Chatbot for the Department Of Justice Website 🤖

Overview

This project implements a chat support application using React for the frontend and a Flask backend that processes queries using a Retrieval-Augmented Generation (RAG) pipeline. Users can select a language, send messages, and receive responses from a chatbot powered by machine learning. The application supports multiple document types and stores relevant data using Chroma as the database.

Features

• Interactive Chatbot: Users can interact with a chatbot that responds to queries.
• Language Selection: Supports multiple languages, allowing users to receive answers in their preferred language.
• Dynamic Suggestions: Provides predefined suggestions to enhance user experience.
• RAG Pipeline: Utilizes a RAG pipeline to fetch and process relevant documents and generate responses.
• Link Extraction: Extracts URLs from the context for users to reference.
• Rate Limiting: Implements rate limiting on API requests to ensure fair use.

Technologies Used

• Frontend: React
• Backend: Flask
• Database: Chroma
• Natural Language Processing: NLTK, LangChain, Google Generative AI
• Document Loaders: Supports PDF, CSV, and text files
• Deployment: Docker (if applicable)

Installation

Prerequisites

• Node.js and npm (for the React frontend)
• Python 3.7 or higher (for the Flask backend)
• Virtual environment (recommended for Python)
• pip (Python package manager)

Clone the Repository

git clone https://github.com/yourusername/chat-support-app.git
cd chat-support-app

Setup Frontend

1. Navigate to the frontend directory:

   cd frontend

2. Install the required npm packages:

   npm install

3. Start the development server:

   npm run dev

Setup Backend

1. Navigate to the backend directory:

   cd backend

2. Create a virtual environment:

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

3. Install the required Python packages:

   pip install -r requirements.txt

4. Create a .env file in the backend directory and add your Google API key:

   GOOGLE_API_KEY=your_api_key_here

5. Run the Flask application:

   python app.py

Usage

1. Open your browser and go to http://localhost:5173 (or the port specified by your frontend setup).
2. Select a language from the dropdown.
3. Type your query in the input box and press the send button.
4. View the chatbot's response along with any relevant links provided.

API Endpoints

• GET /: Returns the main page (frontend).
• POST /predict: Accepts a JSON request with a message field to generate a response based on the provided query.

Example Request

{
  "message": "Tell me about Mann Ki Baat | >English"
}

Rate Limits

• Users can make a maximum of 10 requests per minute to the /predict endpoint.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any enhancements or fixes.

License

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

Acknowledgments

• LangChain for providing the tools for document processing and embeddings.
• NLTK for natural language processing utilities.
• React and Flask for building the frontend and backend.