---
title: "Embedding"
description: "1 posts about Embedding, summarised, each linking to the original."
---

# Embedding
> 1 posts about Embedding, summarised, each linking to the original.

## Articles

### [EmbeddingGemma 2: The Developer Guide](https://yomu.fyi/post/embeddinggemma-2-the-developer-guide.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Maarten Grootendorst, Ian Ballantyne
- Published: Oct 6, 2026
- Type: Tutorial

Modern search and RAG applications need to work across diverse content types, but finding models with strong retrieval accuracy and low latency without massive compute is challenging. EmbeddingGemma 2 is a compact open model under Apache 2.0, based on Gemma 4, that maps text, code, images, video, and audio into a unified 768-dimensional space with a modular architecture scaling from 270M to 740M parameters. Key capabilities include native multimodal retrieval, superior code understanding, modular memory footprint, and flexible vector storage via Matryoshka Representation Learning enabling dynamic truncation from 768 to 128 dimensions. Under the hood, it uses modular encoders—a text/code decoder, vision encoder, and audio encoder—that project into a shared space and can be loaded selectively to minimize memory. The developer guide demonstrates how to use sentence-transformers to load the model, embed with task prompts, handle media inputs, truncate dimensions, and choose configurations based on storage and recall needs.