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Introduction

This repository contains the official code of CRUD-RAG, a novel benchmark for evaluting the RAG systems. It includes the datasets we created for evaluating RAG systems, and a tutorial on how to run the experiments on our benchmark.

Important Notes

• The prompts in this repository are designed for use with ChatGPT. For other models, we recommend selecting appropriate prompts. The 7B models are particularly sensitive to prompts, they can not understand complex prompts. So please exercise caution.
• The use of RAGQuestEval metric relies on GPT, we use GPT as question answer and generator.
• The first time you run the code, you need to build a vector index for the text(It takes about 3 hours). This is a one-time process, so you don't need to repeat it later. Please make sure to omit the construct-index parameter when you use the code again.

Project Structure

├── data  #  This folder comprises the datasets used for evaluation.
│   │
│   ├── crud 
│   │   └── merged.json  # The complete datasets.
│   │
│   ├── crud_split
│   │   └── split_merged.json # The dataset we used for experiments in the paper.
│   │
│   └── 80000_docs
│   │    └── documents_dup_part... # More than 80,000 news documents, which are used to build the retrieval database of the RAG system.
│   │     
├── src 
│   ├── configs  # This folder comprises scripts used to initialize the loading parameters of the LLMs in RAG systems.
│   │
│   ├── datasets # This folder contains scripts used to load the dataset.
│   │
│   ├── embeddings  # The embedding model used to build vector databases.
│   │   
│   ├── llms # This folder contains scripts used to load the large language models.
│   │   ├── api_model.py  # Call GPT-series models.
│   │   ├── local_model.py # Call a locally deployed model.
│   │   └── remote_model.py # Call the model deployed remotely and encapsulated into an API.
│   │
│   ├── metric # The evaluation metric we used in the experiments.
│   │   ├── common.py  # bleu, rouge, bertScore.
│   │   └── quest_eval.py # RAGQuestEval. Note that using such metric requires calling a large language model such as GPT to answer questions, or modifying the code and deploying the question answering model yourself.
│   │
│   ├── prompts # The prompts we used in the experiments.
│   │
│   ├── quest_eval # Question answering dataset for RAGQuestEval metric.
│   │
│   ├── retrievers # The retriever used in RAG system.
│   │
│   └── tasks # The evaluation tasks.
│       ├── base.py
│       ├── continue_writing.py
│       ├── hallucinated_modified.py
│       ├── quest_answer.py
│       └── summary.py

Quick Start

• Install dependency packages

pip install -r requirements.txt

• Start the milvus-lite service(vector database)

milvus-server

• Download the bge-base-zh-v1.5 model to the sentence-transformers/bge-base-zh-v1.5/ directory

• Modify config.py according to your need.

• Run quick_start.py

python quick_start.py \
  --model_name 'gpt-3.5-turbo' \
  --temperature 0.1 \
  --max_new_tokens 1280 \
  --data_path 'path/to/dataset' \
  --shuffle True \
  --docs_path 'path/to/retrieval_database' \
  --docs_type 'txt' \
  --chunk_size 128 \
  --chunk_overlap 0 \
  --retriever_name 'base' \
  --collection_name 'name/of/retrieval_database' \ 
  --retrieve_top_k 8 \
  --task 'all' \
  --num_threads 20 \
  --show_progress_bar True \
  --construct_index \ # you need to build a vector index when you use it first time

Citation

@article{lyu2024crud,
  title={CRUD-RAG: A comprehensive chinese benchmark for retrieval-augmented generation of large language models},
  author={Lyu, Yuanjie and Li, Zhiyu and Niu, Simin and Xiong, Feiyu and Tang, Bo and Wang, Wenjin and Wu, Hao and Liu, Huanyong and Xu, Tong and Chen, Enhong},
  journal={arXiv preprint arXiv:2401.17043},
  year={2024}
}