Vector Embeddings Explained

Vector databases are designed to provide high-quality search results by understanding the meaning behind queries rather than just matching keywords or synonyms. They use semantic searches and question answering, which involve searching by similarity in text or images. The core of a vector database is vector embedding, an array of numbers representing data objects that capture certain features. These vectors are used to efficiently search for similarities between words or paragraphs. Vector embeddings can be generated from various types of data, such as text, images, audio, time series, 3D models, video, and molecules. The quality of the search depends on the model used to generate the vector embeddings, while the speed of the search relies on the performance capabilities of the vector database.

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