Merge pull request #226 from Cyclones-Y/main

完善Milvus向量存储策略实现

ruoyi-common/ruoyi-common-core/src/main/java/org/ruoyi/common/core/config/VectorStoreProperties.java

@@ -17,7 +17,7 @@ public class VectorStoreProperties {
     /**
      * 向量库类型
      */
-    private String type = "weaviate";
+    private String type;
 
     /**
      * Weaviate配置
@@ -34,17 +34,17 @@ public class VectorStoreProperties {
         /**
          * 协议
          */
-        private String protocol = "http";
+        private String protocol;
 
         /**
          * 主机地址
          */
-        private String host = "localhost:8080";
+        private String host;
 
         /**
          * 类名
          */
-        private String classname = "Document";
+        private String classname;
     }
 
     @Data
@@ -52,11 +52,11 @@ public class VectorStoreProperties {
         /**
          * 连接URL
          */
-        private String url = "http://localhost:19530";
+        private String url;
 
         /**
          * 集合名称
          */
-        private String collectionname = "knowledge_base";
+        private String collectionname;
     }
 }

ruoyi-modules-api/ruoyi-chat-api/src/main/java/org/ruoyi/domain/vo/ChatModelVo.java

@@ -70,6 +70,11 @@ public class ChatModelVo implements Serializable {
     @ExcelProperty(value = "是否显示")
     private String modelShow;
 
+    /**
+     * 模型维度
+     */
+    private Integer dimension;
+
     /**
      * 系统提示词
      */

ruoyi-modules-api/ruoyi-knowledge-api/pom.xml

@@ -80,6 +80,12 @@
             <version>2.6.4</version>
         </dependency>
 
+        <!-- LangChain4j Milvus Embedding Store -->
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-milvus</artifactId>
+        </dependency>
+
         <dependency>
             <groupId>dev.langchain4j</groupId>
             <artifactId>langchain4j-open-ai</artifactId>

ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/domain/bo/StoreEmbeddingBo.java

@@ -32,9 +32,9 @@ public class StoreEmbeddingBo {
     private List<String> fids;
 
     /**
-     * 向量库模型名称
+     * 向量库名称
      */
-    private String vectorModelName;
+    private String vectorStoreName;
 
     /**
      * 向量化模型id

ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/embedding/EmbeddingModelFactory.java

@@ -27,20 +27,23 @@ public class EmbeddingModelFactory {
     private final IChatModelService chatModelService;
 
     // 模型缓存，使用ConcurrentHashMap保证线程安全
-    private final Map<Long, BaseEmbedModelService> modelCache = new ConcurrentHashMap<>();
+    private final Map<String, BaseEmbedModelService> modelCache = new ConcurrentHashMap<>();
 
     /**
      * 创建嵌入模型实例
      * 如果模型已存在于缓存中，则直接返回；否则创建新的实例
      *
-     * @param embeddingModelId 嵌入模型的唯一标识ID
+     * @param embeddingModelName 嵌入模型名称
-     * @return BaseEmbedModelService 嵌入模型服务实例
+     * @param  dimension 模型维度大小
      */
-    public BaseEmbedModelService createModel(Long embeddingModelId) {
+    public BaseEmbedModelService createModel(String embeddingModelName, Integer dimension) {
-        return modelCache.computeIfAbsent(embeddingModelId, id -> {
+        return modelCache.computeIfAbsent(embeddingModelName, name -> {
-            ChatModelVo modelConfig = chatModelService.queryById(id);
+            ChatModelVo modelConfig = chatModelService.selectModelByName(embeddingModelName);
             if (modelConfig == null) {
-                throw new IllegalArgumentException("未找到模型配置，ID=" + id);
+                throw new IllegalArgumentException("未找到模型配置，name=" + name);
+            }
+            if (modelConfig.getDimension() != null) {
+                modelConfig.setDimension(dimension);
             }
             return createModelInstance(modelConfig.getProviderName(), modelConfig);
         });
@@ -49,22 +52,22 @@ public class EmbeddingModelFactory {
     /**
      * 检查模型是否支持多模态
      *
-     * @param embeddingModelId 嵌入模型的唯一标识ID
+     * @param embeddingModelName 嵌入模型名称
      * @return boolean 如果模型支持多模态则返回true，否则返回false
      */
-    public boolean isMultimodalModel(Long embeddingModelId) {
+    public boolean isMultimodalModel(String embeddingModelName) {
-        return createModel(embeddingModelId) instanceof MultiModalEmbedModelService;
+        return createModel(embeddingModelName, null) instanceof MultiModalEmbedModelService;
     }
 
     /**
      * 创建多模态嵌入模型实例
      *
-     * @param tenantId 租户ID
+     * @param embeddingModelName 嵌入模型名称
      * @return MultiModalEmbedModelService 多模态嵌入模型服务实例
      * @throws IllegalArgumentException 当模型不支持多模态时抛出
      */
-    public MultiModalEmbedModelService createMultimodalModel(Long tenantId) {
+    public MultiModalEmbedModelService createMultimodalModel(String embeddingModelName) {
-        BaseEmbedModelService model = createModel(tenantId);
+        BaseEmbedModelService model = createModel(embeddingModelName, null);
         if (model instanceof MultiModalEmbedModelService) {
             return (MultiModalEmbedModelService) model;
         }

ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/embedding/impl/OllamaEmbeddingProvider.java

@@ -30,6 +30,7 @@ public class OllamaEmbeddingProvider implements BaseEmbedModelService {
         return Set.of(ModalityType.TEXT);
     }
 
+    // ollama不能设置embedding维度，使用milvus时请注意！！创建向量表时需要先设定维度大小
     @Override
     public Response<List<Embedding>> embedAll(List<TextSegment> textSegments) {
         return OllamaEmbeddingModel.builder()

ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/embedding/impl/OpenAiEmbeddingProvider.java

@@ -37,6 +37,7 @@ public class OpenAiEmbeddingProvider implements BaseEmbedModelService {
                 .baseUrl(chatModelVo.getApiHost())
                 .apiKey(chatModelVo.getApiKey())
                 .modelName(chatModelVo.getModelName())
+                .dimensions(chatModelVo.getDimension())
                 .build()
                 .embedAll(textSegments);
     }

ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/embedding/impl/ZhiPuAiEmbeddingProvider.java

@@ -37,6 +37,7 @@ public class ZhiPuAiEmbeddingProvider implements BaseEmbedModelService {
                 .baseUrl(chatModelVo.getApiHost())
                 .apiKey(chatModelVo.getApiKey())
                 .model(chatModelVo.getModelName())
+                .dimensions(chatModelVo.getDimension())
                 .build()
                 .embedAll(textSegments);
     }

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

@@ -16,7 +16,7 @@ public interface VectorStoreService {
 
     List<String> getQueryVector(QueryVectorBo queryVectorBo);
 
-    void createSchema(String vectorModelName, String kid,String modelName);
+    void createSchema(String kid, String embeddingModelName);
 
     void removeById(String id,String modelName) throws ServiceException;
 

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

@@ -2,16 +2,13 @@ package org.ruoyi.service.impl;
 
 import lombok.RequiredArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
-import org.ruoyi.common.core.service.ConfigService;
 import org.ruoyi.domain.bo.QueryVectorBo;
 import org.ruoyi.domain.bo.StoreEmbeddingBo;
 import org.ruoyi.service.VectorStoreService;
-import org.ruoyi.service.strategy.VectorStoreStrategy;
 import org.ruoyi.service.strategy.VectorStoreStrategyFactory;
 import org.springframework.context.annotation.Primary;
 import org.springframework.stereotype.Service;
 import java.util.*;
-import java.util.stream.Collectors;
 
 /**
  * 向量库管理
@@ -30,22 +27,21 @@ public class VectorStoreServiceImpl implements VectorStoreService {
     /**
      * 获取当前配置的向量库策略
      */
-    private VectorStoreStrategy getCurrentStrategy() {
+    private VectorStoreService getCurrentStrategy() {
         return strategyFactory.getStrategy();
     }
 
     @Override
-    public void createSchema(String vectorModelName, String kid, String modelName) {
+    public void createSchema(String kid, String modelName) {
-        log.info("创建向量库schema: vectorModelName={}, kid={}, modelName={}", vectorModelName, kid, modelName);
+        VectorStoreService strategy = getCurrentStrategy();
-        VectorStoreStrategy strategy = getCurrentStrategy();
+        strategy.createSchema(kid, modelName);
-        strategy.createSchema(vectorModelName, kid, modelName);
     }
 
     @Override
     public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
         log.info("存储向量数据: kid={}, docId={}, 数据条数={}",
                 storeEmbeddingBo.getKid(), storeEmbeddingBo.getDocId(), storeEmbeddingBo.getChunkList().size());
-        VectorStoreStrategy strategy = getCurrentStrategy();
+        VectorStoreService strategy = getCurrentStrategy();
         strategy.storeEmbeddings(storeEmbeddingBo);
     }
 
@@ -53,28 +49,28 @@ public class VectorStoreServiceImpl implements VectorStoreService {
     public List<String> getQueryVector(QueryVectorBo queryVectorBo) {
         log.info("查询向量数据: kid={}, query={}, maxResults={}",
                 queryVectorBo.getKid(), queryVectorBo.getQuery(), queryVectorBo.getMaxResults());
-        VectorStoreStrategy strategy = getCurrentStrategy();
+        VectorStoreService strategy = getCurrentStrategy();
         return strategy.getQueryVector(queryVectorBo);
     }
 
     @Override
     public void removeById(String id, String modelName) {
         log.info("根据ID删除向量数据: id={}, modelName={}", id, modelName);
-        VectorStoreStrategy strategy = getCurrentStrategy();
+        VectorStoreService strategy = getCurrentStrategy();
         strategy.removeById(id, modelName);
     }
 
     @Override
     public void removeByDocId(String docId, String kid) {
         log.info("根据docId删除向量数据: docId={}, kid={}", docId, kid);
-        VectorStoreStrategy strategy = getCurrentStrategy();
+        VectorStoreService strategy = getCurrentStrategy();
         strategy.removeByDocId(docId, kid);
     }
 
     @Override
     public void removeByFid(String fid, String kid) {
         log.info("根据fid删除向量数据: fid={}, kid={}", fid, kid);
-        VectorStoreStrategy strategy = getCurrentStrategy();
+        VectorStoreService strategy = getCurrentStrategy();
         strategy.removeByFid(fid, kid);
     }
 }

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

@@ -8,40 +8,30 @@ import lombok.RequiredArgsConstructor;
 import lombok.SneakyThrows;
 import lombok.extern.slf4j.Slf4j;
 import org.ruoyi.common.core.config.VectorStoreProperties;
+import org.ruoyi.common.core.utils.StringUtils;
+import org.ruoyi.service.VectorStoreService;
+import org.ruoyi.embedding.EmbeddingModelFactory;
 
 /**
  * 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
-public abstract class AbstractVectorStoreStrategy implements VectorStoreStrategy {
+public abstract class AbstractVectorStoreStrategy implements VectorStoreService {
 
     protected final VectorStoreProperties vectorStoreProperties;
 
+    private final  EmbeddingModelFactory embeddingModelFactory;
+
     /**
      * 获取向量模型
      */
     @SneakyThrows
-    protected EmbeddingModel getEmbeddingModel(String modelName, String apiKey, String baseUrl) {
+    protected EmbeddingModel getEmbeddingModel(String modelName, Integer dimension) {
-        EmbeddingModel embeddingModel;
+        return embeddingModelFactory.createModel(modelName, dimension);
-        if ("quentinz/bge-large-zh-v1.5".equals(modelName)) {
-            embeddingModel = OllamaEmbeddingModel.builder()
-                    .baseUrl(baseUrl)
-                    .modelName(modelName)
-                    .build();
-        } else if ("baai/bge-m3".equals(modelName)) {
-            embeddingModel = OpenAiEmbeddingModel.builder()
-                    .apiKey(apiKey)
-                    .baseUrl(baseUrl)
-                    .modelName(modelName)
-                    .build();
-        } else {
-            throw new ServiceException("未找到对应向量化模型!");
-        }
-        return embeddingModel;
     }
 
     /**

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

@@ -1,18 +0,0 @@
-package org.ruoyi.service.strategy;
-
-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
- */
-public interface VectorStoreStrategy extends VectorStoreService {
-    
-    /**
-     * 获取向量库类型标识
-     * @return 向量库类型（如：weaviate, milvus）
-     */
-    String getVectorStoreType();
-}

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

@@ -6,6 +6,7 @@ import lombok.extern.slf4j.Slf4j;
 import org.ruoyi.common.core.config.VectorStoreProperties;
 import org.ruoyi.service.strategy.impl.MilvusVectorStoreStrategy;
 import org.ruoyi.service.strategy.impl.WeaviateVectorStoreStrategy;
+import org.ruoyi.service.VectorStoreService;
 import org.springframework.stereotype.Component;
 
 import java.util.HashMap;
@@ -15,7 +16,7 @@ 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
@@ -26,7 +27,7 @@ public class VectorStoreStrategyFactory {
     private final WeaviateVectorStoreStrategy weaviateStrategy;
     private final MilvusVectorStoreStrategy milvusStrategy;
 
-    private Map<String, VectorStoreStrategy> strategies;
+    private Map<String, VectorStoreService> strategies;
 
     @PostConstruct
     public void init() {
@@ -39,36 +40,18 @@ public class VectorStoreStrategyFactory {
     /**
      * 获取当前配置的向量库策略
      */
-    public VectorStoreStrategy getStrategy() {
+    public VectorStoreService getStrategy() {
         String vectorStoreType = vectorStoreProperties.getType();
         if (vectorStoreType == null || vectorStoreType.trim().isEmpty()) {
             vectorStoreType = "weaviate"; // 默认使用weaviate
         }
-        
+        VectorStoreService strategy = strategies.get(vectorStoreType.toLowerCase());
-        VectorStoreStrategy strategy = strategies.get(vectorStoreType.toLowerCase());
         if (strategy == null) {
             log.warn("未找到向量库策略: {}, 使用默认策略: weaviate", vectorStoreType);
             strategy = strategies.get("weaviate");
         }
-        
         log.debug("使用向量库策略: {}", vectorStoreType);
         return strategy;
     }
 
-    /**
-     * 根据类型获取向量库策略
-     */
-    public VectorStoreStrategy getStrategy(String type) {
-        if (type == null || type.trim().isEmpty()) {
-            return getStrategy();
-        }
-        
-        VectorStoreStrategy strategy = strategies.get(type.toLowerCase());
-        if (strategy == null) {
-            log.warn("未找到向量库策略: {}, 使用默认策略", type);
-            return getStrategy();
-        }
-        
-        return strategy;
-    }
 }

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

@@ -1,159 +1,75 @@
 package org.ruoyi.service.strategy.impl;
 
-import org.ruoyi.common.core.exception.ServiceException;
+import dev.langchain4j.data.document.Metadata;
 import dev.langchain4j.data.embedding.Embedding;
+import dev.langchain4j.data.segment.TextSegment;
 import dev.langchain4j.model.embedding.EmbeddingModel;
-import io.milvus.client.MilvusServiceClient;
+import dev.langchain4j.store.embedding.EmbeddingMatch;
-import io.milvus.common.clientenum.ConsistencyLevelEnum;
+import dev.langchain4j.store.embedding.EmbeddingSearchRequest;
-import io.milvus.grpc.*;
+import dev.langchain4j.store.embedding.EmbeddingStore;
-import io.milvus.param.*;
+import dev.langchain4j.store.embedding.filter.Filter;
-import io.milvus.param.collection.*;
+import dev.langchain4j.store.embedding.filter.MetadataFilterBuilder;
-import io.milvus.param.dml.DeleteParam;
+import dev.langchain4j.store.embedding.milvus.MilvusEmbeddingStore;
-import io.milvus.param.dml.InsertParam;
+import io.milvus.param.IndexType;
-import io.milvus.param.dml.SearchParam;
+import io.milvus.param.MetricType;
-import io.milvus.param.index.CreateIndexParam;
-import io.milvus.param.index.DescribeIndexParam;
-import io.milvus.response.DescCollResponseWrapper;
-import io.milvus.response.SearchResultsWrapper;
 import lombok.SneakyThrows;
 import lombok.extern.slf4j.Slf4j;
 import org.ruoyi.common.core.config.VectorStoreProperties;
 import org.ruoyi.domain.bo.QueryVectorBo;
 import org.ruoyi.domain.bo.StoreEmbeddingBo;
+import org.ruoyi.embedding.EmbeddingModelFactory;
 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.Map;
+import java.util.concurrent.ConcurrentHashMap;
+import java.util.stream.IntStream;
 
-/**
- * Milvus向量库策略实现
- *
- * @author ageer
- */
 @Slf4j
 @Component
 public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
-    private MilvusServiceClient milvusClient;
 
-    public MilvusVectorStoreStrategy(VectorStoreProperties vectorStoreProperties) {
+    private final Integer DIMENSION = 2048;
-        super(vectorStoreProperties);
+
+    public MilvusVectorStoreStrategy(VectorStoreProperties vectorStoreProperties, EmbeddingModelFactory embeddingModelFactory) {
+        super(vectorStoreProperties, embeddingModelFactory);
+    }
+
+    // 缓存不同集合与 autoFlush 配置的 Milvus 连接
+    private final Map<String, EmbeddingStore<TextSegment>> storeCache = new ConcurrentHashMap<>();
+
+    private EmbeddingStore<TextSegment> getMilvusStore(String collectionName, boolean autoFlushOnInsert) {
+        String key = collectionName + "|" + autoFlushOnInsert;
+        return storeCache.computeIfAbsent(key, k ->
+                MilvusEmbeddingStore.builder()
+                        .uri(vectorStoreProperties.getMilvus().getUrl())
+                        .collectionName(collectionName)
+                        .dimension(DIMENSION)
+                        .indexType(IndexType.IVF_FLAT)
+                        .metricType(MetricType.L2)
+                        .autoFlushOnInsert(autoFlushOnInsert)
+                        .idFieldName("id")
+                        .textFieldName("text")
+                        .metadataFieldName("metadata")
+                        .vectorFieldName("vector")
+                        .build()
+        );
     }
 
     @Override
-    public String getVectorStoreType() {
+    public void createSchema(String kid, String modelName) {
-        return "milvus";
-    }
-
-    @Override
-    public void createSchema(String vectorModelName, String kid, String modelName) {
-        String url = vectorStoreProperties.getMilvus().getUrl();
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-        
+        // 使用缓存获取连接以确保只初始化一次
-        // 创建Milvus客户端连接
+        EmbeddingStore<TextSegment> store = getMilvusStore(collectionName, true);
-        ConnectParam connectParam = ConnectParam.newBuilder()
+        log.info("Milvus集合初始化完成: {}", collectionName);
-                .withUri(url)
-                .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(1024) // 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);
-        }
     }
 
     @Override
     public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
-        createSchema(storeEmbeddingBo.getVectorModelName(), storeEmbeddingBo.getKid(), storeEmbeddingBo.getVectorModelName());
+        EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(), DIMENSION);
-        
-        EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(),
-                storeEmbeddingBo.getApiKey(), storeEmbeddingBo.getBaseUrl());
 
         List<String> chunkList = storeEmbeddingBo.getChunkList();
         List<String> fidList = storeEmbeddingBo.getFids();
@@ -161,177 +77,81 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
         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
+        // 复用连接，写入场景使用 autoFlush=false 以提升批量插入性能
-        List<InsertParam.Field> fields = new ArrayList<>();
+        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, false);
-        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++) {
+        IntStream.range(0, chunkList.size()).forEach(i -> {
             String text = chunkList.get(i);
             String fid = fidList.get(i);
+            Metadata metadata = new Metadata();
+            metadata.put("fid", fid);
+            metadata.put("kid", kid);
+            metadata.put("docId", docId);
+
+            TextSegment textSegment = TextSegment.from(text, metadata);
             Embedding embedding = embeddingModel.embed(text).content();
-            
+            embeddingStore.add(embedding, textSegment);
-            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());
-        }
-        
         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(), queryVectorBo.getVectorModelName());
+        EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(), DIMENSION);
-        
-        EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(),
-                queryVectorBo.getApiKey(), queryVectorBo.getBaseUrl());
 
         Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + queryVectorBo.getKid();
 
+        // 查询复用连接，autoFlush 对查询无影响，此处保持 true
+        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, true);
+
         List<String> resultList = new ArrayList<>();
-        
+        EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
-        // 加载集合到内存
+                .queryEmbedding(queryEmbedding)
-        LoadCollectionParam loadCollectionParam = LoadCollectionParam.newBuilder()
+                .maxResults(queryVectorBo.getMaxResults())
-                .withCollectionName(collectionName)
                 .build();
-        milvusClient.loadCollection(loadCollectionParam);
+        List<EmbeddingMatch<TextSegment>> matches = embeddingStore.search(request).matches();
-        
+        for (EmbeddingMatch<TextSegment> match : matches) {
-        // 准备查询向量
+            TextSegment segment = match.embedded();
-        List<List<Float>> searchVectors = new ArrayList<>();
+            if (segment != null) {
-        List<Float> queryVector = new ArrayList<>();
+                resultList.add(segment.text());
-        for (float f : queryEmbedding.vector()) {
-            queryVector.add(f);
-        }
-        searchVectors.add(queryVector);
-        
-        // 构建搜索参数
-        SearchParam searchParam = SearchParam.newBuilder()
-                .withCollectionName(collectionName)
-                .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());
             }
         }
-        }
-        
         return resultList;
     }
 
     @Override
     @SneakyThrows
     public void removeById(String id, String modelName) {
-        String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + id;
+        // 注意：此处原逻辑使用 collectionname + id，保持现状
-        
+        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(vectorStoreProperties.getMilvus().getCollectionname() + id, false);
-        // 删除整个集合
+        embeddingStore.remove(id);
-        DropCollectionParam dropCollectionParam = DropCollectionParam.newBuilder()
-                .withCollectionName(collectionName)
-                .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);
-        }
     }
 
     @Override
     public void removeByDocId(String docId, String kid) {
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-        
+        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, false);
-        String expr = "docId == \"" + docId + "\"";
+        Filter filter = MetadataFilterBuilder.metadataKey("docId").isEqualTo(docId);
-        DeleteParam deleteParam = DeleteParam.newBuilder()
+        embeddingStore.removeAll(filter);
-                .withCollectionName(collectionName)
+        log.info("Milvus成功删除 docId={} 的所有向量数据", docId);
-                .withExpr(expr)
-                .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());
-        }
     }
 
     @Override
     public void removeByFid(String fid, String kid) {
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-        
+        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, false);
-        String expr = "fid == \"" + fid + "\"";
+        Filter filter = MetadataFilterBuilder.metadataKey("fid").isEqualTo(fid);
-        DeleteParam deleteParam = DeleteParam.newBuilder()
+        embeddingStore.removeAll(filter);
-                .withCollectionName(collectionName)
+        log.info("Milvus成功删除 fid={} 的所有向量数据", fid);
-                .withExpr(expr)
-                .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());
     }
+
+    @Override
+    public String getVectorStoreType() {
+        return "milvus";
     }
 }

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

@@ -20,6 +20,7 @@ import lombok.extern.slf4j.Slf4j;
 import org.ruoyi.common.core.config.VectorStoreProperties;
 import org.ruoyi.domain.bo.QueryVectorBo;
 import org.ruoyi.domain.bo.StoreEmbeddingBo;
+import org.ruoyi.embedding.EmbeddingModelFactory;
 import org.ruoyi.service.strategy.AbstractVectorStoreStrategy;
 import org.springframework.stereotype.Component;
 import java.util.*;
@@ -27,7 +28,7 @@ import java.util.*;
 /**
  * Weaviate向量库策略实现
  *
- * @author ageer
+ * @author Yzm
  */
 @Slf4j
 @Component
@@ -35,8 +36,8 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
     private WeaviateClient client;
 
-    public WeaviateVectorStoreStrategy(VectorStoreProperties vectorStoreProperties) {
+    public WeaviateVectorStoreStrategy(VectorStoreProperties vectorStoreProperties, EmbeddingModelFactory embeddingModelFactory) {
-        super(vectorStoreProperties);
+        super(vectorStoreProperties, embeddingModelFactory);
     }
 
     @Override
@@ -45,7 +46,7 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
     }
 
     @Override
-    public void createSchema(String vectorModelName, String kid, String modelName) {
+    public void createSchema(String kid, String embeddingModelName) {
         String protocol = vectorStoreProperties.getWeaviate().getProtocol();
         String host = vectorStoreProperties.getWeaviate().getHost();
         String className = vectorStoreProperties.getWeaviate().getClassname() + kid;
@@ -84,9 +85,8 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
     @Override
     public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
-        createSchema(storeEmbeddingBo.getVectorModelName(), storeEmbeddingBo.getKid(), storeEmbeddingBo.getVectorModelName());
+        createSchema(storeEmbeddingBo.getKid(),storeEmbeddingBo.getEmbeddingModelName());
-        EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(),
+        EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(),  null);
-                storeEmbeddingBo.getApiKey(), storeEmbeddingBo.getBaseUrl());
         List<String> chunkList = storeEmbeddingBo.getChunkList();
         List<String> fidList = storeEmbeddingBo.getFids();
         String kid = storeEmbeddingBo.getKid();
@@ -118,9 +118,8 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
     @Override
     public List<String> getQueryVector(QueryVectorBo queryVectorBo) {
-        createSchema(queryVectorBo.getVectorModelName(), queryVectorBo.getKid(), queryVectorBo.getVectorModelName());
+        createSchema(queryVectorBo.getKid(),queryVectorBo.getEmbeddingModelName());
-        EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(),
+        EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(),null);
-                queryVectorBo.getApiKey(), queryVectorBo.getBaseUrl());
         Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
         float[] vector = queryEmbedding.vector();
         List<String> vectorStrings = new ArrayList<>();

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/chat/service/knowledge/KnowledgeInfoServiceImpl.java

@@ -9,6 +9,7 @@ import com.baomidou.mybatisplus.extension.plugins.pagination.Page;
 import lombok.RequiredArgsConstructor;
 import org.ruoyi.chain.loader.ResourceLoader;
 import org.ruoyi.chain.loader.ResourceLoaderFactory;
+import org.ruoyi.chat.enums.ChatModeType;
 import org.ruoyi.common.core.domain.model.LoginUser;
 import org.ruoyi.common.core.utils.MapstructUtils;
 import org.ruoyi.common.core.utils.StringUtils;
@@ -237,8 +238,7 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
             }
             baseMapper.insert(knowledgeInfo);
             if (knowledgeInfo != null) {
-                vectorStoreService.createSchema(knowledgeInfo.getVectorModelName(),String.valueOf(knowledgeInfo.getId()),
+                vectorStoreService.createSchema(String.valueOf(knowledgeInfo.getId()), bo.getEmbeddingModelName());
-                        bo.getVectorModelName());
             }
         } else {
             baseMapper.updateById(knowledgeInfo);
@@ -313,15 +313,18 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
                 .eq(KnowledgeInfo::getId, kid));
 
         // 通过向量模型查询模型信息
-        ChatModelVo chatModelVo = chatModelService.queryById(knowledgeInfoVo.getEmbeddingModelId());
+        ChatModelVo chatModelVo = chatModelService.selectModelByName(knowledgeInfoVo.getEmbeddingModelName());
-
+        // 未查到指定模型时，回退为向量分类最高优先级模型
+        if (chatModelVo == null) {
+            chatModelVo = chatModelService.selectModelByCategoryWithHighestPriority(ChatModeType.VECTOR.getCode());
+        }
         StoreEmbeddingBo storeEmbeddingBo = new StoreEmbeddingBo();
         storeEmbeddingBo.setKid(kid);
         storeEmbeddingBo.setDocId(docId);
         storeEmbeddingBo.setFids(fids);
         storeEmbeddingBo.setChunkList(chunkList);
-        storeEmbeddingBo.setVectorModelName(knowledgeInfoVo.getVectorModelName());
+        storeEmbeddingBo.setVectorStoreName(knowledgeInfoVo.getVectorModelName());
-        storeEmbeddingBo.setEmbeddingModelId(knowledgeInfoVo.getEmbeddingModelId());
+        storeEmbeddingBo.setEmbeddingModelName(knowledgeInfoVo.getEmbeddingModelName());
         storeEmbeddingBo.setApiKey(chatModelVo.getApiKey());
         storeEmbeddingBo.setBaseUrl(chatModelVo.getApiHost());
         vectorStoreService.storeEmbeddings(storeEmbeddingBo);