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【功能新增】AI：增加 RedisVectorStore 向量库的接入

This commit is contained in:
YunaiV 2025-03-08 22:09:16 +08:00
parent 44bcc9476d
commit 588c9fe323
7 changed files with 103 additions and 71 deletions
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14
yudao-module-ai/yudao-module-ai-biz/src/main/java/cn/iocoder/yudao/module/ai/service/knowledge/AiKnowledgeSegmentServiceImpl.java
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@ -32,6 +32,7 @@ import org.springframework.stereotype.Service;
import java.util.Collections;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import static cn.iocoder.yudao.framework.common.exception.util.ServiceExceptionUtil.exception;
@ -48,9 +49,14 @@ import static cn.iocoder.yudao.module.ai.enums.ErrorCodeConstants.KNOWLEDGE_SEGM
@Slf4j
public class AiKnowledgeSegmentServiceImpl implements AiKnowledgeSegmentService {
public static final String VECTOR_STORE_METADATA_KNOWLEDGE_ID = "knowledgeId";
public static final String VECTOR_STORE_METADATA_DOCUMENT_ID = "documentId";
public static final String VECTOR_STORE_METADATA_SEGMENT_ID = "segmentId";
private static final String VECTOR_STORE_METADATA_KNOWLEDGE_ID = "knowledgeId";
private static final String VECTOR_STORE_METADATA_DOCUMENT_ID = "documentId";
private static final String VECTOR_STORE_METADATA_SEGMENT_ID = "segmentId";
private static final Map<String, Class<?>> VECTOR_STORE_METADATA_TYPES = Map.of(
VECTOR_STORE_METADATA_KNOWLEDGE_ID, String.class,
VECTOR_STORE_METADATA_DOCUMENT_ID, String.class,
VECTOR_STORE_METADATA_SEGMENT_ID, String.class);
@Resource
private AiKnowledgeSegmentMapper segmentMapper;
@ -257,7 +263,7 @@ public class AiKnowledgeSegmentServiceImpl implements AiKnowledgeSegmentService
}
private VectorStore getVectorStoreById(AiKnowledgeDO knowledge) {
return modelService.getOrCreateVectorStore(knowledge.getEmbeddingModelId());
return modelService.getOrCreateVectorStore(knowledge.getEmbeddingModelId(), VECTOR_STORE_METADATA_TYPES);
}
private VectorStore getVectorStoreById(Long knowledgeId) {

4
yudao-module-ai/yudao-module-ai-biz/src/main/java/cn/iocoder/yudao/module/ai/service/model/AiModelService.java
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@ -13,6 +13,7 @@ import org.springframework.ai.vectorstore.VectorStore;
import javax.annotation.Nullable;
import java.util.List;
import java.util.Map;
/**
* AI 模型 Service 接口
@ -125,8 +126,9 @@ public interface AiModelService {
* 获得 VectorStore 对象
*
* @param id 编号
* @param metadataFields 元数据的定义
* @return VectorStore 对象
*/
VectorStore getOrCreateVectorStore(Long id);
VectorStore getOrCreateVectorStore(Long id, Map<String, Class<?>> metadataFields);
}

8
yudao-module-ai/yudao-module-ai-biz/src/main/java/cn/iocoder/yudao/module/ai/service/model/AiModelServiceImpl.java
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@ -22,6 +22,7 @@ import org.springframework.stereotype.Service;
import org.springframework.validation.annotation.Validated;
import java.util.List;
import java.util.Map;
import static cn.iocoder.yudao.framework.common.exception.util.ServiceExceptionUtil.exception;
import static cn.iocoder.yudao.module.ai.enums.ErrorCodeConstants.*;
@ -151,7 +152,7 @@ public class AiModelServiceImpl implements AiModelService {
}
@Override
public VectorStore getOrCreateVectorStore(Long id) {
public VectorStore getOrCreateVectorStore(Long id, Map<String, Class<?>> metadataFields) {
// 获取模型 + 密钥
AiModelDO model = validateModel(id);
AiApiKeyDO apiKey = apiKeyService.validateApiKey(model.getKeyId());
@ -162,8 +163,9 @@ 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);
return modelFactory.getOrCreateVectorStore(QdrantVectorStore.class, embeddingModel);
// return modelFactory.getOrCreateVectorStore(SimpleVectorStore.class, embeddingModel, metadataFields);
return modelFactory.getOrCreateVectorStore(QdrantVectorStore.class, embeddingModel, metadataFields);
// return modelFactory.getOrCreateVectorStore(RedisVectorStore.class, embeddingModel, metadataFields);
}
}

31
yudao-module-ai/yudao-spring-boot-starter-ai/src/main/java/cn/iocoder/yudao/framework/ai/config/YudaoAiAutoConfiguration.java
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@ -12,12 +12,12 @@ 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.qdrant.QdrantVectorStoreProperties;
import org.springframework.ai.autoconfigure.vectorstore.redis.RedisVectorStoreProperties;
import org.springframework.ai.openai.OpenAiChatModel;
import org.springframework.ai.openai.OpenAiChatOptions;
import org.springframework.ai.openai.api.OpenAiApi;
import org.springframework.ai.tokenizer.JTokkitTokenCountEstimator;
import org.springframework.ai.tokenizer.TokenCountEstimator;
import org.springframework.ai.transformer.splitter.TokenTextSplitter;
import org.springframework.boot.autoconfigure.AutoConfiguration;
import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty;
import org.springframework.boot.context.properties.EnableConfigurationProperties;
@ -31,7 +31,8 @@ import org.springframework.context.annotation.Lazy;
*/
@AutoConfiguration
@EnableConfigurationProperties({YudaoAiProperties.class,
QdrantVectorStoreProperties.class // 解析 Qdrant 配置
QdrantVectorStoreProperties.class, // 解析 Qdrant 配置
RedisVectorStoreProperties.class, // 解析 Redis 配置
})
@Slf4j
public class YudaoAiAutoConfiguration {
@ -200,32 +201,6 @@ public class YudaoAiAutoConfiguration {
// return new TransformersEmbeddingModel(MetadataMode.EMBED);
// }
/**
* TODO @xin 默认版本先不弄，目前都先取对应的 EmbeddingModel
*/
// @Bean
// @Lazy // TODO 芋艿：临时注释，避免无法启动
// public RedisVectorStore vectorStore(TransformersEmbeddingModel embeddingModel, RedisVectorStoreProperties properties,
// RedisProperties redisProperties) {
// var config = RedisVectorStore.RedisVectorStoreConfig.builder()
// .withIndexName(properties.getIndex())
// .withPrefix(properties.getPrefix())
// .withMetadataFields(new RedisVectorStore.MetadataField("knowledgeId", Schema.FieldType.NUMERIC))
// .build();
//
// RedisVectorStore redisVectorStore = new RedisVectorStore(config, embeddingModel,
// new JedisPooled(redisProperties.getHost(), redisProperties.getPort()),
// properties.isInitializeSchema());
// redisVectorStore.afterPropertiesSet();
// return redisVectorStore;
// }
@Bean
@Lazy // TODO 芋艿：临时注释，避免无法启动
public TokenTextSplitter tokenTextSplitter() {
//TODO @xin 配置提取
return new TokenTextSplitter(500, 100, 5, 10000, true);
}
@Bean
@Lazy // TODO 芋艿：临时注释，避免无法启动
public TokenCountEstimator tokenCountEstimator() {

9
yudao-module-ai/yudao-spring-boot-starter-ai/src/main/java/cn/iocoder/yudao/framework/ai/core/factory/AiModelFactory.java
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@ -8,6 +8,8 @@ import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.image.ImageModel;
import org.springframework.ai.vectorstore.VectorStore;
import java.util.Map;
/**
* AI Model 模型工厂的接口类
*
@ -96,13 +98,16 @@ public interface AiModelFactory {
/**
* 基于指定配置，获得 VectorStore 对象
* <p>
*
* 如果不存在，则进行创建
*
* @param type 向量存储类型
* @param embeddingModel 向量模型
* @param metadataFields 元数据字段
* @return VectorStore 对象
*/
VectorStore getOrCreateVectorStore(Class<? extends VectorStore> type, EmbeddingModel embeddingModel);
VectorStore getOrCreateVectorStore(Class<? extends VectorStore> type,
EmbeddingModel embeddingModel,
Map<String, Class<?>> metadataFields);
}

100
yudao-module-ai/yudao-spring-boot-starter-ai/src/main/java/cn/iocoder/yudao/framework/ai/core/factory/AiModelFactoryImpl.java
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@ -19,6 +19,7 @@ import cn.iocoder.yudao.framework.ai.core.model.midjourney.api.MidjourneyApi;
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 cn.iocoder.yudao.framework.common.util.spring.SpringUtils;
import com.alibaba.cloud.ai.autoconfigure.dashscope.DashScopeAutoConfiguration;
import com.alibaba.cloud.ai.dashscope.api.DashScopeApi;
import com.alibaba.cloud.ai.dashscope.api.DashScopeImageApi;
@ -39,12 +40,13 @@ import org.springframework.ai.autoconfigure.openai.OpenAiAutoConfiguration;
import org.springframework.ai.autoconfigure.qianfan.QianFanAutoConfiguration;
import org.springframework.ai.autoconfigure.vectorstore.qdrant.QdrantVectorStoreAutoConfiguration;
import org.springframework.ai.autoconfigure.vectorstore.qdrant.QdrantVectorStoreProperties;
import org.springframework.ai.autoconfigure.vectorstore.redis.RedisVectorStoreAutoConfiguration;
import org.springframework.ai.autoconfigure.vectorstore.redis.RedisVectorStoreProperties;
import org.springframework.ai.autoconfigure.zhipuai.ZhiPuAiAutoConfiguration;
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;
@ -67,20 +69,26 @@ import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.observation.DefaultVectorStoreObservationConvention;
import org.springframework.ai.vectorstore.observation.VectorStoreObservationConvention;
import org.springframework.ai.vectorstore.qdrant.QdrantVectorStore;
import org.springframework.ai.vectorstore.redis.RedisVectorStore;
import org.springframework.ai.zhipuai.ZhiPuAiChatModel;
import org.springframework.ai.zhipuai.ZhiPuAiImageModel;
import org.springframework.ai.zhipuai.api.ZhiPuAiApi;
import org.springframework.ai.zhipuai.api.ZhiPuAiImageApi;
import org.springframework.beans.BeansException;
import org.springframework.beans.factory.ObjectProvider;
import org.springframework.boot.autoconfigure.data.redis.RedisProperties;
import org.springframework.web.client.RestClient;
import redis.clients.jedis.JedisPooled;
import java.io.File;
import java.time.Duration;
import java.util.List;
import java.util.Map;
import java.util.Timer;
import java.util.TimerTask;
import static cn.iocoder.yudao.framework.common.util.collection.CollectionUtils.convertList;
/**
* AI Model 模型工厂的实现类
*
@ -225,7 +233,9 @@ public class AiModelFactoryImpl implements AiModelFactory {
}
@Override
public VectorStore getOrCreateVectorStore(Class<? extends VectorStore> type, EmbeddingModel embeddingModel) {
public VectorStore getOrCreateVectorStore(Class<? extends VectorStore> type,
EmbeddingModel embeddingModel,
Map<String, Class<?>> metadataFields) {
String cacheKey = buildClientCacheKey(VectorStore.class, embeddingModel, type);
return Singleton.get(cacheKey, (Func0<VectorStore>) () -> {
if (type == SimpleVectorStore.class) {
@ -234,23 +244,10 @@ public class AiModelFactoryImpl implements AiModelFactory {
if (type == QdrantVectorStore.class) {
return buildQdrantVectorStore(embeddingModel);
}
if (type == RedisVectorStore.class) {
return buildRedisVectorStore(embeddingModel, metadataFields);
}
throw new IllegalArgumentException(StrUtil.format("未知类型({})", type));
// TODO @芋艿：先临时使用 store
// TODO @芋艿：@xin：后续看看，是不是切到阿里云之类的
// String prefix = StrUtil.format("{}#{}:", platform.getPlatform(), apiKey);
// var config = RedisVectorStore.RedisVectorStoreConfig.builder()
// .withIndexName(cacheKey)
// .withPrefix(prefix)
// .withMetadataFields(new RedisVectorStore.MetadataField("knowledgeId",
// Schema.FieldType.NUMERIC))
// .build();
// RedisProperties redisProperties = SpringUtils.getBean(RedisProperties.class);
// RedisVectorStore redisVectorStore = new RedisVectorStore(config,
// embeddingModel,
// new JedisPooled(redisProperties.getHost(), redisProperties.getPort()),
// true);
// redisVectorStore.afterPropertiesSet();
// return redisVectorStore;
});
}
@ -469,21 +466,65 @@ public class AiModelFactoryImpl implements AiModelFactory {
return vectorStore;
}
/**
* 参考 {@link QdrantVectorStoreAutoConfiguration} 的 vectorStore 方法
*/
@SneakyThrows
private QdrantVectorStore buildQdrantVectorStore(EmbeddingModel embeddingModel) {
QdrantVectorStoreAutoConfiguration configuration = new QdrantVectorStoreAutoConfiguration();
QdrantVectorStoreProperties vectorStoreProperties = SpringUtil.getBean(QdrantVectorStoreProperties.class);
QdrantVectorStoreProperties properties = SpringUtil.getBean(QdrantVectorStoreProperties.class);
// 参考 QdrantVectorStoreAutoConfiguration 实现，创建 QdrantClient 对象
QdrantGrpcClient.Builder grpcClientBuilder = QdrantGrpcClient.newBuilder(
vectorStoreProperties.getHost(), vectorStoreProperties.getPort(), vectorStoreProperties.isUseTls());
if (StrUtil.isNotEmpty(vectorStoreProperties.getApiKey())) {
grpcClientBuilder.withApiKey(vectorStoreProperties.getApiKey());
properties.getHost(), properties.getPort(), properties.isUseTls());
if (StrUtil.isNotEmpty(properties.getApiKey())) {
grpcClientBuilder.withApiKey(properties.getApiKey());
}
QdrantClient qdrantClient = new QdrantClient(grpcClientBuilder.build());
// 参考 QdrantVectorStoreAutoConfiguration 实现，实现 batchingStrategy
BatchingStrategy batchingStrategy = ReflectUtil.invoke(configuration, "batchingStrategy");
// 创建 QdrantVectorStore 对象
ObjectProvider<ObservationRegistry> observationRegistry = new ObjectProvider<>() {
QdrantVectorStore vectorStore = configuration.vectorStore(embeddingModel, properties, qdrantClient,
getObservationRegistry(), getCustomObservationConvention(),
ReflectUtil.invoke(configuration, "batchingStrategy"));
// 初始化索引
vectorStore.afterPropertiesSet();
return vectorStore;
}
/**
* 参考 {@link RedisVectorStoreAutoConfiguration} 的 vectorStore 方法
*/
private RedisVectorStore buildRedisVectorStore(EmbeddingModel embeddingModel,
Map<String, Class<?>> metadataFields) {
// 创建 JedisPooled 对象
RedisProperties redisProperties = SpringUtils.getBean(RedisProperties.class);
JedisPooled jedisPooled = new JedisPooled(redisProperties.getHost(), redisProperties.getPort());
// 创建 RedisVectorStoreProperties 对象
RedisVectorStoreAutoConfiguration configuration = new RedisVectorStoreAutoConfiguration();
RedisVectorStoreProperties properties = SpringUtil.getBean(RedisVectorStoreProperties.class);
RedisVectorStore redisVectorStore = RedisVectorStore.builder(jedisPooled, embeddingModel)
.indexName(properties.getIndex()).prefix(properties.getPrefix())
.initializeSchema(properties.isInitializeSchema())
.metadataFields(convertList(metadataFields.entrySet(), entry -> {
String fieldName = entry.getKey();
Class<?> fieldType = entry.getValue();
if (Number.class.isAssignableFrom(fieldType)) {
return RedisVectorStore.MetadataField.numeric(fieldName);
}
if (Boolean.class.isAssignableFrom(fieldType)) {
return RedisVectorStore.MetadataField.tag(fieldName);
}
return RedisVectorStore.MetadataField.text(fieldName);
}))
.observationRegistry(getObservationRegistry().getObject())
.customObservationConvention(getCustomObservationConvention().getObject())
.batchingStrategy(ReflectUtil.invoke(configuration, "batchingStrategy"))
.build();
// 初始化索引
redisVectorStore.afterPropertiesSet();
return redisVectorStore;
}
private static ObjectProvider<ObservationRegistry> getObservationRegistry() {
return new ObjectProvider<>() {
@Override
public ObservationRegistry getObject() throws BeansException {
@ -491,16 +532,15 @@ public class AiModelFactoryImpl implements AiModelFactory {
}
};
ObjectProvider <VectorStoreObservationConvention> customObservationConvention = new ObjectProvider<>() {
}
private static ObjectProvider<VectorStoreObservationConvention> getCustomObservationConvention() {
return new ObjectProvider<>() {
@Override
public VectorStoreObservationConvention getObject() throws BeansException {
return new DefaultVectorStoreObservationConvention();
}
};
return configuration.vectorStore(embeddingModel, vectorStoreProperties, qdrantClient,
observationRegistry, customObservationConvention, batchingStrategy);
}
}

8
yudao-server/src/main/resources/application.yaml
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@ -149,10 +149,12 @@ spring:
ai:
vectorstore: # 向量存储
redis:
index: default-index
prefix: "default:"
initialize-schema: true
index: knowledge_index # Redis 中向量索引的名称：用于存储和检索向量数据的索引标识符，所有相关的向量搜索操作都会基于这个索引进行
prefix: "knowledge_segment:" # Redis 中存储向量数据的键名前缀：这个前缀会添加到每个存储在 Redis 中的向量数据键名前，每个 document 都是一个 hash 结构
qdrant:
collection-name: knowledge_segment
initialize-schema: true
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