MoR

This repository is an official PyTorch implementation of MoR in Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases

Running the Evaluation and Reranking Script

Installation

To set up the environment, you can install dependencies using Conda or pip:

Using Conda

conda env create -f mor_env.yml
conda activate your_env_name  # Replace with actual environment name

Using pip

pip install -r requirements.txt

Checkpoints and embeddings download

Before running the inference, please go to https://drive.google.com/drive/folders/1ldOYiyrIaZ3AVAKAmNeP0ZWfD3DLZu9D?usp=drive_link

(1) download the "checkpoints" and put it under the directory MoR/Planning/

(2) download the "data" and put it under the directory MoR/Reasoning/

(2) download the "model_checkpoint" and put it under the directory MoR/Reasoning/text_retrievers/

Inference

To run the inference script, execute the following command in the terminal:

bash eval_mor.sh

This script will automatically process three datasets using the pre-trained planning graph generator and the pre-trained reranker.

Training (Train MoR from Scratch)

Step1: Training the planning graph generator

bash train_planner.sh

Step2: Train mixed traversal to collect candidates (note: there is no training process for reasoning)

bash run_reasoning.sh

Step3: Training the reranker

bash train_reranker.sh

Generating training data of Planner

We provide codes to generate your own training data to finetune the Planner by using different LLMs.

If you are using Azure API

python get_llm_data.py --model "model_name" \
  --dataset_name "dataset_name" \
  --azure_api_key "your_azure_key" \
  --azure_endpoint "your_azure_endpoint" \
  --azure_api_version "your_azure_version"

If you are using OpenAI API

python get_llm_data.py --model "model_name" \
  --dataset_name "dataset_name" \
  --openai_api_key "your_openai_key" \
  --openai_endpoint "your_openai_endpoint"