curl --request POST \
  --url https://aigw.portkey.ai/v1/responses \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "input": "<string>",
  "metadata": {},
  "temperature": 1,
  "top_p": 1,
  "user": "<string>",
  "previous_response_id": "<string>",
  "reasoning": {
    "effort": "medium"
  },
  "max_output_tokens": 123,
  "instructions": "<string>",
  "text": {
    "format": {
      "type": "text"
    }
  },
  "tools": [
    {
      "type": "file_search",
      "vector_store_ids": [
        "<string>"
      ],
      "max_num_results": 123,
      "filters": {
        "type": "eq",
        "key": "<string>",
        "value": "<string>"
      },
      "ranking_options": {
        "ranker": "auto",
        "score_threshold": 0
      }
    }
  ],
  "truncation": "disabled",
  "include": [],
  "parallel_tool_calls": true,
  "store": true,
  "stream": false
}
'

import requests

url = "https://aigw.portkey.ai/v1/responses"

payload = {
    "input": "<string>",
    "metadata": {},
    "temperature": 1,
    "top_p": 1,
    "user": "<string>",
    "previous_response_id": "<string>",
    "reasoning": { "effort": "medium" },
    "max_output_tokens": 123,
    "instructions": "<string>",
    "text": { "format": { "type": "text" } },
    "tools": [
        {
            "type": "file_search",
            "vector_store_ids": ["<string>"],
            "max_num_results": 123,
            "filters": {
                "type": "eq",
                "key": "<string>",
                "value": "<string>"
            },
            "ranking_options": {
                "ranker": "auto",
                "score_threshold": 0
            }
        }
    ],
    "truncation": "disabled",
    "include": [],
    "parallel_tool_calls": True,
    "store": True,
    "stream": False
}
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({
    input: '<string>',
    metadata: {},
    temperature: 1,
    top_p: 1,
    user: '<string>',
    previous_response_id: '<string>',
    reasoning: {effort: 'medium'},
    max_output_tokens: 123,
    instructions: '<string>',
    text: {format: {type: 'text'}},
    tools: [
      {
        type: 'file_search',
        vector_store_ids: ['<string>'],
        max_num_results: 123,
        filters: {type: 'eq', key: '<string>', value: '<string>'},
        ranking_options: {ranker: 'auto', score_threshold: 0}
      }
    ],
    truncation: 'disabled',
    include: [],
    parallel_tool_calls: true,
    store: true,
    stream: false
  })
};

fetch('https://aigw.portkey.ai/v1/responses', 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://aigw.portkey.ai/v1/responses",
  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([
    'input' => '<string>',
    'metadata' => [
        
    ],
    'temperature' => 1,
    'top_p' => 1,
    'user' => '<string>',
    'previous_response_id' => '<string>',
    'reasoning' => [
        'effort' => 'medium'
    ],
    'max_output_tokens' => 123,
    'instructions' => '<string>',
    'text' => [
        'format' => [
                'type' => 'text'
        ]
    ],
    'tools' => [
        [
                'type' => 'file_search',
                'vector_store_ids' => [
                                '<string>'
                ],
                'max_num_results' => 123,
                'filters' => [
                                'type' => 'eq',
                                'key' => '<string>',
                                'value' => '<string>'
                ],
                'ranking_options' => [
                                'ranker' => 'auto',
                                'score_threshold' => 0
                ]
        ]
    ],
    'truncation' => 'disabled',
    'include' => [
        
    ],
    'parallel_tool_calls' => true,
    'store' => true,
    'stream' => false
  ]),
  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://aigw.portkey.ai/v1/responses"

	payload := strings.NewReader("{\n  \"input\": \"<string>\",\n  \"metadata\": {},\n  \"temperature\": 1,\n  \"top_p\": 1,\n  \"user\": \"<string>\",\n  \"previous_response_id\": \"<string>\",\n  \"reasoning\": {\n    \"effort\": \"medium\"\n  },\n  \"max_output_tokens\": 123,\n  \"instructions\": \"<string>\",\n  \"text\": {\n    \"format\": {\n      \"type\": \"text\"\n    }\n  },\n  \"tools\": [\n    {\n      \"type\": \"file_search\",\n      \"vector_store_ids\": [\n        \"<string>\"\n      ],\n      \"max_num_results\": 123,\n      \"filters\": {\n        \"type\": \"eq\",\n        \"key\": \"<string>\",\n        \"value\": \"<string>\"\n      },\n      \"ranking_options\": {\n        \"ranker\": \"auto\",\n        \"score_threshold\": 0\n      }\n    }\n  ],\n  \"truncation\": \"disabled\",\n  \"include\": [],\n  \"parallel_tool_calls\": true,\n  \"store\": true,\n  \"stream\": false\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://aigw.portkey.ai/v1/responses")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"input\": \"<string>\",\n  \"metadata\": {},\n  \"temperature\": 1,\n  \"top_p\": 1,\n  \"user\": \"<string>\",\n  \"previous_response_id\": \"<string>\",\n  \"reasoning\": {\n    \"effort\": \"medium\"\n  },\n  \"max_output_tokens\": 123,\n  \"instructions\": \"<string>\",\n  \"text\": {\n    \"format\": {\n      \"type\": \"text\"\n    }\n  },\n  \"tools\": [\n    {\n      \"type\": \"file_search\",\n      \"vector_store_ids\": [\n        \"<string>\"\n      ],\n      \"max_num_results\": 123,\n      \"filters\": {\n        \"type\": \"eq\",\n        \"key\": \"<string>\",\n        \"value\": \"<string>\"\n      },\n      \"ranking_options\": {\n        \"ranker\": \"auto\",\n        \"score_threshold\": 0\n      }\n    }\n  ],\n  \"truncation\": \"disabled\",\n  \"include\": [],\n  \"parallel_tool_calls\": true,\n  \"store\": true,\n  \"stream\": false\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://aigw.portkey.ai/v1/responses")

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  \"input\": \"<string>\",\n  \"metadata\": {},\n  \"temperature\": 1,\n  \"top_p\": 1,\n  \"user\": \"<string>\",\n  \"previous_response_id\": \"<string>\",\n  \"reasoning\": {\n    \"effort\": \"medium\"\n  },\n  \"max_output_tokens\": 123,\n  \"instructions\": \"<string>\",\n  \"text\": {\n    \"format\": {\n      \"type\": \"text\"\n    }\n  },\n  \"tools\": [\n    {\n      \"type\": \"file_search\",\n      \"vector_store_ids\": [\n        \"<string>\"\n      ],\n      \"max_num_results\": 123,\n      \"filters\": {\n        \"type\": \"eq\",\n        \"key\": \"<string>\",\n        \"value\": \"<string>\"\n      },\n      \"ranking_options\": {\n        \"ranker\": \"auto\",\n        \"score_threshold\": 0\n      }\n    }\n  ],\n  \"truncation\": \"disabled\",\n  \"include\": [],\n  \"parallel_tool_calls\": true,\n  \"store\": true,\n  \"stream\": false\n}"

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

{
  "metadata": {},
  "temperature": 1,
  "top_p": 1,
  "model": "o1-pro",
  "instructions": "<string>",
  "tools": [
    {
      "type": "file_search",
      "vector_store_ids": [
        "<string>"
      ],
      "max_num_results": 123,
      "filters": {
        "type": "eq",
        "key": "<string>",
        "value": "<string>"
      },
      "ranking_options": {
        "ranker": "auto",
        "score_threshold": 0
      }
    }
  ],
  "tool_choice": "none",
  "id": "<string>",
  "object": "response",
  "created_at": 123,
  "error": {
    "code": "server_error",
    "message": "<string>"
  },
  "incomplete_details": {
    "reason": "max_output_tokens"
  },
  "output": [
    {
      "id": "<string>",
      "type": "message",
      "role": "assistant",
      "content": [
        {
          "type": "output_text",
          "text": "<string>",
          "annotations": [
            {
              "type": "file_citation",
              "index": 123,
              "file_id": "<string>"
            }
          ]
        }
      ],
      "status": "in_progress"
    }
  ],
  "parallel_tool_calls": true,
  "user": "<string>",
  "previous_response_id": "<string>",
  "reasoning": {
    "effort": "medium",
    "generate_summary": "concise"
  },
  "max_output_tokens": 123,
  "text": {
    "format": {
      "type": "text"
    }
  },
  "truncation": "disabled",
  "status": "completed",
  "output_text": "<string>",
  "usage": {
    "input_tokens": 123,
    "input_tokens_details": {
      "cached_tokens": 123
    },
    "output_tokens": 123,
    "output_tokens_details": {
      "reasoning_tokens": 123
    },
    "total_tokens": 123
  }
}