gemini-embedding-2
CommunityEmbed multimodal data for fast cross-modal search
AuthorFandry96
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Gemini Embedding 2 provides a unified 3072-dim vector space to represent text, images, video, audio, and PDFs for efficient cross-modal representation and retrieval.
Core Features & Use Cases
- Natively multimodal embeddings with a single transformer backbone for text, images, video, audio, and PDFs
- Supports MRL truncation (128-3072 dims) for flexible memory and index sizing
- Ideal for semantic search, RAG pipelines, clustering, classification, and cross-modal retrieval
- Works with vector databases and narrative memory for agentic retrieval and memory
Quick Start
Invoke gemini-embedding-2 to generate embeddings for a multimodal dataset and integrate them into your semantic search workflow.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: gemini-embedding-2 Download link: https://github.com/Fandry96/k3-agentic-skills/archive/main.zip#gemini-embedding-2 Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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