Mixedbread: How We Built Multimodal Late-Interaction at Billion Scale (www.mixedbread.com)

Mixedbread has unveiled a groundbreaking multimodal late-interaction retrieval system capable of efficiently handling over one billion documents while maintaining search latency below 50 milliseconds. Traditional semantic search systems often falter with complex inputs due to their reliance on single-vector representations, which dilute important details. Mixedbread's approach employs a novel multi-vector model that retains fine-grained information across text, images, audio, and video, yielding significantly improved search accuracy, especially in challenging multimodal contexts. This architecture not only manages diverse input formats, extracting and optimizing them for retrieval, but also trains on the outputs of its tailored preprocessing pipeline to ensure high accuracy in real-world applications. The implications for the AI/ML community are profound. By utilizing a shared latent space for all data types and implementing optimizations in both the ingress and retrieval stages, Mixedbread facilitates "any-to-any" search without the complexities associated with modality-specific approaches. Their custom multi-vector retrieval engine, "silo," addresses the inherent challenges of storing and scoring vast arrays of vectors, allowing for quick document candidate selection and retrieval. These advancements promise to enhance the performance of AI systems, particularly in fields requiring nuanced understanding of mixed-content documents, while setting a new standard for the scalability and reliability of multimodal search technologies.