google-gemini-embeddings
CommunityBuild intelligent search and RAG systems effortlessly.
Software Engineering#semantic search#embeddings#RAG#vector search#document clustering#cloudflare vectorize
Authorjezweb
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill eliminates the complexity of implementing vector search and retrieval-augmented generation by providing production-ready patterns for Gemini embeddings with Cloudflare Vectorize integration.
Core Features & Use Cases
- Flexible Dimensions: Generate 128-3072 dimension embeddings using Matryoshka Representation Learning.
- Task Type Optimization: Use 8 specialized task types (RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY) for optimized retrieval quality.
- Production RAG: Complete workflows for document ingestion, vector search, and context-aware generation.
Quick Start
Generate a 768-dimension embedding for the text "What is the meaning of life?" using the RETRIEVAL_QUERY task type to match with indexed documents.
Use the google-gemini-embeddings skill to create a semantic search system for your knowledge base.
Dependency Matrix
Required Modules
@google/genaitypescript
Components
scriptsreferencestemplates
💻 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: google-gemini-embeddings Download link: https://github.com/jezweb/claude-skills/archive/main.zip#google-gemini-embeddings Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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