curl --request POST \
  --url https://rag-prod.studio.lyzr.ai/v3/rag/ \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --data '
{
  "user_id": "<string>",
  "llm_credential_id": "<string>",
  "embedding_credential_id": "<string>",
  "vector_db_credential_id": "<string>",
  "collection_name": "<string>",
  "llm_model": "<string>",
  "embedding_model": "<string>",
  "vector_store_provider": "<string>",
  "description": "",
  "semantic_data_model": false,
  "meta_data": {}
}
'

import requests

url = "https://rag-prod.studio.lyzr.ai/v3/rag/"

payload = {
    "user_id": "<string>",
    "llm_credential_id": "<string>",
    "embedding_credential_id": "<string>",
    "vector_db_credential_id": "<string>",
    "collection_name": "<string>",
    "llm_model": "<string>",
    "embedding_model": "<string>",
    "vector_store_provider": "<string>",
    "description": "",
    "semantic_data_model": False,
    "meta_data": {}
}
headers = {
    "x-api-key": "<api-key>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {'x-api-key': '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    user_id: '<string>',
    llm_credential_id: '<string>',
    embedding_credential_id: '<string>',
    vector_db_credential_id: '<string>',
    collection_name: '<string>',
    llm_model: '<string>',
    embedding_model: '<string>',
    vector_store_provider: '<string>',
    description: '',
    semantic_data_model: false,
    meta_data: {}
  })
};

fetch('https://rag-prod.studio.lyzr.ai/v3/rag/', 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://rag-prod.studio.lyzr.ai/v3/rag/",
  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([
    'user_id' => '<string>',
    'llm_credential_id' => '<string>',
    'embedding_credential_id' => '<string>',
    'vector_db_credential_id' => '<string>',
    'collection_name' => '<string>',
    'llm_model' => '<string>',
    'embedding_model' => '<string>',
    'vector_store_provider' => '<string>',
    'description' => '',
    'semantic_data_model' => false,
    'meta_data' => [
        
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json",
    "x-api-key: <api-key>"
  ],
]);

$response = curl_exec($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://rag-prod.studio.lyzr.ai/v3/rag/"

	payload := strings.NewReader("{\n  \"user_id\": \"<string>\",\n  \"llm_credential_id\": \"<string>\",\n  \"embedding_credential_id\": \"<string>\",\n  \"vector_db_credential_id\": \"<string>\",\n  \"collection_name\": \"<string>\",\n  \"llm_model\": \"<string>\",\n  \"embedding_model\": \"<string>\",\n  \"vector_store_provider\": \"<string>\",\n  \"description\": \"\",\n  \"semantic_data_model\": false,\n  \"meta_data\": {}\n}")

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

	req.Header.Add("x-api-key", "<api-key>")
	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://rag-prod.studio.lyzr.ai/v3/rag/")
  .header("x-api-key", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"user_id\": \"<string>\",\n  \"llm_credential_id\": \"<string>\",\n  \"embedding_credential_id\": \"<string>\",\n  \"vector_db_credential_id\": \"<string>\",\n  \"collection_name\": \"<string>\",\n  \"llm_model\": \"<string>\",\n  \"embedding_model\": \"<string>\",\n  \"vector_store_provider\": \"<string>\",\n  \"description\": \"\",\n  \"semantic_data_model\": false,\n  \"meta_data\": {}\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://rag-prod.studio.lyzr.ai/v3/rag/")

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

request = Net::HTTP::Post.new(url)
request["x-api-key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"user_id\": \"<string>\",\n  \"llm_credential_id\": \"<string>\",\n  \"embedding_credential_id\": \"<string>\",\n  \"vector_db_credential_id\": \"<string>\",\n  \"collection_name\": \"<string>\",\n  \"llm_model\": \"<string>\",\n  \"embedding_model\": \"<string>\",\n  \"vector_store_provider\": \"<string>\",\n  \"description\": \"\",\n  \"semantic_data_model\": false,\n  \"meta_data\": {}\n}"

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

{
  "id": "<string>",
  "user_id": "<string>",
  "llm_credential_id": "<string>",
  "embedding_credential_id": "<string>",
  "vector_db_credential_id": "<string>",
  "description": "<string>",
  "collection_name": "<string>",
  "llm_model": "<string>",
  "embedding_model": "<string>",
  "vector_store_provider": "<string>",
  "semantic_data_model": true,
  "meta_data": {}
}

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