Create embeddings

Convert text to vectors

POST /v1/embeddings

Create embeddings

cURL

curl --request POST \
  --url https://api.bianxie.ai/v1/embeddings

import requests

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

response = requests.post(url)

print(response.text)

const options = {method: 'POST'};

fetch('https://api.bianxie.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.bianxie.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",
]);

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

curl_close($curl);

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

package main

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

func main() {

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

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

	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.bianxie.ai/v1/embeddings")
  .asString();

require 'uri'
require 'net/http'

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

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

request = Net::HTTP::Post.new(url)

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

Field	Type	Required	Description
model	string	Yes	Embedding model ID.
input	string/array	Yes	Non-empty text, text array, or token array.
encoding_format	string	No	float or base64.
dimensions	integer	No	Output dimensions for models that support it.
user	string	No	End-user identifier.

cURL

curl https://api.bianxie.ai/v1/embeddings \
  -H "Authorization: Bearer API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "your-model",
    "input": "Text to embed"
  }'

client.embeddings.create(model="your-model", input="Text to embed")

await client.embeddings.create({ model: "your-model", input: "Text to embed" });

The response has object: "list", data[] entries with embedding, index, and object, plus model and usage. Streaming is not supported.