https://github.com/thytranx/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.
https://github.com/thytranx/ragreads

graphdb graphrag knowledge-graph retrieval-augmented-generation

Last synced: 7 months ago
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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.

• Host: GitHub
• URL: https://github.com/thytranx/ragreads
• Owner: thytranx
• License: mit
• Created: 2025-02-08T00:03:59.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2025-02-26T07:34:11.000Z (7 months ago)
• Last Synced: 2025-02-26T08:32:11.177Z (7 months ago)
• Topics: graphdb, graphrag, knowledge-graph, retrieval-augmented-generation
• Homepage:
• Size: 6.84 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 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**

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

[![Python 3.10+](https://img.shields.io/badge/Python-3.10%2B-green.svg)](https://www.python.org/)

[![Neo4j](https://img.shields.io/badge/GraphDB-Neo4j-008CC1.svg)](https://neo4j.com/)

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

![RagReads Architecture](assets/ragreads-arch.png)

    

## 🌟 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

```bash

# 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