Embeddings

The R2R Embedding endpoint generates vector embeddings from text using a specified model. It supports single or batch input, returning semantic vectors for use in search, clustering, and RAG applications.

POST /api/r2r/v3/retrieval/embedding

Reverse Proxy Auth

cURL

curl --request POST \
  --url https://api.intelligence.io.solutions/api/r2r/v3/retrieval/embedding \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "text-embedding-3-large",
  "input": "hello world"
}
'

import requests

url = "https://api.intelligence.io.solutions/api/r2r/v3/retrieval/embedding"

payload = {
    "model": "text-embedding-3-large",
    "input": "hello world"
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({model: 'text-embedding-3-large', input: 'hello world'})
};

fetch('https://api.intelligence.io.solutions/api/r2r/v3/retrieval/embedding', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.intelligence.io.solutions/api/r2r/v3/retrieval/embedding",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'model' => 'text-embedding-3-large',
    'input' => 'hello world'
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.intelligence.io.solutions/api/r2r/v3/retrieval/embedding"

	payload := strings.NewReader("{\n  \"model\": \"text-embedding-3-large\",\n  \"input\": \"hello world\"\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.intelligence.io.solutions/api/r2r/v3/retrieval/embedding")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"text-embedding-3-large\",\n  \"input\": \"hello world\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.intelligence.io.solutions/api/r2r/v3/retrieval/embedding")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"model\": \"text-embedding-3-large\",\n  \"input\": \"hello world\"\n}"

response = http.request(request)
puts response.read_body

422

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>"
    }
  ]
}

The Embedding endpoint generates vector embeddings for the provided text using a specified model.

Embeddings are dense numerical representations of text that capture semantic meaning, enabling downstream applications such as semantic search, clustering, classification, and RAG (Retrieval-Augmented Generation) operations.

This endpoint provides a simple and efficient way to convert text into machine-readable vector form for use within R2R’s retrieval and analysis systems.

Use Cases

Embeddings generated via this endpoint can be used for:

• Semantic search — Compare embedding vectors to find related content.
• Clustering and classification — Group similar documents or classify them by meaning.
• Knowledge graph enhancement — Connect semantically related entities.
• RAG workflows — Retrieve relevant content before passing context to language models.

Authorizations

Authorization · string · header · required

The access token received from the authorization server in the OAuth 2.0 flow.

Headers

token · string

JWT token

Authorization · string

io.net provided API Key

x-api-key · string

API key set by an SDK client

Body

application/json

Raw JSON body for the embedding request. Example inputs shown below.

The body is of type object.

Response

200

application/json

Successful Response