The Only Embedding You Need!

Quick Overview

Google released Gemini Embedding 2, its first natively multimodal embedding model, which maps text, images, video, audio, and documents into a single unified embedding space, significantly improving multimodal retrieval and classification tasks across over 100 languages, offering flexible output dimensions via Matryoshka Representation Learning (MRL), and outperforming previous models across multiple benchmarks.

Key Points: Gemini Embedding 2 is Google's first natively multimodal embedding model, mapping text, images, video, audio, and documents into a single unified embedding space. The model supports semantic intent across over 100 languages, simplifying pipelines for tasks like Retrieval-Augmented Generation (RAG) and semantic search. It features flexible output dimensions using Matryoshka Representation Learning (MRL), allowing developers to choose between dimensions like 3072 (Default/Max Quality) and 768 (Max Efficiency). Input capabilities include supporting up to 8192 text tokens, processing up to 6 images per request, up to 120 seconds of video input, natively ingesting audio without intermediate transcription, and directly embedding PDFs up to 6 pages long. Gemini Embedding 2 achieved state-of-the-art performance on numerous benchmarks, notably scoring 97.4 on Image-Text Recall@RRP and 89.6 on Text-Image TextCaps. The model natively ingests and embeds audio without needing intermediate text transcriptions, preserving tone, urgency, and context, unlike legacy translation paths. The video demonstrates building a multimodal RAG pipeline using Gemini Embedding 2 for retrieval and Gemini Flash for generation, complete with source citation.

Context: This video introduces Gemini Embedding 2, Google's latest embedding model designed to natively handle multimodal inputs—text, images, video, audio, and documents—simultaneously within a single semantic space. This unified approach aims to overcome the limitations of previous unimodal or patched-together multimodal systems by capturing complex, nuanced relationships between different media types for enhanced retrieval and classification.