Flarista - User Guide 🌟

An RAG Chatbot 📚

This project is a Retrieval-Augmented Generation (RAG) application. 💻 It enhances the capabilities of language models by allowing them to access and incorporate information from external sources (Technical documentation in this case).

This application combines: - A backend API built with Python and Flask 🐍 - A user interface built with Next.js and React ⚛️

Developer can benefit from this application, without having to get stuck in the docs. They can easily harness LLMS to provide relevant explainations, code, etc.

Technologies Used 🛠️

• Core RAG Components:

  • Langchain: A framework for developing applications powered by language models. It's used here to orchestrate the RAG pipeline, connecting the language model with the knowledge retrieval process.
  • Gemini Model: A powerful language model from Google, used to generate responses. In this project, Gemini is the model that answers user queries, informed by retrieved external knowledge. (feel free to use any model)

• Frontend:

  • Next.js (React framework)
  • Tailwind CSS (for styling)
  • JavaScript (programming language)
  • npm (package manager)

• Backend:

  • Python (programming language)
  • Flask (web framework)
  • pip (package installer for Python)

User Guide 🚀

This guide will walk you through setting up and running the Flare RAG project on your local machine.

Prerequisites

Before you begin, ensure you have the following installed:

• Node.js and npm: Required for running the frontend. You can download them from nodejs.org.
• Python: Required for running the backend. Ensure you have Python 3.7 or higher installed. You can download it from python.org.

Setup and Run Instructions

Follow these steps to get the project running:

1. Clone the repository:

git clone <repository_url>
cd flare-rag

2. Setup the Backend:

• Navigate to the backend directory:

  cd backend

• Create a virtual environment (recommended):

  python -m venv venv

• Activate the virtual environment:
  • On Windows:

    venv\\Scripts\\activate

  • On macOS/Linux:

    source venv/bin/activate

• Install the required Python packages:

  pip install -r requirements.txt

• Run the backend application:

  python app.py

  The backend server should now be running, typically on http://127.0.0.1:8000.

3. Setup the Frontend:

• Open a new terminal window and navigate to the ui directory:

  cd ui

• Install the required npm packages:

  npm install

• Run the frontend development server:

  npm run dev

  The frontend application should now be running, typically on http://localhost:3000.

4. Access the Application:

• Open your web browser and go to http://localhost:3000 to access the Flare RAG application.

Using the Application

Once both the frontend and backend are running, you can interact with the RAG application through your browser. The user interface should be intuitive, allowing you to input queries and receive responses enhanced by the RAG system.

Contributing

If you wish to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your changes to your fork.
5. Submit a pull request to the main repository.

Hope this comes handy!