A visual exploration of vector embeddings

Vector embedding models map inputs like words or images into lists of numbers, representing them in multidimensional space. Different models have unique characteristics, including dimension length and similarity space. Recent models like OpenAI's text-embedding-3-small offer improved performance and flexibility. Vector search enables semantic similarity comparisons, with various metrics and compression techniques available to optimize storage and search efficiency.