JD Vector - AI-Powered Skill Question Generator

An intelligent interview question generator that uses AI and vector embeddings to create and store relevant questions for any skill.

Features

• 🤖 AI-Powered Generation: Uses OpenAI GPT-4.1 to generate relevant interview questions
• 🔍 Vector Embeddings: Stores question embeddings using OpenAI's text-embedding-3-small model
• 💾 Smart Caching: Automatically caches generated questions to avoid redundant AI calls
• 🎯 Skill-Based Organization: Organizes questions by specific skills
• ⚡ Fast Performance: Built with Next.js 15 and Turbopack for optimal development experience

Tech Stack

• Frontend: Next.js 15, React 19, TailwindCSS 4
• Backend: Next.js API Routes
• Database: PostgreSQL with Prisma ORM
• AI: OpenAI GPT-4.1 for generation, text-embedding-3-small for embeddings
• Language: TypeScript

Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Frontend UI   │───▶│   API Routes     │───▶│   PostgreSQL    │
│                 │    │                  │    │                 │
│ - Skill Input   │    │ - Question Gen   │    │ - Skills        │
│ - Question List │    │ - Embeddings     │    │ - Questions     │
│                 │    │ - Caching        │    │ - Embeddings    │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                │
                                ▼
                       ┌──────────────────┐
                       │   OpenAI API     │
                       │                  │
                       │ - GPT-4.1        │
                       │ - Embeddings     │
                       └──────────────────┘

Prerequisites

• Node.js 18+
• PostgreSQL database
• OpenAI API key

Installation

1. Clone the repository

   git clone <your-repo-url>
   cd jd-vector

2. Install dependencies

   npm install

3. Set up environment variables

   Create a .env.local file in the root directory:

   # OpenAI API Configuration
   OPENAI_API_KEY=your_openai_api_key_here
   
   # Database Configuration
   DATABASE_URL="postgresql://username:password@localhost:5432/jd_vector"

4. Set up the database

   # Generate Prisma client
   npx prisma generate
   
   # Run database migrations
   npx prisma migrate deploy
   
   # (Optional) Open Prisma Studio to view data
   npx prisma studio

Usage

1. Start the development server

   npm run dev

2. Open your browser

   Navigate to http://localhost:3000

3. Generate questions

   • Enter a skill name (e.g., "React", "Python", "Machine Learning")
   • Click "Generate Questions"
   • View the AI-generated interview questions

How It Works

Question Generation Flow

1. Input: User enters a skill name
2. Check Cache: System checks if questions already exist for this skill
3. Generate: If not cached, generates 5 new questions using OpenAI GPT-4.1
4. Embed: Creates vector embeddings for each question using OpenAI's embedding model
5. Store: Saves questions and embeddings to PostgreSQL database
6. Return: Displays questions to the user

Database Schema

-- Skills table
model Skill {
  id        Int         @id @default(autoincrement())
  name      String      @unique
  questions Question[]
}

-- Questions table with embeddings
model Question {
  id        Int     @id @default(autoincrement())
  skill     Skill   @relation(fields: [skillId], references: [id])
  skillId   Int
  text      String
  embedding Json    -- Vector embeddings stored as JSON
}

API Endpoints

• POST /api/skills/generate - Generate questions for a skill
  • Body: { "skillName": "React" }
  • Response: { "questions": [...], "source": "generated|existing" }

Development

Available Scripts

• npm run dev - Start development server with Turbopack
• npm run build - Build for production
• npm run start - Start production server
• npm run lint - Run ESLint

Database Operations

# View your data
npx prisma studio

# Reset database (caution: deletes all data)
npx prisma migrate reset

# Deploy migrations to production
npx prisma migrate deploy

Features in Detail

Smart Caching

• Questions are automatically cached after first generation
• Subsequent requests for the same skill return existing questions instantly
• Reduces API costs and improves response times

Vector Embeddings

• Each question is converted to a vector embedding
• Enables future semantic search capabilities
• Stored efficiently in PostgreSQL as JSON

AI Integration

• Uses GPT-4.1 for high-quality question generation
• Prompts optimized for interview-style questions
• Text-embedding-3-small for efficient vector creation

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Future Enhancements

• 🔍 Semantic Search: Find similar questions using vector similarity
• 📊 Analytics: Track question usage and effectiveness
• 🎨 Enhanced UI: More interactive and visual question management
• 🔒 Authentication: User accounts and personal question libraries
• 📱 Mobile App: React Native companion app
• 🤝 Question Sharing: Community-driven question sharing

License

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

Support

If you encounter any issues or have questions:

1. Check the Issues page
2. Create a new issue with detailed information
3. Include error messages, environment details, and steps to reproduce

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Built with ❤️ using Next.js, OpenAI, and PostgreSQL