CalRAG

A Retrieval-Augmented Generation (RAG) application for course discovery and academic advising, built with Next.js, MongoDB, and OpenAI.

Features

• Semantic Course Search: Find courses based on natural language queries
• AI-Powered Recommendations: Get personalized course recommendations from an AI advisor
• Course Information: Detailed course information including descriptions, prerequisites, and schedules
• Vector Search: Utilizes MongoDB Atlas Vector Search for efficient similarity matching
• Real-time Responses: Quick and relevant responses using OpenAI's GPT-3.5 Turbo

Tech Stack

• Frontend: Next.js 13+ with App Router
• Database: MongoDB Atlas with Vector Search
• AI/ML: OpenAI GPT-3.5 Turbo and Ada Embeddings
• Runtime: Node.js with TypeScript

Prerequisites

• Node.js 18+
• MongoDB Atlas M10+ cluster with Vector Search enabled (Reach out to me if you want one)
• OpenAI API key

Environment Setup

Create a .env.local file in the root directory with the following variables:

MONGODB_URI=your_mongodb_connection_string
OPENAI_API_KEY=your_openai_api_key

Installation

1. Clone the repository:

git clone https://github.com/yourusername/CalRAG.git
cd cal-rag

2. Install dependencies:

npm install

3. Initialize the database:

npm run init-db

4. Start the development server:

npm run dev

MongoDB Atlas Setup

1. Create an M10+ cluster in MongoDB Atlas
2. Create a vector search index on the course_vectors collection:

{
  "mappings": {
    "dynamic": true,
    "fields": {
      "embedding": {
        "dimensions": 1536,
        "similarity": "cosine",
        "type": "knnVector"
      }
    }
  }
}

Project Structure

cal-rag/
├── app/                    # Next.js app directory
│   ├── api/               # API routes
│   └── page.tsx           # Main page
├── lib/                   # Core libraries
│   ├── course-service.ts  # Course-related operations
│   ├── mongodb.ts         # MongoDB configuration
│   ├── openai.ts          # OpenAI configuration
│   └── rag-service.ts     # RAG implementation
├── scripts/               # Utility scripts
│   └── init-db.ts        # Database initialization
└── types/                 # TypeScript type definitions
    └── course.ts         # Course-related types

API Routes

• POST /api/chat: Main endpoint for course queries and recommendations

Contributing

I am super open to open source contributions, always looking to make this product better!

Steps:

1. Fork the repository
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

License

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