/FlashRAG

⚡FlashRAG: A Python Toolkit for Efficient RAG Research

Primary LanguagePythonMIT LicenseMIT

⚡FlashRAG: A Python Toolkit for Efficient RAG Research

License

FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 32 pre-processed benchmark RAG datasets and 12 state-of-the-art RAG algorithms.

With FlashRAG and provided resources, you can effortless reproduce existing SOTA works in the RAG domain or implement your custom RAG processes and components.

✨ Features

  • 🛠 Extensive and Customizable Framework: Includes essential components for RAG scenarios such as retrievers, rerankers, generators, and compressors, allowing for flexible assembly of complex pipelines.

  • 🗂 Comprehensive Benchmark Datasets: A collection of 32 pre-processed RAG benchmark datasets to test and validate RAG models' performances.

  • 🎯 Pre-implemented Advanced RAG Algorithms: Features 12 advancing RAG algorithms with reported results, based on our framework. Easily reproducing results under different settings.

  • 🧩 Efficient Preprocessing Stage: Simplifies the RAG workflow preparation by providing various scripts like corpus processing for retrieval, retrieval index building, and pre-retrieval of documents.

  • 🚀 Optimized Execution: The library's efficiency is enhanced with tools like vLLM, FastChat for LLM inference acceleration, and Faiss for vector index management.

🔧 Installation

To get started with FlashRAG, simply clone it from Github and install (requires Python 3.9+):

git clone https://github.com/RUC-NLPIR/FlashRAG.git
cd FlashRAG
pip install -e . 

🏃 Quick Start

Toy Example

Run the following code to implement a naive RAG pipeline using provided toy datasets. The default retriever is e5 and default generator is llama2-7B-chat. You need to fill in the corresponding model path in the following command. If you wish to use other models, please refer to the detailed instructions below.

cd examples/quick_start
python simple_pipeline.py \
    --model_path=<LLAMA2-7B-Chat-PATH> \
    --retriever_path=<E5-PATH>

After the code is completed, you can view the intermediate results of the run and the final evaluation score in the output folder under the corresponding path.

Note: This toy example is just to help test whether the entire process can run normally. Our toy retrieval document only contains 1000 pieces of data, so it may not yield good results.

Using the ready-made pipeline

You can use the pipeline class we have already built (as shown in pipelines) to implement the RAG process inside. In this case, you just need to configure the config and load the corresponding pipeline.

Firstly, load the entire process's config, which records various hyperparameters required in the RAG process. You can input yaml files as parameters or directly as variables. The priority of variables as input is higher than that of files.

from flashrag.config import Config

config_dict = {'data_dir': 'dataset/'}
my_config = Config(config_file_path = 'my_config.yaml',
                config_dict = config_dict)

You can refer to the basic yaml file we provide to set your own parameters. For specific parameter names and meanings, please refer to the config parameter description.

Next, load the corresponding dataset and initialize the pipeline. The components in the pipeline will be automatically loaded.

from flashrag.utils import get_dataset
from flashrag.pipeline import SequentialPipeline
from flashrag.prompt import PromptTemplate
from flashrag.config import Config

config_dict = {'data_dir': 'dataset/'}
my_config = Config(config_file_path = 'my_config.yaml',
                config_dict = config_dict)
all_split = get_dataset(my_config)
test_data = all_split['test']

pipeline = SequentialPipeline(my_config)

You can specify your own input prompt using PromptTemplete:

prompt_templete = PromptTemplate(
    config, 
    system_prompt = "Answer the question based on the given document. Only give me the answer and do not output any other words.\nThe following are given documents.\n\n{reference}",
    user_prompt = "Question: {question}\nAnswer:"
)
pipeline = SequentialPipeline(my_config, prompt_template=prompt_templete)

Finally, execute pipeline.run to obtain the final result.

output_dataset = pipeline.run(test_data, do_eval=True)

The output_dataset contains the intermediate results and metric scores for each item in the input dataset. Meanwhile, the dataset with intermediate results and the overall evaluation score will also be saved as a file (if save_intermediate_data and save_metric_score are specified).

Build your own pipeline

Sometimes you may need to implement more complex RAG process, and you can build your own pipeline to implement it. You just need to inherit BasicPipeline, initialize the components you need, and complete the run function.

from flashrag.pipeline import BasicPipeline
from flashrag.utils import get_retriever, get_generator

class ToyPipeline(BasicPipeline):
  def __init__(self, config, prompt_templete=None):
    # Load your own components
    pass

  def run(self, dataset, do_eval=True):
    # Complete your own process logic

    # get attribute in dataset using `.`
    input_query = dataset.question
    ...
    # use `update_output` to save intermeidate data
    dataset.update_output("pred",pred_answer_list)
    dataset = self.evaluate(dataset, do_eval=do_eval)
    return dataset

Please first understand the input and output forms of the components you need to use from our documentation.

Just use components

If you already have your own code and only want to use our components to embed the original code, you can refer to the basic introduction of the components to obtain the input and output formats of each component.

⚙️ Components

In FlashRAG, we have built a series of common RAG components, including retrievers, generators, refiners, and more. Based on these components, we have assembled several pipelines to implement the RAG workflow, while also providing the flexibility to combine these components in custom arrangements to create your own pipeline.

RAG-Components

Type Module Description
Judger SKR Judger Judging whether to retrieve using SKR method
Retriever Dense Retriever Bi-encoder models such as dpr, bge, e5, using faiss for search
BM25 Retriever Sparse retrieval method based on Lucene
Bi-Encoder Reranker Calculate matching score using bi-Encoder
Cross-Encoder Reranker Calculate matching score using cross-encoder
Refiner Extractive Refiner Refine input by extracting important context
Abstractive Refiner Refine input through seq2seq model
LLMLingua Refiner LLMLingua-series prompt compressor
SelectiveContext Refiner Selective-Context prompt compressor
Generator Encoder-Decoder Generator Encoder-Decoder model, supporting Fusion-in-Decoder (FiD)
Decoder-only Generator Native transformers implementation
FastChat Generator Accelerate with FastChat
vllm Generator Accelerate with vllm

Pipelines

Referring to a survey on retrieval-augmented generation, we categorized RAG methods into four types based on their inference paths.

  • Sequential: Sequential execuation of RAG process, like Query-(pre-retrieval)-retriever-(post-retrieval)-generator
  • Conditional: Implements different paths for different types of input queries
  • Branching : Executes multiple paths in parallel, merging the responses from each path
  • Loop: Iteratively performs retrieval and generation

In each category, we have implemented corresponding common pipelines. Some pipelines have corresponding work papers.

Type Module Description
Sequential Sequential Pipeline Linear execution of query, supporting refiner, reranker
Conditional Conditional Pipeline With a judger module, distinct execution paths for various query types
Branching REPLUG Pipeline Generate answer by integrating probabilities in multiple generation paths
SuRe Pipeline Ranking and merging generated results based on each document
Loop Iterative Pipeline Alternating retrieval and generation
Self-Ask Pipeline Decompose complex problems into subproblems using self-ask
Self-RAG Pipeline Adaptive retrieval, critique, and generation
FLARE Pipeline Dynamic retrieval during the generation process

🤖 Supporting Methods

We have implemented 12 works with a consistent setting of:

  • Generator: LLAMA3-8B-instruct with input length of 4096
  • Retriever: e5-base-v2 as embedding model, retrieve 5 docs per query
  • Prompt: A consistent default prompt, templete can be found in the code.

For open-source methods, we implemented their processes using our framework. For methods where the author did not provide source code, we will try our best to follow the methods in the original paper for implementation.

For necessary settings and hyperparameters specific to some methods, we have documented them in the specific settings column. For more details, please consult our code.

It’s important to note that, to ensure consistency, we have utilized a uniform setting. However, this setting may differ from the original setting of the method, leading to variations in results compared to the original outcomes.

Method Type NQ (EM) TriviaQA (EM) Hotpotqa (F1) 2Wiki (F1) PopQA (F1) WebQA(EM) Specific setting
Naive Generation Sequential 22.6 55.7 28.4 33.9 21.7 18.8
Standard RAG Sequential 35.1 58.9 35.3 21.0 36.7 15.7
AAR-contriever-kilt Sequential 30.1 56.8 33.4 19.8 36.1 16.1
LongLLMLingua Sequential 32.2 59.2 37.5 25.0 38.7 17.5 Compress Ratio=0.5
RECOMP-abstractive Sequential 33.1 56.4 37.5 32.4 39.9 20.2
Selective-Context Sequential 30.5 55.6 34.4 18.5 33.5 17.3 Compress Ratio=0.5
Ret-Robust Sequential 42.9 68.2 35.8 43.4 57.2 33.7 Use LLAMA2-13B with trained lora
SuRe Branching 37.1 53.2 33.4 20.6 48.1 24.2 Use provided prompt
REPLUG Branching 28.9 57.7 31.2 21.1 27.8 20.2
SKR Conditional 25.5 55.9 29.8 28.5 24.5 18.6 Use infernece-time training data
Self-RAG Loop 36.4 38.2 29.6 25.1 32.7 21.9 Use trained selfrag-llama2-7B
FLARE Loop 22.5 55.8 28.0 33.9 20.7 20.2
Iter-Retgen, ITRG Loop 36.8 60.1 38.3 21.6 37.9 18.2

📓 Supporting Datasets & Document Corpus

Datasets

We have collected and processed 35 datasets widely used in RAG research, pre-processing them to ensure a consistent format for ease of use. For certain datasets (such as Wiki-asp), we have adapted them to fit the requirements of RAG tasks according to the methods commonly used within the community. All datasets are available at Huggingface datasets.

For each dataset, we save each split as a jsonl file, and each line is a dict as follows:

{
  'id': str,
  'question': str,
  'golden_answers': List[str],
  'metadata': dict
}

Below is the list of datasets along with the corresponding sample sizes:

Task Dataset Name Knowledge Source # Train # Dev # Test
QA NQ wiki 79,168 8,757 3,610
QA TriviaQA wiki & web 78,785 8,837 11,313
QA PopQA wiki / / 14,267
QA SQuAD wiki 87,599 10,570 /
QA MSMARCO-QA web 808,731 101,093 /
QA NarrativeQA books and story 32,747 3,461 10,557
QA WikiQA wiki 20,360 2,733 6,165
QA WebQuestions Google Freebase 3,778 / 2,032
QA AmbigQA wiki 10,036 2,002 /
QA SIQA - 33,410 1,954 /
QA CommenseQA - 9,741 1,221 /
QA BoolQ wiki 9,427 3,270 /
QA PIQA - 16,113 1,838 /
QA Fermi wiki 8,000 1,000 1,000
multi-hop QA HotpotQA wiki 90,447 7,405 /
multi-hop QA 2WikiMultiHopQA wiki 15,000 12,576 /
multi-hop QA Musique wiki 19,938 2,417 /
multi-hop QA Bamboogle wiki / / 125
Long-form QA ASQA wiki 4,353 948 /
Long-form QA ELI5 Reddit 272,634 1,507 /
Open-Domain Summarization WikiASP wiki 300,636 37,046 37,368
multiple-choice MMLU - 99,842 1,531 14,042
multiple-choice TruthfulQA wiki / 817 /
multiple-choice HellaSWAG ActivityNet 39,905 10,042 /
multiple-choice ARC - 3,370 869 3,548
multiple-choice OpenBookQA - 4,957 500 500
Fact Verification FEVER wiki 104,966 10,444 /
Dialog Generation WOW wiki 63,734 3,054 /
Entity Linking AIDA CoNll-yago Freebase & wiki 18,395 4,784 /
Entity Linking WNED Wiki / 8,995 /
Slot Filling T-REx DBPedia 2,284,168 5,000 /
Slot Filling Zero-shot RE wiki 147,909 3,724 /

Document Corpus

Our toolkit supports jsonl format for retrieval document collections, with the following structure:

{"id":"0", "contents": "...."}
{"id":"1", "contents": "..."}

The contents key is essential for building the index. For documents that include both text and title, we recommend setting the value of contents to {title}\n{text}. The corpus file can also contain other keys to record additional characteristics of the documents.

In the academic research, Wikipedia and MS MARCO are the most commonly used retrieval document collections. For Wikipedia, we provide a comprehensive script to process any Wikipedia dump into a clean corpus. Additionally, various processed versions of the Wikipedia corpus are available in many works, and we have listed some reference links.

For MS MARCO, it is already processed upon release and can be directly downloaded from its hosting link on Hugging Face.

🙌 Additional FAQs

🔖 License

FlashRAG is licensed under the MIT License.

🌟 Citation

Please kindly cite our paper if helps your research:

@article{FlashRAG,
    author={Jiajie Jin and
            Yutao Zhu and
            Xinyu Yang and
            Chenghao Zhang and
            Zhicheng Dou},
    title={FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research},
    journal={CoRR},
    volume={abs/2405.13576},
    year={2024},
    url={https://arxiv.org/abs/2405.13576},
    eprinttype={arXiv},
    eprint={2405.13576}
}