extremerouter-embeddings

name	extremerouter-embeddings
description	Generate vector embeddings via ExtremeRouter /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.

ExtremeRouter — Embeddings

Requires NINEROUTER_URL (and NINEROUTER_KEY if auth enabled). See https://raw.githubusercontent.com/rsalmn/extremerouter/refs/heads/master/skills/extremerouter/SKILL.md for setup.

Discover

curl $NINEROUTER_URL/v1/models/embedding | jq '.data[].id'
# Per-model dimensions
curl "$NINEROUTER_URL/v1/models/info?id=openai/text-embedding-3-small"

Endpoint

POST $NINEROUTER_URL/v1/embeddings

Field	Required	Notes
model	yes	from /v1/models/embedding
input	yes	string OR array of strings
encoding_format	no	float (default) / base64
dimensions	no	OpenAI v3 only

Examples

curl -X POST $NINEROUTER_URL/v1/embeddings \
  -H "Authorization: Bearer $NINEROUTER_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"openai/text-embedding-3-small","input":["hello","world"]}'

JS:

const r = await fetch(`${process.env.NINEROUTER_URL}/v1/embeddings`, {
  method: "POST",
  headers: { "Authorization": `Bearer ${process.env.NINEROUTER_KEY}`, "Content-Type": "application/json" },
  body: JSON.stringify({ model: "gemini/text-embedding-004", input: "RAG chunk text" }),
});
const { data } = await r.json();
console.log(data[0].embedding.length);  // dimension

Response shape

{ "object": "list", "model": "openai/text-embedding-3-small",
  "data": [
    { "object": "embedding", "index": 0, "embedding": [0.0123, -0.045, ...] },
    { "object": "embedding", "index": 1, "embedding": [...] }
  ],
  "usage": { "prompt_tokens": 5, "total_tokens": 5 } }

Provider quirks

Provider	Notes
openai, openrouter, mistral, voyage-ai, fireworks, together, nebius, github, nvidia, jina-ai	Native OpenAI shape — dimensions works only on OpenAI v3 (text-embedding-3-*)
gemini, google_ai_studio	Server auto-converts to embedContent/batchEmbedContents — send OpenAI shape
openai-compatible-*, custom-embedding-*	Custom baseUrl from credentials

Batch (input as array) is faster; some providers cap batch size.