Apertis Docs

Embeddings & Rerank

Embedding API

Apertis provides the Embedding API for developers to convert text into vectors and find similar text through vector search.

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Usage (Example in Python)

cURL

curl https://api.apertis.ai/v1/embeddings \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer <APERTIS_API_KEY>" \
    -d '{
        "model": "jina-embeddings-v3",
        "input": "The food was delicious and the waiter..."
    }'

Python

from openai import OpenAI

client = OpenAI(
    api_key="<APERTIS_API_KEY>",
    base_url="https://api.apertis.ai/v1"
)

response = client.embeddings.create(
    model="jina-embeddings-v3",
    input="The food was delicious and the waiter..."
)

print(response.data[0].embedding)

JavaScript

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: '<APERTIS_API_KEY>',
  baseURL: 'https://api.apertis.ai/v1'
});

const response = await client.embeddings.create({
  model: 'jina-embeddings-v3',
  input: 'The food was delicious and the waiter...'
});

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

Parameters

  • model: The model to use, currently supports jina-embeddings-v3, jina-embeddings-v4, jina-embeddings-v5-text-small, jina-embeddings-v5-text-nano, jina-clip-v1, jina-colbert-v2 from Jina AI, mistral-embed-2312 from Mistral AI and gemini-embedding-2-preview from Google. See Models for the current list.
  • input: The text to convert
  • APERTIS_API_KEY: Your API key