Main applications of vector databases

Semantic search is a type of search that goes beyond keywords and tries to understand the meaning or intent of the user’s query. It uses a language model that represents words, phrases or documents as vectors, and calculates the similarity between the query and the documents. Thus, the most relevant results can be returned, even if they do not contain exactly the same words as the query.

An example of semantic search is offered by Google with its “voice search” function, which allows the user to ask questions in natural language and get precise answers.

Product recommendation is a type of system that suggests products to the user that may be of interest to them, based on their profile, purchase history, preferences or needs. This is done by using a machine learning model that represents products and users as vectors, and calculates the similarity or affinity between them.Thus, the user can be offered products that match their tastes, that are complementary to those they have already purchased, or that are popular among other similar users.

An example of product recommendation is offered by Amazon with its “customers who bought this product also bought” function.

Anomaly detection is a type of analysis that involves identifying data that deviates from what is normal or expected, and which may indicate a problem, error, fraud or threat. This is done by using a machine learning model that represents the data as vectors and calculates the distance or difference between them.Data that are too far away from the mean, median, interquartile range, or any other statistical criterion can be detected.

An example of anomaly detection is offered by Microsoft with its “Azure Anomaly Detector” service, which allows the detection of anomalies in time series, such as web traffic, energy consumption, sales, etc.

Vector databases are able to store and process any type of data, regardless of its structure, format or origin. Moreover, they do not require the definition of a fixed schema or a previous ontology, which facilitates the integration and updating of data. Thus, it can be adapted to the needs and changes of the business, without losing information or generating inconsistencies.

Vector databases are capable of handling large volumes of data, without losing performance or quality. In addition, they can be easily distributed and parallelised, which makes it possible to take advantage of available computational resources. Thus, the challenges of big data can be met without compromising speed and accuracy.

Vector databases are able to perform complex operations on data, such as similarity, distance, scalar product, transformation, etc., quickly and efficiently. Furthermore, queries can be optimised and customised, using techniques such as filtering, sorting, aggregation, dimensionality reduction, etc. Thus, relevant and useful results can be obtained, without consuming too many resources and time.