Mar 15
Loading video player...
Is RAG dead? Not quite — but the retrieval layer just got a massive upgrade. In this video, I break down Google's new Gemini Embedding 2 model — the first fully multimodal embedding model built on the Gemini architecture. It maps text, images, audio, video, and documents into a single unified vector space. I built a full working demo app called EmbedSearch with 20 Surya-branded products to show you exactly how multimodal embeddings work — the actual vectors, the cosine similarity math, and three different search modes (text, image, and voice) all searching the same vector space. All opinions are my own and do not belong to my employer. ⏱️ Timestamps: 0:00 - Is RAG Dead? 0:15 - What Are Embeddings? (Interactive Demo) 0:55 - The Problem: Legacy AI Architectures 1:25 - The Solution: Gemini Embedding 2 1:55 - 5 Key Features 2:45 - Embedding 20 Products (Live) 4:00 - Text Search: Full Pipeline Breakdown 6:00 - Image Search: Cross-Modal Magic 7:15 - Audio Search: Third Modality 8:15 - Matryoshka Dimensions: Enterprise Cost Savings 9:15 - Honest Assessment + Is RAG Dead? (Final Answer) 🔗 Links: Google Blog Announcement: https://blog.google/technology/developers/gemini-embedding-2/ Gemini Embedding 2 Docs (Vertex AI): https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/embedding-2 Gemini API Colab Notebook: https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/Embeddings.ipynb Vertex AI Colab Notebook: https://colab.research.google.com/github/GoogleCloudPlatform/generative-ai/blob/main/embeddings/gemini-embedding-2.ipynb Google GenAI Python SDK: https://googleapis.github.io/python-genai/ FindMeMedia Demo (Google): https://findmemedia.withgoogle.com/ 📌 What's covered: What embeddings are and how they power search and RAG Why the old approach (separate models per modality) was broken 5 features that make Gemini Embedding 2 different: interleaved input, task types, adjustable dimensions, native audio, PDF + OCR Live demo: embedding 20 products with text, image, and combined vectors Text search with full vector comparison visualization Image search: cross-modal retrieval (search with a picture, find products) Audio search: embed raw voice recordings directly Matryoshka dimensions: 75% storage savings with nearly identical quality Enterprise cost analysis at scale #GeminiEmbedding2 #RAG #GoogleAI #Embeddings #VertexAI #SemanticSearch #MultimodalAI #VectorSearch #GeminiAPI #EnterpriseAI #AIwithSurya