# Gemini Embedding 001

> The semantic memory behind search-by-meaning and Notebook Chat

**Provider:** Google DeepMind

Gemini Embedding 001 converts every source, episode, and library item into vectors so HYR Studio can search your entire content library by meaning — and ground every Notebook Chat answer in your own material.

## How HYR Studio Uses Gemini Embedding 001

Everything you import is embedded on ingest. Those vectors power semantic search across sources, podcast episodes, and library items, Episode Routing content matching, and retrieval-augmented answers in Notebook Chat.

## Capabilities

- High-quality text embeddings
- Semantic search across your full library
- Retrieval-augmented generation (RAG) grounding
- Cross-episode topic matching

## Output Types Powered

- Semantic search results
- Grounded Notebook Chat answers
- Related-content matching

This is why a 1,000-video back-catalogue becomes searchable in an afternoon: every transcript is findable by what it means, not just the words it contains.

**Keywords:** Gemini embeddings, semantic search, RAG, vector search, HYR Studio AI models

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**URL:** https://hyrstudio.com/ai-model/google-gemini-embedding-001
**Markdown:** https://hyrstudio.com/ai-model/google-gemini-embedding-001.md