Embedding models help transform complex data — text, images, audio, and video — into numerical representations that computers can understand. The embeddings capture the semantic meaning of the data, making them useful for tasks like search, recommendation systems, and natural language processing.
Still, they can struggle with more complex materials, such as documents comprising a mix of text and images, so enterprises often have to build pre-processing pipelines to get data ready for AI to use.
Canadian AI company Cohere hopes to solve this problem with Embed 4, its latest multimodal model that supports frontier search and retrieval capabilities. The model can quickly search documents, whether they are solely text-based or include images, diagrams, graphs, tables, code, diagrams, and other components.
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