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【功能新增】AI：增加 MilvusVectorStore 向量库的接入

This commit is contained in:
YunaiV 2025-03-09 09:27:33 +08:00
parent 588c9fe323
commit f2ee2008e6
5 changed files with 75 additions and 22 deletions
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12
yudao-module-ai/yudao-module-ai-biz/src/main/java/cn/iocoder/yudao/module/ai/service/model/AiModelServiceImpl.java
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@ -17,7 +17,7 @@ import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.image.ImageModel;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.qdrant.QdrantVectorStore;
import org.springframework.ai.vectorstore.milvus.MilvusVectorStore;
import org.springframework.stereotype.Service;
import org.springframework.validation.annotation.Validated;
@ -114,8 +114,7 @@ public class AiModelServiceImpl implements AiModelService {
}
@Override
public List<AiModelDO> getModelListByStatusAndType(Integer status, Integer type,
String platform) {
public List<AiModelDO> getModelListByStatusAndType(Integer status, Integer type, String platform) {
return modelMapper.selectListByStatusAndType(status, type, platform);
}
@ -163,9 +162,10 @@ public class AiModelServiceImpl implements AiModelService {
platform, apiKey.getApiKey(), apiKey.getUrl(), model.getModel());
// 创建或获取 VectorStore 对象
// return modelFactory.getOrCreateVectorStore(SimpleVectorStore.class, embeddingModel, metadataFields);
return modelFactory.getOrCreateVectorStore(QdrantVectorStore.class, embeddingModel, metadataFields);
// return modelFactory.getOrCreateVectorStore(RedisVectorStore.class, embeddingModel, metadataFields);
// return modelFactory.getOrCreateVectorStore(SimpleVectorStore.class, embeddingModel, metadataFields);
// return modelFactory.getOrCreateVectorStore(QdrantVectorStore.class, embeddingModel, metadataFields);
// return modelFactory.getOrCreateVectorStore(RedisVectorStore.class, embeddingModel, metadataFields);
return modelFactory.getOrCreateVectorStore(MilvusVectorStore.class, embeddingModel, metadataFields);
}
}

1
yudao-module-ai/yudao-spring-boot-starter-ai/pom.xml
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@ -70,7 +70,6 @@
<groupId>${spring-ai.groupId}</groupId>
<artifactId>spring-ai-qdrant-store</artifactId>
<version>${spring-ai.version}</version>
<!-- <optional>true</optional>-->
</dependency>
<dependency>

20
yudao-module-ai/yudao-spring-boot-starter-ai/src/main/java/cn/iocoder/yudao/framework/ai/config/YudaoAiAutoConfiguration.java
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@ -11,8 +11,12 @@ import cn.iocoder.yudao.framework.ai.core.model.siliconflow.SiliconFlowChatModel
import cn.iocoder.yudao.framework.ai.core.model.suno.api.SunoApi;
import cn.iocoder.yudao.framework.ai.core.model.xinghuo.XingHuoChatModel;
import lombok.extern.slf4j.Slf4j;
import org.springframework.ai.autoconfigure.vectorstore.milvus.MilvusServiceClientProperties;
import org.springframework.ai.autoconfigure.vectorstore.milvus.MilvusVectorStoreProperties;
import org.springframework.ai.autoconfigure.vectorstore.qdrant.QdrantVectorStoreProperties;
import org.springframework.ai.autoconfigure.vectorstore.redis.RedisVectorStoreProperties;
import org.springframework.ai.embedding.BatchingStrategy;
import org.springframework.ai.embedding.TokenCountBatchingStrategy;
import org.springframework.ai.openai.OpenAiChatModel;
import org.springframework.ai.openai.OpenAiChatOptions;
import org.springframework.ai.openai.api.OpenAiApi;
@ -22,7 +26,6 @@ import org.springframework.boot.autoconfigure.AutoConfiguration;
import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty;
import org.springframework.boot.context.properties.EnableConfigurationProperties;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Lazy;
/**
* 芋道 AI 自动配置
@ -33,6 +36,7 @@ import org.springframework.context.annotation.Lazy;
@EnableConfigurationProperties({YudaoAiProperties.class,
QdrantVectorStoreProperties.class, // 解析 Qdrant 配置
RedisVectorStoreProperties.class, // 解析 Redis 配置
MilvusVectorStoreProperties.class, MilvusServiceClientProperties.class // 解析 Milvus 配置
})
@Slf4j
public class YudaoAiAutoConfiguration {
@ -193,18 +197,16 @@ public class YudaoAiAutoConfiguration {
return new SunoApi(yudaoAiProperties.getSuno().getBaseUrl());
}
// ========== rag 相关 ==========
// TODO @xin 免费版本
// @Bean
// @Lazy // TODO 芋艿：临时注释，避免无法启动」
// public TransformersEmbeddingModel transformersEmbeddingClient() {
// return new TransformersEmbeddingModel(MetadataMode.EMBED);
// }
// ========== RAG 相关 ==========
@Bean
@Lazy // TODO 芋艿：临时注释，避免无法启动
public TokenCountEstimator tokenCountEstimator() {
return new JTokkitTokenCountEstimator();
}
@Bean
public BatchingStrategy batchingStrategy() {
return new TokenCountBatchingStrategy();
}
}

55
yudao-module-ai/yudao-spring-boot-starter-ai/src/main/java/cn/iocoder/yudao/framework/ai/core/factory/AiModelFactoryImpl.java
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@ -5,7 +5,6 @@ import cn.hutool.core.lang.Assert;
import cn.hutool.core.lang.Singleton;
import cn.hutool.core.lang.func.Func0;
import cn.hutool.core.util.ArrayUtil;
import cn.hutool.core.util.ReflectUtil;
import cn.hutool.core.util.RuntimeUtil;
import cn.hutool.core.util.StrUtil;
import cn.hutool.extra.spring.SpringUtil;
@ -29,6 +28,7 @@ import com.alibaba.cloud.ai.dashscope.embedding.DashScopeEmbeddingOptions;
import com.alibaba.cloud.ai.dashscope.image.DashScopeImageModel;
import com.azure.ai.openai.OpenAIClientBuilder;
import io.micrometer.observation.ObservationRegistry;
import io.milvus.client.MilvusServiceClient;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import lombok.SneakyThrows;
@ -38,6 +38,10 @@ import org.springframework.ai.autoconfigure.azure.openai.AzureOpenAiConnectionPr
import org.springframework.ai.autoconfigure.ollama.OllamaAutoConfiguration;
import org.springframework.ai.autoconfigure.openai.OpenAiAutoConfiguration;
import org.springframework.ai.autoconfigure.qianfan.QianFanAutoConfiguration;
import org.springframework.ai.autoconfigure.vectorstore.milvus.MilvusServiceClientConnectionDetails;
import org.springframework.ai.autoconfigure.vectorstore.milvus.MilvusServiceClientProperties;
import org.springframework.ai.autoconfigure.vectorstore.milvus.MilvusVectorStoreAutoConfiguration;
import org.springframework.ai.autoconfigure.vectorstore.milvus.MilvusVectorStoreProperties;
import org.springframework.ai.autoconfigure.vectorstore.qdrant.QdrantVectorStoreAutoConfiguration;
import org.springframework.ai.autoconfigure.vectorstore.qdrant.QdrantVectorStoreProperties;
import org.springframework.ai.autoconfigure.vectorstore.redis.RedisVectorStoreAutoConfiguration;
@ -47,6 +51,7 @@ import org.springframework.ai.autoconfigure.zhipuai.ZhiPuAiConnectionProperties;
import org.springframework.ai.azure.openai.AzureOpenAiChatModel;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.document.MetadataMode;
import org.springframework.ai.embedding.BatchingStrategy;
import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.image.ImageModel;
import org.springframework.ai.ollama.OllamaChatModel;
@ -66,6 +71,7 @@ import org.springframework.ai.stabilityai.StabilityAiImageModel;
import org.springframework.ai.stabilityai.api.StabilityAiApi;
import org.springframework.ai.vectorstore.SimpleVectorStore;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.milvus.MilvusVectorStore;
import org.springframework.ai.vectorstore.observation.DefaultVectorStoreObservationConvention;
import org.springframework.ai.vectorstore.observation.VectorStoreObservationConvention;
import org.springframework.ai.vectorstore.qdrant.QdrantVectorStore;
@ -247,6 +253,9 @@ public class AiModelFactoryImpl implements AiModelFactory {
if (type == RedisVectorStore.class) {
return buildRedisVectorStore(embeddingModel, metadataFields);
}
if (type == MilvusVectorStore.class) {
return buildMilvusVectorStore(embeddingModel);
}
throw new IllegalArgumentException(StrUtil.format("未知类型({})", type));
});
}
@ -482,8 +491,7 @@ public class AiModelFactoryImpl implements AiModelFactory {
QdrantClient qdrantClient = new QdrantClient(grpcClientBuilder.build());
// 创建 QdrantVectorStore 对象
QdrantVectorStore vectorStore = configuration.vectorStore(embeddingModel, properties, qdrantClient,
getObservationRegistry(), getCustomObservationConvention(),
ReflectUtil.invoke(configuration, "batchingStrategy"));
getObservationRegistry(), getCustomObservationConvention(), getBatchingStrategy());
// 初始化索引
vectorStore.afterPropertiesSet();
return vectorStore;
@ -516,13 +524,48 @@ public class AiModelFactoryImpl implements AiModelFactory {
}))
.observationRegistry(getObservationRegistry().getObject())
.customObservationConvention(getCustomObservationConvention().getObject())
.batchingStrategy(ReflectUtil.invoke(configuration, "batchingStrategy"))
.batchingStrategy(getBatchingStrategy())
.build();
// 初始化索引
redisVectorStore.afterPropertiesSet();
return redisVectorStore;
}
/**
* 参考 {@link MilvusVectorStoreAutoConfiguration} 的 vectorStore 方法
*/
@SneakyThrows
private MilvusVectorStore buildMilvusVectorStore(EmbeddingModel embeddingModel) {
MilvusVectorStoreAutoConfiguration configuration = new MilvusVectorStoreAutoConfiguration();
// 获取配置属性
MilvusVectorStoreProperties serverProperties = SpringUtil.getBean(MilvusVectorStoreProperties.class);
MilvusServiceClientProperties clientProperties = SpringUtil.getBean(MilvusServiceClientProperties.class);
// 创建 MilvusServiceClient 对象
MilvusServiceClient milvusClient = configuration.milvusClient(serverProperties, clientProperties,
new MilvusServiceClientConnectionDetails() {
@Override
public String getHost() {
return clientProperties.getHost();
}
@Override
public int getPort() {
return clientProperties.getPort();
}
}
);
// 创建 MilvusVectorStore 对象
MilvusVectorStore vectorStore = configuration.vectorStore(milvusClient, embeddingModel, serverProperties,
getBatchingStrategy(), getObservationRegistry(), getCustomObservationConvention());
// 初始化索引
vectorStore.afterPropertiesSet();
return vectorStore;
}
private static ObjectProvider<ObservationRegistry> getObservationRegistry() {
return new ObjectProvider<>() {
@ -543,4 +586,8 @@ public class AiModelFactoryImpl implements AiModelFactory {
};
}
private static BatchingStrategy getBatchingStrategy() {
return SpringUtil.getBean(BatchingStrategy.class);
}
}

9
yudao-server/src/main/resources/application.yaml
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@ -157,8 +157,13 @@ spring:
collection-name: knowledge_segment # Qdrant 中向量集合的名称：用于存储向量数据的集合标识符，所有相关的向量操作都会在这个集合中进行
host: 127.0.0.1
port: 6334
use-tls: false
api-key:
milvus:
initialize-schema: true
database-name: default # Milvus 中数据库的名称
collection-name: knowledge_segment # Milvus 中集合的名称：用于存储向量数据的集合标识符，所有相关的向量操作都会在这个集合中进行
client:
host: 127.0.0.1
port: 19530
qianfan: # 文心一言
api-key: x0cuLZ7XsaTCU08vuJWO87Lg
secret-key: R9mYF9dl9KASgi5RUq0FQt3wRisSnOcK