Chatbot with FARM Stack and RAG Application

Welcome to the Chatbot project! This chatbot is built using the FARM stack (FastAPI, React, MongoDB Atlas) and implements a Retrieval-Augmented Generation (RAG) application to enhance its capabilities.

Table of Contents

• Introduction
• Features
• Technologies Used
• Setup and Installation
• Usage

Introduction

This project is a chatbot application designed to provide intelligent and context-aware responses by leveraging the FARM stack and a RAG application. The chatbot uses MongoDB Atlas as its database to store and retrieve documents, enhancing its response capabilities based on the stored information.

Features

• Intelligent and context-aware responses using RAG application
• User authentication and registration
• Integration with MongoDB Atlas for document storage and retrieval
• Real-time chat interface
• Responsive design with React

Technologies Used

• FastAPI: For building the backend API
• React: For building the frontend user interface
• MongoDB Atlas: For cloud-based database storage
• RAG Application: For enhanced response generation using document retrieval

Setup and Installation

Follow these steps to set up and run the project locally.

Prerequisites

• Node.js and npm installed
• Python 3.7+ installed
• MongoDB Atlas account

Backend Setup

1. Clone the repository:

   git clone https://github.com/yourusername/chatbot.git
   cd chatbot/backend

2. Create a virtual environment and activate it:

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

3. Install the required packages:

   pip install -r requirements.txt

4. Set up environment variables in a .env file:

   MONGO_URL=your_mongo_url
   MONGO_DB=your_database_name
   OPENAI_API_KEY=your_openai_api_key

5. Run the FastAPI server:

   uvicorn main:app --reload
   

Frontend Setup

1. Navigate to the frontend directory:

   cd ../frontend

2. Install the required packages:

   npm install

3. Start the React development server:

   npm start

Usage

Access the frontend at http://localhost:3000. Interact with the chatbot through the chat interface. The chatbot will respond based on the documents stored in the MongoDB Atlas database.