Multimodal RAG: AI Search & Recommender Systems with GPT-4

About this course

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.</

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