Aeca Blog

The mobile applications and web services we use have search functions. Most are developed using basic text search provided by databases or full-text search provided by search engines like Elasticsearch. Full-Text Search is one of the traditional methods mainly used for searching text data, focusing on finding specific keywords, words, phrases, etc., in documents, web pages, databases, and more. It typically involves inputting keywords or short sentences to search text data and finding documents that match the keywords, but it does not consider context or semantic similarity.

Vector embedding is a concept that converts various forms of data (documents, images, audio, video, etc.) into arrays of numbers to measure similarity. For example, colors can be represented as three-dimensional vector data in RGB format. By calculating the distance between these vector embeddings, we can determine the similarity between data. This plays an important role in natural language processing, recommendation algorithms, and more. Various data can be converted into vectors through Transformer models, allowing us to measure the similarity between different types of data. For instance, it is possible to measure the similarity between the text "cat" and a picture of a cat in vector space.