Embeddings in vector search are mathematical representations of data in a numerical vector format. Generated by machine learning models, embeddings encode the essential features and semantics of data, such as words, sentences, images, or audio. For example, the phrase "artificial intelligence" could be represented as a 768-dimensional vector summarizing its linguistic and contextual meaning.
These embeddings allow search systems to identify patterns and relationships within data. For instance, the word "apple" could have different embeddings depending on its context—fruit or tech company—allowing a system to differentiate meaning. This contextual representation is a key advantage of embeddings over traditional keyword matching.
In practice, embeddings are crucial for enabling similarity-based searches. By comparing embeddings, vector search systems can determine semantic closeness. This makes it possible to retrieve data that aligns with the intent of a query, such as finding related articles, visually similar images, or contextually linked pieces of information.