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https://github.com/govinddwivedi-git/chatbot-for-students-queries

An interactive chatbot system designed to help resolve student queries efficiently. The system features both user and admin interfaces, allowing for dynamic query handling and response management.
https://github.com/govinddwivedi-git/chatbot-for-students-queries

flask python rag reactjs

Last synced: about 2 months ago
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An interactive chatbot system designed to help resolve student queries efficiently. The system features both user and admin interfaces, allowing for dynamic query handling and response management.

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README 

          
# Student Query Resolution Chatbot



An intelligent AI-powered chatbot system designed to help resolve student queries efficiently using **Retrieval-Augmented Generation (RAG)** technology. The system features both user and admin interfaces, allowing for dynamic query handling, AI-powered responses, and comprehensive query management with cloud storage integration.



## 🚀 Key Features



### 🤖 AI-Powered Query Resolution

- **RAG (Retrieval-Augmented Generation)** for intelligent responses

- **Google AI (Gemini)** integration for natural language processing

- **LangChain** framework for advanced language processing

- **Pinecone** vector database for semantic search and embedding storage

- **PDF document processing** for knowledge base creation with Cloudinary integration



### 🔐 User Authentication System

- Secure user signup and login

- JWT-based authentication with session management

- Password encryption using Bcrypt

- Admin authentication with role-based access



### 💬 Advanced Query Management

- Real-time AI-powered query responses

- Automatic query storage and categorization

- Historical query search and analytics

- Unanswered query tracking for admin review



### 🎛️ Comprehensive Admin Dashboard

- Dedicated admin login system

- Query analytics and statistics

- Manage unanswered queries with bulk operations

- Add and update responses manually

- PDF management with Cloudinary integration

- One-click embedding rebuilding for knowledge base updates

- Email notification system for critical queries

- Chat history management



### 📧 Email Integration

- Automated email notifications

- Admin alerts for unanswered queries

- Test email functionality for system verification



### ☁️ Cloud Storage Integration

- Cloudinary PDF storage for document management

- PDF preview with Google Docs viewer

- Original filename preservation

- Public/private access management



## 🛠️ Technology Stack



### Backend (Flask)

- **Flask 2.3.3** - Modern Python web framework

- **Flask-CORS** - Cross-Origin Resource Sharing support

- **Gunicorn** - WSGI HTTP Server for production deployment

- **MongoDB with PyMongo** - NoSQL database for scalable data storage

- **Google AI (Gemini)** - Advanced language model integration

- **LangChain** - AI application framework

- **Pinecone** - Vector database for production-ready RAG systems

- **Cloudinary** - Cloud-based PDF storage and management

- **Flask-Mail** - Email service integration

- **JWT** - Secure token-based authentication

- **Flask-CORS** - Cross-origin resource sharing



### Frontend (React + Vite)

- **React 18.3.1** - Modern UI library

- **Vite** - Fast build tool and dev server

- **Tailwind CSS** - Utility-first CSS framework

- **ShadCN/UI** - Modern component library

- **React Router** - Client-side routing

- **Axios** - HTTP client for API communication

- **Lucide React** - Beautiful icons



### AI & ML Stack

- **Google AI API** - Generative AI responses

- **LangChain Community** - Extended AI capabilities

- **Pinecone** - Managed vector database for production RAG deployments

- **TextBlob** - Text processing and analysis

- **PyPDF2** - PDF document processing

- **ReportLab** - PDF generation for Q&A documents

- **Cloudinary SDK** - Cloud storage API integration



## 📡 API Endpoints



### 🔐 Authentication

```http

POST /api/signup              # Register new user

POST /api/login               # User login  

POST /api/admin/login         # Admin authentication

```



### 💬 Chat & Query Management

```http

POST /api/query               # Submit query (AI-powered response)

GET  /api/chat-history        # Retrieve user chat history

POST /api/add-response        # Add manual response to query

```



### 🎛️ Admin Management

```http

GET  /api/admin/stats         # Get system statistics

GET  /api/admin/chat-history  # Get all chat history (admin)

GET  /api/admin/query-analytics # Get query analytics

GET  /api/unanswered-queries  # Get pending queries

DELETE /api/delete-query/ # Delete specific query

```



### 📄 PDF Management

```http

GET  /api/pdfs/               # List all PDFs

POST /api/pdfs/upload         # Upload a PDF file

DELETE /api/pdfs/  # Delete a PDF

POST /api/pdfs/rebuild-embeddings # Rebuild embeddings from PDFs

```



### 🔍 System Endpoints

```http

GET  /health                  # Health check

GET  /debug/routes            # List all available routes

GET  /debug/email             # Test email service

POST /debug/send-test-email   # Send test email

```



## 🚀 Getting Started



### Prerequisites

- **Python 3.8+** (recommended 3.10+)

- **Node.js 16+** and npm

- **MongoDB Atlas** account or local MongoDB

- **Google AI API** key

- **Gmail account** for email services (with app password)

- **LangSmith account** (optional, for AI tracing)



### 🔧 Backend Setup



1. **Clone the repository:**

```bash

git clone https://github.com/Arnavkesari/chatbot-for-students-queries.git

cd chatbot-for-students-queries

```



2. **Navigate to backend directory:**

```bash

cd backend

```



3. **Create and activate virtual environment:**

```bash

# Windows

python -m venv venv

venv\Scripts\activate



# macOS/Linux  

python3 -m venv venv

source venv/bin/activate

```



4. **Install dependencies:**

```bash

pip install -r requirements.txt

```



5. **Set up environment variables:**

Create `.env` file in the backend directory:



```env

# Database Configuration

MONGO_URI=mongodb+srv://username:password@cluster.mongodb.net/chatbot?retryWrites=true&w=majority



# Google AI Configuration  

GOOGLE_API_KEY=your_google_ai_api_key



# LangChain Configuration (Optional - for AI tracing)

LANGSMITH_API_KEY=your_langsmith_api_key

LANGCHAIN_TRACING_V2=true

LANGCHAIN_PROJECT=Faculty Chatbot

LANGCHAIN_ENDPOINT=https://api.smith.langchain.com



# Email Configuration (Gmail SMTP)

EMAIL_USER=your_email@gmail.com

EMAIL_PASS=your_gmail_app_password



# Security

SECRET_KEY=your_secure_random_secret_key



# Flask Configuration

FLASK_ENV=development

PORT=5000



# Admin Credentials

ADMIN_EMAIL=

ADMIN_PASSWORD=

```



6. **Add PDF documents to resources folder:**

```bash

# Place your PDF documents in:

backend/resources/

```



7. **Start the backend server:**

```bash

python app.py

```



### 🎨 Frontend Setup



1. **Navigate to frontend directory:**

```bash

cd frontend

```



2. **Install dependencies:**

```bash

npm install

```



3. **Configure environment variables:**

```bash

# Create .env.local file

cp .env.local.example .env.local



# Edit .env.local to set the API base URL

# For development:

VITE_API_BASE_URL=http://localhost:5000



# For production (when deploying):

# VITE_API_BASE_URL=https://your-backend-url.onrender.com

```



4. **Start the development server:**

```bash

npm run dev

```



The frontend will run on `http://localhost:5173`



## 🏗️ Project Structure



```

chatbot-for-students-queries/

├── backend/

│   ├── app.py                    # Main Flask application

│   ├── requirements.txt          # Python dependencies

│   ├── config/

│   │   ├── config.py            # Configuration settings

│   │   └── database.py          # Database connection

│   ├── controllers/             # Business logic controllers

│   ├── middleware/              # Custom middleware

│   ├── models/                  # Database models

│   ├── routes/                  # API route definitions

│   ├── services/                # Business services

│   ├── utils/                   # Utility functions

│   └── resources/               # PDF documents for RAG

├── frontend/

│   ├── src/

│   │   ├── components/          # React components

│   │   ├── assets/              # Static assets

│   │   └── lib/                 # Utility libraries

│   ├── package.json             # Frontend dependencies

│   └── vite.config.js           # Vite configuration

└── RAG/                         # RAG implementation notes

```



## 🗃️ Database Schema



### Users Collection

```json

{

  "_id": "ObjectId",

  "username": "string",

  "email": "string", 

  "password": "bcrypt_hashed_string",

  "created_at": "datetime",

  "last_login": "datetime"

}

```



### Queries Collection

```json

{

  "_id": "ObjectId",

  "question": "string",

  "answer": "string", 

  "answered": "boolean",

  "user_id": "ObjectId",

  "timestamp": "datetime",

  "ai_generated": "boolean",

  "similarity_score": "float",

  "category": "string"

}

```



## 🔧 Configuration Details



### 🤖 AI Configuration

- **Google AI (Gemini)**: Powers intelligent response generation

- **LangChain**: Handles RAG pipeline and document processing

- **FAISS**: Vector database for semantic similarity search

- **LangSmith**: Optional AI tracing and monitoring



### 📧 Email Service

- **Gmail SMTP** integration for notifications

- **Flask-Mail** for email management

- Automated alerts for unanswered queries



### 🛡️ Security Features

- **Bcrypt password hashing** with salt rounds

- **JWT token authentication** with expiration

- **Admin role-based access control**

- **Environment variable protection**

- **CORS middleware** for secure API access

- **Session timeout management**



## 🎛️ Admin 



### Admin Capabilities:

- View system statistics and analytics

- Manage unanswered queries

- Add manual responses to queries  

- Delete inappropriate queries

- Monitor chat history across all users

- Receive email notifications for critical queries



## 🔍 Development



### Environment Modes

- **Development**: `FLASK_ENV=development` (Hot reloading enabled)

- **Production**: `FLASK_ENV=production` (Optimized for performance)

- **Testing**: `FLASK_ENV=testing` (Test configuration)



### Testing the System

1. **Health Check**: `GET http://localhost:5000/health`

2. **Email Service**: `GET http://localhost:5000/debug/email`  

3. **Send Test Email**: `POST http://localhost:5000/debug/send-test-email`

4. **List Routes**: `GET http://localhost:5000/debug/routes`



### API Testing with cURL

```bash

# Test user signup

curl -X POST http://localhost:5000/api/signup \

  -H "Content-Type: application/json" \

  -d '{"username":"testuser","email":"test@example.com","password":"password123"}'



# Test query submission  

curl -X POST http://localhost:5000/api/query \

  -H "Content-Type: application/json" \

  -H "Authorization: Bearer YOUR_JWT_TOKEN" \

  -d '{"question":"What are the admission requirements?"}'

```



## 📚 RAG Implementation



The system uses **Retrieval-Augmented Generation** to provide accurate, context-aware responses:



1. **Document Processing**: PDF files in `resources/` are processed and chunked

2. **Vector Embeddings**: Text chunks are converted to vectors using Google AI

3. **Vector Storage**: FAISS stores embeddings for fast similarity search  

4. **Query Processing**: User questions are embedded and matched against knowledge base

5. **Response Generation**: Relevant context + user query sent to Google AI for response generation



### Adding New Documents

1. Upload PDF files through the admin dashboard

2. Rebuild embeddings using the dedicated button

3. System automatically processes and indexes new content



## 🚀 Deployment



### Backend Deployment (Render)



The backend is configured for deployment on Render.com:



1. **Render.yaml Configuration**: The project includes a `render.yaml` file with all the necessary configuration for deployment on Render.



2. **Environment Variables**: Make sure to set up all required environment variables in the Render Dashboard:

   - `MONGODB_URI`

   - `JWT_SECRET_KEY`

   - `GOOGLE_API_KEY`

   - All email configuration variables

   - Cloudinary credentials

   - Pinecone API key and environment



3. **CORS Configuration**: The backend is configured to accept requests from the frontend domain. Make sure to update the `CORS_ALLOW_ORIGIN` environment variable with your frontend URL.



### Frontend Deployment (Vercel/Netlify)



1. **Build the frontend**:

```bash

cd frontend

npm run build

```



2. **Environment Variables**: Set the `VITE_API_BASE_URL` to your deployed backend URL:

```

VITE_API_BASE_URL=https://your-backend-url.onrender.com

```



3. **Deploy**: Use Vercel, Netlify, or any static hosting service to deploy the `dist` folder.



## 🤝 Contributing



1. Fork the repository

2. Create feature branch (`git checkout -b feature/amazing-feature`)

3. Make your changes

4. Add tests for new functionality  

5. Commit changes (`git commit -m 'Add amazing feature'`)

6. Push to branch (`git push origin feature/amazing-feature`)

7. Open a Pull Request



### Development Guidelines

- Follow PEP 8 for Python code

- Use ESLint configuration for JavaScript

- Add docstrings to all functions

- Write tests for new features

- Update README for significant changes



## 📄 License



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



## 🆘 Support & Contact



- **Email**: sahayak.iiitdmj@gmail.com

- **GitHub Issues**: [Create an issue](https://github.com/Arnavkesari/chatbot-for-students-queries/issues)

- **Documentation**: See `/docs` folder for detailed documentation



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