Transform natural language queries into Cypher using LLMs and build intelligent knowledge graph applications.
This project demonstrates how to build an intelligent query system combining Large Language Models (LLM), Retrieval-Augmented Generation (RAG), an
d Knowledge Graphs using Neo4j. Perfect for developers and data scientists looking to build natural language interfaces for graph databases.
- 🤖 Natural Language to Cypher query conversion using fine-tuned BART
- 📊 Automated data ingestion and categorization pipeline
- 🎯 Knowledge Graph creation and management
- 🔍 Intelligent query processing and retrieval
- 🎓 Educational examples using organizational data
employees.csv: Sample organizational datasetsync.py: Data ingestion and Neo4j graph population scripttrain-bart.py: LLM training pipeline for natural language to Cypher conversionneo4j_client.py: Neo4j interaction and query processing interface
pip install -r requirements.txt- Set up Neo4j database
- Update connection settings in
neo4j_client.py - Run data ingestion:
python sync.py
- Train the model:
python train-bart.py
- 🏢 Enterprise Knowledge Management
- 👥 HR Analytics and Organizational Insights
- 📊 Data Discovery and Exploration
- 🤖 Conversational AI Interfaces
- 🎯 Intelligent Search Systems
LLM RAG Neo4j Knowledge Graphs Natural Language Processing BART Fine-tuning Graph Database Cypher Transformers Enterprise Data HR Analytics Python Machine Learning AI Data Engineering Information Retrieval Semantic Search Data Science Graph AI
This project serves as a practical example of:
- Building LLM-powered applications
- Implementing RAG systems
- Working with Knowledge Graphs
- Natural Language Understanding
- Enterprise Data Management
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.