🏨 Hotel Receptionist Chatbot

Welcome to the Hotel Receptionist Chatbot project — an AI-powered virtual receptionist designed to handle frequently asked questions (FAQs) from hotel guests. Built with Haystack for natural language processing (NLP) and FastAPI for backend services, this chatbot offers a seamless and interactive experience for users, mimicking a real hotel concierge.

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📁 Table of Contents

1. Project Overview
2. Technical Architecture
3. Tech Stack
4. Features
5. Setup and Installation
6. Usage
7. Contributing
8. Acknowledgments

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🚀 Project Overview

The Hotel Receptionist Chatbot uses Retrieval-Augmented Generation (RAG) to provide accurate, context-aware answers to user queries. It combines retrieval and generation techniques for optimal responses.

Key Components:

• Hugging Face Embeddings Model: Generates embeddings from a predefined dataset (FAQs.txt) to retrieve relevant information.
• Gemini Model: Refines and generates human-like, contextually accurate responses.
• Pinecone: Pinecone vector database to store and retrieve embeddings.

This chatbot is designed to be both intelligent and visually appealing, reflecting the ambiance of a luxurious hotel environment.

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🛠 Technical Architecture

The system is structured as follows:

1. Frontend:

• Responsive and elegant chat interface using HTML, CSS, and JavaScript.
• Bootstrap for responsive UI components.
• SweetAlert2 for attractive and user-friendly alert messages.

2. Backend:

• Built on FastAPI for high-performance API management.
• Handles chat queries, invokes NLP pipeline, and returns refined answers.

3. AI Pipeline:

• Hugging Face Embeddings Model: Retrieves relevant data from FAQs.
• Gemini Model: Generates natural and polished responses.
• Haystack RAG pipeline to integrate retrieval and generation seamlessly.

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⚙️ Tech Stack

Technology	Description
Haystack	NLP framework for document retrieval & QA.
Gemini Model	For interating with the user in Conversational way.
Pinecone	A vector Database for as a RAG Storage
FastAPI	High-performance Python backend framework.
HTML/CSS/JS	Frontend structure and interactivity.
Bootstrap	Responsive, modern UI components.
SweetAlert2	Beautiful and responsive alert modals.

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✨ Features

• Interactive Chat Interface: Real-time conversations with a chatbot.
• AI-Powered, Context-Aware Responses: High-quality, intelligent replies using RAG.
• Luxurious Design: Chat interface mimicking hotel lobby ambiance.
• Error Handling: Graceful error messages for unrecognized queries.
• Fully Responsive: Works seamlessly across devices.

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⚙️ Setup and Installation

✅ Prerequisites

• Python 3.7+
• pip (Python package manager)

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📥 Installation Steps

1. Clone the Repository:

git clone https://github.com/MuhammadAbdullah95/Hotel_Receptionist_AI_Agent.git
cd Hotel_Receptionist_AI_Agent

2. Set Up a Virtual Environment:

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

3. Install Dependencies:

pip install -r requirements.txt

4. Run the FastAPI Server:

uvicorn app:app --reload

5. Open the Chatbot Interface:

• Open index.html located in the templates folder using any browser.
• Or serve via local server:

python -m http.server

• Visit http://localhost:8000 in your browser.

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💬 Usage

1. Open the Chatbot Interface:

• Launch index.html or navigate to the hosted URL.

2. Start Chatting:

• Type your question in the chatbox and hit Send or press Enter.

3. Sample Queries:

• "What time is check-in?"
• "Do you offer room service?"
• "Is breakfast included in the stay?"

4. View Responses:

• AI-generated responses appear instantly in a conversational format.

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🔑 API Keys Setup

Before running the project, make sure you have the following API keys:

1. Google API Key - for accessing Google services (e.g., search, knowledge retrieval).
2. Pinecone API Key - for vector database operations (e.g., storing and retrieving embeddings).
3. Hugging Face Token (HF_TOKEN) - for accessing hosted LLM models and pipelines.

⚙️ How to Set API Keys

You can set these API keys as environment variables to keep them secure and avoid hardcoding them into your code.

🖥️ Option 1: Temporary (per session)

export GOOGLE_API_KEY='your_google_api_key_here'
export PINECONE_API_KEY='your_pinecone_api_key_here'
export HF_TOKEN='your_huggingface_token_here'

📁 Option 2: Permanent (via .env file)

1. Create a .env file in the root directory of your project:

   touch .env

2. Add your keys inside the .env file:

   GOOGLE_API_KEY=your_google_api_key_here
   PINECONE_API_KEY=your_pinecone_api_key_here
   HF_TOKEN=your_huggingface_token_here

3. Make sure to load this .env file in your Python app (if using something like python-dotenv):

   from dotenv import load_dotenv
   load_dotenv()

4. ⚠️ Important: Add .env to your .gitignore to avoid exposing keys:

   # .gitignore
   .env
   

🚨 Notes:

• Never share or commit your API keys publicly.
• Regenerate API keys if you suspect they are compromised.

Feel free to contribute or suggest improvements! ✨

🤝 Contributing

We welcome contributions! Follow these steps to contribute:

1. Fork the repository.
2. Create a branch for your feature or bugfix:

git checkout -b feature-name

3. Commit your changes:

git commit -m "Add feature"

4. Push your branch:

git push origin feature-name

5. Open a Pull Request for review.

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🙏 Acknowledgments

• Haystack: NLP framework for retrieval and generation.
• FastAPI: Backend API framework.
• Bootstrap: Beautiful and responsive UI toolkit.
• SweetAlert2: For stunning alert dialogs.

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Enjoy a seamless hotel experience with our AI Receptionist! 🌐✨