embed-v-4-0

Embed 4 transforms texts and images into numerical vectorsCohere’s Embed 4 is a multilingual multimodal embedding model. It is capable of transforming different modalities such as images, texts, and interleaved images and texts into a single vector representation. Embed 4 offers state-of-the-art performance across all modalities (texts, images, interleaved texts and image) and in both English and multilingual settings. Embed 4 supports a 128k context length and an images can have a maximum of 2MM pixels. Embed 4 is capable of vectorizing interleaved texts and images and capturing key visual features from screenshots of PDFs, slides, tables, figures, and more, thereby eliminating the need for complex document parsing. Embed 4 offers a variety of ways for compression both on the number of dimensions and the number-format precision. The model offers byte and binary quantization and matryoshka embeddings for further compression.