What you can do with Gemini Embedding 2
Skills:
RAG Basics90%
Key Takeaways
Introduces Gemini Embedding 2 for multimodal RAG and agentic workflows with Google for Developers
Original Description
Building multimodal RAG or agentic workflows? This model natively maps text, images, video, audio, and documents into a single unified embedding space-no intermediate conversions required.
With Matryoshka Representation Learning (MRL), you get full control over your output. Truncate from 3072 dimensions down to 1536 or even 768 to optimize for scale while maintaining high-level accuracy.
Resources:
Watch the full deep dive → https://goo.gle/4uFoT63
Subscribe to Google for Developers → https://goo.gle/developers
Speakers: Patrick Loeber
Products Mentioned: Gemini
Watch on YouTube ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
More on: RAG Basics
View skill →Related Reads
📰
📰
📰
📰
Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%
Dev.to AI
Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%
Dev.to · Imus
Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%
Dev.to AI
Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%
Dev.to AI
🎓
Tutor Explanation
DeepCamp AI