Transportation Query Chatbot

This is a web-based chatbot application designed to answer transportation-related queries using an LLM (Microsoft-Phi-3-mini-128k) with a Retrieval-Augmented Generation (RAG) implementation.

Table of Contents

• Overview
• Features
• Tech Stack
• Setup
• Backend
  • Dependencies
  • Running the Backend
• Frontend
  • Dependencies
  • Running the Frontend
• Contributing
• License

Overview

This project is a chatbot application that uses the Microsoft-Phi-3-mini-128k model to generate answers to user queries about transportation. It leverages Retrieval-Augmented Generation (RAG) to provide accurate and contextually relevant responses.

Features

• Real-time query processing
• Retrieval-Augmented Generation (RAG) for enhanced answer accuracy
• Interactive and responsive user interface

Tech Stack

• Model: Microsoft-Phi-3-mini-128k
• Backend Framework: FastAPI
• Frontend Framework: React + Vite
• CSS: Tailwind, Material-Tailwind, hover.dev

Setup

To get the project up and running, follow these steps:

Backend

Dependencies

All necessary dependencies for the backend are listed in the requirements.txt file located in the backend folder. To install them, run:

pip install -r backend/requirements.txt

cd backend

uvicorn main:app --reload

Frontend

Dependencies

Install the required dependencies for the frontend by navigating to the frontend folder and running:

Running the Frontend
Navigate to the frontend directory:
cd frontend

Start the Vite development server:
npm run dev

Contributions

Contributions are welcome! Please fork the repository and create a pull request with your changes. Ensure your code follows the project's coding standards and includes appropriate tests.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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