Embeddings

Embeddings are numerical vectors that represent text’s meaning, enabling machines to compare and analyze language. They power tasks like search, classification, and recommendations by capturing semantic relationships in a compact form.

To get a full list of embedding models visit cortecs.ai and filter by the Embedding tag.

Python

from openai import OpenAI

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

response = client.embeddings.create(
    input="Your text string goes here",
    model="<MODEL_NAME>"
)

print(response.data[0].embedding)

Node.js

import OpenAI from "openai";

const openai = new OpenAI({
    baseURL: 'https://api.cortecs.ai/v1',
    apiKey: '<API_KEY>'
});

const embedding = await openai.embeddings.create({
  model: "<MODEL_NAME>",
  input: "Your text string goes here"
});

console.log(embedding);

Curl

curl https://api.cortecs.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <API_KEY>" \
  -d '{
    "input": "Your text string goes here",
    "model": "<MODEL_NAME>"
  }'