RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
RAGLAB is a modular, research-oriented open-source framework for Retrieval-Augmented Generation (RAG) algorithms. It offers reproductions of 6 existing RAG algorithms and a comprehensive evaluation system with 10 benchmark datasets, enabling fair comparisons between RAG algorithms and easy expansion for efficient development of new algorithms, datasets, and evaluation metrics.
2024.10.6: Our paper has been accepted by EMNLP 2024 System Demonstration.? You can find our paper in RAGLAB.
2024.9.9: RAGLAB has open-sourced all log files and evaluation files in evaluation results?
2024.8.20: RAGLAB has open-sourced 4 models?: llama3-8B-baseline selfrag-llama3-8b llama3-70B-adaptor selfrag-llama3-70B-adaptor
2024.8.6: RAGLAB is released?.
Comprehensive RAG Ecosystem: Supports the entire RAG pipeline from data collection and training to auto-evaluation.
Advanced Algorithm Implementations: Reproduces 6 state-of-the-art RAG algorithms, with an easy-to-extend framework for developing new algorithms.
Interact Mode & Evaluation Mode: Interact Mode is specifically designed for quickly understanding algorithms. Evaluation Mode is specifically designed for reproducing paper results and scientific research.
Fair Comparison Platform: Provides benchmark results for 6 algorithms across 5 task types and 10 datasets.
Efficient Retriever Client: Offers local API for parallel access and caching, with average latency under 1 second.
Versatile Generator Support: Compatible with 70B+ models, VLLM, and quantization techniques.
Flexible Instruction Lab: Customizable instruction templates for various RAG scenarios.
Interesting RAG applications
Autosurvey
dev environment：pytorch:2.0.1-py3.10-cuda11.8.0-devel-ubuntu22.04
install miniconda
git clone RAGLAB
https://github.com/fate-ubw/RAGLAB.git
create environment from yml file
cd RAGLAB conda env create -f environment.yml
install flash-attn, en_core_web_sm, punkt manually
pip install flash-attn==2.2 python -m spacy download en_core_web_sm python -m nltk.downloader punkt
cd RAGLAB mkdir modelcd model mkdir output_models# retriever modelmkdir colbertv2.0 huggingface-cli download colbert-ir/colbertv2.0 --local-dir colbertv2.0/ --local-dir-use-symlinks False mkdir contriever-msmarco huggingface-cli download facebook/contriever-msmarco --local-dir contriever-msmarco/ --local-dir-use-symlinks False# finetuned generator# 8B modelmkdir Llama3-8B-baseline huggingface-cli download RAGLAB/Llama3-8B-baseline --local-dir Llama3-8B-baseline/ --local-dir-use-symlinks False mkdir selfrag_llama3_8b-epoch_0_1 huggingface-cli download RAGLAB/selfrag_llama3-8B --local-dir selfrag_llama3_8b-epoch_0_1/ --local-dir-use-symlinks False# 70B modelmkdir Llama3-70B-baseline-adapter huggingface-cli download RAGLAB/Llama3-70B-baseline-adapter --local-dir Llama3-70B-baseline-adapter/ --local-dir-use-symlinks False mkdir selfrag_llama3_70B-adapter huggingface-cli download RAGLAB/selfrag_llama3-70B-adapter --local-dir selfrag_llama3_70B-adapter/ --local-dir-use-symlinks False mkdir Meta-Llama-3-70B huggingface-cli download meta-llama/Meta-Llama-3-70B --local-dir Meta-Llama-3-70B/ --local-dir-use-symlinks False# base model for finetune and LoRAmkdir Meta-Llama-3-8B huggingface-cli download meta-llama/Meta-Llama-3-8B --local-dir Meta-Llama-3-8B/ --local-dir-use-symlinks False# ALCE Metric Modelsmkdir gpt2-large huggingface-cli download openai-community/gpt2-large --local-dir gpt2-large/ --local-dir-use-symlinks False mkdir roberta-large-squad huggingface-cli download gaotianyu1350/roberta-large-squad --local-dir roberta-large-squad/ --local-dir-use-symlinks False mkdir t5_xxl_true_nli_mixture huggingface-cli download google/t5_xxl_true_nli_mixture --local-dir t5_xxl_true_nli_mixture/ --local-dir-use-symlinks False# factscore model we use gpt3.5 for evaluation, so no need to download local models# models from official selfrag repomkdir selfrag_llama2_7b huggingface-cli download selfrag/selfrag_llama2_7b --local-dir selfrag_llama2_7b/ --local-dir-use-symlinks False# you can download other model as generator from huggingface
If you only need to understand how different algorithms work, the interact mode developed by RAGLAB can meet your needs.
If you want to reproduce the results from the papers, you need to download all the required data from Hugging Face, including training data, knowledge data, and evaluation data. We have packaged all the data for you, so you just need to download it and it's ready to use.
cd RAGLAB huggingface-cli download RAGLAB/data --local-dir data --repo-type dataset
Interact Mode is specifically designed for quickly understanding algorithms. In interact mode, you can run various algorithms very quickly, understand the reasoning process of different algorithms, without needing to download any additional data.
All algorithms integrated in raglab include two modes: interact and evaluation. The test stage demonstrates in interact mode, just for demostration and eduction ?.
Note
Due to colbert's requirement for absolute paths, you need to modify the index_dbPath and text_dbPath in the config file to use absolute paths.
Modify the index_dbPath and text_dbPath in config file:colbert_server-10samples.yaml
index_dbPath: /your_root_path/RAGLAB/data/retrieval/colbertv2.0_embedding/wiki2023-10samples text_dbPath: /your_root_path/RAGLAB/data/retrieval/colbertv2.0_passages/wiki2023-10samples/enwiki-20230401-10samples.tsv
run colbert server
cd RAGLAB sh run/colbert_server/colbert_server-10samples.sh
Note
At this point, colbert embedding will prompt that due to path errors, colbert embedding needs to be reprocessed. Please enter yes and then raglab will automatically help you process the embedding and start the colbert server.
Now please open another terminal and try to request the colbert server
cd RAGLAB sh run/colbert_server/ask_api.sh
If a result is returned, it means the colbert server has started successfully! ?
run selfrag (short form & adaptive retrieval) interact mode test 10-samples embedding
cd RAGLAB sh run/rag_inference/3-selfrag_reproduction-interact-short_form-adaptive_retrieval.sh
Congratulations！！！Now you have already know how to run raglab ?
In raglab, each algorithm has 10 queries built-in in interact mode which are sampled from different benchmarks
Note
remember download wiki2018 konwledge database and model before runing paper results
Due to colbert's requirement for absolute paths, you need to modify the index_dbPath and text_dbPath in config file and process the wiki2018 embedding database
cd RAGLAB/config/colbert_server
vim colbert_server.yaml
index_dbPath: {your_root_path}/RAGLAB/data/retrieval/colbertv2.0_embedding/wiki2018
text_dbPath: {your_root_path}/RAGLAB/data/retrieval/colbertv2.0_passages/wiki2018/wiki2018.tsvvim /data/retrieval/colbertv2.0_embedding/wiki2018/indexes/wiki2018/metadata.json# change root path, other parameters do not need to be modified"collection": "/{your_root_path}/RAGLAB/data/retrieval/colbertv2.0_passages/wiki2018/wiki2018.tsv","experiment": "/{your_root_path}/RAGLAB/data/retrieval/colbertv2.0_embedding/wiki2018",Modify the absolute paths bound in the wiki2018 embedding source file
Modify the paths in the config file
Attention: colbert_server need atleast 60GB ram
cd RAGLAB sh run/colbert_server/colbert_server.sh
open another terminal test your ColBERT server
cd RAGLAB sh run/colbert_server/ask_api.sh
ColBERT server started successfully!!! ?
inference experiments require running hundreds of scripts in parallel, the automatic gpu scheduler needs to be used to automatically allocate GPUs for different bash scripts in Parallel.
install simple_gpu_scheduler
pip install simple_gpu_scheduler
run hundreds of experiments in one line ?
cd RAGLAB simple_gpu_scheduler --gpus 0,1,2,3,4,5,6,7 < auto_gpu_scheduling_scripts/auto_run-llama3_8b-baseline-scripts.txt# Other scripts can be run using the same method
how to write your_script.txt?
# auto_inference_selfreg-7b.txtsh run/rag_inference/selfrag_reproduction/selfrag_reproduction-evaluation-short_form-PubHealth-adaptive_retrieval-pregiven_passages.sh sh run/rag_inference/selfrag_reproduction/selfrag_reproduction-evaluation-short_form-PubHealth-always_retrieval-pregiven_passages.sh
here is an example
RAGLAB includes 3 classic evaluation methods: accuracy, F1, and EM (Exact Match). These 3 methods are simple to calculate, so they can be computed dynamically during the inference process. However, ALCE and Factscore, two advanced metrics, require the completion of the inference process before evaluation.
ALCE: RAGLAB has integrated the ALCE repository into RAGLAB. You only need to set the path for the inference results in the config file.
cd RAGLABcd run/ALCE/# Change the path in each sh file for the inference generated files# For example:# python ./ALCE/eval.py --f './data/eval_results/ASQA/{your_input_file_path}.jsonl' # --mauve # --qasimple_gpu_scheduler --gpus 0,1,2,3,4,5,6,7 < auto_gpu_scheduling_scripts/auto_eval_ALCE.txtThe evaluation results will be in the same directory as the input file, with the file name suffix .score
Factscore: The Factscore environment requires installation of torch 1.13.1, which conflicts with the flash-attn version needed in RAGLAB's training and inference modules. Therefore, RAGLAB currently cannot integrate the Factscore environment, so users need to install the Factscore environment separately for evaluation.
After installing the Factscore environment, please modify the path of the inference results in the bash file
cd RAGLAB/run/Factscore/# change the path in each sh file for the inference generated files# For example:# python ./FActScore/factscore/factscorer.py # --input_path './data/eval_results/Factscore/{your_input_file_path}.jsonl' # --model_name "retrieval+ChatGPT"# --openai_key ./api_keys.txt # --data_dir ./data/retrieval/colbertv2.0_passages/wiki2023 # --verbosesimple_gpu_scheduler --gpus 0,1,2,3,4,5,6,7 < auto_gpu_scheduling_scripts/auto_eval_Factscore.txtThe evaluation results will be in the same directory as the input file, with the file name suffix _factscore_output.json
Note
During the Factscore evaluation process, we used GPT-3.5 as the evaluation model, so there's no need to download a local model. If you need to use a local model to evaluate Factscore, please refer to Factscore
If you wish to process the knowledge database yourself, please refer to the following steps. RAGLAB has already uploaded the processed knowledge database to Hugging Face
document: process_wiki.md
This section covers the process of training models in RAGLAB. You can either download all pre-trained models from HuggingFace?, or use the tutorial below to train from scratch.
All data provides all data necessary for finetuning.
document: train_docs.md
If you find this repository useful, please cite our work.
@inproceedings{zhang-etal-2024-raglab,
title = "{RAGLAB}: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation",
author = "Zhang, Xuanwang and
Song, Yunze and
Wang, Yidong and
Tang, Shuyun and
others",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2024",
publisher = "Association for Computational Linguistics",
}RAGLAB is licensed under the MIT License.
