GFM-RAG Documentation

Welcome to the documentation for GFM-RAG project.

Overview

The GFM-RAG is the first graph foundation model-powered RAG pipeline that combines the power of graph neural networks to reason over knowledge graphs and retrieve relevant documents for question answering.

We first build a knowledge graph index (KG-index) from the documents to capture the relationships between knowledge. Then, we feed the query and constructed KG-index into the pre-trained graph foundation model (GFM) retriever to obtain relevant documents for LLM generation. The GFM retriever experiences large-scale training and can be directly applied to unseen datasets without fine-tuning.

For more details, please refer to our project and paper.

🎉 News

• [2025-02-06] We have released the GFM-RAG codebase and a 8M pre-trained model. 🚀

Features

• Graph Foundation Model (GFM): A graph neural network-based retriever that can reason over the KG-index.
• Knowledge Graph Index: A knowledge graph index that captures the relationships between knowledge.
• Efficiency: The GFM-RAG pipeline is efficient in conducting multi-hop reasoning with single-step retrieval.
• Generalizability: The GFM-RAG can be directly applied to unseen datasets without fine-tuning.
• Transferability: The GFM-RAG can be fine-tuned on your own dataset to improve performance on specific domains.
• Compatibility: The GFM-RAG is compatible with arbitrary agent-based framework to conduct multi-step reasoning.
• Interpretability: The GFM-RAG can illustrate the captured reasoning paths for better understanding.