Embeddings

Use embeddings to convert text into vectors for search, retrieval, clustering, ranking, and similarity.

POST https://api.rout.my/v1/embeddings

Request

curl https://api.rout.my/v1/embeddings \
  -H "Authorization: Bearer $ROUTMY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3-embedding-8b",
    "input": "The food was delicious and the service was excellent."
  }'

Request fields

Field	Type	Required	Notes
model	string	Yes	Exact embedding model ID from /v1/models.
input	string, array of strings, token array, or batch token arrays	Yes	Text or token input to embed.
encoding_format	string	No	float or base64, when supported by the upstream model.
dimensions	integer	No	Requested output dimension count, when supported by the model.
user	string	No	Accepted for compatibility but stripped before upstream requests.

Extra fields are preserved for providers that support them.

Batch input

{
  "model": "qwen/qwen3-embedding-8b",
  "input": [
    "First document.",
    "Second document.",
    "Third document."
  ]
}

The response preserves input order through the index field.

Python

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://api.rout.my/v1",
    api_key=os.environ["ROUTMY_API_KEY"],
)

response = client.embeddings.create(
    model="qwen/qwen3-embedding-8b",
    input=["First document.", "Second document."],
)

for item in response.data:
    print(item.index, len(item.embedding))

Response

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0093, 0.0142]
    }
  ],
  "model": "qwen/qwen3-embedding-8b",
  "usage": {
    "prompt_tokens": 5,
    "total_tokens": 5
  }
}

Quota accounting

Embedding requests count prompt tokens only. The final quota usage is:

input tokens x token_multiplier

Use /v1/models to inspect each model's token_multiplier.