Exploring Rag and Multimodal Rag Systems for Efficient Data Processing
- Authors
- Published on
- Published on
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
Viewer Reactions for Intro to multimodal RAG systems
Accent of the presenter is noted as being useful for tutorials
Positive feedback on the video content and Google Cloud Platform
Mention of difficulty in deploying GCP services compared to others
Comment on the maturity of GCP
Mention of a broken GitHub link in the video
Related Articles
Master Looker Extensions: Develop Custom Apps for Enhanced Data Access
Explore the world of Looker Extensions with Google Cloud Tech. Learn how to develop custom JavaScript web applications integrated with Looker, streamlining data access and enhancing user experiences. Discover marketplace extensions like the Data Dictionary and ER Diagram for optimized data governance and visualization. Start building your own extensions today!
Master Looker Embedding: Private vs. Signed Methods & Embed SDK Interaction
Explore Looker embedding methods: private embedding requires user login, while signed embedding uses unique URLs for authentication. Learn to generate signed URLs and enhance interaction with embedded content using the Embed SDK. Exciting possibilities await in the world of Looker embedding!
Enhance Data Analysis with Gemini and Looker Formula Assistant
Google Cloud Tech introduces Gemini and Looker Formula Assistant, AI tools to streamline data analysis in Looker Studio. From correcting syntax errors to advanced data transformations, these tools enhance efficiency and accuracy, empowering users to extract valuable insights effortlessly.
Mastering Looker Blocks for Data Analysis on Google Cloud
Explore Looker blocks on Google Cloud Tech with Jeremy, discovering pre-built models for data analysis like Google Analytics and Cloud cost management. Learn how to install, extend, and develop blocks to optimize your data visualization.