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【优化】AI 知识库: VectorStore 获取 抽到 AiModelFactory
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xiaoxin
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@ -2,7 +2,6 @@ package cn.iocoder.yudao.module.ai.service.model;
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import cn.iocoder.yudao.framework.ai.core.enums.AiPlatformEnum;
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import cn.iocoder.yudao.framework.ai.core.factory.AiModelFactory;
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import cn.iocoder.yudao.framework.ai.core.factory.AiVectorStoreFactory;
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import cn.iocoder.yudao.framework.ai.core.model.midjourney.api.MidjourneyApi;
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import cn.iocoder.yudao.framework.ai.core.model.suno.api.SunoApi;
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import cn.iocoder.yudao.framework.common.enums.CommonStatusEnum;
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@ -39,8 +38,6 @@ public class AiApiKeyServiceImpl implements AiApiKeyService {
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@Resource
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private AiModelFactory modelFactory;
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@Resource
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private AiVectorStoreFactory vectorFactory;
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@Override
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public Long createApiKey(AiApiKeySaveReqVO createReqVO) {
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@ -149,7 +146,7 @@ public class AiApiKeyServiceImpl implements AiApiKeyService {
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public VectorStore getOrCreateVectorStore(Long id) {
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AiApiKeyDO apiKey = validateApiKey(id);
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AiPlatformEnum platform = AiPlatformEnum.validatePlatform(apiKey.getPlatform());
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return vectorFactory.getOrCreateVectorStore(getEmbeddingModel(id), platform, apiKey.getApiKey(), apiKey.getUrl());
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return modelFactory.getOrCreateVectorStore(getEmbeddingModel(id), platform, apiKey.getApiKey(), apiKey.getUrl());
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}
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}
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@ -2,8 +2,6 @@ package cn.iocoder.yudao.framework.ai.config;
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import cn.iocoder.yudao.framework.ai.core.factory.AiModelFactory;
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import cn.iocoder.yudao.framework.ai.core.factory.AiModelFactoryImpl;
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import cn.iocoder.yudao.framework.ai.core.factory.AiVectorStoreFactory;
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import cn.iocoder.yudao.framework.ai.core.factory.AiVectorStoreFactoryImpl;
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import cn.iocoder.yudao.framework.ai.core.model.deepseek.DeepSeekChatModel;
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import cn.iocoder.yudao.framework.ai.core.model.deepseek.DeepSeekChatOptions;
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import cn.iocoder.yudao.framework.ai.core.model.midjourney.api.MidjourneyApi;
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@ -38,11 +36,6 @@ public class YudaoAiAutoConfiguration {
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return new AiModelFactoryImpl();
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}
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@Bean
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public AiVectorStoreFactory aiVectorFactory() {
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return new AiVectorStoreFactoryImpl();
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}
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// ========== 各种 AI Client 创建 ==========
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@ -6,6 +6,7 @@ import cn.iocoder.yudao.framework.ai.core.model.suno.api.SunoApi;
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import org.springframework.ai.chat.model.ChatModel;
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import org.springframework.ai.embedding.EmbeddingModel;
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import org.springframework.ai.image.ImageModel;
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import org.springframework.ai.vectorstore.VectorStore;
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/**
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* AI Model 模型工厂的接口类
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@ -92,4 +93,17 @@ public interface AiModelFactory {
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*/
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EmbeddingModel getOrCreateEmbeddingModel(AiPlatformEnum platform, String apiKey, String url);
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/**
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* 基于指定配置，获得 VectorStore 对象
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* <p>
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* 如果不存在，则进行创建
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*
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* @param embeddingModel 嵌入模型
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* @param platform 平台
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* @param apiKey API KEY
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* @param url API URL
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* @return VectorStore 对象
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*/
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VectorStore getOrCreateVectorStore(EmbeddingModel embeddingModel, AiPlatformEnum platform, String apiKey, String url);
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}
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@ -13,6 +13,7 @@ import cn.iocoder.yudao.framework.ai.core.model.deepseek.DeepSeekChatModel;
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import cn.iocoder.yudao.framework.ai.core.model.midjourney.api.MidjourneyApi;
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import cn.iocoder.yudao.framework.ai.core.model.suno.api.SunoApi;
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import cn.iocoder.yudao.framework.ai.core.model.xinghuo.XingHuoChatModel;
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import cn.iocoder.yudao.framework.common.util.spring.SpringUtils;
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import com.alibaba.cloud.ai.tongyi.TongYiAutoConfiguration;
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import com.alibaba.cloud.ai.tongyi.TongYiConnectionProperties;
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import com.alibaba.cloud.ai.tongyi.chat.TongYiChatModel;
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@ -54,13 +55,17 @@ import org.springframework.ai.qianfan.api.QianFanApi;
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import org.springframework.ai.qianfan.api.QianFanImageApi;
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import org.springframework.ai.stabilityai.StabilityAiImageModel;
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import org.springframework.ai.stabilityai.api.StabilityAiApi;
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import org.springframework.ai.vectorstore.RedisVectorStore;
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import org.springframework.ai.vectorstore.VectorStore;
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import org.springframework.ai.zhipuai.ZhiPuAiChatModel;
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import org.springframework.ai.zhipuai.ZhiPuAiImageModel;
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import org.springframework.ai.zhipuai.api.ZhiPuAiApi;
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import org.springframework.ai.zhipuai.api.ZhiPuAiImageApi;
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import org.springframework.boot.autoconfigure.data.redis.RedisProperties;
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import org.springframework.retry.support.RetryTemplate;
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import org.springframework.web.client.ResponseErrorHandler;
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import org.springframework.web.client.RestClient;
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import redis.clients.jedis.JedisPooled;
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import java.util.List;
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@ -191,6 +196,28 @@ public class AiModelFactoryImpl implements AiModelFactory {
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});
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}
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@Override
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public VectorStore getOrCreateVectorStore(EmbeddingModel embeddingModel, AiPlatformEnum platform, String apiKey, String url) {
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String cacheKey = buildClientCacheKey(VectorStore.class, platform, apiKey, url);
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return Singleton.get(cacheKey, (Func0<VectorStore>) () -> {
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// TODO 芋艿 @xin 这两个配置取哪好呢
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// TODO 不同模型的向量维度可能会不一样，目前看貌似是以 index 来做区分的，维度不一样存不到一个 index 上
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// TODO 回复：好的哈
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String index = "default-index";
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String prefix = "default:";
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var config = RedisVectorStore.RedisVectorStoreConfig.builder()
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.withIndexName(index)
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.withPrefix(prefix)
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.build();
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RedisProperties redisProperties = SpringUtils.getBean(RedisProperties.class);
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RedisVectorStore redisVectorStore = new RedisVectorStore(config, embeddingModel,
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new JedisPooled(redisProperties.getHost(), redisProperties.getPort()),
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true);
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redisVectorStore.afterPropertiesSet();
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return redisVectorStore;
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});
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}
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private static String buildClientCacheKey(Class<?> clazz, Object... params) {
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if (ArrayUtil.isEmpty(params)) {
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return clazz.getName();
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@ -1,28 +0,0 @@
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package cn.iocoder.yudao.framework.ai.core.factory;
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import cn.iocoder.yudao.framework.ai.core.enums.AiPlatformEnum;
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import org.springframework.ai.embedding.EmbeddingModel;
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import org.springframework.ai.vectorstore.VectorStore;
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// TODO @xin：也放到 AiModelFactory 里面好了，后续改成 AiFactory
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/**
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* AI Vector 模型工厂的接口类
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*
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* @author xiaoxin
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*/
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public interface AiVectorStoreFactory {
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/**
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* 基于指定配置，获得 VectorStore 对象
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* <p>
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* 如果不存在，则进行创建
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*
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* @param embeddingModel 嵌入模型
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* @param platform 平台
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* @param apiKey API KEY
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* @param url API URL
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* @return VectorStore 对象
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*/
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VectorStore getOrCreateVectorStore(EmbeddingModel embeddingModel, AiPlatformEnum platform, String apiKey, String url);
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}
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@ -1,52 +0,0 @@
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package cn.iocoder.yudao.framework.ai.core.factory;
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import cn.hutool.core.lang.Singleton;
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import cn.hutool.core.lang.func.Func0;
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import cn.hutool.core.util.ArrayUtil;
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import cn.hutool.core.util.StrUtil;
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import cn.iocoder.yudao.framework.ai.core.enums.AiPlatformEnum;
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import cn.iocoder.yudao.framework.common.util.spring.SpringUtils;
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import org.springframework.ai.embedding.EmbeddingModel;
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import org.springframework.ai.vectorstore.RedisVectorStore;
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import org.springframework.ai.vectorstore.VectorStore;
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import org.springframework.boot.autoconfigure.data.redis.RedisProperties;
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import redis.clients.jedis.JedisPooled;
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/**
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* AI Vector 模型工厂的实现类
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* 使用 redisVectorStore 实现 VectorStore
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*
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* @author xiaoxin
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*/
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public class AiVectorStoreFactoryImpl implements AiVectorStoreFactory {
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@Override
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public VectorStore getOrCreateVectorStore(EmbeddingModel embeddingModel, AiPlatformEnum platform, String apiKey, String url) {
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String cacheKey = buildClientCacheKey(VectorStore.class, platform, apiKey, url);
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return Singleton.get(cacheKey, (Func0<VectorStore>) () -> {
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// TODO 芋艿 @xin 这两个配置取哪好呢
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// TODO 不同模型的向量维度可能会不一样，目前看貌似是以 index 来做区分的，维度不一样存不到一个 index 上
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// TODO 回复：好的哈
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String index = "default-index";
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String prefix = "default:";
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var config = RedisVectorStore.RedisVectorStoreConfig.builder()
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.withIndexName(index)
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.withPrefix(prefix)
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.build();
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RedisProperties redisProperties = SpringUtils.getBean(RedisProperties.class);
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RedisVectorStore redisVectorStore = new RedisVectorStore(config, embeddingModel,
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new JedisPooled(redisProperties.getHost(), redisProperties.getPort()),
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true);
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redisVectorStore.afterPropertiesSet();
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return redisVectorStore;
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});
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}
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private static String buildClientCacheKey(Class<?> clazz, Object... params) {
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if (ArrayUtil.isEmpty(params)) {
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return clazz.getName();
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}
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return StrUtil.format("{}#{}", clazz.getName(), ArrayUtil.join(params, "_"));
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}
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}
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