Exploring Rag and Multimodal Rag Systems for Efficient Data Processing
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In this riveting video from Google Cloud Tech, they delve into the world of Rag, a cutting-edge system that uses llms and Vector databases to tackle text queries with finesse. This ingenious setup involves two key components: ingestion and query, where text is converted into vectors for efficient matching. But hold on, there's more! Enter Multimodal Rag, a beast that can handle not just text but also images and tables, elevating query capabilities to new heights.
The team takes us on a journey through setting up the environment, importing models, and extracting metadata for text and image processing. By incorporating image descriptions through Gemini models, the system can provide accurate answers by searching within images. The power of Multimodal Rag shines through as it deftly handles complex queries, seamlessly blending text and image contexts for a comprehensive understanding.
Through meticulous prompts and a clever fusion of text and image data, the team showcases the system's prowess in delivering precise answers with proper citations. This session serves as a testament to the versatility and potential of Multimodal Rag systems in diverse enterprise scenarios. Viewers are left inspired to explore the realm of Rag and its multimodal variations, primed to unleash its capabilities in their own projects.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Watch Intro to multimodal RAG systems on Youtube
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