LoLLMs Client JS Detailed Guide

Dive deeper into the capabilities of the LoLLMs Client JS library 🌌, crafted for developers by ParisNeo. This detailed guide will cover the methods provided by the library, including how to use them and their parameters, to empower your applications with cutting-edge AI text generation.

Installation

Ensure you have Node.js installed, then add LoLLMs Client JS to your project:

npm install lollms_client_js

Initialization

Start by importing and initializing the LollmsClient:

const LollmsClient = require('lollms_client_js');

// Initialize the client with your LoLLMs host and the model name.
const client = new LollmsClient('http://your-lollms-host.com', 'modelName');

Methods Overview

The LollmsClient provides several methods for interacting with the LoLLMs backend:

• generateText()
• generate_completion()
• listMountedPersonalities()
• listModels()

generateText(prompt, options)

Generates text based on a given prompt and options.

Parameters:

• prompt (String): The input text to generate further content.
• options (Object): Optional parameters to customize the request. Includes:
  • stream (Boolean): Whether to stream the response.
  • temperature (Number): Controls randomness.
  • topK (Number): Filters the top K candidates before sampling.
  • topP (Number): Nucleus sampling.
  • repeatPenalty (Number): Penalty for repetition.
  • repeatLastN (Number): Number of tokens to check for repetition.
  • seed (Number): Random seed for reproducibility.
  • nThreads (Number): Number of threads to use.

Example:

client.generateText("Hi there, how can I assist you today?", { temperature: 0.9 }).then(response => {
  console.log(response);
});

generate_completion(prompt, options)

Generates a completion for a given prompt with detailed control over the generation process.

Parameters:

Similar to generateText, but with an additional completionFormat parameter to specify the format of the generated completion.

Example:

client.generate_completion("The future of AI in robotics is", { temperature: 0.7, completionFormat: "vllm instruct" }).then(completion => {
  console.log(completion);
});

listMountedPersonalities()

Lists all mounted personalities available on the LoLLMs server.

Example:

client.listMountedPersonalities().then(personalities => {
  console.log(personalities);
});

listModels()

Lists all models available for text generation on the LoLLMs server.

Example:

client.listModels().then(models => {
  console.log(models);
});

Contributing

Your contributions are welcome! Follow the steps below to contribute:

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

TasksLibrary Documentation

The TasksLibrary is a JavaScript library designed to simplify and streamline the use of the LoLLMs (Lord Of Large Language Models) Client for common text processing tasks such as translation and summarization. This documentation provides an overview of the library's functionality and how to utilize it in your projects.

Getting Started

To use the TasksLibrary, you first need an instance of the LollmsClient. This client is responsible for communicating with the LoLLMs service, sending prompts, and receiving generated text.

Installation

Ensure you have the LollmsClient JavaScript class available in your project. The TasksLibrary class then needs to be included in your project:

import { LollmsClient } from './path/to/lollms_client_js.js';
import { TasksLibrary } from './path/to/lollms_client_js.js';

Initialization

Create an instance of the LollmsClient with your service configuration:

const lollmsClient = new LollmsClient(
  'your_host_address',
  'your_model_name',
  // Other parameters as needed
);

Then, instantiate the TasksLibrary with the LollmsClient instance:

const tasksLibrary = new TasksLibrary(lollmsClient);

Features

The TasksLibrary currently supports the following features:

Text Translation

Translate a chunk of text to a specified language without altering the original meaning or adding extraneous information.

Usage

tasksLibrary.translateTextChunk(textChunk, outputLanguage)
  .then(translatedText => {
    console.log(translatedText);
  })
  .catch(error => {
    console.error(error);
  });

Text Summarization

Summarize a given text chunk in a concise manner, ensuring all key points are covered without introducing new information.

Usage

tasksLibrary.summarizeText(textChunk, summaryLength)
  .then(summary => {
    console.log(summary);
  })
  .catch(error => {
    console.error(error);
  });

Parameters

• textChunk: The text to be processed.
• outputLanguage: (For translation) The target language for the translation.
• summaryLength: (For summarization) The desired length of the summary. Can be "short", "medium", or "long".
• hostAddress, modelName, temperature, maxGenerationSize: Additional parameters for customization and optimization of the task.

Conclusion

The TasksLibrary is a powerful tool for developers working with text processing in the context of AI and robotics. By leveraging the capabilities of LoLLMs, it offers an easy and efficient way to perform complex tasks such as translation and summarization.

For more information and updates, follow the project's GitHub repository or join our community on Discord.

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

Contact

Reach out to ParisNeo for any questions or suggestions:

• Twitter: @ParisNeo_AI

Project Repository: https://github.com/ParisNeo/lollms_client_js

Thank you for choosing LoLLMs Client JS for your project. Happy coding!

See ya 👋

This extended guide provides a closer look at the capabilities and usage of the `LollmsClient` methods, offering developers a clear understanding of how to leverage the library for their applications.