🚀 Career Advisor Chatbot

AI Young Guru 2026 – Team LLMagik - AI YOGU GYM 3

An AI-powered career advisory chatbot that helps users explore career paths, discover job information, and receive personalized recommendations using LLM technology.

Polished prototype for interactive career discovery, personalized recommendations, and LLM-driven guidance.

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🌟 Overview

Career Advisor Chatbot is a prototype intelligent system designed to:

• 🔎 Support career exploration
• 💼 Suggest relevant job roles based on user input
• 📚 Provide a searchable job catalog (1000+ roles from O*NET)
• 🤖 Deliver LLM-powered conversational guidance

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🧰 Tech Stack

Layer	Technology
Backend	Node.js
Frontend	Web Application
Database	SQLite
AI Engine	GPT-OSS 120B
Matching	Rule-based Engine (Prototype)

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📁 Project Structure

Below is a complete project structure overview (expanded) to help contributors and reviewers quickly locate modules and assets.

myAIchatbotProject/
├── 632652149_887073670753166_3710026893000461784_n.jpg    # Hero banner
├── ai-young-guru-logo.png
├── index.html
├── 404.html
├── package.json
├── runtime-config.js
├── assets/                      # Generated frontend assets (build)
├── career-icons/                # Icon set used by the UI
├── screenshots/
├── scripts/
├── backend/
│   ├── index.js
│   ├── server.js
│   ├── config.js
   │   ├── package.json
│   ├── testLLM.js
│   ├── data/
│   │   ├── careerLibrary.js
│   │   └── generatedCareerDataset.js
│   ├── database/
│   │   ├── esco_occupations.csv
│   │   ├── schema.sql
│   │   └── seed.sql
│   ├── middleware/
│   │   └── auth.js
│   ├── routes/
│   │   ├── chat.js
│   │   ├── chatbot.js
│   │   ├── auth.js
│   │   ├── admin.js
│   │   └── analytics.js
│   └── services/
│       └── ...
├── frontend/
│   ├── index.html
│   ├── package.json
│   ├── vite.config.js
│   ├── postcss.config.cjs
│   ├── tailwind.config.cjs
│   ├── public/
│   └── src/
│       ├── main.js
│       ├── app/
│       ├── components/
│       └── styles/
└── README.MD

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🛠 Installation & Setup

1️⃣ Backend Setup

cd backend
npm install
npm run setup-db
npm run dev

2️⃣ Frontend Setup

cd frontend
npm install
npm run dev

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🌐 Production Mode (Single URL Deployment)

Serve frontend directly from backend:

cd backend
npm run build      # Run once to build frontend
npm run start

Or run everything with a single command from root:

npm run serve

Access the application at:

http://localhost:3001

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🔌 API Endpoints

Local Development

http://localhost:3001

Deployed Backend API

https://api-myaichatbotproject.onrender.com

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🗄 Database

Main Database

• Uses SQLiteCloud (ONLINE)
• User/profile/chat/recommendation/job catalog data are stored online
• Backend is configured in ONLINE-only mode (no local SQLite fallback)

✅ Online Database Mode (Recommended for production)

Backend now uses ONLINE database via SQLiteCloud.

When configured, user/chat/profile data is stored online and shared across all backend instances.

Environment variables

• DB_PROVIDER=sqlitecloud
• SQLITECLOUD_URL=sqlitecloud://<host>:<port>/<database>?apikey=<api-key>

Use backend/.env.example as template for local setup.

⚠️ Security note:

• Do not commit real .env files.
• Put real values in GitHub Secrets/Variables for CI.
• This repository includes playwright.yml for CI-safe env injection (no plaintext keys in git).

SQLITECLOUD_URL must be a real connection string, not the dashboard URL.

Optional (instead of SQLITECLOUD_URL):

• SQLITECLOUD_HOST=<host>
• SQLITECLOUD_PORT=8860 (default)
• SQLITECLOUD_DATABASE=<database-name>
• One auth mode only:
  • SQLITECLOUD_API_KEY=<api-key> or
  • SQLITECLOUD_TOKEN=<token> or
  • SQLITECLOUD_USERNAME=<username> + SQLITECLOUD_PASSWORD=<password>

Optional TLS/debug option:

• SQLITECLOUD_INSECURE=1 (only for troubleshooting; do not use in production)

Setup steps (SQLiteCloud)

1. Create/identify your SQLiteCloud database in dashboard.

2. Generate API key for backend access.

3. Build connection string:

   • sqlitecloud://<host>:8860/<database>?apikey=<api-key>

4. Set backend environment variables (local .env or Render env):

   • DB_PROVIDER=sqlitecloud
   • SQLITECLOUD_URL=<connection-string-from-step-3>

5. Initialize schema/migrations on online DB:

   • npm --prefix backend run setup-db

6. Start backend:

   • npm --prefix backend run start

7. Verify health:

   • GET /health
   • Test register/login/chat and confirm data persists after backend restart/redeploy.

Render deployment notes

Add these env vars in Render Service → Environment:

• DB_PROVIDER
• SQLITECLOUD_URL
• plus existing vars: JWT_SECRET, ADMIN_EMAIL, ADMIN_PASSWORD, GROQ_API_KEY, ...

After deploy, run one-time DB init command using Render Shell:

• npm run setup-db

Then normal runtime command remains:

• npm run start

Database Commands

Command	Description
npm run setup-db	Initialize ONLINE SQLiteCloud database (idempotent)
npm run reset-db	In ONLINE mode, warns and does not delete cloud DB
Auto-init	Server initializes DB automatically if empty

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📚 Career Catalog (Job Explorer)

The job catalog is also stored in SQLiteCloud (online), alongside app data.

📥 Import 1000+ Jobs from O*NET

npm --prefix backend run import-jobs

🔄 Reset & Re-import Jobs

npm --prefix backend run reset-jobs

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📖 Data Source

• O*NET Database (Text Version)
  https://www.onetcenter.org/dl_files/database/db_30_1_text.zip

• O*NET License Information
  https://www.onetcenter.org/license_db.html

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🤖 LLM Integration

• Model: GPT-OSS 120B
• Provider Playground:
  https://console.groq.com/playground?model=openai/gpt-oss-120b

Used for conversational intelligence, contextual understanding, and personalized career guidance.

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⚙️ Architecture Notes

• ☁️ SQLiteCloud for centralized ONLINE data
• 🧠 Rule-based matching engine (optimized for rapid prototyping)
• 🤖 LLM integration for advanced conversational support
• 🔮 Designed for future ML-based recommendation system expansion

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RESULT:

TOP 5 AI YOGU GYM 3 PROJECT