Google Gemini Embedding 2 Tutorial | Multimodal Image Matching Project

Description

Try our GenAI Free Courses - https://www.analyticsvidhya.com/courses/?utm_source=yt_av&utm_medium=video Google recently released Gemini Embedding 2, their first fully multimodal embedding model built on the Gemini architecture, in Public Preview via the Gemini API and Vertex AI. Gemini Embedding 2 maps text, images, videos, audio, and documents into a single, unified embedding space, and captures semantic intent across over 100 languages. This simplifies complex pipelines and enhances a wide variety of multimodal downstream tasks—from Retrieval-Augmented Generation (RAG) and semantic search to sentiment analysis and data clustering. Timestamps: 0:00 - Introduction to Gemini Embedding 2 0:44 - Text Embeddings vs. Multimodal Embeddings 1:46 - Modalities Supported: Video, Audio, and PDFs 2:10 - Flexible Embedding Dimensions (3072 vs. Smaller) 2:39 - Image Matching Project Overview 3:46 - Dataset Structure & Data Prep 4:46 - Setting up Gemini API & Python Client 5:35 - Loading the Dataset & Generating Embeddings 6:20 - Image Matching Logic (Cosine Similarity) 6:45 - Testing the Results: How Accurate is it? 7:51 - Future Improvements: Vector Databases & RAG #GeminiEmbeddingModel #GeminiEmbeddings #GoogleGeminiEmbedding2