RAG Chatbot Project

This project implements a Retrieval-Augmented Generation (RAG) chatbot. It consists of a backend built with Django and a frontend built with React.

Project Structure

The project is organized as follows:

• backend/: Contains the Django backend application.
  • core/: Core Django project settings and configurations.
  • chat/: Django app for handling chat functionalities.
    • services/: Contains the core logic for document processing, embedding, and question answering.
• frontend/: Contains the React frontend application.
  • components/: React components for the user interface.
• docker-compose.yml: Defines the services for running the application using Docker.

Prerequisites

Before running the application, ensure you have the following installed:

• Docker
• Docker Compose

Running the Application

1. Clone the repository:

   git clone <your-repository-url>
   cd rag-chatbot

2. Build and start the services using Docker Compose:

   docker-compose up --build

   This command will:

   • Build the Docker images for the backend and frontend.
   • Start the containers for the backend and frontend.

3. Access the application:

   • The frontend will be accessible at http://localhost:3000.
   • The backend will be accessible at http://localhost:8000.

Development

Backend

• Navigate to the backend directory:

  cd backend

• To run the backend locally without Docker:

  1. Create a virtual environment:

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

  2. Install dependencies:

     pip install -r requirements.txt

  3. Apply migrations:

     python manage.py migrate

  4. Run the development server:

     python manage.py runserver

• Make changes to the code in the chat app, specifically in the services directory for the core logic.

Frontend

• Navigate to the frontend directory:

  cd frontend

• To run the frontend locally without Docker:

  1. Install dependencies:

     npm install

  2. Start the development server:

     npm start

• Make changes to the code in the src directory, specifically in the components directory for the UI and services for API calls.

Key Components

• Document Upload: Allows users to upload documents that will be processed by the backend.
• Chat Interface: Provides a user interface for interacting with the chatbot.
• Document Processor: Handles the processing of uploaded documents.
• Embedding Service: Generates embeddings for the documents.
• QA Service: Uses the embeddings to answer user questions.

Notes

• The docker-compose.yml file defines the necessary configurations for running the application using Docker.
• The requirements.txt file in the backend directory lists the Python dependencies for the backend.
• The package.json file in the frontend directory lists the JavaScript dependencies for the frontend.

Further Development

• Implement more advanced document processing techniques.
• Improve the accuracy of the question-answering system.
• Add more features to the user interface.