2.0.0 • Published 9 months agoCLI

vsp-bot

Licence

ISC

Version

2.0.0

Deps

Size

29 kB

Vulns

Weekly

Summary Dependency Versions

vsp-bot npm package AI-Powered Edition

Description

This npm package provides information about Vishnu Suresh Perumbavoor with AI-powered conversational capabilities using Qwen2.5-0.5B by Alibaba Cloud.

What's New in v2.0

AI-Powered Conversations - Chat with an AI that knows all about VSP
Qwen2.5-0.5B - Fast and efficient model by Alibaba Cloud (500M parameters)
Lightweight - Only ~500MB model download
Streaming Support - Real-time response streaming
Backward Compatible - Original static data API still works
Runs Locally - No API keys needed, runs on your machine via Transformers.js

Prerequisites

Node.js 18+ (for ESM support in Transformers.js)
1-2GB RAM
Internet connection (first run only, to download ~500MB model)

Installation

npm install vsp-bot

Quick Start

Interactive Chat via CLI (Recommended!)

After installing, chat directly from command line:

# Install globally
npm install -g vsp-bot

# Start chatting! (two commands work)
vsp-bot
# or
vspbot

Or install locally and use:

# Install in your project
npm install vsp-bot

# Run chat
npx vsp-bot
# or
npm run chat

This starts an interactive conversation where you can ask multiple questions in a row!

Option 1: Static Data (Original Feature - Backward Compatible)

const vspbot = require('vsp-bot');

// Access static biodata
console.log(vspbot.name);        // "VSP Bot"
console.log(vspbot.creator);     // "Vishnu Suresh Perumbavoor"
console.log(vspbot.biodata);     // Full biodata object
console.log(vspbot.socials);     // Social media links

Option 2: AI-Powered Chat (New Feature )

const vspbot = require('vsp-bot');

async function chat() {
  // Initialize AI (first time downloads model ~500MB)
  await vspbot.initAI();
  
  // Ask questions about VSP
  const response = await vspbot.chat("Who is Vishnu Suresh Perumbavoor?");
  console.log(response);
  
  // Ask about accomplishments
  const response2 = await vspbot.chat("Tell me about VSP's achievements");
  console.log(response2);
  
  // Get social media info
  const response3 = await vspbot.chat("How can I connect with VSP?");
  console.log(response3);
}

chat();

Option 3: Interactive Continuous Chat (Best! )

# Run the interactive chat interface
node chat.js

This gives you a real continuous conversation:

🤖 VSP Bot - Interactive Chat Mode
============================================================
✅ Model loaded successfully!
============================================================

💬 You: Who is VSP?
🤖 VSP Bot: Vishnu Suresh Perumbavoor is an engineer, entrepreneur...

💬 You: What hackathons did he win?
🤖 VSP Bot: He won 3rd prize in Vaiga Agrihack 2023...

💬 You: Tell me more about his work
🤖 VSP Bot: He works at Trenser and has experience with...

💬 You: exit
👋 Thanks for chatting! Goodbye!

Option 4: Streaming Chat

const vspbot = require('vsp-bot');

async function streamChat() {
  await vspbot.initAI();
  
  // Streaming response (real-time output)
  await vspbot.chatStream("Tell me about VSP's hackathon achievements");
}

streamChat();

API Reference

Static Data (Original API)

All original properties are available:

vspbot.name              // "VSP Bot"
vspbot.creator           // "Vishnu Suresh Perumbavoor"
vspbot.founderOf         // "VSP dot AI"
vspbot.createdOn         // "28 April 2023"
vspbot.whoIsHe           // Array of roles
vspbot.interests         // Array of interests
vspbot.entertainments    // Array of entertainment preferences
vspbot.internships       // Array of internships
vspbot.placement         // Current placement
vspbot.accomplishments   // Array of accomplishments
vspbot.participations    // Array of event participations
vspbot.socials           // Object with social media links
vspbot.featured          // Featured media links
vspbot.biodata           // Complete biodata object

AI-Powered Methods (New)

`initAI(options?)`

Initialize the Qwen2.5-0.5B AI model.

Parameters:

options (object, optional): Model options
- dtype (string): Data type/quantization. Default: "q4"
- device (string): Device to use. Default: "auto"

Returns: Promise

Example:

// Initialize with default settings
await vspbot.initAI();

// Custom options
await vspbot.initAI({
  dtype: "q4",
  device: "cpu"
});

`chat(userMessage, options?)`

Chat with the AI bot about VSP.

Parameters:

userMessage (string): Your question or message
options (object, optional): Generation options
- max_new_tokens (number): Maximum tokens to generate. Default: 512
- temperature (number): Sampling temperature. Default: 0.7
- do_sample (boolean): Enable sampling. Default: false

Returns: Promise - AI response

Example:

const response = await vspbot.chat(
  "What are VSP's interests?",
  { max_new_tokens: 256, temperature: 0.8 }
);

`chatStream(userMessage, options?)`

Stream AI responses in real-time (outputs to console).

Parameters:

userMessage (string): Your question
options (object, optional): Same as chat()

Returns: Promise

Example:

await vspbot.chatStream("Tell me about VSP's achievements");
// Output streams to console in real-time

`getModelInfo()`

Get information about the AI model being used.

Returns: Object with model details

Example:

const modelInfo = vspbot.getModelInfo();
console.log(modelInfo.name);        // "Qwen2.5-0.5B-Instruct"
console.log(modelInfo.size);        // "0.5B parameters"
console.log(modelInfo.provider);    // "Alibaba Cloud - Qwen Team"

AI Model

Qwen2.5-0.5B-Instruct by Alibaba Cloud

Feature	Details
Size	0.5B parameters (~500MB download)
Speed	Very Fast
Quality	Good
RAM	1-2GB
Provider	Alibaba Cloud - Qwen Team
Model ID	`onnx-community/Qwen2.5-0.5B-Instruct`

Why Qwen2.5-0.5B?

Lightweight and fast
Great for conversational AI
Excellent multilingual support (English & Chinese)
Low resource requirements
Optimized with ONNX and quantization

Examples

Basic Usage

const vspbot = require('vsp-bot');

// Check if AI is enabled
console.log('AI Enabled:', vspbot.aiEnabled);    // true
console.log('Version:', vspbot.version);         // "2.0.0"

// Use static data
console.log('Creator:', vspbot.creator);
console.log('LinkedIn:', vspbot.socials.linkedin);

AI Chat Examples

const vspbot = require('vsp-bot');

async function examples() {
  // Initialize once
  await vspbot.initAI();
  
  // Example 1: General info
  const resp1 = await vspbot.chat("Who is VSP?");
  
  // Example 2: Specific questions
  const resp2 = await vspbot.chat("What hackathons did VSP participate in?");
  
  // Example 3: Social media
  const resp3 = await vspbot.chat("Give me VSP's social media links");
  
  // Example 4: Interests
  const resp4 = await vspbot.chat("What does VSP like to do?");
}

examples();

Interactive Chat via Terminal

# If installed globally
vsp-bot

# If installed locally
npx vsp-bot

# Or using npm script
npm run chat

This opens an interactive chat interface where you can have continuous conversations with vspbot!

Performance Tips

First Run: Model downloads on first use (~500MB). Subsequent runs are instant.
Memory: Uses ~1-2GB RAM during inference
Caching: Model is initialized once and cached for subsequent calls
Storage: Model cached in ~/.cache/huggingface/ (~500MB disk space needed)

How It Works

Transformers.js: Uses HuggingFace Transformers.js for Node.js
ONNX Runtime: Model runs via ONNX Runtime (optimized inference)
Local Execution: Everything runs locally, no API calls needed
Qwen2.5: Latest model from Alibaba Cloud's Qwen team
Multilingual: Supports both English and Chinese natively
Quantization: Q4 quantization for faster inference and smaller size

Troubleshooting

Error: Module not found

# Make sure transformers is installed
npm install @huggingface/transformers

Model download fails

Check internet connection
Model downloads to ~/.cache/huggingface/ by default
Need ~500MB free disk space

Out of memory

Close other memory-intensive applications
Reduce max_new_tokens in chat options (try 128 or 256)
Restart Node.js process

Backward Compatibility

All original v1.x features work unchanged:

const vspbot = require('vsp-bot');

// v1.x code still works perfectly
console.log(vspbot);              // Shows all properties
console.log(vspbot.name);         // "VSP Bot"
console.log(vspbot.creator);      // "Vishnu Suresh Perumbavoor"
console.log(vspbot.socials);      // Social links object

Version History

v2.0.0 (Latest) - AI-Powered Edition

Added AI chat capabilities powered by Qwen2.5-0.5B
Fast and lightweight model (500M parameters)
Streaming responses support
Transformers.js integration
Local execution (no API keys)

v1.0.x

Original static biodata API
Basic information export

Technical Details

Framework: Transformers.js v3.3.0+
Model: Qwen2.5-0.5B-Instruct (ONNX format)
Quantization: Q4 (4-bit quantization for efficiency)
Runtime: ONNX Runtime
Languages: English, Chinese (multilingual support)
Model Size: ~500MB download
Memory Usage: 1-2GB RAM during inference

Contact

For any inquiries or support, you can reach out to Vishnu Suresh Perumbavoor

License

ISC

Credits

Created by: Vishnu Suresh Perumbavoor
AI Model: Qwen2.5-0.5B by Alibaba Cloud
Framework: HuggingFace Transformers.js
ONNX Models: onnx-community

Powered by Qwen2.5-0.5B - Alibaba Cloud's Latest AI Model

Keywords

ai chatbot transformers qwen qwen2.5 llm vsp vishnu-suresh-perumbavoor alibaba