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Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity.
Passed 21 of 22 test cases with the skill, and 15 of 22 without it.
and 9 more in the full report
7 fixed14 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
What is the expected model string prefix when sending text to Voyage AI models via 9Router?
| Checked | Without skill | With skill |
|---|---|---|
| The model string uses the voyage-ai/ prefix. | Missed | Passed |
The instructions your model reads when the skill loads.
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" }),
});
test cases passed, vs 15 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.