‘Multimodal RAG’ is all the rage — here’s what it is and how to get started

The rise of multimodal RAG: Retrieval augmented generation (RAG) systems are expanding to include images and videos, offering businesses a more comprehensive view of their data across various file types.

• Multimodal RAG allows companies to surface information from diverse sources such as financial graphs, product catalogs, and informational videos.
• This technology relies on embedding models that transform different data types into numerical representations readable by AI models.
• Companies like Cohere have recently updated their embedding models to process images and videos, reflecting the growing demand for multimodal capabilities.

Best practices for implementation: Experts advise enterprises to start small and gradually scale up when implementing multimodal RAG systems.

• Testing on a limited scale allows companies to assess model performance and suitability for specific use cases before full deployment.
• Some industries, such as healthcare, may require additional training of embedding models to understand nuances in specialized images like radiology scans or microscopic cell photos.
• Data preparation is crucial, involving consistent image resizing and determining appropriate resolution to balance detail preservation and processing efficiency.

Technical considerations: Implementing multimodal RAG requires addressing several technical challenges to ensure smooth integration with existing systems.

• Organizations need to develop custom code to integrate image retrieval with text retrieval systems.
• The RAG system should be capable of processing image pointers (URLs or file paths) alongside text data.
• Enterprises may need to adapt their data storage and retrieval processes to accommodate mixed-modality searches effectively.

Evolving landscape of multimodal search: While text-based RAG has been more common, the demand for multimodal capabilities is growing rapidly.

• Companies like OpenAI and Google already offer multimodal search features in their chatbots, and Meta has recently announced its own multimodal model.
• OpenAI launched its latest generation of embeddings models in January, further advancing the field.
• Other companies, such as Uniphore, are developing tools to help enterprises prepare multimodal datasets for RAG systems.

Potential benefits for enterprises: Multimodal RAG offers several advantages for businesses seeking to leverage their diverse data assets.

• It enables a more holistic view of company information by incorporating various data types into a single search system.
• Multimodal RAG can potentially improve decision-making processes by providing more comprehensive insights from across the organization.
• The technology may lead to more efficient knowledge management and information retrieval within enterprises.

Challenges and considerations: Despite its potential, implementing multimodal RAG comes with certain challenges that organizations must address.

• Ensuring data quality and consistency across different modalities can be complex and resource-intensive.
• Organizations may need to invest in updating their existing infrastructure to support multimodal data processing and storage.
• Privacy and security concerns may arise when dealing with sensitive visual data, requiring robust safeguards and compliance measures.

Looking ahead: The future of multimodal RAG and its impact on enterprise information management.

• As the technology matures, we can expect to see more sophisticated applications of multimodal RAG across various industries.
• The integration of multimodal RAG with other emerging technologies, such as augmented reality or Internet of Things devices, could lead to novel use cases and capabilities.
• Organizations that successfully implement multimodal RAG may gain a competitive advantage through improved data utilization and decision-making processes.