AI Tutor Chatbot (GeminiV3)

A sophisticated AI tutor chatbot application built with Node.js backend, Python microservice for RAG, and React frontend. This application provides an interactive learning experience with document analysis, chat functionality, and personalized tutoring features.

Project Architecture

The project consists of three main components:

1. Node.js Backend Server (Port: 3000)

   • Express.js server handling user authentication, file management, and chat operations
   • MongoDB database for user data and chat history
   • RESTful API endpoints for client communication

2. Python RAG Microservice (Port: 5000)

   • Flask-based service for document processing and RAG operations
   • Handles document chunking, embeddings, and vector search
   • Uses FAISS for efficient similarity search
   • SentenceTransformers for embeddings

3. React Frontend (Port: 3001)

   • Modern UI with responsive design
   • Real-time chat interface
   • File management system
   • User authentication and session management

Prerequisites

• Node.js (v14 or higher)
• Python 3.8 or higher
• MongoDB
• npm or yarn
• Git

Project Setup

1. Clone the Repository

git clone <repository-url>
cd Chatbot-geminiV3

2. Backend Setup (Node.js)

cd server
npm install
# Create .env file with required environment variables
cp .env.example .env
# Edit .env with your configuration
npm start

Required environment variables for backend (.env):

PORT=3000
MONGODB_URI=mongodb://localhost:27017/chatbot
JWT_SECRET=your_jwt_secret
RAG_SERVICE_URL=http://localhost:5000

3. Python RAG Service Setup

cd server/rag_service
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
python app.py

Required environment variables for RAG service (.env):

PORT=5000
EMBEDDING_MODEL=mxbai-embed-large-v1
FAISS_INDEX_PATH=../faiss_indices

4. Frontend Setup

cd client
npm install
npm start

API Documentation

Authentication Endpoints

POST /api/auth/register

Register a new user

{
  "username": "string",
  "password": "string",
  "email": "string"
}

POST /api/auth/login

Login user

{
  "username": "string",
  "password": "string"
}

Chat Endpoints

POST /api/chat/send

Send a message to the chatbot

{
  "message": "string",
  "sessionId": "string",
  "useRag": boolean,
  "subject": "string"
}

GET /api/chat/history

Get chat history for a session

Query Parameters:
- sessionId: string
- limit: number (optional)
- skip: number (optional)

File Management Endpoints

POST /api/files/upload

Upload a document

Content-Type: multipart/form-data
Body:
- file: File
- type: string (docs|images|code|others)

GET /api/files/list

List uploaded files

Query Parameters:
- type: string (optional)

DELETE /api/files/:fileId

Delete a file

Parameters:
- fileId: string

RAG Service Endpoints

POST /api/rag/process

Process a document for RAG

{
  "fileId": "string",
  "userId": "string"
}

POST /api/rag/query

Query the RAG system

{
  "query": "string",
  "userId": "string",
  "subject": "string"
}

Frontend Implementation Guide

Key Components

1. Authentication

   • Login/Register forms
   • JWT token management
   • Protected routes

2. Chat Interface

   • Real-time message display
   • Markdown rendering
   • File upload integration
   • RAG toggle

3. File Management

   • Drag-and-drop upload
   • File type categorization
   • File listing and deletion

4. User Interface

   • Responsive design
   • Dark/Light theme
   • Loading states
   • Error handling

State Management

• Uses React Context for global state
• Local state for component-specific data
• Custom hooks for API communication

API Integration

Example of chat message sending:

const sendMessage = async (message, sessionId) => {
  try {
    const response = await fetch('/api/chat/send', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${token}`
      },
      body: JSON.stringify({
        message,
        sessionId,
        useRag: true,
        subject: currentSubject
      })
    });
    return await response.json();
  } catch (error) {
    console.error('Error sending message:', error);
    throw error;
  }
};

Development Guidelines

1. Code Style

   • Follow ESLint configuration
   • Use Prettier for formatting
   • Write meaningful commit messages

2. Testing

   • Unit tests for utilities
   • Integration tests for API endpoints
   • E2E tests for critical user flows

3. Security

   • Validate all user inputs
   • Sanitize file uploads
   • Use environment variables for secrets
   • Implement rate limiting

4. Performance

   • Optimize bundle size
   • Implement caching where appropriate
   • Use pagination for large datasets

Troubleshooting

Common issues and solutions:

1. MongoDB Connection Issues

   • Verify MongoDB is running
   • Check connection string in .env
   • Ensure network access

2. RAG Service Not Responding

   • Check Python service is running
   • Verify port 5000 is available
   • Check embedding model installation

3. File Upload Failures

   • Verify file size limits
   • Check file type restrictions
   • Ensure proper permissions

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

[Specify your license here]

Support

For support, please [specify contact method or issue reporting process]

PORT=5001 # Port for the backend (make sure it's free)
MONGO_URI=mongodb://localhost:27017/chatbot_gemini # Your MongoDB connection string
JWT_SECRET=your_super_strong_and_secret_jwt_key_12345! # A strong, random secret key for JWT
GEMINI_API_KEY=YOUR_GOOGLE_GEMINI_API_KEY_HERE # Your actual Gemini API Key

• MONGO_URI:
  • For local MongoDB: mongodb://localhost:27017/chatbot_gemini (or your chosen DB name).
  • For MongoDB Atlas: Get the connection string from your Atlas cluster (replace <password> and specify your database name). Example: mongodb+srv://<username>:<password>@yourcluster.mongodb.net/chatbot_gemini?retryWrites=true&w=majority
• JWT_SECRET: Generate a strong random string for security.
• GEMINI_API_KEY: Paste the key you obtained from Google AI Studio.