kelvinc123 / rag-hotel-data

An AI-driven chatbot for intelligent hotel discovery and semantic search. Powered by Qdrant, Traversaal.ai, and OpenAI.

iSEArch Hotels Project

iSEArch Hotels is a Retrieval-Augmented Generation (RAG) hotel search chatbot that leverages Qdrant for enhanced semantic search capabilities, integrating OpenAI for natural language understanding and Traversaal.ai API for dynamic content retrieval.

Awards and Recognition 🏅

We are proud to announce that our iSEArch Hotels Project achieved 2nd place in a hackathon organized by Traversaal.ai, competing against 44 participants. This recognition highlights our team's dedication to leveraging innovative technology integrations to develop a state-of-the-art hotel chatbot.

Getting Started

Prerequisites

• Ensure Docker is installed on your PC. Installation instructions can be found at Docker's official documentation.

Installation Steps

1. Set Up Your Environment

   • Clone this repository to your local machine.
   • Create and activate a virtual environment:

     python -m venv venv
     # On macOS/Linux
     source venv/bin/activate
     # On Windows
     .\venv\Scripts\activate

   • Install required Python dependencies:

     pip install -r requirements.txt

2. Run Qdrant

   • For macOS/Linux, execute:

     ./run_qdrant.sh

   • For Windows, replicate the commands inside run_qdrant.sh in your command prompt.

3. Environment Configuration

   • Obtain an OpenAI API key and a Traversaal API key.
   • Put these API keys along with any other necessary configurations inside the .env file located in the project's root directory.

4. Data Preprocessing and Database Initialization

   • Prepare your dataset and initialize the Qdrant database with:

     python preprocess.py
     python store_to_qdrant.py

Launch the Streamlit Application

Launch the Streamlit app to interact with the chatbot:

streamlit run streamlit_app.py

Additional Information

• To directly interact with the chatbot logic or for debugging, run python rag.py.
• For an illustrative example of a user-chatbot interaction, refer to example_interaction.txt.

Example Interaction

A detailed example showcasing the chatbot's interaction with a user, including handling various hotel-related queries, is available in example_interaction.txt. This provides insight into the chatbot's capabilities and response quality.