refactor(vector): 简化createSchema接口参数并更新相关实现

移除createSchema方法中冗余的modelName参数
更新Milvus向量维度为2048以匹配新模型
添加对embeddingModelName参数的支持

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 vectorModelName, String kid);
 
     void removeById(String id,String modelName) throws ServiceException;
 

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

@@ -35,10 +35,10 @@ public class VectorStoreServiceImpl implements VectorStoreService {
     }
 
     @Override
-    public void createSchema(String vectorModelName, String kid, String modelName) {
+    public void createSchema(String vectorModelName, String kid) {
-        log.info("创建向量库schema: vectorModelName={}, kid={}, modelName={}", vectorModelName, kid, modelName);
+        log.info("创建向量库schema: vectorModelName={}, kid={}, modelName={}", vectorModelName, kid);
         VectorStoreStrategy strategy = getCurrentStrategy();
-        strategy.createSchema(vectorModelName, kid, modelName);
+        strategy.createSchema(vectorModelName, kid);
     }
 
     @Override

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

@@ -8,6 +8,7 @@ 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;
 
 /**
  * 向量库策略抽象基类
@@ -38,6 +39,13 @@ public abstract class AbstractVectorStoreStrategy implements VectorStoreStrategy
                     .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!");
         }

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

@@ -46,7 +46,7 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
     }
 
     @Override
-    public void createSchema(String vectorModelName, String kid, String modelName) {
+    public void createSchema(String vectorModelName, String kid) {
         String url = vectorStoreProperties.getMilvus().getUrl();
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
         
@@ -111,7 +111,7 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
             fields.add(FieldType.newBuilder()
                     .withName("vector")
                     .withDataType(DataType.FloatVector)
-                    .withDimension(1024) // 根据实际embedding维度调整
+                    .withDimension(2048) // 根据实际embedding维度调整
                     .build());
 
             // 创建集合
@@ -150,7 +150,7 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
     @Override
     public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
-        createSchema(storeEmbeddingBo.getVectorModelName(), storeEmbeddingBo.getKid(), storeEmbeddingBo.getVectorModelName());
+        createSchema(storeEmbeddingBo.getVectorModelName(), storeEmbeddingBo.getKid());
         
         EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(),
                 storeEmbeddingBo.getApiKey(), storeEmbeddingBo.getBaseUrl());
@@ -216,7 +216,7 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
     @Override
     public List<String> getQueryVector(QueryVectorBo queryVectorBo) {
-        createSchema(queryVectorBo.getVectorModelName(), queryVectorBo.getKid(), queryVectorBo.getVectorModelName());
+        createSchema(queryVectorBo.getVectorModelName(), queryVectorBo.getKid());
         
         EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(),
                 queryVectorBo.getApiKey(), queryVectorBo.getBaseUrl());
@@ -243,6 +243,7 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
         // 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())

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

@@ -45,7 +45,7 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
     }
 
     @Override
-    public void createSchema(String vectorModelName, String kid, String modelName) {
+    public void createSchema(String vectorModelName, String kid) {
         String protocol = vectorStoreProperties.getWeaviate().getProtocol();
         String host = vectorStoreProperties.getWeaviate().getHost();
         String className = vectorStoreProperties.getWeaviate().getClassname() + kid;
@@ -84,7 +84,7 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
     @Override
     public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
-        createSchema(storeEmbeddingBo.getVectorModelName(), storeEmbeddingBo.getKid(), storeEmbeddingBo.getVectorModelName());
+        createSchema(storeEmbeddingBo.getVectorModelName(), storeEmbeddingBo.getKid());
         EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(),
                 storeEmbeddingBo.getApiKey(), storeEmbeddingBo.getBaseUrl());
         List<String> chunkList = storeEmbeddingBo.getChunkList();
@@ -118,7 +118,7 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
     @Override
     public List<String> getQueryVector(QueryVectorBo queryVectorBo) {
-        createSchema(queryVectorBo.getVectorModelName(), queryVectorBo.getKid(), queryVectorBo.getVectorModelName());
+        createSchema(queryVectorBo.getVectorModelName(), queryVectorBo.getKid());
         EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(),
                 queryVectorBo.getApiKey(), queryVectorBo.getBaseUrl());
         Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();

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

@@ -237,8 +237,7 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
             }
             baseMapper.insert(knowledgeInfo);
             if (knowledgeInfo != null) {
-                vectorStoreService.createSchema(knowledgeInfo.getVectorModelName(),String.valueOf(knowledgeInfo.getId()),
+                vectorStoreService.createSchema(knowledgeInfo.getVectorModelName(),String.valueOf(knowledgeInfo.getId()));
-                        bo.getVectorModelName());
             }
         } else {
             baseMapper.updateById(knowledgeInfo);
@@ -322,6 +321,7 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
         storeEmbeddingBo.setChunkList(chunkList);
         storeEmbeddingBo.setVectorModelName(knowledgeInfoVo.getVectorModelName());
         storeEmbeddingBo.setEmbeddingModelId(knowledgeInfoVo.getEmbeddingModelId());
+        storeEmbeddingBo.setEmbeddingModelName(knowledgeInfoVo.getEmbeddingModelName());
         storeEmbeddingBo.setApiKey(chatModelVo.getApiKey());
         storeEmbeddingBo.setBaseUrl(chatModelVo.getApiHost());
         vectorStoreService.storeEmbeddings(storeEmbeddingBo);