FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research
- URL: http://arxiv.org/abs/2405.13576v1
- Date: Wed, 22 May 2024 12:12:40 GMT
- Title: FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research
- Authors: Jiajie Jin, Yutao Zhu, Xinyu Yang, Chenghao Zhang, Zhicheng Dou,
- Abstract summary: FlashRAG is an efficient and modular open-source toolkit designed to assist researchers in reproducing existing RAG methods and in developing their own RAG algorithms within a unified framework.
Our toolkit has various features, including customizable modular framework, rich collection of pre-implemented RAG works, comprehensive datasets, efficient auxiliary pre-processing scripts, and extensive and standard evaluation metrics.
- Score: 32.820100519805486
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the advent of Large Language Models (LLMs), the potential of Retrieval Augmented Generation (RAG) techniques have garnered considerable research attention. Numerous novel algorithms and models have been introduced to enhance various aspects of RAG systems. However, the absence of a standardized framework for implementation, coupled with the inherently intricate RAG process, makes it challenging and time-consuming for researchers to compare and evaluate these approaches in a consistent environment. Existing RAG toolkits like LangChain and LlamaIndex, while available, are often heavy and unwieldy, failing to meet the personalized needs of researchers. In response to this challenge, we propose FlashRAG, an efficient and modular open-source toolkit designed to assist researchers in reproducing existing RAG methods and in developing their own RAG algorithms within a unified framework. Our toolkit implements 12 advanced RAG methods and has gathered and organized 32 benchmark datasets. Our toolkit has various features, including customizable modular framework, rich collection of pre-implemented RAG works, comprehensive datasets, efficient auxiliary pre-processing scripts, and extensive and standard evaluation metrics. Our toolkit and resources are available at https://github.com/RUC-NLPIR/FlashRAG.
Related papers
- Searching for Best Practices in Retrieval-Augmented Generation [31.438681543849224]
Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information.
Here, we investigate existing RAG approaches and their potential combinations to identify optimal RAG practices.
We suggest several strategies for deploying RAG that balance both performance and efficiency.
arXiv Detail & Related papers (2024-07-01T12:06:34Z) - BERGEN: A Benchmarking Library for Retrieval-Augmented Generation [26.158785168036662]
Retrieval-Augmented Generation allows to enhance Large Language Models with external knowledge.
Inconsistent benchmarking poses a major challenge in comparing approaches and understanding the impact of each component in the pipeline.
In this work, we study best practices that lay the groundwork for a systematic evaluation of RAG and present BERGEN, an end-to-end library for reproducible research standardizing RAG experiments.
arXiv Detail & Related papers (2024-07-01T09:09:27Z) - CodeRAG-Bench: Can Retrieval Augment Code Generation? [78.37076502395699]
We conduct a systematic, large-scale analysis of code generation using retrieval-augmented generation.
We first curate a comprehensive evaluation benchmark, CodeRAG-Bench, encompassing three categories of code generation tasks.
We examine top-performing models on CodeRAG-Bench by providing contexts retrieved from one or multiple sources.
arXiv Detail & Related papers (2024-06-20T16:59:52Z) - R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Models [51.468732121824125]
Large language models have achieved remarkable success on general NLP tasks, but they may fall short for domain-specific problems.
Existing evaluation tools only provide a few baselines and evaluate them on various domains without mining the depth of domain knowledge.
In this paper, we address the challenges of evaluating RALLMs by introducing the R-Eval toolkit, a Python toolkit designed to streamline the evaluation of different RAGs.
arXiv Detail & Related papers (2024-06-17T15:59:49Z) - InspectorRAGet: An Introspection Platform for RAG Evaluation [14.066727601732625]
InspectorRAGet is an introspection platform for RAG evaluation.
It allows the user to analyze aggregate and instance-level performance of RAG systems.
arXiv Detail & Related papers (2024-04-26T11:51:53Z) - Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers [0.0]
Retrieval-Augmented Generation (RAG) is a prevalent approach to infuse a private knowledge base of documents with Large Language Models (LLM) to build Generative Q&A (Question-Answering) systems.
We propose the 'Blended RAG' method of leveraging semantic search techniques, such as Vector indexes and Sparse indexes, blended with hybrid query strategies.
Our study achieves better retrieval results and sets new benchmarks for IR (Information Retrieval) datasets like NQ and TREC-COVID datasets.
arXiv Detail & Related papers (2024-03-22T17:13:46Z) - RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems [51.171355532527365]
We introduce the RAGGED framework to analyze and optimize RAG systems.
We study two classic sparse and dense retrievers, and four top-performing LMs in encoder-decoder and decoder-only architectures.
arXiv Detail & Related papers (2024-03-14T02:26:31Z) - Retrieval-Augmented Generation for AI-Generated Content: A Survey [38.50754568320154]
Retrieval-Augmented Generation (RAG) has emerged as a paradigm to address such challenges.
RAG introduces the information retrieval process, which enhances the generation process by retrieving relevant objects from available data stores.
In this paper, we comprehensively review existing efforts that integrate RAG technique into AIGC scenarios.
arXiv Detail & Related papers (2024-02-29T18:59:01Z) - CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models [49.16989035566899]
Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources.
This paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios.
arXiv Detail & Related papers (2024-01-30T14:25:32Z) - TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series [61.436361263605114]
Time series data are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations.
We introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series.
arXiv Detail & Related papers (2023-05-19T10:11:21Z) - RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems [100.54655931138444]
We propose a more holistic view of robustness for recommender systems that encompasses multiple dimensions.
We present a robustness evaluation toolkit, Robustness Gym for RecSys, that allows us to quickly and uniformly evaluate the robustness of recommender system models.
arXiv Detail & Related papers (2022-01-12T10:32:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.