es实体类中对应的字段

    /**
     * 向量，用于knn，其中type要设置为dense vector，其他参数参考文档，
     * 建议和我的一致，similarity设置为你需要的相似函数（欧几里得或者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;

项目启动后spring data es会根据你的实体类生成对应的索引，可以查看该索引中相应字段的配置：

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

knn搜索代码：

        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());

        //调用embedding服务生成向量
        Double[] v = EmbeddingUtils.vector(name);
        List<Float> vector = Stream.of(v).map(Double::floatValue).toList();

        //构造knn查询条件
        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);

注意事项

spring data es要用最新版5.2.3以上，否则有bug