Hotel Booking Analytics & RAG System

Overview

This project is an interactive system that processes hotel booking data, generates dynamic analytics, and supports retrieval-augmented question answering (RAG). The system uses a REST API built with FastAPI, a PostgreSQL database for data storage, and a Streamlit-based frontend for interactive visualization and user interaction.

Features

• Analytics: Generate interactive charts for revenue trends, cancellation rates, geographical distribution, booking lead time, ADR distribution, stay duration, and monthly trends.
• Question Answering: Utilize a RAG pipeline combining FAISS, LangChain, and the Ollama model to answer natural language questions about hotel bookings.
• Dynamic Data Updates: Insert new booking records and automatically update insights using PostgreSQL triggers and notifications.
• Health Check: Monitor the health status of core components including the database, vector store, and QA chain.

Tech Stack

• FastAPI & Uvicorn: Fast, asynchronous REST API development.
• PostgreSQL: Reliable, scalable relational database with real-time notifications.
• Pandas, Matplotlib, Seaborn, Plotly: Data manipulation and interactive visualization.
• Streamlit: User-friendly frontend for real-time dashboard display.
• LangChain with Ollama (phi4:latest): RAG pipeline for high-quality context-aware question answering.
• Ollama Embedding Model (nomic-embed-text): Advanced text encoder for accurate embeddings.
• FAISS: Efficient vector search and storage for retrieving insights.

Architecture Design

Installation and Setup

Prerequisites

Ensure you have the following installed:

• Python (>=3.8)
• pip (Python package manager)
• Ollama software
• PostgreSQL
• Hotel Booking Dataset [ hotel_bookings_sampled_5k.csv ]

Step-by-Step Guide

1. Clone the Repository

   git clone https://github.com/HR-04/Hotel-Booking.git
   cd Hotel-Booking

2. Create a Virtual Environment (Recommended)

   python -m venv myenv
   source myenv/bin/activate  # On Windows: myenv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Set Up ollama and PostgreSQL DB

• Ollama pull phi4:latest [ Your Choice ]
• Ollama pull nomic-embed-text [ Your Choice ]
• Create a Table and Insert data in PostgreSQL [ hotel_bookings_sampled_5k.csv ]

5. Run the application

• Start the Backend:

  cd backend
  uvicorn main:app --reload

• Run the Frontend:

  cd frontend
  streamlit run main.py

6. Access the Application
   • Open your browser and go to http://localhost:8501