google-gemini-embeddings

Embed text into Gemini vector representations for retrieval and semantic search.

18|4|Updated Jan 15, 2026

Ready to run

npx skills add https://github.com/ataschz/tanstack-start-mastra-example --skill google-gemini-embeddings-ataschz

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: google-gemini-embeddings
Source: https://github.com/ataschz/tanstack-start-mastra-example/tree/main/.agents/skills/google-gemini-embeddings
Command: npx skills add https://github.com/ataschz/tanstack-start-mastra-example --skill google-gemini-embeddings-ataschz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Gemini embeddings enable building robust retrieval-augmented generation (RAG) systems and semantic search by converting text into high-quality vector representations that power fast, accurate similarity, clustering, and indexing.

Core Features & Use Cases

Task-type optimized embeddings for retrieval, clustering, classification, and semantic search
RAG workflows with Cloudflare Vectorize integration for scalable indexing
Real-world use: index documents, run semantic search, and cluster content with vector similarity

Quick Start

Install dependencies and run the basic embedding example to generate a sample embedding, then index and query a small dataset. This section provides a simple path to quickly verify end-to-end functionality without requiring a full production setup.

Frequently Asked Questions about google-gemini-embeddings

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate Gemini embeddings for a RAG pipeline?▼

Generate Gemini embeddings for a RAG pipeline by converting text into high-quality vector representations using gemini-embedding-001. You must explicitly define the taskType and outputDimensionality parameters to ensure the resulting vectors match your specific vector index configuration.

What is the best way to index documents for semantic search with Gemini embeddings?▼

Indexing documents for semantic search with Gemini embeddings involves converting text into task-type optimized vectors and integrating with Cloudflare Vectorize. This approach enables scalable indexing, allowing you to run fast and accurate similarity searches across your content.

Does Cloudflare Vectorize work with Gemini embeddings for document clustering?▼

Cloudflare Vectorize works with Gemini embeddings to support document clustering and scalable indexing. By embedding your text into Gemini vector representations, you can perform vector similarity operations to effectively group similar content within your search apps.

How do I configure taskType and outputDimensionality when embedding text for semantic search?▼

Configuring taskType and outputDimensionality when embedding text for semantic search requires explicit parameter definition in your gemini-embedding-001 API call. Matching outputDimensionality to your vector index ensures proper retrieval, clustering, and classification performance.

Can I use Gemini embeddings for semantic similarity tasks across different search apps?▼

Gemini embeddings are suitable for semantic similarity tasks across search apps, providing task-type optimized vector representations for retrieval and classification. They enable fast, accurate similarity calculations by converting diverse text into a uniform high-quality vector space.

Why do I need to specify outputDimensionality for Gemini vector representations?▼

Specifying outputDimensionality for Gemini vector representations is required to match the dimensionality of your existing vector index. Aligning these dimensions ensures that your document indexing, retrieval, and semantic search pipelines function correctly without dimension mismatch errors.