Crear incrustaciones de texto

import { SDK } from "@meetkai/mka1";

const sdk = new SDK({
  bearerAuth: "<YOUR_BEARER_TOKEN_HERE>",
});

async function run() {
  const result = await sdk.llm.embeddings.embed({
    embeddingsRequest: {
      input: "The quick brown fox jumps over the lazy dog.",
      model: "auto",
    },
  });

  console.log(result);
}

run();

package main

import(
	"context"
	mka1 "github.com/MeetKai/mka1-sdks/go"
	"github.com/MeetKai/mka1-sdks/go/models/components"
	"log"
)

func main() {
    ctx := context.Background()

    s := mka1.New(
        mka1.WithSecurity("<YOUR_BEARER_TOKEN_HERE>"),
    )

    res, err := s.Llm.Embeddings.Embed(ctx, components.EmbeddingsRequest{
        Input: components.CreateEmbeddingsRequestInputStr(
            "The quick brown fox jumps over the lazy dog.",
        ),
        Model: "auto",
    }, nil)
    if err != nil {
        log.Fatal(err)
    }
    if res.EmbeddingsResponse != nil {
        // handle response
    }
}

using MeetKai.MKA1;
using MeetKai.MKA1.Types.Components;

var sdk = new SDK(bearerAuth: "<YOUR_BEARER_TOKEN_HERE>");

var res = await sdk.Llm.Embeddings.EmbedAsync(body: new EmbeddingsRequest() {
    Input = EmbeddingsRequestInput.CreateStr(
        "The quick brown fox jumps over the lazy dog."
    ),
    Model = "auto",
});

// handle response

from meetkai_mka1 import SDK


with SDK(
    bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
) as sdk:

    res = sdk.llm.embeddings.embed(input="The quick brown fox jumps over the lazy dog.", model="auto", encoding_format="float")

    # Handle response
    print(res)

curl --request POST \
  --url https://apigw.mka1.com/api/v1/llm/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "auto",
  "input": "The quick brown fox jumps over the lazy dog.",
  "encoding_format": "float"
}
'

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    model: 'auto',
    input: 'The quick brown fox jumps over the lazy dog.',
    encoding_format: 'float'
  })
};

fetch('https://apigw.mka1.com/api/v1/llm/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://apigw.mka1.com/api/v1/llm/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' => 'auto',
    'input' => 'The quick brown fox jumps over the lazy dog.',
    'encoding_format' => 'float'
  ]),
  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;
}

HttpResponse<String> response = Unirest.post("https://apigw.mka1.com/api/v1/llm/embeddings")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"auto\",\n  \"input\": \"The quick brown fox jumps over the lazy dog.\",\n  \"encoding_format\": \"float\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://apigw.mka1.com/api/v1/llm/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\": \"auto\",\n  \"input\": \"The quick brown fox jumps over the lazy dog.\",\n  \"encoding_format\": \"float\"\n}"

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

{
  "data": [
    {
      "object": "embedding",
      "embedding": [
        0.0023,
        -0.0091,
        0.0156,
        -0.0042,
        0.0089
      ],
      "index": 0
    }
  ],
  "model": "auto",
  "object": "list",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}