Provider Slug: openai

Overview

Portkey integrates with OpenAI’s APIs to help you create production-grade AI sppd with enhanced reliability, observability, and governance features.

Getting Started

Obtain your OpenAI API Key

Visit the OpenAI dashboard to generate your API key.

Add OpenAI to Model Catalog

Add your OpenAI API key to Model Catalog to create an AI Provider. This gives you centralized key management with budget limits and rate limits per provider.

Initialize the Portkey Client

Now that you have your AI Provider, set up the Portkey client:

Portkey Hosted App

Use your Portkey API key and AI Provider slug to initialize the client.

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    provider="@openai-prod"  # Your AI Provider slug from Model Catalog
)

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
    provider:"@openai-prod" // Your AI Provider slug from Model Catalog
})

Open Source Use

Alternatively, use Portkey’s Open Source AI Gateway to enhance your app’s reliability with minimal code:

from portkey_ai import Portkey, PORTKEY_GATEWAY_URL

portkey = Portkey(
    api_key="dummy",  # Replace with your Portkey API key
    base_url=PORTKEY_GATEWAY_URL,
    Authorization="OPENAI_API_KEY", # Replace with your OpenAI API Key
    provider="openai"
)

import Portkey, { PORTKEY_GATEWAY_URL } from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "dummy", // Replace with your Portkey API key
    baseUrl: PORTKEY_GATEWAY_URL,
    Authorization: "OPENAI_API_KEY", // Replace with your OpenAI API Key
    provider: "openai"
})

🔥 That’s it! You’ve integrated Portkey into your application with just a few lines of code. Now let’s explore making requests using the Portkey client.

Supported Models

Supported OpenAI Models

GPT-4o
GPT-4o mini
o1-preview
o1-mini
GPT-4 Turbo
GPT-4
GPT-3.5 Turbo
DALL·E
TTS (Text-to-Speech)
Whisper
Embeddings
Moderation
GPT base

OpenAI Supported Features

Chat Completions

Generate chat completions using OpenAI models through Portkey:

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

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

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

console.log(chatCompletion.choices[0].message.content);

curl -X POST "https://api.portkey.ai/v1/chat/completions" \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer YOUR_PORTKEY_API_KEY" \
     -d '{
       "messages": [{"role": "user", "content": "Say this is a test"}],
       "model": "gpt-4o"
     }'

Streaming

Stream responses for real-time output in your applications:

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

for chunk in chat_complete:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

const stream = await portkey.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 || '');
}

curl -X POST "https://api.portkey.ai/v1/chat/completions" \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer YOUR_PORTKEY_API_KEY" \
     -d '{
       "model": "gpt-4o",
       "messages": [{"role": "user", "content": "Say this is a test"}],
       "stream": true
     }'

Function Calling

Leverage OpenAI’s function calling capabilities through Portkey:

let tools = [{
    type: "function",
    function: {
        name: "getWeather",
        description: "Get the current weather",
        parameters: {
            type: "object",
            properties: {
                location: { type: "string", description: "City and state" },
                unit: { type: "string", enum: ["celsius", "fahrenheit"] }
            },
            required: ["location"]
        }
    }
}];

let response = await portkey.chat.completions.create({
    model: "gpt-4o",
    messages: [
        { role: "system", content: "You are a helpful assistant." },
        { role: "user", content: "What's the weather like in Delhi - respond in JSON" }
    ],
    tools,
    tool_choice: "auto",
});

console.log(response.choices[0].finish_reason);

tools = [{
    "type": "function",
    "function": {
        "name": "getWeather",
        "description": "Get the current weather",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string", "description": "City and state"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["location"]
        }
    }
}]

response = portkey.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What's the weather like in Delhi - respond in JSON"}
    ],
    tools=tools,
    tool_choice="auto"
)

print(response.choices[0].finish_reason)

curl -X POST "https://api.portkey.ai/v1/chat/completions" \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer YOUR_PORTKEY_API_KEY" \
     -d '{
       "model": "gpt-4o",
       "messages": [
         {"role": "system", "content": "You are a helpful assistant."},
         {"role": "user", "content": "What'\''s the weather like in Delhi - respond in JSON"}
       ],
       "tools": [{
         "type": "function",
         "function": {
           "name": "getWeather",
           "description": "Get the current weather",
           "parameters": {
             "type": "object",
             "properties": {
               "location": {"type": "string", "description": "City and state"},
               "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
             },
             "required": ["location"]
           }
         }
       }],
       "tool_choice": "auto"
     }'

Vision

Process images alongside text using OpenAI’s vision capabilities:

response = portkey.chat.completions.create(
    model="gpt-4-vision-preview",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "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",
                },
            ],
        }
    ],
    max_tokens=300,
)

print(response)

const response = await portkey.chat.completions.create({
  model: "gpt-4-vision-preview",
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "What's in this image?" },
        {
          type: "image_url",
          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",
        },
      ],
    },
  ],
  max_tokens: 300,
});

console.log(response);

curl -X POST "https://api.portkey.ai/v1/chat/completions" \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer YOUR_PORTKEY_API_KEY" \
     -d '{
       "model": "gpt-4-vision-preview",
       "messages": [
         {
           "role": "user",
           "content": [
             {"type": "text", "text": "What'\''s in this image?"},
             {
               "type": "image_url",
               "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"
             }
           ]
         }
       ],
       "max_tokens": 300
     }'

Embeddings

Generate embeddings for text using OpenAI’s embedding models:

response = portkey.embeddings.create(
    input="Your text string goes here",
    model="text-embedding-3-small"
)

print(response.data[0].embedding)

const response = await portkey.embeddings.create({
  input: "Your text string goes here",
  model: "text-embedding-3-small"
});

console.log(response.data[0].embedding);

curl -X POST "https://api.portkey.ai/v1/embeddings" \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer YOUR_PORTKEY_API_KEY" \
     -d '{
       "input": "Your text string goes here",
       "model": "text-embedding-3-small"
     }'

Transcription and Translation

Portkey supports both Transcription and Translation methods for STT models:

audio_file= open("/path/to/file.mp3", "rb")

# Transcription
transcription = portkey.audio.transcriptions.create(
  model="whisper-1",
  file=audio_file
)
print(transcription.text)

# Translation
translation = portkey.audio.translations.create(
  model="whisper-1",
  file=audio_file
)
print(translation.text)

import fs from "fs";

// Transcription
async function transcribe() {
  const transcription = await portkey.audio.transcriptions.create({
    file: fs.createReadStream("/path/to/file.mp3"),
    model: "whisper-1",
  });
  console.log(transcription.text);
}
transcribe();

// Translation
async function translate() {
    const translation = await portkey.audio.translations.create({
        file: fs.createReadStream("/path/to/file.mp3"),
        model: "whisper-1",
    });
    console.log(translation.text);
}
translate();

# Transcription
curl -X POST "https://api.portkey.ai/v1/audio/transcriptions" \
     -H "Authorization: Bearer YOUR_PORTKEY_API_KEY" \
     -H "Content-Type: multipart/form-data" \
     -F "file=@/path/to/file.mp3" \
     -F "model=whisper-1"

# Translation
curl -X POST "https://api.portkey.ai/v1/audio/translations" \
     -H "Authorization: Bearer YOUR_PORTKEY_API_KEY" \
     -H "Content-Type: multipart/form-data" \
     -F "file=@/path/to/file.mp3" \
     -F "model=whisper-1"

Text to Speech

Convert text to speech using OpenAI’s TTS models:

from pathlib import Path

speech_file_path = Path(__file__).parent / "speech.mp3"
response = portkey.audio.speech.create(
  model="tts-1",
  voice="alloy",
  input="Today is a wonderful day to build something people love!"
)

with open(speech_file_path, "wb") as f:
    f.write(response.content)

import path from 'path';
import fs from 'fs';

const speechFile = path.resolve("./speech.mp3");

async function main() {
  const mp3 = await portkey.audio.speech.createCertainly! I'll continue with the Text to Speech section and then move on to the additional features and sections:

```javascript Node.js
({
    model: "tts-1",
    voice: "alloy",
    input: "Today is a wonderful day to build something people love!",
  });
  const buffer = Buffer.from(await mp3.arrayBuffer());
  await fs.promises.writeFile(speechFile, buffer);
}

main();

curl -X POST "https://api.portkey.ai/v1/audio/speech" \
     -H "Authorization: Bearer YOUR_PORTKEY_API_KEY" \
     -H "Content-Type: application/json" \
     -d '{
       "model": "tts-1",
       "voice": "alloy",
       "input": "Today is a wonderful day to build something people love!"
     }' \
     --output speech.mp3

Prompt Caching

Implement prompt caching to improve performance and reduce costs:

Prompt Caching Guide

Learn how to implement prompt caching for OpenAI models with Portkey.

Structured Output

Use structured outputs for more consistent and parseable responses:

Structured Outputs Guide

Discover how to use structured outputs with OpenAI models in Portkey.

Supported Endpoints and Parameters

Endpoint	Supported Parameters
`complete`	model, prompt, max_tokens, temperature, top_p, n, stream, logprobs, echo, stop, presence_penalty, frequency_penalty, best_of, logit_bias, user, seed, suffix
`embed`	model, input, encoding_format, dimensions, user
`chatComplete`	model, messages, functions, function_call, max_tokens, temperature, top_p, n, stream, stop, presence_penalty, frequency_penalty, logit_bias, user, seed, tools, tool_choice, response_format, logprobs, top_logprobs, stream_options, service_tier, parallel_tool_calls, max_completion_tokens
`imageGenerate`	prompt, model, n, quality, response_format, size, style, user
`createSpeech`	model, input, voice, response_format, speed
`createTranscription`	All parameters supported
`createTranslation`	All parameters supported

Portkey’s Advanced Features

Track End-User IDs

Portkey allows you to track user IDs passed with the user parameter in OpenAI requests, enabling you to monitor user-level costs, requests, and more:

response = portkey.chat.completions.create(
  model="gpt-4o",
  messages=[{"role": "user", "content": "Say this is a test"}],
  user="user_123456"
)

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

curl -X POST "https://api.portkey.ai/v1/chat/completions" \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer YOUR_PORTKEY_API_KEY" \
     -d '{
       "model": "gpt-4o",
       "messages": [{"role": "user", "content": "Say this is a test"}],
       "user": "user_123456"
     }'

When you include the user parameter in your requests, Portkey logs will display the associated user ID, as shown in the image below:

In addition to the user parameter, Portkey allows you to send arbitrary custom metadata with your requests. This powerful feature enables you to associate additional context or information with each request, which can be useful for analysis, debugging, or other custom use cases.

Learn More About Metadata

Explore how to use custom metadata to enhance your request tracking and analysis.

Using The Gateway Config

Here’s a simplified version of how to use Portkey’s Gateway Configuration:

Create a Gateway Configuration

You can create a Gateway configuration using the Portkey Config Dashboard or by writing a JSON configuration in your code. In this example, requests are routed based on the user’s subscription plan (paid or free).

config = {
  "strategy": {
    "mode": "conditional",
    "conditions": [
      {
        "query": { "metadata.user_plan": { "$eq": "paid" } },
        "then": "gpt4o"
      },
      {
        "query": { "metadata.user_plan": { "$eq": "free" } },
        "then": "gpt-3.5"
      }
    ],
    "default": "base-gpt4"
  },
  "targets": [
    {
      "name": "gpt4o",
      "provider":"@xx"
    },
    {
      "name": "gpt-3.5",
      "provider":"@yy"
    }
  ]
}

Process Requests

When a user makes a request, it will pass through Portkey’s AI Gateway. Based on the configuration, the Gateway routes the request according to the user’s metadata.

Set Up the Portkey Client

Pass the Gateway configuration to your Portkey client. You can either use the config object or the Config ID from Portkey’s hosted version.

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    provider="@PROVIDER",
    config=portkey_config
)

import Portkey from 'portkey-ai'

const portkey = new Portkey({
  apiKey: "PORTKEY_API_KEY",
  provider:"@PROVIDER",
  config: portkeyConfig
})

That’s it! Portkey seamlessly allows you to make your AI app more robust using built-in gateway features. Learn more about advanced gateway features:

Load Balancing

Distribute requests across multiple targets based on defined weights.

Fallbacks

Automatically switch to backup targets if the primary target fails.

Conditional Routing

Route requests to different targets based on specified conditions.

Caching

Enable caching of responses to improve performance and reduce costs.

Guardrails

Portkey’s AI gateway enables you to enforce input/output checks on requests by applying custom hooks before and after processing. Protect your user’s/company’s data by using PII guardrails and many more available on Portkey Guardrails:

{
	"provider:"@openai-xxx",
	"before_request_hooks": [{
		"id": "input-guardrail-id-xx"
	}],
	"after_request_hooks": [{
		"id": "output-guardrail-id-xx"
	}]
}

Learn More About Guardrails

Explore Portkey’s guardrail features to enhance the security and reliability of your AI applications.

Next Steps

The complete list of features supported in the SDK are available in our comprehensive documentation:

Portkey SDK Documentation

Explore the full capabilities of the Portkey SDK and how to leverage them in your projects.

Limitations

Portkey does not support the following OpenAI features:

Streaming for audio endpoints
Chat completions feedback API
File management endpoints

For the most up-to-date information on supported features and endpoints, please refer to our API Reference.

​Overview

​Getting Started

​Portkey Hosted App

​Open Source Use

​Supported Models

​OpenAI Supported Features

​Chat Completions

​Streaming

​Function Calling

​Vision

​Embeddings

​Transcription and Translation

​Text to Speech

​Prompt Caching

Prompt Caching Guide

​Structured Output

Structured Outputs Guide

​Supported Endpoints and Parameters

​Portkey’s Advanced Features

​Track End-User IDs

Learn More About Metadata

​Using The Gateway Config

Create a Gateway Configuration

Process Requests

Set Up the Portkey Client

Load Balancing

Fallbacks

Conditional Routing

Caching

​Guardrails

Learn More About Guardrails

​Next Steps

Portkey SDK Documentation

​Limitations

Portkey is now PRISMA AIRS AI Gateway. See it in action.

Overview

Getting Started

Portkey Hosted App

Open Source Use

Supported Models

OpenAI Supported Features

Chat Completions

Streaming

Function Calling

Vision

Embeddings

Transcription and Translation

Text to Speech

Prompt Caching

Structured Output

Supported Endpoints and Parameters

Portkey’s Advanced Features

Track End-User IDs

Using The Gateway Config

Guardrails

Next Steps

Limitations