feat(embedding): 添加模型维度支持并重构向量存储策略

- 在ChatModelVo中添加dimension字段用于存储模型维度
- 重构EmbeddingModelFactory以支持按模型名称和维度创建实例
- 修改向量存储策略接口参数顺序并统一维度处理
- 为OpenAI和ZhiPuAI嵌入提供者添加维度配置支持
- 优化知识库服务中模型选择逻辑，添加回退机制

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/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);
+    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

@@ -32,10 +32,9 @@ public class VectorStoreServiceImpl implements VectorStoreService {
     }
 
     @Override
-    public void createSchema(String vectorModelName, String kid) {
+    public void createSchema(String kid, String modelName) {
-        log.info("创建向量库schema: vectorModelName={}, kid={}, modelName={}", vectorModelName, kid);
         VectorStoreService strategy = getCurrentStrategy();
-        strategy.createSchema(vectorModelName, kid);
+        strategy.createSchema(kid, modelName);
     }
 
     @Override

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

@@ -10,6 +10,7 @@ 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
@@ -23,34 +24,14 @@ 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 if (StringUtils.isNotEmpty(modelName)){
-            embeddingModel = OpenAiEmbeddingModel.builder()
-                    .apiKey(apiKey)
-                    .baseUrl(baseUrl)
-                    .dimensions(2048)
-                    .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/impl/MilvusVectorStoreStrategy.java

@@ -1,8 +1,8 @@
 package org.ruoyi.service.strategy.impl;
 
+import dev.langchain4j.data.document.Metadata;
 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 dev.langchain4j.store.embedding.EmbeddingMatch;
 import dev.langchain4j.store.embedding.EmbeddingSearchRequest;
@@ -17,22 +17,25 @@ 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.ArrayList;
 import java.util.List;
-import java.util.stream.IntStream;
-// 新增导入
 import java.util.Map;
 import java.util.concurrent.ConcurrentHashMap;
+import java.util.stream.IntStream;
 
 @Slf4j
 @Component
 public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
-    public MilvusVectorStoreStrategy(VectorStoreProperties vectorStoreProperties) {
+
-        super(vectorStoreProperties);
+    private final Integer DIMENSION = 2048;
+
+    public MilvusVectorStoreStrategy(VectorStoreProperties vectorStoreProperties, EmbeddingModelFactory embeddingModelFactory) {
+        super(vectorStoreProperties, embeddingModelFactory);
     }
 
     // 缓存不同集合与 autoFlush 配置的 Milvus 连接
@@ -44,7 +47,7 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
                 MilvusEmbeddingStore.builder()
                         .uri(vectorStoreProperties.getMilvus().getUrl())
                         .collectionName(collectionName)
-                        .dimension(2048)
+                        .dimension(DIMENSION)
                         .indexType(IndexType.IVF_FLAT)
                         .metricType(MetricType.L2)
                         .autoFlushOnInsert(autoFlushOnInsert)
@@ -57,12 +60,7 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
     }
 
     @Override
-    public String getVectorStoreType() {
+    public void createSchema(String kid, String modelName) {
-        return "milvus";
-    }
-
-    @Override
-    public void createSchema(String vectorModelName, String kid) {
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
         // 使用缓存获取连接以确保只初始化一次
         EmbeddingStore<TextSegment> store = getMilvusStore(collectionName, true);
@@ -71,8 +69,7 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
     @Override
     public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
-        EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(),
+        EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(), DIMENSION);
-                storeEmbeddingBo.getApiKey(), storeEmbeddingBo.getBaseUrl());
 
         List<String> chunkList = storeEmbeddingBo.getChunkList();
         List<String> fidList = storeEmbeddingBo.getFids();
@@ -104,8 +101,7 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
     @Override
     public List<String> getQueryVector(QueryVectorBo queryVectorBo) {
-        EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(),
+        EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(), DIMENSION);
-                queryVectorBo.getApiKey(), queryVectorBo.getBaseUrl());
 
         Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + queryVectorBo.getKid();
@@ -153,4 +149,9 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
         embeddingStore.removeAll(filter);
         log.info("Milvus成功删除 fid={} 的所有向量数据", fid);
     }
+
+    @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.*;
@@ -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) {
+    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.getVectorStoreName(), storeEmbeddingBo.getKid());
+        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());
+        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;
@@ -237,7 +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());
             }
         } else {
             baseMapper.updateById(knowledgeInfo);
@@ -312,8 +313,11 @@ 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);