Making Your RAG 10x Better with Images

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​ In this video, I’ll show you how to build an end-to-end multi-modal RAG system using GPT-4 and LLAMA Index. We’ll cover data collection, creating vector stores for text and images, and building a retrieval pipeline. Perfect for those interested in enhancing large language models with multi-modal data.

LINKS:
Colabl: https://tinyurl.com/25sb2rtu
Architecture: https://tinyurl.com/4x9x9bsc
Multi-modal RAG – Previous Video: https://youtu.be/Rg35oYuus-w

? RAG Beyond Basics Course:
https://prompt-s-site.thinkific.com/courses/rag

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TIMESTAMPS:
00:00 Introduction to Multi-Modal RAG Systems
00:23 Overview of the Architecture
02:57 Setting Up the Environment
03:54 Data Collection and Preparation
04:28 Generating Image Descriptions with GPT-4
08:10 Creating Multi-Modal Vector Stores
09:41 Implementing the Retrieval Pipeline
11:05 Generating Final Responses

All Interesting Videos:
Everything LangChain: https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr

Everything LLM: https://youtube.com/playlist?list=PLVEEucA9MYhNF5-zeb4Iw2Nl1OKTH-Txw

Everything Midjourney: https://youtube.com/playlist?list=PLVEEucA9MYhMdrdHZtFeEebl20LPkaSmw

AI Image Generation: https://youtube.com/playlist?list=PLVEEucA9MYhPVgYazU5hx6emMXtargd4z   Read More Prompt Engineering 

#AI #promptengineering

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