-- vector.knowledge_doc_vector definition
-- Drop table
-- DROP TABLE vector.knowledge_doc_vector;
CREATE TABLE vector.knowledge_doc_vector (  id bigserial NOT NULL,  doc_id bigserial NOT NULL,  embedding public.vector NULL,  doc_content text NULL,  doc_name varchar NULL,  doc_page varchar NULL,  CONSTRAINT knowledge_doc_vector_pkey PRIMARY KEY (id));

<dependency>            <groupId>com.baomidou</groupId>            <artifactId>mybatis-plus-boot-starter</artifactId>            <version>3.5.3.1</version>        </dependency>     <dependency>            <groupId>org.postgresql</groupId>            <artifactId>postgresql</artifactId>            <version>42.6.0</version>        </dependency>        <dependency>            <groupId>com.pgvector</groupId>            <artifactId>pgvector</artifactId>            <version>0.1.6</version>        </dependency>

import com.baomidou.mybatisplus.core.mapper.BaseMapper;import com.fawvw.pms.vector.domain.dto.KnowledgeDocVectorDTO;import com.fawvw.pms.vector.domain.entity.KnowledgeDocVector;import com.google.gson.Gson;import org.apache.ibatis.annotations.Mapper;import org.apache.ibatis.annotations.SelectProvider;
import java.util.Arrays;import java.util.List;import java.util.Map;
@Mapperpublic interface VectorMapper extends BaseMapper<KnowledgeDocVector> {    @SelectProvider(type = SqlProvider.class, method = "findUsersWithSimilarVectors")    List<KnowledgeDocVectorDTO> findUsersWithSimilarVectors(double[] targetVector);
    static class SqlProvider {        public String findUsersWithSimilarVectors(Map<String, Object> param) {            System.out.println("param keys:" + new Gson().toJson(param.keySet()));            double[] targetVector = (double[]) param.get("array");            String sql = "SELECT id, doc_id, embedding, doc_content, " +                    "cosine_distance(embedding, '" + Arrays.toString(targetVector) + "') AS similarity " +                    "FROM knowledge_doc_vector " +                    "ORDER BY similarity DESC";            return sql;        }    }}

public List findSimilarVector(String query) {        // query转为向量        TextEmbeddingResult embeddingResult = dashCodeHttpApi.textEmbedding(query);        List<Double> embeddings = embeddingResult.getOutput().getEmbeddings().get(0).getEmbedding();        double[] vector = new double[embeddings.size()];        for (int i = 0; i < embeddings.size(); i++) {            Double embedding = embeddings.get(i);            vector[i] = embedding;        }        log.info("vector:{}", vector);        List<KnowledgeDocVectorDTO> vectorDTOList = vectorMapper.findUsersWithSimilarVectors(vector);        log.info("vectorDTOList:{}", vectorDTOList);        // 过滤掉相似度低的向量检索结果 阈值:0.7        vectorDTOList = vectorDTOList.stream()                .filter(vectorDTO -> vectorDTO.getSimilarity() <= 0.7)                .collect(Collectors.toList());
        return vectorDTOList;    }