Client SDKs

We provide client libraries in a number of popular languages that make it easier to work with the PremAI API.

We give you the option to use the Prem AI API, the PremAI SDK, or the OpenAI SDK.

For API usage documentation, refer to the API Reference.

Create an API Key 🔑

Click the API Key button on the sidebar. Then click the + Create API Key button.

Afterwards, copy the API key and save it in a secure location.

Use with PremAI SDK

Install the PremAI SDK

javascript/typescript

npm install premai

List Models

javascript/typescript

import PremAI from 'premai';

const client = new PremAI({
  apiKey: process.env['PREMAI_API_KEY'], // This is the default and can be omitted
});

const response = await client.models.list();

console.log(response.data);

Check Model Status

This is useful to check if a model is running or not. If it is not running, you can load it up using the load method.

The model name can be replaced with the name of your fine-tuned models as well.

javascript/typescript

import PremAI from 'premai';

const client = new PremAI({
  apiKey: process.env['PREMAI_API_KEY'] // This is the default and can be omitted
});

const response = await client.models.check_status(model="llama3.2-3b");

console.log(response);

Load Model

This is useful to load up a model for inference.

The model name can be replaced with the name of your fine-tuned models as well.

javascript/typescript

import PremAI from 'premai';

const client = new PremAI({
  apiKey: process.env['PREMAI_API_KEY'] // This is the default and can be omitted
});

const response = await client.models.load(model="llama3.2-3b"); // Or any other model you want to load up

const modelStatus = await client.models.check_status(model="llama3.2-3b");

console.log(modelStatus);

Unload Model

This is useful to unload a model from the server.

The model name can be replaced with the name of your fine-tuned models as well.

javascript/typescript

import PremAI from 'premai';

const client = new PremAI({
  apiKey: process.env['PREMAI_API_KEY'] // This is the default and can be omitted
});

const response = await client.models.unload(model="llama3.2-3b"); // Or any other model you want to load up

console.log(response);

Chat Completions

javascript/typescript

import PremAI from 'premai';

const client = new PremAI({
  apiKey: process.env['PREMAI_API_KEY'], // This is the default and can be omitted
});

const response = await client.chat.completions({
    messages: [{
        role: 'user',
        content: 'Write a one-sentence bedtime story about a unicorn.'
    }],
    model: 'llama3.2-3b'
});

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

Chat Completion with Streaming

Streaming is not supported for the PremAI SDK. Will be supported in the future.

Use with OpenAI SDK

Install the OpenAI SDK

javascript/typescript

npm install openai

List Models

javascript/typescript

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: "https://studio.premai.io/api/v1/",
  apiKey: process.env['PREMAI_API_KEY'], // This is the default and can be omitted
});

const response = await client.models.list();

console.log(response.data);

Chat Completions

The model name can be replaced with the name of your fine-tuned models as well.

javascript/typescript

import OpenAI from "openai";

const client = new OpenAI({
    baseURL: "https://studio.premai.io/api/v1/",
    apiKey: process.env.PREMAI_API_KEY,
});

//Create a chat completion
const response = await client.chat.completions.create({
    model: "llama3.2-3b", //Or any other model you want to use
    messages: [{ role: "user", content: "Write a one-sentence bedtime story about a unicorn." }]
});

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

Chat Completion with Streaming

javascript/typescript

import OpenAI from "openai";

const client = new OpenAI({
    baseURL: "https://studio.premai.io/api/v1/",
    apiKey: process.env.PREMAI_API_KEY,
});

//Create a chat completion
const response = await client.chat.completions.create({
    model: "llama3.2-3b", //Or any other model you want to use
    messages: [{ role: "user", content: "Write a one-sentence bedtime story about a unicorn." }],
    stream: true,
});

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