Apertis Docs
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)

import http.client
import json

conn = http.client.HTTPSConnection("api.apertis.ai")
payload = json.dumps({
   "model": "text-embedding-3-large",
   "input": "The food was delicious and the waiter..."
})
headers = {
   'Authorization': 'Bearer <APERTIS_API_KEY>',
   'Content-Type': 'application/json'
}
conn.request("POST", "/v1/embeddings", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))

Parameters

  • model: The model to use, currently supports text-embedding-3-large, text-embedding-3-small, text-embedding-ada-002 from OpenAI and jina-embeddings-v3, jina-clip-v2, jina-colbert-v2, jina-embeddings-v2-base-code, jina-embeddings-v2-base-zh, jina-embeddings-v2-base-en from Jina AI.
  • input: The text to convert
  • APERTIS_API_KEY: Your API key