OpenAI

npm install portkey-ai

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    virtual_key="OPENAI_VIRTUAL_KEY"
)

response = portkey.responses.create(
    model="gpt-4.1",
    input="Tell me a three sentence bedtime story about a unicorn."
)

print(response)

const chatCompletion = await portkey.chat.completions.create({
  messages: [{ role: "user", content: "Say this is a test" }],
  model: "gpt-4o",
  user: "user_12345",
});

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY",
})

// Make the prompt creation call with the variables

const promptCompletion = await portkey.prompts.completions.create({
    promptID: "Your Prompt ID",
    variables: {
       // The variables specified in the prompt
    }
})

// We can also override the hyperparameters

const promptCompletion = await portkey.prompts.completions.create({
    promptID: "Your Prompt ID",
    variables: {
       // The variables specified in the prompt
    },
    max_tokens: 250,
    presence_penalty: 0.2
})

import OpenAI from 'openai';

import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'
const openai = new OpenAI({
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    provider: "openai",
    apiKey: "PORTKEY_API_KEY" // defaults to process.env["PORTKEY_API_KEY"]
  })
});

async function main() {
  const stream = await openai.chat.completions.create({
    model: 'gpt-4',
    messages: [{ role: 'user', content: 'Say this is a test' }],
    stream: true,
  });

  for await (const chunk of stream) {
    process.stdout.write(chunk.choices[0]?.delta?.content || '');
  }
}

main();

response = portkey.responses.create(
    model="gpt-4.1",
    instructions="You are a helpful assistant.",
    input="Hello!",
    stream=True
)

for event in response:
    print(event)

response = portkey.responses.create(
    model="gpt-4.1",
    input=[
        {
            "role": "user",
            "content": [
                { "type": "input_text", "text": "What is in this image?" },
                {
                    "type": "input_image",
                    "image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
                }
            ]
        }
    ]
)

print(response)

tools = [
    {
        "type": "function",
        "name": "get_current_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA"
                },
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["location", "unit"]
        }
    }
]

response = portkey.responses.create(
    model="gpt-4.1",
    tools=tools,
    input="What is the weather like in Boston today?",
    tool_choice="auto"
)

print(response)

// Define the OpenAI client as shown above

const image = await openai.images.generate({
  model:"dall-e-3",
  prompt:"Lucy in the sky with diamonds",
  size:"1024x1024"
})

response = portkey.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "web_search_preview",
        "search_context_size": "medium", # Options: "high", "medium" (default), or "low"
        "user_location": {  # Optional - for localized results
            "type": "approximate",
            "country": "US",
            "city": "San Francisco",
            "region": "California"
        }
    }],
    input="What was a positive news story from today?"
)

print(response)

response = portkey.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "file_search",
        "vector_store_ids": ["vs_1234567890"],
        "max_num_results": 20,
        "filters": {  # Optional - filter by metadata
            "type": "eq",
            "key": "document_type",
            "value": "report"
        }
    }],
    input="What are the attributes of an ancient brown dragon?"
)

print(response)

response = portkey.responses.create(
    model="o3-mini",
    input="How much wood would a woodchuck chuck?",
    reasoning={
        "effort": "high"  # Options: "high", "medium", or "low"
    }
)

print(response)

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

client = OpenAI(
    api_key="OPENAI_API_KEY",
    organization="org-xxxxxxxxxx",
    project="proj_xxxxxxxxx",
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="openai",
        api_key="PORTKEY_API_KEY"
    )
)

chat_complete = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Say this is a test"}],
)

print(chat_complete.choices[0].message.content)