
-            return;
-        }
-        
-        if (!hasCollectionResponse.getData()) {
-            // 创建字段
-            List<FieldType> fields = new ArrayList<>();
-            
-            // ID字段 (主键)
-            fields.add(FieldType.newBuilder()
-                    .withName("id")
-                    .withDataType(DataType.Int64)
-                    .withPrimaryKey(true)
-                    .withAutoID(true)
-                    .build());
-            
-            // 文本字段
-            fields.add(FieldType.newBuilder()
-                    .withName("text")
-                    .withDataType(DataType.VarChar)
-                    .withMaxLength(65535)
-                    .build());
-            
-            // fid字段
-            fields.add(FieldType.newBuilder()
-                    .withName("fid")
-                    .withDataType(DataType.VarChar)
-                    .withMaxLength(255)
-                    .build());
-            
-            // kid字段
-            fields.add(FieldType.newBuilder()
-                    .withName("kid")
-                    .withDataType(DataType.VarChar)
-                    .withMaxLength(255)
-                    .build());
-            
-            // docId字段
-            fields.add(FieldType.newBuilder()
-                    .withName("docId")
-                    .withDataType(DataType.VarChar)
-                    .withMaxLength(255)
-                    .build());
-            
-            // 向量字段
-            fields.add(FieldType.newBuilder()
-                    .withName("vector")
-                    .withDataType(DataType.FloatVector)
-                    .withDimension(2048) // 根据实际embedding维度调整
-                    .build());
-
-            // 创建集合
-            CreateCollectionParam createCollectionParam = CreateCollectionParam.newBuilder()
-                    .withCollectionName(collectionName)
-                    .withDescription("Knowledge base collection for " + kid)
-                    .withShardsNum(2)
-                    .withFieldTypes(fields)
-                    .build();
-
-            R<RpcStatus> createCollectionResponse = milvusClient.createCollection(createCollectionParam);
-            if (createCollectionResponse.getStatus() != R.Status.Success.getCode()) {
-                log.error("创建集合失败: {}", createCollectionResponse.getMessage());
-                return;
-            }
-
-            // 创建索引
-            CreateIndexParam createIndexParam = CreateIndexParam.newBuilder()
-                    .withCollectionName(collectionName)
-                    .withFieldName("vector")
-                    .withIndexType(IndexType.IVF_FLAT)
-                    .withMetricType(MetricType.L2)
-                    .withExtraParam("{\"nlist\":1024}")
-                    .build();
-
-            R<RpcStatus> createIndexResponse = milvusClient.createIndex(createIndexParam);
-            if (createIndexResponse.getStatus() != R.Status.Success.getCode()) {
-                log.error("创建索引失败: {}", createIndexResponse.getMessage());
-            } else {
-                log.info("Milvus集合和索引创建成功: {}", collectionName);
-            }
-        } else {
-            log.info("Milvus集合已存在: {}", collectionName);
-        }
+        log.info("Milvus集合初始化完成: {}", collectionName);
     }
 
     @Override
     public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
         createSchema(storeEmbeddingBo.getVectorModelName(), storeEmbeddingBo.getKid());
-        
+
         EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(),
                 storeEmbeddingBo.getApiKey(), storeEmbeddingBo.getBaseUrl());
-        
+
         List<String> chunkList = storeEmbeddingBo.getChunkList();
         List<String> fidList = storeEmbeddingBo.getFids();
         String kid = storeEmbeddingBo.getKid();
         String docId = storeEmbeddingBo.getDocId();
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-        
-        log.info("Milvus向量存储条数记录: " + chunkList.size());
+
+        log.info("Milvus向量存储条数记录: {}", chunkList.size());
         long startTime = System.currentTimeMillis();
-        
-        // 准备批量插入数据
-        List<InsertParam.Field> fields = new ArrayList<>();
-        List<String> textList = new ArrayList<>();
-        List<String> fidListData = new ArrayList<>();
-        List<String> kidList = new ArrayList<>();
-        List<String> docIdList = new ArrayList<>();
-        List<List<Float>> vectorList = new ArrayList<>();
-        
-        for (int i = 0; i < chunkList.size(); i++) {
+
+        EmbeddingStore<TextSegment> embeddingStore = MilvusEmbeddingStore.builder()
+                .uri(vectorStoreProperties.getMilvus().getUrl())
+                .collectionName(collectionName)
+                .dimension(2048)
+                .indexType(IndexType.IVF_FLAT)
+                .metricType(MetricType.L2)
+                .autoFlushOnInsert(false)
+                .idFieldName("id")
+                .textFieldName("text")
+                .metadataFieldName("metadata")
+                .vectorFieldName("vector")
+                .build();
+
+        IntStream.range(0, chunkList.size()).forEach(i -> {
             String text = chunkList.get(i);
             String fid = fidList.get(i);
             Embedding embedding = embeddingModel.embed(text).content();
-            
-            textList.add(text);
-            fidListData.add(fid);
-            kidList.add(kid);
-            docIdList.add(docId);
-            
-            List<Float> vector = new ArrayList<>();
-            for (float f : embedding.vector()) {
-                vector.add(f);
-            }
-            vectorList.add(vector);
-        }
-        
-        // 构建字段数据
-        fields.add(new InsertParam.Field("text", textList));
-        fields.add(new InsertParam.Field("fid", fidListData));
-        fields.add(new InsertParam.Field("kid", kidList));
-        fields.add(new InsertParam.Field("docId", docIdList));
-        fields.add(new InsertParam.Field("vector", vectorList));
-        
-        // 执行插入
-        InsertParam insertParam = InsertParam.newBuilder()
-                .withCollectionName(collectionName)
-                .withFields(fields)
-                .build();
-        
-        R<MutationResult> insertResponse = milvusClient.insert(insertParam);
-        if (insertResponse.getStatus() != R.Status.Success.getCode()) {
-            log.error("Milvus向量存储失败: {}", insertResponse.getMessage());
-            throw new ServiceException("Milvus向量存储失败");
-        } else {
-            log.info("Milvus向量存储成功，插入条数: {}", insertResponse.getData().getInsertCnt());
-        }
-        
+            Metadata metadata = new Metadata()
+                    .put("fid", fid)
+                    .put("kid", kid)
+                    .put("docId", docId);
+            TextSegment segment = TextSegment.from(text, metadata);
+            embeddingStore.add(embedding, segment);
+        });
+
         long endTime = System.currentTimeMillis();
-        log.info("Milvus向量存储完成消耗时间：" + (endTime - startTime) / 1000 + "秒");
+        log.info("Milvus向量存储完成消耗时间：{}秒", (endTime - startTime) / 1000);
     }
 
     @Override
     public List<String> getQueryVector(QueryVectorBo queryVectorBo) {
         createSchema(queryVectorBo.getVectorModelName(), queryVectorBo.getKid());
-        
+
         EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(),
                 queryVectorBo.getApiKey(), queryVectorBo.getBaseUrl());
-        
+
         Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + queryVectorBo.getKid();
-        
+
+        EmbeddingStore<TextSegment> embeddingStore = MilvusEmbeddingStore.builder()
+                .uri(vectorStoreProperties.getMilvus().getUrl())
+                .collectionName(collectionName)
+                .dimension(2048)
+                .indexType(IndexType.IVF_FLAT)
+                .metricType(MetricType.L2)
+                .autoFlushOnInsert(true)
+                .idFieldName("id")
+                .textFieldName("text")
+                .metadataFieldName("metadata")
+                .vectorFieldName("vector")
+                .build();
+
         List<String> resultList = new ArrayList<>();
-        
-        // 加载集合到内存
-        LoadCollectionParam loadCollectionParam = LoadCollectionParam.newBuilder()
-                .withCollectionName(collectionName)
+        EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
+                .queryEmbedding(queryEmbedding)
+                .maxResults(queryVectorBo.getMaxResults())
                 .build();
-        milvusClient.loadCollection(loadCollectionParam);
-        
-        // 准备查询向量
-        List<List<Float>> searchVectors = new ArrayList<>();
-        List<Float> queryVector = new ArrayList<>();
-        for (float f : queryEmbedding.vector()) {
-            queryVector.add(f);
-        }
-        searchVectors.add(queryVector);
-        
-        // 构建搜索参数
-        SearchParam searchParam = SearchParam.newBuilder()
-                .withCollectionName(collectionName)
-                // 匹配方法
-                .withMetricType(MetricType.L2)
-                .withOutFields(Arrays.asList("text", "fid", "kid", "docId"))
-                .withTopK(queryVectorBo.getMaxResults())
-                .withVectors(searchVectors)
-                .withVectorFieldName("vector")
-                .withParams("{\"nprobe\":10}")
-                .build();
-        
-        R<SearchResults> searchResponse = milvusClient.search(searchParam);
-        if (searchResponse.getStatus() != R.Status.Success.getCode()) {
-            log.error("Milvus查询失败: {}", searchResponse.getMessage());
-            return resultList;
-        }
-        
-        SearchResultsWrapper wrapper = new SearchResultsWrapper(searchResponse.getData().getResults());
-        
-        // 遍历搜索结果
-        for (int i = 0; i < wrapper.getIDScore(0).size(); i++) {
-            SearchResultsWrapper.IDScore idScore = wrapper.getIDScore(0).get(i);
-            
-            // 获取text字段数据
-            List<?> textFieldData = wrapper.getFieldData("text", 0);
-            if (textFieldData != null && i < textFieldData.size()) {
-                Object textObj = textFieldData.get(i);
-                if (textObj != null) {
-                    resultList.add(textObj.toString());
-                    log.debug("找到相似文本，ID: {}, 距离: {}, 内容: {}", 
-                            idScore.getLongID(), idScore.getScore(), textObj.toString());
-                }
+        List<EmbeddingMatch<TextSegment>> matches = embeddingStore.search(request).matches();
+        for (EmbeddingMatch<TextSegment> match : matches) {
+            TextSegment segment = match.embedded();
+            if (segment != null) {
+                resultList.add(segment.text());
             }
         }
-        
         return resultList;
     }
 
     @Override
     @SneakyThrows
     public void removeById(String id, String modelName) {
+        String url = vectorStoreProperties.getMilvus().getUrl();
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + id;
-        
-        // 删除整个集合
-        DropCollectionParam dropCollectionParam = DropCollectionParam.newBuilder()
-                .withCollectionName(collectionName)
+        MilvusEmbeddingStore store = MilvusEmbeddingStore.builder()
+                .uri(url)
+                .collectionName(collectionName)
+                .dimension(2048)
+                .indexType(IndexType.IVF_FLAT)
+                .metricType(MetricType.L2)
+                .autoFlushOnInsert(true)
+                .idFieldName("id")
+                .textFieldName("text")
+                .metadataFieldName("metadata")
+                .vectorFieldName("vector")
                 .build();
-        
-        R<RpcStatus> dropResponse = milvusClient.dropCollection(dropCollectionParam);
-        if (dropResponse.getStatus() != R.Status.Success.getCode()) {
-            log.error("Milvus集合删除失败: {}", dropResponse.getMessage());
-            throw new ServiceException("Milvus集合删除失败");
-        } else {
-            log.info("Milvus集合删除成功: {}", collectionName);
-        }
+        // 修正：MilvusEmbeddingStore 的 dropCollection 需要传入集合名
+        store.dropCollection(collectionName);
+        log.info("Milvus集合删除成功: {}", collectionName);
     }
 
     @Override
     public void removeByDocId(String docId, String kid) {
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-        
-        String expr = "docId == \"" + docId + "\"";
-        DeleteParam deleteParam = DeleteParam.newBuilder()
-                .withCollectionName(collectionName)
-                .withExpr(expr)
+        EmbeddingStore<TextSegment> embeddingStore = MilvusEmbeddingStore.builder()
+                .uri(vectorStoreProperties.getMilvus().getUrl())
+                .collectionName(collectionName)
+                .dimension(2048)
+                .indexType(IndexType.IVF_FLAT)
+                .metricType(MetricType.L2)
+                .autoFlushOnInsert(false)
+                .idFieldName("id")
+                .textFieldName("text")
+                .metadataFieldName("metadata")
+                .vectorFieldName("vector")
                 .build();
-        
-        R<MutationResult> deleteResponse = milvusClient.delete(deleteParam);
-        if (deleteResponse.getStatus() != R.Status.Success.getCode()) {
-            log.error("Milvus删除失败: {}", deleteResponse.getMessage());
-            throw new ServiceException("Milvus删除失败");
-        } else {
-            log.info("Milvus成功删除 docId={} 的所有向量数据，删除条数: {}", docId, deleteResponse.getData().getDeleteCnt());
-        }
+        Filter filter = MetadataFilterBuilder.metadataKey("docId").isEqualTo(docId);
+        embeddingStore.removeAll(filter);
+        log.info("Milvus成功删除 docId={} 的所有向量数据", docId);
     }
 
     @Override
     public void removeByFid(String fid, String kid) {
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-        
-        String expr = "fid == \"" + fid + "\"";
-        DeleteParam deleteParam = DeleteParam.newBuilder()
-                .withCollectionName(collectionName)
-                .withExpr(expr)
+        EmbeddingStore<TextSegment> embeddingStore = MilvusEmbeddingStore.builder()
+                .uri(vectorStoreProperties.getMilvus().getUrl())
+                .collectionName(collectionName)
+                .dimension(2048)
+                .indexType(IndexType.IVF_FLAT)
+                .metricType(MetricType.L2)
+                .autoFlushOnInsert(false)
+                .idFieldName("id")
+                .textFieldName("text")
+                .metadataFieldName("metadata")
+                .vectorFieldName("vector")
                 .build();
-        
-        R<MutationResult> deleteResponse = milvusClient.delete(deleteParam);
-        if (deleteResponse.getStatus() != R.Status.Success.getCode()) {
-            log.error("Milvus删除失败: {}", deleteResponse.getMessage());
-            throw new ServiceException("Milvus删除失败");
-        } else {
-            log.info("Milvus成功删除 fid={} 的所有向量数据，删除条数: {}", fid, deleteResponse.getData().getDeleteCnt());
-        }
+        Filter filter = MetadataFilterBuilder.metadataKey("fid").isEqualTo(fid);
+        embeddingStore.removeAll(filter);
+        log.info("Milvus成功删除 fid={} 的所有向量数据", fid);
     }
-}
+}