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Use Portkey with your Vercel app for:
  1. Calling 1,600+ LLMs (open & closed)
  2. Logging & analysing LLM usage
  3. Caching responses
  4. Automating fallbacks, retries, timeouts, and load balancing
  5. Managing, versioning, and deploying prompts
  6. Continuously improving app with user feedback

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Guide: Create a Portkey + OpenAI Chatbot

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1. Create a NextJS app

Go ahead and create a Next.js application, and install ai and portkey-ai as dependencies. 
pnpm dlx create-next-app my-ai-app
cd my-ai-app
pnpm install ai @ai-sdk/openai portkey-ai

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2. Add Authentication keys to .env

  1. Login to Portkey here
  2. Go to Model Catalog → Add Provider and add your OpenAI credentials
  3. Name your provider (e.g., openai-prod) - this becomes your provider slug @openai-prod
  4. Grab your Portkey API key and add it to .env file:
.env
PORTKEY_API_KEY="xxxxxxxxxx"

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3. Create Route Handler

Create a Next.js Route Handler that utilizes the Edge Runtime to generate a chat completion. Stream back to Next.js. For this example, create a route handler at app/api/chat/route.ts that calls GPT-4 and accepts a POST request with a messages array of strings:
app/api/chat/route.ts
import { streamText } from 'ai';
import { createOpenAI } from '@ai-sdk/openai';
import { createHeaders, PORTKEY_GATEWAY_URL } from 'portkey-ai';

const client = createOpenAI({
    baseURL: PORTKEY_GATEWAY_URL,
    apiKey: "PORTKEY_API_KEY",
    headers: createHeaders({
        apiKey: "PORTKEY_API_KEY"
    }),
})

export const runtime = 'edge';

export async function POST(req: Request) {
  const { messages } = await req.json();

  const response = await streamText({
        model: client('@openai-prod/gpt-4o'),  // @provider-slug/model
    messages
  })

  return response.toTextStreamResponse();
}
Portkey follows the same signature as OpenAI SDK but extends it to work with 1,600+ LLMs. Here, the chat completion call will be sent to the gpt-3.5-turbo model, and the response will be streamed to your Next.js app.

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4. Switch from OpenAI to Anthropic

Portkey is powered by an open-source, universal AI Gateway with which you can route to 1,600+ LLMs using the same, known OpenAI spec. Let’s see how you can switch from GPT-4 to Claude-3-Opus by updating 2 lines of code (without breaking anything else).
  1. Add your Anthropic credentials in Model Catalog → Add Provider (name it e.g., anthropic-prod)
  2. Update the model to use the new provider slug
  3. Update the model name while making your /chat/completions call
  4. Add maxTokens field inside streamText invocation (Anthropic requires this field)
Let’s see it in action: 
const client = createOpenAI({
    baseURL: PORTKEY_GATEWAY_URL,
    apiKey: "PORTKEY_API_KEY",
    headers: createHeaders({
        apiKey: "PORTKEY_API_KEY"
    }),
})

export const runtime = 'edge';

export async function POST(req: Request) {
  const { messages } = await req.json();

  const response = await streamText({
        model: client('@anthropic-prod/claude-3-opus-20240229'),
    messages,
    maxTokens: 200
  })

  return response.toTextStreamResponse();
}

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5. Switch to Gemini 1.5

Similarly, add your Google AI Studio API key in Model Catalog → Add Provider (name it e.g., google-prod) and call Gemini: 
const client = createOpenAI({
    baseURL: PORTKEY_GATEWAY_URL,
    apiKey: "PORTKEY_API_KEY",
    headers: createHeaders({
        apiKey: "PORTKEY_API_KEY"
    }),
})

export const runtime = 'edge';

export async function POST(req: Request) {
  const { messages } = await req.json();

  const response = await streamText({
        model: client('@google-prod/gemini-1.5-flash'),
    messages
  })

  return response.toTextStreamResponse();
}
The same will follow for all the other providers like Azure, Mistral, Anyscale, Together, and more.

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6. Wire up the UI

Let’s create a Client component that will have a form to collect the prompt from the user and stream back the completion. The useChat hook will default use the POST Route Handler we created earlier (/api/chat). However, you can override this default value by passing an api prop to useChat({ api: '...'}).
app/page.tsx
'use client';

import { useChat } from 'ai/react';

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat();

  return (
        <div>
      {messages.map((m) => (
                <div key={m.id}>
          {m.role === 'user' ? 'User: ' : 'AI: '}
          {m.content}
                </div>
      ))}
        </div>
  );
}

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7. Log the Requests

Portkey logs all the requests you’re sending to help you debug errors, and get request-level + aggregate insights on costs, latency, errors, and more. You can enhance the logging by tracing certain requests, passing custom metadata or user feedback.

rolling logs and cachescreens

Segmenting Requests with Metadata While Creating the Client, you can pass any {"key":"value"} pairs inside the metadata header. Portkey segments the requests based on the metadata to give you granular insights. 
const client = createOpenAI({
    baseURL: PORTKEY_GATEWAY_URL,
    apiKey: "PORTKEY_API_KEY",
    headers: createHeaders({
        apiKey: "PORTKEY_API_KEY",
        metadata: {
          _user: 'john doe',
          organization_name: 'acme',
          custom_key: 'custom_value'
        }
    }),
})
Learn more about tracing and feedback.

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Guide: Handle OpenAI Failures

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1. Solve 5xx, 4xx Errors

Portkey helps you automatically trigger a call to any other LLM/provider in case of primary failures.Create a fallback logic with Portkey’s Gateway Config. For example, for setting up a fallback from OpenAI to Anthropic, the Gateway Config would be:
Config
{
    "strategy": {"mode": "fallback"},
    "targets": [
        {"override_params": {"model": "@openai-prod/gpt-4o"}},
        {"override_params": {"model": "@anthropic-prod/claude-3-opus-20240229"}}
    ]
}
You can save this Config in Portkey app and get an associated Config ID that you can pass while instantiating your LLM client:

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2. Apply Config to the Route Handler

const client = createOpenAI({
    baseURL: PORTKEY_GATEWAY_URL,
    apiKey: "PORTKEY_API_KEY",
    headers: createHeaders({
        apiKey: "PORTKEY_API_KEY",
        config: "CONFIG_ID"
    }),
})

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3. Handle Rate Limit Errors

You can loadbalance your requests against multiple LLMs or accounts and prevent any one account from hitting rate limit thresholds. For example, to route your requests between 1 OpenAI and 2 Azure OpenAI accounts:
Config
{
    "strategy": {"mode": "loadbalance"},
  "targets": [
        {"override_params": {"model": "@openai-prod/gpt-4o"}, "weight": 1},
        {"override_params": {"model": "@azure-prod-1/gpt-4o"}, "weight": 1},
        {"override_params": {"model": "@azure-prod-2/gpt-4o"}, "weight": 1}
    ]
}
Save this Config in the Portkey app and pass it while instantiating the LLM Client, just like we did above. Portkey can also trigger automatic retries, set request timeouts, and more.

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Guide: Cache Semantically Similar Requests

Portkey can save LLM costs & reduce latencies 20x by storing responses for semantically similar queries and serving them from cache. For Q&A use cases, cache hit rates go as high as 50%. To enable semantic caching, just set the cache mode to semantic in your Gateway Config: 
{
    "cache": {"mode": "semantic"}
}
Same as above, you can save your cache Config in the Portkey app, and reference the Config ID while instantiating the LLM Client. Moreover, you can set the max-age of the cache and force refresh a cache. See the docs for more information.

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Guide: Manage Prompts Separately

Storing prompt templates and instructions in code is messy. Using Portkey, you can create and manage all of your app’s prompts in a single place and directly hit our prompts API to get responses. Here’s more on what Prompts on Portkey can do. To create a Prompt Template,
  1. From the Dashboard, Open Prompts
  2. In the Prompts page, Click Create
  3. Add your instructions, variables, and You can modify model parameters and click Save

verel app prompt

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Trigger the Prompt in the Route Handler

JavaScript
import Portkey from 'portkey-ai'

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

export async function POST(req: Request) {
  const { movie } = await req.json();

  const moviePromptRender = await portkey.prompts.render({
        promptID: "PROMPT_ID",
        variables: { "movie": movie }
    })

  const messages = moviePromptRender.data.messages

  const response = await streamText({
    model: client('gemini-1.5-flash'),
    messages
  })

  return response.toTextStreamResponse();
}
See docs for more information.

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Talk to the Developers

If you have any questions or issues, reach out to us on Discord here. On Discord, you will also meet many other practitioners who are putting their Vercel AI + Portkey app to production.