Turn text into embeddings, numeric representations of meaning, through 9Router. Choose a model and process individual texts or batches to prepare data for meaning-based search, similarity comparisons, or RAG systems that retrieve reference material.
Generate vector embeddings via 9Router /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.
Review the instructions the agent will receive before you install.
Requires NINEROUTER_URL (and NINEROUTER_KEY if auth enabled). See https://raw.githubusercontent.com/decolua/9router/refs/heads/master/skills/9router/SKILL.md for setup.
curl $NINEROUTER_URL/v1/models/embedding | jq '.data[].id'
# Per-model dimensions
curl "$NINEROUTER_URL/v1/models/info?id=openai/text-embedding-3-small"
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 |
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
{ "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 | 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.
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Set up access to 9Router, a shared gateway for multiple AI providers, and discover available models. Find the relevant capability skills for chat, images, speech, embeddings, web search, and webpage retrieval.
Ask AI questions, generate code, and summarize text through 9Router using OpenAI or Anthropic request formats. Receive responses incrementally with streaming, and use provider combinations that automatically try another provider when needed.
Generate images from written descriptions through 9Router using a choice of image models. Check each model’s supported settings, request an image, and receive a URL, encoded image data, or image bytes for saving to a file.
Transcribe audio files through 9Router using models from providers such as OpenAI, Groq, and Deepgram. Receive plain text, structured results with timestamps, or SRT and VTT subtitles, depending on the selected model’s support.
Convert written text into speech through 9Router, choosing from multiple voice providers. Check available models and voices, then generate MP3 audio for narration, voiceovers, or reading text aloud.
Create videos from text or images through 9Router using xAI Grok Imagine. Set duration, aspect ratio, and resolution, then track the generation job and download the completed video as an MP4 file.