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GFM-RAG

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 graphs and retrieve relevant documents for question answering.

We first build a graph-index from the documents to capture the relationships between knowledge. Then, we feed the query and constructed graph-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.

GFM-RAG is designed to be efficient and generalizable. You can bring your own dataset and directly apply the pre-trained GFM retriever to obtain relevant documents for question answering. You can also fine-tune the GFM retriever on your own dataset to improve performance on specific domains.

For more details, please refer to our project and papers: GFM-RAG, G-reasoner.

Features

  • Graph Foundation Model (GFM): A graph neural network-based retriever that can reason over the graph-index.
  • Universal Graph Index: A universal graph index that can represent various types of structural knowledge such as Knowledge Graphs, Document Graphs, and Hierarchical Graphs.
  • 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.

Choose Your Path

  • Quick Start: shortest runnable path for a new user
  • Workflow: general usage from data formatting through training
  • Experiment: script-first paper reproduction for GFM-RAG and G-reasoner
  • API Reference: developer-facing classes and modules

How GFM-RAG And G-reasoner Relate

  • GFM-RAG is the original graph foundation model retriever and remains the baseline workflow and published checkpoint family.
  • G-reasoner is the latest version of the graph foundation model retriever, which has new architecture and better performance.
  • Both lines share the same indexing, dataset, QA, and documentation structure in this site.

Fastest Path

If you want to run something with minimal setup:

  1. Follow Install.
  2. Prepare a tiny dataset with raw/documents.json.
  3. Start from Quick Start.