{"query":{"sparse_vector":{"field":"field_sparse","query_vector":{"token1":0.5,"token2":0.3,"token3":0.2}}}} 如果你希望使用训练模型,可以使用一个推理端点,该端点会自动将查询文本转换为稀疏向量: 代码语言:json AI代码解释 {"query":{"sparse_vector":{"field
dense_vector存储稠密向量,sparse_vector存储稀疏向量;它们的value都是单一的float数值,可以是0、负数或正数;dense_vector数组的最大长度不能超过1024,每个文档的数组长度可以不同;sparse_vector存储的是个非嵌套类型的json对象,对象key是向量的位置,即integer类型的字符串,范围[0,65535]。 dense_vector与sparse_vector...
SparseVector :稀疏向量 其创建方式有两种: 方法一:Vector.sparse(向量长度,索引数组,与索引数组所对应的数值数组) 方法二:Vector.sparse(向量长度,(索引,数值),(索引,数值),(索引,数值),...(索引,数值)) 示例: 比如向量(1,0,3,4)的创建有三种方法: 稠密向量:直接Vectors.dense(1,0,3,4) 稀疏向量: ...
SparseVector :稀疏向量 其创建方式有两种: 方法一:Vector.sparse(向量长度,索引数组,与索引数组所对应的数值数组) 方法二:Vector.sparse(向量长度,(索引,数值),(索引,数值),(索引,数值),...(索引,数值)) 示例: 比如向量(1,0,3,4)的创建有三种方法: 稠密向量:直接Vectors.dense(1,0,3,4) 稀疏向量: ...
...missing的值 missing: Float = Float.NaN, hasGroup: Boolean = false): (Booster, Map[String, Array...SparseVector和DenseVector都用于表示一个向量,两者之间仅仅是存储结构的不同。 其中,DenseVector就是普通的Vector存储,按序存储Vector中的每一个值。...而事实上XGBoost on Spark也的确将Sparse Vector...
BGE-M3 是由北京智源人工智能研究院(BAAI)于 2024 年发布的一款文本嵌入模型。它基于 XLM-RoBERTa 架构,支持 稠密检索(Dense)、稀疏检索(Sparse)、多向量检索(Multi-Vector) 三种方式,并具备强大的 多语言能力(覆盖 100+ 种语言) 与 超长文本处理能力(最多支持 8192 个 token)。
First elements of a dense vector to be multiplied with first elements of a first row of a sparse array may be determined. The determined first elements of the dense vector may be written into a memory. A dot product for the first elements of the sparse array and the first elements of ...
Infinity is a cutting-edge AI-native database that provides a wide range of search capabilities for rich data types such as dense vector, sparse vector, tensor, full-text, and structured data. It provides robust support for various LLM applications, including search, recommenders, question-answeri...
Multiplies the dense vector x by the sparse matrix A and adds the result to the dense vector y, with all operands containing double-precision values.
As you've mentioned, the OpenAI embedding API typically returns dense embeddings, which are continuous vector representations of the input text. These dense embeddings are typically optimized for similarity search, clustering, and other downstream tasks, but they don't provide ...