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GNN-RAG

(★ 445)

GNN-RAG: Graph Neural Retrieval for Large Language Modeling Reasoning

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Download Latest Version (.zip)

- - base_model.py
- - layer_init.py
  - query_update.py
- - rearev_cwq.sh
- .gitignore
- dataset_load.py
- dataset_load_graft.py
- evaluate.py
- main.py
- parsing.py
- README.md
- requirements.txt
- train_model.py
- utils.py
- - alpaca.txt
  - general_prompt.txt
  - llama2.txt
  - llama2_predict.txt
- - evaluate_multi_hop.sh
  - interpretable_example.py
  - planning.sh
  - plug-and-play.sh
  - rag-reasoning.sh
  - train.sh
- - __init__.py
- .gitignore
- entities_names.json
- README.md
- requirements.txt
.gitignore
GNN-RAG.png
README.md

# Installation Guide

1. Get the code

git clone https://github.com/cmavro/GNN-RAG

Downloads the entire project code from GitHub to your computer.

cd GNN-RAG

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r gnn/requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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This is the code for **GNN-RAG: Graph Neural Retrieval for Large Language Modeling Reasoning**.

![alt GNN-RAG: The GNN reasons over a dense subgraph to retrieve candidate answers, along
with the corresponding reasoning paths (shortest paths from question entities to answers). The
retrieved reasoning paths -optionally combined with retrieval augmentation (RA)- are verbalized
and given to the LLM for RAG](GNN-RAG.png "GNN-RAG")

The directory is the following:

|----`gnn` folder has the implementation of different KGQA GNNs.

You can train your own GNNs or you can skip this folder and  use directly the GNN output (retrieved answer nodes) that we computed (`llm/results/gnn`).

|----`llm` folder has the implementation for RAG-based KGQA with LLMs.

Please see details on how to reproduce results there.

**Results**: We append all the results for Table 2: See `results/KGQA-GNN-RAG-RA` or `results/KGQA-GNN-RAG`. You can look at the actual LLM generations, as well as the KG information retrieved ("input" key) in predictions.jsonl.