together-embeddings

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Dense vector embeddings, semantic search, RAG pipelines, and reranking via Together AI. Generate embeddings with open-source models and rerank results behind dedicated endpoints. Reach for it whenever the user needs vector representations or retrieval quality improvements rather than direct text generation.

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Sourcetogethercomputer/skills

Added on2026-04-05

NPX Install

npx skill4agent add togethercomputer/skills together-embeddings

SKILL.md Content

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Together Embeddings & Reranking

Overview

Use this skill for semantic retrieval components:

create embeddings
batch embeddings
build retrieval or RAG pipelines
rerank retrieved candidates

This skill is for retrieval plumbing, not for the final language-model response itself.

When This Skill Wins

Build vector search or semantic similarity features
Add embedding generation to a data pipeline
Improve retrieval quality with reranking
Assemble a retrieval stage before calling a chat model

Hand Off To Another Skill

Use
```
together-chat-completions
```
for the final answer-generation step
Use
```
together-batch-inference
```
for very large offline embedding backfills
Use
```
together-dedicated-endpoints
```
when reranking requires a dedicated deployment

Quick Routing

Embeddings API usage
- Read references/api-reference.md
- Start with scripts/embed_and_rerank.py or scripts/embed_and_rerank.ts
Semantic search (embed, store, query)
- Start with scripts/semantic_search.py -- includes an in-memory vector store, cosine-similarity retrieval, and optional rerank
RAG pipeline composition
- Start with scripts/rag_pipeline.py
Model selection and rerank constraints
- Read references/models.md

Workflow

Confirm that the user needs vectors or retrieval, not direct generation.
Choose the embedding model and batch shape.
Generate embeddings for corpus and query paths consistently.
Retrieve candidates. An in-memory cosine-similarity store works for prototyping and small corpora (see
```
semantic_search.py
```
). Use a dedicated vector database for production scale.
Rerank only when the extra latency and endpoint requirement are justified. When no dedicated rerank endpoint is available, cosine-similarity ranking is a reasonable fallback.

High-Signal Rules

Python scripts require the Together v2 SDK (
```
together>=2.0.0
```
). If the user is on an older version, they must upgrade first:
```
uv pip install --upgrade "together>=2.0.0"
```
.
Keep embeddings and reranking conceptually separate; rerank is a second-stage precision step.
Reranking in this repo assumes a dedicated endpoint. Do not promise serverless rerank unless the product changes. When no endpoint is available, fall back to cosine-similarity ranking.
The embedding model has a 514-token context limit. Chunk longer documents before embedding.
The
```
rag_pipeline.py
```
example demonstrates retrieval plus generation; treat generation as a hand-off to chat completions.
Preserve model consistency across indexing and querying.

Resource Map

API details: references/api-reference.md
Model guide: references/models.md
Python embeddings example: scripts/embed_and_rerank.py
TypeScript embeddings example: scripts/embed_and_rerank.ts
Python semantic search: scripts/semantic_search.py
Python RAG pipeline: scripts/rag_pipeline.py