es實(shí)體類中對(duì)應(yīng)的字段

    /**
     * 向量徐块，用于knn撑瞧，其中type要設(shè)置為dense vector备畦，其他參數(shù)參考文檔预愤，
     * 建議和我的一致崭捍，similarity設(shè)置為你需要的相似函數(shù)（歐幾里得或者cos）
     * www.elastic.co/guide/en/elasticsearch/reference/8.12/dense-vector.html
     */
    @Field(type = FieldType.Dense_Vector, dims = 1024, index = true, similarity = "cosine")
    private List<Float> vector;

項(xiàng)目啟動(dòng)后spring data es會(huì)根據(jù)你的實(shí)體類生成對(duì)應(yīng)的索引渴丸，可以查看該索引中相應(yīng)字段的配置：

        "vector": {
          "type": "dense_vector",
          "dims": 1024,
          "index": true,
          "similarity": "cosine"
        }

knn搜索代碼：

        BoolQuery.Builder bool = QueryBuilders.bool().boost(1.0f);

        Query multiMatch = QueryBuilders.multiMatch()
                .fields("name", "synonyms")
                .query(name)
                .fuzziness(condition.getFuzziness())
                .operator(Operator.And)
                .type(TextQueryType.BestFields)
                .build()
                ._toQuery();

        bool.must(multiMatch);
        Query query = new Query(bool.build());

        //調(diào)用embedding服務(wù)生成向量
        Double[] v = EmbeddingUtils.vector(name);
        List<Float> vector = Stream.of(v).map(Double::floatValue).toList();

        //構(gòu)造knn查詢條件
        KnnQuery knn = KnnQuery.of(k -> k.field("vector")
                .boost(0.5f)
                .k(20)
                .numCandidates(6000)
                .queryVector(vector)
        );

        NativeQueryBuilder nativeQueryBuilder = NativeQuery.builder()
                .withPageable(condition.toPage())
                .withQuery(query)
                .withSort(scoreSort, timeSort, idSort)
                .withTrackTotalHits(true)
                .withKnnQuery(knnQuery)
                .build();

        SearchHits<Product> hits = esTemplate.search(nativeQuery, Product.class);

注意事項(xiàng)

spring data es要用最新版5.2.3以上氧敢，否則有bug