Apr 5
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🚀 Stop using multiple, separate embedding models for your text and images! In this RAG Tutorial 2026, we dive deep into building a true Multimodal RAG system using the brand new Gemini Embedding 2 model. Previously, developers had to use different models for different data types, creating isolated vector collections that confused the LLM during semantic search. Today, we are solving that problem. The new gemini-embedding-2-preview model acts as a powerful Multimodal Embedding Model, allowing you to process text, images, video, and audio entirely within a single unified embedding space. It is a massive leap forward for generative AI and machine learning What You Will Learn in This Gemini API Tutorial: - Build a search engine to filter any text, image - Why traditional RAG architectures fail when combining text and image embeddings. - How to use Vertex AI for Embeddings to generate vectors for both local images and text documents simultaneously This video gives you the exact Gemini API and Vertex AI code needed to build state-of-the-art multimodal search engines or a recommendation systems Github repo for Notebook https://github.com/theshivamlko/multi_modal_rag_gemini_embedding_2_model ⏳ Video Chapters (Optimized for Viewer Retention): 00:00 - Intro to Multimodal RAG & Gemini Embedding 2 00:57 - The Big Problem with Traditional RAG Architectures 03:24 - How Multimodal Embeddings Fix Semantic Mismatch 04:06 - Code Tutorial: Vertex AI Embeddings Setup with Gemini Embedding 2 05:01 - Generating Vectors for Text and Images 06:07 - Demo 1: Performing Multimodal Search 07:11 - Demo 2: Querying the System for Pet Adoption 07:59 - Conclusion & Next Steps 📌 Don’t Forget: 👍 Like the video 📩 Share with your dev friends 🔔 Subscribe for more Flutter + AI content Follow me: Instagram - https://www.instagram.com/navokitech LinkedIn: https://www.linkedin.com/in/theshivamlko Github: https://github.com/theshivamlko Discord: https://discord.gg/uU6XPkA #vertexai #geminiai #rag #pythonprogramming #langchain