mlsanityData to 14 Oct 2026

FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation

arXiv 2506.12494 ↗First posted 14 Jun 2025cs.CL cs.IRcited by 2

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) plays a pivotal role in modern large language model applications, with numerous existing frameworks offering a wide range of functionalities to facilitate the development of RAG systems. However, we have identified several persistent challenges in these frameworks, including difficulties in algorithm reproduction and sharing, lack of new techniques, and high system overhead. To address these limitations, we introduce FlexRAG, an open-source framework specifically designed for research and prototyping. FlexRAG supports text-based, multimodal, and network-based RAG, providing comprehensive lifecycle support alongside efficient asynchronous processing and persistent caching capabilities. By offering a robust and flexible solution, FlexRAG enables researchers to rapidly develop, deploy, and share advanced RAG systems. Our toolkit and resources are available at hrefhttps://github.com/ictnlp/FlexRAGhttps://github.com/ictnlp/FlexRAG.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 32 of its 46 references that are in the map. First posted on arXiv 14 Jun 2025.

Builds on

32 of 46 references.

Closest ancestors · 6

Same direction, newest first

  1. 2024AutoRAG: Automated Framework for optimization of Retrieval Augmented Generation Pipeline28 Oct
  2. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely23 Sep
  3. 2024ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities19 Jul
  4. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research22 May
  5. 2024Evaluation of Retrieval-Augmented Generation: A Survey13 May
  6. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 26

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  5. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  6. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
  7. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
  8. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,178
  9. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  10. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  11. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
  12. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 573
14 more
  1. 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 476
  2. 2023LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compressioncited by 453
  3. 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 376
  4. 2024LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compressioncited by 348
  5. 2023RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentationcited by 256
  6. 2024PaliGemma 2: A Family of Versatile VLMs for Transfercited by 207
  7. 2018ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithmscited by 130
  8. 2023How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrievalcited by 118
  9. 2024BM25S: Orders of magnitude faster lexical search via eager sparse scoringcited by 88
  10. 2025MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systemscited by 51
  11. 2024Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retrievercited by 25
  12. 2024InRanker: Distilled Rankers for Zero-shot Information Retrievalcited by 8
  13. 2024LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systemscited by 7
  14. 2024EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operationscited by 2

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Enhancing Retrieval-Augmented Generation for Electric Power Industry Customer Supportcited by 0
  2. 2025Living the Novel: A System for Generating Self-Training Timeline-Aware Conversational Agents from Novelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
  2. 2025UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation
  3. 2025FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems
  4. 2024RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
  5. 2026RAGe: A Retrieval-Augmented Generation Evaluation Framework
  6. 2024FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG
  7. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  8. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey