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akrindev
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gemini-embeddings

Generate text embeddings and calculate semantic similarity via the Gemini Embedding API.

4|2|Updated Jan 29, 2026
Search Tags:#semantic search#embeddings#RAG#vector search#gemini#text similarity
One-click install
npx skills add https://github.com/akrindev/google-studio-skills --skill gemini-embeddings
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gemini-embeddings
Source: https://github.com/akrindev/google-studio-skills/tree/main/skills/gemini-embeddings
Command: npx skills add https://github.com/akrindev/google-studio-skills --skill gemini-embeddings

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires google-genai, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the creation of numerical representations (embeddings) for text, enabling AI to understand and compare the semantic meaning of words and documents.

Core Features & Use Cases

  • Generate Embeddings: Create vector representations for any text input.
  • Calculate Similarity: Measure how semantically similar two or more pieces of text are.
  • Support for RAG: Prepare text data for Retrieval Augmented Generation systems.
  • Dimensionality Control: Adjust embedding vector size for performance or accuracy.
  • Use Case: You have a large collection of customer feedback. Use this Skill to generate embeddings for each piece of feedback, then find all feedback entries that are semantically similar to a new customer query, helping you quickly identify recurring issues.

Quick Start

Generate embeddings for the text "What is the meaning of life?".

Frequently Asked Questions about gemini-embeddings

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

FAQPage Schema
How do I generate text embeddings for semantic search using Gemini?▼

To generate text embeddings for semantic search, this Skill utilizes the Gemini Embedding API to create numerical vector representations for any text input. These vectors enable machines to understand and compare the semantic meaning of documents and queries efficiently.

How do I calculate semantic similarity between documents?▼

You can calculate semantic similarity by generating vector embeddings for your text inputs and measuring the distance between them. This Skill automates the vector creation process via the Gemini API, allowing you to directly compare how semantically similar two or more pieces of text are.

Can I use Gemini embeddings to prepare text data for RAG pipelines?▼

Yes, you can prepare text data for Retrieval Augmented Generation pipelines by generating vector representations of your documents. This Skill creates the necessary embeddings that allow RAG systems to retrieve contextually relevant information based on semantic meaning rather than exact keyword matches.

Does this support adjusting embedding dimensionality for performance?▼

Yes, this supports adjusting embedding dimensionality to optimize for either performance or accuracy. You can control the output vector size according to your specific application requirements, ensuring an appropriate balance between computational efficiency and semantic search precision.

What's the best way to cluster large collections of customer feedback by meaning?▼

The best way to cluster customer feedback by meaning is to generate text embeddings for each entry and compare their semantic similarity. This Skill automates the vector creation process, helping you quickly identify recurring issues and group similar feedback together without relying on exact keyword matching.

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