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embeddings

Generate and manage vector embeddings across OpenAI, Voyage AI, and local models.

650|137|Updated Jan 26, 2026
Search Tags:#semantic search#embeddings#vector search#nlp#openai#local models#voyage ai
One-click install
npx skills add https://github.com/alsk1992/CloddsBot --skill embeddings-alsk1992
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: embeddings
Source: https://github.com/alsk1992/CloddsBot/tree/main/src/skills/bundled/embeddings
Command: npx skills add https://github.com/alsk1992/CloddsBot --skill embeddings-alsk1992

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill simplifies the process of generating, managing, and utilizing vector embeddings for semantic search and similarity analysis, making it easier to build AI-powered search and recommendation systems.

Core Features & Use Cases

  • Multiple Providers: Supports OpenAI, Voyage AI, and local models for flexibility.
  • Efficient Embedding: Generates embeddings for single texts or batches.
  • Semantic Search: Enables searching through stored vectors based on query similarity.
  • Similarity Calculation: Computes cosine similarity between texts.
  • Caching: Optimizes performance and reduces costs by caching embeddings.
  • Use Case: Quickly find documents related to "trading strategies" by generating embeddings for your document collection and then performing a semantic search.

Quick Start

Use the embeddings skill to generate an embedding for the text "What is AI?".

Frequently Asked Questions about embeddings

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

FAQPage Schema
How do I perform semantic search over a collection of documents using vector embeddings?▼

To perform semantic search, generate vector embeddings for your document batch and query text, then compute cosine similarity to rank and retrieve the most relevant documents efficiently.

Can I generate vector embeddings locally without sending data to external APIs?▼

Yes, you can generate vector embeddings locally using Transformers.js. This skill provides a unified interface that supports local models alongside external providers like OpenAI and Voyage AI for flexible deployment.

How do I calculate text similarity using embeddings from OpenAI or Voyage AI?▼

Calculate text similarity by generating vector embeddings for your target texts via supported providers like OpenAI or Voyage AI, then computing cosine similarity between the resulting vectors to measure semantic relatedness.

What is the best way to reduce API costs when generating vector embeddings for large datasets?▼

The best way to reduce costs when generating vector embeddings is to use built-in caching mechanisms. This skill supports efficient caching to optimize performance and minimize redundant API calls across batch processing.

Does this tool support batch processing for generating vector embeddings?▼

Yes, this tool supports batch processing for generating vector embeddings. It provides a unified interface to handle multiple texts simultaneously across OpenAI, Voyage AI, and local models for optimized throughput.

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