Multimodal Rag: Ai Search & Recommender Systems With Gpt-4

Mastering Multimodal RAG: Build AI-Powered Search & Recommender Systems with GPT-4, CLIP, and ChromaDB

What you'll learn

Understand and implement Retrieval-Augmented Generation (RAG) with multimodal data (text, images).

Build AI-powered search and recommender systems using GPT-4, CLIP, and ChromaDB.

Generate and utilize text and image embeddings to perform multimodal searches.

Develop interactive applications with Streamlit to handle user queries and provide AI-driven recommendations

Requirements

Basic understanding of Python programming.

Familiarity with machine learning concepts (embeddings, vectors).

No prior experience with multimodal systems is needed, but knowledge of AI tools like GPT or CLIP will be helpful.

A computer with internet access and the ability to install Python libraries like Streamlit, OpenAI, and ChromaDB.

Description

Are you ready to dive into the cutting-edge world of AI-powered search and recommender systems? This course will guide you through the process of building Multimodal Retrieval-Augmented Generation (RAG) systems that combine text and image data for advanced information retrieval and recommendations.In this hands-on course, you'll learn how to leverage state-of-the-art tools such as GPT-4, CLIP, and ChromaDB to build AI systems capable of processing multimodal data—enhancing traditional search methods with the power of machine learning and embeddings.What You’ll Learn:Master Multimodal RAG: Understand the concept of Retrieval-Augmented Generation (RAG) and how to implement it for both text and image-based data.Build AI-Powered Search & Recommendation Systems: Learn how to construct search engines and recommender systems that can handle multimodal queries, using powerful AI models like GPT-4 and CLIP.Utilize Embeddings for Cross-Modal Search: Gain practical experience generating and using embeddings to enable search and recommendations based on text or image input.Develop Interactive Applications with Streamlit: Create user-friendly applications that allow real-time querying and recommendations based on user-provided text or image data.Key Technologies You'll Work With:GPT-4: A cutting-edge language model that powers the AI-driven recommendations.CLIP: An advanced AI model for generating image and text embeddings, making it possible to search images with text.ChromaDB: A high-performance vector database that enables fast and efficient querying for multimodal embeddings.Streamlit: A simple yet powerful framework for building interactive web applications.No prior experience with multimodal systems? No problem!This course is designed to make advanced AI concepts accessible, with detailed, step-by-step instructions that guide you through each process—from generating embeddings to building complete AI systems. Basic Python knowledge and a curiosity for AI are all you need to get started.Enroll today and take your AI development skills to the next level by mastering the art of multimodal RAG systems!

Overview

Section 1: Introduction

Lecture 1 Introduction & Prerequisites

Lecture 2 Course Structure

Lecture 3 WATCH THIS DEMO - What You'll Build in This Course

Section 2: Download Source code and Resources

Lecture 4 Download source code

Section 3: Development environment Setup

Lecture 5 Development Environment Setup - Overview

Section 4: RAG (Retrieval Augmented Generation) and Multimodal Systems Deep Dive

Lecture 6 RAG Systems - Deep Dive Crush Course

Lecture 7 RAG Benefits and Practical Application

Lecture 8 Multimodal RAG - Overview & Motivation and Benefits - How it Works

Section 5: Search in a Multimodal RAG System

Lecture 9 How Search is Integrated into a Multimodal RAG System - Full Workflow

Lecture 10 Why Multimodal Search is so Powerful

Lecture 11 Visual Explanation Why Multimodal Search is so Powerful

Section 6: Hands-on: Multimodal Search RAG System

Lecture 12 Multimodal Search System Setup - Create Embeddings of Images

Lecture 13 Finish the Multimodal Search System

Section 7: Hands-On - Multimodal Recommender System

Lecture 14 Multimodal Recommender System - Overview

Lecture 15 Getting our Dataset from Hugging Face & Showing Number of Rows

Lecture 16 Saving all Images Locally

Lecture 17 Saving Image Embeddings to Vector Database

Lecture 18 Testing our Multimodal Recommender System - Fetching the Correct Images

Lecture 19 Setting up the RAG Flow - Part 1

Lecture 20 Putting it all Together and Testing the Multimodal Recommender RAG System

Lecture 21 Adding a UI to the Multimodal Recommender System - Streamlit

Section 8: Next Steps

Lecture 22 Next steps

Aspiring AI Developers: Individuals looking to build AI-powered applications that integrate text and image data.,Data Scientists: Professionals aiming to enhance their skills in multimodal data processing and retrieval.,Machine Learning Engineers: Those seeking to implement advanced search and recommender systems using state-of-the-art models.