RAGReads

RAGReads is a GraphDB-powered book recommendation app that utilizes a Graph-based Retrieval-Augmented Generation (RAG) model. It analyzes book relationships to suggest personalized reading recommendations, enhancing discovery with AI-driven insights from interconnected data for an enriched reading experience.

RAGReads 📚🔍

Graph-Powered Book Recommendation Engine with LLM Insights

RagReads combines graph database relationships with large language models to deliver contextual book recommendations through semantic understanding of user preferences and literary content.

🌟 Features

Graph RAG Engine

• 📊 Neo4j knowledge graph with 50+ relationship types
• 🔗 Context-aware node connections (GENRE, AUTHOR_STYLE, THEMATIC_SIMILARITY)
• 🧠 User preference vector embeddings (768d)

LLM Integration

• 📚 GPT-4 for content understanding & summary generation
• 🤖 Custom fine-tuned recommendation model (LoRA adapters)
• 🎯 Semantic similarity scoring with Sentence-BERT

Core Capabilities

• Personalized reading lists based on graph walks
• "Why Recommended" explainable AI feature
• Multi-hop relationship discovery
• Real-time graph updates from user feedback

🚀 Installation

# Clone repository
git clone https://github.com/yourusername/RagReads.git
cd RagReads

# Install dependencies
pip install -r requirements.txt

# Set up environment
cp .env.example .env
# Update Neo4j and OpenAI credentials in .env

# Initialize graph database
python scripts/init_graph.py