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【新增】AI 知识库: AiVectorFactory 负责管理不同 EmbeddingModel 对应的 VectorStore
This commit is contained in:
xiaoxin
parent
024109dac9
commit
f97fb0a8fe
@ -45,7 +45,7 @@ public class AiKnowledgeDO extends BaseDO {
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@TableField(typeHandler = JacksonTypeHandler.class)
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private List<Long> visibilityPermissions;
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/**
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* 嵌入模型编号，高质量模式时维护
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* 嵌入模型编号
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*/
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private Long modelId;
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/**
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@ -24,10 +24,14 @@ public class AiKnowledgeSegmentDO extends BaseDO {
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* 向量库的编号
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*/
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private String vectorId;
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// TODO @新：knowledgeId 加个，会方便点
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/**
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* 知识库编号
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* 关联 {@link AiKnowledgeDO#getId()}
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*/
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private Long knowledgeId;
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/**
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* 文档编号
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*
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* <p>
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* 关联 {@link AiKnowledgeDocumentDO#getId()}
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*/
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private Long documentId;
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@ -6,24 +6,27 @@ import cn.iocoder.yudao.framework.common.enums.CommonStatusEnum;
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import cn.iocoder.yudao.framework.common.util.collection.CollectionUtils;
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import cn.iocoder.yudao.framework.common.util.object.BeanUtils;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.AiKnowledgeDocumentCreateReqVO;
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import cn.iocoder.yudao.module.ai.dal.dataobject.knowledge.AiKnowledgeDO;
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import cn.iocoder.yudao.module.ai.dal.dataobject.knowledge.AiKnowledgeDocumentDO;
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import cn.iocoder.yudao.module.ai.dal.dataobject.knowledge.AiKnowledgeSegmentDO;
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import cn.iocoder.yudao.module.ai.dal.dataobject.model.AiChatModelDO;
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import cn.iocoder.yudao.module.ai.dal.mysql.knowledge.AiKnowledgeDocumentMapper;
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import cn.iocoder.yudao.module.ai.dal.mysql.knowledge.AiKnowledgeSegmentMapper;
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import cn.iocoder.yudao.module.ai.enums.knowledge.AiKnowledgeDocumentStatusEnum;
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import cn.iocoder.yudao.module.ai.service.model.AiApiKeyService;
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import cn.iocoder.yudao.module.ai.service.model.AiChatModelService;
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import jakarta.annotation.Resource;
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import lombok.extern.slf4j.Slf4j;
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import org.springframework.ai.document.Document;
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import org.springframework.ai.reader.tika.TikaDocumentReader;
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import org.springframework.ai.tokenizer.TokenCountEstimator;
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import org.springframework.ai.transformer.splitter.TokenTextSplitter;
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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.core.io.ByteArrayResource;
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import org.springframework.stereotype.Service;
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import org.springframework.transaction.annotation.Transactional;
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import java.util.List;
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import java.util.Objects;
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/**
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* AI 知识库-文档 Service 实现类
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@ -42,9 +45,14 @@ public class AiKnowledgeDocumentServiceImpl implements AiKnowledgeDocumentServic
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@Resource
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private TokenTextSplitter tokenTextSplitter;
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@Resource
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private TokenCountEstimator TOKEN_COUNT_ESTIMATOR;
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private TokenCountEstimator tokenCountEstimator;
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@Resource
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private RedisVectorStore vectorStore;
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private AiApiKeyService apiKeyService;
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@Resource
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private AiKnowledgeService knowledgeService;
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@Resource
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private AiChatModelService chatModelService;
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// TODO 芋艿：需要 review 下，代码格式；
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@ -53,18 +61,18 @@ public class AiKnowledgeDocumentServiceImpl implements AiKnowledgeDocumentServic
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public Long createKnowledgeDocument(AiKnowledgeDocumentCreateReqVO createReqVO) {
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// 1.1 下载文档
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String url = createReqVO.getUrl();
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TikaDocumentReader loader = new TikaDocumentReader(downloadFile(url));
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// 1.2 加载文档
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TikaDocumentReader loader = new TikaDocumentReader(downloadFile(url));
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List<Document> documents = loader.get();
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Document document = CollUtil.getFirst(documents);
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// TODO @xin：是不是不存在，就抛出异常呀；厚泽 return 呀；
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Integer tokens = Objects.nonNull(document) ? TOKEN_COUNT_ESTIMATOR.estimate(document.getContent()) : 0;
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Integer wordCount = Objects.nonNull(document) ? document.getContent().length() : 0;
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String content = document.getContent();
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Integer tokens = tokenCountEstimator.estimate(content);
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Integer wordCount = content.length();
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// 1.3 文档记录入库
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AiKnowledgeDocumentDO documentDO = BeanUtils.toBean(createReqVO, AiKnowledgeDocumentDO.class)
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.setTokens(tokens).setWordCount(wordCount)
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.setStatus(CommonStatusEnum.ENABLE.getStatus()).setSliceStatus(AiKnowledgeDocumentStatusEnum.SUCCESS.getStatus());
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// 1.2 文档记录入库
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documentMapper.insert(documentDO);
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Long documentId = documentDO.getId();
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if (CollUtil.isEmpty(documents)) {
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@ -75,11 +83,16 @@ public class AiKnowledgeDocumentServiceImpl implements AiKnowledgeDocumentServic
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List<Document> segments = tokenTextSplitter.apply(documents);
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// 2.2 分段内容入库
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List<AiKnowledgeSegmentDO> segmentDOList = CollectionUtils.convertList(segments,
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segment -> new AiKnowledgeSegmentDO().setContent(segment.getContent()).setDocumentId(documentId)
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.setTokens(TOKEN_COUNT_ESTIMATOR.estimate(segment.getContent())).setWordCount(segment.getContent().length())
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segment -> new AiKnowledgeSegmentDO().setContent(segment.getContent()).setDocumentId(documentId).setKnowledgeId(createReqVO.getKnowledgeId())
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.setTokens(tokenCountEstimator.estimate(segment.getContent())).setWordCount(segment.getContent().length())
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.setStatus(CommonStatusEnum.ENABLE.getStatus()));
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segmentMapper.insertBatch(segmentDOList);
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// 3 向量化并存储
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AiKnowledgeDO knowledge = knowledgeService.validateKnowledgeExists(createReqVO.getKnowledgeId());
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AiChatModelDO model = chatModelService.validateChatModel(knowledge.getModelId());
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// 3.1 获取向量存储实例
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VectorStore vectorStore = apiKeyService.getOrCreateVectorStore(model.getKeyId());
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// 3.2 向量化并存储
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vectorStore.add(segments);
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return documentId;
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}
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@ -1,6 +1,8 @@
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package cn.iocoder.yudao.module.ai.service.knowledge;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.AiKnowledgeCreateMyReqVO;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.AiKnowledgeUpdateMyReqVO;
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import cn.iocoder.yudao.module.ai.dal.dataobject.knowledge.AiKnowledgeDO;
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/**
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* AI 知识库-基础信息 Service 接口
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@ -27,4 +29,12 @@ public interface AiKnowledgeService {
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*/
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void updateKnowledgeMy(AiKnowledgeUpdateMyReqVO updateReqVO, Long userId);
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/**
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* 校验知识库是否存在
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*
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* @param id 记录编号
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*/
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AiKnowledgeDO validateKnowledgeExists(Long id);
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}
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@ -29,7 +29,7 @@ public class AiKnowledgeServiceImpl implements AiKnowledgeService {
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private AiChatModelService chatModalService;
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@Resource
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private AiKnowledgeMapper knowledgeBaseMapper;
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private AiKnowledgeMapper knowledgeMapper;
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@Override
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public Long createKnowledgeMy(AiKnowledgeCreateMyReqVO createReqVO, Long userId) {
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@ -39,7 +39,7 @@ public class AiKnowledgeServiceImpl implements AiKnowledgeService {
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// 2. 插入知识库
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AiKnowledgeDO knowledgeBase = BeanUtils.toBean(createReqVO, AiKnowledgeDO.class)
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.setModel(model.getModel()).setUserId(userId).setStatus(CommonStatusEnum.ENABLE.getStatus());
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knowledgeBaseMapper.insert(knowledgeBase);
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knowledgeMapper.insert(knowledgeBase);
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return knowledgeBase.getId();
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}
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@ -56,11 +56,12 @@ public class AiKnowledgeServiceImpl implements AiKnowledgeService {
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// 2. 更新知识库
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AiKnowledgeDO updateDO = BeanUtils.toBean(updateReqVO, AiKnowledgeDO.class);
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updateDO.setModel(model.getModel());
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knowledgeBaseMapper.updateById(updateDO);
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knowledgeMapper.updateById(updateDO);
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}
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@Override
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public AiKnowledgeDO validateKnowledgeExists(Long id) {
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AiKnowledgeDO knowledgeBase = knowledgeBaseMapper.selectById(id);
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AiKnowledgeDO knowledgeBase = knowledgeMapper.selectById(id);
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if (knowledgeBase == null) {
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throw exception(KNOWLEDGE_NOT_EXISTS);
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}
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@ -9,7 +9,9 @@ import cn.iocoder.yudao.module.ai.controller.admin.model.vo.apikey.AiApiKeySaveR
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import cn.iocoder.yudao.module.ai.dal.dataobject.model.AiApiKeyDO;
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import jakarta.validation.Valid;
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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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import java.util.List;
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@ -83,6 +85,14 @@ public interface AiApiKeyService {
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*/
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ChatModel getChatModel(Long id);
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/**
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* 获得 EmbeddingModel 对象
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*
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* @param id 编号
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* @return EmbeddingModel 对象
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*/
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EmbeddingModel getEmbeddingModel(Long id);
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/**
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* 获得 ImageModel 对象
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*
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@ -111,4 +121,12 @@ public interface AiApiKeyService {
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*/
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SunoApi getSunoApi();
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/**
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* 获得 vector 对象
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*
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* @param id 编号
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* @return VectorStore 对象
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*/
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VectorStore getOrCreateVectorStore(Long id);
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}
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@ -2,6 +2,7 @@ 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.AiVectorFactory;
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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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@ -13,7 +14,9 @@ import cn.iocoder.yudao.module.ai.dal.dataobject.model.AiApiKeyDO;
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import cn.iocoder.yudao.module.ai.dal.mysql.model.AiApiKeyMapper;
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import jakarta.annotation.Resource;
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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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import org.springframework.stereotype.Service;
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import org.springframework.validation.annotation.Validated;
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@ -36,6 +39,8 @@ 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 AiVectorFactory vectorFactory;
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@Override
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public Long createApiKey(AiApiKeySaveReqVO createReqVO) {
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@ -104,6 +109,13 @@ public class AiApiKeyServiceImpl implements AiApiKeyService {
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return modelFactory.getOrCreateChatModel(platform, apiKey.getApiKey(), apiKey.getUrl());
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}
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@Override
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public EmbeddingModel getEmbeddingModel(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 modelFactory.getOrCreateEmbeddingModel(platform, apiKey.getApiKey(), apiKey.getUrl());
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}
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@Override
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public ImageModel getImageModel(AiPlatformEnum platform) {
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AiApiKeyDO apiKey = apiKeyMapper.selectFirstByPlatformAndStatus(platform.getPlatform(), CommonStatusEnum.ENABLE.getStatus());
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@ -132,4 +144,11 @@ public class AiApiKeyServiceImpl implements AiApiKeyService {
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}
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return modelFactory.getOrCreateSunoApi(apiKey.getApiKey(), apiKey.getUrl());
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}
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@Override
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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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}
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}
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@ -2,6 +2,8 @@ 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.AiVectorFactory;
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import cn.iocoder.yudao.framework.ai.core.factory.AiVectorFactoryImpl;
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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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@ -10,22 +12,15 @@ import cn.iocoder.yudao.framework.ai.core.model.xinghuo.XingHuoChatModel;
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import cn.iocoder.yudao.framework.ai.core.model.xinghuo.XingHuoChatOptions;
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import com.alibaba.cloud.ai.tongyi.TongYiAutoConfiguration;
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import lombok.extern.slf4j.Slf4j;
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import org.springframework.ai.autoconfigure.vectorstore.redis.RedisVectorStoreProperties;
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import org.springframework.ai.document.MetadataMode;
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import org.springframework.ai.embedding.EmbeddingModel;
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import org.springframework.ai.tokenizer.JTokkitTokenCountEstimator;
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import org.springframework.ai.tokenizer.TokenCountEstimator;
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import org.springframework.ai.transformer.splitter.TokenTextSplitter;
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import org.springframework.ai.transformers.TransformersEmbeddingModel;
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import org.springframework.ai.vectorstore.RedisVectorStore;
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import org.springframework.boot.autoconfigure.AutoConfiguration;
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import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty;
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import org.springframework.boot.autoconfigure.data.redis.RedisProperties;
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import org.springframework.boot.context.properties.EnableConfigurationProperties;
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import org.springframework.context.annotation.Bean;
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import org.springframework.context.annotation.Import;
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import org.springframework.context.annotation.Lazy;
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import redis.clients.jedis.JedisPooled;
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/**
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* 芋道 AI 自动配置
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@ -43,6 +38,12 @@ public class YudaoAiAutoConfiguration {
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return new AiModelFactoryImpl();
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}
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@Bean
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public AiVectorFactory aiVectorFactory() {
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return new AiVectorFactoryImpl();
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}
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// ========== 各种 AI Client 创建 ==========
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@Bean
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@ -85,30 +86,31 @@ public class YudaoAiAutoConfiguration {
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}
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// ========== rag 相关 ==========
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@Bean
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@Lazy // TODO 芋艿：临时注释，避免无法启动
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public EmbeddingModel transformersEmbeddingClient() {
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return new TransformersEmbeddingModel(MetadataMode.EMBED);
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}
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// TODO @xin 免费版本
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// @Bean
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// @Lazy // TODO 芋艿：临时注释，避免无法启动」
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// public EmbeddingModel transformersEmbeddingClient() {
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// return new TransformersEmbeddingModel(MetadataMode.EMBED);
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// }
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/**
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* TODO @xin 抽离出去，根据具体模型走
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* TODO @xin 默认版本先不弄，目前都先取对应的 EmbeddingModel
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*/
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@Bean
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@Lazy // TODO 芋艿：临时注释，避免无法启动
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public RedisVectorStore vectorStore(TransformersEmbeddingModel transformersEmbeddingModel, RedisVectorStoreProperties properties,
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RedisProperties redisProperties) {
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var config = RedisVectorStore.RedisVectorStoreConfig.builder()
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.withIndexName(properties.getIndex())
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.withPrefix(properties.getPrefix())
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.build();
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RedisVectorStore redisVectorStore = new RedisVectorStore(config, transformersEmbeddingModel,
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new JedisPooled(redisProperties.getHost(), redisProperties.getPort()),
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properties.isInitializeSchema());
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redisVectorStore.afterPropertiesSet();
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return redisVectorStore;
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}
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// @Bean
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// @Lazy // TODO 芋艿：临时注释，避免无法启动
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// public RedisVectorStore vectorStore(TongYiTextEmbeddingModel tongYiTextEmbeddingModel, RedisVectorStoreProperties properties,
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// RedisProperties redisProperties) {
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// var config = RedisVectorStore.RedisVectorStoreConfig.builder()
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// .withIndexName(properties.getIndex())
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// .withPrefix(properties.getPrefix())
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// .build();
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//
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// RedisVectorStore redisVectorStore = new RedisVectorStore(config, tongYiTextEmbeddingModel,
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// new JedisPooled(redisProperties.getHost(), redisProperties.getPort()),
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// properties.isInitializeSchema());
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// redisVectorStore.afterPropertiesSet();
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// return redisVectorStore;
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// }
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@Bean
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@Lazy // TODO 芋艿：临时注释，避免无法启动
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@ -4,6 +4,7 @@ import cn.iocoder.yudao.framework.ai.core.enums.AiPlatformEnum;
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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 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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/**
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@ -25,6 +26,18 @@ public interface AiModelFactory {
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*/
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ChatModel getOrCreateChatModel(AiPlatformEnum platform, String apiKey, String url);
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/**
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* 基于指定配置，获得 EmbeddingModel 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 platform 平台
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* @param apiKey API KEY
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* @param url API URL
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* @return ChatModel 对象
|
||||
*/
|
||||
EmbeddingModel getOrCreateEmbeddingModel(AiPlatformEnum platform, String apiKey, String url);
|
||||
|
||||
/**
|
||||
* 基于默认配置，获得 ChatModel 对象
|
||||
*
|
||||
|
||||
@ -21,6 +21,7 @@ import com.alibaba.cloud.ai.tongyi.image.TongYiImagesModel;
|
||||
import com.alibaba.cloud.ai.tongyi.image.TongYiImagesProperties;
|
||||
import com.alibaba.dashscope.aigc.generation.Generation;
|
||||
import com.alibaba.dashscope.aigc.imagesynthesis.ImageSynthesis;
|
||||
import com.alibaba.dashscope.embeddings.TextEmbedding;
|
||||
import com.azure.ai.openai.OpenAIClient;
|
||||
import org.springframework.ai.autoconfigure.azure.openai.AzureOpenAiAutoConfiguration;
|
||||
import org.springframework.ai.autoconfigure.azure.openai.AzureOpenAiChatProperties;
|
||||
@ -37,6 +38,7 @@ import org.springframework.ai.autoconfigure.zhipuai.ZhiPuAiConnectionProperties;
|
||||
import org.springframework.ai.autoconfigure.zhipuai.ZhiPuAiImageProperties;
|
||||
import org.springframework.ai.azure.openai.AzureOpenAiChatModel;
|
||||
import org.springframework.ai.chat.model.ChatModel;
|
||||
import org.springframework.ai.embedding.EmbeddingModel;
|
||||
import org.springframework.ai.image.ImageModel;
|
||||
import org.springframework.ai.model.function.FunctionCallbackContext;
|
||||
import org.springframework.ai.ollama.OllamaChatModel;
|
||||
@ -97,6 +99,21 @@ public class AiModelFactoryImpl implements AiModelFactory {
|
||||
});
|
||||
}
|
||||
|
||||
@Override
|
||||
public EmbeddingModel getOrCreateEmbeddingModel(AiPlatformEnum platform, String apiKey, String url) {
|
||||
String cacheKey = buildClientCacheKey(EmbeddingModel.class, platform, apiKey, url);
|
||||
return Singleton.get(cacheKey, (Func0<EmbeddingModel>) () -> {
|
||||
// TODO @xin 先测试一个
|
||||
switch (platform) {
|
||||
case TONG_YI:
|
||||
return buildTongYiEmbeddingModel(apiKey);
|
||||
default:
|
||||
throw new IllegalArgumentException(StrUtil.format("未知平台({})", platform));
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
|
||||
@Override
|
||||
public ChatModel getDefaultChatModel(AiPlatformEnum platform) {
|
||||
//noinspection EnhancedSwitchMigration
|
||||
@ -239,7 +256,7 @@ public class AiModelFactoryImpl implements AiModelFactory {
|
||||
|
||||
/**
|
||||
* 可参考 {@link ZhiPuAiAutoConfiguration#zhiPuAiChatModel(
|
||||
* ZhiPuAiConnectionProperties, ZhiPuAiChatProperties, RestClient.Builder, List, FunctionCallbackContext, RetryTemplate, ResponseErrorHandler)}
|
||||
*ZhiPuAiConnectionProperties, ZhiPuAiChatProperties, RestClient.Builder, List, FunctionCallbackContext, RetryTemplate, ResponseErrorHandler)}
|
||||
*/
|
||||
private ZhiPuAiChatModel buildZhiPuChatModel(String apiKey, String url) {
|
||||
url = StrUtil.blankToDefault(url, ZhiPuAiConnectionProperties.DEFAULT_BASE_URL);
|
||||
@ -249,7 +266,7 @@ public class AiModelFactoryImpl implements AiModelFactory {
|
||||
|
||||
/**
|
||||
* 可参考 {@link ZhiPuAiAutoConfiguration#zhiPuAiImageModel(
|
||||
* ZhiPuAiConnectionProperties, ZhiPuAiImageProperties, RestClient.Builder, RetryTemplate, ResponseErrorHandler)}
|
||||
*ZhiPuAiConnectionProperties, ZhiPuAiImageProperties, RestClient.Builder, RetryTemplate, ResponseErrorHandler)}
|
||||
*/
|
||||
private ZhiPuAiImageModel buildZhiPuAiImageModel(String apiKey, String url) {
|
||||
url = StrUtil.blankToDefault(url, ZhiPuAiConnectionProperties.DEFAULT_BASE_URL);
|
||||
@ -315,4 +332,15 @@ public class AiModelFactoryImpl implements AiModelFactory {
|
||||
return new StabilityAiImageModel(stabilityAiApi);
|
||||
}
|
||||
|
||||
// ========== 各种创建 EmbeddingModel 的方法 ==========
|
||||
|
||||
/**
|
||||
* 可参考 {@link TongYiAutoConfiguration#tongYiTextEmbeddingClient(TextEmbedding, TongYiConnectionProperties)}
|
||||
*/
|
||||
private EmbeddingModel buildTongYiEmbeddingModel(String apiKey) {
|
||||
TongYiConnectionProperties connectionProperties = new TongYiConnectionProperties();
|
||||
connectionProperties.setApiKey(apiKey);
|
||||
return new TongYiAutoConfiguration().tongYiTextEmbeddingClient(SpringUtil.getBean(TextEmbedding.class), connectionProperties);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
@ -0,0 +1,27 @@
|
||||
package cn.iocoder.yudao.framework.ai.core.factory;
|
||||
|
||||
import cn.iocoder.yudao.framework.ai.core.enums.AiPlatformEnum;
|
||||
import org.springframework.ai.embedding.EmbeddingModel;
|
||||
import org.springframework.ai.vectorstore.VectorStore;
|
||||
|
||||
/**
|
||||
* AI Vector 模型工厂的接口类
|
||||
* @author xiaoxin
|
||||
*/
|
||||
public interface AiVectorFactory {
|
||||
|
||||
|
||||
/**
|
||||
* 基于指定配置，获得 VectorStore 对象
|
||||
* <p>
|
||||
* 如果不存在，则进行创建
|
||||
*
|
||||
* @param embeddingModel 嵌入模型
|
||||
* @param platform 平台
|
||||
* @param apiKey API KEY
|
||||
* @param url API URL
|
||||
* @return VectorStore 对象
|
||||
*/
|
||||
VectorStore getOrCreateVectorStore(EmbeddingModel embeddingModel, AiPlatformEnum platform, String apiKey, String url);
|
||||
|
||||
}
|
||||
@ -0,0 +1,51 @@
|
||||
package cn.iocoder.yudao.framework.ai.core.factory;
|
||||
|
||||
import cn.hutool.core.lang.Singleton;
|
||||
import cn.hutool.core.lang.func.Func0;
|
||||
import cn.hutool.core.util.ArrayUtil;
|
||||
import cn.hutool.core.util.StrUtil;
|
||||
import cn.iocoder.yudao.framework.ai.core.enums.AiPlatformEnum;
|
||||
import cn.iocoder.yudao.framework.common.util.spring.SpringUtils;
|
||||
import org.springframework.ai.embedding.EmbeddingModel;
|
||||
import org.springframework.ai.vectorstore.RedisVectorStore;
|
||||
import org.springframework.ai.vectorstore.VectorStore;
|
||||
import org.springframework.boot.autoconfigure.data.redis.RedisProperties;
|
||||
import redis.clients.jedis.JedisPooled;
|
||||
|
||||
/**
|
||||
* AI Vector 模型工厂的实现类
|
||||
* 使用 redisVectorStore 实现 VectorStore
|
||||
*
|
||||
* @author xiaoxin
|
||||
*/
|
||||
public class AiVectorFactoryImpl implements AiVectorFactory {
|
||||
|
||||
@Override
|
||||
public VectorStore getOrCreateVectorStore(EmbeddingModel embeddingModel, AiPlatformEnum platform, String apiKey, String url) {
|
||||
String cacheKey = buildClientCacheKey(VectorStore.class, platform, apiKey, url);
|
||||
return Singleton.get(cacheKey, (Func0<VectorStore>) () -> {
|
||||
// TODO 芋艿 @xin 这两个配置取哪好呢
|
||||
// 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
|
||||
String index = "default-index";
|
||||
String prefix = "default:";
|
||||
var config = RedisVectorStore.RedisVectorStoreConfig.builder()
|
||||
.withIndexName(index)
|
||||
.withPrefix(prefix)
|
||||
.build();
|
||||
RedisProperties redisProperties = SpringUtils.getBean(RedisProperties.class);
|
||||
RedisVectorStore redisVectorStore = new RedisVectorStore(config, embeddingModel,
|
||||
new JedisPooled(redisProperties.getHost(), redisProperties.getPort()),
|
||||
true);
|
||||
redisVectorStore.afterPropertiesSet();
|
||||
return redisVectorStore;
|
||||
});
|
||||
}
|
||||
|
||||
|
||||
private static String buildClientCacheKey(Class<?> clazz, Object... params) {
|
||||
if (ArrayUtil.isEmpty(params)) {
|
||||
return clazz.getName();
|
||||
}
|
||||
return StrUtil.format("{}#{}", clazz.getName(), ArrayUtil.join(params, "_"));
|
||||
}
|
||||
}
|
||||
@ -19,6 +19,7 @@ import org.springframework.ai.embedding.EmbeddingModel;
|
||||
import org.springframework.ai.vectorstore.RedisVectorStore;
|
||||
import org.springframework.ai.vectorstore.RedisVectorStore.RedisVectorStoreConfig;
|
||||
import org.springframework.boot.autoconfigure.AutoConfiguration;
|
||||
import org.springframework.boot.autoconfigure.condition.ConditionalOnBean;
|
||||
import org.springframework.boot.autoconfigure.condition.ConditionalOnClass;
|
||||
import org.springframework.boot.autoconfigure.condition.ConditionalOnMissingBean;
|
||||
import org.springframework.boot.autoconfigure.data.redis.RedisAutoConfiguration;
|
||||
@ -38,7 +39,7 @@ import redis.clients.jedis.JedisPooled;
|
||||
*/
|
||||
@AutoConfiguration(after = RedisAutoConfiguration.class)
|
||||
@ConditionalOnClass({JedisPooled.class, JedisConnectionFactory.class, RedisVectorStore.class, EmbeddingModel.class})
|
||||
//@ConditionalOnBean(JedisConnectionFactory.class)
|
||||
@ConditionalOnBean(JedisConnectionFactory.class)
|
||||
@EnableConfigurationProperties(RedisVectorStoreProperties.class)
|
||||
public class RedisVectorStoreAutoConfiguration {
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user