mirror of https://github.com/decolua/9router.git synced 2026-05-08 12:01:28 +00:00

Files

decolua 5c62e73cc6 - Cowork: ComboFormModal

- BaseUrlSelect: add cloud endpoint option, custom URL local state, always
  default to first option; new cliEndpointMatch helper; CLI tool cards refactor
- API: new /v1/audio/voices and /v1/models/info; /v1/models filters disabled
  models, drop unused timestamp
- initializeApp: guard tunnel/tailscale auto-resume to once-per-process
- geminiHelper: ensureObjectType for schemas with properties but no type
- skills: minor SKILL.md tweaks (chat/embeddings/image/stt/tts/web-*)

2026-05-07 15:45:09 +07:00

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name, description

name	description
9router-embeddings	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.

9Router — Embeddings

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.

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.

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