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together-embeddings

Generate text embeddings via the Together AI embeddings API.

2|1|Updated Feb 20, 2026
Search Tags:#embeddings#rag#multilingual#vector-search#semantic-similarity#model-usage#retrieval-augmentation
One-click install
npx skills add https://github.com/zainhas/togetherai-skills --skill together-embeddings
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: together-embeddings
Source: https://github.com/zainhas/togetherai-skills/tree/main/skills/together-embeddings
Command: npx skills add https://github.com/zainhas/togetherai-skills --skill together-embeddings

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires together, together-ai, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Generate vector embeddings for text to enable semantic search, similarity, and retrieval-augmented generation with Together AI.

Core Features & Use Cases

  • Generate text embeddings using the Multilingual E5 model for multilingual retrieval.
  • Use embeddings for vector search, RAG pipelines, and semantic similarity.
  • Access dedicated rerank endpoints when needed for ranking documents.

Quick Start

Provide a list of texts and call the embeddings API to obtain vector representations for downstream retrieval tasks.

Frequently Asked Questions about together-embeddings

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

FAQPage Schema
How do I generate text embeddings for a RAG pipeline using Together AI?▼

You can generate text embeddings by providing a list of texts and calling the Together AI embeddings API, which returns vector representations for downstream semantic search and retrieval-augmented generation.

Can I use Together AI embeddings for multilingual semantic search?▼

Yes, you can use Together AI embeddings for multilingual semantic search by leveraging the Multilingual E5 model to generate vector representations that support cross-lingual retrieval and similarity tasks.

Does Together AI provide reranking endpoints for document retrieval?▼

Yes, Together AI provides dedicated rerank endpoints for ranking documents, which can be integrated alongside vector embeddings to refine results in retrieval-augmented generation pipelines.

What is the best way to batch generate vector embeddings in Python and TypeScript?▼

The best way to batch generate vector embeddings in Python and TypeScript is to pass a list of texts to the Together AI embeddings API, utilizing the provided runnable scripts to process multiple inputs efficiently.

When do I need vector embeddings for semantic similarity tasks?▼

You need vector embeddings for semantic similarity tasks when converting text data into numerical representations to compare contextual meaning, enabling vector search and retrieval-augmented generation within production data pipelines.

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