venice-embeddings

Generates vector embeddings for text data with OpenAI-compatible SDK support.

130|15|Updated Apr 21, 2026

Search Tags:#RAG #retrieval #semantic analysis #clustering #text embeddings

Ready to run

npx skills add https://github.com/veniceai/skills --skill venice-embeddings

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: venice-embeddings
Source: https://github.com/veniceai/skills/tree/main/skills/venice-embeddings
Command: npx skills add https://github.com/veniceai/skills --skill venice-embeddings

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides the ability to generate vector embeddings for text, enabling advanced applications like retrieval, clustering, and reasoning about aggregates (RAG).

Core Features & Use Cases

Text Embeddings: Generate embeddings for text data.
OpenAI Compatibility: Directly compatible with OpenAI SDK for embeddings.
Use Case: For building applications that require understanding the semantic similarity between texts, such as information retrieval systems or content recommendation engines.

Quick Start

Generate embeddings for the text "Why is the sky blue?" using the venice-embeddings skill.

Frequently Asked Questions about venice-embeddings

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

FAQPage Schema

How do I generate text embeddings for retrieval and clustering?▼

You can generate text embeddings for semantic analysis by passing your text data to this Skill, which outputs vector representations for retrieval, clustering, and RAG applications.

Can I use the OpenAI SDK to generate vector embeddings for RAG?▼

Yes, this Skill is directly compatible with the OpenAI SDK for embeddings, allowing you to generate vector representations for RAG without changing your existing codebase.

What are text embeddings used for in semantic analysis?▼

Text embeddings are vector representations of text used to measure semantic similarity. They power information retrieval systems, content recommendation engines, and reasoning about data aggregates.

Does this Skill support reasoning about aggregates for RAG applications?▼

Yes, this Skill supports reasoning about aggregates for RAG applications by generating vector embeddings that enable semantic understanding and similarity analysis across text data clusters.