Google: Gemini Embedding 2 (batch)

curl -s "https://llmmart.ru/api/v1/embeddings" \
  -H "Authorization: Bearer $LLM_ROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-embedding-2:batch",
    "input": "Привет!"
  }'

from openai import OpenAI

client = OpenAI(
    api_key="sk-...",  # API-ключ из личного кабинета
    base_url="https://llmmart.ru/api/v1",
)

response = client.embeddings.create(
    model="google/gemini-embedding-2:batch",
    input="Привет!",
)

print(len(response.data[0].embedding))

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLM_ROUTER_API_KEY,
  baseURL: "https://llmmart.ru/api/v1",
});

const response = await client.embeddings.create({
  model: "google/gemini-embedding-2:batch",
  input: "Привет!",
});

console.log(response.data[0]?.embedding.length);