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【新增】AI 知识库:文档 token、字符数计算
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@ -21,7 +21,7 @@ public interface AiEmbeddingService {
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/**
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* 相似查询
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*
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* @param content 查询内容
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* @param request 查询实体
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*/
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List<Document> similaritySearch(SearchRequest request);
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}
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@ -4,7 +4,6 @@ import jakarta.annotation.Resource;
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import org.springframework.ai.document.Document;
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import org.springframework.ai.vectorstore.RedisVectorStore;
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import org.springframework.ai.vectorstore.SearchRequest;
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import org.springframework.scheduling.annotation.Async;
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import org.springframework.stereotype.Service;
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import java.util.List;
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@ -21,6 +20,8 @@ public class AiEmbeddingServiceImpl implements AiEmbeddingService {
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private RedisVectorStore vectorStore;
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@Override
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// @Async
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// TODO xiaoxin 报错先注释
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public void add(List<Document> documents) {
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vectorStore.add(documents);
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}
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@ -14,12 +14,14 @@ 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.JTokkitTokenCountEstimator;
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import org.springframework.ai.transformer.splitter.TokenTextSplitter;
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import org.springframework.beans.factory.annotation.Value;
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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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@ -41,7 +43,9 @@ public class AiKnowledgeDocumentServiceImpl implements AiKnowledgeDocumentServic
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@Resource
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private AiEmbeddingService embeddingService;
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// TODO @xin 临时测试用,后续删
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private static final JTokkitTokenCountEstimator TOKEN_COUNT_ESTIMATOR = new JTokkitTokenCountEstimator();
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// TODO xiaoxin 临时测试用,后续删
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@Value("classpath:/webapp/test/Fel.pdf")
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private org.springframework.core.io.Resource data;
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@ -49,18 +53,23 @@ public class AiKnowledgeDocumentServiceImpl implements AiKnowledgeDocumentServic
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@Override
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@Transactional(rollbackFor = Exception.class)
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public Long createKnowledgeDocument(AiKnowledgeDocumentCreateReqVO createReqVO) {
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AiKnowledgeDocumentDO documentDO = BeanUtils.toBean(createReqVO, AiKnowledgeDocumentDO.class);
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documentDO
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//todo
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.setTokens(0).setWordCount(0)
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.setStatus(CommonStatusEnum.ENABLE.getStatus()).setSliceStatus(AiKnowledgeDocumentStatusEnum.SUCCESS.getStatus());
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documentMapper.insert(documentDO);
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// TODO xiaoxin 后续从 url 加载
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TikaDocumentReader loader = new TikaDocumentReader(data);
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// 加载文档
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List<Document> documents = loader.get();
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Document document = CollUtil.getFirst(documents);
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// TODO 芋艿 文档层面有没有可能会比较大,这两个字段是否可以从分段表计算得出?
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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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AiKnowledgeDocumentDO documentDO = BeanUtils.toBean(createReqVO, AiKnowledgeDocumentDO.class);
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documentDO.setTokens(tokens).setWordCount(wordCount)
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.setStatus(CommonStatusEnum.ENABLE.getStatus()).setSliceStatus(AiKnowledgeDocumentStatusEnum.SUCCESS.getStatus());
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// 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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log.info("文档内容为空");
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return documentId;
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}
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@ -69,10 +78,8 @@ public class AiKnowledgeDocumentServiceImpl implements AiKnowledgeDocumentServic
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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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//todo
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.setTokens(0).setWordCount(0)
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.setTokens(TOKEN_COUNT_ESTIMATOR.estimate(segment.getContent())).setWordCount(segment.getContent().length())
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.setStatus(CommonStatusEnum.ENABLE.getStatus()));
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// 分段内容入库
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segmentMapper.insertBatch(segmentDOList);
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