package cn.iocoder.yudao.module.ai.service.knowledge;
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import cn.hutool.core.collection.CollUtil;
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import cn.hutool.core.collection.ListUtil;
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import cn.hutool.core.util.ObjUtil;
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import cn.hutool.core.util.StrUtil;
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import cn.iocoder.yudao.framework.common.enums.CommonStatusEnum;
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import cn.iocoder.yudao.framework.common.pojo.PageResult;
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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.segment.AiKnowledgeSegmentPageReqVO;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.segment.AiKnowledgeSegmentProcessRespVO;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.segment.AiKnowledgeSegmentSaveReqVO;
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import cn.iocoder.yudao.module.ai.controller.admin.knowledge.vo.segment.AiKnowledgeSegmentUpdateStatusReqVO;
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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.mysql.knowledge.AiKnowledgeSegmentMapper;
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import cn.iocoder.yudao.module.ai.enums.AiDocumentSplitStrategyEnum;
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import cn.iocoder.yudao.module.ai.service.knowledge.bo.AiKnowledgeSegmentSearchReqBO;
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import cn.iocoder.yudao.module.ai.service.knowledge.bo.AiKnowledgeSegmentSearchRespBO;
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import cn.iocoder.yudao.module.ai.service.knowledge.splitter.MarkdownQaSplitter;
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import cn.iocoder.yudao.module.ai.service.knowledge.splitter.SemanticTextSplitter;
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import cn.iocoder.yudao.module.ai.service.model.AiModelService;
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import com.alibaba.cloud.ai.dashscope.rerank.DashScopeRerankOptions;
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import com.alibaba.cloud.ai.model.RerankModel;
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import com.alibaba.cloud.ai.model.RerankRequest;
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import com.alibaba.cloud.ai.model.RerankResponse;
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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.tokenizer.TokenCountEstimator;
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import org.springframework.ai.transformer.splitter.TextSplitter;
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import org.springframework.ai.transformer.splitter.TokenTextSplitter;
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import org.springframework.ai.vectorstore.SearchRequest;
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import org.springframework.ai.vectorstore.VectorStore;
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import org.springframework.ai.vectorstore.filter.FilterExpressionBuilder;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.context.annotation.Lazy;
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import org.springframework.stereotype.Service;
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import java.util.*;
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import static cn.iocoder.yudao.framework.common.exception.util.ServiceExceptionUtil.exception;
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import static cn.iocoder.yudao.framework.common.util.collection.CollectionUtils.convertList;
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import static cn.iocoder.yudao.module.ai.enums.ErrorCodeConstants.*;
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import static org.springframework.ai.vectorstore.SearchRequest.SIMILARITY_THRESHOLD_ACCEPT_ALL;
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/**
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* AI 知识库分片 Service 实现类
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*
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* @author xiaoxin
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*/
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@Service
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@Slf4j
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public class AiKnowledgeSegmentServiceImpl implements AiKnowledgeSegmentService {
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private static final String VECTOR_STORE_METADATA_KNOWLEDGE_ID = "knowledgeId";
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private static final String VECTOR_STORE_METADATA_DOCUMENT_ID = "documentId";
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private static final String VECTOR_STORE_METADATA_SEGMENT_ID = "segmentId";
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private static final Map<String, Class<?>> VECTOR_STORE_METADATA_TYPES = Map.of(
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VECTOR_STORE_METADATA_KNOWLEDGE_ID, String.class,
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VECTOR_STORE_METADATA_DOCUMENT_ID, String.class,
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VECTOR_STORE_METADATA_SEGMENT_ID, String.class);
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/**
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* Rerank 在向量检索时，检索数量 * 该系数，目的是为了提升 Rerank 的效果
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*/
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private static final Integer RERANK_RETRIEVAL_FACTOR = 4;
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@Resource
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private AiKnowledgeSegmentMapper segmentMapper;
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@Resource
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private AiKnowledgeService knowledgeService;
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@Resource
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@Lazy // 延迟加载，避免循环依赖
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private AiKnowledgeDocumentService knowledgeDocumentService;
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@Resource
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private AiModelService modelService;
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@Resource
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private TokenCountEstimator tokenCountEstimator;
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@Autowired(required = false) // 由于 spring.ai.model.rerank 配置项，可以关闭 RerankModel 的功能，所以这里只能不强制注入
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private RerankModel rerankModel;
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@Override
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public PageResult<AiKnowledgeSegmentDO> getKnowledgeSegmentPage(AiKnowledgeSegmentPageReqVO pageReqVO) {
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return segmentMapper.selectPage(pageReqVO);
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}
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@Override
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public void createKnowledgeSegmentBySplitContent(Long documentId, String content) {
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// 1. 校验
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AiKnowledgeDocumentDO documentDO = knowledgeDocumentService.validateKnowledgeDocumentExists(documentId);
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AiKnowledgeDO knowledgeDO = knowledgeService.validateKnowledgeExists(documentDO.getKnowledgeId());
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VectorStore vectorStore = getVectorStoreById(knowledgeDO);
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// 2. 文档切片（使用自动检测策略）
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List<Document> documentSegments = splitContentByStrategy(content, documentDO.getSegmentMaxTokens(),
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AiDocumentSplitStrategyEnum.AUTO, documentDO.getUrl());
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// 3.1 存储切片
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List<AiKnowledgeSegmentDO> segmentDOs = convertList(documentSegments, segment -> {
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if (StrUtil.isEmpty(segment.getText())) {
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return null;
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}
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return new AiKnowledgeSegmentDO().setKnowledgeId(documentDO.getKnowledgeId()).setDocumentId(documentId)
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.setContent(segment.getText()).setContentLength(segment.getText().length())
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.setVectorId(AiKnowledgeSegmentDO.VECTOR_ID_EMPTY)
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.setTokens(tokenCountEstimator.estimate(segment.getText()))
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.setStatus(CommonStatusEnum.ENABLE.getStatus());
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});
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segmentMapper.insertBatch(segmentDOs);
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// 3.2 切片向量化
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for (int i = 0; i < documentSegments.size(); i++) {
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Document segment = documentSegments.get(i);
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AiKnowledgeSegmentDO segmentDO = segmentDOs.get(i);
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writeVectorStore(vectorStore, segmentDO, segment);
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}
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}
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@Override
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public void updateKnowledgeSegment(AiKnowledgeSegmentSaveReqVO reqVO) {
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// 1. 校验
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AiKnowledgeSegmentDO oldSegment = validateKnowledgeSegmentExists(reqVO.getId());
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// 2. 删除向量
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VectorStore vectorStore = getVectorStoreById(oldSegment.getKnowledgeId());
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deleteVectorStore(vectorStore, oldSegment);
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// 3.1 更新切片
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AiKnowledgeSegmentDO newSegment = BeanUtils.toBean(reqVO, AiKnowledgeSegmentDO.class);
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segmentMapper.updateById(newSegment);
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// 3.2 重新向量化，必须开启状态
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if (CommonStatusEnum.isEnable(oldSegment.getStatus())) {
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newSegment.setKnowledgeId(oldSegment.getKnowledgeId()).setDocumentId(oldSegment.getDocumentId());
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writeVectorStore(vectorStore, newSegment, new Document(newSegment.getContent()));
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}
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}
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@Override
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public void deleteKnowledgeSegment(Long id) {
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// 1. 校验段落存在
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AiKnowledgeSegmentDO segment = validateKnowledgeSegmentExists(id);
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// 2. 删除向量
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VectorStore vectorStore = getVectorStoreById(segment.getKnowledgeId());
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deleteVectorStore(vectorStore, segment);
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// 3. 删除段落记录
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segmentMapper.deleteById(id);
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}
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@Override
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public void deleteKnowledgeSegmentByDocumentId(Long documentId) {
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// 1. 查询需要删除的段落
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List<AiKnowledgeSegmentDO> segments = segmentMapper.selectListByDocumentId(documentId);
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if (CollUtil.isEmpty(segments)) {
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return;
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}
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// 2. 批量删除段落记录
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segmentMapper.deleteByIds(convertList(segments, AiKnowledgeSegmentDO::getId));
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// 3. 删除向量存储中的段落
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VectorStore vectorStore = getVectorStoreById(segments.getFirst().getKnowledgeId());
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vectorStore.delete(convertList(segments, AiKnowledgeSegmentDO::getVectorId));
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}
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@Override
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public void updateKnowledgeSegmentStatus(AiKnowledgeSegmentUpdateStatusReqVO reqVO) {
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// 1. 校验
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AiKnowledgeSegmentDO segment = validateKnowledgeSegmentExists(reqVO.getId());
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// 2. 获取知识库向量实例
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VectorStore vectorStore = getVectorStoreById(segment.getKnowledgeId());
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// 3. 更新状态
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segmentMapper.updateById(new AiKnowledgeSegmentDO().setId(reqVO.getId()).setStatus(reqVO.getStatus()));
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// 4. 更新向量
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if (CommonStatusEnum.isEnable(reqVO.getStatus())) {
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writeVectorStore(vectorStore, segment, new Document(segment.getContent()));
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} else {
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deleteVectorStore(vectorStore, segment);
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}
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}
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@Override
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public void reindexKnowledgeSegmentByKnowledgeId(Long knowledgeId) {
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// 1.1 校验知识库存在
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AiKnowledgeDO knowledge = knowledgeService.validateKnowledgeExists(knowledgeId);
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// 1.2 获取知识库向量实例
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VectorStore vectorStore = getVectorStoreById(knowledge);
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// 2.1 查询知识库下的所有启用状态的段落
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List<AiKnowledgeSegmentDO> segments = segmentMapper.selectListByKnowledgeIdAndStatus(
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knowledgeId, CommonStatusEnum.ENABLE.getStatus());
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if (CollUtil.isEmpty(segments)) {
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return;
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}
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// 2.2 遍历所有段落，重新索引
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for (AiKnowledgeSegmentDO segment : segments) {
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// 删除旧的向量
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deleteVectorStore(vectorStore, segment);
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// 重新创建向量
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writeVectorStore(vectorStore, segment, new Document(segment.getContent()));
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}
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log.info("[reindexKnowledgeSegmentByKnowledgeId][知识库({}) 重新索引完成，共处理 {} 个段落]",
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knowledgeId, segments.size());
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}
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private void writeVectorStore(VectorStore vectorStore, AiKnowledgeSegmentDO segmentDO, Document segment) {
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// 1. 向量存储
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// 为什么要 toString 呢？因为部分 VectorStore 实现，不支持 Long 类型，例如说 QdrantVectorStore
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segment.getMetadata().put(VECTOR_STORE_METADATA_KNOWLEDGE_ID, segmentDO.getKnowledgeId().toString());
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segment.getMetadata().put(VECTOR_STORE_METADATA_DOCUMENT_ID, segmentDO.getDocumentId().toString());
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segment.getMetadata().put(VECTOR_STORE_METADATA_SEGMENT_ID, segmentDO.getId().toString());
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vectorStore.add(List.of(segment));
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// 2. 更新向量 ID
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segmentMapper.updateById(new AiKnowledgeSegmentDO().setId(segmentDO.getId()).setVectorId(segment.getId()));
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}
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private void deleteVectorStore(VectorStore vectorStore, AiKnowledgeSegmentDO segmentDO) {
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// 1. 更新向量 ID
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if (StrUtil.isEmpty(segmentDO.getVectorId())) {
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return;
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}
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segmentMapper.updateById(new AiKnowledgeSegmentDO().setId(segmentDO.getId())
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.setVectorId(AiKnowledgeSegmentDO.VECTOR_ID_EMPTY));
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// 2. 删除向量
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vectorStore.delete(List.of(segmentDO.getVectorId()));
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}
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@Override
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public List<AiKnowledgeSegmentSearchRespBO> searchKnowledgeSegment(AiKnowledgeSegmentSearchReqBO reqBO) {
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// 1. 校验
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AiKnowledgeDO knowledge = knowledgeService.validateKnowledgeExists(reqBO.getKnowledgeId());
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// 2. 检索
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List<Document> documents = searchDocument(knowledge, reqBO);
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if (CollUtil.isEmpty(documents)) {
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return ListUtil.empty();
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}
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// 3.1 段落召回
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List<AiKnowledgeSegmentDO> segments = segmentMapper
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.selectListByVectorIds(convertList(documents, Document::getId));
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if (CollUtil.isEmpty(segments)) {
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return ListUtil.empty();
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}
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// 3.2 增加召回次数
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segmentMapper.updateRetrievalCountIncrByIds(convertList(segments, AiKnowledgeSegmentDO::getId));
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// 4. 构建结果
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List<AiKnowledgeSegmentSearchRespBO> result = convertList(segments, segment -> {
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Document document = CollUtil.findOne(documents, // 找到对应的文档
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doc -> Objects.equals(doc.getId(), segment.getVectorId()));
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if (document == null) {
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return null;
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}
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return BeanUtils.toBean(segment, AiKnowledgeSegmentSearchRespBO.class)
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.setScore(document.getScore());
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});
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result.sort((o1, o2) -> Double.compare(o2.getScore(), o1.getScore())); // 按照分数降序排序
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return result;
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}
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/**
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* 基于 Embedding + Rerank Model，检索知识库中的文档
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*
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* @param knowledge 知识库
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* @param reqBO 检索请求
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* @return 文档列表
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*/
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private List<Document> searchDocument(AiKnowledgeDO knowledge, AiKnowledgeSegmentSearchReqBO reqBO) {
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VectorStore vectorStore = getVectorStoreById(knowledge);
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Integer topK = ObjUtil.defaultIfNull(reqBO.getTopK(), knowledge.getTopK());
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Double similarityThreshold = ObjUtil.defaultIfNull(reqBO.getSimilarityThreshold(), knowledge.getSimilarityThreshold());
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// 1. 向量检索
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int searchTopK = rerankModel != null ? topK * RERANK_RETRIEVAL_FACTOR : topK;
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double searchSimilarityThreshold = rerankModel != null ? SIMILARITY_THRESHOLD_ACCEPT_ALL : similarityThreshold;
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SearchRequest.Builder searchRequestBuilder = SearchRequest.builder()
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.query(reqBO.getContent())
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.topK(searchTopK).similarityThreshold(searchSimilarityThreshold)
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.filterExpression(new FilterExpressionBuilder()
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.eq(VECTOR_STORE_METADATA_KNOWLEDGE_ID, reqBO.getKnowledgeId().toString()).build());
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List<Document> documents = vectorStore.similaritySearch(searchRequestBuilder.build());
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if (CollUtil.isEmpty(documents)) {
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return documents;
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}
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// 2. Rerank 重排序
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if (rerankModel != null) {
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RerankResponse rerankResponse = rerankModel.call(new RerankRequest(reqBO.getContent(), documents,
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DashScopeRerankOptions.builder().topN(topK).build()));
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documents = convertList(rerankResponse.getResults(),
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documentWithScore -> documentWithScore.getScore() >= similarityThreshold
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? documentWithScore.getOutput() : null);
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}
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return documents;
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}
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@Override
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public List<AiKnowledgeSegmentDO> splitContent(String url, Integer segmentMaxTokens) {
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// 1. 读取 URL 内容
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String content = knowledgeDocumentService.readUrl(url);
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// 2.1 自动检测文档类型并选择策略
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AiDocumentSplitStrategyEnum strategy = detectDocumentStrategy(content, url);
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// 2.2 文档切片
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List<Document> documentSegments = splitContentByStrategy(content, segmentMaxTokens, strategy, url);
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// 3. 转换为段落对象
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return convertList(documentSegments, segment -> {
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if (StrUtil.isEmpty(segment.getText())) {
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return null;
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}
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return new AiKnowledgeSegmentDO()
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.setContent(segment.getText())
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.setContentLength(segment.getText().length())
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.setTokens(tokenCountEstimator.estimate(segment.getText()));
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});
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}
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/**
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* 校验段落是否存在
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*
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* @param id 文档编号
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* @return 段落信息
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*/
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private AiKnowledgeSegmentDO validateKnowledgeSegmentExists(Long id) {
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AiKnowledgeSegmentDO knowledgeSegment = segmentMapper.selectById(id);
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if (knowledgeSegment == null) {
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throw exception(KNOWLEDGE_SEGMENT_NOT_EXISTS);
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}
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return knowledgeSegment;
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}
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private VectorStore getVectorStoreById(AiKnowledgeDO knowledge) {
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return modelService.getOrCreateVectorStore(knowledge.getEmbeddingModelId(), VECTOR_STORE_METADATA_TYPES);
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}
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private VectorStore getVectorStoreById(Long knowledgeId) {
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AiKnowledgeDO knowledge = knowledgeService.validateKnowledgeExists(knowledgeId);
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return getVectorStoreById(knowledge);
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}
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/**
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* 根据策略切分内容
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*
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* @param content 文档内容
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* @param segmentMaxTokens 分段的最大 Token 数
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* @param strategy 切片策略
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* @param url 文档 URL（用于自动检测文件类型）
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* @return 切片后的文档列表
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*/
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@SuppressWarnings("EnhancedSwitchMigration")
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private List<Document> splitContentByStrategy(String content, Integer segmentMaxTokens,
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AiDocumentSplitStrategyEnum strategy, String url) {
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// 自动检测策略
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if (strategy == AiDocumentSplitStrategyEnum.AUTO) {
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strategy = detectDocumentStrategy(content, url);
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log.info("[splitContentByStrategy][自动检测到文档策略: {}]", strategy.getName());
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}
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// 根据策略切分
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TextSplitter textSplitter;
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switch (strategy) {
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case MARKDOWN_QA:
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textSplitter = new MarkdownQaSplitter(segmentMaxTokens);
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break;
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case SEMANTIC:
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textSplitter = new SemanticTextSplitter(segmentMaxTokens);
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break;
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case PARAGRAPH:
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textSplitter = new SemanticTextSplitter(segmentMaxTokens, 0); // 段落切分，无重叠
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break;
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case TOKEN:
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default:
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textSplitter = buildTokenTextSplitter(segmentMaxTokens);
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break;
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}
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// 执行切分
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return textSplitter.apply(Collections.singletonList(new Document(content)));
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}
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/**
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* 自动检测文档类型并选择切片策略
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*
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* @param content 文档内容
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* @param url 文档 URL
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* @return 推荐的切片策略
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*/
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private AiDocumentSplitStrategyEnum detectDocumentStrategy(String content, String url) {
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if (StrUtil.isEmpty(content)) {
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return AiDocumentSplitStrategyEnum.TOKEN;
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}
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// 1. 检测 Markdown QA 格式
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if (isMarkdownQaFormat(content, url)) {
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return AiDocumentSplitStrategyEnum.MARKDOWN_QA;
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}
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// 2. 检测普通 Markdown 文档
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if (isMarkdownDocument(url)) {
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return AiDocumentSplitStrategyEnum.SEMANTIC;
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}
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// 3. 默认使用语义切分（比 Token 切分更智能）
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return AiDocumentSplitStrategyEnum.SEMANTIC;
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}
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/**
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* 检测是否为 Markdown QA 格式
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* 特征：包含多个二级标题（## ）且标题后紧跟答案内容
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*/
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private boolean isMarkdownQaFormat(String content, String url) {
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// 文件扩展名判断
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if (StrUtil.isNotEmpty(url) && !url.toLowerCase().endsWith(".md")) {
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return false;
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}
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// 统计二级标题数量
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long h2Count = content.lines()
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.filter(line -> line.trim().startsWith("## "))
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.count();
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// 要求一：至少包含 2 个二级标题才认为是 QA 格式
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if (h2Count < 2) {
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return false;
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}
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// 要求二：检查标题占比（QA 文档标题行数相对较多），如果二级标题占比超过 10%，认为是 QA 格式
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long totalLines = content.lines().count();
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double h2Ratio = (double) h2Count / totalLines;
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return h2Ratio > 0.1;
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}
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/**
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* 检测是否为 Markdown 文档
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*/
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private boolean isMarkdownDocument(String url) {
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return StrUtil.endWithAnyIgnoreCase(url, ".md", ".markdown");
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}
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/**
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* 构建基于 Token 的文本切片器（原有逻辑保留）
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*/
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private static TextSplitter buildTokenTextSplitter(Integer segmentMaxTokens) {
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return TokenTextSplitter.builder()
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.withChunkSize(segmentMaxTokens)
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.withMinChunkSizeChars(Integer.MAX_VALUE) // 忽略字符的截断
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.withMinChunkLengthToEmbed(1) // 允许的最小有效分段长度
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.withMaxNumChunks(Integer.MAX_VALUE)
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.withKeepSeparator(true) // 保留分隔符
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.build();
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}
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@Override
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public List<AiKnowledgeSegmentProcessRespVO> getKnowledgeSegmentProcessList(List<Long> documentIds) {
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if (CollUtil.isEmpty(documentIds)) {
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return Collections.emptyList();
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}
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return segmentMapper.selectProcessList(documentIds);
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}
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@Override
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public Long createKnowledgeSegment(AiKnowledgeSegmentSaveReqVO createReqVO) {
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// 1.1 校验文档是否存在
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AiKnowledgeDocumentDO document = knowledgeDocumentService
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.validateKnowledgeDocumentExists(createReqVO.getDocumentId());
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// 1.2 获取知识库信息
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AiKnowledgeDO knowledge = knowledgeService.validateKnowledgeExists(document.getKnowledgeId());
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// 1.3 校验 token 熟练
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Integer tokens = tokenCountEstimator.estimate(createReqVO.getContent());
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if (tokens > document.getSegmentMaxTokens()) {
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throw exception(KNOWLEDGE_SEGMENT_CONTENT_TOO_LONG, tokens, document.getSegmentMaxTokens());
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}
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// 2. 保存段落
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AiKnowledgeSegmentDO segment = BeanUtils.toBean(createReqVO, AiKnowledgeSegmentDO.class)
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.setKnowledgeId(knowledge.getId()).setDocumentId(document.getId())
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.setContentLength(createReqVO.getContent().length()).setTokens(tokens)
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.setVectorId(AiKnowledgeSegmentDO.VECTOR_ID_EMPTY)
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.setRetrievalCount(0).setStatus(CommonStatusEnum.ENABLE.getStatus());
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segmentMapper.insert(segment);
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// 3. 向量化
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writeVectorStore(getVectorStoreById(knowledge), segment, new Document(segment.getContent()));
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return segment.getId();
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}
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@Override
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public AiKnowledgeSegmentDO getKnowledgeSegment(Long id) {
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return segmentMapper.selectById(id);
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}
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|
@Override
|
public List<AiKnowledgeSegmentDO> getKnowledgeSegmentList(Collection<Long> ids) {
|
if (CollUtil.isEmpty(ids)) {
|
return Collections.emptyList();
|
}
|
return segmentMapper.selectByIds(ids);
|
}
|
|
}
|
