Gemini Embedding 2 Hands-on in 8 mins!

2026-03-11 · Source: 1littlecoder · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

Google has released Gemini Embedding 2, a new multimodal embedding model built on the Gemini architecture, capable of processing text, images, video, audio, and PDFs. This model supports up to 120 seconds of video (MP4/MOV), six images per request, text up to 8,192 input tokens, and PDFs up to six pages long. A key feature is Matrioska Representation Learning (MRL), which allows dynamic scaling of output dimensions (e.g., 372, 768, 1536) to balance performance and storage costs, with a default of 372. Gemini Embedding 2 demonstrates state-of-the-art performance, surpassing previous models like Gemini Embedding 01, and is both multimodal and multilingual. It can be accessed via the Gemini API or Vertex AI API, with a Google Colab notebook provided for hands-on demonstration.

Key takeaway

For AI Engineers and Data Scientists building Retrieval Augmented Generation (RAG) pipelines or semantic search systems, Gemini Embedding 2 simplifies multimodal data processing. Its native support for text, images, video, audio, and PDFs, combined with flexible output dimensions via MRL, means you can consolidate multiple embedding models into one, potentially reducing complexity and cost. Consider integrating this model to streamline your data ingestion and improve the accuracy of your multimodal applications.

Key insights

Gemini Embedding 2 offers native multimodal and multilingual embeddings with flexible dimensions for diverse data types.

Principles

• Multimodal embeddings simplify complex RAG pipelines.
• MRL enables dynamic dimension scaling for efficiency.

Method

Access Gemini Embedding 2 via Gemini or Vertex AI APIs, pass various modalities (text, image, video, audio, PDF) to generate embeddings, and use cosine similarity for semantic comparison.

In practice

• Embed internal organization documents for semantic search.
• Build multimodal chatbots or Q&A systems.
• Optimize embedding dimensions for cost/performance.

Topics

• Gemini Embedding 2
• Multimodal Embeddings
• Matrioska Representation Learning
• Retrieval-Augmented Generation
• Semantic Search

Best for: Machine Learning Engineer, AI Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by 1littlecoder.