What are vector embeddings?

Vector embeddings are dense representations of objects (including words, images or user profiles) in a continuous vector space. Each object is represented by a point (or vector) in this space, where the distance and direction between points capture semantic or contextual relationships between the objects. For example in NLP, similar words are mapped close together in the embedding space.

Types of vector embeddings

Generative AI applications are built using vector embeddings and the data source can range from text, audio, video and any type of structured and unstructured data. We can broadly divide these embeddings into four major categories:

1. Word embeddings: These are the most common types of embeddings, used to represent words in NLP. Popular models include Word2Vec, GloVe and FastText.
2. Sentence and document embeddings: These capture the semantic meaning of sentences and documents. Techniques like BERT and Doc2Vec are examples.
3. Graph embeddings: These are used to represent nodes and edges of graphs in vector space, facilitating tasks like link prediction and node classification.
4. Image embeddings: Generated by deep learning models, these represent images in a compact vector form, useful for tasks like image recognition and classification.

Embeddings are instrumental in bridging the gap between human language and machine understanding, providing a foundation for numerous applications in AI, from chatbots to content recommendation systems.