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https://github.com/fate-ubw/raglab

RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
https://github.com/fate-ubw/raglab

rag retrieval-augmented-generation

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RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation

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# 
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation









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- 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.



![figure-1](./figures/Raglab-figure-1.png)



# News

- 2024.8.20: RAGLAB has open-sourced 4 models🤗:

  [llama3-8B-baseline](https://huggingface.co/RAGLAB/Llama3-8B-baseline)

  [selfrag-llama3-8b](https://huggingface.co/RAGLAB/selfrag_llama3-8B)

  [llama3-70B-adaptor](https://huggingface.co/RAGLAB/Llama3-70B-baseline-adapter)

  [selfrag-llama3-70B-adaptor](https://huggingface.co/RAGLAB/selfrag_llama3_70B-adapter)

- 2024.8.6: RAGLAB is released🌈.



# 🌟Features

- **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.



# 📎Related works

- Interesting RAG applications

  - [Autosurvey](https://github.com/AutoSurveys/AutoSurvey)



# 🔨Install environment

- dev environment：pytorch:2.0.1-py3.10-cuda11.8.0-devel-ubuntu22.04

- [install miniconda](https://docs.anaconda.com/free/miniconda/index.html)



- git clone RAGLAB

  ~~~bash

  https://github.com/fate-ubw/RAGLAB.git

  ~~~



- create environment from yml file 

  ~~~bash

  cd RAGLAB

  conda env create -f environment.yml

  ~~~

- install flash-attn, en_core_web_sm, punkt manually

  ~~~bash

  pip install flash-attn==2.2

  python -m spacy download en_core_web_sm

  python -m nltk.downloader punkt

  ~~~



# 🤗 Models



raglab need several models please download them



  ~~~bash

  cd RAGLAB

  mkdir model

  cd model

  mkdir output_models

  # retriever model

  mkdir 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 model

  mkdir 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 model

  mkdir 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 LoRA

  mkdir 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 Models

  mkdir 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 repo

  mkdir 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

  ~~~



# 🤗 Whole Data

- 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.

  ~~~bash

  cd RAGLAB

  huggingface-cli download RAGLAB/data --local-dir data --repo-type dataset

  ~~~



# Run Raglab in Interact Mode

- 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.



## Setup colbert server

- 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](./config/colbert_server/colbert_server-10samples.yaml)

  ~~~bash

  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 

  ~~~bash

  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

  ~~~bash

  cd RAGLAB

  sh run/colbert_server/ask_api.sh

  ~~~

  - If a result is returned, it means the colbert server has started successfully! 🌈

- run [selfrag](https://arxiv.org/abs/2310.11511) (short form & adaptive retrieval) interact mode test 10-samples embedding

  ~~~bash

  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



# Reproduce paper results 

> [!NOTE]

> - remember download  [wiki2018 konwledge database](#🤗whole-data) and [model](#🤗models) before runing paper results

## Retrieval server & api

- 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

  - Modify the paths in the config file

  ~~~bash

  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.tsv

  ~~~

  - Modify the absolute paths bound in the wiki2018 embedding source file

  ~~~bash

  vim /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",

  ~~~

- Attention: colbert_server need atleast 60GB ram 

  ~~~bash

  cd RAGLAB

  sh run/colbert_server/colbert_server.sh

  ~~~

- open another terminal test your ColBERT server

  ~~~bash

  cd RAGLAB

  sh run/colbert_server/ask_api.sh

  ~~~

- ColBERT server started successfully!!! 🌈

## Automatic GPU Scheduler

- inference experiments require running hundreds of scripts in parallel, the [automatic gpu scheduler](https://github.com/ExpectationMax/simple_gpu_scheduler) needs to be used to automatically allocate GPUs for different bash scripts in Parallel.

- install `simple_gpu_scheduler`

  ~~~bash

  pip install simple_gpu_scheduler

  ~~~

- run hundreds of experiments in one line 😎

  ~~~bash

  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?

  - here is an example

  ~~~bash

  # auto_inference_selfreg-7b.txt

  sh 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

  ~~~

## Evaluation for ALCE & Factscore



- 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.

  ~~~bash

  cd RAGLAB

  cd 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 \

  #     --qa



  simple_gpu_scheduler --gpus 0,1,2,3,4,5,6,7 < auto_gpu_scheduling_scripts/auto_eval_ALCE.txt



  ~~~

- The 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](https://github.com/shmsw25/FActScore) environment separately for evaluation.

- After installing the Factscore environment, please modify the path of the inference results in the bash file

  ~~~bash

  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 \

  #  --verbose



  simple_gpu_scheduler --gpus 0,1,2,3,4,5,6,7 < auto_gpu_scheduling_scripts/auto_eval_Factscore.txt

  ~~~

- The 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](https://github.com/shmsw25/FActScore)



# Process knowledge database from source

- 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](https://huggingface.co/datasets/RAGLAB/data)

- document: [process_wiki.md](./docs/process_wiki.md)



# 🤖 Train models

- 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](#all-data-for-reproduce-paper-results) provides all data necessary for finetuning.

- document: [train_docs.md](./docs/train_docs.md)



## :bookmark: License



RAGLAB is licensed under the MIT License.