helixml / demo-recipes

Helix ML Connection - helixml/demo-recipes

This document explains the role of Helix ML in the system, how it handles authorization, and how it connects to the GPTScript backend. We will also cover the possible options and provide examples for each option.

The Role of Helix ML

Helix ML is a machine learning library that provides a set of tools for building and deploying machine learning models. It is designed to work seamlessly with the Helix platform, which is a distributed data processing system. Helix ML enables data scientists and engineers to build and deploy machine learning models in a scalable and efficient manner.

Authorization in Helix ML

Helix ML uses a token-based authentication system for authorization. The system uses JSON Web Tokens (JWT) for authentication and authorization. The tokens are generated by the Helix platform and are passed to Helix ML during the connection process. The tokens contain the necessary information to authenticate and authorize the user.

Here’s an example of how to generate a JWT token using the Helix platform:

const jwt = require('jsonwebtoken');
const secret = 'your-secret-key';

const payload = {
sub: '1234567890',
name: 'John Doe',
iat: Math.floor(Date.now() / 1000) - 30,
exp: Math.floor(Date.now() / 1000) + (60 * 60),
};

const token = jwt.sign(payload, secret);

The sub field in the payload is the user’s ID, and the exp field is the expiration time of the token.

Connecting to the GPTScript Backend

Helix ML connects to the GPTScript backend using the Helix Client Library. The library provides a set of tools for connecting to the Helix platform and interacting with the GPTScript backend.

Here’s an example of how to connect to the Helix platform using the Helix Client Library:

import { HelixClient } from '@helix/client';

const client = new HelixClient({
host: 'your-helix-platform-host',
token: 'your-jwt-token',
});

client.connect();

Once connected, you can interact with the GPTScript backend using the client object.

Possible Options

There are several options for connecting to the Helix platform and the GPTScript backend. Here are some examples:

Option 1: Using the Helix Client Library

This is the recommended option for connecting to the Helix platform and the GPTScript backend. The Helix Client Library provides a simple and efficient way to connect to the Helix platform and interact with the GPTScript backend.

Option 2: Using the Helix REST API

You can also connect to the Helix platform using the Helix REST API. The API provides a set of endpoints for interacting with the Helix platform and the GPTScript backend. However, this option is less efficient than using the Helix Client Library.

Option 3: Using the Helix gRPC API

The Helix platform also provides a gRPC API for interacting with the platform and the GPTScript backend. This option is more efficient than the REST API but requires more setup and configuration.

Conclusion

In this document, we explained the role of Helix ML in the system, how it handles authorization, and how it connects to the GPTScript backend. We also covered the possible options and provided examples for each option.

For more information, please refer to the following resources: