⚡FlashRAG: A Python Toolkit for Efficient RAG Research

Installation | Features | Quick-Start | Components | Supporting Methods | Supporting Datasets | FAQs

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 13 state-of-the-art RAG algorithms.

With FlashRAG and provided resources, you can effortlessly 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 13 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.

🔎 Roadmap

FlashRAG is still under development and there are many issues and room for improvement. We will continue to update. And we also sincerely welcome contributions on this open-source toolkit.

• Support OpenAI models
• Support Claude and Gemini models
• Provdide instructions for each component
• Integrate sentence Transformers
• Inlcude more RAG approaches
• Add more evaluation metrics (e.g., Unieval, name-entity F1) and benchmarks (e.g., RGB benchmark)
• Enhance code adaptability and readability

📃 Changelog

[24/06/19] We add support for a new method: IRCoT, and update the result table.

[24/06/15] We provide a demo to perform the RAG process using our toolkit.

[24/06/11] We have integrated sentence transformers in the retriever module. Now it's easier to use the retriever without setting pooling methods.

[24/06/05] We have provided detailed document for reproducing existing methods (see how to reproduce, baseline details), and configurations settings.

[24/06/02] We have provided an introduction of FlashRAG for beginners, see an introduction to flashrag (中文版).

[24/05/31] We supported Openai-series models as generator.

🔧 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 . 

Due to the incompatibility when installing faiss using pip, it is necessary to use the following conda command for installation.

# CPU-only version
conda install -c pytorch faiss-cpu=1.8.0

# GPU(+CPU) version
conda install -c pytorch -c nvidia faiss-gpu=1.8.0

🚀 Quick Start

Toy Example

For beginners, we provide a an introduction to flashrag (中文版) to help you familiarize yourself with our toolkit. Alternatively, you can directly refer to the code below.

Demo

We provide a toy demo to implement a simple RAG process. You can freely change the corpus and model you want to use. The English demo uses general knowledge as the corpus, e5-base-v2 as the retriever, and Llama3-8B-instruct as generator. The Chinese demo uses data crawled from the official website of Remin University of China as the corpus, bge-large-zh-v1.5 as the retriever, and qwen1.5-14B as the generator. Please fill in the corresponding path in the file.

To run the demo:

cd examples/quick_start

# copy the config file here, otherwise, streamlit will complain that file s
cp ../methods/my_config.yaml .

# run english demo
streamlit run demo_en.py

# run chinese demo
streamlit run demo_zh.py

Pipeline

We also provide an example to use our framework for pipeline execution. Run the following code to implement a naive RAG pipeline using provided toy datasets. The default retriever is e5-base-v2 and default generator is Llama3-8B-instruct. 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=<Llama-3-8B-instruct-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.

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)

We provide comprehensive guidance on how to set configurations, you can see our configuration guidance. You can also refer to the basic yaml file we provide to set your own parameters.

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
	IRCoT Pipeline	Integrate retrieval process with CoT

🤖 Supporting Methods

We have implemented 13 works with a consistent setting of:

• Generator: LLAMA3-8B-instruct with input length of 2048
• Retriever: e5-base-v2 as embedding model, retrieve 5 docs per query
• Prompt: A consistent default prompt, template can be found in the method details.

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 reproduce guidance and method details.

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	33.2	56.0	32.4	23.4	31.7	17.0	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
IRCoT	Loop	33.3	56.9	41.5	32.4	45.6	20.7

📓 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

• How should I set different experimental parameters?
• How to build my own corpus, such as a specific segmented Wikipedia?
• How to index my own corpus?
• How to reproduce supporting methods?

🔖 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}
}