ReadingHub

Overview

This project is a full-stack book recommendation engine that leverages semantic search to help users find books based on natural language queries. It combines a modern React frontend with a robust FastAPI backend, utilizing vector embeddings for intelligent search results.

Technology Stack

Frontend

• Framework: React (Vite)
• Styling: Tailwind CSS
• Routing: React Router
• State/Auth: Supabase Client

Backend

• Framework: FastAPI
• Vector Database: Qdrant (for semantic search)
• Database: Supabase (PostgreSQL for metadata and user data)
• ML Models: HuggingFace transformers (all-MiniLM-L6-v2) for text embeddings
• Caching: Custom Redis-based caching implementation
• Orchestration: Apache Airflow (DAGs for data pipelines)

Features

• Semantic Search: Find books by describing plot, mood, or context rather than just keywords.
• User Authentication: Secure sign-up and login via Supabase.
• Book Management: Authenticated users can add new books to the database.
• Responsive Design: Modern interface built with Tailwind CSS.

Project Structure

• backend/: Contains the FastAPI application, Airflow DAGs, and Docker configuration.
• frontend/: Contains the React application.
• models/: Directory for ML models.
• notebooks/: Jupyter notebooks for data exploration and testing.

Setup Instructions

Prerequisites

• Python 3.11+
• Node.js & npm
• Docker (optional, for containerization)
• Redis server (running locally or remotely)
• Apache Airflow (for pipeline orchestration)
• Supabase account
• Qdrant account

Backend Setup

1. Navigate to the backend directory.
2. Create a virtual environment and activate it.
3. Install dependencies:

   pip install -r requirements.txt

4. Configure environment variables in a .env file (refer to .env.example).
   • Ensure REDIS_HOST and REDIS_PORT are set correctly.
5. Run the server:

   uvicorn src.api:app --reload

Airflow Pipeline Setup

1. Ensure your Airflow environment is active.
2. Point Airflow to the backend/dags directory.
3. Available DAGs:
   • update_embeddings_dag: Updates vector embeddings for new books.
   • validation_dag: Validates system integrity and data consistency.

Frontend Setup

1. Navigate to the frontend directory.
2. Install dependencies:

   npm install

3. Configure environment variables (if required).
4. Start the development server:

   npm run dev

API Documentation

Once the backend is running, you can access the interactive API documentation at http://localhost:8000/docs.