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Multimodal RAG is growing, here’s the best way to get started
Enterprises want to use RAG systems to search for more than just text files, multimodal embeddings models help them do that.
This enables you to assess the model’s performance and suitability for specific use cases and should provide insights into any adjustments needed before full deployment,” a blog post from Cohere staff solutions architect Yann Stoneman said. Stoneman said, depending on some industries, models may also need “additional training to pick up fine-grain details and variations in images.” He used medical applications as an example, where radiology scans or photos of microscopic cells require a specialized embedding system that understands the nuances in those kinds of images. “The system should be able to process image pointers (e.g. URLs or file paths) alongside text data, which may not be possible with text-based embeddings.
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