GraphRag-Neo4j_llama3.1

Description

GraphRag-Neo4j_llama3.1 is an implementation of Graph RAG using Neo4j and llama_3.1. This project aims to leverage the power of graph databases and machine learning for advanced relational data processing and analysis.

Features

• Integration with Neo4j for graph data storage and queries.
• Utilizes llama_3.1 for machine learning tasks.
• Scalable and efficient data management.

Installation

Prerequisites

• Python 3.8 or higher
• Neo4j Database
• Docker (optional, for containerized deployment)

Steps

1. Clone the repository:

   git clone https://github.com/Jakee4488/GraphRag-Neo4j_llama3.1.git
   cd GraphRag-Neo4j_llama3.1

2. Install the required Python packages:

   pip install -r requirements.txt

3. Set up the Neo4j database and configure connection settings in the project.

Usage

Basic Example

1. Start the Neo4j database.
2. Run the main script to process and analyze your graph data:

   python main.py

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.

License

This project is licensed under the MIT License.

Contact

For further information, please reach out to the repository owner Jakee4488.

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Feel free to edit and expand on this draft as needed.