Embeddings generieren

curl --request POST \
  --url https://api.venice.ai/api/v1/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "encoding_format": "float",
  "input": "The quick brown fox jumped over the lazy dog",
  "model": "text-embedding-bge-m3"
}
'

import requests

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

payload = {
    "encoding_format": "float",
    "input": "The quick brown fox jumped over the lazy dog",
    "model": "text-embedding-bge-m3"
}
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({
    encoding_format: 'float',
    input: 'The quick brown fox jumped over the lazy dog',
    model: 'text-embedding-bge-m3'
  })
};

fetch('https://api.venice.ai/api/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.venice.ai/api/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([
    'encoding_format' => 'float',
    'input' => 'The quick brown fox jumped over the lazy dog',
    'model' => 'text-embedding-bge-m3'
  ]),
  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.venice.ai/api/v1/embeddings"

	payload := strings.NewReader("{\n  \"encoding_format\": \"float\",\n  \"input\": \"The quick brown fox jumped over the lazy dog\",\n  \"model\": \"text-embedding-bge-m3\"\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.venice.ai/api/v1/embeddings")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"encoding_format\": \"float\",\n  \"input\": \"The quick brown fox jumped over the lazy dog\",\n  \"model\": \"text-embedding-bge-m3\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.venice.ai/api/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  \"encoding_format\": \"float\",\n  \"input\": \"The quick brown fox jumped over the lazy dog\",\n  \"model\": \"text-embedding-bge-m3\"\n}"

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

{
  "data": [
    {
      "embedding": [
        0.0023064255,
        -0.009327292,
        0.015797377
      ],
      "index": 0,
      "object": "embedding"
    }
  ],
  "model": "text-embedding-bge-m3",
  "object": "list",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

{
  "error": "<string>",
  "details": {
    "_errors": [],
    "field": {
      "_errors": [
        "Field is required"
      ]
    }
  }
}

{
  "error": "<string>"
}

{
  "error": "<string>"
}

{
  "error": "<string>"
}

{
  "error": "<string>"
}

{
  "error": "<string>"
}

{
  "error": "<string>"
}