Embeddings

Turn text into vectors for search, clustering and retrieval. The endpoint is compatible with the OpenAI Embeddings API, so the official OpenAI SDKs work with only a new base URL.

POST /v1/embeddings

Only embedding models are served here. Calling it with a chat model returns 400. List the current embedding models with a public call that needs no key:

curl "https://kenari.id/v1/models?modality=embedding"

A plain GET /v1/models does not list embedding, rerank, moderation or System One models. It does list chat, image, speech, transcription and video models. The examples below use bge-m3. Swap in any id the call above returns. Prices are on Models and pricing.

Request

Field	Type	Required	Description
`model`	string	yes	An embedding model id.
`input`	string or array of strings	yes	One text, or several texts in one request. An array needs at least one text, and every text must be a string with some non-whitespace content. Up to 2,048 texts and 1 MiB of text in total.

Other OpenAI fields, such as encoding_format, are passed on to the provider. Whether one has an effect depends on the model.

Response

The response has the OpenAI shape. The example vector is shortened for readability. A real vector has many more values. object is "list", model is the id you requested, and data holds one entry per input, in the same order, with an index and the embedding vector. A usage object appears only when the provider reports token counts.

{
  "object": "list",
  "model": "bge-m3",
  "data": [
    { "object": "embedding", "index": 0, "embedding": [0.0123, -0.0456] }
  ]
}

Examples

curl

curl https://kenari.id/v1/embeddings \
  -H "Authorization: Bearer $KENARI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "bge-m3",
    "input": "a cat sitting on a rug"
  }'

Python

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://kenari.id/v1",
    api_key=os.environ["KENARI_API_KEY"],
)

result = client.embeddings.create(
    model="bge-m3",
    input="a cat sitting on a rug",
)

print(result.data[0].embedding[:5])

JavaScript

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://kenari.id/v1",
  apiKey: process.env.KENARI_API_KEY,
});

const result = await client.embeddings.create({
  model: "bge-m3",
  input: "a cat sitting on a rug",
});

console.log(result.data[0].embedding.slice(0, 5));

Billing

Embeddings are billed per input token. kenari estimates the count from the text you send, so the charge does not depend on what the provider reports. See How billing works for balance and price dimensions.

Errors

Status	Code	When
400	`bad_request`	`model` is unknown or is not an embedding model, `input` is missing, is an empty array, or contains an empty, whitespace-only or non-string item, or the request is over the 2,048 text or 1 MiB limit.
400	`model_not_found`	The model is listed but no provider serves it right now. Retry later.
503	`upstream_error`, `all_providers_failed`	No provider returned a usable response. You are not charged. Retry.

When model is not an embedding model, the error message lists the embedding ids that are available. See Errors for the other codes.