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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
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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 Read More Prompt Engineering
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