h9-tect/RAG_with_Kg

Knowledge Graph RAG Setup

This repository provides a structured setup for configuring a Nebula graph database, building a knowledge graph, and integrating it with a Retrieval-Augmented Generation (RAG) model.

Prerequisites

• Docker
• Python 3.9+

Setup Instructions

1. Clone the Repository

   git clone ttps://github.com/h9-tect/RAG_with_Kg.git
   cd knowledge-graph-rag

2. Install Docker

   Ensure Docker is installed and running on your machine. You can download Docker from here.

3. Run Nebula Container

   Execute the following Docker commands to pull and run the Nebula container:

   docker pull vesoft/nebula-graph:nightly
   docker run -d --name nebula-server vesoft/nebula-graph:nightly

4. Set Environment Variables

   Adjust environment variables as needed in config/nebula_config.py.

5. Install Python Dependencies

   Install the required Python packages:

   pip install -r requirements.txt

6. Setup Nebula Database

   Run the following command to set up the Nebula database:

   python database/setup_nebula.py

7. Run the Main Application

   Run the main application to build the knowledge graph and query it:

   python main.py

Overview of Project Files

• main.py: The entry point of the application. It sets up the Nebula database, builds the knowledge graph, and performs queries.
• config/nebula_config.py: Contains the configuration for connecting to the Nebula database.
• database/setup_nebula.py: Sets up the Nebula database schema.
• kg_rag/query_kg_rag.py: Contains functions to load documents, build a knowledge graph, and query it. Also includes a custom retriever to combine results from vector and graph databases.
• requirements.txt: Lists the Python dependencies for the project.
• Dockerfile: Dockerfile to containerize the application.

License

This project is licensed under the MIT License.