GPT - vector - Tikway

GPT - vector

curl --request POST \
  --url https://api.tikway.ai/v1/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "openai/text-embedding-3-large",
  "input": "Hello, world!"
}
'

import requests

url = "https://api.tikway.ai/v1/embeddings"

payload = {
    "model": "openai/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: 'openai/text-embedding-3-large', input: 'Hello, world!'})
};

fetch('https://api.tikway.ai/v1/embeddings', 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.tikway.ai/v1/embeddings",
  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' => 'openai/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.tikway.ai/v1/embeddings"

	payload := strings.NewReader("{\n  \"model\": \"openai/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.tikway.ai/v1/embeddings")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"openai/text-embedding-3-large\",\n  \"input\": \"Hello, world!\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.tikway.ai/v1/embeddings")

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\": \"openai/text-embedding-3-large\",\n  \"input\": \"Hello, world!\"\n}"

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

{
  "data": [
    {
      "embedding": [
        -0.005828857421875,
        -0.0241241455078125,
        -0.02203369140625
      ],
      "index": 0,
      "object": "embedding"
    }
  ],
  "model": "openai/text-embedding-3-large",
  "object": "list",
  "usage": {
    "prompt_tokens": 4,
    "total_tokens": 4
  }
}

POST

https://api.tikway.ai

/

v1

/

embeddings

GPT - vector

curl --request POST \
  --url https://api.tikway.ai/v1/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "openai/text-embedding-3-large",
  "input": "Hello, world!"
}
'

import requests

url = "https://api.tikway.ai/v1/embeddings"

payload = {
    "model": "openai/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: 'openai/text-embedding-3-large', input: 'Hello, world!'})
};

fetch('https://api.tikway.ai/v1/embeddings', 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.tikway.ai/v1/embeddings",
  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' => 'openai/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.tikway.ai/v1/embeddings"

	payload := strings.NewReader("{\n  \"model\": \"openai/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.tikway.ai/v1/embeddings")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"openai/text-embedding-3-large\",\n  \"input\": \"Hello, world!\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.tikway.ai/v1/embeddings")

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\": \"openai/text-embedding-3-large\",\n  \"input\": \"Hello, world!\"\n}"

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

{
  "data": [
    {
      "embedding": [
        -0.005828857421875,
        -0.0241241455078125,
        -0.02203369140625
      ],
      "index": 0,
      "object": "embedding"
    }
  ],
  "model": "openai/text-embedding-3-large",
  "object": "list",
  "usage": {
    "prompt_tokens": 4,
    "total_tokens": 4
  }
}

Authorizations

Authorization

string

header

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

application/json

Convert text or token ID to vector representation.

model

string

required

Vector model ID.

Minimum string length: 1

Examples:

"openai/text-embedding-3-small"

"openai/text-embedding-3-large"

"bailian/text-embedding-v4"

"google/gemini-embedding-2-preview"

input

required

Requires input to generate vectors. Supports a single string, string array, token ID array or token ID two-dimensional array. The Gemini embedding model currently only supports single strings.

Minimum string length: 1

Example:

"OfoxAI is an LLM Gateway"

encoding_format

enum<string>

default:float

Vector encoding format. float returns an array of floating point numbers; base64 returns Base64-encoded float32 data.

Available options:

float,

base64

dimensions

integer

Output vector dimension, only supported by some models. Gemini embedding models are limited to 128 to 3072 in the current gateway.

Required range: x >= 1

user

string

End user identification, used for upstream abuse monitoring.

Response

200 - application/json

OpenAI Embeddings compatible responses.

object

string

required

Fixed to list.

Allowed value: "list"

data

object[]

required

Enter the corresponding vector result list.

Minimum array length: 1

Hide child attributes

data.object

string

required

Allowed value: "embedding"

data.embedding

required

vector data. The specific type is determined by encoding_format in the request.

Minimum array length: 1

data.index

integer

required

The index corresponding to the input content.

Required range: x >= 0

model

string

required

The actual model ID used.

usage

object

required

Hide child attributes

usage.prompt_tokens

integer

required

Enter the number of tokens consumed.

Required range: x >= 0

usage.total_tokens

integer

required

The total number of tokens in this request.

Required range: x >= 0