Elastic AI: Vectors

Elastic AI: Vectors

Vectors mean an advance and an innovation in the field of data searches and analysis, vectors are numerical representations of data as words or phrases in a multidimensional space.

These vectors are saved and cross-referenced with searches to provide better relevance and accuracy because they are quickly analyzed and compared.

Elastic can use different vectors, dense and sparse, dense vectors are used in natural processing, representing words in a continuous vector space, thanks to that it is possible to capture their meaning and semantic relationships. Sparse vectors are used for representations and storing terms and weights.

The creation of vectors in Elastic is achieved thanks to embedding techniques, converting textual data into vectors.

This conversion allows searches based on semantic similarity (it is possible to use ELSER)

We can also use Hugging Face, which offers a wide range of pre-trained natural language processing (NLP) models, they can be used for multiple tasks such as analysis, text classification, etc., to import a model you can use Eland, a library of Elastic.

In summary, thanks to this technology, it is possible to perform more intuitive searches and obtain more precise and relevant results, representing a significant advance in the field of data analysis and searches.