# Together AI Embeddings & Reranking Skill for Agents

> Skill that teaches Claude Code Together AI's embeddings and reranking API. Covers dense vector generation, semantic search, RAG pipelines, and result reranking patterns.

## Install

Save the content below to `.claude/skills/` or append to your `CLAUDE.md`:

## Quick Use

```bash
npx skills add togethercomputer/skills
```

## What is This Skill?

This skill teaches AI coding agents how to use Together AI's embeddings and reranking APIs. It covers dense vector generation for semantic search, RAG pipeline construction, and result reranking for improved relevance.

**Answer-Ready**: Together AI Embeddings Skill for coding agents. Covers dense embeddings, semantic search, RAG pipelines, and reranking. Correct model IDs and SDK patterns. Part of official 12-skill collection.

**Best for**: Developers building RAG or semantic search with Together AI. **Works with**: Claude Code, Cursor, Codex CLI.

## What the Agent Learns

### Generate Embeddings

```python
from together import Together

client = Together()
response = client.embeddings.create(
    model="togethercomputer/m2-bert-80M-8k-retrieval",
    input=["What is machine learning?", "How does AI work?"],
)
vectors = [e.embedding for e in response.data]
```

### Semantic Search Pattern

```python
# 1. Embed documents
doc_embeddings = embed(documents)
# 2. Embed query
query_embedding = embed([query])
# 3. Cosine similarity search
# 4. Return top-k results
```

### Reranking

```python
response = client.rerank.create(
    model="Salesforce/Llama-Rank-V1",
    query="What is deep learning?",
    documents=["Deep learning is...", "Machine learning uses...", "AI refers to..."],
    top_n=3,
)
for result in response.results:
    print(f"Score: {result.relevance_score}")
```

## FAQ

**Q: What embedding models are available?**
A: Multiple models including M2-BERT, BGE, and others. Choose based on dimension needs and performance.

## Source & Thanks

> Part of [togethercomputer/skills](https://github.com/togethercomputer/skills) — MIT licensed.

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## Quick Start

```bash
npx skills add togethercomputer/skills
```

## What Is This Skill?

Teaches AI agents how to use Together AI's embeddings and reranking APIs to build semantic search and RAG pipelines.

**In one sentence**: Together AI embeddings Skill — vector generation + semantic search + RAG + reranking. Official.

## Source & Thanks

> [togethercomputer/skills](https://github.com/togethercomputer/skills) — MIT

---
Source: https://tokrepo.com/en/workflows/together-ai-embeddings-reranking-skill-agents-da3bf81c
Author: Together AI