A sophisticated book recommendation system that combines semantic search, emotion analysis, and AI embeddings to deliver personalized book suggestions based on user preferences and emotional tone.

• Semantic Search: Uses OpenAI embeddings for intelligent content understanding
• Emotion-Based Filtering: Recommends books based on desired emotional tone (Happy, Sad, Suspenseful, etc.)
• Multi-Modal Search: Supports natural language queries like "adventurous fantasy with strong characters"

• Category Filtering: Fiction, Non-fiction, Children's books
• Tone Selection: Happy, Surprising, Angry, Suspenseful, Sad
• Flexible Limits: Customizable result count (8-32 books)

• Real-time Search: Instant book search with autocomplete
• Hybrid Approach: Combines keyword matching with semantic understanding
• Rich Metadata: Displays ratings, categories, descriptions, and cover images

• Python 3.9+
• Node.js 16+
• OpenAI API Key

git clone https://github.com/Bagusdevaa/Book-Recommender.git
cd book-recommender

cd backend

# Install dependencies
pip install -r requirements.txt

# Setup environment variables
cp .env.example .env
# Add your OPENAI_API_KEY to .env

# Run the server
uvicorn app.main:app --reload

Backend will be available at http://localhost:8000

cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

Frontend will be available at http://localhost:5173

• Size: 7,000 books with rich metadata
• Sources: Kaggle - 7k Books

Each book analyzed across 5 emotional dimensions:

• Joy (0.0-1.0): Happiness, hope, uplift
• Sadness (0.0-1.0): Melancholy, tragedy, loss
• Fear (0.0-1.0): Suspense, tension, thriller elements
• Anger (0.0-1.0): Conflict, rage, social issues
• Surprise (0.0-1.0): Plot twists, unexpected elements

• Model: OpenAI text-embedding-ada-002
• Storage: ChromaDB for efficient similarity search
• Persistence: Local vector database for cost optimization

• OpenAI for powerful embedding models
• LangChain for seamless AI integration
• FastAPI for excellent async API framework
• React & Tailwind for modern frontend development
• Book data sources: Google Books API, Open Library

• FastAPI Documentation
• LangChain Guide
• OpenAI Embeddings
• ChromaDB Documentation
• React Best Practices

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