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Cohere enhances RAG search with multimodal capabilities: Cohere has upgraded its Embed 3 model to include multimodal embeddings, allowing for image-based retrieval augmented generation (RAG) in enterprise search.

Key features of the new Embed 3 model:

  • Generates embeddings for both images and text
  • Utilizes a unified latent space for encoders, enabling mixed modality searches
  • Available in over 100 languages
  • Accessible on Cohere’s platform and Amazon SageMaker

Expanding enterprise data accessibility:

  • Enables businesses to search complex reports, product catalogs, and design files
  • Increases the volume of data accessible through RAG search
  • Allows incorporation of charts, graphs, product images, and design templates

Performance improvements and advantages:

  • Claims to be the “most generally capable multimodal embedding model on the market”
  • Prioritizes meaning behind data without biasing towards specific modalities
  • Eliminates the need for separate databases for images and text
  • Offers better mixed modality search results compared to other models

Industry context and competitive landscape:

  • Multimodal search is becoming increasingly familiar to consumers through platforms like Google and ChatGPT
  • Other companies, including Google and OpenAI, offer multimodal embedding options
  • Open-source models also facilitate embeddings for various modalities

Cohere’s market positioning:

  • Founded by researchers behind the Transformer model
  • Has faced challenges in gaining top-of-mind status in the enterprise space
  • Recently updated APIs to allow easy switching from competitor models
  • Aims to align with industry standards where customers often toggle between models

Implications for enterprise users:

  • Enables more comprehensive search capabilities across various file formats
  • Potentially boosts workforce productivity through improved data accessibility
  • Aligns enterprise search capabilities with consumer expectations for multimodal search

Looking ahead: The race for superior multimodal embeddings: As enterprises increasingly recognize the benefits of multimodal search, competition among model developers is likely to intensify, focusing on speed, accuracy, and security to meet demanding enterprise requirements.

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