With this integration, your LLM requests will go through the Keywords AI gateway, and the requests will be automatically logged to Keywords AI.

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1. Get Keywords AI API key

After you create an account on Keywords AI, you can get your API key from the API keys page.

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2. Add provider credentials

You have to add your own credentials to activate AI gateway otherwise your LLM calls will cause errors. We will use your credentials to call LLMs on your behalf.

We won’t use your credentials for any other purposes and no extra charges will be applied.

Go to the Providers page to add your credentials.

Learn how to add credentials to a specific provider here.

​
3. Integrate Keywords AI gateway

Here’s an example of how to use the Vercel AI SDK with OpenAI:

import { createOpenAI, OpenAIProvider } from '@ai-sdk/openai'
import { streamText, streamObject } from 'ai'

async function main() {
  // Initialize OpenAI with Keywords proxy
  const client: OpenAIProvider = createOpenAI({
    baseURL: 'https://api.keywordsai.co',
    apiKey: 'YOUR_KEYWORDS_AI_API_KEY',
    compatibility: 'strict',
  })


  const requestParamsDefault: Parameters<typeof streamText>[0] = {
    model: client.chat('gpt-3.5-turbo'),
    messages: [
      {
        role: 'user',
        content: 'Hello! How are you doing today?',
      },
    ],
    temperature: 0.5,
  }

  try {
    console.log('Calling OpenAI with Keywords proxy...')

    const { textStream: proxyTextStream } = await streamText(requestParamsDefault)
    for await (const textPart of proxyTextStream) {
      console.log('Keywords Proxy Response:', textPart)
    }
  } catch (error) {
    console.error('Error:', error)
  }
}

main()

​
4. Add Keywords AI parameters

Adding Keywords AI parameters to the Vercel AI SDK is different than other frameworks. Here is an example of how to do it:

1

Specify Keywords AI params in an object

You should create an object with the Keywords AI parameters you want to use. Add parameters you want to use as keys in the object.

const keywordsAIHeaderContent = {
    "customer_params": {
        "customer_identifier": "customer_123",
        "name": "Hendrix Liu", //optional
        "email": "hendrix@keywordsai.co" //optional
    }
    // "cache_enabled": true or other parameters
}
2

Encode the object as a string

You should encode the object as a string and then you can send it as a header in your request.

const encoded = Buffer.from(JSON.stringify(keywordsAIHeaderContent)).toString('base64');
3

Add the header to your request

You should send it in the X-Data-Keywordsai-Params header.

const client = createOpenAI({
  baseURL: process.env.KEYWORDSAI_ENDPOINT_LOCAL,
  apiKey: process.env.KEYWORDSAI_API_KEY_TEST,
  compatibility: "strict",
  headers: {
    "X-Data-Keywordsai-Params": encoded
  }
});
4

Full example

import { streamText, streamObject } from "ai";
import { createOpenAI } from "@ai-sdk/openai";

const keywordsAIHeaderContent = {
    "customer_identifier": "test_customer_identifier_from_header"
}
const encoded = Buffer.from(JSON.stringify(keywordsAIHeaderContent)).toString('base64');

const client = createOpenAI({
  baseURL: process.env.KEYWORDSAI_ENDPOINT_LOCAL,
  apiKey: process.env.KEYWORDSAI_API_KEY_TEST,
  compatibility: "strict",
  headers: {
    "X-Data-Keywordsai-Params": encoded
  }
});

const requestParamsDefault: Parameters<typeof streamText>[0] = {
  model: client.chat("gpt-4o"),
  messages: [
    {
      role: "user",
      content: "Hello! How are you doing today?",
    },
  ],
  temperature: 0.5,
};

try {
  console.log("Calling OpenAI with Keywords proxy...");

  const { textStream: proxyTextStream } = await streamText(
    requestParamsDefault
  );
  for await (const textPart of proxyTextStream) {
    console.log("Keywords Proxy Response:", textPart);
  }
} catch (error) {
  console.error("Error:", error);
}