Real-time vector databases

Vector databases are the computational backbone required by modern AI applications, enabling efficient interaction with vector embeddings. Shapelets REC adds real-time capabilities to vector databases, offering unprecedented indexing and querying speeds on mid-tier computing systems.

only the manipulation of more data, but also more complex, multi-dimensional data.

Traditional databases, designed to store scalar values, are not a good fit for manipulating the amounts of complex data required today, limiting the ability to build scalable systems with real-time performance.

Vector databases offer a different architecture than traditional ones, tailored to handle the complexity of multi-dimensional data points at scale. These data points, also known as vectors, are basically a numerical representation in the form of a list of numbers, each one being a dimension that represents specific attributes or qualities extracted from raw data (i.e., audio, images, or text).

The number of dimensions in each vector can vary significantly depending on the complexity of the data, ranging from a mere few to several thousand. Various processes, such as feature extraction techniques, embeddings or some machine learning models are employed to transform various types of raw data into these vectors, much easier to handle by machine learning systems.

These vectors are basically a distinctive code that encapsulates the meaning or essence of a text, image, audio sample or any other entity. This code enables computers to comprehend and compare these elements in a more efficient and meaningful manner. As an example, in the case of word embeddings, words are transmuted into vectors in a manner that situates words with analogous meanings in proximity within the vector space.

The primary advantage of a vector database lies in its ability to quickly and accurately find and extract data based on their proximity or similarity in vector space. This enables searches based on semantic or contextual relevance, instead of relying on exact matches as conventional databases do.
To achieve superior performance, vector databases rely on various algorithms to perform vector indexing, which consists in further transforming the data into a structure that allows for faster searches.

This indexing process is slow in most cases, complicating the use of vector databases in real-time applications. Not only query time is important, indexing time is also relevant, and Shapelets REC can index large vector sets up to 200 times faster than any other solution.

Shapelets REC is a cutting-edge vector database with indexing and querying times that exceed the capabilities of the most advanced vector databases available today in the market. Our technology offers the necessary performance, scalability, and flexibility to maximize the utility of your data, in any modality from image and audio to text and geographical information. With its unprecedented capabilities, it can ingest and index one million documents in the fraction of a second on a modest computer.

If you are designing, building or maintaining AI systems that have to manipulate large amounts of complex data and need to improve their performance or you believe the operating costs of your data infrastructure are too high, contact us today for a demo.