  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.
