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telnyx-embeddings

Generate text embeddings and perform semantic search via Telnyx AI APIs.

3|2|Updated Feb 10, 2026
Search Tags:#semantic search#embeddings#ai#vector search#telnyx#data indexing
One-click install
npx skills add https://github.com/team-telnyx/telnyx-toolkit --skill telnyx-embeddings
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: telnyx-embeddings
Source: https://github.com/team-telnyx/telnyx-toolkit/tree/main/tools/embeddings
Command: npx skills add https://github.com/team-telnyx/telnyx-toolkit --skill telnyx-embeddings

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill allows you to convert text into high-dimensional vectors and perform semantic searches across your data without needing external API keys like OpenAI or Google.

Core Features & Use Cases

  • Text-to-Vector Embeddings: Generate embedding vectors for any text input using Telnyx's OpenAI-compatible AI API.
  • Semantic Search: Search your Telnyx Storage buckets using natural language queries.
  • Indexing: Upload files to Telnyx Storage and trigger embedding to make them searchable.
  • Use Case: You have a collection of documents in a Telnyx Storage bucket and want to find information using natural language questions, rather than keyword searches.

Quick Start

Generate an embedding vector for the text "Hello, world!".

Frequently Asked Questions about telnyx-embeddings

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

FAQPage Schema
How do I generate text-to-vector embeddings without an OpenAI API key?▼

Text-to-vector embeddings can be generated without an OpenAI API key by using Telnyx's OpenAI-compatible AI API. This allows you to convert text inputs into high-dimensional vectors directly through the Telnyx infrastructure.

How do I perform a semantic search across files in a storage bucket?▼

Semantic search across storage buckets is performed by using natural language queries to find information. Indexing files in Telnyx Storage triggers embedding generation, making the document contents searchable by meaning rather than exact keywords.

Can I search Telnyx Storage buckets using natural language queries?▼

Yes, you can search Telnyx Storage buckets using natural language queries. The skill generates embeddings for your indexed files and matches your natural language input against them to retrieve semantically relevant results.

What is the best way to index documents for semantic searchability?▼

Indexing documents for semantic searchability is best done by uploading files to Telnyx Storage. Once uploaded, the system triggers embedding generation automatically, converting the text into vectors so users can query the data naturally.

Does the Telnyx AI API work with OpenAI-compatible embeddings tools?▼

Yes, the Telnyx AI API works with OpenAI-compatible embeddings tools. It provides an OpenAI-compatible interface for generating text-to-vector embeddings, allowing you to integrate it into existing workflows without needing external provider keys.

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