curl "https://api.portkey.ai/v1/assistants" \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-virtual-key: $PORTKEY_PROVIDER_VIRTUAL_KEY" \
  -H "OpenAI-Beta: assistants=v2" \
  -d '{
    "instructions": "You are a personal math tutor. When asked a question, write and run Python code to answer the question.",
    "name": "Math Tutor",
    "tools": [{"type": "code_interpreter"}],
    "model": "gpt-4-turbo"
  }'

from portkey_ai import Portkey

client = Portkey(
  api_key = "PORTKEY_API_KEY",
  virtual_key = "PROVIDER_VIRTUAL_KEY"
)

my_assistant = client.beta.assistants.create(
    instructions="You are a personal math tutor. When asked a question, write and run Python code to answer the question.",
    name="Math Tutor",
    tools=[{"type": "code_interpreter"}],
    model="gpt-4-turbo",
)
print(my_assistant)

import Portkey from 'portkey-ai';

const client = new Portkey({
  apiKey: 'PORTKEY_API_KEY',
  virtualKey: 'PROVIDER_VIRTUAL_KEY'
});

async function main() {
  const myAssistant = await client.beta.assistants.create({
    instructions:
      "You are a personal math tutor. When asked a question, write and run Python code to answer the question.",
    name: "Math Tutor",
    tools: [{ type: "code_interpreter" }],
    model: "gpt-4-turbo",
  });

  console.log(myAssistant);
}

main();

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.portkey.ai/v1/assistants",
  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' => 'gpt-4-turbo',
    'name' => '<string>',
    'description' => '<string>',
    'instructions' => '<string>',
    'tools' => [
        
    ],
    'tool_resources' => [
        'code_interpreter' => [
                'file_ids' => [
                                
                ]
        ],
        'file_search' => [
                'vector_store_ids' => [
                                '<string>'
                ],
                'vector_stores' => [
                                [
                                                                'file_ids' => [
                                                                                                                                '<string>'
                                                                ],
                                                                'chunking_strategy' => [
                                                                                                                                'type' => 'auto'
                                                                ],
                                                                'metadata' => [
                                                                                                                                
                                                                ]
                                ]
                ]
        ]
    ],
    'metadata' => [
        
    ],
    'temperature' => 1,
    'top_p' => 1
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json",
    "x-portkey-api-key: <api-key>",
    "x-portkey-virtual-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://api.portkey.ai/v1/assistants"

	payload := strings.NewReader("{\n  \"model\": \"gpt-4-turbo\",\n  \"name\": \"<string>\",\n  \"description\": \"<string>\",\n  \"instructions\": \"<string>\",\n  \"tools\": [],\n  \"tool_resources\": {\n    \"code_interpreter\": {\n      \"file_ids\": []\n    },\n    \"file_search\": {\n      \"vector_store_ids\": [\n        \"<string>\"\n      ],\n      \"vector_stores\": [\n        {\n          \"file_ids\": [\n            \"<string>\"\n          ],\n          \"chunking_strategy\": {\n            \"type\": \"auto\"\n          },\n          \"metadata\": {}\n        }\n      ]\n    }\n  },\n  \"metadata\": {},\n  \"temperature\": 1,\n  \"top_p\": 1\n}")

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

	req.Header.Add("x-portkey-api-key", "<api-key>")
	req.Header.Add("x-portkey-virtual-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://api.portkey.ai/v1/assistants")
  .header("x-portkey-api-key", "<api-key>")
  .header("x-portkey-virtual-key", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"gpt-4-turbo\",\n  \"name\": \"<string>\",\n  \"description\": \"<string>\",\n  \"instructions\": \"<string>\",\n  \"tools\": [],\n  \"tool_resources\": {\n    \"code_interpreter\": {\n      \"file_ids\": []\n    },\n    \"file_search\": {\n      \"vector_store_ids\": [\n        \"<string>\"\n      ],\n      \"vector_stores\": [\n        {\n          \"file_ids\": [\n            \"<string>\"\n          ],\n          \"chunking_strategy\": {\n            \"type\": \"auto\"\n          },\n          \"metadata\": {}\n        }\n      ]\n    }\n  },\n  \"metadata\": {},\n  \"temperature\": 1,\n  \"top_p\": 1\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.portkey.ai/v1/assistants")

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

request = Net::HTTP::Post.new(url)
request["x-portkey-api-key"] = '<api-key>'
request["x-portkey-virtual-key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"model\": \"gpt-4-turbo\",\n  \"name\": \"<string>\",\n  \"description\": \"<string>\",\n  \"instructions\": \"<string>\",\n  \"tools\": [],\n  \"tool_resources\": {\n    \"code_interpreter\": {\n      \"file_ids\": []\n    },\n    \"file_search\": {\n      \"vector_store_ids\": [\n        \"<string>\"\n      ],\n      \"vector_stores\": [\n        {\n          \"file_ids\": [\n            \"<string>\"\n          ],\n          \"chunking_strategy\": {\n            \"type\": \"auto\"\n          },\n          \"metadata\": {}\n        }\n      ]\n    }\n  },\n  \"metadata\": {},\n  \"temperature\": 1,\n  \"top_p\": 1\n}"

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

{
  "id": "<string>",
  "object": "assistant",
  "created_at": 123,
  "name": "<string>",
  "description": "<string>",
  "model": "<string>",
  "instructions": "<string>",
  "tools": [],
  "metadata": {},
  "tool_resources": {
    "code_interpreter": {
      "file_ids": []
    },
    "file_search": {
      "vector_store_ids": [
        "<string>"
      ]
    }
  },
  "temperature": 1,
  "top_p": 1,
  "response_format": "none"
}