🚌 Bus Ticket Booking Application

A comprehensive bus ticket booking system with AI-powered search capabilities, built using FastAPI and a RAG (Retrieval-Augmented Generation) pipeline for intelligent query handling.

Demo at:https://www.loom.com/share/986076c39f7d46b396593969bbc1cc46

🎯 Project Overview

This application allows users to:

• 🔍 Search for buses using natural language queries
• 🎫 Book tickets with basic information (name and phone number)
• 📋 View and manage their bookings
• 🚌 Access detailed bus provider information (routes, fares, policies, contact details)
• 💬 Ask questions about bus services and get AI-powered responses

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✨ Key Features

• RAG Pipeline Integration: Uses LangChain with Google Generative AI for intelligent query processing
• Vector Search: ChromaDB with sentence transformers for semantic search capabilities
• Natural Language Queries: Ask questions like "Are there any buses from Dhaka to Rajshahi under 500 taka?"
• SQLite Database: Reliable booking data persistence
• JSON-based Route Data: Flexible data management for districts, routes, and providers
• Complete CRUD Operations: Create, read, update, and delete bookings
• HTML/CSS/JS Frontend: Served directly by FastAPI via static files and Jinja2 templates

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📂 Project Structure

Bus-ticket-booking-application/
│
├── backend/
│   ├── main.py                 # FastAPI application entry point
│   ├── database.py             # SQLite database configuration
│   ├── models.py               # Pydantic models
│   ├── rag_pipeline.py         # RAG implementation
│   └── data_loader.py          # Data loading utilities
│
├── static/
│   ├── css/
│   │   └── style.css           # Application styles
│   └── js/
│       └── main.js             # Frontend JavaScript
│
├── templates/                  # Jinja2 HTML templates
│
├── data.json                   # Main data file (routes, districts, providers)
│
├── attachments/
│   ├── hanif.txt               # Hanif bus provider information
│   ├── ena.txt                 # Ena bus provider information
│   └── ...                     # Other bus provider files
│
├── bus_booking.db              # SQLite database (auto-generated)
├── requirements.txt            # Python dependencies
├── Dockerfile                  # Docker build instructions
├── docker-compose.yaml         # Docker Compose configuration
├── .env                        # Environment variables (create this)
└── README.md                   # This file

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🚀 Option 1: Run with Docker Compose (Recommended)

Prerequisites

• Docker Desktop installed and running

1️⃣ Clone the Repository

git clone https://github.com/yourusername/Bus-ticket-booking-application.git
cd Bus-ticket-booking-application

2️⃣ Create a .env File

Create a .env file in the root directory:

GOOGLE_API_KEY=your_google_api_key_here
DATABASE_PATH=./bus_booking.db
CHROMA_PERSIST_DIRECTORY=./chroma_db

To get a Google API Key: Visit Google AI Studio, create a new key, and paste it above.

3️⃣ Build and Run

docker compose up --build

4️⃣ Access the Application

Service	URL
Web Frontend	http://localhost:8000
FastAPI Swagger Docs	http://localhost:8000/docs
FastAPI Redoc	http://localhost:8000/redoc

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🛠️ Option 2: Run Locally (Without Docker)

Prerequisites

• Python 3.10
• pip

1️⃣ Clone & Set Up Virtual Environment

git clone https://github.com/yourusername/Bus-ticket-booking-application.git
cd Bus-ticket-booking-application
python -m venv venv

Activate — Windows:

venv\Scripts\activate

Activate — Mac/Linux:

source venv/bin/activate

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Configure Environment Variables

Create a .env file in the root directory:

GOOGLE_API_KEY=your_google_api_key_here
DATABASE_PATH=./bus_booking.db
CHROMA_PERSIST_DIRECTORY=./chroma_db

4️⃣ Run the Application

uvicorn backend.main:app --reload --port 8000

Then open http://localhost:8000 in your browser.

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🐳 Docker Details

Overview

The image is based on python:3.10-slim-bookworm (Debian 12) which satisfies ChromaDB's sqlite3 >= 3.35 requirement. Key points:

• numpy==1.26.4 and torch==2.1.0+cpu are installed first to prevent version conflicts
• The full LangChain stack is pinned to ensure compatibility
• libgomp1 is included for PyTorch CPU threading support
• FastAPI serves both the API and the HTML/CSS/JS frontend on a single port 8000

Useful Docker Compose Commands

# Build and start
docker compose up --build

# Start in detached mode (background)
docker compose up -d --build

# View logs
docker compose logs -f

# Stop the application
docker compose down

# Stop and remove volumes
docker compose down -v

# Rebuild from scratch
docker compose build --no-cache
docker compose up

Useful Docker Commands

# View running containers
docker ps

# View container logs
docker logs <container_name>

# Remove stopped containers
docker container prune

# Remove unused images
docker image prune

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🤖 RAG Pipeline Architecture

1. Data Ingestion — Bus provider information and route data are loaded from data.json and .txt files in attachments/
2. Embedding Generation — Sentence transformers convert text into vector embeddings
3. Vector Storage — ChromaDB stores embeddings for fast semantic search
4. Query Processing — User questions are embedded and matched against stored vectors
5. Context Retrieval — Relevant information is retrieved from the vector database
6. Response Generation — Google Generative AI (Gemini) generates natural language responses based on retrieved context

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📝 Example Queries

• "Are there any buses from Dhaka to Rajshahi under 500 taka?"
• "Show all bus providers operating from Chittagong to Sylhet."
• "What are the contact details of Hanif Bus?"
• "Can I cancel my booking for the bus from Dhaka to Barishal on 15th November?"
• "What is the privacy policy of Ena Paribahan?"
• "Which bus is cheapest from Dhaka to Cox's Bazar?"

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🛠️ Troubleshooting

Port already in use

# Windows — find and kill the process
netstat -ano | findstr :8000
taskkill /PID <PID> /F

Google API Key Error

• Ensure your .env file contains a valid GOOGLE_API_KEY
• Check that the key has access to Generative AI services

ChromaDB sqlite3 Error (local only)

• Use Python 3.10+ and ensure your system sqlite3 is >= 3.35
• The Docker image handles this automatically via the bookworm base image

ChromaDB Permission Error

• Delete the chroma_db directory and restart the application

Database Locked

• Close any other processes accessing the SQLite database and restart the server

Module Not Found

• Ensure your virtual environment is activated
• Run pip install -r requirements.txt again

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🎥 Demo Video

https://youtu.be/wBabHF555jU

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📄 License

This project is open source and available under the MIT License.

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Built with: FastAPI • HTML/CSS/JS • LangChain • Google Generative AI • ChromaDB • SQLite • Docker