We Index Images for RAG (www.kapa.ai)

Kapa has introduced a novel approach to enhancing AI assistants that respond to technical documentation by effectively indexing images within their Retrieval-Augmented Generation (RAG) pipeline. Instead of sending images directly to models during query time—which significantly increases costs and complicates processing—Kapa describes images during indexing using a low-cost vision model. This method turns images into descriptive text, allowing the system to achieve a substantial improvement in answer quality across various customer projects while limiting per-query costs to just 1% to 6% above text-only queries. This innovative solution is significant for the AI/ML community as it addresses the common challenges of multimodal retrieval in a cost-effective manner. By implementing an initial indexing phase where images are described once, Kapa maximizes the efficiency of subsequent queries and eliminates the overhead of processing raw images repeatedly. The results demonstrate that processing images in this way leads to significantly better answers, with users benefiting from clearer, actionable responses without the increased costs associated with real-time image analysis. This method not only enhances the usability of AI assistants in technical domains but also advances the conversation around effective multimodal integration in AI systems.