Hands-On Multimodal RAG: Images, Tables & Text

Learn how to build a vision-based RAG pipeline that directly indexes and retrieves images, tables, and text—no captions needed! We’ll compare Cohere’s Embed-v4 API with a fully local ColPali based solution, then plug the results into a vision-language model like Gemini for accurate, context-rich answers. Whether you need a cloud-powered workflow or a private on-prem setup, this hands-on tutorial shows you every step. LINKS: - https://youtu.be/DI9Q60T_054 - https://youtu.be/Ra8n_9wnHFs - https://x.com/Nils_Reimers/status/1915431608980586874 - Embed-v4: https://cohere.com/blog/embed-4 - Notebook: https://colab.research.google.com/drive/1JwZ_nWhBUFbrzJnHKmyd0qKJ3gVt5lCe?usp=sharing RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag Let's Connect: 🦾 Discord: https://discord.com/invite/t4eYQRUcXB ☕ Buy me a Coffee: https://ko-fi.com/promptengineering |🔴 Patreon: https://www.patreon.com/PromptEngineering 💼Consulting: https://calendly.com/engineerprompt/consulting-call 📧 Business Contact: [email protected] Become Member: http://tinyurl.com/y5h28s6h 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 00:00 Introduction to Multimodal RAG Systems 00:31 Traditional Text-Based RAG Systems 02:13 Cohere's Embed Form for Multimodal Search 02:56 Workflow Overview 05:17 Code Implementation: Proprietary API 14:04 Code Implementation: Local Model 15:07 Using ColPali for Local Vision-Based Retrieval