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2026-08-04 cfbddbd52f8c4d35b06c8b7e7e68b6cf22ce400a 
 yudao-module-ai/src/main/java/cn/iocoder/yudao/module/ai/framework/ai/core/model/AiModelFactoryImpl.java


@@ -7,22 +7,20 @@


import cn.hutool.extra.spring.SpringUtil;


import cn.iocoder.yudao.module.ai.enums.model.AiPlatformEnum;


import cn.iocoder.yudao.module.ai.framework.ai.config.AiAutoConfiguration;


import cn.iocoder.yudao.module.ai.framework.ai.config.YudaoAiProperties;


import lombok.extern.slf4j.Slf4j;


import org.springframework.ai.chat.model.ChatModel;


import org.springframework.ai.embedding.BatchingStrategy;


import org.springframework.ai.embedding.EmbeddingModel;


import org.springframework.ai.embedding.TokenCountBatchingStrategy;


import org.springframework.ai.openai.OpenAiChatModel;


import org.springframework.ai.vectorstore.VectorStore;


import org.springframework.ai.vectorstore.milvus.MilvusVectorStore;


import org.springframework.ai.vectorstore.milvus.autoconfigure.MilvusServiceClientProperties;


import org.springframework.ai.vectorstore.milvus.autoconfigure.MilvusVectorStoreProperties;


import java.util.Map;




/**


 * AI Model 模型工厂实现类


 *


 * 使用 OpenAI 兼容接口对接通义千问 + Milvus 向量存储


 * 使用 OpenAI 兼容接口对接通义千问 + 可配置向量存储（Milvus）


 */


@Slf4j


public class AiModelFactoryImpl implements AiModelFactory {


@@ -61,7 +59,6 @@


    public VectorStore getOrCreateVectorStore(Class<? extends VectorStore> type,


                                              EmbeddingModel embeddingModel,


                                              Map<String, Class<?>> metadataFields) {


        // metadataFields 参与缓存 key，确保不同知识库使用不同配置时不会复用


        String cacheKey = buildCacheKey(VectorStore.class, embeddingModel, type, metadataFields.hashCode());


        return Singleton.get(cacheKey, (Func0<VectorStore>) () -> {


            if (type == MilvusVectorStore.class) {


@@ -72,20 +69,20 @@


    }




    private MilvusVectorStore buildMilvusVectorStore(EmbeddingModel embeddingModel) {


        MilvusVectorStoreProperties serverProperties = SpringUtil.getBean(MilvusVectorStoreProperties.class);


        MilvusServiceClientProperties clientProperties = SpringUtil.getBean(MilvusServiceClientProperties.class);


        YudaoAiProperties aiProperties = SpringUtil.getBean(YudaoAiProperties.class);


        YudaoAiProperties.VectorStore.Milvus milvusConfig = aiProperties.getVectorStore().getMilvus();




        var connectParam = io.milvus.param.ConnectParam.newBuilder()


                .withHost(clientProperties.getHost())


                .withPort(clientProperties.getPort())


                .withDatabaseName(serverProperties.getDatabaseName())


                .withHost(milvusConfig.getHost())


                .withPort(milvusConfig.getPort())


                .withDatabaseName(milvusConfig.getDatabaseName())


                .build();


        var milvusClient = new io.milvus.client.MilvusServiceClient(connectParam);




        MilvusVectorStore vectorStore = MilvusVectorStore.builder(milvusClient, embeddingModel)


                .databaseName(serverProperties.getDatabaseName())


                .collectionName(serverProperties.getCollectionName())


                .initializeSchema(serverProperties.isInitializeSchema())


                .databaseName(milvusConfig.getDatabaseName())


                .collectionName(milvusConfig.getCollectionName())


                .initializeSchema(milvusConfig.isInitializeSchema())


                .batchingStrategy(new TokenCountBatchingStrategy())


                .build();


        try {