Google Gemini Embedding 2 Tutorial | Multimodal Image Matching Project
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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 t…
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Chapters (11)
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
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