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- 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-*)
70 lines
2.4 KiB
Markdown
70 lines
2.4 KiB
Markdown
---
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name: 9router-embeddings
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description: 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.
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---
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# 9Router — Embeddings
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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.
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## Discover
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```bash
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curl $NINEROUTER_URL/v1/models/embedding | jq '.data[].id'
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# Per-model dimensions
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curl "$NINEROUTER_URL/v1/models/info?id=openai/text-embedding-3-small"
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```
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## Endpoint
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`POST $NINEROUTER_URL/v1/embeddings`
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| Field | Required | Notes |
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| `model` | yes | from `/v1/models/embedding` |
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| `input` | yes | string OR array of strings |
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| `encoding_format` | no | `float` (default) / `base64` |
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| `dimensions` | no | OpenAI v3 only |
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## Examples
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```bash
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curl -X POST $NINEROUTER_URL/v1/embeddings \
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-H "Authorization: Bearer $NINEROUTER_KEY" \
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-H "Content-Type: application/json" \
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-d '{"model":"openai/text-embedding-3-small","input":["hello","world"]}'
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```
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JS:
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```js
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const r = await fetch(`${process.env.NINEROUTER_URL}/v1/embeddings`, {
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method: "POST",
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headers: { "Authorization": `Bearer ${process.env.NINEROUTER_KEY}`, "Content-Type": "application/json" },
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body: JSON.stringify({ model: "gemini/text-embedding-004", input: "RAG chunk text" }),
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});
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const { data } = await r.json();
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console.log(data[0].embedding.length); // dimension
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```
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## Response shape
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```json
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{ "object": "list", "model": "openai/text-embedding-3-small",
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"data": [
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{ "object": "embedding", "index": 0, "embedding": [0.0123, -0.045, ...] },
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{ "object": "embedding", "index": 1, "embedding": [...] }
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],
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"usage": { "prompt_tokens": 5, "total_tokens": 5 } }
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```
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## Provider quirks
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| Provider | Notes |
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|---|---|
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| `openai`, `openrouter`, `mistral`, `voyage-ai`, `fireworks`, `together`, `nebius`, `github`, `nvidia`, `jina-ai` | Native OpenAI shape — `dimensions` works only on OpenAI v3 (`text-embedding-3-*`) |
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| `gemini`, `google_ai_studio` | Server auto-converts to `embedContent`/`batchEmbedContents` — send OpenAI shape |
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| `openai-compatible-*`, `custom-embedding-*` | Custom `baseUrl` from credentials |
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Batch (`input` as array) is faster; some providers cap batch size.
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